Untitled
Jhoan Correa, Darcy Meneses y Breiner Omen
2026-06-08
APRENDIZAJE SUPERVISADO
¿Qué es el machine learning?
El machine learning es el subconjunto de la inteligencia artificial (IA) centrado en algoritmos que pueden “aprender” los patrones de los datos de entrenamiento y, posteriormente, hacer inferencias precisas sobre nuevos datos. Esta capacidad de reconocimiento de patrones permite a los modelos de machine learning tomar decisiones o realizar predicciones sin instrucciones explícitas y codificadas de forma rígida.
¿Cómo funciona el machine learning?
Funciona a través de la lógica matemática. Por lo tanto, las características relevantes de cada punto de datos deben expresarse numéricamente para poder introducirse en un algoritmo matemático que “aprenderá” a asignar una entrada determinada al output deseado. Los puntos de datos en el machine learning suelen representarse en forma de vector, en el que cada elemento (o dimensión) del embedding vectorial de un punto de datos corresponde a su valor numérico para una característica específica.
Tipos de machines learning
¿QUÉ ES APRENDIZAJE SUPERVISADO?
El aprendizaje supervisado es una técnica de machine learning que utiliza conjuntos de datos etiquetados para entrenar modelos de inteligencia artificial (IA) para identificar los patrones y relaciones subyacentes. El objetivo del proceso de aprendizaje es crear un modelo que pueda predecir resultados correctos en nuevos datos del mundo real. El machine learning supervisado ayuda a las organizaciones a resolver diversos problemas del mundo real a escala, como clasificar el spam o predecir los precios de las acciones. Se puede utilizar para crear modelos de machine learning de alta precisión.
¿Qué son los datos de verdad sobre el terreno?
Características Principales
Datos Etiquetados: Es el pilar fundamental. Los ejemplos históricos incluyen tanto las características de entrada como la etiqueta esperada (la “solución” o “resultado”).
Entrenamiento Guiado: El algoritmo ajusta sus parámetros internos comparando continuamente sus propias predicciones con las etiquetas correctas, aprendiendo mediante la corrección de errores.
Dos tareas principales: Se divide en dos problemas centrales dependiendo del tipo de variable a predecir: Clasificación y Regresación.
Generalización: El objetivo principal es que el modelo logre identificar patrones generales y aplicar ese conocimiento para predecir o clasificar correctamente datos que nunca ha visto en el mundo real.
¿Cómo funciona el aprendizaje supervisado?
Las técnicas de aprendizaje supervisado utilizan un conjunto de datos etiquetado para comprender las relaciones entre los datos de entrada y salida. Los científicos de datos crean manualmente conjuntos de datos de entrenamiento de verdad que contienen datos de entrada junto con las etiquetas correspondientes. El aprendizaje supervisado entrena el modelo para aplicar los outputs correctos a datos no vistos en casos de uso del mundo real. Durante el entrenamiento, el algoritmo del modelo procesa grandes conjuntos de datos para explorar posibles correlaciones entre entradas y salidas.
Un flujo de trabajo típico de aprendizaje supervisado incluye:
Recopilar datos históricos de entrenamiento con resultados conocidos
Preparar y validar conjuntos de datos de entrenamiento etiquetados
Ingeniería de características que capturan señales relevantes
Entrenar y evaluar modelos contra la verdad fundamental
Desplegar modelos y rastrear el rendimiento a lo largo del tiempo
Aplicaciones comunes de aprendizaje supervisado
El aprendizaje automático supervisado se utiliza comúnmente para:
Filtrado de correo electrónico y moderación de contenido
Análisis de sentimiento en comentarios de clientes
Pronóstico y análisis predictivo
Clasificación de imágenes y documentos
Ejemplos de aprendizaje supervisado del mundo real
Los modelos de aprendizaje supervisado se pueden usar para varios casos de uso empresariales que abordan una amplia gama de problemas. Estos son algunos ejemplos comunes de aprendizaje supervisado:
Evaluación de riesgos
Clasificación de imágenes
Detección de fraudes
Sistemas de recomendación
Aprendizaje supervisado en diversas industrias
Las aplicaciones de aprendizaje supervisado abarcan prácticamente todos los sectores, y algunos casos de uso se han convertido en la base de la infraestructura digital moderna.
Ciberseguridad
Salud y ciencias de la vida
Servicios financieros
Retail y bienes de consumo
Experiencias del cliente
Medios y entretenimiento
Aplicaciones y Beneficios en la Ingeniería
Es vital en la ingeniería porque permite a los sistemas aprender a partir de datos históricos etiquetados. Esto facilita el desarrollo de modelos predictivos y clasificatorios que automatizan la resolución de problemas reales, optimizando el diseño, la toma de decisiones y el análisis de procesos complejos en múltiples disciplinas; además, del Control de Calidad y Clasificación.
Tipos de aprendizaje supervisado
Las tareas de aprendizaje supervisado se pueden dividir en términos generales en problemas de clasificación y regresión:
Clasificación: Utiliza un algoritmo para clasificar los datos en categorías. Reconoce entidades específicas dentro del conjunto de datos e intenta determinar cómo se deben etiquetar o definir esas entidades. Los algoritmos de clasificación más comunes son los clasificadores lineales, las máquinas de vectores de soporte (SVM), los árboles de decisión, el k vecino más cercano (KNN),la regresión logística y el bosque aleatorio.
Regresión: Se usa para entender la relación entre variables dependientes e independientes. En los problemas de regresión, el resultado es un valor continuo y los modelos intentan predecir el output objetivo. Las tareas de regresión incluyen proyecciones de ingresos por ventas o planificación financiera. Los algoritmos de regresión incluyen la regresión lineal, la regresión de Lasso, la regresión de cresta y la regresión polinomial son tres ejemplos de algoritmos de regresión.
Aprendizaje conjunto: es un metaenfoque del aprendizaje supervisado en el que se entrenan varios modelos en la misma tarea de clasificación o regresión. Los resultados de todos los modelos del grupo se agregan para descubrir el mejor enfoque general para resolver el desafío. Los algoritmos individuales dentro del modelo de conjunto más grande se conocen como aprendices débiles o modelos base.
Algoritmos de aprendiazaje supervisado
Los algoritmos de optimización, como el descenso de gradiente, entrenan una amplia gama de algoritmos de machine learning que destacan en tareas de aprendizaje supervisado.
Naïve Bayes: Es un algoritmo de clasificación que adopta el principio de independencia condicional de clase del teorema de Bayes. Esto significa que la presencia de una característica no influye en la presencia de otra en la probabilidad de un resultado, y que cada predictor tiene el mismo efecto en ese resultado.
Regresión lineal: La regresión lineal se utiliza para identificar la relación entre una variable dependiente continua y una o más variables independientes. Por lo general, se utiliza para hacer predicciones sobre resultados futuros.
Regresión no lineal: A veces, no se puede reproducir una salida a partir de entradas lineales. En estos casos, las salidas deben modelarse con una función no lineal. La regresión no lineal expresa una relación entre variables a través de una línea no lineal o curva. Los modelos no lineales pueden manejar relaciones complejas con muchos parámetros.
Regresión logística: Gestiona variables dependientes categóricas, cuando tienen resultados binarios, como verdadero o falso o positivo o negativo. Mientras que los modelos de regresión lineal busca comprender las relaciones entre las entradas de datos, la regresión logística se utiliza principalmente para resolver problemas de clasificación binaria, como la identificación de spam.
Regresión polinomial: Al igual que otros modelos de regresión, la regresión polinomial modela una relación entre variables en un gráfico. Las funciones utilizadas en la regresión polinomial expresan esta relación a través de un grado exponencial. La regresión polinómica es un caso especial de regresión en el que las características de entrada se elevan a potencias, lo que permite que los modelos lineales se ajusten a patrones no lineales.
Máquina de vectores de soporte (SVM): Se utiliza tanto para la clasificación como para la regresión de datos. Dicho esto, suele gestionar problemas de clasificación. Aquí, SVM separa las clases de puntos de datos con un límite de decisión o hiperplano. El objetivo del algoritmo SVM es trazar el hiperplano que maximiza la distancia entre los grupos de puntos de datos.
K-vecino más cercano: K-vecino más cercano (KNN) es un algoritmo no paramétrico que clasifica los puntos de datos en función de su proximidad y asociación con otros datos disponibles. Este algoritmo supone que se pueden encontrar puntos de datos similares cerca unos de otros cuando se trazan matemáticamente
Bosque aleatorio: Es un algoritmo de machine learning supervisado flexible que se utiliza para fines de clasificación y regresión. El “bosque” hace referencia a una colección de árboles de decisión no correlacionados que se fusionan para reducir la varianza y aumentar la precisión.
ALGORITMO KNN
¿Qué es el algoritmo KNN?
El algoritmo de k-nearest neighbors (KNN) es un clasificador de aprendizaje supervisado no paramétrico, que emplea la proximidad para realizar clasificaciones o predicciones sobre la agrupación de un punto de datos individual. Es uno de los clasificadores de clasificación y regression más populares y sencillos que se emplean actualmente en machine learning.
Cálculo de KNN: métricas de distancia
Para resumir, el objetivo del algoritmo de k vecinos más cercanos es identificar los vecinos más cercanos de un punto de consulta determinado, de modo que podamos asignar una etiqueta de clase a ese punto. Para ello, KNN tiene algunos requisitos:
Determinar las métricas de distancia
Para determinar qué puntos de datos están más cerca de un punto de consulta determinado, será necesario calcular la distancia entre el punto de consulta y los demás puntos de datos. Esas métricas de distancia ayudan a formar límites de decisión, que dividen los puntos de consulta en diferentes regiones. Por lo general, los límites de decisión se visualizan con diagramas de Voronoi. Si bien hay varias medidas de distancia entre las que puede elegir, solo cubrirá lo siguiente:
Distancia euclidiana (p=2): Esta es la medida de distancia más comúnmente empleada y está limitada a vectores de valores reales. Empleando la siguiente fórmula, se mide una línea recta entre el punto de consulta y el otro punto que se está midiendo.
Fórmula de la distancia euclidiana:
\[d(x,y) = \sqrt{\sum_{i=1}^{n} (y_i - x_i)^2}\]
Métricas de distancia adicionales
Distancia de Manhattan (p=1): También conocida como distancia de “taxi” o distancia L1. Mide la suma de las diferencias absolutas entre las coordenadas de dos puntos. Es útil cuando los datos tienen muchas dimensiones o cuando se trabaja con cuadrículas.
Fórmula de la distancia de Manhattan:
\[d(x,y) = \sum_{i=1}^{n} |y_i - x_i|\]
Distancia de Minkowski (p=p): Es una generalización de las distancias Euclidiana y Manhattan. Cuando p=1 se convierte en la distancia de Manhattan y cuando p=2 se convierte en la distancia Euclidiana. Permite ajustar el parámetro p según las necesidades del problema.
Fórmula de la distancia de Minkowski:
\[d(x,y) = \left(\sum_{i=1}^{n} |y_i - x_i|^p\right)^{1/p}\]
Distancia de Hamming: Se utiliza principalmente con variables categóricas o datos binarios. Mide el número de posiciones en las que dos cadenas de igual longitud difieren. Es especialmente útil en procesamiento de lenguaje natural y en problemas donde las características no son numéricas.
Fórmula de la distancia de Hamming:
\[d(x,y) = \frac{1}{n}\sum_{i=1}^{n} \mathbb{1}(x_i \neq y_i)\]
Resumen comparativo de métricas de distancia
| Métrica | Fórmula resumida | Tipo de datos | Uso típico |
|---|---|---|---|
| Euclidiana | Raíz de la suma de diferencias al cuadrado | Numérico continuo | Datos con escala similar, geometría plana |
| Manhattan | Suma de diferencias absolutas | Numérico continuo | Datos con muchas dimensiones, cuadrículas |
| Minkowski | Generalización paramétrica (p variable) | Numérico continuo | Cuando se quiere ajustar el tipo de distancia |
| Hamming | Proporción de posiciones distintas | Categórico / binario | Texto, cadenas de caracteres, datos binarios |
¿Cómo funciona el algoritmo KNN paso a paso?
El funcionamiento del algoritmo KNN puede resumirse en los siguientes pasos:
Paso 1 – Cargar el conjunto de datos: Se introduce el conjunto de datos de entrenamiento, que contiene los puntos con sus respectivas etiquetas o valores conocidos.
Paso 2 – Elegir el valor de K: Se define el número de vecinos K que el algoritmo tomará en cuenta para hacer la predicción. Este valor es un hiperparámetro que debe ajustarse según el problema.
Paso 3 – Calcular distancias: Para cada nuevo punto de consulta, se calcula la distancia entre ese punto y todos los puntos del conjunto de entrenamiento usando alguna de las métricas disponibles (Euclidiana, Manhattan, etc.).
Paso 4 – Seleccionar los K vecinos más cercanos: Se ordenan todas las distancias de menor a mayor y se seleccionan los K puntos con menor distancia al punto de consulta.
Paso 5 – Realizar la predicción:
Para clasificación: se asigna al nuevo punto la clase que aparece con mayor frecuencia entre sus K vecinos (votación por mayoría).
Para regresión: se calcula el promedio de los valores de los K vecinos y ese promedio es la predicción.
Paso 6 – Evaluar el modelo: Se mide el rendimiento del modelo usando métricas como exactitud (accuracy), precisión, recall o error cuadrático medio (MSE), según el tipo de problema.
¿Cómo elegir el valor de K?
La elección del valor de K es uno de los aspectos más importantes al aplicar KNN, ya que influye directamente en la precisión y el comportamiento del modelo:
K pequeño (ej. K=1 o K=3): El modelo es muy sensible a los datos de entrenamiento. Capta bien los patrones locales pero también el ruido, lo que puede llevar a sobreajuste (overfitting). El límite de decisión resultante es muy irregular.
K grande (ej. K=50 o K=100): El modelo se vuelve más generalizado y suaviza el límite de decisión, pero puede ignorar patrones importantes, derivando en subajuste (underfitting). Además, incrementa el costo computacional.
Regla práctica: Se recomienda comenzar con \(K = \sqrt{n}\), donde n es el número de puntos del conjunto de entrenamiento, y luego ajustar mediante validación cruzada.
Usar K impar: En problemas de clasificación binaria se recomienda usar valores impares de K para evitar empates en la votación por mayoría.
Validación cruzada (Cross-Validation): Es el método más recomendado para encontrar el K óptimo. Consiste en dividir los datos en múltiples subconjuntos, entrenar y evaluar el modelo con distintos valores de K, y escoger aquel que minimice el error de validación.
Ventajas y Desventajas del algoritmo KNN
| Ventajas | Desventajas |
|---|---|
| Simple e intuitivo de implementar | Alto costo computacional en predicción |
| No requiere fase de entrenamiento (lazy learner) | Sensible a la escala de los datos |
| Versátil: sirve para clasificación y regresión | Sensible a variables irrelevantes (maldición dimensional) |
| Se adapta fácilmente con nuevos datos | Requiere mucha memoria (almacena todos los datos) |
Ventajas
Simple e intuitivo: Es uno de los algoritmos más fáciles de entender e implementar, lo que lo hace ideal para introducirse en el machine learning.
Sin fase de entrenamiento: KNN es un algoritmo “perezoso” (lazy learner), lo que significa que no construye un modelo explícito durante el entrenamiento. Simplemente almacena los datos y realiza los cálculos en el momento de la predicción.
Versátil: Puede usarse tanto para clasificación como para regresión.
Adaptable: Se adapta fácilmente cuando se agregan nuevos datos de entrenamiento, sin necesidad de reentrenar el modelo.
Desventajas
Alto costo computacional: Al no tener fase de entrenamiento, todo el cálculo ocurre en la predicción. Con conjuntos de datos grandes, calcular la distancia a cada punto es muy costoso en tiempo y memoria.
Sensible a la escala de los datos: Variables con rangos muy diferentes pueden dominar el cálculo de distancias, distorsionando los resultados. Por eso es indispensable escalar los datos antes de aplicar KNN.
Sensible a características irrelevantes: Si el conjunto de datos contiene muchas variables irrelevantes o redundantes, el rendimiento del modelo disminuye considerablemente (maldición de la dimensionalidad).
Requiere mucha memoria: Almacena todos los datos de entrenamiento, lo que puede ser un problema con datasets de gran tamaño.
Importancia de escalar los datos en KNN
Dado que KNN se basa en el cálculo de distancias entre puntos, es fundamental que todas las variables estén en una escala comparable. Si una variable tiene valores en el rango [0, 1] y otra en el rango [0, 10.000], la segunda dominará el cálculo de distancias y el modelo ignorará efectivamente a la primera.
Los dos métodos de escalado más utilizados son:
Normalización Min-Max: Transforma los valores al rango [0, 1]. Es útil cuando se conocen los límites de los datos.
\[X_{norm} = \frac{X - X_{min}}{X_{max} - X_{min}}\]
Estandarización Z-score: Transforma los datos para que tengan media 0 y desviación estándar 1. Es más robusta cuando hay valores atípicos.
\[X_{std} = \frac{X - \mu}{\sigma}\]
En R, el escalado puede realizarse fácilmente con las siguientes funciones:
# Estandarización Z-score
datos_escalados <- scale(datos)
# Normalización Min-Max
normalizar <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}
datos_normalizados <- as.data.frame(lapply(datos, normalizar))Comparación de KNN vs otros algoritmos de clasificación
| Algoritmo | Tipo | Entrenamiento | Interpretabilidad | Escala bien | Cuándo preferirlo |
|---|---|---|---|---|---|
| KNN | No paramétrico | Ninguno (lazy) | Media | No | Datos pequeños, límites no lineales |
| Regresión Logística | Paramétrico | Rápido | Alta | Sí | Relación lineal, necesidad de interpretación |
| Árbol de Decisión | No paramétrico | Rápido | Muy alta | Sí | Reglas explícitas, datos mixtos |
| SVM | Paramétrico | Moderado | Baja | Sí | Alta dimensionalidad, margen claro entre clases |
| Bosque Clasificación | Ensemble | Moderado/lento | Baja | Sí | Alta precisión, datos complejos o ruidosos |
| Naïve Bayes | Probabilístico | Muy rápido | Media | Sí | Texto, datos con independencia entre variables |
Para elegir el algoritmo más adecuado para un problema, es importante conocer cómo se compara KNN frente a otros métodos populares de clasificación:
KNN vs Regresión Logística: La regresión logística es un modelo paramétrico que aprende una función matemática explícita durante el entrenamiento, asumiendo una relación lineal entre las variables y la clase. KNN, en cambio, es no paramétrico y no hace supuestos sobre la distribución de los datos. KNN funciona mejor cuando los límites de decisión son no lineales y complejos, mientras que la regresión logística es más eficiente computacionalmente y más fácil de interpretar.
KNN vs Árbol de Decisión: Los árboles de decisión crean reglas explícitas y jerárquicas que son muy fáciles de visualizar e interpretar. KNN no genera reglas interpretables. Sin embargo, KNN puede capturar límites de decisión más suaves y continuos, mientras que los árboles de decisión generan fronteras rectangulares. Los árboles son más rápidos en la predicción, ya que no necesitan calcular distancias.
KNN vs Máquina de Vectores de Soporte (SVM): SVM busca el hiperplano óptimo que maximiza el margen entre clases, lo que lo hace muy robusto en espacios de alta dimensionalidad. KNN, en cambio, sufre con muchas dimensiones. SVM es generalmente más preciso en datos de alta dimensión, pero KNN es más simple de implementar y no requiere ajustar kernels ni parámetros de regularización.
KNN vs Bosque de Clasificación: El Bosque de Clasificación combina múltiples árboles y suele superar a KNN en precisión, manejo de datos ruidosos y características irrelevantes. KNN, sin embargo, no requiere entrenamiento previo y es más intuitivo. Los bosques escalan mejor a conjuntos de datos grandes y proporcionan medidas de importancia de variables.
KNN vs Naïve Bayes: Naïve Bayes es extremadamente rápido y funciona bien con datos de texto y alta dimensionalidad, pero asume independencia entre las variables. KNN no hace este supuesto y puede capturar relaciones más complejas entre variables, aunque a un mayor costo computacional.
En resumen, KNN es ideal cuando el conjunto de datos es pequeño o mediano, los límites de decisión son no lineales, y se prioriza la simplicidad sobre la velocidad de predicción. Para conjuntos de datos grandes o de alta dimensionalidad, suelen preferirse alternativas como SVM, Bosques de Clasificación o redes neuronales.
Aplicaciones reales del algoritmo KNN
El algoritmo KNN se aplica en una amplia variedad de campos del mundo real gracias a su simplicidad y versatilidad:
Sistemas de recomendación: Plataformas como Netflix o Spotify utilizan variantes de KNN para recomendar películas, series o canciones basándose en los gustos de usuarios con perfiles similares al del usuario actual.
Diagnóstico médico: KNN se utiliza para clasificar tumores como benignos o malignos a partir de características clínicas, o para identificar enfermedades basándose en los síntomas y resultados de laboratorio de pacientes similares.
Reconocimiento de imágenes y escritura: Se aplica para identificar dígitos escritos a mano o clasificar imágenes comparándolas con ejemplos previamente etiquetados en el conjunto de entrenamiento.
Detección de fraudes financieros: Los bancos e instituciones financieras usan KNN para detectar transacciones inusuales comparando el comportamiento de una operación con el de transacciones históricas similares.
Predicción de precios: En el sector inmobiliario, KNN puede predecir el precio de una propiedad basándose en los precios de propiedades cercanas con características similares (tamaño, ubicación, número de habitaciones).
Clasificación de textos: En procesamiento de lenguaje natural (NLP), KNN se usa para clasificar documentos o correos electrónicos según su contenido, comparándolos con documentos ya etiquetados.
Base de Datos
Clasificación de la flor de iris
La clasificación de flores de iris es una tarea clásica de aprendizaje automático utilizada para el aprendizaje y la práctica de algoritmos de clasificación. El conjunto de datos contiene características como la longitud y el ancho del sépalo, así como la longitud y el ancho del pétalo, para tres especies diferentes de flores de iris.
Objetivo
Desarrollar un modelo de clasificación basado en el algoritmo K-Nearest Neighbors (KNN) para predecir la especie de una flor Iris (Iris setosa, Iris versicolor e Iris virginica) a partir de sus características morfológicas: longitud y ancho del sépalo, y longitud y ancho del pétalo. Por lo que, KNN es ideal para este dataset porque las tres especies tienen patrones de medidas morfológicas distintos, lo que permite que el algoritmo clasifique correctamente basándose en la similitud entre observaciones vecinas.
Datos exportados desde excel
iris <- iris
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaHipótesis
Hipótesis principal: El algoritmo K-Nearest Neighbors (KNN) puede clasificar correctamente las especies de la flor Iris (setosa, versicolor y virginica) con una precisión superior al 90%, utilizando como variables predictoras la longitud y ancho del sépalo, y la longitud y ancho del pétalo.
Hipótesis nula: Las características morfológicas de la flor Iris (longitud y ancho del sépalo y pétalo) no permiten clasificar correctamente la especie mediante el algoritmo KNN, obteniendo una precisión igual o inferior al 50%.
Hipótesis alternativa: Las características morfológicas de la flor Iris sí permiten clasificar correctamente la especie mediante el algoritmo KNN, obteniendo una precisión superior al 50%, lo que demuestra que existe una relación entre las medidas morfológicas y la especie de la flor.
Código en RStudio
# Cargar librerías
library(class)## Warning: package 'class' was built under R version 4.5.3library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.5.3# Cargar datos
iris <- iris
# Normalización
normalizar <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}
iris_norm <- as.data.frame(lapply(iris[, 1:4], normalizar))
iris_norm$Species <- iris$Species
# División en entrenamiento (70%) y prueba (30%)
set.seed(123)
indice <- sample(1:nrow(iris_norm), size = 0.7 * nrow(iris_norm))
entrenamiento <- iris_norm[indice, ]
prueba <- iris_norm[-indice, ]
# Separar predictoras y objetivo
train_x <- entrenamiento[, 1:4]
test_x <- prueba[, 1:4]
train_y <- entrenamiento$Species
test_y <- prueba$Species
# Aplicar KNN con k = 5
predicciones <- knn(train = train_x,
test = test_x,
cl = train_y,
k = 5)
# Matriz de confusión manual
confusion <- table(Predicho = predicciones, Real = test_y)
print(confusion)## Real
## Predicho setosa versicolor virginica
## setosa 14 0 0
## versicolor 0 17 1
## virginica 0 1 12# Accuracy manual
accuracy <- sum(diag(confusion)) / sum(confusion)
cat("Precisión del modelo KNN:", round(accuracy * 100, 2), "%\n")## Precisión del modelo KNN: 95.56 %# Gráfico con ggplot2
prueba$Predicho <- predicciones
ggplot(prueba, aes(x = Petal.Length, y = Petal.Width, color = Predicho)) +
geom_point(size = 3) +
labs(title = "Clasificación KNN - Flor Iris",
x = "Longitud del Pétalo (normalizada)",
y = "Ancho del Pétalo (normalizada)",
color = "Especie Predicha") +
theme_minimal()
Análisis de Resultados
El modelo KNN con k=5 obtuvo una precisión del 95.56%, evaluado sobre 45 observaciones del conjunto de prueba (30% del dataset).
Matriz de Confusión
| Predicho | Setosa | Versicolor | Virginica |
|---|---|---|---|
| Setosa | 14 | 0 | 0 |
| Versicolor | 0 | 17 | 1 |
| Virginica | 0 | 1 | 12 |
Iris setosa fue clasificada perfectamente (14/14 correctas)
Iris versicolor tuvo 1 error (clasificada como virginica)
Iris virginica tuvo 1 error (clasificada como versicolor)
Interpretación respecto a las Hipótesis
Se rechaza la hipótesis nula: El modelo no obtuvo una precisión menor o igual al 50%, por lo tanto H₀ queda descartada.
Se acepta la hipótesis alternativa: El modelo alcanzó un 95.56% de precisión, superando ampliamente el umbral del 50%, lo que confirma que las características morfológicas (longitud y ancho del sépalo y pétalo) sí tienen poder predictivo para clasificar la especie de la flor Iris.
Se confirma la hipótesis principal: Se planteó una precisión superior al 90% y el modelo alcanzó 95.56%, superando dicho umbral y validando completamente la hipótesis.
El algoritmo KNN demostró ser altamente efectivo para clasificar las especies de la flor Iris, con solo 2 errores de 45 predicciones. La especie más fácil de clasificar fue Iris setosa, mientras que versicolor y virginica presentaron una leve confusión entre sí, lo cual es esperado dado que estas dos especies tienen características morfológicas más similares entre ellas.
BOSQUES DE CLASIFICACIÓN
¿Qué es un Bosque de Clasificación?
Un Bosque de Clasificación es un algoritmo de aprendizaje supervisado que construye un conjunto de árboles de decisión durante el entrenamiento y combina sus predicciones mediante votación por mayoría para determinar la clase final de un nuevo punto de datos. Es la aplicación del método de Bosque Aleatorio (Random Forest) específicamente orientada a tareas de clasificación, donde la variable objetivo es categórica (por ejemplo: sano/enfermo, fraude/no fraude, aprobado/reprobado).
A diferencia de un único árbol de decisión, el bosque de clasificación no depende de un solo modelo que puede ser inestable o propenso al sobreajuste. En cambio, consolida las opiniones de cientos de árboles independientes, donde cada uno fue entrenado con datos y características distintas, logrando así una clasificación más robusta, precisa y generalizable.
Árboles de Decisión: la base del Bosque de Clasificación
Un árbol de decisión es un modelo que divide los datos en subconjuntos más pequeños de forma jerárquica mediante condiciones sobre las características. En el contexto de clasificación, cada camino desde la raíz hasta una hoja representa una secuencia de reglas que asigna una clase a los datos. Su estructura se compone de:
Nodo raíz: Punto de partida que representa el conjunto completo de datos y contiene la primera división basada en la característica más discriminante.
Nodos internos: Cada nodo representa una condición sobre una característica (ej. ¿temperatura > 38°C?). Dependiendo de la respuesta, los datos se dirigen hacia una rama u otra.
Ramas: Conexiones entre nodos que representan el resultado de una condición (verdadero/falso) y conducen a otros nodos o a hojas.
Nodos hoja: Nodos finales que contienen la etiqueta de clase asignada a los datos que llegaron hasta ese punto. En clasificación, cada hoja representa una clase predicha.
Los criterios más usados para dividir los nodos en árboles de clasificación son:
Índice de Gini: Mide la impureza de un nodo. Un valor de 0 indica que todos los elementos del nodo pertenecen a la misma clase (nodo puro). El algoritmo busca la división que minimice el Gini ponderado de los nodos hijos.
\[Gini = 1 - \sum_{i=1}^{C} p_i^2\]
Entropía (Ganancia de información): Mide el desorden o la incertidumbre dentro de un nodo. El algoritmo selecciona la división que maximice la reducción de entropía, es decir, la ganancia de información.
\[Entropía = -\sum_{i=1}^{C} p_i \log_2(p_i)\]
donde \(p_i\) es la proporción de elementos de la clase \(i\) en el nodo y \(C\) es el número total de clases.
¿Cómo funciona el Bosque de Clasificación paso a paso?
El Bosque de Clasificación se basa en dos técnicas clave: Bagging y selección aleatoria de características. Su proceso de construcción y predicción es el siguiente:
Paso 1 – Muestreo Bootstrap: Se generan múltiples subconjuntos del conjunto de entrenamiento seleccionando muestras con reemplazo. Cada subconjunto contiene aproximadamente el 63% de los datos originales únicos; el 37% restante (llamado muestra Out-of-Bag u OOB) se usará para validar cada árbol.
Paso 2 – Selección aleatoria de características: Al construir cada nodo de cada árbol, se evalúa únicamente un subconjunto aleatorio de características, no todas. En clasificación, el número recomendado es \(\sqrt{p}\), donde \(p\) es el total de características disponibles. Esto garantiza diversidad entre los árboles.
Paso 3 – Construcción de árboles de clasificación: Con cada muestra bootstrap y su subconjunto de características, se entrena un árbol de decisión de clasificación de forma independiente, usando Gini o Entropía como criterio de división. Los árboles generalmente se dejan crecer sin poda para capturar patrones detallados.
Paso 4 – Predicción individual por árbol: Cuando llega un nuevo punto de datos, cada árbol del bosque lo clasifica de forma independiente, asignándole una etiqueta de clase según las reglas aprendidas.
Paso 5 – Votación por mayoría: La clase predicha por el bosque es aquella que recibe más votos entre todos los árboles. Por ejemplo, si 70 de 100 árboles predicen “clase A” y 30 predicen “clase B”, la predicción final es “clase A”.
Paso 6 – Evaluación del modelo: Se evalúa el rendimiento con métricas de clasificación como exactitud (accuracy), precisión, recall, F1-score y matriz de confusión. También se puede usar el error OOB como estimación interna del error sin necesidad de un conjunto de validación separado.
Hiperparámetros importantes del Bosque de Clasificación
Los hiperparámetros son configuraciones definidas antes del entrenamiento que controlan el comportamiento del bosque. Los más relevantes para clasificación son:
ntree (número de árboles): Define cuántos árboles de clasificación se construirán. Más árboles generalmente mejoran la estabilidad y precisión de las predicciones, pero aumentan el costo computacional. Valores típicos: entre 100 y 500.
mtry (características por nodo): Número de características evaluadas en cada división. Para clasificación, el valor por defecto es \(\sqrt{p}\). Reducirlo aumenta la diversidad entre árboles; aumentarlo los hace más similares entre sí.
max_depth (profundidad máxima): Limita cuántas divisiones puede tener cada árbol desde la raíz hasta una hoja. Árboles muy profundos tienden al sobreajuste; muy poco profundos generan subajuste.
min_samples_split: Número mínimo de muestras que debe tener un nodo para poder dividirse. Valores más altos generan árboles más simples y reducen el sobreajuste.
min_samples_leaf: Número mínimo de muestras que debe tener un nodo hoja. Ayuda a evitar que el árbol se ajuste al ruido de los datos.
class.weights: Permite asignar mayor peso a clases minoritarias, lo cual es especialmente importante en problemas de clasificación con clases desbalanceadas (ej. detección de fraude o enfermedades raras).
En R, el Bosque de Clasificación se
implementa con el paquete randomForest:
library(randomForest)
modelo_bosque <- randomForest(
clase ~ ., # Variable objetivo categórica
data = datos_train,
ntree = 300, # Número de árboles
mtry = floor(sqrt(ncol(datos_train) - 1)), # Características por nodo
importance = TRUE, # Calcular importancia de variables
classwt = c("0" = 1, "1" = 3) # Pesos por clase (opcional)
)
# Resumen del modelo
print(modelo_bosque)
# Predicciones sobre datos nuevos
predicciones <- predict(modelo_bosque, newdata = datos_test)
# Matriz de confusión
table(predicciones, datos_test$clase)Importancia de Variables en el Bosque de Clasificación
Una de las ventajas más valiosas del Bosque de Clasificación es su capacidad para medir la importancia de cada variable en la tarea de clasificación. Esto permite identificar qué características son más determinantes para distinguir entre clases y cuáles pueden eliminarse sin pérdida significativa de rendimiento.
Las dos métricas principales son:
Mean Decrease in Impurity (MDI) o Importancia Gini: Mide cuánto reduce cada variable la impureza total del bosque (índice de Gini) a lo largo de todos los árboles y todas las divisiones. Variables que separan bien las clases en muchos árboles reciben mayor puntuación. Es rápida de calcular pero puede favorecer variables con muchas categorías.
Mean Decrease in Accuracy (MDA) o Permutation Importance: Mide cuánto cae la exactitud de clasificación cuando los valores de una variable se permutan aleatoriamente, destruyendo su relación con la clase objetivo. Si permutar una variable causa una gran caída en la exactitud, esa variable es muy importante para clasificar correctamente. Es más confiable que MDI pero más costosa computacionalmente.
En R, la importancia puede visualizarse así:
# Ver tabla de importancia
importance(modelo_bosque)
# Gráfico de importancia de variables
varImpPlot(modelo_bosque,
main = "Importancia de Variables - Bosque de Clasificación",
col = "steelblue")Error Out-of-Bag (OOB) en Clasificación
El error Out-of-Bag (OOB) es una estimación interna del error de clasificación que el Bosque de Clasificación calcula automáticamente durante el entrenamiento, sin necesidad de un conjunto de validación separado.
Su funcionamiento se basa en el muestreo bootstrap: como cada árbol se entrena con aproximadamente el 63% de los datos, el 37% restante (las muestras OOB de ese árbol) se usa para evaluarlo. Cada observación es clasificada únicamente por los árboles en los que no participó durante el entrenamiento. La tasa de error OOB global es el porcentaje de observaciones mal clasificadas en estas predicciones OOB.
El error OOB es una estimación honesta y confiable del error de generalización, comparable a la validación cruzada pero con menor costo computacional. En R se obtiene directamente del modelo:
# El error OOB aparece en el resumen del modelo
print(modelo_bosque)
# También puede extraerse directamente
modelo_bosque$err.rate[nrow(modelo_bosque$err.rate), "OOB"]Bosque de Clasificación vs Árbol de Decisión Individual
| Criterio | Árbol de Decisión Individual | Bosque de Clasificación |
|---|---|---|
| Precisión | Moderada | Alta |
| Sobreajuste | Alto riesgo | Bajo riesgo |
| Interpretabilidad | Muy alta (reglas visibles) | Baja (caja negra) |
| Estabilidad | Baja (sensible a cambios) | Alta |
| Clases desbalanceadas | Manejo deficiente | Buen manejo con class.weights |
| Costo computacional | Bajo | Moderado/alto |
| Importancia de variables | No disponible directamente | Sí (MDI y MDA) |
| Cuándo usarlo | Interpretabilidad prioritaria | Máxima precisión y robustez |
Para entender por qué el Bosque de Clasificación supera al árbol de decisión individual en la mayoría de los escenarios, es importante comparar ambos enfoques directamente:
Precisión en clasificación: Un árbol individual puede tener una exactitud aceptable, pero es muy sensible a los datos de entrenamiento. El bosque, al combinar cientos de árboles mediante votación, reduce los errores individuales y obtiene predicciones de clase significativamente más precisas y estables.
Sobreajuste: Los árboles de clasificación profundos memorizan los datos de entrenamiento, clasificando perfectamente el conjunto de entrenamiento pero fallando en datos nuevos. El Bosque de Clasificación combate esto gracias a la diversidad entre árboles, generalizando mucho mejor.
Interpretabilidad: El árbol individual es altamente interpretable: sus reglas de clasificación pueden visualizarse y explicarse fácilmente. El bosque es una “caja negra”: no es posible seguir la lógica de cientos de árboles simultáneamente, aunque la importancia de variables ofrece cierta explicabilidad.
Estabilidad: Pequeños cambios en los datos de entrenamiento pueden producir árboles de clasificación completamente diferentes. El Bosque de Clasificación es mucho más estable, ya que la variabilidad de un árbol queda compensada por los demás.
Clases desbalanceadas: Ante clases con muy diferente frecuencia (ej. 95% clase A, 5% clase B), el árbol individual tiende a ignorar la clase minoritaria. El bosque maneja mejor este problema, especialmente al usar el parámetro
class.weights.Cuándo usar cada uno: Se recomienda el árbol individual cuando la interpretabilidad es prioritaria o el conjunto de datos es muy pequeño. El Bosque de Clasificación es preferible cuando se busca máxima exactitud, los datos son complejos o ruidosos, o cuando hay clases desbalanceadas.
Bagging vs Boosting en Clasificación
| Característica | Bagging (Bosque de Clasificación) | Boosting (AdaBoost, XGBoost) |
|---|---|---|
| Entrenamiento | Paralelo e independiente | Secuencial (cada árbol corrige al anterior) |
| Objetivo principal | Reducir varianza | Reducir sesgo |
| Sensibilidad al ruido | Robusto | Sensible (puede sobreajustarse) |
| Velocidad de entrenamiento | Rápido (paralelizable) | Más lento (secuencial) |
| Riesgo de sobreajuste | Bajo | Moderado/alto si no se ajusta bien |
| Complejidad de ajuste | Simple | Requiere ajuste cuidadoso |
| Ejemplo típico | Random Forest | AdaBoost, Gradient Boosting, XGBoost |
El Bosque de Clasificación pertenece a la familia del Bagging. Existe otra familia llamada Boosting que también combina múltiples clasificadores, pero con una filosofía completamente diferente:
Bagging (Bosque de Clasificación): Entrena múltiples árboles de clasificación de forma paralela e independiente, cada uno sobre una muestra bootstrap distinta. Los árboles no se comunican entre sí. La predicción final se obtiene por votación por mayoría. El objetivo es reducir la varianza y evitar el sobreajuste.
Boosting (AdaBoost, Gradient Boosting): Entrena los clasificadores de forma secuencial. Cada nuevo clasificador se enfoca en corregir los errores del anterior: las observaciones mal clasificadas reciben mayor peso en la siguiente iteración. El objetivo es reducir el sesgo y mejorar clasificadores débiles. Es más preciso que Bagging en muchos casos, pero más propenso al sobreajuste con datos ruidosos y más lento de entrenar.
Diferencias clave para clasificación:
Bagging es más robusto ante ruido y valores atípicos en las etiquetas de clase; Boosting puede verse muy afectado por etiquetas incorrectas.
Bagging es más fácil de paralelizar y escalar; Boosting es inherentemente secuencial.
En problemas con clases desbalanceadas, Boosting (especialmente XGBoost) suele superar al Bosque de Clasificación con el ajuste correcto.
Variantes del Bosque de Clasificación
| Variante | Familia | Aleatoriedad | Velocidad entrenamiento | Riesgo sobreajuste | Fortaleza principal |
|---|---|---|---|---|---|
| Random Forest | Bagging | Alta | Rápido | Bajo | Robustez y estabilidad general |
| Extra Trees | Bagging | Muy alta | Muy rápido | Muy bajo | Datos con ruido, velocidad |
| Gradient Boosting | Boosting | Baja | Lento | Moderado | Alta precisión en clasificación |
| XGBoost | Boosting | Baja | Rápido (optimizado) | Moderado | Regularización, clasificación binaria/multiclase |
| LightGBM | Boosting | Baja | Muy rápido | Moderado | Grandes conjuntos de datos |
| CatBoost | Boosting | Baja | Rápido | Moderado | Variables categóricas nativas |
A partir del concepto del Bosque de Clasificación se han desarrollado variantes que mejoran o adaptan sus características para distintos escenarios de clasificación:
Extra Trees para Clasificación (Extremely Randomized Trees): Variante que introduce aún más aleatoriedad al seleccionar el punto de corte de cada nodo de forma completamente aleatoria, en lugar de buscar el mejor corte entre las características evaluadas. Esto hace que el entrenamiento sea más rápido, el modelo menos propenso al sobreajuste en clasificación, y que los límites de decisión sean más suaves. Es especialmente útil cuando los datos de clasificación tienen mucho ruido o cuando la velocidad de entrenamiento es una prioridad.
Gradient Boosting para Clasificación: Construye los árboles de clasificación de forma secuencial, donde cada árbol corrige los errores de clasificación del conjunto anterior ajustándose al gradiente de la función de pérdida (generalmente log-loss o entropía cruzada para clasificación). Produce modelos de clasificación muy precisos, pero requiere un ajuste cuidadoso de hiperparámetros como la tasa de aprendizaje y la profundidad de los árboles para evitar sobreajuste.
XGBoost para Clasificación: Implementación optimizada de Gradient Boosting especialmente eficiente para clasificación. Incorpora regularización L1 y L2 para controlar el sobreajuste, manejo nativo de valores faltantes, y soporte para clasificación binaria y multiclase mediante los objetivos
binary:logisticymulti:softmaxrespectivamente. Es uno de los algoritmos de clasificación más usados en competencias de ciencia de datos.LightGBM para Clasificación: Variante de Gradient Boosting optimizada para velocidad y eficiencia en grandes conjuntos de datos de clasificación. Crece los árboles hoja por hoja (leaf-wise) en lugar de nivel por nivel, lo que permite capturar patrones complejos de clasificación con menos árboles. Soporta clasificación binaria y multiclase de forma nativa.
CatBoost para Clasificación: Variante diseñada para manejar variables categóricas de forma nativa en problemas de clasificación, sin necesidad de codificación previa (one-hot encoding). Es especialmente útil cuando el conjunto de datos de clasificación contiene muchas variables categóricas con alta cardinalidad.
En R, estas variantes para clasificación pueden implementarse así:
# Extra Trees para clasificación
library(extraTrees)
modelo_et <- extraTrees(x = datos_train[, -ncol(datos_train)],
y = datos_train$clase,
ntree = 300)
pred_et <- predict(modelo_et, newdata = datos_test[, -ncol(datos_test)])
# Gradient Boosting para clasificación
library(gbm)
modelo_gbm <- gbm(clase ~ ., data = datos_train,
distribution = "bernoulli", # binaria; "multinomial" para multiclase
n.trees = 300,
interaction.depth = 4,
shrinkage = 0.01)
# XGBoost para clasificación binaria
library(xgboost)
modelo_xgb <- xgboost(
data = as.matrix(datos_train[, -ncol(datos_train)]),
label = as.numeric(datos_train$clase) - 1,
nrounds = 100,
objective = "binary:logistic",
eval_metric = "logloss"
)Ventajas y Desventajas del Bosque de Clasificación
| Ventajas | Desventajas |
|---|---|
| Alta exactitud en clasificación | Poca interpretabilidad (caja negra) |
| Resistente al sobreajuste | Alto costo computacional |
| Maneja variables numéricas y categóricas | Lento en predicción en tiempo real |
| Proporciona importancia de variables | No óptimo con datos muy escasos |
| Estimación interna de error (OOB) | Requiere más memoria que un árbol individual |
| Maneja clases desbalanceadas con class.weights | Difícil de explicar a usuarios no técnicos |
Ventajas
Alta exactitud en clasificación: Al combinar cientos de árboles mediante votación, el bosque obtiene predicciones de clase mucho más precisas que un clasificador individual.
Resistente al sobreajuste: La diversidad entre árboles generada por el muestreo aleatorio hace que el bosque generalice bien a datos nuevos, incluso en problemas de clasificación complejos.
Manejo de variables mixtas: Puede trabajar con variables numéricas y categóricas simultáneamente, y es robusto ante valores faltantes, lo que lo hace versátil para distintos tipos de problemas de clasificación.
Importancia de variables: Identifica qué características son más relevantes para distinguir entre clases, lo que apoya la selección de variables y la interpretación del problema.
Estimación OOB: Permite estimar el error de clasificación de forma interna durante el entrenamiento, sin necesitar un conjunto de validación separado.
Manejo de clases desbalanceadas: Mediante el ajuste de pesos por clase (
class.weights), el bosque puede mejorar la clasificación de clases minoritarias.
Desventajas
Poca interpretabilidad: A diferencia de un árbol individual, el bosque de cientos de clasificadores es difícil de interpretar y explicar a usuarios no técnicos. Se le considera un modelo de “caja negra”.
Alto costo computacional: Entrenar y almacenar cientos de árboles de clasificación requiere más tiempo y memoria, especialmente con conjuntos de datos muy grandes o muchas características.
Lento en predicción en tiempo real: Al recorrer todos los árboles para clasificar cada nueva observación, puede ser lento en aplicaciones que requieren respuestas instantáneas.
No óptimo para datos muy dispersos: En problemas de clasificación con muy pocas muestras por clase o con datos muy escasos, su rendimiento puede deteriorarse.
Aplicaciones reales del Bosque de Clasificación
El Bosque de Clasificación es uno de los algoritmos más utilizados en la industria para tareas donde la variable objetivo es una categoría. Algunas de sus aplicaciones más destacadas son:
Diagnóstico médico: Permite clasificar pacientes como enfermos o sanos, o identificar el tipo de enfermedad a partir de datos clínicos (análisis de sangre, síntomas, historial médico). Se ha aplicado con éxito en la detección de cáncer, diabetes y enfermedades cardiovasculares.
Detección de fraudes financieros: Los bancos e instituciones financieras usan el Bosque de Clasificación para categorizar transacciones como fraudulentas o legítimas, basándose en patrones históricos de comportamiento.
Clasificación de imágenes satelitales: En teledetección, el bosque clasifica cada píxel de una imagen satelital en categorías como bosque, agua, suelo urbano o cultivos, con alta precisión incluso en paisajes complejos.
Predicción de abandono de clientes (Churn): Las empresas de telecomunicaciones, banca y e-commerce usan el bosque para clasificar a sus clientes como propensos a abandonar el servicio o a permanecer, permitiendo tomar acciones preventivas.
Mantenimiento predictivo en ingeniería: Clasifica el estado de maquinaria industrial (normal, advertencia, fallo inminente) a partir de datos de sensores, permitiendo programar el mantenimiento antes de que ocurra una falla costosa.
Bioinformática: Clasifica tipos de cáncer o variantes genéticas a partir de datos de expresión génica, identificando qué genes son más relevantes para distinguir entre grupos de pacientes.
Base de Datos
Conjunto de datos sintéticos de la trayectoria profesional de un jugador de fútbol
Este conjunto de datos presenta una representación sintética pero realista del desarrollo profesional de los futbolistas a lo largo de varias temporadas. Cada registro corresponde a una única combinación de jugador y temporada, lo que permite observar cómo evolucionan los jugadores con el tiempo en términos de rendimiento, tiempo de juego, lesiones y valor de mercado. Si bien los datos son completamente sintéticos, las distribuciones y las relaciones entre las variables están diseñadas para asemejarse lo más posible a la dinámica real del fútbol.
Objetivo
Desarrollar un modelo de clasificación basado en el algoritmo Bosques Aleatorios (Random Forest) para predecir la etapa de carrera (temprana, pico y declive) de un jugador de fútbol, a partir de sus características de rendimiento como partidos jugados, minutos, goles, asistencias, valoración, lesiones y valor de mercado. Para ello, se cargará y explorará el conjunto de datos sintético de trayectorias de jugadores de fútbol obtenido de Kaggle, se preprocesarán los datos seleccionando las variables más relevantes, y se evaluará el rendimiento del modelo mediante la matriz de confusión y la precisión, con el fin de identificar qué características del rendimiento deportivo son más determinantes para predecir la etapa de carrera de un jugador.
Datos exportados desde excel
datos_futbol <- read.csv(file.choose())
datos_futbol## player_id nationality position birth_year season age club_level matches
## 1 1 Argentina GK 1993 2010/2011 18 4 29
## 2 1 Argentina GK 1993 2011/2012 19 4 23
## 3 1 Argentina GK 1993 2012/2013 20 1 18
## 4 1 Argentina GK 1993 2013/2014 21 1 14
## 5 1 Argentina GK 1993 2014/2015 22 2 13
## 6 1 Argentina GK 1993 2015/2016 23 1 8
## 7 1 Argentina GK 1993 2016/2017 24 2 30
## 8 1 Argentina GK 1993 2017/2018 25 5 32
## 9 1 Argentina GK 1993 2018/2019 26 5 20
## 10 1 Argentina GK 1993 2019/2020 27 1 7
## 11 1 Argentina GK 1993 2020/2021 28 5 12
## 12 2 Brazil GK 2001 2010/2011 20 5 32
## 13 2 Brazil GK 2001 2011/2012 21 5 29
## 14 2 Brazil GK 2001 2012/2013 22 1 15
## 15 2 Brazil GK 2001 2013/2014 23 1 25
## 16 2 Brazil GK 2001 2014/2015 24 5 17
## 17 2 Brazil GK 2001 2015/2016 25 2 7
## 18 2 Brazil GK 2001 2016/2017 26 5 25
## 19 2 Brazil GK 2001 2017/2018 27 2 22
## 20 3 Germany GK 1994 2010/2011 17 4 14
## 21 3 Germany GK 1994 2011/2012 18 5 6
## 22 3 Germany GK 1994 2012/2013 19 3 20
## 23 3 Germany GK 1994 2013/2014 20 3 33
## 24 3 Germany GK 1994 2014/2015 21 5 10
## 25 3 Germany GK 1994 2015/2016 22 2 31
## 26 3 Germany GK 1994 2016/2017 23 1 30
## 27 3 Germany GK 1994 2017/2018 24 2 32
## 28 3 Germany GK 1994 2018/2019 25 3 22
## 29 3 Germany GK 1994 2019/2020 26 1 21
## 30 3 Germany GK 1994 2020/2021 27 5 22
## 31 3 Germany GK 1994 2021/2022 28 2 10
## 32 4 Italy MID 1993 2010/2011 19 5 28
## 33 4 Italy MID 1993 2011/2012 20 2 8
## 34 4 Italy MID 1993 2012/2013 21 4 17
## 35 4 Italy MID 1993 2013/2014 22 5 6
## 36 4 Italy MID 1993 2014/2015 23 1 9
## 37 4 Italy MID 1993 2015/2016 24 4 33
## 38 4 Italy MID 1993 2016/2017 25 5 10
## 39 4 Italy MID 1993 2017/2018 26 3 33
## 40 4 Italy MID 1993 2018/2019 27 5 8
## 41 5 Germany DEF 2004 2010/2011 20 2 16
## 42 5 Germany DEF 2004 2011/2012 21 1 26
## 43 5 Germany DEF 2004 2012/2013 22 5 30
## 44 5 Germany DEF 2004 2013/2014 23 1 33
## 45 5 Germany DEF 2004 2014/2015 24 3 23
## 46 5 Germany DEF 2004 2015/2016 25 3 36
## 47 5 Germany DEF 2004 2016/2017 26 3 12
## 48 5 Germany DEF 2004 2017/2018 27 1 27
## 49 5 Germany DEF 2004 2018/2019 28 4 24
## 50 5 Germany DEF 2004 2019/2020 29 2 21
## 51 5 Germany DEF 2004 2020/2021 30 5 15
## 52 6 Germany FWD 1990 2010/2011 20 5 11
## 53 6 Germany FWD 1990 2011/2012 21 3 30
## 54 6 Germany FWD 1990 2012/2013 22 3 13
## 55 6 Germany FWD 1990 2013/2014 23 2 34
## 56 6 Germany FWD 1990 2014/2015 24 2 25
## 57 6 Germany FWD 1990 2015/2016 25 3 7
## 58 6 Germany FWD 1990 2016/2017 26 5 6
## 59 6 Germany FWD 1990 2017/2018 27 4 33
## 60 6 Germany FWD 1990 2018/2019 28 5 18
## 61 6 Germany FWD 1990 2019/2020 29 3 10
## 62 6 Germany FWD 1990 2020/2021 30 2 30
## 63 6 Germany FWD 1990 2021/2022 31 5 15
## 64 6 Germany FWD 1990 2022/2023 32 4 19
## 65 6 Germany FWD 1990 2023/2024 33 2 36
## 66 7 Germany GK 1989 2010/2011 17 3 35
## 67 7 Germany GK 1989 2011/2012 18 2 6
## 68 7 Germany GK 1989 2012/2013 19 2 28
## 69 7 Germany GK 1989 2013/2014 20 2 32
## 70 7 Germany GK 1989 2014/2015 21 1 26
## 71 7 Germany GK 1989 2015/2016 22 4 16
## 72 7 Germany GK 1989 2016/2017 23 2 33
## 73 8 Netherlands DEF 2004 2010/2011 18 3 31
## 74 8 Netherlands DEF 2004 2011/2012 19 4 20
## 75 8 Netherlands DEF 2004 2012/2013 20 3 24
## 76 8 Netherlands DEF 2004 2013/2014 21 3 17
## 77 8 Netherlands DEF 2004 2014/2015 22 3 29
## 78 8 Netherlands DEF 2004 2015/2016 23 2 37
## 79 8 Netherlands DEF 2004 2016/2017 24 5 7
## 80 8 Netherlands DEF 2004 2017/2018 25 3 11
## 81 8 Netherlands DEF 2004 2018/2019 26 4 17
## 82 8 Netherlands DEF 2004 2019/2020 27 2 23
## 83 8 Netherlands DEF 2004 2020/2021 28 4 34
## 84 9 France FWD 1997 2010/2011 19 1 30
## 85 9 France FWD 1997 2011/2012 20 1 24
## 86 9 France FWD 1997 2012/2013 21 3 14
## 87 9 France FWD 1997 2013/2014 22 4 5
## 88 9 France FWD 1997 2014/2015 23 4 19
## 89 9 France FWD 1997 2015/2016 24 5 5
## 90 9 France FWD 1997 2016/2017 25 3 27
## 91 9 France FWD 1997 2017/2018 26 5 36
## 92 9 France FWD 1997 2018/2019 27 5 9
## 93 9 France FWD 1997 2019/2020 28 5 13
## 94 9 France FWD 1997 2020/2021 29 1 28
## 95 9 France FWD 1997 2021/2022 30 1 37
## 96 10 Italy FWD 1989 2010/2011 19 3 27
## 97 10 Italy FWD 1989 2011/2012 20 5 8
## 98 10 Italy FWD 1989 2012/2013 21 3 11
## 99 10 Italy FWD 1989 2013/2014 22 4 24
## 100 10 Italy FWD 1989 2014/2015 23 2 13
## 101 10 Italy FWD 1989 2015/2016 24 3 14
## 102 10 Italy FWD 1989 2016/2017 25 1 26
## 103 10 Italy FWD 1989 2017/2018 26 5 22
## 104 10 Italy FWD 1989 2018/2019 27 4 7
## 105 11 Spain DEF 2001 2010/2011 17 4 31
## 106 11 Spain DEF 2001 2011/2012 18 1 20
## 107 11 Spain DEF 2001 2012/2013 19 4 5
## 108 11 Spain DEF 2001 2013/2014 20 2 18
## 109 11 Spain DEF 2001 2014/2015 21 2 8
## 110 11 Spain DEF 2001 2015/2016 22 2 34
## 111 11 Spain DEF 2001 2016/2017 23 1 20
## 112 11 Spain DEF 2001 2017/2018 24 4 22
## 113 12 Brazil DEF 1994 2010/2011 18 5 35
## 114 12 Brazil DEF 1994 2011/2012 19 4 33
## 115 12 Brazil DEF 1994 2012/2013 20 2 9
## 116 12 Brazil DEF 1994 2013/2014 21 3 25
## 117 12 Brazil DEF 1994 2014/2015 22 1 5
## 118 12 Brazil DEF 1994 2015/2016 23 3 26
## 119 12 Brazil DEF 1994 2016/2017 24 1 28
## 120 12 Brazil DEF 1994 2017/2018 25 5 9
## 121 12 Brazil DEF 1994 2018/2019 26 1 13
## 122 12 Brazil DEF 1994 2019/2020 27 4 16
## 123 12 Brazil DEF 1994 2020/2021 28 4 29
## 124 12 Brazil DEF 1994 2021/2022 29 4 36
## 125 13 England GK 1989 2010/2011 19 3 27
## 126 13 England GK 1989 2011/2012 20 1 9
## 127 13 England GK 1989 2012/2013 21 3 29
## 128 13 England GK 1989 2013/2014 22 1 24
## 129 13 England GK 1989 2014/2015 23 2 28
## 130 13 England GK 1989 2015/2016 24 4 33
## 131 13 England GK 1989 2016/2017 25 2 34
## 132 13 England GK 1989 2017/2018 26 4 34
## 133 13 England GK 1989 2018/2019 27 4 32
## 134 13 England GK 1989 2019/2020 28 4 15
## 135 13 England GK 1989 2020/2021 29 3 17
## 136 13 England GK 1989 2021/2022 30 5 24
## 137 14 Germany GK 2002 2010/2011 20 1 35
## 138 14 Germany GK 2002 2011/2012 21 2 20
## 139 14 Germany GK 2002 2012/2013 22 4 22
## 140 14 Germany GK 2002 2013/2014 23 2 30
## 141 14 Germany GK 2002 2014/2015 24 1 25
## 142 14 Germany GK 2002 2015/2016 25 1 21
## 143 14 Germany GK 2002 2016/2017 26 1 26
## 144 15 Netherlands MID 2000 2010/2011 19 4 28
## 145 15 Netherlands MID 2000 2011/2012 20 2 32
## 146 15 Netherlands MID 2000 2012/2013 21 4 32
## 147 15 Netherlands MID 2000 2013/2014 22 1 32
## 148 15 Netherlands MID 2000 2014/2015 23 2 8
## 149 15 Netherlands MID 2000 2015/2016 24 1 24
## 150 15 Netherlands MID 2000 2016/2017 25 3 16
## 151 15 Netherlands MID 2000 2017/2018 26 2 22
## 152 15 Netherlands MID 2000 2018/2019 27 1 24
## 153 15 Netherlands MID 2000 2019/2020 28 4 16
## 154 16 Germany DEF 1987 2010/2011 20 1 29
## 155 16 Germany DEF 1987 2011/2012 21 5 21
## 156 16 Germany DEF 1987 2012/2013 22 4 12
## 157 16 Germany DEF 1987 2013/2014 23 4 15
## 158 16 Germany DEF 1987 2014/2015 24 1 12
## 159 16 Germany DEF 1987 2015/2016 25 5 24
## 160 16 Germany DEF 1987 2016/2017 26 5 16
## 161 16 Germany DEF 1987 2017/2018 27 5 18
## 162 16 Germany DEF 1987 2018/2019 28 5 13
## 163 17 Italy GK 1993 2010/2011 18 3 15
## 164 17 Italy GK 1993 2011/2012 19 5 16
## 165 17 Italy GK 1993 2012/2013 20 5 27
## 166 17 Italy GK 1993 2013/2014 21 4 37
## 167 17 Italy GK 1993 2014/2015 22 3 17
## 168 17 Italy GK 1993 2015/2016 23 5 6
## 169 18 Brazil DEF 2001 2010/2011 19 3 5
## 170 18 Brazil DEF 2001 2011/2012 20 4 25
## 171 18 Brazil DEF 2001 2012/2013 21 1 27
## 172 18 Brazil DEF 2001 2013/2014 22 4 22
## 173 18 Brazil DEF 2001 2014/2015 23 4 16
## 174 18 Brazil DEF 2001 2015/2016 24 3 10
## 175 18 Brazil DEF 2001 2016/2017 25 4 22
## 176 18 Brazil DEF 2001 2017/2018 26 3 7
## 177 18 Brazil DEF 2001 2018/2019 27 4 25
## 178 18 Brazil DEF 2001 2019/2020 28 2 23
## 179 18 Brazil DEF 2001 2020/2021 29 4 9
## 180 18 Brazil DEF 2001 2021/2022 30 3 37
## 181 19 Germany MID 1992 2010/2011 19 5 17
## 182 19 Germany MID 1992 2011/2012 20 3 23
## 183 19 Germany MID 1992 2012/2013 21 5 17
## 184 19 Germany MID 1992 2013/2014 22 3 18
## 185 19 Germany MID 1992 2014/2015 23 2 20
## 186 19 Germany MID 1992 2015/2016 24 5 5
## 187 19 Germany MID 1992 2016/2017 25 1 27
## 188 19 Germany MID 1992 2017/2018 26 2 22
## 189 19 Germany MID 1992 2018/2019 27 2 12
## 190 19 Germany MID 1992 2019/2020 28 4 22
## 191 20 Italy MID 1994 2010/2011 17 3 17
## 192 20 Italy MID 1994 2011/2012 18 1 22
## 193 20 Italy MID 1994 2012/2013 19 1 13
## 194 20 Italy MID 1994 2013/2014 20 5 17
## 195 20 Italy MID 1994 2014/2015 21 1 18
## 196 20 Italy MID 1994 2015/2016 22 1 32
## 197 21 Italy DEF 1985 2010/2011 17 4 20
## 198 21 Italy DEF 1985 2011/2012 18 4 30
## 199 21 Italy DEF 1985 2012/2013 19 2 23
## 200 21 Italy DEF 1985 2013/2014 20 1 15
## 201 21 Italy DEF 1985 2014/2015 21 2 18
## 202 21 Italy DEF 1985 2015/2016 22 1 20
## 203 21 Italy DEF 1985 2016/2017 23 4 33
## 204 21 Italy DEF 1985 2017/2018 24 3 26
## 205 21 Italy DEF 1985 2018/2019 25 3 5
## 206 22 France GK 2001 2010/2011 17 4 31
## 207 22 France GK 2001 2011/2012 18 2 15
## 208 22 France GK 2001 2012/2013 19 5 15
## 209 22 France GK 2001 2013/2014 20 2 9
## 210 22 France GK 2001 2014/2015 21 4 31
## 211 23 France GK 1988 2010/2011 20 3 27
## 212 23 France GK 1988 2011/2012 21 5 24
## 213 23 France GK 1988 2012/2013 22 3 9
## 214 23 France GK 1988 2013/2014 23 3 20
## 215 23 France GK 1988 2014/2015 24 3 13
## 216 23 France GK 1988 2015/2016 25 5 5
## 217 23 France GK 1988 2016/2017 26 2 19
## 218 23 France GK 1988 2017/2018 27 1 33
## 219 23 France GK 1988 2018/2019 28 3 30
## 220 23 France GK 1988 2019/2020 29 2 8
## 221 23 France GK 1988 2020/2021 30 1 9
## 222 23 France GK 1988 2021/2022 31 4 27
## 223 23 France GK 1988 2022/2023 32 2 20
## 224 23 France GK 1988 2023/2024 33 1 10
## 225 24 Netherlands GK 2002 2010/2011 18 5 6
## 226 24 Netherlands GK 2002 2011/2012 19 3 33
## 227 24 Netherlands GK 2002 2012/2013 20 5 14
## 228 24 Netherlands GK 2002 2013/2014 21 4 31
## 229 24 Netherlands GK 2002 2014/2015 22 2 28
## 230 24 Netherlands GK 2002 2015/2016 23 3 6
## 231 24 Netherlands GK 2002 2016/2017 24 1 37
## 232 24 Netherlands GK 2002 2017/2018 25 2 34
## 233 24 Netherlands GK 2002 2018/2019 26 1 23
## 234 24 Netherlands GK 2002 2019/2020 27 3 25
## 235 25 Germany GK 2001 2010/2011 18 2 24
## 236 25 Germany GK 2001 2011/2012 19 3 15
## 237 25 Germany GK 2001 2012/2013 20 2 22
## 238 25 Germany GK 2001 2013/2014 21 3 21
## 239 25 Germany GK 2001 2014/2015 22 5 13
## 240 25 Germany GK 2001 2015/2016 23 4 19
## 241 25 Germany GK 2001 2016/2017 24 5 28
## 242 25 Germany GK 2001 2017/2018 25 5 33
## 243 25 Germany GK 2001 2018/2019 26 2 27
## 244 25 Germany GK 2001 2019/2020 27 2 20
## 245 25 Germany GK 2001 2020/2021 28 1 9
## 246 25 Germany GK 2001 2021/2022 29 4 27
## 247 25 Germany GK 2001 2022/2023 30 2 18
## 248 26 Argentina MID 2003 2010/2011 20 3 13
## 249 26 Argentina MID 2003 2011/2012 21 1 33
## 250 26 Argentina MID 2003 2012/2013 22 1 33
## 251 26 Argentina MID 2003 2013/2014 23 3 6
## 252 26 Argentina MID 2003 2014/2015 24 1 22
## 253 27 Germany FWD 1988 2010/2011 19 2 35
## 254 27 Germany FWD 1988 2011/2012 20 1 36
## 255 27 Germany FWD 1988 2012/2013 21 3 25
## 256 27 Germany FWD 1988 2013/2014 22 4 14
## 257 27 Germany FWD 1988 2014/2015 23 1 7
## 258 27 Germany FWD 1988 2015/2016 24 2 19
## 259 27 Germany FWD 1988 2016/2017 25 5 25
## 260 27 Germany FWD 1988 2017/2018 26 3 24
## 261 27 Germany FWD 1988 2018/2019 27 3 15
## 262 27 Germany FWD 1988 2019/2020 28 3 7
## 263 27 Germany FWD 1988 2020/2021 29 3 6
## 264 27 Germany FWD 1988 2021/2022 30 3 9
## 265 27 Germany FWD 1988 2022/2023 31 2 27
## 266 27 Germany FWD 1988 2023/2024 32 5 18
## 267 28 Germany FWD 1990 2010/2011 18 5 17
## 268 28 Germany FWD 1990 2011/2012 19 4 20
## 269 28 Germany FWD 1990 2012/2013 20 1 12
## 270 28 Germany FWD 1990 2013/2014 21 3 15
## 271 28 Germany FWD 1990 2014/2015 22 2 36
## 272 28 Germany FWD 1990 2015/2016 23 5 30
## 273 28 Germany FWD 1990 2016/2017 24 3 27
## 274 29 Spain DEF 1992 2010/2011 17 2 17
## 275 29 Spain DEF 1992 2011/2012 18 5 21
## 276 29 Spain DEF 1992 2012/2013 19 5 20
## 277 29 Spain DEF 1992 2013/2014 20 1 35
## 278 29 Spain DEF 1992 2014/2015 21 5 27
## 279 29 Spain DEF 1992 2015/2016 22 5 36
## 280 29 Spain DEF 1992 2016/2017 23 4 37
## 281 29 Spain DEF 1992 2017/2018 24 3 12
## 282 29 Spain DEF 1992 2018/2019 25 4 6
## 283 29 Spain DEF 1992 2019/2020 26 1 23
## 284 29 Spain DEF 1992 2020/2021 27 5 29
## 285 30 Germany FWD 2003 2010/2011 19 2 31
## 286 30 Germany FWD 2003 2011/2012 20 4 26
## 287 30 Germany FWD 2003 2012/2013 21 1 27
## 288 30 Germany FWD 2003 2013/2014 22 5 33
## 289 30 Germany FWD 2003 2014/2015 23 5 32
## 290 30 Germany FWD 2003 2015/2016 24 2 25
## 291 30 Germany FWD 2003 2016/2017 25 3 18
## 292 30 Germany FWD 2003 2017/2018 26 5 30
## 293 31 Spain MID 2004 2010/2011 20 4 28
## 294 31 Spain MID 2004 2011/2012 21 2 35
## 295 31 Spain MID 2004 2012/2013 22 2 8
## 296 31 Spain MID 2004 2013/2014 23 3 7
## 297 31 Spain MID 2004 2014/2015 24 3 26
## 298 31 Spain MID 2004 2015/2016 25 4 27
## 299 31 Spain MID 2004 2016/2017 26 2 15
## 300 31 Spain MID 2004 2017/2018 27 3 30
## 301 31 Spain MID 2004 2018/2019 28 2 30
## 302 31 Spain MID 2004 2019/2020 29 2 11
## 303 31 Spain MID 2004 2020/2021 30 3 33
## 304 31 Spain MID 2004 2021/2022 31 1 23
## 305 31 Spain MID 2004 2022/2023 32 2 8
## 306 32 Italy GK 2003 2010/2011 19 4 14
## 307 32 Italy GK 2003 2011/2012 20 3 28
## 308 32 Italy GK 2003 2012/2013 21 3 32
## 309 32 Italy GK 2003 2013/2014 22 2 15
## 310 32 Italy GK 2003 2014/2015 23 4 32
## 311 32 Italy GK 2003 2015/2016 24 1 17
## 312 32 Italy GK 2003 2016/2017 25 1 16
## 313 33 Italy FWD 2004 2010/2011 17 3 28
## 314 33 Italy FWD 2004 2011/2012 18 1 36
## 315 33 Italy FWD 2004 2012/2013 19 1 27
## 316 33 Italy FWD 2004 2013/2014 20 3 37
## 317 33 Italy FWD 2004 2014/2015 21 4 27
## 318 33 Italy FWD 2004 2015/2016 22 3 9
## 319 33 Italy FWD 2004 2016/2017 23 3 31
## 320 33 Italy FWD 2004 2017/2018 24 3 16
## 321 33 Italy FWD 2004 2018/2019 25 4 32
## 322 34 Spain DEF 1995 2010/2011 19 5 27
## 323 34 Spain DEF 1995 2011/2012 20 4 33
## 324 34 Spain DEF 1995 2012/2013 21 2 14
## 325 34 Spain DEF 1995 2013/2014 22 4 30
## 326 34 Spain DEF 1995 2014/2015 23 2 8
## 327 34 Spain DEF 1995 2015/2016 24 5 11
## 328 34 Spain DEF 1995 2016/2017 25 1 37
## 329 35 Germany MID 2002 2010/2011 18 4 37
## 330 35 Germany MID 2002 2011/2012 19 2 19
## 331 35 Germany MID 2002 2012/2013 20 5 33
## 332 35 Germany MID 2002 2013/2014 21 5 11
## 333 35 Germany MID 2002 2014/2015 22 1 29
## 334 35 Germany MID 2002 2015/2016 23 1 32
## 335 35 Germany MID 2002 2016/2017 24 5 17
## 336 35 Germany MID 2002 2017/2018 25 3 23
## 337 35 Germany MID 2002 2018/2019 26 4 32
## 338 35 Germany MID 2002 2019/2020 27 5 30
## 339 35 Germany MID 2002 2020/2021 28 5 22
## 340 36 England GK 2000 2010/2011 19 4 32
## 341 36 England GK 2000 2011/2012 20 5 10
## 342 36 England GK 2000 2012/2013 21 5 28
## 343 36 England GK 2000 2013/2014 22 1 20
## 344 36 England GK 2000 2014/2015 23 2 29
## 345 36 England GK 2000 2015/2016 24 2 14
## 346 36 England GK 2000 2016/2017 25 5 13
## 347 36 England GK 2000 2017/2018 26 5 24
## 348 36 England GK 2000 2018/2019 27 5 17
## 349 37 Argentina FWD 1986 2010/2011 19 5 27
## 350 37 Argentina FWD 1986 2011/2012 20 5 23
## 351 37 Argentina FWD 1986 2012/2013 21 3 29
## 352 37 Argentina FWD 1986 2013/2014 22 1 28
## 353 37 Argentina FWD 1986 2014/2015 23 3 24
## 354 37 Argentina FWD 1986 2015/2016 24 1 21
## 355 37 Argentina FWD 1986 2016/2017 25 2 19
## 356 37 Argentina FWD 1986 2017/2018 26 4 18
## 357 37 Argentina FWD 1986 2018/2019 27 1 18
## 358 37 Argentina FWD 1986 2019/2020 28 1 29
## 359 37 Argentina FWD 1986 2020/2021 29 2 33
## 360 37 Argentina FWD 1986 2021/2022 30 5 21
## 361 37 Argentina FWD 1986 2022/2023 31 2 5
## 362 38 Netherlands MID 2004 2010/2011 18 1 28
## 363 38 Netherlands MID 2004 2011/2012 19 2 21
## 364 38 Netherlands MID 2004 2012/2013 20 3 30
## 365 38 Netherlands MID 2004 2013/2014 21 1 16
## 366 38 Netherlands MID 2004 2014/2015 22 3 9
## 367 38 Netherlands MID 2004 2015/2016 23 3 27
## 368 38 Netherlands MID 2004 2016/2017 24 4 21
## 369 38 Netherlands MID 2004 2017/2018 25 3 6
## 370 38 Netherlands MID 2004 2018/2019 26 3 28
## 371 38 Netherlands MID 2004 2019/2020 27 5 34
## 372 38 Netherlands MID 2004 2020/2021 28 4 13
## 373 39 Spain GK 1998 2010/2011 20 5 7
## 374 39 Spain GK 1998 2011/2012 21 2 12
## 375 39 Spain GK 1998 2012/2013 22 1 29
## 376 39 Spain GK 1998 2013/2014 23 1 18
## 377 39 Spain GK 1998 2014/2015 24 3 8
## 378 39 Spain GK 1998 2015/2016 25 2 30
## 379 40 England MID 1999 2010/2011 18 4 6
## 380 40 England MID 1999 2011/2012 19 4 26
## 381 40 England MID 1999 2012/2013 20 3 9
## 382 40 England MID 1999 2013/2014 21 2 28
## 383 40 England MID 1999 2014/2015 22 3 35
## 384 40 England MID 1999 2015/2016 23 4 12
## 385 40 England MID 1999 2016/2017 24 1 5
## 386 40 England MID 1999 2017/2018 25 2 12
## 387 41 Netherlands DEF 1996 2010/2011 18 1 36
## 388 41 Netherlands DEF 1996 2011/2012 19 5 34
## 389 41 Netherlands DEF 1996 2012/2013 20 1 12
## 390 41 Netherlands DEF 1996 2013/2014 21 4 22
## 391 41 Netherlands DEF 1996 2014/2015 22 5 17
## 392 41 Netherlands DEF 1996 2015/2016 23 4 25
## 393 41 Netherlands DEF 1996 2016/2017 24 3 20
## 394 41 Netherlands DEF 1996 2017/2018 25 4 9
## 395 41 Netherlands DEF 1996 2018/2019 26 1 11
## 396 41 Netherlands DEF 1996 2019/2020 27 4 35
## 397 41 Netherlands DEF 1996 2020/2021 28 5 19
## 398 41 Netherlands DEF 1996 2021/2022 29 3 30
## 399 41 Netherlands DEF 1996 2022/2023 30 3 28
## 400 42 Italy MID 2004 2010/2011 20 5 25
## 401 42 Italy MID 2004 2011/2012 21 2 9
## 402 42 Italy MID 2004 2012/2013 22 5 12
## 403 42 Italy MID 2004 2013/2014 23 1 35
## 404 42 Italy MID 2004 2014/2015 24 4 9
## 405 42 Italy MID 2004 2015/2016 25 3 25
## 406 43 Germany GK 1990 2010/2011 17 1 21
## 407 43 Germany GK 1990 2011/2012 18 3 21
## 408 43 Germany GK 1990 2012/2013 19 1 8
## 409 43 Germany GK 1990 2013/2014 20 1 34
## 410 43 Germany GK 1990 2014/2015 21 3 23
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## 412 43 Germany GK 1990 2016/2017 23 2 11
## 413 43 Germany GK 1990 2017/2018 24 1 12
## 414 43 Germany GK 1990 2018/2019 25 2 18
## 415 43 Germany GK 1990 2019/2020 26 1 5
## 416 43 Germany GK 1990 2020/2021 27 4 22
## 417 43 Germany GK 1990 2021/2022 28 4 10
## 418 43 Germany GK 1990 2022/2023 29 2 7
## 419 43 Germany GK 1990 2023/2024 30 2 8
## 420 44 Germany GK 1997 2010/2011 20 3 7
## 421 44 Germany GK 1997 2011/2012 21 4 29
## 422 44 Germany GK 1997 2012/2013 22 3 30
## 423 44 Germany GK 1997 2013/2014 23 5 16
## 424 44 Germany GK 1997 2014/2015 24 5 30
## 425 44 Germany GK 1997 2015/2016 25 5 11
## 426 44 Germany GK 1997 2016/2017 26 1 15
## 427 44 Germany GK 1997 2017/2018 27 4 29
## 428 44 Germany GK 1997 2018/2019 28 2 19
## 429 44 Germany GK 1997 2019/2020 29 2 34
## 430 44 Germany GK 1997 2020/2021 30 2 31
## 431 44 Germany GK 1997 2021/2022 31 4 19
## 432 44 Germany GK 1997 2022/2023 32 4 7
## 433 45 Italy DEF 1991 2010/2011 19 4 15
## 434 45 Italy DEF 1991 2011/2012 20 1 6
## 435 45 Italy DEF 1991 2012/2013 21 1 7
## 436 45 Italy DEF 1991 2013/2014 22 5 7
## 437 45 Italy DEF 1991 2014/2015 23 1 11
## 438 45 Italy DEF 1991 2015/2016 24 5 13
## 439 45 Italy DEF 1991 2016/2017 25 5 37
## 440 45 Italy DEF 1991 2017/2018 26 2 10
## 441 45 Italy DEF 1991 2018/2019 27 4 5
## 442 45 Italy DEF 1991 2019/2020 28 4 30
## 443 45 Italy DEF 1991 2020/2021 29 3 5
## 444 45 Italy DEF 1991 2021/2022 30 1 19
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## 446 45 Italy DEF 1991 2023/2024 32 5 33
## 447 46 Germany GK 1996 2010/2011 18 2 36
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## 453 46 Germany GK 1996 2016/2017 24 1 34
## 454 46 Germany GK 1996 2017/2018 25 4 16
## 455 46 Germany GK 1996 2018/2019 26 4 26
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## 457 47 Netherlands DEF 1992 2011/2012 19 2 34
## 458 47 Netherlands DEF 1992 2012/2013 20 3 18
## 459 47 Netherlands DEF 1992 2013/2014 21 4 6
## 460 47 Netherlands DEF 1992 2014/2015 22 1 16
## 461 47 Netherlands DEF 1992 2015/2016 23 3 26
## 462 48 Argentina MID 1986 2010/2011 20 2 33
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## 464 48 Argentina MID 1986 2012/2013 22 5 32
## 465 48 Argentina MID 1986 2013/2014 23 5 33
## 466 48 Argentina MID 1986 2014/2015 24 4 34
## 467 48 Argentina MID 1986 2015/2016 25 2 24
## 468 48 Argentina MID 1986 2016/2017 26 3 36
## 469 48 Argentina MID 1986 2017/2018 27 1 37
## 470 49 Germany DEF 2003 2010/2011 20 5 24
## 471 49 Germany DEF 2003 2011/2012 21 3 20
## 472 49 Germany DEF 2003 2012/2013 22 3 24
## 473 49 Germany DEF 2003 2013/2014 23 1 18
## 474 49 Germany DEF 2003 2014/2015 24 3 16
## 475 49 Germany DEF 2003 2015/2016 25 4 26
## 476 49 Germany DEF 2003 2016/2017 26 4 15
## 477 49 Germany DEF 2003 2017/2018 27 5 9
## 478 49 Germany DEF 2003 2018/2019 28 1 21
## 479 49 Germany DEF 2003 2019/2020 29 4 18
## 480 49 Germany DEF 2003 2020/2021 30 3 26
## 481 50 Argentina DEF 2002 2010/2011 20 2 12
## 482 50 Argentina DEF 2002 2011/2012 21 4 18
## 483 50 Argentina DEF 2002 2012/2013 22 2 24
## 484 50 Argentina DEF 2002 2013/2014 23 3 14
## 485 50 Argentina DEF 2002 2014/2015 24 1 22
## 486 50 Argentina DEF 2002 2015/2016 25 5 23
## 487 50 Argentina DEF 2002 2016/2017 26 5 24
## 488 50 Argentina DEF 2002 2017/2018 27 4 36
## 489 50 Argentina DEF 2002 2018/2019 28 1 24
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## 491 50 Argentina DEF 2002 2020/2021 30 5 15
## 492 50 Argentina DEF 2002 2021/2022 31 1 17
## 493 51 France FWD 2004 2010/2011 17 3 29
## 494 51 France FWD 2004 2011/2012 18 4 31
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## 497 51 France FWD 2004 2014/2015 21 1 16
## 498 51 France FWD 2004 2015/2016 22 4 11
## 499 51 France FWD 2004 2016/2017 23 4 34
## 500 52 Italy DEF 2003 2010/2011 20 3 16
## 501 52 Italy DEF 2003 2011/2012 21 5 34
## 502 52 Italy DEF 2003 2012/2013 22 5 17
## 503 52 Italy DEF 2003 2013/2014 23 1 25
## 504 52 Italy DEF 2003 2014/2015 24 4 10
## 505 53 France GK 1987 2010/2011 17 5 30
## 506 53 France GK 1987 2011/2012 18 5 29
## 507 53 France GK 1987 2012/2013 19 5 20
## 508 53 France GK 1987 2013/2014 20 2 19
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## 510 53 France GK 1987 2015/2016 22 1 34
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## 512 53 France GK 1987 2017/2018 24 3 10
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## 514 54 France FWD 1999 2011/2012 21 2 28
## 515 54 France FWD 1999 2012/2013 22 1 11
## 516 54 France FWD 1999 2013/2014 23 3 5
## 517 54 France FWD 1999 2014/2015 24 4 16
## 518 54 France FWD 1999 2015/2016 25 2 6
## 519 55 France FWD 1991 2010/2011 19 3 14
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## 521 55 France FWD 1991 2012/2013 21 5 20
## 522 55 France FWD 1991 2013/2014 22 3 14
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## 528 55 France FWD 1991 2019/2020 28 1 8
## 529 55 France FWD 1991 2020/2021 29 4 5
## 530 55 France FWD 1991 2021/2022 30 4 24
## 531 56 Brazil DEF 2003 2010/2011 19 1 15
## 532 56 Brazil DEF 2003 2011/2012 20 1 32
## 533 56 Brazil DEF 2003 2012/2013 21 1 5
## 534 56 Brazil DEF 2003 2013/2014 22 5 7
## 535 56 Brazil DEF 2003 2014/2015 23 5 23
## 536 56 Brazil DEF 2003 2015/2016 24 5 16
## 537 56 Brazil DEF 2003 2016/2017 25 4 31
## 538 56 Brazil DEF 2003 2017/2018 26 5 13
## 539 57 Argentina MID 1997 2010/2011 17 3 12
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## 541 57 Argentina MID 1997 2012/2013 19 1 15
## 542 57 Argentina MID 1997 2013/2014 20 1 12
## 543 57 Argentina MID 1997 2014/2015 21 2 7
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## 548 58 Brazil GK 2000 2013/2014 22 1 21
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## 554 58 Brazil GK 2000 2019/2020 28 5 18
## 555 59 Netherlands GK 1994 2010/2011 18 5 7
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## 563 59 Netherlands GK 1994 2018/2019 26 1 29
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## 565 60 Italy MID 1989 2010/2011 19 4 28
## 566 60 Italy MID 1989 2011/2012 20 1 8
## 567 60 Italy MID 1989 2012/2013 21 1 7
## 568 60 Italy MID 1989 2013/2014 22 4 33
## 569 60 Italy MID 1989 2014/2015 23 2 9
## 570 60 Italy MID 1989 2015/2016 24 1 30
## 571 60 Italy MID 1989 2016/2017 25 2 7
## 572 60 Italy MID 1989 2017/2018 26 4 7
## 573 60 Italy MID 1989 2018/2019 27 4 28
## 574 60 Italy MID 1989 2019/2020 28 1 25
## 575 60 Italy MID 1989 2020/2021 29 5 31
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## 578 60 Italy MID 1989 2023/2024 32 5 18
## 579 61 Germany FWD 1989 2010/2011 20 3 28
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## 606 64 Germany MID 1988 2013/2014 20 4 15
## 607 64 Germany MID 1988 2014/2015 21 2 35
## 608 64 Germany MID 1988 2015/2016 22 2 20
## 609 64 Germany MID 1988 2016/2017 23 5 9
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## 611 64 Germany MID 1988 2018/2019 25 3 32
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## 623 66 Italy FWD 1990 2010/2011 19 4 11
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## 647 68 Spain MID 1990 2012/2013 21 1 11
## 648 68 Spain MID 1990 2013/2014 22 1 11
## 649 68 Spain MID 1990 2014/2015 23 5 16
## 650 68 Spain MID 1990 2015/2016 24 3 7
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## 652 68 Spain MID 1990 2017/2018 26 5 20
## 653 68 Spain MID 1990 2018/2019 27 4 31
## 654 68 Spain MID 1990 2019/2020 28 3 33
## 655 68 Spain MID 1990 2020/2021 29 1 19
## 656 68 Spain MID 1990 2021/2022 30 4 34
## 657 69 England DEF 1989 2010/2011 19 5 30
## 658 69 England DEF 1989 2011/2012 20 4 12
## 659 69 England DEF 1989 2012/2013 21 3 9
## 660 69 England DEF 1989 2013/2014 22 3 32
## 661 69 England DEF 1989 2014/2015 23 5 27
## 662 69 England DEF 1989 2015/2016 24 1 33
## 663 69 England DEF 1989 2016/2017 25 5 21
## 664 69 England DEF 1989 2017/2018 26 5 10
## 665 69 England DEF 1989 2018/2019 27 3 15
## 666 69 England DEF 1989 2019/2020 28 1 31
## 667 69 England DEF 1989 2020/2021 29 5 24
## 668 70 France FWD 1998 2010/2011 19 4 31
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## 693 72 Germany MID 1986 2010/2011 19 4 13
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## 696 72 Germany MID 1986 2013/2014 22 2 10
## 697 72 Germany MID 1986 2014/2015 23 5 26
## 698 72 Germany MID 1986 2015/2016 24 4 19
## 699 72 Germany MID 1986 2016/2017 25 1 26
## 700 72 Germany MID 1986 2017/2018 26 4 25
## 701 72 Germany MID 1986 2018/2019 27 2 10
## 702 73 Germany DEF 1998 2010/2011 18 1 34
## 703 73 Germany DEF 1998 2011/2012 19 3 13
## 704 73 Germany DEF 1998 2012/2013 20 2 25
## 705 73 Germany DEF 1998 2013/2014 21 5 28
## 706 73 Germany DEF 1998 2014/2015 22 1 17
## 707 73 Germany DEF 1998 2015/2016 23 5 14
## 708 74 Netherlands DEF 1990 2010/2011 17 1 10
## 709 74 Netherlands DEF 1990 2011/2012 18 4 23
## 710 74 Netherlands DEF 1990 2012/2013 19 4 37
## 711 74 Netherlands DEF 1990 2013/2014 20 1 24
## 712 74 Netherlands DEF 1990 2014/2015 21 2 21
## 713 74 Netherlands DEF 1990 2015/2016 22 3 30
## 714 74 Netherlands DEF 1990 2016/2017 23 5 17
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## 737 76 England GK 1989 2018/2019 25 1 25
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## 740 76 England GK 1989 2021/2022 28 5 24
## 741 76 England GK 1989 2022/2023 29 4 36
## 742 76 England GK 1989 2023/2024 30 4 14
## 743 77 Spain DEF 1988 2010/2011 17 1 34
## 744 77 Spain DEF 1988 2011/2012 18 1 17
## 745 77 Spain DEF 1988 2012/2013 19 4 24
## 746 77 Spain DEF 1988 2013/2014 20 4 21
## 747 77 Spain DEF 1988 2014/2015 21 1 21
## 748 77 Spain DEF 1988 2015/2016 22 4 32
## 749 77 Spain DEF 1988 2016/2017 23 4 22
## 750 77 Spain DEF 1988 2017/2018 24 5 37
## 751 77 Spain DEF 1988 2018/2019 25 2 22
## 752 77 Spain DEF 1988 2019/2020 26 4 26
## 753 77 Spain DEF 1988 2020/2021 27 5 16
## 754 77 Spain DEF 1988 2021/2022 28 5 32
## 755 77 Spain DEF 1988 2022/2023 29 3 8
## 756 78 Netherlands FWD 1987 2010/2011 20 2 6
## 757 78 Netherlands FWD 1987 2011/2012 21 5 27
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## 777 80 Italy MID 1998 2013/2014 21 3 32
## 778 80 Italy MID 1998 2014/2015 22 3 20
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## 780 80 Italy MID 1998 2016/2017 24 2 5
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## 787 81 France MID 1994 2010/2011 20 2 20
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## 798 82 Spain DEF 2000 2010/2011 17 1 33
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## 802 82 Spain DEF 2000 2014/2015 21 5 24
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## 808 82 Spain DEF 2000 2020/2021 27 3 35
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## 811 83 Argentina FWD 1998 2010/2011 19 2 34
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## 823 84 Netherlands GK 1993 2010/2011 18 5 20
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## 840 86 Germany FWD 1994 2010/2011 17 4 31
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## 852 87 Argentina FWD 1990 2010/2011 19 2 33
## 853 87 Argentina FWD 1990 2011/2012 20 3 28
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## 856 87 Argentina FWD 1990 2014/2015 23 3 27
## 857 87 Argentina FWD 1990 2015/2016 24 4 29
## 858 87 Argentina FWD 1990 2016/2017 25 1 26
## 859 88 England DEF 1996 2010/2011 19 3 29
## 860 88 England DEF 1996 2011/2012 20 3 7
## 861 88 England DEF 1996 2012/2013 21 4 33
## 862 88 England DEF 1996 2013/2014 22 3 5
## 863 88 England DEF 1996 2014/2015 23 4 24
## 864 89 England DEF 1985 2010/2011 18 4 37
## 865 89 England DEF 1985 2011/2012 19 5 25
## 866 89 England DEF 1985 2012/2013 20 5 19
## 867 89 England DEF 1985 2013/2014 21 4 12
## 868 89 England DEF 1985 2014/2015 22 1 20
## 869 89 England DEF 1985 2015/2016 23 5 25
## 870 89 England DEF 1985 2016/2017 24 3 26
## 871 89 England DEF 1985 2017/2018 25 5 8
## 872 89 England DEF 1985 2018/2019 26 4 14
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## 879 90 Spain FWD 1990 2013/2014 20 5 20
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## 886 91 England MID 1998 2013/2014 23 2 22
## 887 91 England MID 1998 2014/2015 24 4 24
## 888 91 England MID 1998 2015/2016 25 4 14
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## 890 92 Brazil GK 1993 2010/2011 20 3 11
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## 894 92 Brazil GK 1993 2014/2015 24 2 33
## 895 93 Netherlands FWD 1988 2010/2011 18 4 17
## 896 93 Netherlands FWD 1988 2011/2012 19 3 7
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## 898 93 Netherlands FWD 1988 2013/2014 21 1 25
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## 900 93 Netherlands FWD 1988 2015/2016 23 1 21
## 901 93 Netherlands FWD 1988 2016/2017 24 4 11
## 902 93 Netherlands FWD 1988 2017/2018 25 1 18
## 903 93 Netherlands FWD 1988 2018/2019 26 4 33
## 904 94 Germany DEF 1989 2010/2011 18 5 31
## 905 94 Germany DEF 1989 2011/2012 19 3 17
## 906 94 Germany DEF 1989 2012/2013 20 3 26
## 907 94 Germany DEF 1989 2013/2014 21 5 14
## 908 94 Germany DEF 1989 2014/2015 22 5 16
## 909 94 Germany DEF 1989 2015/2016 23 4 29
## 910 94 Germany DEF 1989 2016/2017 24 3 18
## 911 94 Germany DEF 1989 2017/2018 25 1 7
## 912 94 Germany DEF 1989 2018/2019 26 4 29
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## 918 95 Argentina GK 1994 2010/2011 20 5 30
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## 932 96 Brazil MID 1985 2013/2014 21 5 22
## 933 96 Brazil MID 1985 2014/2015 22 5 18
## 934 96 Brazil MID 1985 2015/2016 23 4 24
## 935 96 Brazil MID 1985 2016/2017 24 3 8
## 936 96 Brazil MID 1985 2017/2018 25 3 25
## 937 96 Brazil MID 1985 2018/2019 26 4 33
## 938 96 Brazil MID 1985 2019/2020 27 4 9
## 939 96 Brazil MID 1985 2020/2021 28 2 34
## 940 96 Brazil MID 1985 2021/2022 29 3 6
## 941 96 Brazil MID 1985 2022/2023 30 3 18
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## 947 97 France MID 1992 2014/2015 24 2 19
## 948 97 France MID 1992 2015/2016 25 5 14
## 949 97 France MID 1992 2016/2017 26 5 23
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## 952 97 France MID 1992 2019/2020 29 1 11
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## 969 99 Argentina DEF 1998 2012/2013 22 5 28
## 970 99 Argentina DEF 1998 2013/2014 23 4 6
## 971 99 Argentina DEF 1998 2014/2015 24 4 24
## 972 99 Argentina DEF 1998 2015/2016 25 2 10
## 973 99 Argentina DEF 1998 2016/2017 26 1 32
## 974 99 Argentina DEF 1998 2017/2018 27 3 19
## 975 99 Argentina DEF 1998 2018/2019 28 4 22
## 976 99 Argentina DEF 1998 2019/2020 29 4 10
## 977 99 Argentina DEF 1998 2020/2021 30 4 5
## 978 99 Argentina DEF 1998 2021/2022 31 2 36
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## 980 100 Germany DEF 1986 2010/2011 17 5 6
## 981 100 Germany DEF 1986 2011/2012 18 4 16
## 982 100 Germany DEF 1986 2012/2013 19 2 25
## 983 100 Germany DEF 1986 2013/2014 20 3 20
## 984 100 Germany DEF 1986 2014/2015 21 2 17
## 985 100 Germany DEF 1986 2015/2016 22 2 15
## 986 100 Germany DEF 1986 2016/2017 23 1 12
## 987 100 Germany DEF 1986 2017/2018 24 3 30
## 988 100 Germany DEF 1986 2018/2019 25 4 37
## 989 100 Germany DEF 1986 2019/2020 26 1 30
## 990 100 Germany DEF 1986 2020/2021 27 1 33
## 991 100 Germany DEF 1986 2021/2022 28 2 16
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## 1007 103 England FWD 1985 2014/2015 21 5 27
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## 1010 103 England FWD 1985 2017/2018 24 3 34
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## 1020 104 Argentina MID 1990 2016/2017 25 4 14
## 1021 104 Argentina MID 1990 2017/2018 26 2 8
## 1022 104 Argentina MID 1990 2018/2019 27 1 12
## 1023 104 Argentina MID 1990 2019/2020 28 2 22
## 1024 104 Argentina MID 1990 2020/2021 29 1 13
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## 1028 105 Netherlands GK 1992 2013/2014 22 1 28
## 1029 105 Netherlands GK 1992 2014/2015 23 2 32
## 1030 105 Netherlands GK 1992 2015/2016 24 4 17
## 1031 105 Netherlands GK 1992 2016/2017 25 1 28
## 1032 105 Netherlands GK 1992 2017/2018 26 3 11
## 1033 105 Netherlands GK 1992 2018/2019 27 1 14
## 1034 105 Netherlands GK 1992 2019/2020 28 3 6
## 1035 105 Netherlands GK 1992 2020/2021 29 2 28
## 1036 105 Netherlands GK 1992 2021/2022 30 3 12
## 1037 106 Spain DEF 1999 2010/2011 20 2 27
## 1038 106 Spain DEF 1999 2011/2012 21 4 11
## 1039 106 Spain DEF 1999 2012/2013 22 5 28
## 1040 106 Spain DEF 1999 2013/2014 23 1 7
## 1041 106 Spain DEF 1999 2014/2015 24 1 20
## 1042 106 Spain DEF 1999 2015/2016 25 3 34
## 1043 106 Spain DEF 1999 2016/2017 26 2 14
## 1044 106 Spain DEF 1999 2017/2018 27 1 31
## 1045 106 Spain DEF 1999 2018/2019 28 3 11
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## 1050 107 Brazil FWD 2004 2014/2015 23 4 25
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## 1056 108 Germany MID 1997 2013/2014 23 3 13
## 1057 108 Germany MID 1997 2014/2015 24 4 21
## 1058 108 Germany MID 1997 2015/2016 25 2 14
## 1059 108 Germany MID 1997 2016/2017 26 5 26
## 1060 108 Germany MID 1997 2017/2018 27 2 30
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## 1062 108 Germany MID 1997 2019/2020 29 4 22
## 1063 108 Germany MID 1997 2020/2021 30 3 23
## 1064 108 Germany MID 1997 2021/2022 31 3 8
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## 1069 109 Spain MID 2002 2014/2015 21 4 20
## 1070 109 Spain MID 2002 2015/2016 22 1 15
## 1071 109 Spain MID 2002 2016/2017 23 4 33
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## 1077 110 Netherlands FWD 1993 2012/2013 22 2 6
## 1078 110 Netherlands FWD 1993 2013/2014 23 2 19
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## 1082 111 Germany DEF 1998 2010/2011 17 2 8
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## 1084 111 Germany DEF 1998 2012/2013 19 3 35
## 1085 111 Germany DEF 1998 2013/2014 20 1 8
## 1086 111 Germany DEF 1998 2014/2015 21 1 31
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## 1088 112 Germany GK 1986 2011/2012 21 3 34
## 1089 112 Germany GK 1986 2012/2013 22 4 13
## 1090 112 Germany GK 1986 2013/2014 23 4 35
## 1091 112 Germany GK 1986 2014/2015 24 1 7
## 1092 112 Germany GK 1986 2015/2016 25 5 11
## 1093 112 Germany GK 1986 2016/2017 26 1 37
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## 1095 113 England GK 1992 2011/2012 19 5 5
## 1096 113 England GK 1992 2012/2013 20 4 35
## 1097 113 England GK 1992 2013/2014 21 2 33
## 1098 113 England GK 1992 2014/2015 22 5 9
## 1099 113 England GK 1992 2015/2016 23 2 23
## 1100 113 England GK 1992 2016/2017 24 5 25
## 1101 113 England GK 1992 2017/2018 25 2 36
## 1102 113 England GK 1992 2018/2019 26 3 29
## 1103 114 Netherlands DEF 1991 2010/2011 17 3 35
## 1104 114 Netherlands DEF 1991 2011/2012 18 2 8
## 1105 114 Netherlands DEF 1991 2012/2013 19 2 28
## 1106 114 Netherlands DEF 1991 2013/2014 20 4 7
## 1107 114 Netherlands DEF 1991 2014/2015 21 2 37
## 1108 114 Netherlands DEF 1991 2015/2016 22 4 30
## 1109 114 Netherlands DEF 1991 2016/2017 23 1 9
## 1110 115 Spain GK 1999 2010/2011 17 5 12
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## 1120 116 France MID 2001 2015/2016 23 3 14
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## 1143 118 Argentina FWD 2003 2015/2016 25 4 23
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## 1159 119 Italy FWD 1990 2020/2021 27 2 36
## 1160 120 Netherlands DEF 1986 2010/2011 20 1 36
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## 1162 120 Netherlands DEF 1986 2012/2013 22 5 23
## 1163 120 Netherlands DEF 1986 2013/2014 23 3 26
## 1164 120 Netherlands DEF 1986 2014/2015 24 3 37
## 1165 120 Netherlands DEF 1986 2015/2016 25 1 11
## 1166 120 Netherlands DEF 1986 2016/2017 26 4 18
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## 1168 120 Netherlands DEF 1986 2018/2019 28 4 17
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## 1184 121 Brazil MID 1988 2021/2022 29 4 25
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## 1187 122 Germany FWD 1999 2010/2011 20 5 11
## 1188 122 Germany FWD 1999 2011/2012 21 3 21
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## 1198 122 Germany FWD 1999 2021/2022 31 3 36
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## 1200 122 Germany FWD 1999 2023/2024 33 5 24
## 1201 123 Spain DEF 2001 2010/2011 19 1 14
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## 1203 123 Spain DEF 2001 2012/2013 21 3 21
## 1204 123 Spain DEF 2001 2013/2014 22 1 16
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## 1211 124 Netherlands GK 1992 2013/2014 20 3 7
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## 1232 127 Spain DEF 1993 2010/2011 17 3 29
## 1233 127 Spain DEF 1993 2011/2012 18 4 33
## 1234 127 Spain DEF 1993 2012/2013 19 1 13
## 1235 127 Spain DEF 1993 2013/2014 20 3 35
## 1236 127 Spain DEF 1993 2014/2015 21 1 25
## 1237 127 Spain DEF 1993 2015/2016 22 5 32
## 1238 127 Spain DEF 1993 2016/2017 23 3 29
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## 1242 128 Spain GK 1987 2012/2013 22 1 7
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## 1252 129 England MID 2001 2013/2014 22 4 30
## 1253 129 England MID 2001 2014/2015 23 5 31
## 1254 129 England MID 2001 2015/2016 24 2 24
## 1255 129 England MID 2001 2016/2017 25 4 7
## 1256 129 England MID 2001 2017/2018 26 5 36
## 1257 129 England MID 2001 2018/2019 27 1 24
## 1258 129 England MID 2001 2019/2020 28 4 37
## 1259 129 England MID 2001 2020/2021 29 3 26
## 1260 130 England GK 2001 2010/2011 17 5 10
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## 1268 130 England GK 2001 2018/2019 25 3 10
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## 1271 131 France FWD 1995 2010/2011 19 1 31
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## 1276 131 France FWD 1995 2015/2016 24 5 25
## 1277 131 France FWD 1995 2016/2017 25 4 13
## 1278 131 France FWD 1995 2017/2018 26 1 15
## 1279 132 England DEF 1992 2010/2011 19 4 25
## 1280 132 England DEF 1992 2011/2012 20 1 21
## 1281 132 England DEF 1992 2012/2013 21 4 24
## 1282 132 England DEF 1992 2013/2014 22 5 32
## 1283 132 England DEF 1992 2014/2015 23 1 26
## 1284 132 England DEF 1992 2015/2016 24 4 25
## 1285 132 England DEF 1992 2016/2017 25 1 34
## 1286 132 England DEF 1992 2017/2018 26 4 23
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## 1297 134 Spain MID 1992 2010/2011 20 4 11
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## 1299 134 Spain MID 1992 2012/2013 22 5 12
## 1300 134 Spain MID 1992 2013/2014 23 5 8
## 1301 134 Spain MID 1992 2014/2015 24 4 22
## 1302 134 Spain MID 1992 2015/2016 25 3 17
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## 1304 134 Spain MID 1992 2017/2018 27 2 12
## 1305 134 Spain MID 1992 2018/2019 28 2 19
## 1306 134 Spain MID 1992 2019/2020 29 2 25
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## 1309 135 Italy MID 1985 2012/2013 21 1 5
## 1310 135 Italy MID 1985 2013/2014 22 4 16
## 1311 135 Italy MID 1985 2014/2015 23 3 25
## 1312 135 Italy MID 1985 2015/2016 24 2 6
## 1313 136 Germany FWD 1996 2010/2011 18 2 11
## 1314 136 Germany FWD 1996 2011/2012 19 2 16
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## 1316 136 Germany FWD 1996 2013/2014 21 3 34
## 1317 136 Germany FWD 1996 2014/2015 22 4 33
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## 1334 137 Spain FWD 2001 2017/2018 27 5 11
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## 1338 137 Spain FWD 2001 2021/2022 31 4 36
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## 1340 138 Argentina GK 1988 2010/2011 17 5 7
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## 1346 138 Argentina GK 1988 2016/2017 23 4 10
## 1347 138 Argentina GK 1988 2017/2018 24 3 35
## 1348 138 Argentina GK 1988 2018/2019 25 1 28
## 1349 139 Brazil MID 1994 2010/2011 20 5 6
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## 1351 139 Brazil MID 1994 2012/2013 22 4 11
## 1352 139 Brazil MID 1994 2013/2014 23 4 22
## 1353 139 Brazil MID 1994 2014/2015 24 3 7
## 1354 139 Brazil MID 1994 2015/2016 25 5 15
## 1355 139 Brazil MID 1994 2016/2017 26 4 14
## 1356 139 Brazil MID 1994 2017/2018 27 4 7
## 1357 139 Brazil MID 1994 2018/2019 28 3 7
## 1358 139 Brazil MID 1994 2019/2020 29 5 7
## 1359 140 France DEF 1985 2010/2011 20 5 13
## 1360 140 France DEF 1985 2011/2012 21 2 18
## 1361 140 France DEF 1985 2012/2013 22 5 17
## 1362 140 France DEF 1985 2013/2014 23 5 11
## 1363 140 France DEF 1985 2014/2015 24 2 9
## 1364 140 France DEF 1985 2015/2016 25 5 20
## 1365 140 France DEF 1985 2016/2017 26 3 9
## 1366 140 France DEF 1985 2017/2018 27 4 21
## 1367 141 Spain GK 1987 2010/2011 17 1 5
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## 1369 141 Spain GK 1987 2012/2013 19 1 6
## 1370 141 Spain GK 1987 2013/2014 20 3 18
## 1371 141 Spain GK 1987 2014/2015 21 4 29
## 1372 141 Spain GK 1987 2015/2016 22 5 15
## 1373 141 Spain GK 1987 2016/2017 23 5 36
## 1374 141 Spain GK 1987 2017/2018 24 4 10
## 1375 141 Spain GK 1987 2018/2019 25 4 27
## 1376 141 Spain GK 1987 2019/2020 26 2 5
## 1377 141 Spain GK 1987 2020/2021 27 2 13
## 1378 141 Spain GK 1987 2021/2022 28 5 14
## 1379 141 Spain GK 1987 2022/2023 29 5 18
## 1380 141 Spain GK 1987 2023/2024 30 1 13
## 1381 142 Spain DEF 1990 2010/2011 18 4 7
## 1382 142 Spain DEF 1990 2011/2012 19 1 11
## 1383 142 Spain DEF 1990 2012/2013 20 5 20
## 1384 142 Spain DEF 1990 2013/2014 21 2 8
## 1385 142 Spain DEF 1990 2014/2015 22 3 36
## 1386 142 Spain DEF 1990 2015/2016 23 2 9
## 1387 142 Spain DEF 1990 2016/2017 24 2 23
## 1388 142 Spain DEF 1990 2017/2018 25 3 22
## 1389 142 Spain DEF 1990 2018/2019 26 1 9
## 1390 142 Spain DEF 1990 2019/2020 27 1 22
## 1391 142 Spain DEF 1990 2020/2021 28 3 24
## 1392 143 England GK 1993 2010/2011 19 4 6
## 1393 143 England GK 1993 2011/2012 20 3 18
## 1394 143 England GK 1993 2012/2013 21 1 37
## 1395 143 England GK 1993 2013/2014 22 1 5
## 1396 143 England GK 1993 2014/2015 23 1 35
## 1397 143 England GK 1993 2015/2016 24 5 9
## 1398 143 England GK 1993 2016/2017 25 3 28
## 1399 143 England GK 1993 2017/2018 26 3 12
## 1400 143 England GK 1993 2018/2019 27 4 8
## 1401 143 England GK 1993 2019/2020 28 4 14
## 1402 143 England GK 1993 2020/2021 29 1 7
## 1403 143 England GK 1993 2021/2022 30 4 24
## 1404 143 England GK 1993 2022/2023 31 4 9
## 1405 144 Italy DEF 1993 2010/2011 17 3 20
## 1406 144 Italy DEF 1993 2011/2012 18 1 17
## 1407 144 Italy DEF 1993 2012/2013 19 3 24
## 1408 144 Italy DEF 1993 2013/2014 20 2 18
## 1409 144 Italy DEF 1993 2014/2015 21 4 34
## 1410 144 Italy DEF 1993 2015/2016 22 1 20
## 1411 144 Italy DEF 1993 2016/2017 23 1 20
## 1412 144 Italy DEF 1993 2017/2018 24 4 23
## 1413 144 Italy DEF 1993 2018/2019 25 1 14
## 1414 144 Italy DEF 1993 2019/2020 26 1 20
## 1415 144 Italy DEF 1993 2020/2021 27 2 14
## 1416 145 Spain FWD 2004 2010/2011 18 4 15
## 1417 145 Spain FWD 2004 2011/2012 19 1 14
## 1418 145 Spain FWD 2004 2012/2013 20 5 15
## 1419 145 Spain FWD 2004 2013/2014 21 2 8
## 1420 145 Spain FWD 2004 2014/2015 22 1 30
## 1421 146 Netherlands GK 1991 2010/2011 19 1 28
## 1422 146 Netherlands GK 1991 2011/2012 20 3 9
## 1423 146 Netherlands GK 1991 2012/2013 21 3 15
## 1424 146 Netherlands GK 1991 2013/2014 22 5 27
## 1425 146 Netherlands GK 1991 2014/2015 23 3 33
## 1426 147 Italy MID 1995 2010/2011 19 5 30
## 1427 147 Italy MID 1995 2011/2012 20 4 19
## 1428 147 Italy MID 1995 2012/2013 21 5 7
## 1429 147 Italy MID 1995 2013/2014 22 4 25
## 1430 147 Italy MID 1995 2014/2015 23 4 35
## 1431 147 Italy MID 1995 2015/2016 24 5 22
## 1432 147 Italy MID 1995 2016/2017 25 3 10
## 1433 147 Italy MID 1995 2017/2018 26 4 9
## 1434 147 Italy MID 1995 2018/2019 27 5 13
## 1435 147 Italy MID 1995 2019/2020 28 5 27
## 1436 147 Italy MID 1995 2020/2021 29 5 8
## 1437 147 Italy MID 1995 2021/2022 30 2 13
## 1438 147 Italy MID 1995 2022/2023 31 5 7
## 1439 148 Netherlands MID 1999 2010/2011 17 5 23
## 1440 148 Netherlands MID 1999 2011/2012 18 1 17
## 1441 148 Netherlands MID 1999 2012/2013 19 4 25
## 1442 148 Netherlands MID 1999 2013/2014 20 5 33
## 1443 148 Netherlands MID 1999 2014/2015 21 2 16
## 1444 148 Netherlands MID 1999 2015/2016 22 4 12
## 1445 148 Netherlands MID 1999 2016/2017 23 3 13
## 1446 148 Netherlands MID 1999 2017/2018 24 5 19
## 1447 148 Netherlands MID 1999 2018/2019 25 2 11
## 1448 148 Netherlands MID 1999 2019/2020 26 2 14
## 1449 148 Netherlands MID 1999 2020/2021 27 4 37
## 1450 148 Netherlands MID 1999 2021/2022 28 1 31
## 1451 148 Netherlands MID 1999 2022/2023 29 5 30
## 1452 149 Netherlands GK 1991 2010/2011 18 2 7
## 1453 149 Netherlands GK 1991 2011/2012 19 4 7
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## 1462 150 Germany MID 2004 2012/2013 21 3 36
## 1463 150 Germany MID 2004 2013/2014 22 2 9
## 1464 150 Germany MID 2004 2014/2015 23 2 8
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## 1466 150 Germany MID 2004 2016/2017 25 1 11
## 1467 150 Germany MID 2004 2017/2018 26 4 35
## 1468 150 Germany MID 2004 2018/2019 27 5 22
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## 1470 150 Germany MID 2004 2020/2021 29 4 21
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## 1472 150 Germany MID 2004 2022/2023 31 3 30
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## 1477 151 England FWD 2002 2014/2015 22 3 9
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## 1479 151 England FWD 2002 2016/2017 24 5 10
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## 1483 152 England MID 1999 2013/2014 21 3 28
## 1484 152 England MID 1999 2014/2015 22 1 12
## 1485 152 England MID 1999 2015/2016 23 4 5
## 1486 152 England MID 1999 2016/2017 24 5 17
## 1487 152 England MID 1999 2017/2018 25 3 34
## 1488 152 England MID 1999 2018/2019 26 1 10
## 1489 152 England MID 1999 2019/2020 27 5 22
## 1490 152 England MID 1999 2020/2021 28 3 27
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## 1502 154 Spain DEF 1986 2011/2012 20 3 30
## 1503 154 Spain DEF 1986 2012/2013 21 4 12
## 1504 154 Spain DEF 1986 2013/2014 22 2 17
## 1505 154 Spain DEF 1986 2014/2015 23 5 26
## 1506 154 Spain DEF 1986 2015/2016 24 2 29
## 1507 154 Spain DEF 1986 2016/2017 25 2 19
## 1508 154 Spain DEF 1986 2017/2018 26 3 12
## 1509 154 Spain DEF 1986 2018/2019 27 5 15
## 1510 154 Spain DEF 1986 2019/2020 28 2 35
## 1511 154 Spain DEF 1986 2020/2021 29 5 10
## 1512 154 Spain DEF 1986 2021/2022 30 2 10
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## 1528 156 England MID 1991 2012/2013 22 4 12
## 1529 156 England MID 1991 2013/2014 23 3 11
## 1530 156 England MID 1991 2014/2015 24 2 17
## 1531 156 England MID 1991 2015/2016 25 5 15
## 1532 156 England MID 1991 2016/2017 26 3 21
## 1533 156 England MID 1991 2017/2018 27 5 20
## 1534 156 England MID 1991 2018/2019 28 1 10
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## 1536 157 England GK 1993 2011/2012 20 1 6
## 1537 157 England GK 1993 2012/2013 21 2 36
## 1538 157 England GK 1993 2013/2014 22 5 32
## 1539 157 England GK 1993 2014/2015 23 2 33
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## 1542 158 France FWD 1993 2012/2013 22 1 14
## 1543 158 France FWD 1993 2013/2014 23 2 22
## 1544 158 France FWD 1993 2014/2015 24 2 9
## 1545 158 France FWD 1993 2015/2016 25 2 8
## 1546 158 France FWD 1993 2016/2017 26 3 35
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## 1548 159 England FWD 1996 2011/2012 20 2 16
## 1549 159 England FWD 1996 2012/2013 21 4 30
## 1550 159 England FWD 1996 2013/2014 22 3 20
## 1551 159 England FWD 1996 2014/2015 23 3 14
## 1552 159 England FWD 1996 2015/2016 24 4 26
## 1553 159 England FWD 1996 2016/2017 25 4 5
## 1554 159 England FWD 1996 2017/2018 26 3 8
## 1555 159 England FWD 1996 2018/2019 27 2 18
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## 1557 159 England FWD 1996 2020/2021 29 2 15
## 1558 159 England FWD 1996 2021/2022 30 1 32
## 1559 159 England FWD 1996 2022/2023 31 1 16
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## 1571 160 Argentina FWD 2001 2020/2021 30 5 16
## 1572 160 Argentina FWD 2001 2021/2022 31 4 33
## 1573 161 France DEF 1995 2010/2011 20 2 21
## 1574 161 France DEF 1995 2011/2012 21 5 21
## 1575 161 France DEF 1995 2012/2013 22 5 12
## 1576 161 France DEF 1995 2013/2014 23 1 30
## 1577 161 France DEF 1995 2014/2015 24 3 31
## 1578 161 France DEF 1995 2015/2016 25 2 23
## 1579 161 France DEF 1995 2016/2017 26 5 27
## 1580 161 France DEF 1995 2017/2018 27 2 23
## 1581 161 France DEF 1995 2018/2019 28 1 26
## 1582 161 France DEF 1995 2019/2020 29 3 20
## 1583 162 France DEF 1995 2010/2011 20 5 7
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## 1585 162 France DEF 1995 2012/2013 22 2 8
## 1586 162 France DEF 1995 2013/2014 23 2 27
## 1587 162 France DEF 1995 2014/2015 24 3 24
## 1588 162 France DEF 1995 2015/2016 25 1 8
## 1589 162 France DEF 1995 2016/2017 26 4 18
## 1590 162 France DEF 1995 2017/2018 27 3 26
## 1591 162 France DEF 1995 2018/2019 28 5 37
## 1592 162 France DEF 1995 2019/2020 29 5 27
## 1593 162 France DEF 1995 2020/2021 30 3 11
## 1594 162 France DEF 1995 2021/2022 31 3 27
## 1595 163 Italy DEF 2001 2010/2011 18 2 15
## 1596 163 Italy DEF 2001 2011/2012 19 1 32
## 1597 163 Italy DEF 2001 2012/2013 20 4 11
## 1598 163 Italy DEF 2001 2013/2014 21 3 5
## 1599 163 Italy DEF 2001 2014/2015 22 3 6
## 1600 163 Italy DEF 2001 2015/2016 23 4 34
## 1601 163 Italy DEF 2001 2016/2017 24 1 15
## 1602 163 Italy DEF 2001 2017/2018 25 3 23
## 1603 163 Italy DEF 2001 2018/2019 26 2 29
## 1604 163 Italy DEF 2001 2019/2020 27 4 10
## 1605 163 Italy DEF 2001 2020/2021 28 2 18
## 1606 163 Italy DEF 2001 2021/2022 29 5 27
## 1607 163 Italy DEF 2001 2022/2023 30 1 7
## 1608 164 France DEF 1989 2010/2011 19 2 24
## 1609 164 France DEF 1989 2011/2012 20 5 22
## 1610 164 France DEF 1989 2012/2013 21 3 24
## 1611 164 France DEF 1989 2013/2014 22 4 28
## 1612 164 France DEF 1989 2014/2015 23 2 6
## 1613 164 France DEF 1989 2015/2016 24 5 30
## 1614 164 France DEF 1989 2016/2017 25 2 12
## 1615 164 France DEF 1989 2017/2018 26 4 25
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## 1618 165 Brazil DEF 1996 2012/2013 19 5 15
## 1619 165 Brazil DEF 1996 2013/2014 20 1 11
## 1620 165 Brazil DEF 1996 2014/2015 21 2 23
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## 1622 166 Spain DEF 1993 2011/2012 20 2 29
## 1623 166 Spain DEF 1993 2012/2013 21 5 10
## 1624 166 Spain DEF 1993 2013/2014 22 2 29
## 1625 166 Spain DEF 1993 2014/2015 23 2 32
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## 1641 168 Spain FWD 1993 2011/2012 20 1 22
## 1642 168 Spain FWD 1993 2012/2013 21 1 8
## 1643 168 Spain FWD 1993 2013/2014 22 2 24
## 1644 168 Spain FWD 1993 2014/2015 23 1 23
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## 1646 168 Spain FWD 1993 2016/2017 25 4 24
## 1647 168 Spain FWD 1993 2017/2018 26 3 8
## 1648 169 Argentina DEF 2003 2010/2011 20 4 11
## 1649 169 Argentina DEF 2003 2011/2012 21 1 18
## 1650 169 Argentina DEF 2003 2012/2013 22 3 24
## 1651 169 Argentina DEF 2003 2013/2014 23 1 19
## 1652 169 Argentina DEF 2003 2014/2015 24 3 10
## 1653 169 Argentina DEF 2003 2015/2016 25 3 16
## 1654 169 Argentina DEF 2003 2016/2017 26 2 11
## 1655 169 Argentina DEF 2003 2017/2018 27 4 32
## 1656 169 Argentina DEF 2003 2018/2019 28 2 32
## 1657 169 Argentina DEF 2003 2019/2020 29 4 31
## 1658 169 Argentina DEF 2003 2020/2021 30 3 27
## 1659 170 Argentina GK 1991 2010/2011 18 3 5
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## 1661 170 Argentina GK 1991 2012/2013 20 5 17
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## 1668 171 Brazil MID 1987 2013/2014 23 5 19
## 1669 171 Brazil MID 1987 2014/2015 24 3 13
## 1670 171 Brazil MID 1987 2015/2016 25 1 32
## 1671 171 Brazil MID 1987 2016/2017 26 4 6
## 1672 172 Germany FWD 2000 2010/2011 19 5 17
## 1673 172 Germany FWD 2000 2011/2012 20 3 22
## 1674 172 Germany FWD 2000 2012/2013 21 4 22
## 1675 172 Germany FWD 2000 2013/2014 22 5 13
## 1676 172 Germany FWD 2000 2014/2015 23 1 11
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## 1681 173 England GK 1995 2014/2015 24 3 25
## 1682 173 England GK 1995 2015/2016 25 5 20
## 1683 173 England GK 1995 2016/2017 26 1 36
## 1684 173 England GK 1995 2017/2018 27 2 6
## 1685 173 England GK 1995 2018/2019 28 1 34
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## 1687 174 England GK 1990 2011/2012 18 4 11
## 1688 174 England GK 1990 2012/2013 19 5 30
## 1689 174 England GK 1990 2013/2014 20 4 10
## 1690 174 England GK 1990 2014/2015 21 4 10
## 1691 175 England GK 1987 2010/2011 18 5 14
## 1692 175 England GK 1987 2011/2012 19 5 12
## 1693 175 England GK 1987 2012/2013 20 5 5
## 1694 175 England GK 1987 2013/2014 21 4 5
## 1695 175 England GK 1987 2014/2015 22 5 25
## 1696 175 England GK 1987 2015/2016 23 4 15
## 1697 175 England GK 1987 2016/2017 24 4 8
## 1698 175 England GK 1987 2017/2018 25 3 33
## 1699 175 England GK 1987 2018/2019 26 5 18
## 1700 175 England GK 1987 2019/2020 27 1 33
## 1701 175 England GK 1987 2020/2021 28 4 13
## 1702 175 England GK 1987 2021/2022 29 4 25
## 1703 176 France DEF 1996 2010/2011 19 2 34
## 1704 176 France DEF 1996 2011/2012 20 3 19
## 1705 176 France DEF 1996 2012/2013 21 5 25
## 1706 176 France DEF 1996 2013/2014 22 4 25
## 1707 176 France DEF 1996 2014/2015 23 3 26
## 1708 177 Netherlands FWD 1993 2010/2011 18 3 13
## 1709 177 Netherlands FWD 1993 2011/2012 19 3 21
## 1710 177 Netherlands FWD 1993 2012/2013 20 2 26
## 1711 177 Netherlands FWD 1993 2013/2014 21 2 6
## 1712 177 Netherlands FWD 1993 2014/2015 22 4 7
## 1713 178 England MID 1987 2010/2011 18 2 28
## 1714 178 England MID 1987 2011/2012 19 5 23
## 1715 178 England MID 1987 2012/2013 20 1 35
## 1716 178 England MID 1987 2013/2014 21 5 28
## 1717 178 England MID 1987 2014/2015 22 1 10
## 1718 178 England MID 1987 2015/2016 23 5 22
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## 1723 179 Italy GK 2002 2014/2015 21 1 24
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## 1750 182 Brazil FWD 1988 2014/2015 22 5 15
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## 1760 184 England FWD 1988 2010/2011 19 5 27
## 1761 184 England FWD 1988 2011/2012 20 5 22
## 1762 184 England FWD 1988 2012/2013 21 2 34
## 1763 184 England FWD 1988 2013/2014 22 5 23
## 1764 184 England FWD 1988 2014/2015 23 5 36
## 1765 185 France DEF 1990 2010/2011 18 5 23
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## 1774 186 Spain MID 2003 2012/2013 19 3 29
## 1775 186 Spain MID 2003 2013/2014 20 5 26
## 1776 186 Spain MID 2003 2014/2015 21 1 25
## 1777 186 Spain MID 2003 2015/2016 22 1 22
## 1778 187 Brazil MID 1991 2010/2011 17 1 26
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## 1780 187 Brazil MID 1991 2012/2013 19 5 34
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## 1782 187 Brazil MID 1991 2014/2015 21 2 24
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## 1798 188 France GK 1993 2021/2022 30 1 37
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## 1803 189 Spain MID 1992 2012/2013 22 5 9
## 1804 189 Spain MID 1992 2013/2014 23 2 17
## 1805 189 Spain MID 1992 2014/2015 24 1 36
## 1806 189 Spain MID 1992 2015/2016 25 4 26
## 1807 189 Spain MID 1992 2016/2017 26 3 26
## 1808 189 Spain MID 1992 2017/2018 27 4 32
## 1809 189 Spain MID 1992 2018/2019 28 5 32
## 1810 189 Spain MID 1992 2019/2020 29 1 5
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## 1843 193 Argentina DEF 1989 2012/2013 21 4 23
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## 1845 193 Argentina DEF 1989 2014/2015 23 5 6
## 1846 193 Argentina DEF 1989 2015/2016 24 3 11
## 1847 193 Argentina DEF 1989 2016/2017 25 1 21
## 1848 193 Argentina DEF 1989 2017/2018 26 2 29
## 1849 193 Argentina DEF 1989 2018/2019 27 1 14
## 1850 193 Argentina DEF 1989 2019/2020 28 3 13
## 1851 193 Argentina DEF 1989 2020/2021 29 1 32
## 1852 193 Argentina DEF 1989 2021/2022 30 3 9
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## 1854 194 France GK 1994 2011/2012 18 5 18
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## 1856 194 France GK 1994 2013/2014 20 3 34
## 1857 194 France GK 1994 2014/2015 21 3 18
## 1858 195 England DEF 1995 2010/2011 19 4 37
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## 1860 195 England DEF 1995 2012/2013 21 2 29
## 1861 195 England DEF 1995 2013/2014 22 2 33
## 1862 195 England DEF 1995 2014/2015 23 3 32
## 1863 195 England DEF 1995 2015/2016 24 1 11
## 1864 195 England DEF 1995 2016/2017 25 2 24
## 1865 195 England DEF 1995 2017/2018 26 2 6
## 1866 195 England DEF 1995 2018/2019 27 3 9
## 1867 195 England DEF 1995 2019/2020 28 5 7
## 1868 196 Netherlands MID 1993 2010/2011 17 5 34
## 1869 196 Netherlands MID 1993 2011/2012 18 2 20
## 1870 196 Netherlands MID 1993 2012/2013 19 3 28
## 1871 196 Netherlands MID 1993 2013/2014 20 2 21
## 1872 196 Netherlands MID 1993 2014/2015 21 1 16
## 1873 196 Netherlands MID 1993 2015/2016 22 5 15
## 1874 196 Netherlands MID 1993 2016/2017 23 1 22
## 1875 196 Netherlands MID 1993 2017/2018 24 4 29
## 1876 196 Netherlands MID 1993 2018/2019 25 1 37
## 1877 196 Netherlands MID 1993 2019/2020 26 1 12
## 1878 196 Netherlands MID 1993 2020/2021 27 4 8
## 1879 196 Netherlands MID 1993 2021/2022 28 3 34
## 1880 197 Italy GK 2001 2010/2011 17 4 37
## 1881 197 Italy GK 2001 2011/2012 18 3 15
## 1882 197 Italy GK 2001 2012/2013 19 4 19
## 1883 197 Italy GK 2001 2013/2014 20 3 22
## 1884 197 Italy GK 2001 2014/2015 21 5 29
## 1885 197 Italy GK 2001 2015/2016 22 5 33
## 1886 197 Italy GK 2001 2016/2017 23 4 12
## 1887 197 Italy GK 2001 2017/2018 24 1 15
## 1888 197 Italy GK 2001 2018/2019 25 5 37
## 1889 198 France MID 2004 2010/2011 18 5 7
## 1890 198 France MID 2004 2011/2012 19 3 15
## 1891 198 France MID 2004 2012/2013 20 5 32
## 1892 198 France MID 2004 2013/2014 21 4 9
## 1893 198 France MID 2004 2014/2015 22 4 6
## 1894 198 France MID 2004 2015/2016 23 2 36
## 1895 198 France MID 2004 2016/2017 24 1 6
## 1896 198 France MID 2004 2017/2018 25 3 13
## 1897 199 France GK 1993 2010/2011 17 2 18
## 1898 199 France GK 1993 2011/2012 18 1 6
## 1899 199 France GK 1993 2012/2013 19 5 33
## 1900 199 France GK 1993 2013/2014 20 3 17
## 1901 199 France GK 1993 2014/2015 21 3 31
## 1902 199 France GK 1993 2015/2016 22 2 22
## 1903 200 France MID 1999 2010/2011 19 3 23
## 1904 200 France MID 1999 2011/2012 20 3 25
## 1905 200 France MID 1999 2012/2013 21 1 17
## 1906 200 France MID 1999 2013/2014 22 3 29
## 1907 200 France MID 1999 2014/2015 23 3 18
## 1908 200 France MID 1999 2015/2016 24 1 35
## 1909 200 France MID 1999 2016/2017 25 2 18
## 1910 200 France MID 1999 2017/2018 26 1 9
## 1911 200 France MID 1999 2018/2019 27 2 27
## 1912 200 France MID 1999 2019/2020 28 5 17
## 1913 201 Brazil MID 2004 2010/2011 20 5 33
## 1914 201 Brazil MID 2004 2011/2012 21 5 37
## 1915 201 Brazil MID 2004 2012/2013 22 1 34
## 1916 201 Brazil MID 2004 2013/2014 23 3 8
## 1917 201 Brazil MID 2004 2014/2015 24 2 18
## 1918 201 Brazil MID 2004 2015/2016 25 3 12
## 1919 201 Brazil MID 2004 2016/2017 26 3 24
## 1920 201 Brazil MID 2004 2017/2018 27 3 35
## 1921 201 Brazil MID 2004 2018/2019 28 5 37
## 1922 201 Brazil MID 2004 2019/2020 29 2 15
## 1923 202 France DEF 1990 2010/2011 18 4 14
## 1924 202 France DEF 1990 2011/2012 19 3 10
## 1925 202 France DEF 1990 2012/2013 20 2 30
## 1926 202 France DEF 1990 2013/2014 21 3 15
## 1927 202 France DEF 1990 2014/2015 22 1 10
## 1928 203 Brazil FWD 1985 2010/2011 17 3 29
## 1929 203 Brazil FWD 1985 2011/2012 18 4 8
## 1930 203 Brazil FWD 1985 2012/2013 19 2 32
## 1931 203 Brazil FWD 1985 2013/2014 20 5 25
## 1932 203 Brazil FWD 1985 2014/2015 21 3 31
## 1933 203 Brazil FWD 1985 2015/2016 22 5 10
## 1934 203 Brazil FWD 1985 2016/2017 23 4 22
## 1935 203 Brazil FWD 1985 2017/2018 24 3 5
## 1936 203 Brazil FWD 1985 2018/2019 25 5 33
## 1937 203 Brazil FWD 1985 2019/2020 26 1 32
## 1938 203 Brazil FWD 1985 2020/2021 27 5 25
## 1939 203 Brazil FWD 1985 2021/2022 28 4 31
## 1940 203 Brazil FWD 1985 2022/2023 29 5 8
## 1941 204 England GK 1990 2010/2011 20 2 8
## 1942 204 England GK 1990 2011/2012 21 3 8
## 1943 204 England GK 1990 2012/2013 22 1 7
## 1944 204 England GK 1990 2013/2014 23 4 7
## 1945 204 England GK 1990 2014/2015 24 4 26
## 1946 204 England GK 1990 2015/2016 25 2 22
## 1947 204 England GK 1990 2016/2017 26 4 22
## 1948 204 England GK 1990 2017/2018 27 4 14
## 1949 204 England GK 1990 2018/2019 28 1 30
## 1950 204 England GK 1990 2019/2020 29 1 7
## 1951 205 Spain FWD 2001 2010/2011 18 1 11
## 1952 205 Spain FWD 2001 2011/2012 19 3 37
## 1953 205 Spain FWD 2001 2012/2013 20 2 22
## 1954 205 Spain FWD 2001 2013/2014 21 2 19
## 1955 205 Spain FWD 2001 2014/2015 22 1 19
## 1956 205 Spain FWD 2001 2015/2016 23 1 13
## 1957 205 Spain FWD 2001 2016/2017 24 3 30
## 1958 206 Argentina DEF 1999 2010/2011 17 3 28
## 1959 206 Argentina DEF 1999 2011/2012 18 1 16
## 1960 206 Argentina DEF 1999 2012/2013 19 3 21
## 1961 206 Argentina DEF 1999 2013/2014 20 2 18
## 1962 206 Argentina DEF 1999 2014/2015 21 2 17
## 1963 206 Argentina DEF 1999 2015/2016 22 2 14
## 1964 206 Argentina DEF 1999 2016/2017 23 5 34
## 1965 206 Argentina DEF 1999 2017/2018 24 1 36
## 1966 207 Netherlands DEF 1999 2010/2011 17 3 15
## 1967 207 Netherlands DEF 1999 2011/2012 18 3 11
## 1968 207 Netherlands DEF 1999 2012/2013 19 2 18
## 1969 207 Netherlands DEF 1999 2013/2014 20 5 18
## 1970 207 Netherlands DEF 1999 2014/2015 21 3 5
## 1971 207 Netherlands DEF 1999 2015/2016 22 2 7
## 1972 207 Netherlands DEF 1999 2016/2017 23 1 25
## 1973 207 Netherlands DEF 1999 2017/2018 24 2 19
## 1974 208 Netherlands FWD 1989 2010/2011 17 2 16
## 1975 208 Netherlands FWD 1989 2011/2012 18 2 20
## 1976 208 Netherlands FWD 1989 2012/2013 19 3 26
## 1977 208 Netherlands FWD 1989 2013/2014 20 3 28
## 1978 208 Netherlands FWD 1989 2014/2015 21 3 25
## 1979 208 Netherlands FWD 1989 2015/2016 22 3 14
## 1980 208 Netherlands FWD 1989 2016/2017 23 3 21
## 1981 209 Spain DEF 1997 2010/2011 18 1 19
## 1982 209 Spain DEF 1997 2011/2012 19 1 19
## 1983 209 Spain DEF 1997 2012/2013 20 3 30
## 1984 209 Spain DEF 1997 2013/2014 21 2 32
## 1985 209 Spain DEF 1997 2014/2015 22 3 10
## 1986 209 Spain DEF 1997 2015/2016 23 1 29
## 1987 209 Spain DEF 1997 2016/2017 24 2 33
## 1988 209 Spain DEF 1997 2017/2018 25 1 25
## 1989 209 Spain DEF 1997 2018/2019 26 5 11
## 1990 209 Spain DEF 1997 2019/2020 27 2 30
## 1991 209 Spain DEF 1997 2020/2021 28 4 30
## 1992 209 Spain DEF 1997 2021/2022 29 2 17
## 1993 209 Spain DEF 1997 2022/2023 30 5 15
## 1994 209 Spain DEF 1997 2023/2024 31 2 5
## 1995 210 England DEF 1987 2010/2011 18 4 15
## 1996 210 England DEF 1987 2011/2012 19 4 35
## 1997 210 England DEF 1987 2012/2013 20 4 11
## 1998 210 England DEF 1987 2013/2014 21 5 37
## 1999 210 England DEF 1987 2014/2015 22 5 36
## 2000 210 England DEF 1987 2015/2016 23 1 34
## 2001 210 England DEF 1987 2016/2017 24 5 29
## 2002 211 England DEF 1996 2010/2011 20 4 35
## 2003 211 England DEF 1996 2011/2012 21 3 29
## 2004 211 England DEF 1996 2012/2013 22 2 7
## 2005 211 England DEF 1996 2013/2014 23 3 32
## 2006 211 England DEF 1996 2014/2015 24 4 22
## 2007 211 England DEF 1996 2015/2016 25 1 36
## 2008 211 England DEF 1996 2016/2017 26 4 27
## 2009 212 Spain FWD 1990 2010/2011 18 5 9
## 2010 212 Spain FWD 1990 2011/2012 19 4 37
## 2011 212 Spain FWD 1990 2012/2013 20 1 26
## 2012 212 Spain FWD 1990 2013/2014 21 2 26
## 2013 212 Spain FWD 1990 2014/2015 22 4 37
## 2014 212 Spain FWD 1990 2015/2016 23 2 20
## 2015 212 Spain FWD 1990 2016/2017 24 3 13
## 2016 212 Spain FWD 1990 2017/2018 25 4 5
## 2017 213 Germany GK 1987 2010/2011 19 3 25
## 2018 213 Germany GK 1987 2011/2012 20 1 16
## 2019 213 Germany GK 1987 2012/2013 21 4 13
## 2020 213 Germany GK 1987 2013/2014 22 2 7
## 2021 213 Germany GK 1987 2014/2015 23 2 8
## 2022 213 Germany GK 1987 2015/2016 24 4 33
## 2023 213 Germany GK 1987 2016/2017 25 5 33
## 2024 213 Germany GK 1987 2017/2018 26 2 36
## 2025 213 Germany GK 1987 2018/2019 27 5 8
## 2026 213 Germany GK 1987 2019/2020 28 5 23
## 2027 213 Germany GK 1987 2020/2021 29 1 10
## 2028 214 Argentina DEF 1985 2010/2011 18 4 33
## 2029 214 Argentina DEF 1985 2011/2012 19 1 15
## 2030 214 Argentina DEF 1985 2012/2013 20 2 15
## 2031 214 Argentina DEF 1985 2013/2014 21 4 32
## 2032 214 Argentina DEF 1985 2014/2015 22 3 14
## 2033 214 Argentina DEF 1985 2015/2016 23 3 8
## 2034 214 Argentina DEF 1985 2016/2017 24 4 25
## 2035 214 Argentina DEF 1985 2017/2018 25 1 37
## 2036 215 Italy DEF 2001 2010/2011 18 4 11
## 2037 215 Italy DEF 2001 2011/2012 19 3 6
## 2038 215 Italy DEF 2001 2012/2013 20 5 27
## 2039 215 Italy DEF 2001 2013/2014 21 5 28
## 2040 215 Italy DEF 2001 2014/2015 22 1 37
## 2041 215 Italy DEF 2001 2015/2016 23 5 6
## 2042 215 Italy DEF 2001 2016/2017 24 3 16
## 2043 215 Italy DEF 2001 2017/2018 25 4 25
## 2044 215 Italy DEF 2001 2018/2019 26 4 19
## 2045 215 Italy DEF 2001 2019/2020 27 2 33
## 2046 215 Italy DEF 2001 2020/2021 28 5 27
## 2047 215 Italy DEF 2001 2021/2022 29 3 31
## 2048 215 Italy DEF 2001 2022/2023 30 5 9
## 2049 216 Italy DEF 1987 2010/2011 20 5 28
## 2050 216 Italy DEF 1987 2011/2012 21 2 30
## 2051 216 Italy DEF 1987 2012/2013 22 2 21
## 2052 216 Italy DEF 1987 2013/2014 23 5 34
## 2053 216 Italy DEF 1987 2014/2015 24 4 32
## 2054 217 Italy DEF 1998 2010/2011 19 2 23
## 2055 217 Italy DEF 1998 2011/2012 20 3 25
## 2056 217 Italy DEF 1998 2012/2013 21 2 20
## 2057 217 Italy DEF 1998 2013/2014 22 4 10
## 2058 217 Italy DEF 1998 2014/2015 23 4 8
## 2059 217 Italy DEF 1998 2015/2016 24 2 37
## 2060 217 Italy DEF 1998 2016/2017 25 3 9
## 2061 217 Italy DEF 1998 2017/2018 26 4 26
## 2062 217 Italy DEF 1998 2018/2019 27 4 25
## 2063 217 Italy DEF 1998 2019/2020 28 1 24
## 2064 217 Italy DEF 1998 2020/2021 29 4 31
## 2065 217 Italy DEF 1998 2021/2022 30 4 23
## 2066 218 Spain GK 1986 2010/2011 19 5 19
## 2067 218 Spain GK 1986 2011/2012 20 2 27
## 2068 218 Spain GK 1986 2012/2013 21 4 37
## 2069 218 Spain GK 1986 2013/2014 22 3 11
## 2070 218 Spain GK 1986 2014/2015 23 4 11
## 2071 218 Spain GK 1986 2015/2016 24 4 22
## 2072 218 Spain GK 1986 2016/2017 25 2 14
## 2073 218 Spain GK 1986 2017/2018 26 5 28
## 2074 218 Spain GK 1986 2018/2019 27 3 33
## 2075 218 Spain GK 1986 2019/2020 28 5 15
## 2076 218 Spain GK 1986 2020/2021 29 1 28
## 2077 219 England MID 1993 2010/2011 19 3 27
## 2078 219 England MID 1993 2011/2012 20 3 37
## 2079 219 England MID 1993 2012/2013 21 4 33
## 2080 219 England MID 1993 2013/2014 22 1 22
## 2081 219 England MID 1993 2014/2015 23 4 19
## 2082 219 England MID 1993 2015/2016 24 3 34
## 2083 220 Brazil DEF 1995 2010/2011 19 1 6
## 2084 220 Brazil DEF 1995 2011/2012 20 4 36
## 2085 220 Brazil DEF 1995 2012/2013 21 2 30
## 2086 220 Brazil DEF 1995 2013/2014 22 5 34
## 2087 220 Brazil DEF 1995 2014/2015 23 2 29
## 2088 220 Brazil DEF 1995 2015/2016 24 2 24
## 2089 220 Brazil DEF 1995 2016/2017 25 5 31
## 2090 220 Brazil DEF 1995 2017/2018 26 1 31
## 2091 220 Brazil DEF 1995 2018/2019 27 4 14
## 2092 220 Brazil DEF 1995 2019/2020 28 1 14
## 2093 220 Brazil DEF 1995 2020/2021 29 5 17
## 2094 220 Brazil DEF 1995 2021/2022 30 4 19
## 2095 221 Germany GK 1994 2010/2011 17 4 28
## 2096 221 Germany GK 1994 2011/2012 18 4 30
## 2097 221 Germany GK 1994 2012/2013 19 3 16
## 2098 221 Germany GK 1994 2013/2014 20 1 17
## 2099 221 Germany GK 1994 2014/2015 21 3 21
## 2100 222 England GK 1991 2010/2011 17 4 33
## 2101 222 England GK 1991 2011/2012 18 4 34
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## 2104 222 England GK 1991 2014/2015 21 1 11
## 2105 222 England GK 1991 2015/2016 22 1 19
## 2106 222 England GK 1991 2016/2017 23 4 12
## 2107 222 England GK 1991 2017/2018 24 4 19
## 2108 222 England GK 1991 2018/2019 25 2 25
## 2109 222 England GK 1991 2019/2020 26 5 22
## 2110 222 England GK 1991 2020/2021 27 2 6
## 2111 222 England GK 1991 2021/2022 28 5 21
## 2112 222 England GK 1991 2022/2023 29 3 6
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## 2115 223 Argentina MID 2001 2012/2013 21 5 23
## 2116 223 Argentina MID 2001 2013/2014 22 5 9
## 2117 223 Argentina MID 2001 2014/2015 23 3 20
## 2118 223 Argentina MID 2001 2015/2016 24 2 9
## 2119 223 Argentina MID 2001 2016/2017 25 4 32
## 2120 223 Argentina MID 2001 2017/2018 26 5 24
## 2121 223 Argentina MID 2001 2018/2019 27 3 36
## 2122 223 Argentina MID 2001 2019/2020 28 3 12
## 2123 223 Argentina MID 2001 2020/2021 29 4 33
## 2124 223 Argentina MID 2001 2021/2022 30 4 29
## 2125 223 Argentina MID 2001 2022/2023 31 3 21
## 2126 224 England DEF 1986 2010/2011 20 4 22
## 2127 224 England DEF 1986 2011/2012 21 4 13
## 2128 224 England DEF 1986 2012/2013 22 4 23
## 2129 224 England DEF 1986 2013/2014 23 4 21
## 2130 224 England DEF 1986 2014/2015 24 1 14
## 2131 224 England DEF 1986 2015/2016 25 5 32
## 2132 225 Spain GK 1985 2010/2011 18 3 7
## 2133 225 Spain GK 1985 2011/2012 19 5 6
## 2134 225 Spain GK 1985 2012/2013 20 2 5
## 2135 225 Spain GK 1985 2013/2014 21 4 9
## 2136 225 Spain GK 1985 2014/2015 22 2 14
## 2137 225 Spain GK 1985 2015/2016 23 2 11
## 2138 225 Spain GK 1985 2016/2017 24 3 29
## 2139 225 Spain GK 1985 2017/2018 25 3 31
## 2140 225 Spain GK 1985 2018/2019 26 2 36
## 2141 225 Spain GK 1985 2019/2020 27 2 13
## 2142 225 Spain GK 1985 2020/2021 28 1 10
## 2143 226 Brazil GK 1989 2010/2011 18 4 6
## 2144 226 Brazil GK 1989 2011/2012 19 5 18
## 2145 226 Brazil GK 1989 2012/2013 20 4 20
## 2146 226 Brazil GK 1989 2013/2014 21 2 34
## 2147 226 Brazil GK 1989 2014/2015 22 2 8
## 2148 226 Brazil GK 1989 2015/2016 23 4 8
## 2149 226 Brazil GK 1989 2016/2017 24 1 18
## 2150 226 Brazil GK 1989 2017/2018 25 3 13
## 2151 226 Brazil GK 1989 2018/2019 26 5 26
## 2152 226 Brazil GK 1989 2019/2020 27 3 29
## 2153 226 Brazil GK 1989 2020/2021 28 4 9
## 2154 226 Brazil GK 1989 2021/2022 29 2 24
## 2155 226 Brazil GK 1989 2022/2023 30 3 34
## 2156 226 Brazil GK 1989 2023/2024 31 5 35
## 2157 227 England GK 1995 2010/2011 19 3 25
## 2158 227 England GK 1995 2011/2012 20 5 7
## 2159 227 England GK 1995 2012/2013 21 5 8
## 2160 227 England GK 1995 2013/2014 22 1 34
## 2161 227 England GK 1995 2014/2015 23 2 20
## 2162 227 England GK 1995 2015/2016 24 2 30
## 2163 227 England GK 1995 2016/2017 25 1 32
## 2164 228 Brazil GK 1991 2010/2011 20 5 23
## 2165 228 Brazil GK 1991 2011/2012 21 3 30
## 2166 228 Brazil GK 1991 2012/2013 22 4 24
## 2167 228 Brazil GK 1991 2013/2014 23 5 26
## 2168 228 Brazil GK 1991 2014/2015 24 4 36
## 2169 228 Brazil GK 1991 2015/2016 25 2 22
## 2170 228 Brazil GK 1991 2016/2017 26 1 7
## 2171 228 Brazil GK 1991 2017/2018 27 2 30
## 2172 228 Brazil GK 1991 2018/2019 28 1 34
## 2173 228 Brazil GK 1991 2019/2020 29 1 27
## 2174 228 Brazil GK 1991 2020/2021 30 3 31
## 2175 229 Germany FWD 1996 2010/2011 20 2 10
## 2176 229 Germany FWD 1996 2011/2012 21 1 31
## 2177 229 Germany FWD 1996 2012/2013 22 3 33
## 2178 229 Germany FWD 1996 2013/2014 23 5 21
## 2179 229 Germany FWD 1996 2014/2015 24 5 37
## 2180 229 Germany FWD 1996 2015/2016 25 1 37
## 2181 229 Germany FWD 1996 2016/2017 26 3 37
## 2182 229 Germany FWD 1996 2017/2018 27 1 36
## 2183 229 Germany FWD 1996 2018/2019 28 3 18
## 2184 229 Germany FWD 1996 2019/2020 29 4 16
## 2185 229 Germany FWD 1996 2020/2021 30 1 13
## 2186 229 Germany FWD 1996 2021/2022 31 5 18
## 2187 229 Germany FWD 1996 2022/2023 32 4 20
## 2188 229 Germany FWD 1996 2023/2024 33 5 5
## 2189 230 Netherlands GK 1998 2010/2011 17 3 13
## 2190 230 Netherlands GK 1998 2011/2012 18 1 25
## 2191 230 Netherlands GK 1998 2012/2013 19 4 7
## 2192 230 Netherlands GK 1998 2013/2014 20 1 16
## 2193 230 Netherlands GK 1998 2014/2015 21 5 21
## 2194 230 Netherlands GK 1998 2015/2016 22 3 25
## 2195 230 Netherlands GK 1998 2016/2017 23 2 26
## 2196 230 Netherlands GK 1998 2017/2018 24 1 29
## 2197 230 Netherlands GK 1998 2018/2019 25 2 6
## 2198 230 Netherlands GK 1998 2019/2020 26 5 19
## 2199 230 Netherlands GK 1998 2020/2021 27 2 16
## 2200 230 Netherlands GK 1998 2021/2022 28 2 20
## 2201 230 Netherlands GK 1998 2022/2023 29 1 33
## 2202 231 Brazil FWD 2003 2010/2011 20 2 26
## 2203 231 Brazil FWD 2003 2011/2012 21 5 34
## 2204 231 Brazil FWD 2003 2012/2013 22 5 12
## 2205 231 Brazil FWD 2003 2013/2014 23 5 5
## 2206 231 Brazil FWD 2003 2014/2015 24 3 27
## 2207 231 Brazil FWD 2003 2015/2016 25 2 13
## 2208 231 Brazil FWD 2003 2016/2017 26 2 31
## 2209 231 Brazil FWD 2003 2017/2018 27 1 34
## 2210 232 Germany MID 1992 2010/2011 17 2 34
## 2211 232 Germany MID 1992 2011/2012 18 2 29
## 2212 232 Germany MID 1992 2012/2013 19 2 32
## 2213 232 Germany MID 1992 2013/2014 20 1 33
## 2214 232 Germany MID 1992 2014/2015 21 2 23
## 2215 233 Brazil DEF 1992 2010/2011 18 5 12
## 2216 233 Brazil DEF 1992 2011/2012 19 1 8
## 2217 233 Brazil DEF 1992 2012/2013 20 1 11
## 2218 233 Brazil DEF 1992 2013/2014 21 5 33
## 2219 233 Brazil DEF 1992 2014/2015 22 4 23
## 2220 233 Brazil DEF 1992 2015/2016 23 3 37
## 2221 234 Germany MID 2004 2010/2011 19 1 10
## 2222 234 Germany MID 2004 2011/2012 20 3 29
## 2223 234 Germany MID 2004 2012/2013 21 1 10
## 2224 234 Germany MID 2004 2013/2014 22 5 6
## 2225 234 Germany MID 2004 2014/2015 23 3 23
## 2226 235 Netherlands DEF 1990 2010/2011 19 1 23
## 2227 235 Netherlands DEF 1990 2011/2012 20 3 26
## 2228 235 Netherlands DEF 1990 2012/2013 21 5 29
## 2229 235 Netherlands DEF 1990 2013/2014 22 5 10
## 2230 235 Netherlands DEF 1990 2014/2015 23 2 15
## 2231 235 Netherlands DEF 1990 2015/2016 24 5 12
## 2232 235 Netherlands DEF 1990 2016/2017 25 3 6
## 2233 235 Netherlands DEF 1990 2017/2018 26 4 16
## 2234 235 Netherlands DEF 1990 2018/2019 27 2 33
## 2235 236 France GK 1996 2010/2011 18 1 13
## 2236 236 France GK 1996 2011/2012 19 5 13
## 2237 236 France GK 1996 2012/2013 20 1 29
## 2238 236 France GK 1996 2013/2014 21 5 32
## 2239 236 France GK 1996 2014/2015 22 2 21
## 2240 236 France GK 1996 2015/2016 23 2 15
## 2241 236 France GK 1996 2016/2017 24 3 30
## 2242 236 France GK 1996 2017/2018 25 2 35
## 2243 236 France GK 1996 2018/2019 26 3 19
## 2244 237 Netherlands DEF 1996 2010/2011 18 3 13
## 2245 237 Netherlands DEF 1996 2011/2012 19 1 18
## 2246 237 Netherlands DEF 1996 2012/2013 20 4 20
## 2247 237 Netherlands DEF 1996 2013/2014 21 4 34
## 2248 237 Netherlands DEF 1996 2014/2015 22 3 27
## 2249 237 Netherlands DEF 1996 2015/2016 23 3 29
## 2250 237 Netherlands DEF 1996 2016/2017 24 2 6
## 2251 237 Netherlands DEF 1996 2017/2018 25 2 14
## 2252 237 Netherlands DEF 1996 2018/2019 26 5 24
## 2253 237 Netherlands DEF 1996 2019/2020 27 3 12
## 2254 237 Netherlands DEF 1996 2020/2021 28 3 32
## 2255 238 France MID 1989 2010/2011 17 2 13
## 2256 238 France MID 1989 2011/2012 18 2 29
## 2257 238 France MID 1989 2012/2013 19 1 17
## 2258 238 France MID 1989 2013/2014 20 2 19
## 2259 238 France MID 1989 2014/2015 21 1 10
## 2260 238 France MID 1989 2015/2016 22 4 37
## 2261 238 France MID 1989 2016/2017 23 5 16
## 2262 238 France MID 1989 2017/2018 24 3 27
## 2263 238 France MID 1989 2018/2019 25 2 27
## 2264 238 France MID 1989 2019/2020 26 3 19
## 2265 238 France MID 1989 2020/2021 27 1 22
## 2266 238 France MID 1989 2021/2022 28 2 27
## 2267 238 France MID 1989 2022/2023 29 4 20
## 2268 239 Netherlands GK 2004 2010/2011 20 5 19
## 2269 239 Netherlands GK 2004 2011/2012 21 1 8
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## 2276 239 Netherlands GK 2004 2018/2019 28 2 37
## 2277 239 Netherlands GK 2004 2019/2020 29 5 34
## 2278 239 Netherlands GK 2004 2020/2021 30 5 30
## 2279 239 Netherlands GK 2004 2021/2022 31 5 33
## 2280 239 Netherlands GK 2004 2022/2023 32 4 36
## 2281 240 Netherlands GK 1986 2010/2011 20 1 11
## 2282 240 Netherlands GK 1986 2011/2012 21 5 10
## 2283 240 Netherlands GK 1986 2012/2013 22 5 13
## 2284 240 Netherlands GK 1986 2013/2014 23 4 5
## 2285 240 Netherlands GK 1986 2014/2015 24 4 34
## 2286 240 Netherlands GK 1986 2015/2016 25 5 11
## 2287 240 Netherlands GK 1986 2016/2017 26 4 18
## 2288 241 Brazil FWD 1996 2010/2011 19 4 7
## 2289 241 Brazil FWD 1996 2011/2012 20 2 18
## 2290 241 Brazil FWD 1996 2012/2013 21 4 24
## 2291 241 Brazil FWD 1996 2013/2014 22 1 27
## 2292 241 Brazil FWD 1996 2014/2015 23 5 29
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## 2294 241 Brazil FWD 1996 2016/2017 25 5 30
## 2295 242 Argentina MID 1990 2010/2011 19 4 17
## 2296 242 Argentina MID 1990 2011/2012 20 4 28
## 2297 242 Argentina MID 1990 2012/2013 21 4 30
## 2298 242 Argentina MID 1990 2013/2014 22 5 19
## 2299 242 Argentina MID 1990 2014/2015 23 4 24
## 2300 242 Argentina MID 1990 2015/2016 24 3 11
## 2301 242 Argentina MID 1990 2016/2017 25 1 13
## 2302 243 England FWD 1993 2010/2011 19 4 23
## 2303 243 England FWD 1993 2011/2012 20 2 22
## 2304 243 England FWD 1993 2012/2013 21 1 25
## 2305 243 England FWD 1993 2013/2014 22 2 12
## 2306 243 England FWD 1993 2014/2015 23 4 29
## 2307 243 England FWD 1993 2015/2016 24 3 5
## 2308 243 England FWD 1993 2016/2017 25 1 26
## 2309 243 England FWD 1993 2017/2018 26 2 25
## 2310 244 Argentina DEF 1998 2010/2011 17 2 19
## 2311 244 Argentina DEF 1998 2011/2012 18 3 16
## 2312 244 Argentina DEF 1998 2012/2013 19 1 19
## 2313 244 Argentina DEF 1998 2013/2014 20 4 24
## 2314 244 Argentina DEF 1998 2014/2015 21 3 22
## 2315 244 Argentina DEF 1998 2015/2016 22 1 22
## 2316 244 Argentina DEF 1998 2016/2017 23 2 9
## 2317 244 Argentina DEF 1998 2017/2018 24 5 22
## 2318 244 Argentina DEF 1998 2018/2019 25 3 37
## 2319 244 Argentina DEF 1998 2019/2020 26 4 23
## 2320 244 Argentina DEF 1998 2020/2021 27 5 30
## 2321 244 Argentina DEF 1998 2021/2022 28 5 31
## 2322 245 Netherlands MID 1995 2010/2011 17 1 29
## 2323 245 Netherlands MID 1995 2011/2012 18 5 32
## 2324 245 Netherlands MID 1995 2012/2013 19 4 30
## 2325 245 Netherlands MID 1995 2013/2014 20 4 16
## 2326 245 Netherlands MID 1995 2014/2015 21 2 27
## 2327 245 Netherlands MID 1995 2015/2016 22 4 20
## 2328 245 Netherlands MID 1995 2016/2017 23 2 36
## 2329 245 Netherlands MID 1995 2017/2018 24 1 27
## 2330 245 Netherlands MID 1995 2018/2019 25 2 27
## 2331 245 Netherlands MID 1995 2019/2020 26 1 22
## 2332 246 France DEF 2004 2010/2011 18 1 8
## 2333 246 France DEF 2004 2011/2012 19 1 19
## 2334 246 France DEF 2004 2012/2013 20 5 37
## 2335 246 France DEF 2004 2013/2014 21 1 13
## 2336 246 France DEF 2004 2014/2015 22 4 30
## 2337 246 France DEF 2004 2015/2016 23 3 24
## 2338 246 France DEF 2004 2016/2017 24 2 9
## 2339 246 France DEF 2004 2017/2018 25 2 6
## 2340 246 France DEF 2004 2018/2019 26 1 29
## 2341 246 France DEF 2004 2019/2020 27 1 33
## 2342 246 France DEF 2004 2020/2021 28 3 24
## 2343 246 France DEF 2004 2021/2022 29 5 5
## 2344 247 France MID 1995 2010/2011 20 1 7
## 2345 247 France MID 1995 2011/2012 21 4 30
## 2346 247 France MID 1995 2012/2013 22 3 16
## 2347 247 France MID 1995 2013/2014 23 3 14
## 2348 247 France MID 1995 2014/2015 24 3 12
## 2349 248 England FWD 2002 2010/2011 18 3 12
## 2350 248 England FWD 2002 2011/2012 19 4 12
## 2351 248 England FWD 2002 2012/2013 20 4 26
## 2352 248 England FWD 2002 2013/2014 21 1 20
## 2353 248 England FWD 2002 2014/2015 22 4 35
## 2354 248 England FWD 2002 2015/2016 23 2 6
## 2355 248 England FWD 2002 2016/2017 24 5 5
## 2356 248 England FWD 2002 2017/2018 25 2 14
## 2357 248 England FWD 2002 2018/2019 26 2 26
## 2358 248 England FWD 2002 2019/2020 27 1 31
## 2359 248 England FWD 2002 2020/2021 28 3 12
## 2360 248 England FWD 2002 2021/2022 29 4 34
## 2361 249 Argentina DEF 1993 2010/2011 17 5 30
## 2362 249 Argentina DEF 1993 2011/2012 18 5 26
## 2363 249 Argentina DEF 1993 2012/2013 19 1 24
## 2364 249 Argentina DEF 1993 2013/2014 20 5 24
## 2365 249 Argentina DEF 1993 2014/2015 21 1 24
## 2366 249 Argentina DEF 1993 2015/2016 22 2 7
## 2367 249 Argentina DEF 1993 2016/2017 23 2 22
## 2368 249 Argentina DEF 1993 2017/2018 24 5 8
## 2369 250 Italy DEF 2004 2010/2011 20 2 15
## 2370 250 Italy DEF 2004 2011/2012 21 2 15
## 2371 250 Italy DEF 2004 2012/2013 22 1 30
## 2372 250 Italy DEF 2004 2013/2014 23 2 34
## 2373 250 Italy DEF 2004 2014/2015 24 1 34
## 2374 251 Italy MID 1986 2010/2011 19 3 20
## 2375 251 Italy MID 1986 2011/2012 20 3 25
## 2376 251 Italy MID 1986 2012/2013 21 5 6
## 2377 251 Italy MID 1986 2013/2014 22 4 11
## 2378 251 Italy MID 1986 2014/2015 23 2 21
## 2379 251 Italy MID 1986 2015/2016 24 5 15
## 2380 251 Italy MID 1986 2016/2017 25 5 26
## 2381 251 Italy MID 1986 2017/2018 26 1 22
## 2382 251 Italy MID 1986 2018/2019 27 4 24
## 2383 251 Italy MID 1986 2019/2020 28 4 8
## 2384 251 Italy MID 1986 2020/2021 29 1 36
## 2385 251 Italy MID 1986 2021/2022 30 4 33
## 2386 252 France DEF 1998 2010/2011 17 1 17
## 2387 252 France DEF 1998 2011/2012 18 1 18
## 2388 252 France DEF 1998 2012/2013 19 4 13
## 2389 252 France DEF 1998 2013/2014 20 3 27
## 2390 252 France DEF 1998 2014/2015 21 3 17
## 2391 252 France DEF 1998 2015/2016 22 1 10
## 2392 252 France DEF 1998 2016/2017 23 3 33
## 2393 252 France DEF 1998 2017/2018 24 4 24
## 2394 253 Netherlands MID 1998 2010/2011 20 3 8
## 2395 253 Netherlands MID 1998 2011/2012 21 3 17
## 2396 253 Netherlands MID 1998 2012/2013 22 2 29
## 2397 253 Netherlands MID 1998 2013/2014 23 1 21
## 2398 253 Netherlands MID 1998 2014/2015 24 5 15
## 2399 253 Netherlands MID 1998 2015/2016 25 1 14
## 2400 253 Netherlands MID 1998 2016/2017 26 1 15
## 2401 253 Netherlands MID 1998 2017/2018 27 5 36
## 2402 253 Netherlands MID 1998 2018/2019 28 4 29
## 2403 253 Netherlands MID 1998 2019/2020 29 2 25
## 2404 254 France GK 1987 2010/2011 18 1 15
## 2405 254 France GK 1987 2011/2012 19 4 33
## 2406 254 France GK 1987 2012/2013 20 1 25
## 2407 254 France GK 1987 2013/2014 21 5 36
## 2408 254 France GK 1987 2014/2015 22 4 10
## 2409 254 France GK 1987 2015/2016 23 1 10
## 2410 254 France GK 1987 2016/2017 24 2 10
## 2411 254 France GK 1987 2017/2018 25 5 9
## 2412 254 France GK 1987 2018/2019 26 1 29
## 2413 254 France GK 1987 2019/2020 27 3 9
## 2414 254 France GK 1987 2020/2021 28 5 11
## 2415 254 France GK 1987 2021/2022 29 2 34
## 2416 255 Germany GK 1985 2010/2011 19 4 27
## 2417 255 Germany GK 1985 2011/2012 20 2 25
## 2418 255 Germany GK 1985 2012/2013 21 1 33
## 2419 255 Germany GK 1985 2013/2014 22 5 10
## 2420 255 Germany GK 1985 2014/2015 23 4 25
## 2421 255 Germany GK 1985 2015/2016 24 2 13
## 2422 255 Germany GK 1985 2016/2017 25 3 36
## 2423 255 Germany GK 1985 2017/2018 26 1 27
## 2424 256 France MID 1988 2010/2011 17 3 13
## 2425 256 France MID 1988 2011/2012 18 2 28
## 2426 256 France MID 1988 2012/2013 19 4 37
## 2427 256 France MID 1988 2013/2014 20 2 10
## 2428 256 France MID 1988 2014/2015 21 4 18
## 2429 256 France MID 1988 2015/2016 22 4 33
## 2430 256 France MID 1988 2016/2017 23 5 26
## 2431 257 England DEF 1989 2010/2011 17 4 17
## 2432 257 England DEF 1989 2011/2012 18 3 19
## 2433 257 England DEF 1989 2012/2013 19 4 18
## 2434 257 England DEF 1989 2013/2014 20 3 26
## 2435 257 England DEF 1989 2014/2015 21 4 5
## 2436 257 England DEF 1989 2015/2016 22 1 10
## 2437 257 England DEF 1989 2016/2017 23 2 18
## 2438 257 England DEF 1989 2017/2018 24 5 27
## 2439 257 England DEF 1989 2018/2019 25 3 33
## 2440 257 England DEF 1989 2019/2020 26 2 14
## 2441 257 England DEF 1989 2020/2021 27 4 18
## 2442 257 England DEF 1989 2021/2022 28 2 12
## 2443 257 England DEF 1989 2022/2023 29 5 30
## 2444 258 Argentina MID 1987 2010/2011 18 2 21
## 2445 258 Argentina MID 1987 2011/2012 19 4 33
## 2446 258 Argentina MID 1987 2012/2013 20 2 28
## 2447 258 Argentina MID 1987 2013/2014 21 1 15
## 2448 258 Argentina MID 1987 2014/2015 22 2 10
## 2449 258 Argentina MID 1987 2015/2016 23 2 18
## 2450 258 Argentina MID 1987 2016/2017 24 4 17
## 2451 259 Germany DEF 2000 2010/2011 17 5 7
## 2452 259 Germany DEF 2000 2011/2012 18 4 21
## 2453 259 Germany DEF 2000 2012/2013 19 4 36
## 2454 259 Germany DEF 2000 2013/2014 20 5 29
## 2455 259 Germany DEF 2000 2014/2015 21 2 32
## 2456 259 Germany DEF 2000 2015/2016 22 3 5
## 2457 259 Germany DEF 2000 2016/2017 23 4 21
## 2458 259 Germany DEF 2000 2017/2018 24 3 7
## 2459 259 Germany DEF 2000 2018/2019 25 2 10
## 2460 259 Germany DEF 2000 2019/2020 26 1 7
## 2461 259 Germany DEF 2000 2020/2021 27 1 22
## 2462 259 Germany DEF 2000 2021/2022 28 5 29
## 2463 259 Germany DEF 2000 2022/2023 29 3 36
## 2464 260 France MID 1988 2010/2011 18 1 12
## 2465 260 France MID 1988 2011/2012 19 3 12
## 2466 260 France MID 1988 2012/2013 20 5 18
## 2467 260 France MID 1988 2013/2014 21 5 33
## 2468 260 France MID 1988 2014/2015 22 5 25
## 2469 260 France MID 1988 2015/2016 23 1 24
## 2470 261 England FWD 1999 2010/2011 20 3 21
## 2471 261 England FWD 1999 2011/2012 21 5 33
## 2472 261 England FWD 1999 2012/2013 22 4 9
## 2473 261 England FWD 1999 2013/2014 23 1 19
## 2474 261 England FWD 1999 2014/2015 24 3 8
## 2475 261 England FWD 1999 2015/2016 25 1 5
## 2476 262 England DEF 2002 2010/2011 20 5 24
## 2477 262 England DEF 2002 2011/2012 21 2 30
## 2478 262 England DEF 2002 2012/2013 22 3 23
## 2479 262 England DEF 2002 2013/2014 23 2 18
## 2480 262 England DEF 2002 2014/2015 24 5 13
## 2481 262 England DEF 2002 2015/2016 25 2 8
## 2482 263 Spain MID 1987 2010/2011 19 4 12
## 2483 263 Spain MID 1987 2011/2012 20 1 15
## 2484 263 Spain MID 1987 2012/2013 21 3 18
## 2485 263 Spain MID 1987 2013/2014 22 5 27
## 2486 263 Spain MID 1987 2014/2015 23 1 16
## 2487 263 Spain MID 1987 2015/2016 24 2 16
## 2488 263 Spain MID 1987 2016/2017 25 1 29
## 2489 263 Spain MID 1987 2017/2018 26 2 8
## 2490 263 Spain MID 1987 2018/2019 27 4 30
## 2491 263 Spain MID 1987 2019/2020 28 4 5
## 2492 264 Brazil DEF 1987 2010/2011 18 3 27
## 2493 264 Brazil DEF 1987 2011/2012 19 1 11
## 2494 264 Brazil DEF 1987 2012/2013 20 1 25
## 2495 264 Brazil DEF 1987 2013/2014 21 4 32
## 2496 264 Brazil DEF 1987 2014/2015 22 4 11
## 2497 264 Brazil DEF 1987 2015/2016 23 2 32
## 2498 264 Brazil DEF 1987 2016/2017 24 4 30
## 2499 265 England GK 1992 2010/2011 18 3 27
## 2500 265 England GK 1992 2011/2012 19 2 32
## 2501 265 England GK 1992 2012/2013 20 3 14
## 2502 265 England GK 1992 2013/2014 21 4 33
## 2503 265 England GK 1992 2014/2015 22 3 19
## 2504 265 England GK 1992 2015/2016 23 4 10
## 2505 265 England GK 1992 2016/2017 24 2 37
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## 2582 274 Germany DEF 2000 2013/2014 22 3 32
## 2583 274 Germany DEF 2000 2014/2015 23 5 17
## 2584 274 Germany DEF 2000 2015/2016 24 5 34
## 2585 274 Germany DEF 2000 2016/2017 25 2 25
## 2586 274 Germany DEF 2000 2017/2018 26 2 20
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## 2589 274 Germany DEF 2000 2020/2021 29 3 22
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## 2591 275 Germany DEF 1989 2011/2012 20 2 5
## 2592 275 Germany DEF 1989 2012/2013 21 3 32
## 2593 275 Germany DEF 1989 2013/2014 22 5 25
## 2594 275 Germany DEF 1989 2014/2015 23 1 17
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## 2608 278 Italy FWD 1988 2011/2012 20 3 6
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## 2610 278 Italy FWD 1988 2013/2014 22 1 27
## 2611 278 Italy FWD 1988 2014/2015 23 4 26
## 2612 278 Italy FWD 1988 2015/2016 24 4 18
## 2613 278 Italy FWD 1988 2016/2017 25 1 31
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## 2617 279 Spain DEF 1991 2013/2014 23 2 24
## 2618 279 Spain DEF 1991 2014/2015 24 3 11
## 2619 279 Spain DEF 1991 2015/2016 25 4 37
## 2620 279 Spain DEF 1991 2016/2017 26 5 16
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## 2632 280 Brazil FWD 1993 2018/2019 25 2 11
## 2633 280 Brazil FWD 1993 2019/2020 26 2 7
## 2634 280 Brazil FWD 1993 2020/2021 27 1 17
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## 2637 281 Netherlands DEF 1988 2012/2013 19 1 14
## 2638 281 Netherlands DEF 1988 2013/2014 20 1 11
## 2639 281 Netherlands DEF 1988 2014/2015 21 2 22
## 2640 281 Netherlands DEF 1988 2015/2016 22 1 37
## 2641 281 Netherlands DEF 1988 2016/2017 23 1 37
## 2642 281 Netherlands DEF 1988 2017/2018 24 4 35
## 2643 281 Netherlands DEF 1988 2018/2019 25 2 9
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## 2648 282 Spain FWD 1988 2013/2014 21 4 8
## 2649 282 Spain FWD 1988 2014/2015 22 5 20
## 2650 282 Spain FWD 1988 2015/2016 23 3 12
## 2651 282 Spain FWD 1988 2016/2017 24 2 11
## 2652 282 Spain FWD 1988 2017/2018 25 1 35
## 2653 282 Spain FWD 1988 2018/2019 26 5 27
## 2654 282 Spain FWD 1988 2019/2020 27 4 35
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## 2669 284 Germany MID 1997 2014/2015 24 4 17
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## 2671 284 Germany MID 1997 2016/2017 26 1 30
## 2672 285 England DEF 1991 2010/2011 17 1 6
## 2673 285 England DEF 1991 2011/2012 18 1 8
## 2674 285 England DEF 1991 2012/2013 19 2 7
## 2675 285 England DEF 1991 2013/2014 20 4 12
## 2676 285 England DEF 1991 2014/2015 21 4 28
## 2677 285 England DEF 1991 2015/2016 22 5 6
## 2678 285 England DEF 1991 2016/2017 23 4 28
## 2679 285 England DEF 1991 2017/2018 24 1 24
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## 2686 286 Brazil DEF 1989 2014/2015 22 2 6
## 2687 286 Brazil DEF 1989 2015/2016 23 4 14
## 2688 286 Brazil DEF 1989 2016/2017 24 5 34
## 2689 286 Brazil DEF 1989 2017/2018 25 3 26
## 2690 286 Brazil DEF 1989 2018/2019 26 3 13
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## 2697 287 England FWD 1998 2013/2014 21 4 8
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## 2713 289 France DEF 1991 2014/2015 21 1 34
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## 2716 290 Spain DEF 2000 2012/2013 22 1 20
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## 2718 290 Spain DEF 2000 2014/2015 24 5 16
## 2719 291 Netherlands FWD 2003 2010/2011 17 1 23
## 2720 291 Netherlands FWD 2003 2011/2012 18 3 11
## 2721 291 Netherlands FWD 2003 2012/2013 19 4 6
## 2722 291 Netherlands FWD 2003 2013/2014 20 2 25
## 2723 291 Netherlands FWD 2003 2014/2015 21 2 6
## 2724 291 Netherlands FWD 2003 2015/2016 22 1 34
## 2725 291 Netherlands FWD 2003 2016/2017 23 1 13
## 2726 291 Netherlands FWD 2003 2017/2018 24 1 35
## 2727 291 Netherlands FWD 2003 2018/2019 25 5 14
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## 2729 292 Brazil DEF 1987 2011/2012 21 3 12
## 2730 292 Brazil DEF 1987 2012/2013 22 2 16
## 2731 292 Brazil DEF 1987 2013/2014 23 2 17
## 2732 292 Brazil DEF 1987 2014/2015 24 1 12
## 2733 292 Brazil DEF 1987 2015/2016 25 3 21
## 2734 292 Brazil DEF 1987 2016/2017 26 4 14
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## 2737 293 Netherlands DEF 2001 2012/2013 20 1 14
## 2738 293 Netherlands DEF 2001 2013/2014 21 3 27
## 2739 293 Netherlands DEF 2001 2014/2015 22 2 6
## 2740 293 Netherlands DEF 2001 2015/2016 23 4 9
## 2741 293 Netherlands DEF 2001 2016/2017 24 1 30
## 2742 293 Netherlands DEF 2001 2017/2018 25 2 22
## 2743 294 Netherlands FWD 1999 2010/2011 17 2 18
## 2744 294 Netherlands FWD 1999 2011/2012 18 2 22
## 2745 294 Netherlands FWD 1999 2012/2013 19 1 7
## 2746 294 Netherlands FWD 1999 2013/2014 20 5 14
## 2747 294 Netherlands FWD 1999 2014/2015 21 4 36
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## 2755 296 Germany MID 2004 2010/2011 20 2 32
## 2756 296 Germany MID 2004 2011/2012 21 1 5
## 2757 296 Germany MID 2004 2012/2013 22 4 32
## 2758 296 Germany MID 2004 2013/2014 23 4 15
## 2759 296 Germany MID 2004 2014/2015 24 1 31
## 2760 297 Germany DEF 1992 2010/2011 18 4 23
## 2761 297 Germany DEF 1992 2011/2012 19 4 25
## 2762 297 Germany DEF 1992 2012/2013 20 3 28
## 2763 297 Germany DEF 1992 2013/2014 21 1 21
## 2764 297 Germany DEF 1992 2014/2015 22 5 21
## 2765 297 Germany DEF 1992 2015/2016 23 5 20
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## 2768 297 Germany DEF 1992 2018/2019 26 4 15
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## 2771 298 France DEF 1997 2012/2013 22 2 25
## 2772 298 France DEF 1997 2013/2014 23 4 14
## 2773 298 France DEF 1997 2014/2015 24 2 24
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## 2776 298 France DEF 1997 2017/2018 27 2 9
## 2777 298 France DEF 1997 2018/2019 28 2 19
## 2778 298 France DEF 1997 2019/2020 29 1 14
## 2779 299 England MID 2000 2010/2011 20 3 15
## 2780 299 England MID 2000 2011/2012 21 5 20
## 2781 299 England MID 2000 2012/2013 22 5 26
## 2782 299 England MID 2000 2013/2014 23 5 23
## 2783 299 England MID 2000 2014/2015 24 5 25
## 2784 299 England MID 2000 2015/2016 25 2 28
## 2785 299 England MID 2000 2016/2017 26 3 12
## 2786 300 Italy GK 1998 2010/2011 20 4 15
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## 2795 300 Italy GK 1998 2019/2020 29 2 12
## 2796 300 Italy GK 1998 2020/2021 30 3 7
## 2797 300 Italy GK 1998 2021/2022 31 4 12
## 2798 300 Italy GK 1998 2022/2023 32 1 24
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## 2805 301 France GK 1990 2016/2017 24 2 28
## 2806 301 France GK 1990 2017/2018 25 5 11
## 2807 301 France GK 1990 2018/2019 26 2 34
## 2808 302 Spain DEF 1994 2010/2011 20 5 29
## 2809 302 Spain DEF 1994 2011/2012 21 3 18
## 2810 302 Spain DEF 1994 2012/2013 22 3 11
## 2811 302 Spain DEF 1994 2013/2014 23 2 36
## 2812 302 Spain DEF 1994 2014/2015 24 1 23
## 2813 302 Spain DEF 1994 2015/2016 25 1 36
## 2814 302 Spain DEF 1994 2016/2017 26 3 36
## 2815 302 Spain DEF 1994 2017/2018 27 5 19
## 2816 302 Spain DEF 1994 2018/2019 28 1 6
## 2817 302 Spain DEF 1994 2019/2020 29 5 33
## 2818 302 Spain DEF 1994 2020/2021 30 3 26
## 2819 302 Spain DEF 1994 2021/2022 31 1 12
## 2820 303 England DEF 1996 2010/2011 17 1 24
## 2821 303 England DEF 1996 2011/2012 18 5 20
## 2822 303 England DEF 1996 2012/2013 19 1 30
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## 2827 304 France MID 1999 2012/2013 21 1 9
## 2828 304 France MID 1999 2013/2014 22 3 14
## 2829 304 France MID 1999 2014/2015 23 1 34
## 2830 304 France MID 1999 2015/2016 24 1 21
## 2831 304 France MID 1999 2016/2017 25 2 20
## 2832 304 France MID 1999 2017/2018 26 3 22
## 2833 304 France MID 1999 2018/2019 27 1 33
## 2834 305 Brazil FWD 2001 2010/2011 17 2 37
## 2835 305 Brazil FWD 2001 2011/2012 18 2 22
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## 2853 307 Argentina FWD 1994 2017/2018 25 5 11
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## 2855 308 Argentina MID 1990 2011/2012 18 5 20
## 2856 308 Argentina MID 1990 2012/2013 19 3 26
## 2857 308 Argentina MID 1990 2013/2014 20 5 7
## 2858 308 Argentina MID 1990 2014/2015 21 3 7
## 2859 308 Argentina MID 1990 2015/2016 22 4 31
## 2860 308 Argentina MID 1990 2016/2017 23 5 19
## 2861 308 Argentina MID 1990 2017/2018 24 3 12
## 2862 308 Argentina MID 1990 2018/2019 25 1 19
## 2863 309 Argentina FWD 1990 2010/2011 17 1 29
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## 2873 309 Argentina FWD 1990 2020/2021 27 1 20
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## 2883 311 France FWD 1997 2014/2015 23 2 23
## 2884 311 France FWD 1997 2015/2016 24 4 29
## 2885 311 France FWD 1997 2016/2017 25 3 28
## 2886 312 Netherlands GK 1992 2010/2011 18 4 11
## 2887 312 Netherlands GK 1992 2011/2012 19 2 33
## 2888 312 Netherlands GK 1992 2012/2013 20 1 19
## 2889 312 Netherlands GK 1992 2013/2014 21 2 23
## 2890 312 Netherlands GK 1992 2014/2015 22 2 5
## 2891 312 Netherlands GK 1992 2015/2016 23 4 31
## 2892 313 England GK 1993 2010/2011 20 3 32
## 2893 313 England GK 1993 2011/2012 21 5 10
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## 2896 313 England GK 1993 2014/2015 24 5 35
## 2897 313 England GK 1993 2015/2016 25 1 33
## 2898 313 England GK 1993 2016/2017 26 2 8
## 2899 313 England GK 1993 2017/2018 27 5 7
## 2900 313 England GK 1993 2018/2019 28 4 7
## 2901 313 England GK 1993 2019/2020 29 5 35
## 2902 313 England GK 1993 2020/2021 30 4 18
## 2903 313 England GK 1993 2021/2022 31 1 5
## 2904 313 England GK 1993 2022/2023 32 1 11
## 2905 314 Brazil GK 2001 2010/2011 18 2 9
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## 2907 314 Brazil GK 2001 2012/2013 20 1 16
## 2908 314 Brazil GK 2001 2013/2014 21 5 8
## 2909 314 Brazil GK 2001 2014/2015 22 5 25
## 2910 314 Brazil GK 2001 2015/2016 23 4 18
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## 2912 314 Brazil GK 2001 2017/2018 25 4 17
## 2913 314 Brazil GK 2001 2018/2019 26 4 13
## 2914 314 Brazil GK 2001 2019/2020 27 3 12
## 2915 314 Brazil GK 2001 2020/2021 28 2 26
## 2916 314 Brazil GK 2001 2021/2022 29 1 37
## 2917 314 Brazil GK 2001 2022/2023 30 3 18
## 2918 314 Brazil GK 2001 2023/2024 31 5 26
## 2919 315 Spain FWD 1986 2010/2011 19 3 32
## 2920 315 Spain FWD 1986 2011/2012 20 2 11
## 2921 315 Spain FWD 1986 2012/2013 21 4 22
## 2922 315 Spain FWD 1986 2013/2014 22 1 22
## 2923 315 Spain FWD 1986 2014/2015 23 1 16
## 2924 315 Spain FWD 1986 2015/2016 24 4 27
## 2925 315 Spain FWD 1986 2016/2017 25 2 35
## 2926 315 Spain FWD 1986 2017/2018 26 5 21
## 2927 315 Spain FWD 1986 2018/2019 27 4 34
## 2928 315 Spain FWD 1986 2019/2020 28 3 34
## 2929 316 France MID 1992 2010/2011 19 2 23
## 2930 316 France MID 1992 2011/2012 20 2 20
## 2931 316 France MID 1992 2012/2013 21 1 5
## 2932 316 France MID 1992 2013/2014 22 3 10
## 2933 316 France MID 1992 2014/2015 23 3 12
## 2934 316 France MID 1992 2015/2016 24 5 21
## 2935 316 France MID 1992 2016/2017 25 5 25
## 2936 316 France MID 1992 2017/2018 26 3 26
## 2937 316 France MID 1992 2018/2019 27 2 21
## 2938 316 France MID 1992 2019/2020 28 3 20
## 2939 316 France MID 1992 2020/2021 29 2 33
## 2940 316 France MID 1992 2021/2022 30 2 27
## 2941 316 France MID 1992 2022/2023 31 2 21
## 2942 316 France MID 1992 2023/2024 32 4 16
## 2943 317 Netherlands FWD 2000 2010/2011 18 2 16
## 2944 317 Netherlands FWD 2000 2011/2012 19 1 22
## 2945 317 Netherlands FWD 2000 2012/2013 20 1 24
## 2946 317 Netherlands FWD 2000 2013/2014 21 2 18
## 2947 317 Netherlands FWD 2000 2014/2015 22 4 26
## 2948 317 Netherlands FWD 2000 2015/2016 23 4 23
## 2949 317 Netherlands FWD 2000 2016/2017 24 5 21
## 2950 318 England GK 1986 2010/2011 17 1 14
## 2951 318 England GK 1986 2011/2012 18 3 5
## 2952 318 England GK 1986 2012/2013 19 1 27
## 2953 318 England GK 1986 2013/2014 20 2 26
## 2954 318 England GK 1986 2014/2015 21 2 6
## 2955 318 England GK 1986 2015/2016 22 2 24
## 2956 318 England GK 1986 2016/2017 23 2 36
## 2957 318 England GK 1986 2017/2018 24 2 15
## 2958 318 England GK 1986 2018/2019 25 1 13
## 2959 318 England GK 1986 2019/2020 26 2 17
## 2960 318 England GK 1986 2020/2021 27 1 24
## 2961 318 England GK 1986 2021/2022 28 4 7
## 2962 319 Italy MID 1999 2010/2011 19 1 15
## 2963 319 Italy MID 1999 2011/2012 20 1 13
## 2964 319 Italy MID 1999 2012/2013 21 2 12
## 2965 319 Italy MID 1999 2013/2014 22 5 17
## 2966 319 Italy MID 1999 2014/2015 23 5 37
## 2967 319 Italy MID 1999 2015/2016 24 5 32
## 2968 319 Italy MID 1999 2016/2017 25 5 13
## 2969 319 Italy MID 1999 2017/2018 26 2 30
## 2970 319 Italy MID 1999 2018/2019 27 3 35
## 2971 319 Italy MID 1999 2019/2020 28 5 19
## 2972 319 Italy MID 1999 2020/2021 29 4 18
## 2973 319 Italy MID 1999 2021/2022 30 1 27
## 2974 320 France MID 1991 2010/2011 18 3 33
## 2975 320 France MID 1991 2011/2012 19 5 9
## 2976 320 France MID 1991 2012/2013 20 3 18
## 2977 320 France MID 1991 2013/2014 21 4 6
## 2978 320 France MID 1991 2014/2015 22 3 35
## 2979 320 France MID 1991 2015/2016 23 5 29
## 2980 321 Italy MID 1999 2010/2011 19 1 27
## 2981 321 Italy MID 1999 2011/2012 20 5 25
## 2982 321 Italy MID 1999 2012/2013 21 3 36
## 2983 321 Italy MID 1999 2013/2014 22 1 16
## 2984 321 Italy MID 1999 2014/2015 23 1 25
## 2985 321 Italy MID 1999 2015/2016 24 5 11
## 2986 321 Italy MID 1999 2016/2017 25 4 34
## 2987 321 Italy MID 1999 2017/2018 26 3 25
## 2988 321 Italy MID 1999 2018/2019 27 5 10
## 2989 321 Italy MID 1999 2019/2020 28 2 18
## 2990 322 Netherlands DEF 2002 2010/2011 17 3 10
## 2991 322 Netherlands DEF 2002 2011/2012 18 5 28
## 2992 322 Netherlands DEF 2002 2012/2013 19 1 14
## 2993 322 Netherlands DEF 2002 2013/2014 20 5 19
## 2994 322 Netherlands DEF 2002 2014/2015 21 4 16
## 2995 322 Netherlands DEF 2002 2015/2016 22 5 21
## 2996 322 Netherlands DEF 2002 2016/2017 23 3 12
## 2997 322 Netherlands DEF 2002 2017/2018 24 3 17
## 2998 322 Netherlands DEF 2002 2018/2019 25 1 11
## 2999 322 Netherlands DEF 2002 2019/2020 26 5 24
## 3000 322 Netherlands DEF 2002 2020/2021 27 3 9
## 3001 323 France DEF 1985 2010/2011 19 1 16
## 3002 323 France DEF 1985 2011/2012 20 4 5
## 3003 323 France DEF 1985 2012/2013 21 2 34
## 3004 323 France DEF 1985 2013/2014 22 1 24
## 3005 323 France DEF 1985 2014/2015 23 4 12
## 3006 323 France DEF 1985 2015/2016 24 2 33
## 3007 324 England FWD 1996 2010/2011 17 2 17
## 3008 324 England FWD 1996 2011/2012 18 4 23
## 3009 324 England FWD 1996 2012/2013 19 4 31
## 3010 324 England FWD 1996 2013/2014 20 4 21
## 3011 324 England FWD 1996 2014/2015 21 5 16
## 3012 324 England FWD 1996 2015/2016 22 1 26
## 3013 324 England FWD 1996 2016/2017 23 5 30
## 3014 324 England FWD 1996 2017/2018 24 3 32
## 3015 324 England FWD 1996 2018/2019 25 3 35
## 3016 324 England FWD 1996 2019/2020 26 4 7
## 3017 324 England FWD 1996 2020/2021 27 5 33
## 3018 324 England FWD 1996 2021/2022 28 2 12
## 3019 324 England FWD 1996 2022/2023 29 4 11
## 3020 324 England FWD 1996 2023/2024 30 4 27
## 3021 325 Spain FWD 1990 2010/2011 20 1 19
## 3022 325 Spain FWD 1990 2011/2012 21 1 15
## 3023 325 Spain FWD 1990 2012/2013 22 3 30
## 3024 325 Spain FWD 1990 2013/2014 23 4 30
## 3025 325 Spain FWD 1990 2014/2015 24 5 31
## 3026 325 Spain FWD 1990 2015/2016 25 5 27
## 3027 326 Germany FWD 1993 2010/2011 18 4 28
## 3028 326 Germany FWD 1993 2011/2012 19 2 6
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## 3030 326 Germany FWD 1993 2013/2014 21 4 34
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## 3033 326 Germany FWD 1993 2016/2017 24 5 20
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## 3090 332 Italy DEF 1997 2014/2015 21 1 20
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## 3192 341 Italy DEF 2004 2014/2015 21 1 16
## 3193 341 Italy DEF 2004 2015/2016 22 2 37
## 3194 341 Italy DEF 2004 2016/2017 23 3 25
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## 3203 342 Spain DEF 1990 2014/2015 22 1 5
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## 3205 342 Spain DEF 1990 2016/2017 24 3 22
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## 3208 342 Spain DEF 1990 2019/2020 27 2 15
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## 3368 359 Brazil GK 1991 2010/2011 19 4 23
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## 3434 366 Spain DEF 2003 2010/2011 17 3 9
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## 3451 368 Italy DEF 2003 2010/2011 17 1 9
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## 3455 368 Italy DEF 2003 2014/2015 21 4 36
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## 3458 368 Italy DEF 2003 2017/2018 24 2 26
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## 3460 369 Spain MID 1998 2010/2011 17 2 37
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## 3513 375 England DEF 1997 2010/2011 19 2 13
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## 3524 376 Netherlands GK 1991 2010/2011 18 1 19
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## 3535 376 Netherlands GK 1991 2021/2022 29 3 10
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## 3544 378 Brazil DEF 1996 2010/2011 20 5 14
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## 3609 385 England DEF 1986 2010/2011 20 4 14
## 3610 385 England DEF 1986 2011/2012 21 3 10
## 3611 385 England DEF 1986 2012/2013 22 4 7
## 3612 385 England DEF 1986 2013/2014 23 2 11
## 3613 385 England DEF 1986 2014/2015 24 3 26
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## 3633 386 Spain FWD 1995 2023/2024 31 1 35
## 3634 387 Brazil DEF 1996 2010/2011 20 5 26
## 3635 387 Brazil DEF 1996 2011/2012 21 5 25
## 3636 387 Brazil DEF 1996 2012/2013 22 5 34
## 3637 387 Brazil DEF 1996 2013/2014 23 2 12
## 3638 387 Brazil DEF 1996 2014/2015 24 1 35
## 3639 387 Brazil DEF 1996 2015/2016 25 4 18
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## 3674 390 England FWD 1999 2018/2019 28 4 17
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## 3676 390 England FWD 1999 2020/2021 30 1 6
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## 3722 395 Argentina DEF 1994 2014/2015 22 4 17
## 3723 395 Argentina DEF 1994 2015/2016 23 5 14
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## 3725 395 Argentina DEF 1994 2017/2018 25 3 31
## 3726 395 Argentina DEF 1994 2018/2019 26 5 24
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## 3794 403 Italy DEF 2004 2012/2013 22 4 20
## 3795 403 Italy DEF 2004 2013/2014 23 5 22
## 3796 403 Italy DEF 2004 2014/2015 24 2 18
## 3797 403 Italy DEF 2004 2015/2016 25 4 34
## 3798 403 Italy DEF 2004 2016/2017 26 3 37
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## 3800 403 Italy DEF 2004 2018/2019 28 2 24
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## 3853 408 England GK 1991 2023/2024 30 1 7
## 3854 409 Spain FWD 1990 2010/2011 20 5 16
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## 3858 409 Spain FWD 1990 2014/2015 24 5 37
## 3859 409 Spain FWD 1990 2015/2016 25 1 11
## 3860 409 Spain FWD 1990 2016/2017 26 4 19
## 3861 410 Spain DEF 1991 2010/2011 19 4 13
## 3862 410 Spain DEF 1991 2011/2012 20 1 8
## 3863 410 Spain DEF 1991 2012/2013 21 1 14
## 3864 410 Spain DEF 1991 2013/2014 22 1 29
## 3865 410 Spain DEF 1991 2014/2015 23 5 7
## 3866 410 Spain DEF 1991 2015/2016 24 1 37
## 3867 410 Spain DEF 1991 2016/2017 25 5 37
## 3868 410 Spain DEF 1991 2017/2018 26 4 19
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## 3893 413 Germany GK 1988 2010/2011 17 4 8
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## 3906 414 Netherlands MID 2000 2014/2015 22 1 5
## 3907 415 Italy MID 1999 2010/2011 19 5 8
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## 3915 416 Germany GK 2001 2010/2011 17 1 7
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## 3938 418 England MID 2001 2010/2011 17 3 18
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## 3942 418 England MID 2001 2014/2015 21 3 29
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## 3948 418 England MID 2001 2020/2021 27 5 35
## 3949 418 England MID 2001 2021/2022 28 1 33
## 3950 418 England MID 2001 2022/2023 29 1 14
## 3951 418 England MID 2001 2023/2024 30 4 16
## 3952 419 Argentina DEF 1999 2010/2011 17 1 12
## 3953 419 Argentina DEF 1999 2011/2012 18 3 16
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## 3956 419 Argentina DEF 1999 2014/2015 21 2 9
## 3957 419 Argentina DEF 1999 2015/2016 22 1 9
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## 3959 419 Argentina DEF 1999 2017/2018 24 2 15
## 3960 419 Argentina DEF 1999 2018/2019 25 4 36
## 3961 420 Brazil DEF 2003 2010/2011 19 3 9
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## 3965 420 Brazil DEF 2003 2014/2015 23 4 28
## 3966 420 Brazil DEF 2003 2015/2016 24 4 35
## 3967 420 Brazil DEF 2003 2016/2017 25 4 24
## 3968 420 Brazil DEF 2003 2017/2018 26 5 14
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## 3970 420 Brazil DEF 2003 2019/2020 28 5 15
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## 3978 421 France MID 2003 2016/2017 25 5 24
## 3979 421 France MID 2003 2017/2018 26 4 31
## 3980 422 England MID 1992 2010/2011 17 1 34
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## 3985 423 England MID 1991 2010/2011 20 2 29
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## 3987 423 England MID 1991 2012/2013 22 4 31
## 3988 423 England MID 1991 2013/2014 23 2 36
## 3989 423 England MID 1991 2014/2015 24 5 32
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## 3991 423 England MID 1991 2016/2017 26 2 7
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## 3994 424 Brazil FWD 1997 2010/2011 18 4 27
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## 4015 425 Brazil FWD 2002 2020/2021 28 4 11
## 4016 425 Brazil FWD 2002 2021/2022 29 3 8
## 4017 425 Brazil FWD 2002 2022/2023 30 4 30
## 4018 425 Brazil FWD 2002 2023/2024 31 2 25
## 4019 426 Germany MID 1995 2010/2011 20 4 5
## 4020 426 Germany MID 1995 2011/2012 21 4 6
## 4021 426 Germany MID 1995 2012/2013 22 1 9
## 4022 426 Germany MID 1995 2013/2014 23 5 11
## 4023 426 Germany MID 1995 2014/2015 24 3 33
## 4024 426 Germany MID 1995 2015/2016 25 5 34
## 4025 426 Germany MID 1995 2016/2017 26 1 33
## 4026 426 Germany MID 1995 2017/2018 27 3 26
## 4027 426 Germany MID 1995 2018/2019 28 2 13
## 4028 426 Germany MID 1995 2019/2020 29 1 29
## 4029 426 Germany MID 1995 2020/2021 30 4 16
## 4030 426 Germany MID 1995 2021/2022 31 3 26
## 4031 426 Germany MID 1995 2022/2023 32 1 13
## 4032 426 Germany MID 1995 2023/2024 33 2 18
## 4033 427 Brazil FWD 1988 2010/2011 18 4 22
## 4034 427 Brazil FWD 1988 2011/2012 19 1 5
## 4035 427 Brazil FWD 1988 2012/2013 20 3 18
## 4036 427 Brazil FWD 1988 2013/2014 21 2 25
## 4037 427 Brazil FWD 1988 2014/2015 22 3 25
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## 4043 428 England FWD 1997 2010/2011 20 4 37
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## 4047 428 England FWD 1997 2014/2015 24 1 19
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## 4049 428 England FWD 1997 2016/2017 26 5 18
## 4050 428 England FWD 1997 2017/2018 27 3 15
## 4051 428 England FWD 1997 2018/2019 28 1 26
## 4052 429 Netherlands FWD 1986 2010/2011 19 2 27
## 4053 429 Netherlands FWD 1986 2011/2012 20 2 21
## 4054 429 Netherlands FWD 1986 2012/2013 21 2 36
## 4055 429 Netherlands FWD 1986 2013/2014 22 2 37
## 4056 429 Netherlands FWD 1986 2014/2015 23 3 28
## 4057 429 Netherlands FWD 1986 2015/2016 24 4 10
## 4058 429 Netherlands FWD 1986 2016/2017 25 3 26
## 4059 429 Netherlands FWD 1986 2017/2018 26 5 27
## 4060 430 Germany DEF 2001 2010/2011 19 3 16
## 4061 430 Germany DEF 2001 2011/2012 20 5 37
## 4062 430 Germany DEF 2001 2012/2013 21 5 33
## 4063 430 Germany DEF 2001 2013/2014 22 4 23
## 4064 430 Germany DEF 2001 2014/2015 23 1 20
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## 4076 432 Italy MID 1998 2012/2013 21 4 36
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## 4078 432 Italy MID 1998 2014/2015 23 2 18
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## 4080 432 Italy MID 1998 2016/2017 25 3 11
## 4081 432 Italy MID 1998 2017/2018 26 5 36
## 4082 432 Italy MID 1998 2018/2019 27 1 32
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## 4169 441 Italy GK 1996 2010/2011 18 4 26
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## 4283 456 Germany MID 1993 2014/2015 21 5 22
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## 4285 456 Germany MID 1993 2016/2017 23 1 15
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## 4287 456 Germany MID 1993 2018/2019 25 1 34
## 4288 456 Germany MID 1993 2019/2020 26 4 10
## 4289 456 Germany MID 1993 2020/2021 27 1 33
## 4290 456 Germany MID 1993 2021/2022 28 4 22
## 4291 456 Germany MID 1993 2022/2023 29 5 13
## 4292 456 Germany MID 1993 2023/2024 30 1 21
## 4293 457 Brazil GK 2002 2010/2011 20 3 6
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## 4301 458 England MID 1989 2012/2013 21 5 18
## 4302 458 England MID 1989 2013/2014 22 1 36
## 4303 458 England MID 1989 2014/2015 23 3 9
## 4304 458 England MID 1989 2015/2016 24 4 15
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## 4329 461 Germany MID 1992 2020/2021 28 1 29
## 4330 462 Germany DEF 1989 2010/2011 20 5 28
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## 4365 465 Spain MID 1994 2014/2015 23 4 33
## 4366 465 Spain MID 1994 2015/2016 24 5 16
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## 4368 465 Spain MID 1994 2017/2018 26 5 18
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## 4371 465 Spain MID 1994 2020/2021 29 1 19
## 4372 465 Spain MID 1994 2021/2022 30 4 14
## 4373 465 Spain MID 1994 2022/2023 31 4 8
## 4374 465 Spain MID 1994 2023/2024 32 1 13
## 4375 466 England GK 1996 2010/2011 19 5 8
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## 4407 468 England MID 1996 2014/2015 23 2 7
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## 4429 471 Argentina DEF 1987 2010/2011 17 2 28
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## 4477 476 Netherlands MID 2003 2010/2011 17 1 28
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## 4481 476 Netherlands MID 2003 2014/2015 21 1 10
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## 4488 476 Netherlands MID 2003 2021/2022 28 2 20
## 4489 477 Netherlands GK 1995 2010/2011 18 3 8
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## 4497 478 Germany MID 1996 2010/2011 20 2 25
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## 4501 478 Germany MID 1996 2014/2015 24 2 27
## 4502 478 Germany MID 1996 2015/2016 25 2 8
## 4503 478 Germany MID 1996 2016/2017 26 5 19
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## 4507 478 Germany MID 1996 2020/2021 30 4 7
## 4508 478 Germany MID 1996 2021/2022 31 3 16
## 4509 478 Germany MID 1996 2022/2023 32 3 26
## 4510 479 Argentina FWD 1990 2010/2011 20 5 13
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## 4532 481 Spain DEF 1986 2010/2011 17 2 23
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## 4534 481 Spain DEF 1986 2012/2013 19 2 37
## 4535 481 Spain DEF 1986 2013/2014 20 2 27
## 4536 481 Spain DEF 1986 2014/2015 21 2 18
## 4537 481 Spain DEF 1986 2015/2016 22 5 6
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## 4540 482 England MID 2000 2010/2011 19 1 26
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## 4550 482 England MID 2000 2020/2021 29 3 5
## 4551 482 England MID 2000 2021/2022 30 1 14
## 4552 482 England MID 2000 2022/2023 31 1 7
## 4553 482 England MID 2000 2023/2024 32 3 14
## 4554 483 Brazil FWD 1988 2010/2011 20 1 11
## 4555 483 Brazil FWD 1988 2011/2012 21 4 10
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## 4561 483 Brazil FWD 1988 2017/2018 27 3 37
## 4562 483 Brazil FWD 1988 2018/2019 28 4 9
## 4563 484 Netherlands DEF 1990 2010/2011 19 4 33
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## 4576 485 France MID 2000 2010/2011 19 3 18
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## 4584 485 France MID 2000 2018/2019 27 1 34
## 4585 485 France MID 2000 2019/2020 28 2 32
## 4586 485 France MID 2000 2020/2021 29 4 35
## 4587 485 France MID 2000 2021/2022 30 3 35
## 4588 486 Netherlands GK 1999 2010/2011 19 2 35
## 4589 486 Netherlands GK 1999 2011/2012 20 5 27
## 4590 486 Netherlands GK 1999 2012/2013 21 2 37
## 4591 486 Netherlands GK 1999 2013/2014 22 5 11
## 4592 486 Netherlands GK 1999 2014/2015 23 1 13
## 4593 486 Netherlands GK 1999 2015/2016 24 4 34
## 4594 486 Netherlands GK 1999 2016/2017 25 1 29
## 4595 487 Netherlands GK 1999 2010/2011 19 3 9
## 4596 487 Netherlands GK 1999 2011/2012 20 3 15
## 4597 487 Netherlands GK 1999 2012/2013 21 1 25
## 4598 487 Netherlands GK 1999 2013/2014 22 3 18
## 4599 487 Netherlands GK 1999 2014/2015 23 4 25
## 4600 488 Netherlands MID 2000 2010/2011 17 5 30
## 4601 488 Netherlands MID 2000 2011/2012 18 4 29
## 4602 488 Netherlands MID 2000 2012/2013 19 2 18
## 4603 488 Netherlands MID 2000 2013/2014 20 4 9
## 4604 488 Netherlands MID 2000 2014/2015 21 5 31
## 4605 488 Netherlands MID 2000 2015/2016 22 2 15
## 4606 488 Netherlands MID 2000 2016/2017 23 2 28
## 4607 489 Brazil DEF 1996 2010/2011 19 4 32
## 4608 489 Brazil DEF 1996 2011/2012 20 5 28
## 4609 489 Brazil DEF 1996 2012/2013 21 1 34
## 4610 489 Brazil DEF 1996 2013/2014 22 2 5
## 4611 489 Brazil DEF 1996 2014/2015 23 1 20
## 4612 489 Brazil DEF 1996 2015/2016 24 4 9
## 4613 489 Brazil DEF 1996 2016/2017 25 4 19
## 4614 489 Brazil DEF 1996 2017/2018 26 1 34
## 4615 490 Italy MID 1991 2010/2011 20 5 36
## 4616 490 Italy MID 1991 2011/2012 21 4 19
## 4617 490 Italy MID 1991 2012/2013 22 1 9
## 4618 490 Italy MID 1991 2013/2014 23 3 22
## 4619 490 Italy MID 1991 2014/2015 24 2 36
## 4620 490 Italy MID 1991 2015/2016 25 3 5
## 4621 491 France DEF 2001 2010/2011 19 1 7
## 4622 491 France DEF 2001 2011/2012 20 4 29
## 4623 491 France DEF 2001 2012/2013 21 5 31
## 4624 491 France DEF 2001 2013/2014 22 4 21
## 4625 491 France DEF 2001 2014/2015 23 1 25
## 4626 492 Spain GK 1995 2010/2011 18 5 32
## 4627 492 Spain GK 1995 2011/2012 19 3 15
## 4628 492 Spain GK 1995 2012/2013 20 2 20
## 4629 492 Spain GK 1995 2013/2014 21 3 28
## 4630 492 Spain GK 1995 2014/2015 22 5 5
## 4631 492 Spain GK 1995 2015/2016 23 5 22
## 4632 492 Spain GK 1995 2016/2017 24 5 34
## 4633 492 Spain GK 1995 2017/2018 25 5 16
## 4634 492 Spain GK 1995 2018/2019 26 4 6
## 4635 492 Spain GK 1995 2019/2020 27 2 9
## 4636 492 Spain GK 1995 2020/2021 28 5 36
## 4637 493 Brazil GK 1998 2010/2011 18 5 31
## 4638 493 Brazil GK 1998 2011/2012 19 5 30
## 4639 493 Brazil GK 1998 2012/2013 20 1 36
## 4640 493 Brazil GK 1998 2013/2014 21 1 37
## 4641 493 Brazil GK 1998 2014/2015 22 3 23
## 4642 493 Brazil GK 1998 2015/2016 23 3 36
## 4643 493 Brazil GK 1998 2016/2017 24 4 16
## 4644 493 Brazil GK 1998 2017/2018 25 5 35
## 4645 493 Brazil GK 1998 2018/2019 26 1 9
## 4646 493 Brazil GK 1998 2019/2020 27 1 24
## 4647 493 Brazil GK 1998 2020/2021 28 5 21
## 4648 493 Brazil GK 1998 2021/2022 29 2 17
## 4649 494 Spain MID 1985 2010/2011 18 2 6
## 4650 494 Spain MID 1985 2011/2012 19 3 16
## 4651 494 Spain MID 1985 2012/2013 20 5 16
## 4652 494 Spain MID 1985 2013/2014 21 5 12
## 4653 494 Spain MID 1985 2014/2015 22 4 27
## 4654 494 Spain MID 1985 2015/2016 23 3 18
## 4655 494 Spain MID 1985 2016/2017 24 5 12
## 4656 494 Spain MID 1985 2017/2018 25 4 32
## 4657 494 Spain MID 1985 2018/2019 26 2 37
## 4658 494 Spain MID 1985 2019/2020 27 5 12
## 4659 494 Spain MID 1985 2020/2021 28 1 25
## 4660 495 Brazil GK 2001 2010/2011 17 2 34
## 4661 495 Brazil GK 2001 2011/2012 18 2 31
## 4662 495 Brazil GK 2001 2012/2013 19 4 8
## 4663 495 Brazil GK 2001 2013/2014 20 4 14
## 4664 495 Brazil GK 2001 2014/2015 21 2 12
## 4665 495 Brazil GK 2001 2015/2016 22 1 36
## 4666 495 Brazil GK 2001 2016/2017 23 1 22
## 4667 495 Brazil GK 2001 2017/2018 24 1 13
## 4668 495 Brazil GK 2001 2018/2019 25 3 8
## 4669 496 Italy MID 1991 2010/2011 19 1 29
## 4670 496 Italy MID 1991 2011/2012 20 1 5
## 4671 496 Italy MID 1991 2012/2013 21 1 13
## 4672 496 Italy MID 1991 2013/2014 22 5 19
## 4673 496 Italy MID 1991 2014/2015 23 1 34
## 4674 497 Italy FWD 1998 2010/2011 18 3 30
## 4675 497 Italy FWD 1998 2011/2012 19 3 37
## 4676 497 Italy FWD 1998 2012/2013 20 2 18
## 4677 497 Italy FWD 1998 2013/2014 21 5 27
## 4678 497 Italy FWD 1998 2014/2015 22 2 34
## 4679 497 Italy FWD 1998 2015/2016 23 4 13
## 4680 497 Italy FWD 1998 2016/2017 24 2 12
## 4681 497 Italy FWD 1998 2017/2018 25 5 16
## 4682 497 Italy FWD 1998 2018/2019 26 2 11
## 4683 497 Italy FWD 1998 2019/2020 27 1 24
## 4684 497 Italy FWD 1998 2020/2021 28 4 31
## 4685 498 Argentina GK 1985 2010/2011 17 1 37
## 4686 498 Argentina GK 1985 2011/2012 18 5 32
## 4687 498 Argentina GK 1985 2012/2013 19 2 27
## 4688 498 Argentina GK 1985 2013/2014 20 2 24
## 4689 498 Argentina GK 1985 2014/2015 21 2 31
## 4690 498 Argentina GK 1985 2015/2016 22 5 35
## 4691 498 Argentina GK 1985 2016/2017 23 1 20
## 4692 498 Argentina GK 1985 2017/2018 24 4 6
## 4693 498 Argentina GK 1985 2018/2019 25 4 29
## 4694 498 Argentina GK 1985 2019/2020 26 4 21
## 4695 498 Argentina GK 1985 2020/2021 27 3 33
## 4696 499 Brazil GK 1995 2010/2011 20 5 26
## 4697 499 Brazil GK 1995 2011/2012 21 3 8
## 4698 499 Brazil GK 1995 2012/2013 22 4 15
## 4699 499 Brazil GK 1995 2013/2014 23 4 6
## 4700 499 Brazil GK 1995 2014/2015 24 2 16
## 4701 499 Brazil GK 1995 2015/2016 25 4 13
## 4702 499 Brazil GK 1995 2016/2017 26 2 27
## 4703 499 Brazil GK 1995 2017/2018 27 1 16
## 4704 499 Brazil GK 1995 2018/2019 28 3 8
## 4705 499 Brazil GK 1995 2019/2020 29 2 6
## 4706 499 Brazil GK 1995 2020/2021 30 1 28
## 4707 499 Brazil GK 1995 2021/2022 31 3 27
## 4708 499 Brazil GK 1995 2022/2023 32 2 34
## 4709 500 Netherlands DEF 1986 2010/2011 17 4 10
## 4710 500 Netherlands DEF 1986 2011/2012 18 2 26
## 4711 500 Netherlands DEF 1986 2012/2013 19 1 29
## 4712 500 Netherlands DEF 1986 2013/2014 20 1 34
## 4713 500 Netherlands DEF 1986 2014/2015 21 1 22
## 4714 500 Netherlands DEF 1986 2015/2016 22 5 8
## 4715 500 Netherlands DEF 1986 2016/2017 23 1 10
## minutes goals assists rating injuries market_value_m. career_stage
## 1 2349 0 1 6.70 0 192.03 early
## 2 1725 0 2 6.24 1 171.35 early
## 3 1620 0 0 7.21 1 61.67 early
## 4 1064 0 0 6.70 0 48.53 early
## 5 1118 0 1 7.08 0 118.09 early
## 6 520 0 1 6.26 0 42.42 early
## 7 2340 0 2 7.46 0 122.30 peak
## 8 2688 0 3 6.49 2 171.92 peak
## 9 1600 0 1 7.11 1 266.01 peak
## 10 525 0 3 6.75 0 38.38 decline
## 11 1044 0 1 6.10 0 168.23 decline
## 12 2816 0 1 5.43 2 142.57 early
## 13 1769 0 2 7.20 0 257.15 early
## 14 1200 0 3 6.28 0 32.09 early
## 15 1625 0 6 6.13 0 33.84 early
## 16 1071 0 1 5.98 1 168.40 early
## 17 497 0 2 6.02 1 70.14 early
## 18 1825 0 2 7.77 0 283.80 early
## 19 1958 0 1 5.29 1 65.04 early
## 20 1064 0 1 7.48 0 266.82 early
## 21 432 0 0 6.54 1 210.61 early
## 22 1340 0 1 5.47 0 103.15 early
## 23 3003 0 1 6.23 0 101.55 early
## 24 600 0 1 5.90 1 148.38 early
## 25 2108 0 1 5.69 1 61.51 early
## 26 1800 0 2 6.32 1 46.73 early
## 27 2624 0 2 6.70 0 102.15 early
## 28 2068 0 1 5.91 1 91.40 early
## 29 1701 0 1 7.14 1 56.93 early
## 30 1782 0 3 6.94 0 227.68 early
## 31 920 0 0 7.20 0 118.13 peak
## 32 2520 3 6 5.16 1 117.47 early
## 33 624 0 1 6.19 1 76.90 early
## 34 1054 0 1 6.57 0 206.45 early
## 35 474 0 1 5.06 1 120.48 early
## 36 657 2 0 5.70 2 35.39 early
## 37 2211 8 7 5.69 1 131.37 early
## 38 650 2 0 5.49 1 150.61 early
## 39 3036 5 3 7.33 0 163.65 peak
## 40 568 1 1 8.35 1 362.67 peak
## 41 1360 3 6 5.63 1 69.37 early
## 42 1820 4 5 7.44 0 62.12 early
## 43 2820 6 2 5.15 0 154.71 early
## 44 2673 5 3 6.47 0 40.18 early
## 45 2093 5 3 6.32 0 111.95 early
## 46 2484 3 3 7.42 0 147.37 peak
## 47 744 6 2 7.00 0 154.76 peak
## 48 2160 1 4 7.17 0 48.95 peak
## 49 1536 4 2 6.29 1 138.14 decline
## 50 1533 4 1 6.27 2 84.55 decline
## 51 975 2 1 6.07 1 147.97 decline
## 52 759 2 1 6.76 0 250.50 early
## 53 2640 4 5 6.80 0 113.96 early
## 54 1105 2 2 5.74 0 82.09 early
## 55 3162 7 10 6.35 1 96.81 early
## 56 2025 2 7 6.03 1 59.31 early
## 57 595 1 0 6.77 1 144.62 peak
## 58 564 1 1 7.42 2 308.33 peak
## 59 2904 3 1 8.17 0 213.89 peak
## 60 1602 5 0 6.58 0 184.20 decline
## 61 830 2 1 6.68 2 134.69 decline
## 62 1860 7 6 7.31 0 123.77 decline
## 63 930 2 1 6.56 0 196.51 decline
## 64 1273 1 2 6.41 1 196.96 decline
## 65 3168 9 2 6.26 2 68.61 decline
## 66 2870 0 0 5.99 1 95.80 early
## 67 438 0 1 6.38 0 83.25 early
## 68 2632 0 1 6.20 0 74.86 early
## 69 2368 0 4 6.32 0 82.47 early
## 70 2210 0 1 6.19 2 40.98 early
## 71 1056 0 0 6.87 1 170.01 early
## 72 2079 0 2 6.07 1 79.44 early
## 73 1922 6 4 6.54 0 121.46 early
## 74 1720 7 0 6.39 0 190.54 early
## 75 1776 2 2 6.56 0 117.32 early
## 76 1292 0 0 6.16 0 112.90 early
## 77 2668 5 1 6.20 0 127.85 early
## 78 2257 5 4 5.73 1 77.44 early
## 79 455 2 0 6.17 2 212.14 early
## 80 770 3 2 6.76 0 113.02 early
## 81 1428 3 0 6.29 1 161.65 early
## 82 1863 3 1 8.37 0 123.53 peak
## 83 2720 6 7 7.70 0 209.73 peak
## 84 2010 6 1 6.59 0 45.04 early
## 85 1464 2 2 5.49 1 26.98 early
## 86 868 2 1 7.27 0 164.27 early
## 87 395 0 0 7.25 0 227.36 early
## 88 1235 1 1 6.39 1 191.79 early
## 89 470 0 1 6.96 0 237.70 early
## 90 2133 1 3 6.67 0 145.93 early
## 91 2304 5 4 6.39 1 240.19 early
## 92 765 1 1 6.68 1 266.81 early
## 93 936 1 3 6.72 2 258.95 peak
## 94 1848 0 2 7.48 1 53.41 peak
## 95 3367 3 6 6.72 0 43.35 peak
## 96 1755 6 3 5.73 0 86.57 early
## 97 688 2 0 6.46 1 235.49 early
## 98 990 2 0 6.27 1 102.98 early
## 99 1512 2 1 6.62 0 182.06 early
## 100 962 4 0 7.43 1 100.34 early
## 101 1176 0 2 6.82 0 123.61 early
## 102 1924 3 1 6.71 0 40.93 early
## 103 1364 3 3 5.73 2 173.44 early
## 104 476 0 0 7.73 0 198.05 peak
## 105 2821 8 3 5.80 1 136.71 early
## 106 1420 4 1 6.21 3 45.22 early
## 107 445 1 0 6.37 1 180.43 early
## 108 1656 2 1 6.42 0 93.27 early
## 109 616 2 2 6.14 2 71.82 early
## 110 2482 5 5 6.25 1 91.35 early
## 111 1240 4 1 6.29 0 41.11 early
## 112 1936 2 2 6.65 0 196.81 early
## 113 2590 3 2 6.58 0 243.69 early
## 114 3102 5 2 6.50 0 186.68 early
## 115 540 2 0 6.44 0 75.15 early
## 116 2125 3 5 6.28 0 125.92 early
## 117 305 0 0 7.38 1 57.91 early
## 118 1742 4 2 6.20 0 126.56 early
## 119 2632 0 4 5.78 1 29.02 early
## 120 801 1 1 6.57 1 250.13 early
## 121 793 1 0 7.78 0 56.24 peak
## 122 1376 6 2 7.27 0 205.87 peak
## 123 2349 4 4 6.48 1 200.06 peak
## 124 2520 5 4 6.80 0 178.70 decline
## 125 2376 0 1 5.70 1 99.85 early
## 126 558 0 1 6.38 0 45.64 early
## 127 2001 0 5 6.42 1 139.91 early
## 128 1680 0 4 6.62 0 46.31 early
## 129 2632 0 2 6.29 0 76.08 early
## 130 2409 0 2 5.71 1 107.07 early
## 131 2618 0 1 6.00 0 77.73 early
## 132 3060 0 3 7.29 1 190.27 peak
## 133 2240 0 2 6.44 1 178.02 peak
## 134 1020 0 1 7.09 1 239.79 peak
## 135 1513 0 2 7.02 0 167.89 decline
## 136 1920 0 2 6.40 0 223.65 decline
## 137 2765 0 3 6.06 0 42.71 early
## 138 1500 0 3 6.09 0 60.27 early
## 139 1760 0 2 6.13 2 176.77 early
## 140 2190 0 4 6.56 1 84.77 early
## 141 1775 0 1 6.93 0 54.80 peak
## 142 1743 0 0 6.69 1 48.82 peak
## 143 1976 0 3 6.74 0 38.09 peak
## 144 1932 8 2 6.83 0 219.62 early
## 145 2720 4 4 5.82 0 59.28 early
## 146 2560 5 3 5.97 2 123.62 early
## 147 1920 3 4 5.77 1 36.31 early
## 148 696 2 0 6.28 1 79.73 early
## 149 2232 3 2 5.64 1 28.64 early
## 150 1392 2 0 6.84 1 135.94 early
## 151 1540 6 1 6.27 0 68.35 early
## 152 1440 5 1 6.60 0 46.24 early
## 153 1216 5 1 7.85 0 251.32 peak
## 154 2407 2 9 6.24 1 38.15 early
## 155 1869 1 2 6.83 0 192.49 early
## 156 732 3 0 7.26 0 235.92 early
## 157 1200 1 2 6.63 0 199.14 early
## 158 984 1 2 6.62 1 42.84 early
## 159 2112 3 3 6.95 0 193.68 early
## 160 1040 0 0 7.19 0 307.43 peak
## 161 1692 4 0 6.86 1 231.63 peak
## 162 1079 0 1 7.69 0 296.38 peak
## 163 1080 0 1 6.79 0 147.75 early
## 164 1472 0 3 6.47 1 171.94 early
## 165 2133 0 2 6.06 0 177.43 early
## 166 2627 0 3 6.15 1 142.77 early
## 167 1173 0 1 6.35 0 127.61 early
## 168 528 0 0 6.14 1 161.59 early
## 169 370 0 0 6.93 1 152.98 early
## 170 1925 2 3 6.07 0 151.90 early
## 171 1890 6 1 5.82 1 27.42 early
## 172 1914 0 3 6.08 1 149.95 early
## 173 960 1 3 6.44 0 194.31 early
## 174 740 1 1 6.42 0 122.42 early
## 175 1496 5 1 7.09 1 227.68 early
## 176 651 0 0 6.63 0 123.38 early
## 177 1750 3 3 6.85 3 192.67 peak
## 178 1840 4 3 7.75 0 131.93 peak
## 179 684 2 3 6.14 1 150.16 peak
## 180 3071 10 3 6.26 1 108.11 decline
## 181 1530 3 4 6.61 0 243.79 early
## 182 1541 2 0 7.36 0 171.29 early
## 183 1139 5 1 6.16 0 178.39 early
## 184 1296 2 1 6.43 0 123.98 early
## 185 1220 1 1 6.91 0 88.80 early
## 186 380 2 0 6.20 1 163.37 early
## 187 2457 3 5 6.50 1 38.34 early
## 188 2046 3 2 6.85 1 108.56 peak
## 189 780 2 0 7.66 0 108.71 peak
## 190 1628 5 0 6.86 1 220.36 peak
## 191 1564 1 2 6.50 0 120.86 early
## 192 1606 1 2 6.01 0 39.36 early
## 193 910 4 2 6.13 1 38.75 early
## 194 1173 1 1 7.08 2 284.73 early
## 195 1242 0 3 7.01 1 48.20 early
## 196 2272 1 3 6.22 0 35.54 early
## 197 1500 4 4 6.45 0 182.79 early
## 198 1890 7 1 5.68 0 135.29 early
## 199 1610 7 2 6.52 1 85.05 early
## 200 1380 3 1 6.60 0 48.90 early
## 201 1242 2 4 5.00 3 53.18 early
## 202 1260 6 1 6.60 1 51.93 early
## 203 3069 8 8 6.06 1 147.51 early
## 204 2236 1 2 6.73 0 158.88 early
## 205 455 1 1 6.46 0 113.87 early
## 206 2387 0 2 5.76 2 111.72 early
## 207 960 0 0 6.30 0 93.58 early
## 208 1200 0 2 6.84 0 271.05 early
## 209 720 0 3 6.07 0 76.45 early
## 210 1953 0 3 5.78 1 109.64 early
## 211 2214 0 4 7.18 0 136.26 early
## 212 1488 0 1 6.08 0 170.67 early
## 213 648 0 1 6.93 0 125.79 early
## 214 1620 0 3 6.59 1 141.19 early
## 215 988 0 1 5.71 0 106.04 early
## 216 420 0 0 7.30 2 260.74 peak
## 217 1729 0 0 6.89 1 77.15 peak
## 218 2244 0 6 7.37 2 54.25 peak
## 219 2220 0 3 6.43 1 129.71 decline
## 220 664 0 2 5.94 2 57.88 decline
## 221 774 0 1 6.31 2 44.74 decline
## 222 1647 0 5 6.15 1 175.78 decline
## 223 1340 0 0 5.80 1 63.30 decline
## 224 920 0 2 5.95 0 33.35 decline
## 225 534 0 0 6.79 2 242.98 early
## 226 2607 0 2 6.67 2 110.49 early
## 227 1064 0 0 6.32 1 175.04 early
## 228 2201 0 1 6.49 1 184.63 early
## 229 1932 0 2 7.38 0 107.18 early
## 230 534 0 1 6.19 0 117.73 early
## 231 3367 0 6 6.39 0 42.30 early
## 232 2856 0 1 6.64 0 103.52 early
## 233 1380 0 2 7.42 1 55.06 peak
## 234 2100 0 2 6.54 1 103.15 peak
## 235 1968 0 0 6.06 2 59.28 early
## 236 930 0 0 6.18 1 118.88 early
## 237 1716 0 2 5.66 0 76.61 early
## 238 1428 0 2 6.35 1 140.89 early
## 239 1001 0 2 6.20 0 187.61 early
## 240 1653 0 3 7.36 0 224.48 early
## 241 1764 0 1 7.09 1 293.61 peak
## 242 2112 0 7 6.82 2 227.50 peak
## 243 2268 0 4 6.84 2 88.05 peak
## 244 1840 0 2 6.46 1 78.51 decline
## 245 738 0 3 6.54 1 37.25 decline
## 246 2052 0 5 6.04 0 140.77 decline
## 247 1242 0 2 6.05 0 65.72 decline
## 248 1196 3 0 6.25 0 112.88 early
## 249 2376 7 9 6.47 1 45.23 early
## 250 2673 7 1 6.79 0 51.68 early
## 251 546 0 0 6.83 0 142.57 early
## 252 1342 4 1 6.53 1 44.51 early
## 253 3185 1 2 6.43 1 68.44 early
## 254 2736 1 4 7.26 0 47.56 early
## 255 2125 5 1 6.12 0 109.02 early
## 256 980 1 2 5.96 1 158.73 early
## 257 434 1 0 5.99 0 37.29 early
## 258 1406 3 2 6.85 0 84.83 early
## 259 2275 2 4 5.87 2 177.29 early
## 260 1704 6 1 6.82 0 131.84 early
## 261 1335 2 0 7.77 1 147.62 peak
## 262 525 1 0 7.61 0 183.58 peak
## 263 414 0 1 6.36 1 126.96 peak
## 264 720 4 0 5.80 0 108.00 decline
## 265 1890 5 2 6.30 0 84.74 decline
## 266 1422 1 1 7.41 0 294.93 decline
## 267 1037 4 1 6.10 0 193.77 early
## 268 1320 6 1 6.23 1 161.42 early
## 269 888 1 1 6.27 2 46.22 early
## 270 1395 0 0 6.65 0 131.19 early
## 271 2664 7 2 7.18 0 85.17 early
## 272 2430 5 4 5.73 1 151.31 early
## 273 2052 5 1 6.39 1 135.36 early
## 274 1275 4 1 5.78 1 64.59 early
## 275 1365 4 3 6.01 1 160.04 early
## 276 1620 5 4 6.08 0 221.05 early
## 277 2800 3 5 6.48 0 49.12 early
## 278 1809 4 2 6.05 0 189.80 early
## 279 3168 6 4 6.11 0 174.49 early
## 280 2701 6 7 6.85 0 206.21 early
## 281 1128 1 1 7.10 0 160.70 early
## 282 378 0 1 7.35 0 231.57 early
## 283 2162 3 5 7.70 0 53.89 peak
## 284 2697 1 1 6.61 1 198.42 peak
## 285 2325 8 1 6.57 0 90.02 early
## 286 2080 4 2 6.54 0 174.20 early
## 287 1701 5 2 6.38 0 33.79 early
## 288 2112 3 2 6.58 0 222.65 early
## 289 2848 4 1 6.78 0 207.02 early
## 290 1850 5 1 5.83 0 67.54 early
## 291 1206 4 1 6.27 2 95.83 early
## 292 2370 6 3 7.28 1 280.60 early
## 293 2128 7 2 5.88 2 139.35 early
## 294 2170 10 6 6.03 1 73.92 early
## 295 704 1 1 6.64 1 81.93 early
## 296 490 2 0 5.62 0 108.20 early
## 297 1560 4 3 6.32 2 106.59 early
## 298 2403 5 3 6.72 0 168.96 peak
## 299 1050 3 1 7.22 0 119.21 peak
## 300 2250 1 1 7.09 1 144.51 peak
## 301 2580 5 4 5.31 1 64.69 decline
## 302 968 1 1 5.99 1 80.66 decline
## 303 2607 4 4 5.45 2 82.50 decline
## 304 1495 4 0 5.68 1 32.75 decline
## 305 704 1 0 7.12 0 101.69 decline
## 306 854 0 2 5.95 1 114.57 early
## 307 1680 0 1 5.47 1 88.57 early
## 308 2912 0 3 6.38 1 102.71 early
## 309 1050 0 2 7.11 1 104.20 early
## 310 2688 0 0 6.96 0 228.02 early
## 311 1275 0 1 6.48 1 40.55 early
## 312 1360 0 0 6.68 0 40.35 peak
## 313 2408 6 3 6.74 1 163.26 early
## 314 3312 6 2 6.85 0 50.40 early
## 315 1971 1 4 5.63 1 29.10 early
## 316 3330 3 9 6.96 0 144.22 early
## 317 2457 7 2 5.72 1 111.80 early
## 318 774 0 1 6.87 1 137.34 early
## 319 2232 6 3 6.84 0 158.85 early
## 320 1184 2 1 7.10 0 162.73 early
## 321 1920 4 11 7.21 2 222.79 peak
## 322 2349 4 1 6.59 0 183.13 early
## 323 2442 2 4 6.18 0 134.09 early
## 324 924 1 2 6.51 1 69.00 early
## 325 1890 7 4 7.49 0 186.57 early
## 326 712 0 1 5.61 1 70.01 early
## 327 660 2 1 5.85 1 205.30 peak
## 328 2553 4 3 6.61 1 41.60 peak
## 329 3330 12 4 7.20 0 209.31 early
## 330 1254 2 1 6.78 0 88.10 early
## 331 2574 4 0 6.40 1 180.38 early
## 332 814 4 0 6.30 1 195.08 early
## 333 2059 5 2 5.97 2 38.08 early
## 334 2752 4 2 6.05 1 43.37 early
## 335 1088 1 3 6.01 2 169.67 early
## 336 2162 7 0 6.32 0 134.78 early
## 337 2944 5 5 6.60 1 150.43 peak
## 338 2400 5 3 7.16 0 261.68 peak
## 339 1826 1 3 7.79 1 331.80 peak
## 340 2432 0 2 6.66 0 206.17 early
## 341 700 0 2 6.62 0 223.31 early
## 342 2632 0 2 5.92 1 180.74 early
## 343 1860 0 3 6.03 0 31.70 early
## 344 1885 0 1 6.06 1 63.91 early
## 345 1246 0 2 6.51 0 96.95 peak
## 346 1105 0 1 8.04 0 373.14 peak
## 347 1776 0 5 6.96 1 215.25 peak
## 348 1224 0 1 6.53 1 235.05 decline
## 349 2376 6 3 6.16 0 200.51 early
## 350 1863 2 6 5.21 2 129.04 early
## 351 2175 3 6 6.37 1 113.70 early
## 352 1960 4 2 6.48 1 46.25 early
## 353 2184 5 5 6.75 0 146.45 early
## 354 1974 8 1 6.06 1 33.87 early
## 355 1406 4 3 6.51 0 100.95 early
## 356 1584 4 0 5.97 2 128.22 early
## 357 1512 4 3 7.34 0 50.04 peak
## 358 1856 1 4 7.93 1 63.14 peak
## 359 3036 5 3 7.25 0 117.74 peak
## 360 1554 2 1 6.07 1 171.19 decline
## 361 315 2 1 5.79 0 75.58 decline
## 362 1848 3 4 6.24 0 46.02 early
## 363 1533 5 0 7.15 1 87.60 early
## 364 2250 3 8 6.22 1 101.73 early
## 365 1040 3 2 6.95 0 55.13 early
## 366 549 1 1 6.83 1 131.14 early
## 367 2376 6 2 6.65 0 149.30 early
## 368 1932 2 3 6.56 1 151.66 peak
## 369 396 1 0 7.86 0 201.86 peak
## 370 2576 2 1 7.42 0 193.28 peak
## 371 2380 3 4 7.64 1 300.99 decline
## 372 936 1 0 6.33 1 187.29 decline
## 373 427 0 2 7.06 0 278.84 early
## 374 792 0 1 6.03 0 79.89 early
## 375 2001 0 1 7.43 1 54.76 early
## 376 1530 0 1 5.96 1 35.28 early
## 377 680 0 1 6.53 0 138.52 early
## 378 2400 0 1 7.77 0 143.97 peak
## 379 474 2 2 6.51 0 169.50 early
## 380 2002 6 2 6.67 1 155.15 early
## 381 738 1 0 7.33 0 183.06 early
## 382 2632 3 1 6.36 1 68.02 early
## 383 2100 9 4 6.42 1 108.74 early
## 384 1056 3 1 5.46 0 137.96 early
## 385 425 1 0 8.12 0 65.57 peak
## 386 960 2 1 7.48 0 94.28 peak
## 387 3060 8 2 6.95 1 45.34 early
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## 395 737 2 0 6.58 0 38.21 peak
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## 403 2730 4 5 5.00 1 22.79 early
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## 417 930 0 1 6.36 1 138.01 peak
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## 437 880 1 0 7.53 1 59.73 early
## 438 962 4 2 6.66 0 224.26 early
## 439 2516 10 4 6.45 1 216.77 peak
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## 447 2484 0 6 6.28 0 72.97 early
## 448 2442 0 2 5.80 1 60.10 early
## 449 1738 0 3 7.03 1 126.52 early
## 450 1183 0 2 6.31 1 175.33 early
## 451 2312 0 4 5.82 2 140.08 early
## 452 2135 0 3 6.72 1 53.83 early
## 453 2244 0 2 7.78 0 56.80 peak
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## 458 1458 2 2 6.43 0 101.85 early
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## 462 2046 3 4 6.42 1 70.57 early
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## 465 2607 5 2 5.76 1 196.43 early
## 466 2176 2 3 6.48 1 192.51 early
## 467 1800 3 1 6.23 0 81.52 early
## 468 2664 5 4 7.25 1 173.89 peak
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## 472 1560 7 2 6.59 0 118.95 early
## 473 1494 5 3 6.59 1 43.65 early
## 474 1184 2 1 6.81 0 116.99 early
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## 476 1260 2 1 6.81 0 166.94 peak
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## 481 756 3 1 6.82 0 81.56 early
## 482 1134 1 2 6.96 0 184.98 early
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## 485 1540 3 7 6.84 1 47.49 early
## 486 2093 5 2 6.66 0 262.14 early
## 487 2016 4 1 6.42 1 206.25 early
## 488 2664 8 1 6.50 1 169.48 peak
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## 493 2088 4 4 6.37 0 128.86 early
## 494 2852 7 3 6.90 1 177.62 early
## 495 2805 3 5 6.10 1 192.72 early
## 496 693 2 0 5.86 1 30.98 early
## 497 1392 6 1 5.95 0 32.37 early
## 498 902 1 1 7.48 0 210.21 early
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## 515 814 2 0 6.09 0 31.68 early
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## 518 450 0 2 6.94 1 97.57 early
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## 520 2244 8 5 5.70 0 29.00 early
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## 528 672 4 1 7.58 0 57.93 peak
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## 533 345 0 0 5.61 2 31.60 early
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## 543 483 0 1 6.75 0 85.74 early
## 544 1786 2 1 6.81 0 242.01 early
## 545 1260 0 3 6.86 0 113.19 early
## 546 1106 0 3 6.62 3 113.32 early
## 547 2059 0 2 6.21 2 172.12 early
## 548 1764 0 2 6.24 0 35.43 early
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## 551 1701 0 3 6.20 2 123.45 early
## 552 1368 0 2 6.81 1 194.41 early
## 553 2108 0 3 7.30 1 85.77 peak
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## 557 1452 0 4 6.53 2 45.31 early
## 558 3255 0 6 6.87 0 279.71 early
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## 563 1885 0 4 5.76 1 36.68 early
## 564 1955 0 1 6.96 2 88.91 peak
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## 566 616 1 1 6.14 1 35.74 early
## 567 511 1 2 6.73 1 39.17 early
## 568 1980 11 2 6.70 0 175.77 early
## 569 819 1 1 6.61 0 90.99 early
## 570 2160 3 5 6.33 0 39.99 early
## 571 546 1 1 6.79 0 75.16 early
## 572 441 1 0 6.93 1 174.18 peak
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## 579 2128 2 4 6.64 0 129.15 early
## 580 2016 5 4 5.85 1 109.83 early
## 581 2805 2 3 6.35 1 157.15 early
## 582 1960 5 3 5.99 0 102.70 early
## 583 1472 2 0 6.70 1 38.41 early
## 584 2520 4 6 6.72 0 42.26 early
## 585 2002 1 3 7.42 1 212.86 peak
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## 588 468 0 2 6.69 0 49.75 early
## 589 2686 0 6 8.12 0 296.51 early
## 590 2376 0 4 6.75 1 111.33 early
## 591 1752 0 2 6.49 0 132.94 early
## 592 1840 0 4 6.88 0 53.48 early
## 593 355 0 0 6.57 1 36.55 early
## 594 744 0 0 6.45 1 115.17 early
## 595 1980 0 3 5.74 1 91.55 early
## 596 2916 0 3 6.58 2 42.28 peak
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## 598 2322 0 0 6.56 0 200.66 early
## 599 1976 0 2 6.71 1 97.84 early
## 600 2310 0 5 6.71 1 255.25 early
## 601 1368 0 4 6.32 1 209.20 early
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## 603 2250 6 0 6.41 1 140.18 early
## 604 1241 2 0 7.29 0 286.44 early
## 605 1326 1 2 6.34 0 90.12 early
## 606 1005 3 2 7.41 0 235.22 early
## 607 3185 3 5 6.42 1 85.88 early
## 608 1480 1 7 6.54 0 97.94 early
## 609 846 0 0 6.32 1 196.28 early
## 610 1914 3 3 7.58 1 250.57 peak
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## 615 814 2 1 6.19 1 70.11 decline
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## 617 1281 1 1 7.26 0 132.58 early
## 618 2520 5 6 6.24 0 179.72 early
## 619 2886 4 6 6.90 0 80.70 early
## 620 3036 3 2 5.74 1 82.85 early
## 621 1152 2 1 7.34 0 228.37 peak
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## 624 2108 7 2 6.58 0 144.41 early
## 625 1056 0 3 5.41 2 134.23 early
## 626 2016 4 2 6.67 1 72.49 early
## 627 1134 4 2 6.11 0 38.63 early
## 628 1608 3 6 5.90 0 34.21 early
## 629 2220 4 2 7.23 1 115.85 peak
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## 634 756 3 2 6.47 1 33.78 decline
## 635 1403 2 2 5.85 0 109.69 decline
## 636 2790 5 4 6.76 1 241.83 decline
## 637 1827 0 3 5.59 1 104.42 early
## 638 2139 0 3 5.67 0 180.14 early
## 639 1980 0 5 6.28 0 138.96 early
## 640 1260 0 2 6.20 0 73.18 early
## 641 3003 0 2 6.42 0 219.32 early
## 642 420 0 0 6.80 0 40.21 early
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## 644 1140 0 1 6.29 2 218.70 early
## 645 3003 7 3 6.83 1 42.05 early
## 646 1080 2 1 6.17 1 37.78 early
## 647 803 1 2 6.66 0 38.61 early
## 648 825 3 3 5.99 0 39.92 early
## 649 1504 3 2 7.23 0 312.79 early
## 650 574 1 0 6.54 1 122.88 early
## 651 2494 1 1 7.40 0 145.17 early
## 652 1660 7 3 6.48 1 237.29 early
## 653 2325 4 3 5.61 1 116.66 early
## 654 2145 4 6 7.41 0 177.22 peak
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## 658 900 2 2 6.23 1 140.81 early
## 659 648 1 0 6.10 1 117.97 early
## 660 2688 5 7 6.28 1 104.27 early
## 661 2430 5 2 6.63 0 235.38 early
## 662 2673 4 2 6.34 2 33.55 early
## 663 1848 3 1 8.16 1 390.26 peak
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## 669 1625 3 3 7.04 0 164.09 early
## 670 1178 1 3 6.26 1 183.86 early
## 671 595 2 1 6.29 1 34.08 early
## 672 1620 7 7 6.08 0 36.54 early
## 673 2288 1 4 6.16 1 69.05 early
## 674 1209 3 3 7.92 0 305.84 peak
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## 679 1462 4 4 6.72 0 255.09 decline
## 680 540 3 1 6.17 1 184.42 decline
## 681 803 1 0 5.56 1 30.09 early
## 682 992 4 3 7.40 0 120.11 early
## 683 2958 3 0 6.95 0 274.60 early
## 684 1846 3 3 6.54 0 111.10 early
## 685 1541 0 1 6.63 0 220.48 early
## 686 2844 3 6 6.46 0 92.30 early
## 687 1320 5 2 7.00 0 91.43 early
## 688 1376 0 2 6.63 0 49.26 peak
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## 692 2668 3 1 5.76 0 28.96 decline
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## 694 1830 1 1 6.98 1 166.98 early
## 695 2556 4 4 6.47 0 186.60 early
## 696 670 2 1 6.43 1 96.78 early
## 697 1664 5 2 6.46 1 238.45 early
## 698 1710 6 3 7.03 0 179.05 early
## 699 1872 6 5 6.35 0 36.98 early
## 700 1900 2 0 5.45 0 127.72 early
## 701 760 1 0 8.32 0 161.36 peak
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## 703 1027 2 3 5.59 0 100.01 early
## 704 2225 4 5 5.68 0 66.58 early
## 705 2436 5 3 6.56 1 216.92 early
## 706 1394 6 1 6.89 0 38.68 early
## 707 1162 1 3 5.69 1 137.42 early
## 708 640 1 2 6.54 0 44.90 early
## 709 1541 3 7 6.85 1 169.29 early
## 710 3145 6 3 7.09 0 227.69 early
## 711 2208 4 2 5.54 2 30.63 early
## 712 1407 2 1 6.17 0 69.19 early
## 713 2220 2 2 6.83 1 152.66 early
## 714 1020 2 3 6.78 1 272.40 early
## 715 2553 7 5 7.52 0 197.36 peak
## 716 736 0 1 6.30 0 199.93 peak
## 717 553 0 1 6.26 1 98.76 early
## 718 2480 0 1 6.29 1 159.05 early
## 719 1925 0 2 6.13 1 163.29 early
## 720 2937 0 2 6.88 1 161.30 early
## 721 1178 0 1 6.47 1 247.21 early
## 722 1530 0 2 7.21 0 116.88 early
## 723 1365 0 7 7.91 0 297.34 peak
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## 727 1032 0 0 6.45 1 40.95 decline
## 728 1300 0 3 6.50 0 49.11 decline
## 729 630 0 2 6.80 0 106.64 early
## 730 1400 0 2 5.78 0 37.20 early
## 731 1742 0 2 6.22 1 128.16 early
## 732 438 0 0 6.34 1 78.48 early
## 733 1207 0 0 6.11 0 86.01 early
## 734 2108 0 5 7.33 0 219.31 early
## 735 2870 0 2 5.76 1 167.76 early
## 736 602 0 1 6.56 0 103.85 early
## 737 2050 0 1 6.07 1 36.42 early
## 738 1054 0 1 6.45 1 166.93 early
## 739 2592 0 7 6.59 1 79.75 peak
## 740 1560 0 1 6.77 0 227.07 peak
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## 743 2142 5 4 7.05 0 42.96 early
## 744 1598 1 0 7.11 0 50.60 early
## 745 1992 5 0 6.10 0 175.76 early
## 746 1554 2 1 6.41 1 136.63 early
## 747 1470 3 2 5.86 1 33.86 early
## 748 1952 7 2 5.61 1 110.00 early
## 749 1408 1 2 6.50 2 178.68 early
## 750 2923 7 6 7.17 0 211.18 early
## 751 1672 3 1 6.14 0 75.82 early
## 752 2158 4 2 6.43 0 148.53 early
## 753 1088 3 2 7.32 0 264.22 peak
## 754 2752 7 3 7.08 1 288.24 peak
## 755 624 2 1 7.19 1 174.00 peak
## 756 492 0 1 6.67 1 92.31 early
## 757 2484 2 3 6.42 1 186.08 early
## 758 581 0 1 7.15 0 166.00 early
## 759 1794 5 3 5.12 1 118.16 early
## 760 2490 6 5 6.59 1 40.76 peak
## 761 1162 1 1 7.19 0 183.36 peak
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## 764 1460 1 2 5.35 0 158.30 decline
## 765 1248 2 2 6.39 2 180.29 decline
## 766 1116 1 2 5.29 3 107.24 decline
## 767 1428 4 1 6.56 0 104.02 decline
## 768 1157 4 0 5.46 1 177.44 decline
## 769 792 0 2 6.71 1 106.35 early
## 770 1798 0 5 5.54 3 146.21 early
## 771 1650 0 1 6.86 0 164.91 early
## 772 1020 0 2 6.67 3 198.38 early
## 773 960 0 1 6.33 1 175.47 early
## 774 780 1 3 6.76 1 85.32 early
## 775 1479 3 1 6.86 0 242.51 early
## 776 1664 3 2 6.93 0 114.93 early
## 777 2464 5 0 6.60 0 152.29 early
## 778 1580 1 0 5.73 1 97.21 early
## 779 1458 4 0 5.66 1 66.15 early
## 780 450 0 0 6.43 2 95.82 early
## 781 1998 5 1 6.42 0 140.98 early
## 782 1952 7 6 6.08 0 80.83 early
## 783 384 0 1 6.43 2 81.62 early
## 784 2340 4 3 7.34 1 100.11 peak
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## 788 1128 0 4 6.05 1 93.83 early
## 789 1748 3 2 6.52 1 95.86 early
## 790 730 1 1 6.43 1 187.04 early
## 791 1680 4 1 5.83 0 63.89 early
## 792 1118 3 0 7.04 0 235.37 early
## 793 864 1 1 6.85 1 92.40 early
## 794 2047 3 1 8.02 0 354.38 peak
## 795 552 2 1 6.42 1 104.11 peak
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## 797 1496 0 5 5.00 0 126.94 decline
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## 799 1254 5 2 6.27 1 100.76 early
## 800 1500 0 5 6.53 0 86.13 early
## 801 603 1 0 6.39 1 142.47 early
## 802 2208 3 6 6.00 0 171.80 early
## 803 780 1 0 6.52 0 119.32 early
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## 805 793 2 0 6.57 0 234.35 early
## 806 790 0 1 5.54 0 90.14 early
## 807 2418 6 5 6.86 0 196.95 peak
## 808 2275 7 4 7.02 1 118.49 peak
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## 812 528 4 0 6.14 1 71.37 early
## 813 2175 3 2 6.70 0 175.07 early
## 814 1850 3 5 6.76 1 137.32 early
## 815 1350 2 0 6.61 1 36.17 early
## 816 1708 4 3 6.54 0 188.12 early
## 817 512 0 4 5.70 1 55.76 early
## 818 1820 2 3 5.51 0 157.63 early
## 819 2001 4 5 6.36 0 135.83 peak
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## 824 2610 0 5 6.24 1 129.10 early
## 825 2720 0 1 5.50 1 169.12 early
## 826 1617 0 2 5.89 1 189.81 early
## 827 780 0 1 5.56 1 117.78 early
## 828 1376 1 1 6.49 0 147.44 early
## 829 884 2 0 5.82 0 150.51 early
## 830 2080 7 5 6.41 0 141.04 early
## 831 2262 2 3 6.43 0 146.83 early
## 832 1978 6 1 6.88 0 243.38 early
## 833 1254 4 2 6.70 0 175.43 early
## 834 2100 6 4 6.24 1 116.71 early
## 835 1577 3 3 7.71 0 199.58 peak
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## 839 1720 3 3 5.77 2 119.72 decline
## 840 1860 6 3 6.36 0 140.17 early
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## 842 1950 2 4 6.14 0 43.54 early
## 843 1580 6 0 7.07 0 234.12 early
## 844 900 1 1 5.99 1 150.39 early
## 845 910 0 2 7.14 0 105.86 early
## 846 1260 1 4 6.20 0 192.04 early
## 847 632 0 1 5.72 0 55.59 early
## 848 1460 0 3 7.37 0 182.42 peak
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## 852 3102 4 2 5.38 1 48.41 early
## 853 2576 5 4 6.17 1 128.12 early
## 854 1694 1 4 5.86 0 33.18 early
## 855 2484 6 2 6.52 0 90.65 early
## 856 1890 2 3 6.20 1 102.68 early
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## 858 2028 2 4 6.06 1 37.61 early
## 859 1856 3 8 6.62 0 112.60 early
## 860 539 1 2 5.55 1 108.13 early
## 861 3003 3 2 6.44 0 138.90 early
## 862 390 0 1 6.71 2 140.86 early
## 863 2064 3 3 6.55 1 188.80 early
## 864 3034 3 2 5.99 1 127.47 early
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## 867 768 4 1 6.58 0 158.88 early
## 868 1380 4 3 6.83 2 44.20 early
## 869 1625 9 2 5.79 1 144.64 early
## 870 2340 1 4 6.27 2 102.98 early
## 871 656 1 1 6.67 1 252.73 peak
## 872 1260 1 0 7.29 0 186.77 peak
## 873 2010 7 1 8.10 0 292.34 peak
## 874 2088 6 0 6.73 0 77.62 decline
## 875 2046 3 0 6.41 0 159.81 decline
## 876 2706 5 4 6.67 0 153.52 early
## 877 2001 3 5 6.91 0 173.95 early
## 878 936 4 0 5.97 2 96.89 early
## 879 1320 1 1 6.58 1 196.06 early
## 880 2184 0 3 6.84 0 152.08 early
## 881 1525 2 0 6.69 0 198.13 early
## 882 1638 0 2 6.08 0 173.98 early
## 883 2387 6 1 5.80 1 74.54 early
## 884 440 1 0 6.69 0 115.92 early
## 885 3010 8 5 6.66 0 105.21 early
## 886 1386 4 4 6.09 1 72.26 early
## 887 1752 3 4 6.49 0 135.54 early
## 888 1260 3 1 6.55 0 197.96 early
## 889 860 2 1 6.39 0 67.77 early
## 890 748 0 1 5.81 1 109.59 early
## 891 2405 0 3 5.81 0 70.87 early
## 892 1848 0 3 6.04 2 153.36 early
## 893 837 0 2 7.05 1 142.56 early
## 894 2277 0 1 6.26 0 93.66 early
## 895 1530 2 1 5.53 2 129.62 early
## 896 609 2 0 6.74 1 148.46 early
## 897 1554 2 3 6.34 1 216.18 early
## 898 1725 3 4 6.38 1 38.33 early
## 899 3034 5 4 7.27 0 89.15 early
## 900 1449 2 4 6.51 1 48.02 early
## 901 759 0 1 6.40 1 177.60 early
## 902 1224 3 2 6.84 1 45.47 early
## 903 2046 7 4 6.28 0 148.71 early
## 904 2573 7 3 6.50 0 173.67 early
## 905 1462 2 0 6.42 1 138.25 early
## 906 2158 5 0 6.42 1 128.86 early
## 907 1148 2 1 6.72 0 182.78 early
## 908 1392 4 1 6.10 0 175.32 early
## 909 2001 5 3 6.85 1 221.34 early
## 910 1296 1 1 5.96 0 98.34 early
## 911 651 1 1 6.73 1 38.35 early
## 912 1972 6 4 6.20 0 153.61 early
## 913 1998 3 1 8.49 0 137.60 peak
## 914 2257 5 4 8.02 0 270.77 peak
## 915 1920 6 2 6.69 2 71.76 peak
## 916 2268 6 3 6.50 1 96.92 decline
## 917 1880 1 0 6.12 0 44.75 decline
## 918 2430 0 5 5.94 1 202.88 early
## 919 2312 0 1 6.04 2 167.49 early
## 920 510 0 2 5.83 0 108.84 early
## 921 704 0 1 6.26 0 196.97 early
## 922 2250 0 4 5.98 1 40.35 early
## 923 1980 0 2 6.55 1 246.23 early
## 924 385 0 0 6.85 0 245.63 early
## 925 1406 0 2 7.21 0 52.64 peak
## 926 1463 0 0 7.39 1 257.48 peak
## 927 420 0 1 7.11 1 56.30 peak
## 928 2225 0 3 6.54 0 191.65 decline
## 929 576 1 3 5.46 1 29.13 early
## 930 988 1 1 6.86 1 50.66 early
## 931 1008 0 4 7.04 0 161.96 early
## 932 1892 1 3 6.37 0 170.64 early
## 933 1206 5 2 5.88 1 156.36 early
## 934 1728 2 1 6.20 0 174.98 early
## 935 512 0 1 7.26 0 163.89 peak
## 936 2075 4 4 7.26 0 128.46 peak
## 937 2640 2 2 6.70 1 199.28 peak
## 938 630 0 1 5.82 2 108.78 decline
## 939 2754 3 8 6.10 1 78.05 decline
## 940 438 0 1 6.54 1 118.00 decline
## 941 1098 3 2 5.51 1 104.05 decline
## 942 1023 3 3 6.63 0 74.15 decline
## 943 1380 1 1 6.39 0 33.23 early
## 944 992 4 0 5.97 2 102.92 early
## 945 1904 2 2 5.50 1 132.93 early
## 946 1716 3 1 6.31 0 100.89 early
## 947 1767 3 0 6.69 1 107.31 early
## 948 938 4 2 6.62 2 198.64 peak
## 949 1909 3 1 7.59 0 241.97 peak
## 950 832 3 1 7.60 2 192.36 peak
## 951 2340 5 0 5.96 0 33.76 decline
## 952 682 2 1 6.33 0 36.50 decline
## 953 2160 7 5 5.80 2 55.27 decline
## 954 1008 2 1 6.12 0 209.03 decline
## 955 783 7 1 5.76 1 132.91 decline
## 956 310 0 0 6.49 0 83.06 early
## 957 2059 0 1 7.30 0 53.96 early
## 958 828 0 1 7.01 1 83.83 early
## 959 600 0 0 6.51 0 235.79 early
## 960 3094 0 6 6.54 0 208.38 early
## 961 2343 0 1 6.75 1 158.00 early
## 962 2250 0 3 8.06 1 69.34 peak
## 963 444 0 1 7.13 2 169.58 peak
## 964 1274 0 1 6.43 1 123.55 peak
## 965 1633 0 3 6.18 1 76.47 decline
## 966 2268 0 1 6.52 1 49.02 decline
## 967 574 0 0 6.33 2 171.38 early
## 968 2652 7 1 6.47 0 186.91 early
## 969 1792 5 0 6.63 0 259.75 early
## 970 468 1 0 5.55 1 119.64 early
## 971 1632 4 1 5.79 0 156.20 early
## 972 920 1 1 6.07 0 75.24 early
## 973 1920 7 3 7.49 0 59.16 early
## 974 1482 0 3 7.45 1 147.38 early
## 975 2046 5 4 6.51 0 200.09 peak
## 976 610 0 1 7.30 1 183.74 peak
## 977 355 0 0 7.22 0 214.14 peak
## 978 2844 9 4 7.23 0 92.01 decline
## 979 1449 2 3 6.28 0 81.14 decline
## 980 474 1 2 6.28 1 222.58 early
## 981 1024 5 3 6.14 1 148.88 early
## 982 1575 8 1 6.80 2 86.19 early
## 983 1780 3 1 6.81 0 123.55 early
## 984 1496 2 2 6.45 0 99.51 early
## 985 1050 2 2 6.15 1 87.85 early
## 986 804 0 1 6.77 0 46.65 early
## 987 2040 5 3 7.41 0 164.61 peak
## 988 2294 9 4 6.93 0 165.45 peak
## 989 2040 4 4 8.19 0 59.41 peak
## 990 2739 8 4 6.65 0 43.48 decline
## 991 1344 0 2 6.90 1 93.08 decline
## 992 1890 0 0 7.15 0 264.13 early
## 993 488 0 1 5.88 1 188.45 early
## 994 760 0 2 6.71 1 218.52 early
## 995 2262 0 3 6.34 0 110.34 early
## 996 1400 0 1 5.99 0 120.24 early
## 997 602 0 0 6.35 0 128.71 early
## 998 1615 0 2 6.34 0 163.50 early
## 999 595 0 1 6.16 0 119.86 early
## 1000 1403 0 2 5.55 2 67.77 early
## 1001 2812 0 3 6.53 0 241.34 early
## 1002 576 0 2 6.26 1 118.29 early
## 1003 819 3 0 6.89 0 206.54 early
## 1004 2112 3 1 7.25 0 150.85 early
## 1005 1404 3 2 5.92 1 33.25 early
## 1006 2668 2 3 5.77 0 69.19 early
## 1007 2430 5 2 6.88 0 283.98 early
## 1008 1690 3 3 6.08 1 87.41 early
## 1009 1036 5 2 5.90 1 163.12 early
## 1010 2924 10 1 6.48 1 109.56 early
## 1011 2100 8 1 5.28 1 121.29 early
## 1012 310 1 1 8.01 0 292.19 peak
## 1013 756 4 2 5.88 1 144.48 peak
## 1014 657 1 1 6.50 0 179.48 early
## 1015 2128 7 1 6.09 0 137.14 early
## 1016 1139 1 2 6.95 0 261.12 early
## 1017 1998 0 0 6.09 1 40.50 early
## 1018 405 1 1 5.75 0 174.16 early
## 1019 623 1 0 6.88 0 160.37 early
## 1020 1022 1 0 6.36 0 190.47 early
## 1021 576 1 2 6.74 0 102.39 early
## 1022 816 3 3 6.33 0 47.78 early
## 1023 1738 1 0 7.01 0 90.53 peak
## 1024 1170 2 2 6.72 1 53.82 peak
## 1025 1596 0 4 5.63 3 32.83 early
## 1026 2125 0 1 6.15 2 142.74 early
## 1027 1406 0 0 5.62 0 63.97 early
## 1028 2520 0 3 6.53 0 37.32 early
## 1029 2368 0 3 5.52 1 55.00 early
## 1030 1462 0 0 6.43 2 198.40 early
## 1031 2464 0 2 6.87 0 49.75 early
## 1032 792 0 0 6.98 0 136.98 early
## 1033 1008 0 1 7.33 1 46.11 peak
## 1034 444 0 1 7.40 0 142.59 peak
## 1035 2296 0 4 7.80 0 114.51 peak
## 1036 864 0 0 7.09 0 126.15 decline
## 1037 2241 1 0 6.41 0 95.94 early
## 1038 935 1 1 6.23 1 175.77 early
## 1039 2464 5 0 6.51 0 241.43 early
## 1040 609 3 0 5.62 1 32.27 early
## 1041 1480 2 0 7.14 0 51.02 early
## 1042 2482 5 4 7.41 0 196.41 peak
## 1043 952 1 2 7.26 0 119.11 peak
## 1044 2883 6 4 7.01 0 50.70 peak
## 1045 737 1 0 6.28 0 110.98 decline
## 1046 720 0 1 6.17 1 90.99 early
## 1047 1826 5 2 6.05 0 163.63 early
## 1048 1206 3 1 5.25 1 110.19 early
## 1049 900 2 2 6.06 0 72.43 early
## 1050 2000 3 3 6.58 1 161.90 early
## 1051 2820 7 5 6.22 0 43.46 early
## 1052 504 1 0 6.43 0 191.73 early
## 1053 2590 5 3 6.44 1 46.61 early
## 1054 2013 2 5 6.37 0 221.60 early
## 1055 1512 3 0 6.69 0 110.51 early
## 1056 897 3 1 6.17 1 112.19 early
## 1057 1932 5 4 6.04 0 158.29 early
## 1058 1008 1 2 6.58 2 78.79 early
## 1059 2392 4 2 6.83 0 262.54 early
## 1060 2730 0 3 6.97 1 98.81 early
## 1061 2479 8 3 7.07 0 167.30 peak
## 1062 1430 2 3 7.29 0 193.61 peak
## 1063 1564 3 3 6.81 0 148.04 peak
## 1064 728 0 2 6.87 0 146.84 decline
## 1065 2050 2 2 6.24 0 177.78 early
## 1066 1794 2 2 6.32 0 37.91 early
## 1067 1690 3 4 7.18 0 228.39 early
## 1068 2821 3 6 6.58 0 50.96 early
## 1069 1720 4 4 6.26 1 151.04 early
## 1070 1290 1 3 6.62 0 44.76 early
## 1071 2277 5 3 6.99 0 181.63 early
## 1072 1392 4 0 6.57 0 246.48 early
## 1073 2210 3 3 7.13 0 231.48 early
## 1074 2640 8 3 7.93 0 128.65 peak
## 1075 2322 3 2 6.56 0 237.37 early
## 1076 511 0 2 6.20 1 37.66 early
## 1077 420 1 0 6.11 0 59.76 early
## 1078 1140 0 1 5.54 1 51.24 early
## 1079 810 2 1 5.89 1 40.18 early
## 1080 2550 0 3 6.95 0 42.71 early
## 1081 1892 2 1 5.84 2 120.75 early
## 1082 560 1 1 6.76 0 97.00 early
## 1083 1001 2 1 7.39 0 60.44 early
## 1084 2100 5 4 6.69 1 128.37 early
## 1085 496 3 2 5.41 3 26.06 early
## 1086 1860 5 2 5.85 1 40.79 early
## 1087 1170 0 1 6.61 0 140.43 early
## 1088 2550 0 2 6.37 0 143.48 early
## 1089 1144 0 1 5.93 0 123.00 early
## 1090 3220 0 4 6.56 0 189.64 early
## 1091 497 0 1 6.78 0 49.59 peak
## 1092 902 0 0 7.20 0 269.32 peak
## 1093 2923 0 3 6.77 0 52.96 peak
## 1094 1152 0 2 5.75 1 63.57 early
## 1095 350 0 0 6.29 1 195.13 early
## 1096 2345 0 3 6.82 0 199.66 early
## 1097 2838 0 3 6.13 0 64.95 early
## 1098 747 0 2 5.36 2 117.19 early
## 1099 1380 0 2 7.13 1 114.15 early
## 1100 2250 0 1 6.41 0 176.16 early
## 1101 3240 0 2 6.53 0 74.38 early
## 1102 2175 0 5 6.40 1 99.58 early
## 1103 3010 7 3 5.58 1 95.05 early
## 1104 616 1 1 6.55 0 75.51 early
## 1105 2240 4 1 6.58 0 75.88 early
## 1106 441 2 1 6.13 0 159.38 early
## 1107 2701 8 4 5.68 1 66.81 early
## 1108 2460 4 1 6.64 0 170.91 early
## 1109 837 0 0 6.17 0 33.37 early
## 1110 1104 0 1 6.40 1 203.98 early
## 1111 1386 0 5 6.62 2 38.78 early
## 1112 444 0 1 5.99 1 186.27 early
## 1113 1872 0 3 5.64 2 104.04 early
## 1114 608 0 0 6.92 0 269.16 early
## 1115 1065 4 1 6.96 0 127.04 early
## 1116 2560 4 2 5.62 1 149.59 early
## 1117 2295 5 2 6.27 0 169.20 early
## 1118 630 2 1 5.97 1 97.81 early
## 1119 2730 8 1 6.30 1 78.43 early
## 1120 896 1 3 6.11 0 113.03 early
## 1121 693 2 1 5.92 0 144.33 early
## 1122 1472 2 1 7.23 1 124.46 early
## 1123 1278 2 1 6.13 2 64.24 early
## 1124 1974 6 2 6.98 0 269.48 early
## 1125 924 3 0 6.02 0 72.78 early
## 1126 2552 6 5 6.36 2 133.48 early
## 1127 1911 0 4 7.33 0 145.00 early
## 1128 480 0 1 6.43 1 38.97 early
## 1129 748 1 0 7.66 0 234.99 peak
## 1130 2511 10 1 7.39 1 64.11 peak
## 1131 2070 2 5 6.91 0 225.99 peak
## 1132 1020 3 2 5.77 1 32.26 decline
## 1133 1480 3 2 6.69 0 225.98 decline
## 1134 3384 4 3 6.88 1 92.95 decline
## 1135 1014 2 4 6.61 0 219.13 decline
## 1136 2016 6 2 6.14 0 121.16 decline
## 1137 2706 3 4 5.94 0 113.24 decline
## 1138 1168 4 1 6.74 0 77.92 early
## 1139 525 2 0 5.97 0 81.42 early
## 1140 1525 2 2 6.53 1 203.92 early
## 1141 792 2 2 6.56 1 251.07 early
## 1142 1729 3 4 5.31 0 123.12 early
## 1143 1978 5 2 6.28 1 182.22 early
## 1144 1470 3 2 6.24 0 136.15 early
## 1145 2025 4 4 6.69 0 198.45 early
## 1146 1020 3 2 6.82 0 134.98 peak
## 1147 670 1 0 6.87 1 136.46 peak
## 1148 2240 5 3 7.43 1 63.63 peak
## 1149 1694 1 3 6.28 2 98.75 early
## 1150 1035 3 0 5.88 0 121.14 early
## 1151 2923 3 5 6.27 0 110.90 early
## 1152 1152 4 3 6.78 0 146.41 early
## 1153 3132 6 2 6.94 0 56.91 early
## 1154 935 2 1 6.36 0 234.19 early
## 1155 1350 0 2 5.22 0 118.31 early
## 1156 1300 3 1 6.97 1 39.82 peak
## 1157 410 1 1 6.86 1 104.09 peak
## 1158 468 0 2 6.79 0 126.51 peak
## 1159 2412 3 5 6.54 0 78.86 decline
## 1160 2520 3 5 5.78 2 39.16 early
## 1161 3168 5 2 6.25 1 222.78 early
## 1162 1564 3 4 6.39 1 235.60 early
## 1163 2028 4 6 7.07 0 173.87 early
## 1164 3071 6 3 6.27 1 104.90 early
## 1165 825 5 0 6.72 1 48.29 early
## 1166 1260 3 1 6.23 0 156.66 early
## 1167 1200 3 2 7.08 2 181.94 early
## 1168 1343 3 1 7.02 0 175.67 peak
## 1169 2775 10 4 6.68 1 189.38 peak
## 1170 1809 2 3 8.17 0 291.98 peak
## 1171 2436 3 2 6.75 0 37.80 decline
## 1172 3367 2 1 5.65 0 109.20 decline
## 1173 712 1 1 6.65 2 39.68 early
## 1174 1728 3 2 6.16 0 33.87 early
## 1175 1400 2 1 6.39 2 154.61 early
## 1176 592 5 1 6.73 0 147.88 early
## 1177 2160 7 3 5.37 1 111.13 early
## 1178 1440 8 2 6.12 0 190.71 early
## 1179 1183 3 1 6.54 0 175.35 early
## 1180 1104 2 2 7.30 0 94.70 peak
## 1181 2418 6 0 7.32 1 186.79 peak
## 1182 2100 4 5 7.64 1 338.73 peak
## 1183 2576 4 2 7.09 0 49.29 decline
## 1184 2350 4 1 6.86 0 157.46 decline
## 1185 2464 7 3 6.59 0 249.25 decline
## 1186 2686 4 2 6.79 1 183.60 decline
## 1187 891 1 0 6.23 1 200.67 early
## 1188 1302 4 0 6.14 0 98.49 early
## 1189 945 2 2 6.29 1 91.81 early
## 1190 792 0 0 7.35 0 98.10 early
## 1191 558 0 0 6.22 2 103.38 early
## 1192 1870 4 2 6.03 1 131.49 early
## 1193 2345 5 8 6.68 0 228.16 early
## 1194 2496 3 3 8.05 0 66.42 peak
## 1195 2184 4 0 6.60 0 112.60 peak
## 1196 1040 1 0 6.66 2 40.66 peak
## 1197 2190 3 5 6.30 0 44.88 decline
## 1198 2844 5 0 5.43 3 89.88 decline
## 1199 2738 6 5 6.55 0 109.67 decline
## 1200 1896 4 5 6.85 0 261.07 decline
## 1201 1120 6 3 6.84 1 43.50 early
## 1202 2112 6 3 7.06 0 238.40 early
## 1203 1323 1 2 6.18 1 116.99 early
## 1204 1488 3 2 6.67 0 41.96 early
## 1205 1701 0 4 6.30 1 83.86 early
## 1206 2548 6 5 6.34 0 179.18 early
## 1207 1958 1 4 5.70 1 146.44 early
## 1208 2277 0 2 6.85 0 52.22 early
## 1209 2236 0 3 6.52 2 116.61 early
## 1210 660 0 0 5.85 1 83.00 early
## 1211 518 0 0 5.81 0 108.96 early
## 1212 2448 0 2 5.94 0 75.28 early
## 1213 1368 0 0 5.50 1 61.45 early
## 1214 1460 0 3 5.85 0 121.70 early
## 1215 1106 0 1 6.50 0 111.25 early
## 1216 871 0 0 6.45 1 84.84 early
## 1217 410 0 1 6.76 2 212.52 early
## 1218 1260 0 1 5.51 1 142.22 early
## 1219 402 2 0 5.61 1 32.10 early
## 1220 1080 2 1 6.50 0 174.87 early
## 1221 2541 5 1 5.38 1 65.30 early
## 1222 871 3 3 6.62 0 232.87 early
## 1223 1680 4 2 6.93 0 51.30 early
## 1224 720 4 0 6.73 1 101.28 early
## 1225 585 1 2 6.36 0 234.28 early
## 1226 1581 2 0 6.58 0 116.89 early
## 1227 920 0 1 6.27 0 33.19 early
## 1228 564 0 0 5.65 1 123.79 early
## 1229 1740 5 0 6.74 1 144.00 peak
## 1230 2200 6 4 7.30 1 183.88 peak
## 1231 624 1 0 6.97 1 189.01 peak
## 1232 2233 3 3 7.31 0 190.44 early
## 1233 1980 3 4 6.37 1 174.51 early
## 1234 884 2 1 6.22 0 34.06 early
## 1235 2450 7 2 7.09 0 163.29 early
## 1236 2325 4 2 6.13 2 33.60 early
## 1237 2016 0 1 6.97 1 233.21 early
## 1238 1914 7 2 5.91 1 98.14 early
## 1239 2555 7 1 5.82 1 197.28 early
## 1240 1520 0 2 5.73 0 85.92 early
## 1241 1024 0 1 6.41 0 142.88 early
## 1242 462 0 0 5.77 3 27.93 early
## 1243 924 0 2 5.89 0 82.11 early
## 1244 3185 0 4 5.66 1 67.17 early
## 1245 1690 0 2 6.29 0 32.51 early
## 1246 1472 0 3 6.36 0 133.72 early
## 1247 2296 0 1 6.49 0 69.88 peak
## 1248 552 0 1 7.30 1 308.47 peak
## 1249 1728 2 3 6.76 0 118.17 early
## 1250 1564 1 1 5.59 2 129.45 early
## 1251 1512 4 1 5.17 1 31.04 early
## 1252 2010 2 2 7.27 1 186.60 early
## 1253 2852 4 4 5.94 0 159.82 early
## 1254 2184 2 3 6.13 0 77.78 early
## 1255 455 0 1 6.08 1 119.26 early
## 1256 3024 3 2 5.75 0 134.44 early
## 1257 2232 4 2 7.08 1 59.11 early
## 1258 3219 5 5 7.08 1 199.26 peak
## 1259 2418 2 3 7.83 0 172.95 peak
## 1260 730 0 1 6.47 1 236.58 early
## 1261 2952 0 4 6.26 0 186.40 early
## 1262 540 0 0 6.29 1 188.07 early
## 1263 1932 0 2 7.26 1 44.55 early
## 1264 539 0 1 6.45 0 37.52 early
## 1265 2436 0 5 5.91 0 76.39 early
## 1266 1610 0 5 6.41 1 136.06 early
## 1267 2494 0 1 6.43 0 222.49 early
## 1268 900 0 0 6.47 0 113.81 early
## 1269 976 0 2 6.73 1 128.41 early
## 1270 896 0 5 6.01 2 131.80 early
## 1271 2449 5 7 5.25 1 26.22 early
## 1272 1722 3 3 5.16 1 144.62 early
## 1273 1148 2 0 7.47 0 131.42 early
## 1274 1680 4 4 5.83 1 93.68 early
## 1275 1984 2 4 6.45 1 162.56 early
## 1276 2000 4 3 6.49 0 171.57 early
## 1277 806 0 2 6.83 0 207.28 early
## 1278 900 3 0 7.25 1 45.72 peak
## 1279 1675 2 1 6.55 0 179.46 early
## 1280 1764 1 3 6.40 1 35.31 early
## 1281 1560 3 3 5.75 1 150.85 early
## 1282 2592 4 3 6.09 1 199.47 early
## 1283 2444 8 1 6.53 1 39.08 early
## 1284 1600 6 5 5.95 0 150.84 early
## 1285 2686 0 2 6.20 0 42.96 early
## 1286 2047 4 6 8.23 0 293.44 peak
## 1287 1800 6 1 6.26 1 46.86 peak
## 1288 840 0 1 6.48 2 84.40 peak
## 1289 2686 5 5 6.77 0 161.94 decline
## 1290 549 1 1 6.23 1 222.70 early
## 1291 568 0 0 5.78 1 75.96 early
## 1292 2640 5 1 6.63 0 40.48 early
## 1293 1309 2 1 6.35 1 128.38 early
## 1294 2664 6 5 5.33 0 78.43 early
## 1295 1134 3 1 7.23 1 182.42 peak
## 1296 2257 7 4 7.22 1 179.85 peak
## 1297 726 1 1 6.10 0 151.42 early
## 1298 1320 0 2 5.59 0 55.38 early
## 1299 816 3 1 6.26 0 219.27 early
## 1300 632 2 0 5.19 1 138.52 early
## 1301 1980 4 1 5.92 0 134.92 early
## 1302 1275 1 3 6.04 0 105.39 early
## 1303 2244 5 5 7.36 1 118.14 peak
## 1304 816 0 1 7.22 1 87.78 peak
## 1305 1140 5 2 8.26 0 138.81 peak
## 1306 1625 3 7 6.52 0 101.72 decline
## 1307 2204 4 3 7.38 0 116.34 early
## 1308 2800 8 3 6.82 0 46.77 early
## 1309 385 3 0 7.18 0 58.97 early
## 1310 1200 1 0 7.35 0 206.91 early
## 1311 1525 4 0 6.50 1 139.16 early
## 1312 366 1 1 6.85 1 104.52 early
## 1313 1012 3 1 5.90 1 74.00 early
## 1314 1392 3 2 6.99 1 80.45 early
## 1315 448 0 1 6.54 0 37.51 early
## 1316 3196 7 5 7.83 0 174.84 early
## 1317 2904 3 5 6.19 0 145.67 early
## 1318 1148 4 1 6.47 2 109.43 early
## 1319 2002 7 4 5.92 0 29.03 early
## 1320 1430 5 3 6.29 0 103.00 peak
## 1321 1672 8 4 7.06 0 201.30 peak
## 1322 1600 4 0 7.01 1 40.63 peak
## 1323 1729 3 2 6.57 1 91.04 decline
## 1324 1780 4 5 6.66 1 202.84 decline
## 1325 3162 4 2 6.43 1 48.60 decline
## 1326 1425 6 4 6.97 1 221.71 decline
## 1327 2349 3 4 6.07 1 85.55 early
## 1328 3276 1 7 6.53 0 137.76 early
## 1329 581 2 0 6.41 0 73.93 early
## 1330 480 1 1 5.48 2 76.84 early
## 1331 1729 7 3 6.86 0 46.07 early
## 1332 498 0 1 6.95 2 79.52 early
## 1333 2408 5 1 6.82 1 80.86 peak
## 1334 1012 2 1 7.62 1 308.76 peak
## 1335 1235 5 5 7.44 0 241.94 peak
## 1336 2340 8 4 5.83 2 35.20 decline
## 1337 594 0 1 5.88 1 73.14 decline
## 1338 3024 4 3 5.81 1 138.38 decline
## 1339 2697 1 6 7.22 0 167.37 decline
## 1340 546 0 1 6.67 1 261.05 early
## 1341 1144 0 2 6.29 0 71.32 early
## 1342 3196 0 1 6.14 0 142.13 early
## 1343 2156 0 3 5.63 0 56.36 early
## 1344 2145 0 4 6.67 0 228.05 early
## 1345 2492 0 5 6.78 1 272.21 early
## 1346 910 0 3 7.03 0 169.97 early
## 1347 2765 0 7 6.89 1 166.04 early
## 1348 1932 0 2 7.19 1 52.59 early
## 1349 468 2 0 5.96 0 142.29 early
## 1350 1005 1 1 6.85 0 154.01 early
## 1351 704 0 1 6.35 1 165.71 early
## 1352 1672 6 1 6.43 0 133.31 early
## 1353 595 2 0 6.72 0 117.64 early
## 1354 1110 5 0 6.30 0 168.26 early
## 1355 1064 1 0 6.06 0 158.66 early
## 1356 588 1 1 6.93 0 197.06 early
## 1357 469 1 0 8.19 1 213.83 peak
## 1358 532 0 1 7.09 1 286.10 peak
## 1359 845 1 1 5.84 0 185.51 early
## 1360 1242 2 1 6.80 0 104.29 early
## 1361 1292 3 0 6.70 1 214.98 early
## 1362 781 2 1 5.95 2 169.97 early
## 1363 837 2 0 6.76 1 95.50 early
## 1364 1800 1 1 6.04 0 199.56 early
## 1365 639 1 1 6.75 0 139.01 peak
## 1366 1512 2 3 7.14 2 178.90 peak
## 1367 425 0 0 5.93 0 39.56 early
## 1368 1950 0 1 6.95 0 219.07 early
## 1369 396 0 1 6.60 1 40.69 early
## 1370 1098 0 4 5.88 1 89.53 early
## 1371 2378 0 4 6.39 0 170.70 early
## 1372 1410 0 1 6.58 2 218.89 early
## 1373 3132 0 4 6.29 0 164.69 early
## 1374 670 0 3 6.35 1 181.90 early
## 1375 2079 0 4 6.05 0 163.95 peak
## 1376 400 0 0 7.34 2 98.95 peak
## 1377 1027 0 0 7.31 1 99.38 peak
## 1378 1050 0 1 6.44 0 196.04 decline
## 1379 1584 0 0 6.46 1 179.45 decline
## 1380 806 0 1 6.62 1 36.59 decline
## 1381 518 1 1 5.70 2 151.75 early
## 1382 803 2 0 6.04 0 42.34 early
## 1383 1280 7 3 6.55 1 247.41 early
## 1384 608 3 2 6.44 1 92.13 early
## 1385 2592 3 4 7.35 0 175.51 early
## 1386 657 0 2 7.36 2 120.58 early
## 1387 1909 3 1 6.56 0 86.23 early
## 1388 1650 4 2 6.18 1 102.13 early
## 1389 828 1 0 5.95 1 37.69 early
## 1390 1562 6 1 7.65 1 66.34 peak
## 1391 1848 1 5 7.28 1 158.91 peak
## 1392 414 0 0 6.58 1 157.73 early
## 1393 1674 0 0 6.99 0 125.89 early
## 1394 3367 0 3 5.62 2 31.46 early
## 1395 385 0 1 7.23 0 53.78 early
## 1396 2100 0 5 7.58 2 56.87 early
## 1397 819 0 4 6.64 0 264.82 early
## 1398 2520 0 2 7.96 0 223.78 peak
## 1399 744 0 1 7.42 1 167.44 peak
## 1400 496 0 2 7.50 0 192.51 peak
## 1401 1120 0 0 7.62 0 230.28 decline
## 1402 595 0 0 6.70 1 43.74 decline
## 1403 1560 0 3 5.62 2 128.65 decline
## 1404 576 0 0 6.96 0 192.75 decline
## 1405 1860 7 2 5.83 0 104.12 early
## 1406 1428 0 2 6.79 0 42.11 early
## 1407 2208 6 2 5.86 1 122.06 early
## 1408 1242 4 2 6.13 0 73.59 early
## 1409 2584 8 3 5.41 2 119.86 early
## 1410 1440 5 0 6.14 1 39.60 early
## 1411 1520 4 1 6.99 0 58.56 early
## 1412 1794 1 2 6.69 0 211.79 early
## 1413 910 0 0 6.85 0 52.35 early
## 1414 1740 4 3 6.67 1 37.51 early
## 1415 1246 1 1 5.59 2 50.94 early
## 1416 1365 1 3 5.90 1 120.62 early
## 1417 938 2 3 6.26 2 37.21 early
## 1418 945 2 2 6.73 1 229.14 early
## 1419 600 2 1 5.00 0 41.55 early
## 1420 2610 2 1 5.88 1 35.12 early
## 1421 2324 0 1 5.90 0 30.18 early
## 1422 828 0 0 6.49 0 145.08 early
## 1423 1035 0 3 7.23 0 157.35 early
## 1424 2484 0 3 7.34 0 287.13 early
## 1425 2937 0 4 6.96 0 172.10 early
## 1426 2430 9 5 5.80 1 180.14 early
## 1427 1501 0 1 6.58 1 147.54 early
## 1428 616 2 0 6.93 0 213.23 early
## 1429 1875 8 3 6.11 1 160.24 early
## 1430 2170 11 3 5.29 1 113.66 early
## 1431 1738 3 0 6.26 1 219.93 early
## 1432 900 5 0 6.73 1 162.70 early
## 1433 693 3 1 6.11 0 169.53 early
## 1434 1144 2 0 7.14 0 249.02 peak
## 1435 2430 2 5 7.27 1 313.47 peak
## 1436 752 0 2 7.35 0 311.44 peak
## 1437 975 1 2 6.51 0 85.26 decline
## 1438 497 0 1 6.36 1 179.84 decline
## 1439 2001 3 3 5.88 1 141.82 early
## 1440 1530 1 0 6.01 1 34.19 early
## 1441 2000 5 3 6.01 0 125.08 early
## 1442 2244 6 5 6.83 1 267.31 early
## 1443 1456 3 0 6.82 2 99.02 early
## 1444 732 0 1 6.42 1 177.18 early
## 1445 1222 1 2 7.08 0 153.41 early
## 1446 1539 1 1 6.46 0 209.54 early
## 1447 726 0 2 6.39 0 86.67 early
## 1448 1190 2 1 7.01 0 95.14 early
## 1449 3108 5 4 5.90 0 141.81 early
## 1450 2356 6 7 8.02 0 74.41 peak
## 1451 1950 5 4 7.71 0 256.25 peak
## 1452 595 0 0 6.79 1 92.76 early
## 1453 595 0 1 6.95 0 167.32 early
## 1454 2175 0 1 6.96 0 218.73 early
## 1455 870 0 2 6.28 0 165.13 early
## 1456 378 0 0 6.79 1 180.30 early
## 1457 460 0 0 5.89 1 36.06 early
## 1458 2730 0 3 6.17 2 167.55 early
## 1459 1760 0 2 6.57 1 201.63 early
## 1460 2175 4 2 5.80 1 166.09 early
## 1461 2528 6 3 6.94 0 99.19 early
## 1462 3096 8 4 6.43 0 131.25 early
## 1463 657 3 3 5.93 0 70.29 early
## 1464 632 1 0 6.87 0 88.54 early
## 1465 3182 7 3 7.01 0 54.71 early
## 1466 715 2 3 6.04 0 43.68 early
## 1467 2905 12 1 8.62 0 312.76 peak
## 1468 2046 2 2 7.33 0 221.48 peak
## 1469 3008 5 0 6.64 1 224.37 peak
## 1470 1344 1 3 5.68 1 117.70 decline
## 1471 2511 7 4 5.80 1 171.60 decline
## 1472 2190 2 1 6.82 1 162.67 decline
## 1473 2520 11 6 6.68 2 39.58 early
## 1474 1377 5 3 6.43 0 135.85 early
## 1475 1716 5 3 7.03 0 175.86 early
## 1476 3441 5 6 6.24 0 94.84 early
## 1477 801 0 2 6.47 1 104.01 early
## 1478 1209 2 1 6.04 2 146.04 early
## 1479 620 1 1 6.14 0 167.92 early
## 1480 1564 3 1 6.84 0 218.94 early
## 1481 584 0 1 6.07 1 149.45 early
## 1482 2077 8 7 6.03 0 122.82 early
## 1483 2268 1 5 6.62 0 112.73 early
## 1484 1104 4 4 6.43 0 47.62 early
## 1485 300 1 0 6.25 0 125.42 early
## 1486 1207 1 2 6.74 0 235.81 peak
## 1487 2652 5 2 7.10 0 142.16 peak
## 1488 720 1 1 7.27 0 42.44 peak
## 1489 1452 5 2 6.82 1 246.01 decline
## 1490 2106 3 4 5.88 0 106.40 decline
## 1491 2232 4 4 6.61 0 70.15 early
## 1492 1500 5 2 7.15 0 301.17 early
## 1493 2952 5 2 6.76 1 197.41 early
## 1494 1406 1 4 6.10 1 168.92 early
## 1495 1914 3 3 6.39 0 98.67 early
## 1496 2706 7 3 6.09 0 153.88 early
## 1497 1449 3 2 6.68 0 112.84 early
## 1498 2268 2 5 6.40 1 84.14 early
## 1499 3256 4 3 6.49 0 201.67 early
## 1500 1302 5 0 7.65 0 241.57 peak
## 1501 888 0 2 6.95 2 102.58 early
## 1502 2190 7 2 6.06 1 99.90 early
## 1503 936 0 2 6.04 1 133.41 early
## 1504 1207 3 2 5.27 1 61.12 early
## 1505 2028 7 4 7.46 2 289.49 early
## 1506 2204 4 2 7.11 2 102.13 early
## 1507 1558 3 0 6.17 0 77.80 early
## 1508 756 3 2 7.35 1 176.08 early
## 1509 1080 3 6 6.24 1 208.89 peak
## 1510 3290 8 1 7.26 1 114.49 peak
## 1511 850 1 1 6.89 1 283.98 peak
## 1512 750 1 1 5.85 2 71.25 decline
## 1513 820 0 1 7.60 0 138.44 early
## 1514 1349 0 2 6.62 1 75.03 early
## 1515 1036 0 1 6.37 0 194.78 early
## 1516 2291 0 2 6.01 0 126.66 early
## 1517 2592 0 1 6.90 0 48.78 early
## 1518 2450 0 2 7.05 1 93.24 peak
## 1519 1634 0 3 8.19 0 223.74 peak
## 1520 1035 0 1 7.30 0 51.43 peak
## 1521 726 0 2 6.61 0 98.63 decline
## 1522 2635 0 1 6.74 1 197.83 decline
## 1523 2988 0 3 6.64 1 83.27 decline
## 1524 1407 0 3 6.30 0 223.32 decline
## 1525 375 0 0 6.87 1 52.64 decline
## 1526 1891 6 3 6.77 0 168.39 early
## 1527 819 1 1 6.25 1 214.47 early
## 1528 972 0 4 6.48 0 162.33 early
## 1529 748 4 0 6.97 1 130.19 early
## 1530 1020 2 1 5.51 0 62.41 early
## 1531 1080 1 5 7.49 0 320.12 peak
## 1532 1323 3 3 7.05 0 161.72 peak
## 1533 1340 1 1 6.21 2 174.62 peak
## 1534 600 0 0 6.35 1 41.50 decline
## 1535 1700 0 3 5.51 1 58.46 early
## 1536 522 0 0 5.70 0 26.95 early
## 1537 2520 0 2 5.52 3 72.32 early
## 1538 2784 0 6 6.03 0 184.16 early
## 1539 2937 0 3 6.59 0 103.03 early
## 1540 924 2 0 6.37 1 93.89 early
## 1541 1520 4 0 6.77 2 153.83 early
## 1542 994 1 0 6.35 3 35.43 early
## 1543 1562 3 2 6.44 0 86.75 early
## 1544 756 1 1 7.17 2 117.62 peak
## 1545 480 0 2 7.21 0 120.21 peak
## 1546 2905 7 3 7.16 0 152.01 peak
## 1547 1190 3 0 7.18 0 44.79 early
## 1548 1024 4 1 6.14 0 65.94 early
## 1549 2250 7 4 6.35 0 165.79 early
## 1550 1860 1 2 6.75 1 146.85 early
## 1551 938 2 0 7.33 0 159.73 early
## 1552 1898 3 2 7.15 0 189.07 early
## 1553 445 1 1 5.00 0 96.41 early
## 1554 512 1 0 6.41 1 132.40 early
## 1555 1404 3 1 7.12 1 101.75 early
## 1556 1980 6 2 7.84 1 228.93 peak
## 1557 1365 2 1 6.45 0 80.50 peak
## 1558 1920 7 6 7.41 1 65.60 peak
## 1559 1136 2 2 5.65 0 26.29 decline
## 1560 1971 6 1 6.47 0 67.62 decline
## 1561 1080 0 1 7.12 0 222.80 early
## 1562 2480 4 2 6.51 1 126.86 early
## 1563 2324 4 3 6.98 0 261.47 early
## 1564 1260 3 4 6.47 0 153.28 early
## 1565 682 1 1 6.58 0 153.34 peak
## 1566 860 1 1 8.10 1 387.81 peak
## 1567 1035 5 0 6.37 1 92.40 peak
## 1568 1554 1 2 5.00 2 83.03 decline
## 1569 1596 4 1 6.19 1 76.69 decline
## 1570 3008 2 1 6.41 0 190.50 decline
## 1571 1248 1 1 6.38 0 170.58 decline
## 1572 2310 4 7 6.09 1 125.75 decline
## 1573 1806 5 1 6.81 0 96.68 early
## 1574 1806 6 2 6.52 0 232.46 early
## 1575 768 2 2 6.43 0 177.98 early
## 1576 2700 7 3 6.36 0 34.97 early
## 1577 2201 4 2 6.96 1 129.48 early
## 1578 1679 1 3 6.84 0 110.22 early
## 1579 2079 4 0 7.40 0 231.45 peak
## 1580 1403 4 2 7.16 0 116.87 peak
## 1581 1820 3 3 7.43 0 53.80 peak
## 1582 1540 1 3 6.81 0 142.65 decline
## 1583 609 0 0 5.89 0 180.36 early
## 1584 902 1 0 6.98 0 223.29 early
## 1585 568 2 1 6.79 0 80.89 early
## 1586 2295 8 3 6.56 0 95.68 early
## 1587 2040 5 2 6.24 0 96.49 early
## 1588 512 1 1 6.95 0 51.93 early
## 1589 1386 4 0 6.62 0 153.70 peak
## 1590 2080 7 3 7.48 1 185.00 peak
## 1591 3330 4 6 7.01 0 265.95 peak
## 1592 1782 6 2 6.18 0 189.75 decline
## 1593 1034 1 2 6.16 0 109.92 decline
## 1594 2133 7 1 5.22 2 89.41 decline
## 1595 1185 3 4 6.64 1 84.25 early
## 1596 2784 7 7 6.00 1 30.08 early
## 1597 792 3 1 6.64 0 186.07 early
## 1598 430 2 0 6.21 1 128.17 early
## 1599 474 0 2 6.65 0 149.46 early
## 1600 3196 6 3 6.31 1 171.76 early
## 1601 975 3 1 6.77 0 47.78 early
## 1602 1403 4 4 7.22 0 179.88 early
## 1603 1740 1 3 7.60 0 97.60 peak
## 1604 650 2 0 6.71 2 153.88 peak
## 1605 1530 1 1 7.15 0 99.48 peak
## 1606 1755 4 2 6.81 2 238.65 decline
## 1607 567 1 0 6.66 1 44.05 decline
## 1608 1800 4 3 6.13 0 74.19 early
## 1609 1562 4 3 6.43 1 193.09 early
## 1610 1512 2 1 6.15 0 121.13 early
## 1611 1680 4 1 6.39 0 151.57 early
## 1612 462 0 0 6.84 2 79.35 early
## 1613 1890 2 1 7.99 1 280.65 peak
## 1614 768 2 1 8.02 0 108.47 peak
## 1615 1875 5 5 8.09 0 301.39 peak
## 1616 1220 4 3 5.70 1 189.43 early
## 1617 1584 3 0 7.59 0 249.53 early
## 1618 1170 1 3 5.75 2 192.92 early
## 1619 847 2 0 5.87 1 29.57 early
## 1620 1794 4 1 7.00 1 99.28 early
## 1621 1584 4 1 6.11 0 165.57 early
## 1622 1856 6 4 6.81 0 88.63 early
## 1623 770 3 1 7.10 0 210.99 early
## 1624 2465 1 8 6.70 0 106.75 early
## 1625 2304 4 2 7.10 0 101.73 early
## 1626 1222 0 1 6.06 0 131.94 early
## 1627 2349 0 1 6.72 0 140.37 early
## 1628 696 0 2 6.31 1 206.95 early
## 1629 2760 0 2 6.23 0 39.48 early
## 1630 1560 0 1 5.07 1 84.38 early
## 1631 576 0 2 5.84 1 158.35 early
## 1632 1394 0 3 5.65 0 155.33 early
## 1633 2211 0 2 6.91 1 223.63 early
## 1634 558 0 1 6.24 1 118.95 early
## 1635 1122 0 2 5.30 0 45.85 early
## 1636 1458 0 6 7.35 0 103.85 early
## 1637 1547 0 0 6.43 1 38.90 peak
## 1638 1053 0 2 6.71 0 152.83 peak
## 1639 3220 0 4 7.18 0 218.50 peak
## 1640 486 1 0 6.31 1 69.22 early
## 1641 2068 5 2 5.94 2 39.69 early
## 1642 584 0 0 7.59 0 61.94 early
## 1643 2256 2 3 6.43 2 98.93 early
## 1644 1978 4 3 5.30 2 29.79 early
## 1645 1440 3 2 6.26 3 76.25 early
## 1646 2184 2 6 7.04 1 159.78 peak
## 1647 728 1 0 7.49 0 135.67 peak
## 1648 836 1 3 6.05 2 117.29 early
## 1649 1512 3 2 6.61 0 40.80 early
## 1650 1848 2 1 7.32 0 153.85 early
## 1651 1729 8 0 6.14 2 45.10 early
## 1652 820 2 3 6.77 0 114.79 early
## 1653 976 3 1 6.50 2 118.43 early
## 1654 781 0 1 6.11 2 79.73 early
## 1655 2816 4 4 8.06 1 211.53 peak
## 1656 2624 7 3 7.27 0 110.01 peak
## 1657 2387 3 3 7.54 2 229.43 peak
## 1658 1917 4 5 5.82 1 86.04 decline
## 1659 390 0 2 5.56 1 94.75 early
## 1660 780 0 1 6.60 0 72.65 early
## 1661 1071 0 2 5.63 1 165.30 early
## 1662 2250 0 4 5.96 0 211.18 early
## 1663 845 0 2 5.83 2 203.70 early
## 1664 1672 0 1 6.39 0 161.51 early
## 1665 378 1 1 6.27 1 120.86 early
## 1666 1920 2 1 6.77 0 44.43 early
## 1667 2025 5 3 7.29 1 172.38 early
## 1668 1330 2 2 6.10 0 163.03 early
## 1669 1170 4 1 7.19 1 185.90 early
## 1670 2368 5 4 6.78 0 54.93 early
## 1671 378 0 0 7.02 1 181.96 peak
## 1672 1462 6 0 6.14 1 154.99 early
## 1673 1584 4 2 7.29 0 172.66 early
## 1674 1606 5 3 5.22 2 106.93 early
## 1675 1105 1 3 5.96 1 200.83 early
## 1676 836 2 1 6.04 1 34.07 early
## 1677 1222 0 0 6.07 1 86.58 early
## 1678 630 0 0 6.49 1 150.04 early
## 1679 402 0 1 6.32 1 165.97 early
## 1680 1050 0 1 6.28 0 123.61 early
## 1681 2075 0 3 6.81 0 130.05 peak
## 1682 1460 0 3 7.06 0 228.16 peak
## 1683 2520 0 1 7.16 1 41.92 peak
## 1684 414 0 1 6.33 0 69.57 decline
## 1685 2142 0 6 5.91 0 31.46 decline
## 1686 2625 0 4 6.53 1 96.67 early
## 1687 759 0 0 6.80 0 189.78 early
## 1688 2550 0 3 7.23 0 244.30 early
## 1689 840 0 2 7.52 0 199.95 early
## 1690 740 0 3 6.91 0 196.72 early
## 1691 1120 0 3 5.99 1 205.85 early
## 1692 828 0 0 5.41 1 134.07 early
## 1693 465 0 2 6.69 2 244.81 early
## 1694 450 0 1 5.86 1 153.24 early
## 1695 1550 0 1 6.27 2 166.32 early
## 1696 915 0 1 6.35 0 164.39 early
## 1697 696 0 1 6.37 1 192.27 early
## 1698 2541 0 4 6.39 1 131.56 early
## 1699 1548 0 1 7.19 0 298.65 peak
## 1700 2079 0 2 7.32 1 50.27 peak
## 1701 1183 0 1 6.27 2 188.04 peak
## 1702 2025 0 2 6.45 1 140.29 decline
## 1703 3128 6 1 6.57 1 92.93 early
## 1704 1178 2 1 6.33 0 102.20 early
## 1705 2000 3 2 6.92 1 275.50 early
## 1706 2075 3 5 6.34 2 183.27 early
## 1707 2184 4 1 7.00 1 127.80 early
## 1708 1066 1 1 6.17 0 120.98 early
## 1709 1638 6 5 6.54 1 134.21 early
## 1710 1950 5 3 6.06 1 85.92 early
## 1711 450 0 0 6.47 1 77.52 early
## 1712 469 0 2 6.71 0 203.85 early
## 1713 2632 6 1 6.27 1 75.19 early
## 1714 1679 4 2 6.58 0 173.95 early
## 1715 2205 9 4 6.35 0 40.92 early
## 1716 2492 1 2 5.85 2 181.41 early
## 1717 790 1 0 6.58 2 40.65 early
## 1718 1540 1 3 6.52 0 231.74 early
## 1719 984 0 0 6.14 1 201.96 early
## 1720 603 0 2 7.18 1 61.64 early
## 1721 2997 0 1 5.76 1 142.63 early
## 1722 876 0 0 7.76 0 122.88 early
## 1723 1464 0 1 6.54 0 47.06 early
## 1724 1320 0 2 6.20 0 39.08 early
## 1725 781 0 3 6.28 0 158.71 early
## 1726 1309 0 2 7.19 1 250.69 early
## 1727 1260 0 3 6.44 1 118.89 early
## 1728 2025 0 3 6.29 0 134.53 early
## 1729 420 0 0 6.08 0 131.26 early
## 1730 2064 0 3 6.08 1 146.36 early
## 1731 504 0 2 5.71 0 156.13 early
## 1732 1659 0 1 7.21 0 249.81 peak
## 1733 1034 0 0 7.74 1 159.96 peak
## 1734 2739 0 2 7.36 0 317.52 peak
## 1735 602 0 1 7.08 0 50.45 early
## 1736 730 0 0 7.30 0 252.34 early
## 1737 1328 0 2 6.62 0 48.92 early
## 1738 1470 0 3 6.22 0 164.50 early
## 1739 616 0 0 6.30 2 130.33 early
## 1740 1232 0 1 6.75 1 91.35 early
## 1741 1050 0 1 7.86 0 127.73 peak
## 1742 3290 0 2 6.80 1 271.06 peak
## 1743 825 0 2 7.21 2 173.99 peak
## 1744 2030 0 3 6.43 2 115.94 decline
## 1745 1909 0 2 5.96 1 191.29 decline
## 1746 2988 4 3 6.90 1 50.63 early
## 1747 1647 8 5 5.96 2 137.70 early
## 1748 430 1 0 6.34 0 165.96 early
## 1749 3185 5 4 6.30 1 141.14 early
## 1750 930 1 1 5.00 1 121.91 early
## 1751 2352 4 4 6.36 1 122.35 early
## 1752 1581 3 3 6.62 0 39.08 early
## 1753 2244 6 4 6.85 1 214.88 peak
## 1754 1470 5 3 6.87 0 99.13 peak
## 1755 1640 0 2 7.09 1 262.48 early
## 1756 1512 0 0 7.14 1 216.81 early
## 1757 2403 0 1 5.79 1 95.28 early
## 1758 996 0 0 6.74 0 212.08 early
## 1759 1281 0 4 6.45 2 69.33 early
## 1760 1836 2 2 6.87 0 256.63 early
## 1761 1892 6 3 6.65 3 227.81 early
## 1762 3162 4 2 5.94 1 65.04 early
## 1763 1748 4 4 6.57 1 194.32 early
## 1764 2988 4 3 6.64 0 187.49 early
## 1765 2001 4 2 6.52 2 174.94 early
## 1766 2349 5 3 5.87 1 198.94 early
## 1767 1440 4 1 6.44 1 33.62 early
## 1768 320 2 0 6.62 0 120.40 early
## 1769 783 0 0 7.32 1 60.23 early
## 1770 1584 6 5 6.64 1 181.02 early
## 1771 510 1 0 7.43 0 241.51 peak
## 1772 2044 6 4 6.58 1 258.39 early
## 1773 1872 2 3 7.19 0 234.60 early
## 1774 2146 5 1 6.15 0 120.10 early
## 1775 2418 2 1 6.31 0 232.19 early
## 1776 1650 3 6 6.63 0 50.69 early
## 1777 1606 3 4 5.80 2 35.77 early
## 1778 2028 3 3 6.02 1 32.39 early
## 1779 2555 4 7 5.79 0 162.83 early
## 1780 2992 4 2 6.33 0 164.70 early
## 1781 948 3 1 5.67 0 33.68 early
## 1782 1968 5 0 6.75 0 93.45 early
## 1783 2952 4 8 7.41 0 48.10 early
## 1784 913 0 3 5.94 1 205.20 early
## 1785 1562 6 0 6.34 0 210.99 early
## 1786 2176 5 4 6.73 1 44.46 early
## 1787 1638 0 4 6.37 1 212.25 early
## 1788 1764 0 2 6.60 1 154.55 early
## 1789 2520 0 6 6.98 1 53.53 early
## 1790 711 0 0 6.50 0 99.70 early
## 1791 3102 0 4 6.73 0 195.27 early
## 1792 651 0 3 7.95 0 348.98 peak
## 1793 2701 0 3 7.24 1 182.77 peak
## 1794 1680 0 6 7.84 1 190.35 peak
## 1795 1292 0 1 6.68 0 197.74 decline
## 1796 310 0 1 5.68 0 169.84 decline
## 1797 1909 0 2 7.62 0 216.33 decline
## 1798 3071 0 7 6.54 0 36.21 decline
## 1799 462 0 0 5.17 1 29.08 decline
## 1800 738 0 0 6.58 0 218.24 decline
## 1801 540 0 0 6.86 0 117.17 early
## 1802 2772 3 3 5.65 1 176.69 early
## 1803 657 3 1 6.45 0 182.69 early
## 1804 1020 2 2 6.96 0 89.81 early
## 1805 2448 7 4 6.09 0 31.10 early
## 1806 2132 4 3 5.88 1 139.71 early
## 1807 2132 3 5 7.23 0 135.30 peak
## 1808 2592 9 7 6.95 0 196.52 peak
## 1809 2656 9 1 6.78 0 242.80 peak
## 1810 390 0 1 6.06 0 32.14 decline
## 1811 1800 0 4 6.99 0 143.57 early
## 1812 1360 0 1 6.18 0 117.45 early
## 1813 756 0 2 5.92 1 73.83 early
## 1814 1458 0 1 6.53 0 187.05 early
## 1815 975 0 0 6.59 0 112.48 early
## 1816 2232 0 1 7.00 1 85.19 early
## 1817 1800 0 1 6.11 0 69.14 early
## 1818 2624 0 6 6.20 0 148.79 early
## 1819 1836 0 9 7.77 0 134.09 peak
## 1820 1173 0 4 7.81 0 124.35 peak
## 1821 2116 0 5 6.04 2 203.18 peak
## 1822 2142 0 4 6.01 0 175.79 early
## 1823 2070 0 1 6.38 1 234.57 early
## 1824 2664 0 2 6.60 1 80.15 early
## 1825 1275 0 0 6.33 0 162.37 early
## 1826 780 0 2 7.63 1 52.42 peak
## 1827 2552 0 2 7.68 0 157.62 peak
## 1828 1040 0 2 7.01 0 279.47 peak
## 1829 984 0 3 6.17 0 62.78 decline
## 1830 621 0 1 6.89 0 276.74 early
## 1831 3367 0 5 5.53 1 56.02 early
## 1832 3220 0 3 6.56 0 182.35 early
## 1833 1449 0 4 6.13 0 96.89 early
## 1834 2739 0 4 6.06 0 38.48 early
## 1835 810 0 0 6.48 2 173.45 peak
## 1836 1482 0 3 7.70 0 119.46 peak
## 1837 897 0 1 6.95 0 166.81 peak
## 1838 1587 0 1 6.54 0 138.53 decline
## 1839 1900 0 5 5.95 0 40.09 decline
## 1840 1780 0 2 5.61 0 63.80 decline
## 1841 2538 6 4 6.96 1 51.30 early
## 1842 2790 1 1 6.77 1 212.21 early
## 1843 1702 5 3 5.98 0 151.79 early
## 1844 1710 4 3 6.47 0 182.45 early
## 1845 474 1 2 5.83 0 156.51 early
## 1846 847 2 0 6.26 0 97.76 early
## 1847 1302 1 1 6.24 0 34.62 early
## 1848 1769 2 1 6.60 0 75.07 peak
## 1849 980 2 1 7.01 1 52.02 peak
## 1850 1027 2 1 7.10 1 147.48 peak
## 1851 2944 5 5 6.53 0 49.62 decline
## 1852 765 2 0 6.40 1 122.99 decline
## 1853 2883 0 3 6.14 1 165.72 early
## 1854 1116 0 1 6.07 1 206.02 early
## 1855 592 0 1 5.90 1 41.36 early
## 1856 3196 0 3 5.91 0 116.05 early
## 1857 1602 0 1 6.03 0 129.35 early
## 1858 2294 3 1 5.52 2 130.61 early
## 1859 1040 1 2 6.16 1 63.69 early
## 1860 2465 7 2 6.62 0 104.07 early
## 1861 3069 2 2 6.87 0 107.20 early
## 1862 2048 2 0 7.28 1 128.68 early
## 1863 1023 1 3 6.23 0 41.09 early
## 1864 1560 4 6 7.60 1 126.94 peak
## 1865 498 1 1 7.85 0 114.23 peak
## 1866 828 1 0 7.40 1 153.41 peak
## 1867 532 1 0 6.80 0 267.47 decline
## 1868 2346 6 4 6.59 1 240.16 early
## 1869 1600 2 4 6.84 0 84.14 early
## 1870 1988 3 5 6.60 1 149.86 early
## 1871 1512 6 0 6.23 1 74.32 early
## 1872 1344 4 1 6.03 1 37.29 early
## 1873 915 2 0 5.76 0 171.89 early
## 1874 1386 9 2 6.74 0 48.02 early
## 1875 2349 7 1 6.88 1 167.69 early
## 1876 3293 10 6 7.64 0 61.68 peak
## 1877 888 4 3 6.58 1 35.78 peak
## 1878 592 0 1 7.94 0 288.42 peak
## 1879 2754 7 3 6.01 2 106.52 decline
## 1880 3182 0 4 5.59 1 146.41 early
## 1881 1365 0 0 6.68 0 157.60 early
## 1882 1368 0 3 6.48 1 198.37 early
## 1883 1804 0 2 6.71 0 152.77 early
## 1884 2204 0 3 7.40 2 296.21 early
## 1885 2475 0 4 6.35 1 219.75 early
## 1886 948 0 2 5.51 1 134.14 early
## 1887 1365 0 0 5.70 1 33.07 early
## 1888 2849 0 2 7.04 0 254.09 peak
## 1889 630 0 1 6.27 2 235.24 early
## 1890 1335 2 1 6.58 0 122.44 early
## 1891 2016 7 5 6.24 0 170.29 early
## 1892 801 3 2 6.30 0 146.64 early
## 1893 498 1 1 7.02 1 232.51 early
## 1894 2628 1 5 5.98 1 80.75 early
## 1895 414 1 2 5.87 0 33.54 early
## 1896 1144 3 1 6.36 1 113.29 early
## 1897 1332 0 3 6.74 1 83.72 early
## 1898 474 0 1 6.70 1 51.71 early
## 1899 2574 0 3 5.76 0 190.01 early
## 1900 1496 0 1 5.80 1 94.73 early
## 1901 2790 0 1 6.10 0 115.22 early
## 1902 1848 0 1 6.81 0 93.63 early
## 1903 1380 3 1 6.12 0 128.37 early
## 1904 2250 1 1 5.81 0 105.39 early
## 1905 1598 3 0 6.88 1 46.03 early
## 1906 1769 8 2 5.87 2 86.29 early
## 1907 1260 2 1 6.50 0 127.01 early
## 1908 2590 4 2 7.08 0 55.73 peak
## 1909 1206 2 0 7.43 0 117.42 peak
## 1910 783 1 2 7.70 0 57.52 peak
## 1911 2187 8 3 5.31 1 50.00 decline
## 1912 1190 1 0 6.14 0 154.03 decline
## 1913 2673 5 3 6.08 2 192.94 early
## 1914 2220 3 4 6.67 0 196.16 early
## 1915 2278 6 3 6.57 0 35.89 early
## 1916 520 2 2 6.29 0 104.25 early
## 1917 1098 1 1 6.14 0 81.22 early
## 1918 1008 0 2 6.54 0 127.71 early
## 1919 1824 0 0 6.97 1 154.47 early
## 1920 2310 1 2 6.66 2 153.44 peak
## 1921 2220 3 3 7.67 0 296.24 peak
## 1922 1020 2 0 7.48 1 106.37 peak
## 1923 952 2 1 6.69 2 157.37 early
## 1924 640 2 1 6.30 0 120.99 early
## 1925 2250 3 4 6.25 1 69.55 early
## 1926 1260 0 0 6.93 0 148.92 early
## 1927 800 2 1 6.12 2 31.24 early
## 1928 2436 9 1 5.71 3 80.11 early
## 1929 536 1 0 6.03 1 125.84 early
## 1930 2656 6 2 6.86 1 108.21 early
## 1931 2100 1 2 6.58 1 199.38 early
## 1932 2759 3 2 6.23 0 111.22 early
## 1933 710 0 1 6.38 1 196.20 early
## 1934 1848 4 1 6.26 1 181.51 early
## 1935 465 0 2 6.92 0 146.17 early
## 1936 2937 2 3 6.84 0 221.36 early
## 1937 2752 3 4 6.39 1 42.15 peak
## 1938 2225 2 2 7.63 1 319.79 peak
## 1939 2635 10 0 7.26 0 218.08 peak
## 1940 752 2 0 6.20 1 202.49 decline
## 1941 504 0 0 6.60 1 82.74 early
## 1942 560 0 1 7.24 0 187.21 early
## 1943 595 0 1 6.68 1 39.49 early
## 1944 504 0 0 6.76 0 197.22 early
## 1945 1664 0 3 6.49 1 156.90 early
## 1946 1496 0 2 6.60 0 80.67 early
## 1947 1980 0 5 5.93 0 163.36 early
## 1948 1050 0 0 6.40 0 158.79 early
## 1949 1980 0 5 7.82 1 55.66 peak
## 1950 546 0 0 7.38 0 50.61 peak
## 1951 957 2 1 5.61 0 26.35 early
## 1952 2775 2 2 6.63 0 113.27 early
## 1953 1738 6 1 6.27 2 70.85 early
## 1954 1520 5 0 6.58 0 91.59 early
## 1955 1159 4 5 6.54 1 35.54 early
## 1956 806 1 1 5.95 0 38.06 early
## 1957 2010 7 5 6.33 1 142.02 early
## 1958 2436 6 3 6.38 0 133.41 early
## 1959 1408 3 2 6.83 0 42.81 early
## 1960 1365 5 2 7.11 0 180.93 early
## 1961 1350 0 1 6.91 0 88.99 early
## 1962 1360 3 1 6.62 0 89.81 early
## 1963 1134 4 3 5.74 1 64.86 early
## 1964 3060 4 6 5.56 1 155.01 early
## 1965 2808 8 2 6.31 0 46.09 peak
## 1966 1275 1 2 6.81 1 126.50 early
## 1967 979 0 2 5.10 2 77.92 early
## 1968 1206 0 2 5.63 0 61.47 early
## 1969 1224 5 1 6.71 0 256.47 early
## 1970 360 3 0 6.56 1 146.12 early
## 1971 567 2 1 6.33 1 81.38 early
## 1972 1875 1 3 7.24 0 47.98 early
## 1973 1691 4 1 6.32 1 80.49 early
## 1974 1024 4 1 5.59 2 68.78 early
## 1975 1860 2 2 7.60 0 129.59 early
## 1976 2132 4 0 6.70 1 115.74 early
## 1977 2156 5 0 6.43 0 110.31 early
## 1978 2225 10 4 7.16 0 177.88 early
## 1979 1036 3 0 7.28 1 156.59 early
## 1980 1869 3 2 6.56 1 148.14 early
## 1981 1311 3 0 7.89 0 58.11 early
## 1982 1577 2 3 6.99 0 50.23 early
## 1983 2430 5 3 6.22 0 99.71 early
## 1984 2432 1 3 5.73 1 55.90 early
## 1985 690 2 0 6.93 0 156.50 early
## 1986 2407 3 1 5.00 1 24.94 early
## 1987 2739 2 4 6.82 0 78.19 early
## 1988 2100 6 4 6.27 2 34.37 peak
## 1989 1001 1 1 6.11 0 199.01 peak
## 1990 2700 7 1 7.21 0 83.59 peak
## 1991 2460 4 3 6.50 1 158.79 decline
## 1992 1326 2 0 6.28 0 86.10 decline
## 1993 1395 1 4 6.04 0 151.50 decline
## 1994 455 0 0 6.75 1 105.09 decline
## 1995 900 2 1 7.44 0 232.73 early
## 1996 2800 3 3 6.48 1 151.19 early
## 1997 737 1 1 6.38 1 163.73 early
## 1998 3367 6 1 6.40 0 233.37 early
## 1999 2232 12 5 6.50 0 245.04 early
## 2000 2856 6 3 5.57 1 32.62 early
## 2001 2436 1 3 6.81 1 270.98 early
## 2002 2870 4 3 6.33 1 169.98 early
## 2003 1769 1 3 5.94 0 115.60 early
## 2004 476 1 1 6.83 0 101.53 early
## 2005 2432 1 5 6.30 0 113.33 early
## 2006 1804 6 1 6.12 0 135.93 early
## 2007 2700 7 0 7.93 0 66.29 peak
## 2008 2214 3 5 6.31 2 158.02 peak
## 2009 846 3 0 6.34 0 168.46 early
## 2010 2664 7 4 5.71 1 119.59 early
## 2011 1976 1 5 6.45 1 38.47 early
## 2012 2080 5 2 6.91 0 76.71 early
## 2013 2960 9 2 5.77 0 114.87 early
## 2014 1880 3 1 6.74 1 75.26 early
## 2015 1001 3 0 7.03 0 162.98 peak
## 2016 410 1 1 7.06 1 179.96 peak
## 2017 1550 0 2 6.86 0 150.90 early
## 2018 1376 0 0 6.46 1 46.09 early
## 2019 871 0 1 6.87 0 211.48 early
## 2020 420 0 1 5.91 1 57.39 early
## 2021 752 0 2 6.36 0 73.17 early
## 2022 3102 0 4 7.01 0 232.63 early
## 2023 2673 0 7 6.24 0 179.96 early
## 2024 2196 0 4 6.91 2 98.08 peak
## 2025 728 0 2 7.62 1 314.84 peak
## 2026 2162 0 1 8.11 0 321.46 peak
## 2027 640 0 0 6.15 0 35.39 decline
## 2028 3102 7 1 7.51 1 264.15 early
## 2029 1125 2 3 6.67 1 38.25 early
## 2030 1395 3 2 6.84 0 78.21 early
## 2031 2528 6 3 5.64 1 142.56 early
## 2032 1064 1 1 6.73 1 112.20 early
## 2033 528 1 1 5.63 0 106.57 early
## 2034 2300 0 0 7.01 1 210.83 early
## 2035 3256 4 7 6.09 1 33.53 early
## 2036 1023 2 2 6.05 1 153.14 early
## 2037 522 2 2 5.83 0 114.72 early
## 2038 1647 9 0 7.18 0 262.74 early
## 2039 2184 2 2 6.64 0 185.01 early
## 2040 3034 3 2 6.44 0 40.84 early
## 2041 486 1 0 6.41 0 229.53 early
## 2042 1040 3 3 7.22 0 130.54 early
## 2043 2075 6 2 6.70 1 215.05 early
## 2044 1710 3 3 6.68 0 176.21 early
## 2045 2310 5 4 5.98 1 61.64 early
## 2046 1755 5 4 7.96 0 370.76 peak
## 2047 2480 5 6 7.03 0 129.25 peak
## 2048 747 2 0 6.92 2 192.29 peak
## 2049 1680 3 2 5.89 0 202.34 early
## 2050 2160 5 2 5.66 2 61.12 early
## 2051 1575 4 3 6.10 1 71.27 early
## 2052 2244 7 5 6.69 0 216.59 early
## 2053 2112 4 3 6.51 0 136.04 early
## 2054 1909 3 3 6.29 0 80.97 early
## 2055 2250 3 2 7.28 0 152.05 early
## 2056 1740 5 3 6.02 3 83.38 early
## 2057 680 0 2 6.66 0 176.67 early
## 2058 592 1 0 6.76 0 178.06 early
## 2059 2701 3 2 6.39 1 90.83 early
## 2060 639 2 1 6.39 0 125.06 early
## 2061 2444 3 1 7.41 0 195.11 peak
## 2062 1800 4 6 6.91 2 156.99 peak
## 2063 1872 5 1 6.54 1 41.26 peak
## 2064 2883 4 1 7.03 1 204.61 decline
## 2065 2047 1 7 6.64 0 208.60 decline
## 2066 1368 0 0 6.09 1 177.58 early
## 2067 1674 0 0 7.07 0 85.76 early
## 2068 3108 0 8 6.35 0 150.92 early
## 2069 946 0 2 6.20 0 130.32 early
## 2070 803 0 1 7.20 0 186.76 early
## 2071 2002 0 3 6.04 1 163.94 early
## 2072 896 0 0 6.81 0 110.54 peak
## 2073 2044 0 7 6.83 2 277.85 peak
## 2074 3036 0 0 6.64 1 155.46 peak
## 2075 1365 0 0 6.02 1 149.08 decline
## 2076 2436 0 2 6.66 0 41.33 decline
## 2077 2295 6 5 6.85 1 163.76 early
## 2078 2738 2 5 6.75 0 136.92 early
## 2079 2838 2 3 6.35 0 163.55 early
## 2080 1914 3 3 5.96 1 29.34 early
## 2081 1368 1 1 6.43 1 170.75 early
## 2082 2890 6 3 6.28 1 117.32 early
## 2083 492 2 1 7.33 0 52.57 early
## 2084 3060 3 4 6.54 0 187.66 early
## 2085 2730 5 3 6.88 1 112.54 early
## 2086 2856 3 3 6.41 1 164.83 early
## 2087 2262 5 4 5.43 1 53.63 early
## 2088 2064 1 1 7.49 0 107.76 peak
## 2089 2201 5 2 6.23 1 167.83 peak
## 2090 2759 5 4 7.78 0 51.40 peak
## 2091 1302 3 2 6.85 1 213.45 decline
## 2092 1204 2 1 5.90 0 31.03 decline
## 2093 1513 0 3 6.16 1 222.67 decline
## 2094 1368 2 5 6.84 1 201.36 decline
## 2095 1764 0 4 6.04 0 148.15 early
## 2096 1830 0 2 6.68 0 150.57 early
## 2097 1504 0 1 6.23 0 101.41 early
## 2098 1513 0 4 5.66 0 30.00 early
## 2099 1806 0 3 7.03 0 121.53 early
## 2100 2970 0 2 5.50 1 109.99 early
## 2101 2278 0 2 6.37 0 192.07 early
## 2102 2268 0 0 7.01 1 153.84 early
## 2103 2275 0 2 6.38 0 48.00 early
## 2104 990 0 0 6.21 1 40.44 early
## 2105 1216 0 1 5.86 0 30.56 early
## 2106 744 0 0 6.10 0 140.01 early
## 2107 1159 0 1 6.97 2 231.09 early
## 2108 1725 0 1 6.29 1 78.30 early
## 2109 1958 0 3 7.30 1 253.72 peak
## 2110 402 0 0 7.69 0 134.30 peak
## 2111 1869 0 2 7.58 1 245.87 peak
## 2112 366 0 0 5.89 0 109.72 decline
## 2113 760 2 1 6.89 0 47.39 early
## 2114 522 1 0 6.64 1 48.16 early
## 2115 1472 4 2 6.93 0 265.43 early
## 2116 630 0 1 6.46 1 204.88 early
## 2117 1260 2 2 5.20 1 83.45 early
## 2118 621 1 1 6.64 1 99.51 early
## 2119 2624 3 4 6.36 2 177.38 early
## 2120 1704 3 2 6.48 0 227.70 early
## 2121 2844 5 2 7.19 0 135.12 peak
## 2122 1092 2 2 7.15 2 172.65 peak
## 2123 2508 7 5 6.63 1 174.22 peak
## 2124 2639 1 4 7.21 1 169.85 decline
## 2125 1260 1 4 5.00 3 65.10 decline
## 2126 1452 0 0 5.59 0 112.61 early
## 2127 1170 0 1 7.47 0 233.48 early
## 2128 1702 6 3 6.19 0 159.83 early
## 2129 1407 6 4 6.29 1 173.59 early
## 2130 1120 2 3 6.94 1 39.96 peak
## 2131 2688 5 2 7.71 1 349.06 peak
## 2132 560 0 1 5.69 1 88.29 early
## 2133 372 0 1 6.36 0 190.97 early
## 2134 355 0 0 6.42 1 70.84 early
## 2135 711 0 0 6.14 3 170.46 early
## 2136 1120 0 1 6.02 1 74.83 early
## 2137 693 0 0 6.32 1 66.35 early
## 2138 1769 0 1 6.25 1 96.83 early
## 2139 1984 0 7 6.24 2 136.68 early
## 2140 2268 0 2 6.85 0 78.49 early
## 2141 1066 0 2 6.81 0 86.21 early
## 2142 940 0 2 7.90 0 51.62 peak
## 2143 510 0 1 6.44 1 138.92 early
## 2144 1278 0 1 6.35 0 195.58 early
## 2145 1220 0 2 6.17 1 177.21 early
## 2146 2040 0 3 5.46 1 63.31 early
## 2147 744 0 0 5.76 1 76.10 early
## 2148 528 0 1 5.49 1 122.96 early
## 2149 1098 0 1 6.54 0 36.48 early
## 2150 1209 0 2 6.35 0 131.02 peak
## 2151 1638 0 3 7.45 0 247.63 peak
## 2152 2117 0 2 6.71 2 158.81 peak
## 2153 711 0 1 5.57 2 119.50 decline
## 2154 1824 0 3 6.39 1 67.23 decline
## 2155 2618 0 5 6.35 2 123.99 decline
## 2156 2275 0 0 6.51 0 218.63 decline
## 2157 2225 0 3 6.34 1 102.89 early
## 2158 574 0 0 5.53 0 180.04 early
## 2159 696 0 2 6.87 0 276.32 early
## 2160 2380 0 2 5.31 2 31.69 early
## 2161 1720 0 1 6.14 0 75.05 early
## 2162 2280 0 3 6.46 1 89.04 early
## 2163 2144 0 1 7.13 0 58.70 peak
## 2164 2093 0 2 6.45 0 202.78 early
## 2165 1860 0 5 7.27 0 128.63 early
## 2166 1728 0 4 5.80 0 149.23 early
## 2167 1768 0 4 6.56 0 234.83 early
## 2168 2700 0 2 6.74 0 210.10 early
## 2169 1760 0 1 6.92 1 77.37 early
## 2170 637 0 4 7.20 0 48.43 early
## 2171 1950 0 6 5.88 1 81.01 early
## 2172 2482 0 5 6.96 0 39.82 peak
## 2173 1971 0 0 7.38 0 51.06 peak
## 2174 2790 0 4 7.00 1 157.16 peak
## 2175 910 2 4 6.17 0 84.79 early
## 2176 2604 2 4 6.56 0 44.79 early
## 2177 2706 3 4 6.48 0 140.83 early
## 2178 1785 3 3 6.09 1 150.36 early
## 2179 3478 5 1 6.24 2 233.62 early
## 2180 3367 3 3 6.01 0 37.33 early
## 2181 3145 7 3 6.41 0 118.71 early
## 2182 2232 5 3 6.09 1 44.31 early
## 2183 1530 2 2 7.78 2 175.33 peak
## 2184 976 2 0 7.30 0 172.62 peak
## 2185 1040 2 3 6.80 2 41.47 peak
## 2186 1620 4 4 6.05 0 169.23 decline
## 2187 1280 3 1 6.76 0 178.17 decline
## 2188 325 0 1 6.44 0 169.39 decline
## 2189 832 0 4 5.68 2 82.10 early
## 2190 1700 0 4 6.72 2 37.40 early
## 2191 441 0 3 6.03 0 168.92 early
## 2192 1392 0 0 5.66 0 37.34 early
## 2193 1491 0 5 6.70 1 227.41 early
## 2194 2050 0 1 6.73 0 145.68 early
## 2195 2444 0 2 6.21 1 87.94 early
## 2196 2001 0 5 6.27 0 43.05 early
## 2197 408 0 3 7.20 1 91.31 peak
## 2198 1140 0 1 7.15 0 285.15 peak
## 2199 1152 0 1 6.56 1 83.05 peak
## 2200 1800 0 2 6.27 2 63.86 decline
## 2201 2574 0 1 6.41 1 42.44 decline
## 2202 1950 3 4 7.01 1 91.62 early
## 2203 2584 5 3 6.93 1 219.95 early
## 2204 816 0 3 5.80 0 149.11 early
## 2205 450 0 0 6.97 1 252.58 early
## 2206 2403 6 3 6.74 1 128.85 early
## 2207 1001 3 0 6.12 1 67.68 early
## 2208 2139 2 4 6.83 1 75.26 early
## 2209 2210 9 4 6.25 1 34.77 early
## 2210 3196 4 5 6.74 0 85.77 early
## 2211 2175 2 3 6.44 0 83.89 early
## 2212 2400 5 2 6.52 1 85.38 early
## 2213 2772 3 1 6.56 0 39.43 early
## 2214 1702 2 1 6.64 1 95.32 early
## 2215 1128 1 0 6.16 0 203.41 early
## 2216 504 0 0 6.88 0 51.64 early
## 2217 803 0 2 6.52 1 47.98 early
## 2218 2739 3 3 6.28 1 168.44 early
## 2219 1472 7 1 5.68 1 117.66 early
## 2220 2664 6 2 5.76 1 108.95 early
## 2221 820 0 0 6.25 1 37.81 early
## 2222 2088 9 3 5.90 1 121.06 early
## 2223 710 3 1 6.29 1 41.04 early
## 2224 504 3 2 7.12 1 220.94 early
## 2225 1725 5 3 6.57 1 147.84 early
## 2226 1702 6 2 7.17 0 51.94 early
## 2227 1690 5 2 7.01 0 155.78 early
## 2228 2436 5 5 6.81 1 198.73 early
## 2229 610 0 1 6.01 2 216.50 early
## 2230 1155 3 2 7.05 0 95.73 early
## 2231 828 0 1 5.80 0 177.24 early
## 2232 486 1 1 6.02 0 91.61 early
## 2233 1408 1 0 6.02 1 140.31 early
## 2234 2376 8 3 7.02 1 110.23 early
## 2235 1118 0 2 6.35 1 47.11 early
## 2236 897 0 1 6.44 1 183.15 early
## 2237 2639 0 6 6.02 1 36.78 early
## 2238 2912 0 6 7.88 0 366.91 early
## 2239 1890 0 1 6.22 1 86.98 early
## 2240 1275 0 5 6.94 1 85.66 early
## 2241 2550 0 1 6.93 0 137.33 early
## 2242 2275 0 6 6.52 0 98.70 early
## 2243 1577 0 1 6.22 1 100.46 early
## 2244 858 3 2 6.94 2 146.32 early
## 2245 1116 3 1 6.20 1 34.19 early
## 2246 1300 1 1 6.30 1 129.65 early
## 2247 3128 8 1 6.42 2 150.51 early
## 2248 1701 5 2 5.87 0 105.11 early
## 2249 2610 3 2 5.79 0 81.67 early
## 2250 480 2 0 7.05 0 116.69 peak
## 2251 1022 1 1 7.58 0 94.59 peak
## 2252 1536 5 3 6.81 0 276.13 peak
## 2253 1020 0 1 7.00 0 145.97 decline
## 2254 2752 4 5 6.03 0 93.54 decline
## 2255 1131 3 3 6.70 1 107.46 early
## 2256 2262 5 3 6.49 1 96.95 early
## 2257 1360 2 0 5.75 2 28.01 early
## 2258 1672 2 0 6.34 0 83.50 early
## 2259 710 1 1 6.05 1 32.38 early
## 2260 2405 5 2 6.11 0 178.57 early
## 2261 1376 1 2 6.54 0 199.87 early
## 2262 1809 6 2 6.22 1 130.83 peak
## 2263 2349 4 2 6.85 0 111.04 peak
## 2264 1520 3 3 7.29 0 152.02 peak
## 2265 1496 4 2 7.09 1 47.84 decline
## 2266 1809 8 2 7.15 1 98.93 decline
## 2267 1280 2 3 6.53 0 165.32 decline
## 2268 1387 0 1 6.37 0 225.02 early
## 2269 496 0 0 6.56 0 45.40 early
## 2270 2937 0 0 6.56 1 172.89 early
## 2271 1638 0 2 5.82 0 80.79 early
## 2272 1140 0 1 6.38 2 35.55 peak
## 2273 2376 0 2 7.05 0 124.30 peak
## 2274 1014 0 0 7.17 0 123.60 peak
## 2275 1742 0 2 6.73 0 191.07 decline
## 2276 2627 0 3 6.71 0 103.75 decline
## 2277 2720 0 2 6.49 0 190.17 decline
## 2278 2070 0 4 6.22 0 231.67 decline
## 2279 2475 0 4 5.41 1 142.61 decline
## 2280 3024 0 4 6.85 0 182.63 decline
## 2281 803 0 0 6.70 0 50.23 early
## 2282 800 0 1 6.13 1 160.03 early
## 2283 1170 0 2 5.74 0 192.62 early
## 2284 395 0 0 7.60 0 253.54 early
## 2285 2720 0 3 6.69 2 157.90 peak
## 2286 682 0 2 7.15 0 232.58 peak
## 2287 1404 0 7 7.24 0 235.86 peak
## 2288 497 1 0 5.82 2 125.42 early
## 2289 1350 6 0 6.74 1 90.88 early
## 2290 2112 1 2 5.08 2 117.13 early
## 2291 1674 4 2 5.52 1 24.85 early
## 2292 1798 7 3 6.99 1 279.67 early
## 2293 752 2 0 6.60 0 126.61 early
## 2294 2430 5 3 5.76 1 164.61 early
## 2295 1564 2 1 6.75 0 173.65 early
## 2296 2100 3 2 6.84 0 203.73 early
## 2297 2160 6 2 6.59 1 184.20 early
## 2298 1672 4 1 6.10 0 173.09 early
## 2299 1584 5 2 6.84 0 217.99 early
## 2300 869 1 1 6.34 1 102.44 early
## 2301 1014 3 0 6.80 1 41.94 early
## 2302 1978 4 3 6.81 0 160.40 early
## 2303 1628 2 3 5.92 1 58.59 early
## 2304 1875 7 3 6.64 2 36.53 early
## 2305 912 1 4 5.86 0 70.20 early
## 2306 2581 5 2 6.21 0 140.70 early
## 2307 440 1 0 6.11 1 115.16 early
## 2308 1638 1 4 6.60 0 41.45 peak
## 2309 1775 5 2 8.39 0 115.16 peak
## 2310 1349 1 2 6.52 0 100.42 early
## 2311 1344 2 2 6.81 1 125.21 early
## 2312 1577 4 2 5.04 1 28.04 early
## 2313 1512 6 3 6.87 0 182.24 early
## 2314 1958 2 1 6.95 1 139.22 early
## 2315 1848 2 3 6.39 0 42.74 early
## 2316 657 1 1 6.45 0 74.47 early
## 2317 2046 3 3 6.81 1 240.03 early
## 2318 2368 7 2 6.79 0 115.47 peak
## 2319 1564 9 2 6.21 1 157.68 peak
## 2320 2430 4 3 7.70 0 320.19 peak
## 2321 2573 5 1 6.68 1 239.16 decline
## 2322 2001 3 3 5.33 1 30.52 early
## 2323 2784 7 6 5.91 0 183.53 early
## 2324 2790 4 2 5.96 1 144.98 early
## 2325 1472 1 1 6.69 2 197.75 early
## 2326 1674 7 1 6.61 0 77.25 early
## 2327 1320 5 1 5.42 2 116.89 early
## 2328 2880 6 0 6.32 1 69.59 early
## 2329 1863 5 1 6.57 1 44.78 early
## 2330 2538 4 6 5.94 2 65.36 early
## 2331 1672 5 3 7.67 0 53.63 peak
## 2332 656 3 0 7.33 0 43.68 early
## 2333 1254 2 1 6.51 0 47.42 early
## 2334 3293 4 2 6.19 0 193.61 early
## 2335 1131 2 0 6.30 0 43.85 early
## 2336 2490 4 7 5.61 0 114.71 early
## 2337 1584 4 2 6.73 0 140.89 early
## 2338 549 1 0 5.97 1 67.75 early
## 2339 510 0 2 6.66 1 101.76 early
## 2340 1740 1 0 6.60 1 40.09 peak
## 2341 3003 1 3 7.72 1 54.02 peak
## 2342 2208 2 2 8.08 0 217.69 peak
## 2343 395 2 0 6.55 0 199.48 decline
## 2344 630 2 0 7.08 0 58.31 early
## 2345 1980 6 2 5.32 1 100.75 early
## 2346 1440 4 0 5.66 0 81.27 early
## 2347 966 2 3 7.13 0 139.35 early
## 2348 1044 1 1 6.90 0 117.83 early
## 2349 852 3 3 7.40 1 162.06 early
## 2350 912 3 1 7.23 0 230.94 early
## 2351 2002 5 1 7.26 1 239.32 early
## 2352 1640 2 3 6.83 1 47.41 early
## 2353 2975 1 3 6.60 0 147.27 early
## 2354 444 1 1 6.38 2 95.63 early
## 2355 330 1 0 6.56 1 230.96 peak
## 2356 1162 0 1 7.81 1 130.40 peak
## 2357 1638 6 1 7.29 0 91.36 peak
## 2358 2852 6 3 5.84 1 29.31 decline
## 2359 1044 0 0 7.11 0 154.14 decline
## 2360 2312 6 2 5.78 1 133.99 decline
## 2361 2100 6 3 5.62 1 148.30 early
## 2362 2236 4 2 6.05 0 156.02 early
## 2363 1704 4 1 6.83 1 55.68 early
## 2364 2184 6 1 5.13 0 138.17 early
## 2365 1560 3 3 6.21 0 34.86 early
## 2366 623 2 1 6.51 1 79.13 early
## 2367 1782 4 2 6.40 1 92.18 early
## 2368 648 4 2 6.84 0 214.05 early
## 2369 1320 1 4 6.59 0 70.27 early
## 2370 975 1 2 6.23 1 91.41 early
## 2371 1980 5 5 7.04 0 39.74 early
## 2372 2380 10 6 6.99 2 98.00 early
## 2373 2516 7 4 6.19 0 41.86 early
## 2374 1500 2 4 6.66 1 152.08 early
## 2375 1625 1 2 6.92 1 144.70 early
## 2376 438 2 1 6.93 1 264.27 early
## 2377 880 0 1 6.25 1 172.37 early
## 2378 1428 3 3 6.64 0 102.75 early
## 2379 1275 3 1 6.94 0 273.38 early
## 2380 2028 5 1 6.28 0 176.57 early
## 2381 1474 2 2 5.45 1 34.87 early
## 2382 1608 4 1 6.69 1 152.84 peak
## 2383 696 1 0 6.92 0 183.26 peak
## 2384 3384 6 2 6.62 0 40.94 peak
## 2385 2244 3 6 6.60 0 149.39 decline
## 2386 1547 2 2 6.28 1 39.06 early
## 2387 1242 2 1 5.74 1 33.54 early
## 2388 1092 3 1 7.36 1 201.24 early
## 2389 1863 1 3 7.58 0 169.33 early
## 2390 1479 0 2 6.39 1 100.31 early
## 2391 690 3 2 6.72 0 42.49 early
## 2392 2211 1 2 5.00 0 85.54 early
## 2393 1944 2 4 6.13 1 169.77 early
## 2394 480 2 2 5.59 0 89.03 early
## 2395 1207 2 2 6.13 1 121.10 early
## 2396 2146 5 2 6.70 2 87.49 early
## 2397 1974 3 0 5.85 0 36.61 early
## 2398 1335 2 0 6.53 1 226.11 early
## 2399 952 2 1 6.70 1 42.48 early
## 2400 1275 1 2 6.38 1 45.61 early
## 2401 2340 3 3 6.44 0 192.53 early
## 2402 2552 5 4 7.72 1 279.09 peak
## 2403 2075 4 0 6.89 1 82.40 peak
## 2404 1170 0 0 5.27 0 26.54 early
## 2405 2508 0 4 7.65 1 243.15 early
## 2406 1600 0 3 5.64 1 26.32 early
## 2407 2448 0 2 6.49 1 239.95 early
## 2408 790 0 0 6.54 1 149.89 early
## 2409 730 0 1 5.96 1 37.99 early
## 2410 940 0 2 6.01 1 84.95 early
## 2411 702 0 1 6.04 1 159.08 early
## 2412 2146 0 4 5.77 1 39.57 early
## 2413 648 0 1 6.88 1 128.13 early
## 2414 990 0 2 6.99 1 276.39 peak
## 2415 2040 0 1 6.86 0 103.25 peak
## 2416 1728 0 5 6.09 0 147.96 early
## 2417 2075 0 0 6.19 1 76.05 early
## 2418 2277 0 4 6.30 1 40.69 early
## 2419 650 0 2 5.55 1 182.27 early
## 2420 2175 0 0 5.31 0 92.61 early
## 2421 910 0 0 5.63 2 58.69 early
## 2422 2556 0 4 6.06 0 114.74 early
## 2423 1728 0 2 7.09 0 48.19 peak
## 2424 1196 2 0 7.06 0 179.18 early
## 2425 2380 4 1 5.87 1 68.01 early
## 2426 2405 13 5 5.96 0 156.86 early
## 2427 730 2 2 5.70 1 75.13 early
## 2428 1278 3 0 6.71 0 171.25 early
## 2429 2409 9 2 5.82 0 120.19 early
## 2430 2002 8 5 6.03 0 172.37 early
## 2431 1156 2 2 6.33 1 172.18 early
## 2432 1463 5 6 6.81 0 145.80 early
## 2433 1224 2 3 6.71 0 155.55 early
## 2434 2366 5 2 7.18 1 137.59 early
## 2435 340 2 0 7.23 0 248.21 early
## 2436 870 3 2 6.43 0 45.92 early
## 2437 1314 3 2 6.23 0 64.56 early
## 2438 2079 3 0 6.51 1 198.85 early
## 2439 2244 1 1 6.21 1 109.10 early
## 2440 1148 2 1 8.25 2 109.93 peak
## 2441 1170 5 2 7.01 2 199.45 peak
## 2442 732 0 1 7.29 0 120.98 peak
## 2443 2280 6 1 6.59 2 187.90 decline
## 2444 1428 7 1 6.25 1 87.92 early
## 2445 2706 5 1 6.40 1 185.24 early
## 2446 2240 3 4 6.57 1 101.30 early
## 2447 1140 3 2 6.76 1 52.72 early
## 2448 760 2 0 6.36 1 91.00 early
## 2449 1458 1 1 6.30 0 69.89 early
## 2450 1513 1 0 5.63 1 143.53 early
## 2451 630 0 1 6.80 0 276.03 early
## 2452 1386 6 2 5.79 0 135.24 early
## 2453 3204 3 2 5.86 2 152.37 early
## 2454 2552 9 3 6.67 0 245.33 early
## 2455 2464 6 2 5.53 1 64.16 early
## 2456 360 0 0 6.56 0 127.57 early
## 2457 1302 0 2 6.42 2 138.22 early
## 2458 651 0 0 6.80 0 120.34 peak
## 2459 700 2 2 6.96 0 79.01 peak
## 2460 637 1 4 7.38 1 51.10 peak
## 2461 1540 3 1 7.21 0 45.28 decline
## 2462 2523 2 5 5.59 1 134.46 decline
## 2463 2520 5 3 6.67 1 123.23 decline
## 2464 804 1 1 6.00 1 37.59 early
## 2465 996 0 1 7.31 1 141.69 early
## 2466 1638 0 1 5.25 0 129.29 early
## 2467 2475 8 3 5.52 1 161.24 early
## 2468 1850 3 3 7.07 1 290.50 early
## 2469 2232 2 4 5.53 1 36.57 early
## 2470 1890 6 1 6.00 0 107.35 early
## 2471 2640 6 1 5.25 0 141.04 early
## 2472 648 1 2 6.31 1 172.02 early
## 2473 1140 2 4 6.19 2 32.00 early
## 2474 720 1 1 6.16 1 125.91 early
## 2475 320 0 0 6.20 2 45.05 early
## 2476 1752 3 3 6.38 1 170.13 early
## 2477 2340 4 1 6.86 0 107.98 early
## 2478 1863 4 0 6.41 1 104.52 early
## 2479 1296 5 2 7.59 1 100.98 early
## 2480 1066 0 1 7.57 2 258.15 peak
## 2481 640 2 2 7.90 1 124.72 peak
## 2482 1068 4 1 5.83 1 161.28 early
## 2483 1230 2 1 7.01 1 40.89 early
## 2484 1242 7 3 5.54 1 85.23 early
## 2485 2079 8 4 6.41 3 196.70 early
## 2486 1376 1 1 6.66 1 35.95 early
## 2487 1312 1 0 7.20 0 83.65 peak
## 2488 2262 6 0 6.42 0 44.20 peak
## 2489 568 0 2 7.27 0 105.60 peak
## 2490 2070 1 5 5.83 0 109.27 decline
## 2491 355 2 1 5.65 1 152.53 decline
## 2492 2052 2 0 6.78 1 150.19 early
## 2493 682 2 1 6.60 0 36.60 early
## 2494 1900 5 2 7.29 0 48.89 early
## 2495 2912 6 3 6.91 1 188.89 early
## 2496 1001 0 1 6.86 0 200.87 early
## 2497 2880 4 4 6.81 1 105.73 early
## 2498 2460 6 4 6.80 0 153.73 early
## 2499 1944 0 1 6.69 1 127.69 early
## 2500 3008 0 5 6.13 0 62.11 early
## 2501 1316 0 1 6.31 0 138.65 early
## 2502 2013 0 5 6.55 0 169.77 early
## 2503 1387 0 0 6.03 0 101.19 early
## 2504 840 0 1 6.80 0 199.57 early
## 2505 2886 0 5 6.32 1 95.20 early
## 2506 1950 0 3 6.49 0 104.97 early
## 2507 1488 0 0 7.75 0 270.00 peak
## 2508 1564 0 2 7.37 1 180.00 peak
## 2509 2516 0 5 7.30 0 171.43 peak
## 2510 1420 0 3 6.09 3 67.08 decline
## 2511 1680 0 3 6.98 0 47.06 decline
## 2512 2108 5 4 5.81 1 33.05 early
## 2513 1840 5 2 6.29 0 131.06 early
## 2514 540 0 1 7.13 1 232.80 early
## 2515 2300 3 0 5.98 1 123.40 early
## 2516 2552 2 2 7.99 0 125.76 peak
## 2517 474 1 0 6.57 2 41.60 peak
## 2518 2130 5 2 7.50 1 243.19 peak
## 2519 390 0 1 5.43 1 101.95 decline
## 2520 1176 0 1 6.67 1 42.83 early
## 2521 1702 0 1 6.92 0 79.34 early
## 2522 2508 0 4 6.48 0 165.42 early
## 2523 1230 0 2 5.45 2 127.61 early
## 2524 1349 0 0 6.28 2 76.95 early
## 2525 1656 0 1 5.78 1 74.22 early
## 2526 2225 0 3 5.86 1 77.89 early
## 2527 1300 0 0 6.58 1 199.97 early
## 2528 946 0 1 7.38 0 132.11 early
## 2529 612 0 1 6.19 0 45.12 early
## 2530 2201 0 6 6.73 0 101.45 early
## 2531 2511 0 3 5.46 1 25.73 early
## 2532 512 0 2 6.19 1 167.23 early
## 2533 2366 3 2 5.86 0 158.46 early
## 2534 1349 2 2 5.64 0 61.44 early
## 2535 768 1 1 6.86 1 50.03 early
## 2536 3276 6 4 6.09 0 207.50 early
## 2537 3108 10 7 6.48 0 35.36 early
## 2538 1364 0 2 5.80 1 130.30 early
## 2539 3196 0 3 6.25 1 119.43 early
## 2540 1896 0 0 6.98 1 201.03 early
## 2541 3108 0 3 6.72 1 117.85 early
## 2542 2310 0 2 6.64 1 218.51 early
## 2543 1932 0 1 6.36 1 165.87 early
## 2544 576 0 1 6.72 0 196.43 early
## 2545 576 0 1 6.87 0 214.79 peak
## 2546 1728 0 3 6.42 0 164.66 peak
## 2547 320 0 0 6.31 1 69.72 early
## 2548 984 0 2 6.55 1 79.40 early
## 2549 1672 0 3 6.35 0 219.27 early
## 2550 2176 0 4 6.28 1 133.54 early
## 2551 1536 0 5 6.89 0 41.78 early
## 2552 540 0 0 6.42 0 76.73 early
## 2553 2553 0 3 6.93 0 53.18 peak
## 2554 504 0 0 6.57 1 190.29 peak
## 2555 2312 2 0 5.88 1 148.65 early
## 2556 1577 4 2 6.24 0 35.29 early
## 2557 1800 2 1 6.04 2 126.28 early
## 2558 2196 4 8 6.23 0 31.99 early
## 2559 1377 3 1 6.21 0 150.85 early
## 2560 2640 3 3 6.59 2 150.58 early
## 2561 518 0 1 6.05 0 152.20 early
## 2562 2706 7 2 7.27 0 124.60 peak
## 2563 2738 2 6 6.97 2 157.87 peak
## 2564 1230 5 2 7.04 0 223.48 peak
## 2565 1260 4 4 5.93 2 115.04 decline
## 2566 438 0 1 7.04 0 54.99 decline
## 2567 1558 1 2 6.81 0 127.20 early
## 2568 1280 2 0 6.32 1 37.28 early
## 2569 1105 6 2 5.88 0 106.97 early
## 2570 1120 0 0 6.47 0 144.43 early
## 2571 1729 2 2 6.27 0 35.51 early
## 2572 2400 5 2 6.46 0 111.14 early
## 2573 2838 6 4 6.96 0 186.59 peak
## 2574 1782 4 2 7.48 0 62.43 peak
## 2575 1144 1 1 8.07 0 266.76 peak
## 2576 480 1 0 6.58 0 41.37 decline
## 2577 1800 4 3 6.76 1 40.25 decline
## 2578 2176 5 2 6.40 0 88.41 decline
## 2579 2208 1 3 5.95 1 67.15 early
## 2580 1890 7 1 5.77 0 107.86 early
## 2581 624 2 2 7.21 0 199.37 early
## 2582 2688 3 1 7.01 0 122.52 early
## 2583 1581 1 2 6.24 0 229.61 early
## 2584 3196 2 6 5.80 1 164.84 early
## 2585 1600 0 6 6.78 1 105.24 early
## 2586 1340 2 0 6.31 0 70.50 early
## 2587 1376 1 2 7.60 1 265.01 peak
## 2588 480 3 0 7.03 2 161.58 peak
## 2589 1430 3 1 7.48 1 178.89 peak
## 2590 774 0 1 6.26 1 150.09 early
## 2591 350 1 2 6.55 0 86.13 early
## 2592 2624 6 6 5.49 1 95.36 early
## 2593 1900 4 3 6.54 0 254.10 early
## 2594 1547 4 4 6.74 1 51.64 early
## 2595 2144 5 2 7.01 1 139.37 peak
## 2596 1131 2 0 7.05 2 92.83 peak
## 2597 2275 5 2 6.04 0 124.29 early
## 2598 558 2 0 6.50 2 104.85 early
## 2599 1288 3 0 6.34 1 151.19 early
## 2600 1349 1 2 6.99 0 158.31 early
## 2601 1035 1 3 5.50 0 97.11 early
## 2602 2268 3 2 6.47 0 200.63 early
## 2603 1860 6 1 6.31 1 201.87 early
## 2604 2080 7 2 6.56 2 45.32 early
## 2605 2145 2 3 6.49 0 82.70 early
## 2606 1292 4 0 5.78 0 113.67 early
## 2607 2340 7 4 5.82 1 57.47 early
## 2608 474 0 0 6.36 2 110.99 early
## 2609 1932 2 2 5.60 0 161.50 early
## 2610 1863 5 1 6.69 1 46.31 early
## 2611 2236 4 1 5.81 2 118.42 early
## 2612 1134 2 4 7.14 1 171.18 early
## 2613 2015 4 5 5.83 1 38.47 early
## 2614 1742 4 1 5.68 0 73.23 early
## 2615 2392 4 1 5.92 1 36.84 early
## 2616 2240 4 2 6.42 0 138.52 early
## 2617 2184 7 1 5.42 2 63.35 early
## 2618 869 1 0 6.28 0 118.04 early
## 2619 2775 6 7 6.60 0 177.68 early
## 2620 1296 4 1 6.36 1 234.93 early
## 2621 3010 2 4 5.71 0 35.70 early
## 2622 320 0 0 7.65 1 283.32 peak
## 2623 1350 1 0 6.73 2 206.70 peak
## 2624 2139 5 3 5.66 1 134.81 early
## 2625 780 2 0 6.22 1 140.15 early
## 2626 832 1 1 6.20 0 103.33 early
## 2627 522 0 1 6.15 1 65.51 early
## 2628 2156 4 1 6.36 0 216.75 early
## 2629 864 1 4 6.40 0 65.64 early
## 2630 952 1 1 7.05 0 172.85 early
## 2631 940 3 0 6.06 0 122.89 early
## 2632 704 2 0 7.81 1 114.49 peak
## 2633 574 3 2 7.58 0 136.76 peak
## 2634 1020 6 3 7.44 0 52.78 peak
## 2635 2726 9 3 6.70 2 218.95 early
## 2636 1769 5 3 7.81 0 152.66 early
## 2637 1008 1 0 6.71 0 40.18 early
## 2638 781 0 2 6.20 1 40.16 early
## 2639 1452 5 1 6.81 0 77.85 early
## 2640 3293 5 5 6.30 1 36.58 early
## 2641 2849 4 3 6.96 0 51.35 early
## 2642 3290 4 7 6.50 1 184.37 early
## 2643 810 1 3 7.21 0 106.04 peak
## 2644 693 1 3 7.31 1 46.39 peak
## 2645 2430 4 2 5.65 2 32.86 early
## 2646 666 2 1 6.56 0 38.58 early
## 2647 712 3 0 7.40 1 87.71 early
## 2648 744 0 1 6.85 1 196.34 early
## 2649 1700 3 6 7.11 0 208.38 early
## 2650 768 1 0 5.94 1 88.93 early
## 2651 913 0 1 6.58 1 101.12 early
## 2652 2940 6 5 6.39 1 46.77 early
## 2653 2079 4 1 6.65 0 211.56 early
## 2654 3185 5 2 6.54 1 191.42 peak
## 2655 1156 2 1 6.75 1 163.56 early
## 2656 2112 5 3 6.30 0 163.12 early
## 2657 720 4 3 6.69 0 38.92 early
## 2658 992 6 0 6.27 3 73.31 early
## 2659 1892 5 3 6.46 0 101.89 early
## 2660 2448 5 7 6.24 1 94.62 early
## 2661 2484 4 2 6.07 0 152.59 early
## 2662 520 1 1 7.14 1 55.78 early
## 2663 1550 1 2 7.40 1 147.52 peak
## 2664 1615 1 2 7.47 1 141.83 peak
## 2665 3367 8 4 6.11 0 177.23 early
## 2666 2380 2 1 6.56 0 206.24 early
## 2667 910 2 1 5.95 2 109.57 early
## 2668 3145 1 6 7.46 1 231.92 early
## 2669 1173 3 1 5.44 0 116.54 early
## 2670 1520 1 6 7.18 1 83.50 peak
## 2671 2310 2 3 6.74 2 36.62 peak
## 2672 414 0 0 6.55 0 50.79 early
## 2673 504 2 0 6.82 0 50.29 early
## 2674 518 2 0 6.72 2 101.97 early
## 2675 1116 3 2 6.89 0 198.73 early
## 2676 2128 3 2 7.41 0 195.91 early
## 2677 510 1 0 6.32 1 175.21 early
## 2678 2576 4 6 5.94 0 158.28 early
## 2679 1944 0 2 7.02 0 42.66 early
## 2680 1162 4 1 7.24 0 132.66 early
## 2681 1170 1 0 6.57 1 44.98 peak
## 2682 504 0 1 6.64 0 86.46 early
## 2683 1598 6 2 5.70 1 145.03 early
## 2684 1917 3 5 5.62 1 131.91 early
## 2685 1176 2 1 7.03 0 107.04 early
## 2686 378 0 1 6.86 1 103.41 early
## 2687 966 1 2 6.97 2 184.53 early
## 2688 2652 2 2 6.94 1 265.22 peak
## 2689 2366 7 1 7.10 1 173.04 peak
## 2690 1144 5 1 6.84 1 156.33 peak
## 2691 1326 3 3 6.46 1 162.79 decline
## 2692 2403 3 5 7.08 0 196.56 decline
## 2693 2232 4 5 5.77 1 32.22 decline
## 2694 1104 2 1 6.12 0 32.55 early
## 2695 2046 4 2 6.08 1 127.01 early
## 2696 660 1 0 7.07 0 153.77 early
## 2697 536 1 1 6.75 1 185.45 early
## 2698 438 2 2 5.86 0 35.27 early
## 2699 730 3 0 5.89 0 38.28 early
## 2700 1056 0 3 7.17 1 271.40 early
## 2701 780 0 0 7.09 1 99.81 early
## 2702 2160 0 1 7.34 0 97.23 early
## 2703 469 0 0 6.63 1 80.83 early
## 2704 1600 0 1 5.59 0 128.98 early
## 2705 2432 0 8 7.36 0 243.46 early
## 2706 1950 0 2 7.81 0 197.40 peak
## 2707 1488 0 2 7.76 1 147.61 peak
## 2708 1922 0 4 7.37 1 174.63 peak
## 2709 1364 3 2 5.88 2 116.26 early
## 2710 405 0 0 6.29 0 36.95 early
## 2711 1560 3 2 7.08 0 157.60 early
## 2712 1742 1 3 6.28 0 161.29 early
## 2713 2822 5 3 6.63 0 35.94 early
## 2714 2484 3 1 6.07 0 167.54 early
## 2715 1738 4 1 6.72 1 269.89 early
## 2716 1500 3 2 7.42 0 44.12 early
## 2717 702 1 0 6.11 0 191.87 early
## 2718 1312 3 0 5.99 2 195.31 early
## 2719 2139 4 6 7.37 0 44.14 early
## 2720 1012 3 1 6.32 0 121.79 early
## 2721 366 1 2 5.83 1 139.26 early
## 2722 2250 1 3 6.35 1 81.23 early
## 2723 420 0 2 6.60 0 98.17 early
## 2724 2720 6 4 6.76 2 38.92 early
## 2725 1001 1 2 6.38 0 35.03 early
## 2726 2835 2 5 6.22 1 33.43 early
## 2727 1316 0 1 5.94 1 154.92 early
## 2728 1134 0 2 6.67 1 82.27 early
## 2729 720 1 2 6.47 0 150.70 early
## 2730 1504 1 1 6.49 2 81.72 early
## 2731 1071 1 1 7.07 0 111.47 early
## 2732 1104 4 0 5.41 1 29.99 early
## 2733 1848 4 1 5.36 1 86.04 early
## 2734 1120 1 1 7.23 0 200.46 early
## 2735 3045 6 3 6.00 0 58.19 early
## 2736 2592 5 3 6.91 0 54.10 early
## 2737 1176 1 3 6.12 1 39.80 early
## 2738 2160 7 2 5.97 1 120.86 early
## 2739 486 2 1 6.54 0 88.69 early
## 2740 648 1 1 5.71 1 141.55 early
## 2741 2610 5 3 6.75 1 36.48 early
## 2742 1760 4 3 7.36 0 98.81 peak
## 2743 1332 3 0 5.52 2 61.05 early
## 2744 1584 4 0 7.13 1 87.52 early
## 2745 651 0 0 6.72 0 54.05 early
## 2746 1022 4 1 6.69 1 266.03 early
## 2747 3132 1 1 5.72 0 144.86 early
## 2748 1296 3 5 5.68 0 107.47 early
## 2749 2835 6 1 6.67 0 76.70 early
## 2750 2130 3 2 6.35 0 206.94 early
## 2751 2686 5 7 6.66 0 197.97 early
## 2752 1680 2 2 7.00 0 225.41 early
## 2753 910 3 3 6.05 1 113.70 early
## 2754 900 2 2 5.73 1 65.67 early
## 2755 2464 3 3 6.94 1 101.53 early
## 2756 330 1 0 5.79 0 28.04 early
## 2757 1984 3 5 6.00 1 170.08 early
## 2758 1185 1 0 6.02 1 149.04 early
## 2759 2821 8 0 6.48 1 49.35 early
## 2760 1909 3 2 7.00 0 166.95 early
## 2761 1500 6 3 6.57 1 205.48 early
## 2762 2604 6 3 5.97 1 96.87 early
## 2763 1785 0 1 6.34 2 47.65 early
## 2764 1260 3 2 6.71 0 229.90 early
## 2765 1880 3 1 5.70 0 177.35 early
## 2766 1184 0 1 5.69 1 32.48 early
## 2767 3010 10 5 6.65 2 179.86 early
## 2768 1110 3 1 7.51 0 206.15 peak
## 2769 1105 3 2 5.61 0 36.66 early
## 2770 1769 4 2 6.69 0 104.53 early
## 2771 2025 5 2 7.37 1 120.48 early
## 2772 1092 1 3 6.39 1 157.90 early
## 2773 1656 3 3 6.71 1 84.40 early
## 2774 1496 3 2 6.38 0 68.80 early
## 2775 3080 4 6 5.76 0 79.53 early
## 2776 630 0 1 6.66 1 94.57 peak
## 2777 1197 0 3 7.70 0 131.51 peak
## 2778 896 3 0 7.25 1 53.39 peak
## 2779 1320 3 2 6.64 1 155.47 early
## 2780 1240 1 2 6.51 0 222.96 early
## 2781 1950 3 1 6.79 0 238.75 early
## 2782 1702 2 4 6.90 0 237.99 early
## 2783 2175 7 1 7.42 1 294.83 early
## 2784 2492 4 3 5.83 2 54.97 early
## 2785 852 4 0 5.89 2 111.50 early
## 2786 960 0 0 6.86 0 183.19 early
## 2787 630 0 0 6.30 0 112.54 early
## 2788 680 0 1 6.21 0 133.81 early
## 2789 847 0 1 5.83 1 73.12 early
## 2790 1748 0 2 7.04 1 56.92 early
## 2791 948 0 1 6.49 1 102.69 peak
## 2792 3102 0 3 7.28 1 98.10 peak
## 2793 2100 0 2 7.28 0 130.67 peak
## 2794 858 0 2 6.01 2 104.86 decline
## 2795 744 0 2 5.83 1 57.79 decline
## 2796 504 0 0 6.18 0 136.23 decline
## 2797 732 0 2 6.93 1 157.91 decline
## 2798 1656 0 5 6.29 0 39.46 decline
## 2799 1638 0 4 6.54 1 155.95 early
## 2800 1530 0 2 6.26 2 31.51 early
## 2801 2436 0 1 6.43 1 134.34 early
## 2802 2240 0 5 5.98 0 39.26 early
## 2803 2664 0 2 6.20 1 158.25 early
## 2804 2108 0 5 6.39 0 104.53 early
## 2805 2072 0 3 6.83 0 95.35 early
## 2806 847 0 3 6.31 0 227.25 early
## 2807 2924 0 1 5.84 1 78.79 early
## 2808 2262 7 3 5.97 0 208.21 early
## 2809 1242 6 3 6.13 0 102.93 early
## 2810 715 2 4 5.74 1 91.02 early
## 2811 2952 2 2 7.02 1 83.84 early
## 2812 1564 3 1 6.59 0 47.94 early
## 2813 2412 6 4 6.95 1 57.84 early
## 2814 2556 3 7 6.53 0 131.63 early
## 2815 1387 2 3 8.54 0 382.54 peak
## 2816 558 0 0 6.83 0 41.89 peak
## 2817 1980 5 3 7.40 0 311.72 peak
## 2818 2392 3 2 6.04 0 115.35 decline
## 2819 1092 2 1 6.69 1 39.55 decline
## 2820 1824 4 0 6.50 0 37.55 early
## 2821 1780 2 2 6.59 1 218.68 early
## 2822 1860 2 3 5.91 0 29.03 early
## 2823 2100 1 1 6.77 1 149.52 early
## 2824 2624 3 1 6.22 1 164.29 early
## 2825 852 3 2 6.47 0 33.53 early
## 2826 588 2 1 6.69 0 100.34 early
## 2827 702 1 0 6.34 0 47.21 early
## 2828 882 1 2 5.06 1 64.54 early
## 2829 2856 8 1 6.34 3 34.85 early
## 2830 1785 4 3 6.00 2 36.35 early
## 2831 1760 4 6 5.60 1 70.75 early
## 2832 1892 8 2 7.59 1 148.69 peak
## 2833 2838 3 3 7.04 1 48.44 peak
## 2834 3256 5 1 6.85 0 99.95 early
## 2835 1364 2 1 6.02 0 72.50 early
## 2836 2590 2 1 5.66 0 180.47 early
## 2837 2952 3 2 6.27 0 37.77 early
## 2838 408 0 0 7.02 1 152.70 early
## 2839 2790 3 4 5.96 0 82.54 early
## 2840 2304 5 3 5.73 1 184.69 early
## 2841 1890 0 1 5.72 0 34.07 early
## 2842 637 0 1 6.08 0 79.54 early
## 2843 2205 0 3 6.50 0 107.17 early
## 2844 1400 0 1 5.90 2 139.28 early
## 2845 1068 0 0 6.08 2 164.85 early
## 2846 684 0 1 6.00 2 65.75 early
## 2847 1602 1 0 6.41 0 134.37 early
## 2848 803 2 2 6.67 0 90.01 early
## 2849 913 1 1 5.85 1 190.88 early
## 2850 1914 5 1 5.54 1 62.93 early
## 2851 720 2 1 5.65 0 124.18 early
## 2852 2030 4 5 7.08 0 53.87 early
## 2853 990 0 0 5.96 1 157.68 early
## 2854 1786 1 1 6.50 1 101.79 early
## 2855 1340 5 0 7.41 1 322.22 early
## 2856 2314 2 1 6.81 1 129.34 early
## 2857 476 1 0 6.45 0 172.23 early
## 2858 623 0 0 6.53 0 124.69 early
## 2859 2356 9 2 6.50 0 197.42 early
## 2860 1159 2 1 5.93 1 147.79 early
## 2861 1056 3 2 6.05 1 122.38 early
## 2862 1558 5 2 5.86 0 35.25 early
## 2863 1798 7 1 6.03 0 37.36 early
## 2864 1079 1 0 6.52 0 117.20 early
## 2865 2200 8 3 6.43 1 46.18 early
## 2866 2214 8 2 5.96 2 38.69 early
## 2867 3312 2 2 5.62 1 173.54 early
## 2868 1196 0 1 6.38 2 92.62 early
## 2869 3384 7 2 6.02 1 166.79 early
## 2870 1836 6 3 5.76 1 110.29 early
## 2871 1615 1 1 6.18 1 40.29 early
## 2872 2263 11 3 5.48 1 162.32 early
## 2873 1680 0 5 7.43 1 65.39 early
## 2874 2484 0 2 6.38 0 44.36 early
## 2875 1204 0 2 7.37 0 55.31 early
## 2876 504 0 0 7.00 0 115.52 early
## 2877 1190 0 1 5.95 1 120.70 early
## 2878 2142 0 4 6.74 1 108.87 early
## 2879 2040 5 1 6.22 1 114.13 early
## 2880 2765 6 5 6.27 1 88.43 early
## 2881 1530 3 2 6.38 0 131.12 early
## 2882 770 1 2 5.55 0 72.73 early
## 2883 1656 1 3 6.48 1 100.59 early
## 2884 2320 4 3 5.34 1 122.63 early
## 2885 2632 4 2 6.86 1 162.93 peak
## 2886 759 0 1 6.93 0 193.20 early
## 2887 2541 0 0 6.12 0 89.49 early
## 2888 1653 0 1 6.20 2 33.02 early
## 2889 1449 0 5 5.05 1 45.09 early
## 2890 465 0 0 6.25 1 70.21 early
## 2891 2325 0 6 6.36 1 154.81 early
## 2892 2912 0 3 6.56 0 119.37 early
## 2893 620 0 3 6.44 0 185.38 early
## 2894 1260 0 1 6.00 1 162.17 early
## 2895 2133 0 5 6.15 2 166.86 early
## 2896 2905 0 5 5.80 1 179.10 early
## 2897 2673 0 8 7.04 1 44.83 early
## 2898 632 0 0 7.44 1 113.50 peak
## 2899 434 0 1 7.45 1 296.45 peak
## 2900 574 0 0 7.55 1 205.05 peak
## 2901 2170 0 1 5.92 1 188.34 decline
## 2902 1386 0 2 6.80 1 194.23 decline
## 2903 445 0 0 5.62 1 28.02 decline
## 2904 968 0 0 5.85 2 40.00 decline
## 2905 540 0 1 6.18 2 86.44 early
## 2906 1760 0 1 6.40 0 43.29 early
## 2907 1376 0 2 5.32 0 25.64 early
## 2908 720 0 1 6.62 0 209.99 early
## 2909 1800 0 1 6.74 1 225.56 early
## 2910 1188 0 1 6.74 1 169.69 early
## 2911 1638 0 0 6.14 1 131.82 early
## 2912 1343 0 2 6.64 0 187.74 peak
## 2913 1183 0 0 7.44 0 199.47 peak
## 2914 936 0 0 6.15 2 113.24 peak
## 2915 2444 0 2 5.96 1 65.18 decline
## 2916 3256 0 3 6.83 0 45.44 decline
## 2917 1656 0 0 6.02 0 128.91 decline
## 2918 2444 0 5 5.90 2 164.50 decline
## 2919 2880 1 4 6.41 0 130.39 early
## 2920 682 1 1 5.66 0 54.11 early
## 2921 1936 1 2 5.65 0 131.92 early
## 2922 1870 4 1 6.39 1 45.15 early
## 2923 1424 2 1 6.46 1 34.55 early
## 2924 1863 4 4 5.85 0 115.90 early
## 2925 2800 5 4 8.10 0 131.13 peak
## 2926 1344 3 0 7.12 1 289.36 peak
## 2927 2108 5 5 7.59 0 193.33 peak
## 2928 2414 7 1 5.35 0 86.78 decline
## 2929 2001 3 2 5.71 0 77.48 early
## 2930 1200 3 4 6.09 1 78.48 early
## 2931 410 1 1 6.61 0 41.02 early
## 2932 700 3 0 5.73 0 85.27 early
## 2933 780 2 2 6.13 1 126.69 early
## 2934 1743 1 1 6.85 0 190.37 early
## 2935 2000 3 2 6.53 0 186.37 early
## 2936 1742 4 2 6.15 2 124.87 peak
## 2937 1302 2 0 6.88 0 112.55 peak
## 2938 1480 3 3 7.34 0 132.12 peak
## 2939 2937 3 3 6.27 0 84.77 decline
## 2940 2025 7 1 6.38 0 85.27 decline
## 2941 1722 2 2 6.98 0 79.20 decline
## 2942 1072 5 3 6.17 1 124.39 decline
## 2943 1488 5 0 6.05 2 80.29 early
## 2944 1782 3 3 6.00 2 29.98 early
## 2945 1632 1 1 6.20 1 38.78 early
## 2946 1548 4 1 6.79 1 76.88 early
## 2947 2002 3 1 6.96 0 160.63 early
## 2948 1587 1 3 6.92 1 226.21 early
## 2949 1869 4 0 6.18 1 227.43 early
## 2950 952 0 2 6.45 0 46.66 early
## 2951 425 0 0 6.60 0 120.82 early
## 2952 1890 0 2 6.16 1 40.05 early
## 2953 2132 0 5 6.73 0 99.19 early
## 2954 384 0 0 6.66 0 87.66 early
## 2955 1920 0 2 6.16 0 87.40 early
## 2956 3096 0 5 5.73 1 77.08 early
## 2957 1275 0 1 7.27 0 95.39 early
## 2958 910 0 0 7.39 0 47.62 peak
## 2959 1088 0 0 7.28 2 109.82 peak
## 2960 1440 0 4 6.85 0 50.93 peak
## 2961 497 0 0 7.06 1 237.16 decline
## 2962 1395 1 3 6.55 1 47.57 early
## 2963 897 3 4 6.25 1 45.94 early
## 2964 840 2 2 6.29 0 80.92 early
## 2965 1462 1 0 6.45 0 189.28 early
## 2966 2442 3 4 6.44 0 202.10 early
## 2967 1984 5 6 6.61 0 223.52 early
## 2968 1222 1 5 6.06 0 170.06 early
## 2969 2400 7 4 6.35 1 70.22 early
## 2970 3080 7 2 6.35 1 132.86 early
## 2971 1482 3 1 7.47 2 237.61 peak
## 2972 1440 1 2 7.62 0 261.99 peak
## 2973 2538 3 3 8.07 1 70.08 peak
## 2974 2343 4 5 6.16 1 115.39 early
## 2975 783 1 1 5.41 1 153.60 early
## 2976 1134 3 0 6.20 0 103.10 early
## 2977 438 1 1 5.70 0 145.42 early
## 2978 3080 6 3 6.62 0 139.37 early
## 2979 2059 1 3 7.18 0 290.94 early
## 2980 1701 4 3 6.89 1 40.44 early
## 2981 2025 5 5 5.86 2 148.69 early
## 2982 3060 7 8 6.91 1 141.59 early
## 2983 1408 3 1 6.56 0 35.19 early
## 2984 1725 6 0 5.62 1 28.96 early
## 2985 682 4 1 6.93 0 252.85 early
## 2986 2788 8 0 6.65 0 141.48 early
## 2987 1900 3 4 7.49 1 182.34 peak
## 2988 830 3 0 7.36 1 315.60 peak
## 2989 1692 1 1 7.83 0 141.48 peak
## 2990 920 3 0 6.90 0 159.97 early
## 2991 2324 0 2 6.23 1 208.74 early
## 2992 1134 0 0 6.96 0 52.35 early
## 2993 1140 2 2 6.31 1 186.86 early
## 2994 1424 2 1 6.75 1 168.92 early
## 2995 1680 3 4 5.36 0 140.68 early
## 2996 1116 1 2 6.60 0 133.61 early
## 2997 1207 1 2 6.87 0 120.61 early
## 2998 979 0 0 7.74 0 61.91 early
## 2999 2232 8 3 6.24 1 218.64 early
## 3000 729 3 1 6.87 1 150.63 peak
## 3001 1120 2 2 6.80 0 37.67 early
## 3002 340 0 1 7.09 0 185.29 early
## 3003 2958 5 3 6.28 1 92.35 early
## 3004 1560 3 2 7.22 0 60.96 early
## 3005 852 2 2 6.60 1 187.95 early
## 3006 2244 4 4 7.09 1 91.00 early
## 3007 1207 4 3 6.22 1 76.96 early
## 3008 1610 2 3 5.93 1 114.73 early
## 3009 2325 5 1 5.05 1 118.77 early
## 3010 1932 2 5 6.53 1 140.14 early
## 3011 1104 3 0 5.84 1 151.31 early
## 3012 1638 3 4 5.52 0 27.65 early
## 3013 1800 4 2 6.22 0 204.91 early
## 3014 2752 7 7 6.70 0 120.48 early
## 3015 2310 10 1 6.65 1 144.91 early
## 3016 651 1 1 6.63 1 141.07 early
## 3017 2574 10 4 5.99 1 149.73 early
## 3018 732 1 0 7.62 1 115.86 peak
## 3019 1012 1 1 6.71 0 157.47 peak
## 3020 2484 2 3 6.98 2 161.26 peak
## 3021 1349 3 3 5.74 1 29.88 early
## 3022 1380 3 4 6.47 0 41.95 early
## 3023 2640 4 2 6.47 2 148.37 early
## 3024 1950 1 2 6.95 0 194.87 early
## 3025 2418 3 2 6.80 0 202.37 early
## 3026 2457 4 3 6.17 1 225.62 early
## 3027 1848 4 1 5.28 1 128.06 early
## 3028 486 0 0 7.49 0 111.31 early
## 3029 882 0 1 5.83 1 101.44 early
## 3030 2448 10 3 6.70 0 205.82 early
## 3031 946 1 1 5.17 1 58.46 early
## 3032 1584 3 3 6.78 1 158.69 early
## 3033 1600 3 2 7.18 0 233.29 peak
## 3034 1320 2 2 6.74 0 111.18 peak
## 3035 450 3 2 6.72 1 154.14 peak
## 3036 1800 2 4 6.50 0 98.18 decline
## 3037 648 3 0 5.85 0 159.26 decline
## 3038 806 1 3 6.58 0 92.38 decline
## 3039 2924 7 2 5.71 1 105.85 decline
## 3040 1449 6 4 5.91 2 139.75 decline
## 3041 366 0 0 7.84 0 165.62 early
## 3042 2800 0 1 6.44 1 75.69 early
## 3043 444 0 0 6.82 0 260.59 early
## 3044 522 0 0 6.79 1 211.10 early
## 3045 2870 0 2 5.72 0 69.72 early
## 3046 1248 1 2 6.57 0 104.52 early
## 3047 1944 6 6 6.91 0 162.55 early
## 3048 2668 5 1 7.04 0 47.67 early
## 3049 760 2 3 6.32 0 130.53 early
## 3050 595 0 0 6.72 1 162.21 early
## 3051 2407 2 3 6.50 1 174.82 early
## 3052 2325 2 2 7.01 1 152.77 early
## 3053 1708 7 3 6.68 0 136.40 early
## 3054 1958 5 3 7.21 0 55.36 peak
## 3055 1953 2 3 7.53 1 147.14 peak
## 3056 1530 2 3 7.58 0 234.92 peak
## 3057 1176 1 0 6.91 0 53.01 decline
## 3058 1752 0 1 6.86 0 46.61 early
## 3059 2511 0 3 6.84 1 217.94 early
## 3060 1260 0 0 5.50 2 126.04 early
## 3061 730 0 0 6.85 0 216.23 early
## 3062 1296 0 3 6.10 1 88.87 early
## 3063 2432 0 3 5.00 0 56.17 early
## 3064 1769 0 2 6.17 1 156.33 early
## 3065 3182 0 2 6.98 1 55.16 early
## 3066 3024 0 3 5.84 2 40.54 early
## 3067 595 0 2 8.06 0 338.01 peak
## 3068 2350 0 4 7.30 0 110.64 peak
## 3069 1106 0 2 8.08 0 296.78 peak
## 3070 1580 0 1 6.23 0 152.38 decline
## 3071 1260 0 2 7.18 1 233.47 decline
## 3072 3312 0 1 6.38 0 115.35 decline
## 3073 2016 0 1 6.21 1 218.60 decline
## 3074 603 0 0 6.92 0 158.62 decline
## 3075 935 0 1 5.88 1 164.12 decline
## 3076 2618 5 5 6.30 1 210.11 early
## 3077 2256 3 1 6.80 0 194.11 early
## 3078 450 1 0 6.20 1 36.11 early
## 3079 720 2 0 5.75 1 60.18 early
## 3080 2576 3 2 6.93 2 188.29 early
## 3081 1914 4 1 6.90 0 162.33 early
## 3082 2752 9 4 6.75 1 135.60 early
## 3083 1216 5 0 6.86 0 53.02 early
## 3084 1080 4 1 6.54 1 128.37 early
## 3085 1740 5 2 6.74 1 206.08 peak
## 3086 2408 2 2 6.14 1 177.27 early
## 3087 630 2 1 6.59 0 177.21 early
## 3088 1840 7 2 6.72 0 160.07 early
## 3089 2136 5 3 7.33 0 126.48 early
## 3090 1460 1 1 5.77 1 34.97 early
## 3091 1360 0 3 6.54 0 39.87 early
## 3092 1825 5 6 7.07 1 96.26 early
## 3093 1241 6 2 6.40 2 159.27 early
## 3094 1638 3 3 6.79 0 206.12 early
## 3095 1245 3 2 8.06 1 385.16 peak
## 3096 984 2 0 6.93 2 117.46 peak
## 3097 3240 5 7 6.31 1 43.26 early
## 3098 1728 3 2 6.44 1 197.21 early
## 3099 858 1 2 6.48 0 139.03 early
## 3100 2295 3 2 6.20 1 182.68 early
## 3101 854 6 0 6.14 2 88.93 early
## 3102 2890 6 3 7.40 0 318.07 early
## 3103 2088 2 4 6.46 1 48.80 early
## 3104 2106 2 0 7.85 0 177.51 peak
## 3105 1738 6 1 7.44 1 233.10 peak
## 3106 585 0 1 8.05 2 107.41 peak
## 3107 1027 2 1 6.29 0 122.47 decline
## 3108 402 1 1 6.03 2 67.88 decline
## 3109 1449 2 3 7.04 1 162.50 decline
## 3110 1008 0 1 6.17 2 107.34 decline
## 3111 880 1 1 6.72 0 44.43 early
## 3112 1159 5 3 6.18 0 178.57 early
## 3113 1458 2 2 6.25 0 186.73 early
## 3114 2272 5 4 5.80 1 167.84 early
## 3115 2639 9 2 5.86 2 201.71 early
## 3116 910 1 2 6.07 0 134.09 early
## 3117 2822 4 2 7.83 0 263.94 peak
## 3118 2730 8 7 6.92 0 202.40 peak
## 3119 1222 1 3 6.73 1 76.17 peak
## 3120 2001 7 4 6.00 0 178.91 decline
## 3121 2025 3 7 6.48 0 78.67 decline
## 3122 2349 6 3 6.77 0 206.97 early
## 3123 1273 4 1 5.09 1 139.57 early
## 3124 1836 5 5 5.94 1 155.37 early
## 3125 1241 1 0 6.08 2 174.90 early
## 3126 1185 1 1 7.04 0 218.30 early
## 3127 2415 2 2 6.84 0 50.75 early
## 3128 792 1 0 7.09 1 181.49 early
## 3129 2484 1 4 5.92 1 56.95 early
## 3130 2449 3 3 6.02 0 43.10 early
## 3131 630 0 0 7.42 1 275.73 peak
## 3132 3204 5 3 6.86 0 40.32 peak
## 3133 990 2 0 6.30 1 225.48 peak
## 3134 2492 2 1 6.25 1 73.52 decline
## 3135 790 2 1 6.84 0 189.24 decline
## 3136 1139 2 1 5.48 2 71.60 early
## 3137 720 3 1 7.10 2 230.30 early
## 3138 1944 4 3 5.93 0 98.72 early
## 3139 2232 2 3 6.06 0 171.35 early
## 3140 2904 5 5 7.00 0 98.28 early
## 3141 3204 8 1 5.70 1 172.87 early
## 3142 2726 8 4 5.91 1 148.15 early
## 3143 1053 0 1 7.13 0 47.45 early
## 3144 2310 0 2 6.63 1 100.74 early
## 3145 1316 0 1 5.62 0 26.21 early
## 3146 511 0 0 6.36 0 41.91 early
## 3147 1254 0 4 7.27 0 50.80 early
## 3148 1088 0 5 5.90 0 78.52 early
## 3149 960 0 3 6.53 2 218.97 early
## 3150 1809 0 0 6.43 0 175.88 early
## 3151 744 0 2 5.69 0 129.56 early
## 3152 1406 0 0 6.39 0 93.37 early
## 3153 1680 0 1 6.74 0 123.63 early
## 3154 1647 0 2 6.29 1 39.43 early
## 3155 1488 0 4 6.49 0 227.05 early
## 3156 2046 0 2 7.05 1 85.30 early
## 3157 2772 0 4 7.28 1 259.59 peak
## 3158 744 0 0 6.45 1 49.13 peak
## 3159 2904 0 4 7.16 0 154.75 peak
## 3160 2080 0 2 5.89 2 27.99 decline
## 3161 539 0 0 6.87 1 199.92 decline
## 3162 1958 3 1 6.62 1 207.37 early
## 3163 2117 3 8 6.23 0 127.78 early
## 3164 1488 4 2 6.37 0 240.75 early
## 3165 1496 0 0 6.44 0 45.82 early
## 3166 1200 4 0 5.98 0 114.68 early
## 3167 3293 3 1 6.04 0 65.02 early
## 3168 1525 7 2 6.81 2 109.76 early
## 3169 845 1 2 6.42 1 131.01 peak
## 3170 1273 5 2 7.59 0 201.96 peak
## 3171 712 1 0 7.24 2 85.81 peak
## 3172 2340 3 2 7.31 1 88.88 decline
## 3173 2624 4 3 5.58 1 143.46 decline
## 3174 576 1 1 6.54 1 80.21 early
## 3175 1159 3 3 6.67 0 148.73 early
## 3176 2844 5 5 6.17 1 75.04 early
## 3177 1608 0 2 6.33 0 37.90 early
## 3178 1037 2 0 6.17 1 116.12 early
## 3179 962 0 3 5.19 1 25.28 early
## 3180 2628 6 7 7.07 0 89.91 peak
## 3181 1694 4 2 7.31 1 117.35 peak
## 3182 2133 2 2 6.85 1 203.83 peak
## 3183 1692 1 3 6.16 0 149.42 decline
## 3184 1925 7 1 7.21 0 154.93 decline
## 3185 968 0 1 7.30 1 143.31 decline
## 3186 2139 4 3 6.21 0 40.34 decline
## 3187 1610 4 3 6.17 0 89.08 decline
## 3188 1316 1 1 6.38 2 130.40 early
## 3189 325 1 1 6.08 0 170.85 early
## 3190 2028 1 5 7.14 0 48.44 early
## 3191 2210 3 1 6.04 0 114.55 early
## 3192 1088 1 2 5.52 1 32.01 early
## 3193 2775 4 4 7.50 0 101.18 early
## 3194 1875 3 5 6.50 0 122.41 early
## 3195 3060 4 3 5.77 1 164.41 early
## 3196 1428 5 3 6.02 1 99.23 early
## 3197 2697 4 6 6.35 0 33.77 peak
## 3198 1122 4 2 6.84 2 119.77 peak
## 3199 2343 2 4 6.85 0 44.70 early
## 3200 372 0 0 6.24 0 74.65 early
## 3201 1292 2 2 6.32 1 71.36 early
## 3202 2697 3 4 5.76 1 139.82 early
## 3203 320 1 0 6.94 0 46.05 early
## 3204 902 1 2 6.85 0 153.20 early
## 3205 1760 2 2 6.85 0 115.85 early
## 3206 2496 4 5 5.75 2 125.66 early
## 3207 915 2 2 5.93 2 67.86 early
## 3208 1020 1 3 6.60 0 101.35 early
## 3209 1580 3 4 7.82 0 275.65 peak
## 3210 2275 3 2 7.08 1 216.27 peak
## 3211 792 1 3 8.26 0 77.74 peak
## 3212 1408 0 2 6.42 0 48.19 early
## 3213 1092 0 2 5.95 1 169.26 early
## 3214 2574 0 3 6.19 0 103.30 early
## 3215 2250 0 0 5.99 2 37.97 early
## 3216 2204 0 2 6.57 1 39.19 early
## 3217 558 0 0 6.53 0 39.22 early
## 3218 1617 0 0 6.53 0 71.05 early
## 3219 946 0 2 5.71 2 136.02 early
## 3220 2849 0 4 6.54 0 139.90 early
## 3221 675 0 3 7.33 1 137.27 early
## 3222 2392 0 1 6.73 0 243.61 early
## 3223 1806 0 1 6.02 0 79.42 early
## 3224 2322 0 4 6.01 2 199.43 early
## 3225 740 0 0 7.02 0 133.53 early
## 3226 1014 0 2 5.75 2 161.93 early
## 3227 520 0 2 6.80 0 144.35 early
## 3228 845 0 2 5.37 1 80.20 early
## 3229 1380 5 1 6.47 2 182.48 early
## 3230 405 1 0 6.23 1 71.61 early
## 3231 390 0 0 5.59 0 64.40 early
## 3232 1736 7 3 7.01 0 258.02 early
## 3233 1680 3 2 6.90 0 54.09 early
## 3234 2581 1 4 5.86 1 158.49 early
## 3235 2210 8 6 6.39 0 78.74 early
## 3236 2356 4 4 6.43 1 46.96 early
## 3237 1694 7 0 5.98 0 87.03 early
## 3238 720 1 3 6.54 1 138.05 early
## 3239 486 1 2 7.18 2 82.86 early
## 3240 2550 3 2 7.36 0 279.86 peak
## 3241 2436 8 1 7.88 0 269.74 peak
## 3242 2720 9 4 6.95 0 46.35 peak
## 3243 666 0 2 5.95 0 109.56 decline
## 3244 2160 3 3 6.12 1 43.96 decline
## 3245 420 1 1 6.47 0 147.75 early
## 3246 990 2 2 6.31 1 76.50 early
## 3247 1140 3 0 7.13 0 167.60 early
## 3248 1035 2 1 6.69 0 121.75 early
## 3249 1596 1 0 6.89 1 233.03 peak
## 3250 1820 1 1 7.71 0 55.31 peak
## 3251 1462 4 0 7.14 0 172.99 peak
## 3252 656 0 2 5.73 2 177.95 early
## 3253 1001 3 0 6.17 0 43.17 early
## 3254 2788 3 4 5.45 1 121.76 early
## 3255 510 2 0 6.26 1 46.66 early
## 3256 690 1 0 6.59 0 189.15 early
## 3257 2838 4 7 6.75 1 126.53 early
## 3258 2100 2 1 6.15 0 108.16 early
## 3259 1326 4 4 7.47 0 169.87 peak
## 3260 378 0 0 7.21 0 42.01 peak
## 3261 2314 2 2 7.28 1 53.76 peak
## 3262 806 4 0 6.37 0 175.38 decline
## 3263 488 2 0 6.96 1 50.25 early
## 3264 1365 6 4 5.17 1 22.26 early
## 3265 512 0 1 6.10 0 134.18 early
## 3266 2905 4 3 7.07 0 188.28 early
## 3267 1863 1 3 6.72 0 141.89 early
## 3268 1425 1 2 6.58 0 47.91 early
## 3269 672 0 0 6.69 0 209.91 early
## 3270 2997 0 2 6.78 0 157.97 early
## 3271 1924 0 1 6.79 1 113.97 early
## 3272 737 0 3 6.49 0 140.58 early
## 3273 2059 0 4 6.94 0 53.31 early
## 3274 2277 0 3 5.83 0 109.07 early
## 3275 3196 0 4 6.00 2 29.04 early
## 3276 1540 0 4 5.79 0 58.23 early
## 3277 441 0 0 7.30 1 254.01 early
## 3278 979 0 1 6.14 2 148.28 early
## 3279 1001 0 1 6.85 1 96.84 early
## 3280 1218 0 1 7.15 1 179.35 early
## 3281 1088 0 4 6.16 0 127.94 early
## 3282 2280 0 5 6.00 2 165.33 early
## 3283 1587 0 2 6.55 1 42.29 early
## 3284 2262 0 4 7.02 0 218.18 early
## 3285 1922 0 4 8.13 0 223.68 peak
## 3286 1173 4 4 6.29 1 160.29 early
## 3287 1309 2 0 5.99 1 204.94 early
## 3288 426 2 0 7.80 0 114.35 early
## 3289 896 3 0 6.43 1 113.02 early
## 3290 1656 4 0 6.56 0 140.92 early
## 3291 752 1 0 7.44 1 224.00 early
## 3292 588 0 0 5.88 1 138.21 peak
## 3293 1755 2 3 7.65 1 226.28 peak
## 3294 2849 2 3 6.95 1 153.22 peak
## 3295 891 0 2 6.11 0 217.00 decline
## 3296 3115 4 4 7.33 0 205.69 early
## 3297 2668 2 3 6.19 1 155.42 early
## 3298 1343 1 1 5.54 0 136.59 early
## 3299 2340 6 2 6.73 1 134.33 early
## 3300 2448 4 1 5.98 0 193.43 early
## 3301 2523 2 1 6.44 2 79.70 early
## 3302 2904 3 4 5.94 0 39.26 early
## 3303 1729 3 2 6.55 1 96.94 early
## 3304 1365 1 1 6.31 2 177.96 early
## 3305 3240 3 6 5.63 1 95.65 early
## 3306 672 2 1 7.25 1 113.00 early
## 3307 2730 7 6 6.46 1 222.00 early
## 3308 1380 3 3 6.32 1 82.91 early
## 3309 1027 3 1 5.88 1 31.59 early
## 3310 2300 5 2 7.31 0 286.30 early
## 3311 1216 2 0 6.38 0 184.87 peak
## 3312 1782 4 5 6.73 0 45.46 peak
## 3313 539 0 0 6.61 1 47.11 peak
## 3314 1246 2 3 7.21 0 181.01 decline
## 3315 1786 1 2 6.03 0 82.15 decline
## 3316 1200 2 2 6.37 1 178.61 early
## 3317 1876 2 2 6.09 0 123.00 early
## 3318 315 0 1 5.97 2 211.51 early
## 3319 504 3 1 6.17 1 173.85 early
## 3320 1376 1 2 6.09 1 68.56 early
## 3321 2580 3 4 6.47 0 120.80 early
## 3322 1056 3 2 6.08 1 148.60 early
## 3323 1863 2 2 6.18 0 164.94 early
## 3324 2025 5 6 6.90 2 50.01 early
## 3325 3276 6 3 5.64 1 100.98 early
## 3326 2196 4 3 5.47 2 96.46 early
## 3327 924 0 1 6.65 0 97.51 peak
## 3328 2916 9 5 7.60 1 253.11 peak
## 3329 366 0 1 7.10 1 101.13 peak
## 3330 1512 0 0 6.21 0 168.37 early
## 3331 2800 0 3 6.06 0 164.08 early
## 3332 1452 0 1 5.63 1 180.28 early
## 3333 2240 0 1 6.33 1 147.87 early
## 3334 2838 0 3 7.05 2 40.58 early
## 3335 750 0 0 6.38 2 216.86 early
## 3336 2310 0 2 6.18 1 162.53 early
## 3337 1342 0 3 5.62 1 26.21 early
## 3338 460 0 0 7.64 0 213.40 peak
## 3339 1079 0 2 6.78 1 46.20 peak
## 3340 1449 0 1 6.30 2 173.56 early
## 3341 847 0 4 5.39 0 148.65 early
## 3342 639 0 0 6.33 0 77.18 early
## 3343 1230 0 0 8.06 0 144.86 early
## 3344 516 0 1 5.29 1 32.88 early
## 3345 2523 0 3 6.51 0 170.02 early
## 3346 504 0 0 6.79 0 42.79 early
## 3347 1106 0 2 6.10 0 219.47 early
## 3348 1092 0 0 6.15 1 176.29 peak
## 3349 2871 0 5 6.91 1 280.52 peak
## 3350 2628 0 5 6.74 2 184.70 peak
## 3351 979 0 3 6.96 0 239.53 decline
## 3352 1710 0 3 6.05 1 93.57 decline
## 3353 1944 0 5 6.81 2 81.66 decline
## 3354 2976 9 6 5.57 1 103.63 early
## 3355 1273 4 2 6.72 0 214.28 early
## 3356 2106 2 3 6.12 0 41.99 early
## 3357 2108 5 3 6.56 0 95.54 early
## 3358 2772 5 6 6.41 0 199.45 early
## 3359 1185 1 1 5.92 1 120.21 early
## 3360 2340 2 3 6.00 1 81.16 early
## 3361 2844 4 7 6.81 1 81.77 early
## 3362 960 2 1 7.20 1 47.48 early
## 3363 1672 3 4 8.29 1 230.95 peak
## 3364 1702 5 1 7.20 2 212.95 peak
## 3365 1476 1 2 7.31 0 152.46 peak
## 3366 567 1 0 6.61 0 148.58 decline
## 3367 1968 2 1 6.31 0 212.57 decline
## 3368 1909 0 4 6.66 0 164.77 early
## 3369 1610 0 0 5.92 1 41.17 early
## 3370 3182 0 5 6.32 0 67.37 early
## 3371 372 0 2 6.78 0 209.72 early
## 3372 2604 0 1 5.76 1 130.84 early
## 3373 2275 0 4 6.24 1 65.09 early
## 3374 1541 0 8 6.10 1 98.80 early
## 3375 1260 0 3 6.93 0 210.86 early
## 3376 2010 0 2 8.09 1 55.56 peak
## 3377 1185 0 2 7.88 0 101.46 peak
## 3378 1725 3 3 6.04 1 79.51 early
## 3379 1260 1 2 6.69 1 157.12 early
## 3380 2904 5 4 6.23 1 35.86 early
## 3381 790 2 2 6.18 1 121.34 early
## 3382 1274 3 3 6.37 0 47.87 early
## 3383 2790 3 3 6.58 1 141.84 early
## 3384 996 2 1 5.54 2 177.12 early
## 3385 930 5 0 6.86 0 146.32 early
## 3386 405 1 2 7.05 0 54.88 early
## 3387 2573 3 4 7.38 0 194.68 early
## 3388 3384 9 6 6.10 0 115.26 early
## 3389 1520 2 0 6.43 1 85.56 early
## 3390 512 0 0 8.08 0 156.77 peak
## 3391 900 0 3 5.85 2 94.83 early
## 3392 1608 0 1 7.61 0 221.36 early
## 3393 704 0 3 6.21 0 112.77 early
## 3394 2112 0 5 6.30 0 230.19 early
## 3395 1494 0 1 5.19 0 123.14 early
## 3396 1596 0 0 7.35 1 235.98 peak
## 3397 1974 0 2 8.28 0 170.25 peak
## 3398 3045 0 1 6.79 0 214.62 peak
## 3399 960 0 2 6.79 0 238.62 decline
## 3400 3219 0 8 5.65 1 26.97 decline
## 3401 2150 0 1 5.91 0 165.92 decline
## 3402 1260 0 3 6.58 2 151.45 decline
## 3403 468 0 1 6.37 0 177.10 decline
## 3404 711 0 0 5.22 2 45.80 decline
## 3405 2046 0 1 7.26 0 57.53 early
## 3406 1598 0 2 6.09 1 167.97 early
## 3407 1909 0 3 6.29 0 175.64 early
## 3408 1530 0 4 7.18 2 216.28 early
## 3409 684 0 1 6.10 0 41.06 early
## 3410 935 0 1 5.68 0 83.21 early
## 3411 600 0 0 7.50 1 166.63 peak
## 3412 2916 0 6 6.20 0 145.28 peak
## 3413 1701 0 2 6.85 2 85.30 peak
## 3414 1560 0 2 6.17 2 88.01 decline
## 3415 1020 4 0 6.68 0 207.46 early
## 3416 1220 4 0 6.41 0 188.99 early
## 3417 2516 8 6 6.88 0 204.66 early
## 3418 2322 5 2 6.18 1 166.37 early
## 3419 1886 5 5 6.52 0 46.42 early
## 3420 2240 6 4 6.62 1 251.48 peak
## 3421 1200 0 1 7.18 0 182.36 peak
## 3422 549 2 2 6.60 1 237.42 peak
## 3423 1260 2 0 6.42 0 47.67 decline
## 3424 468 0 0 6.34 0 43.70 decline
## 3425 666 1 0 6.24 0 106.25 early
## 3426 1504 2 1 6.52 1 38.90 early
## 3427 864 2 2 7.00 1 115.81 early
## 3428 2158 2 4 5.93 0 132.20 early
## 3429 2720 5 5 6.46 1 167.61 early
## 3430 1500 5 1 6.06 0 118.44 early
## 3431 945 3 4 5.06 1 84.62 early
## 3432 3108 6 2 7.30 0 54.64 early
## 3433 2250 6 3 5.65 0 31.07 early
## 3434 756 3 0 6.32 1 119.77 early
## 3435 1131 0 1 5.59 2 63.42 early
## 3436 2788 7 5 7.36 0 189.97 early
## 3437 2184 5 2 6.72 0 202.77 early
## 3438 1406 6 4 6.91 3 42.39 early
## 3439 2088 4 3 6.77 0 99.69 early
## 3440 962 1 0 6.00 1 100.87 early
## 3441 1071 3 0 5.85 0 79.09 early
## 3442 854 1 2 6.55 0 68.91 early
## 3443 1240 1 7 6.00 0 155.21 early
## 3444 1944 8 3 6.32 2 190.91 early
## 3445 644 0 2 6.27 1 46.59 early
## 3446 414 0 1 6.34 1 213.54 early
## 3447 2660 7 5 6.45 1 34.91 early
## 3448 1400 3 2 7.87 0 50.42 peak
## 3449 1170 5 2 7.12 2 114.50 peak
## 3450 2448 6 2 7.28 0 110.90 peak
## 3451 720 1 0 5.60 1 31.34 early
## 3452 2592 9 4 6.58 1 39.72 early
## 3453 975 1 1 5.57 1 170.82 early
## 3454 2625 4 3 6.51 0 49.62 early
## 3455 2772 5 2 6.05 1 138.36 early
## 3456 1976 1 6 6.13 2 76.13 early
## 3457 3145 3 3 6.66 2 184.15 early
## 3458 1950 4 2 6.38 0 69.88 early
## 3459 1840 2 5 6.53 0 152.92 early
## 3460 3108 3 2 6.98 0 93.25 early
## 3461 1479 4 3 6.48 0 164.62 early
## 3462 1309 3 1 6.67 2 44.13 early
## 3463 2046 6 1 6.45 1 36.84 early
## 3464 1098 6 2 6.94 0 214.35 early
## 3465 3478 5 0 5.56 1 135.25 early
## 3466 1869 5 2 5.72 2 87.06 early
## 3467 448 2 0 6.11 1 196.47 early
## 3468 2108 4 8 5.07 1 140.10 early
## 3469 1230 0 0 6.90 0 276.36 early
## 3470 2184 0 2 5.86 1 161.51 early
## 3471 2325 0 3 6.39 0 67.25 early
## 3472 730 0 1 5.72 1 164.85 early
## 3473 2250 0 5 5.96 0 73.76 early
## 3474 1027 0 2 6.53 0 240.47 early
## 3475 1850 0 2 6.55 0 95.35 early
## 3476 1314 0 3 7.36 0 273.53 peak
## 3477 1785 0 2 5.76 1 84.15 peak
## 3478 470 0 1 7.93 0 67.79 peak
## 3479 1952 0 6 6.15 0 126.82 decline
## 3480 710 0 1 6.88 1 47.59 early
## 3481 1984 0 3 6.24 1 40.63 early
## 3482 3404 0 3 6.61 0 48.17 early
## 3483 656 0 0 6.98 0 160.02 early
## 3484 1775 0 1 5.11 1 129.25 early
## 3485 2236 0 2 6.37 0 34.65 early
## 3486 1360 0 0 6.55 1 178.39 early
## 3487 1462 0 3 5.59 2 159.27 early
## 3488 1558 3 3 6.13 0 64.98 early
## 3489 2444 2 2 5.85 1 163.39 early
## 3490 1785 0 3 6.22 0 101.43 early
## 3491 546 2 0 7.37 0 196.23 early
## 3492 2635 5 1 5.08 1 115.32 early
## 3493 806 3 1 6.13 0 73.40 early
## 3494 395 0 0 6.74 0 200.58 early
## 3495 792 2 2 6.14 2 144.13 peak
## 3496 1491 4 1 7.30 0 202.42 peak
## 3497 2392 1 4 7.41 0 50.71 peak
## 3498 2106 1 2 6.64 0 155.97 early
## 3499 427 2 0 6.80 1 97.02 early
## 3500 1232 1 1 5.52 1 28.32 early
## 3501 1472 5 1 6.24 0 43.20 early
## 3502 2772 5 0 6.83 1 148.73 early
## 3503 1309 4 0 6.89 1 199.56 peak
## 3504 1230 4 1 8.44 0 131.22 peak
## 3505 972 2 0 7.04 0 256.63 peak
## 3506 2880 1 5 6.09 0 156.07 early
## 3507 2100 4 2 6.60 0 138.01 early
## 3508 680 2 0 6.17 0 41.45 early
## 3509 2170 3 1 6.18 0 161.87 early
## 3510 1314 6 1 5.73 3 155.07 early
## 3511 840 0 1 6.57 0 154.69 early
## 3512 2479 7 2 7.23 0 61.23 peak
## 3513 988 2 0 5.43 0 47.53 early
## 3514 2130 4 6 6.18 0 64.52 early
## 3515 930 1 1 6.14 0 121.70 early
## 3516 1185 4 0 5.99 1 91.99 early
## 3517 2511 8 3 7.08 0 235.21 early
## 3518 2210 2 2 5.89 1 64.03 early
## 3519 1950 1 1 6.14 1 35.51 early
## 3520 744 3 2 5.34 3 100.26 early
## 3521 2697 4 1 7.16 0 152.04 early
## 3522 1482 3 3 7.47 1 46.23 peak
## 3523 910 2 3 6.80 0 206.12 peak
## 3524 1710 0 2 6.50 1 40.48 early
## 3525 390 0 2 7.31 0 132.41 early
## 3526 450 0 0 5.00 2 24.35 early
## 3527 2175 0 5 5.78 0 66.77 early
## 3528 2240 0 1 6.18 1 157.03 early
## 3529 1520 0 2 6.32 0 78.52 early
## 3530 1425 0 2 6.16 0 218.84 early
## 3531 2263 0 2 6.97 0 162.05 peak
## 3532 3256 0 2 8.10 1 217.70 peak
## 3533 2752 0 2 7.78 0 138.35 peak
## 3534 455 0 0 6.25 0 36.13 decline
## 3535 930 0 0 6.46 1 114.06 decline
## 3536 2409 6 2 6.14 1 85.18 early
## 3537 553 1 0 6.53 0 43.12 early
## 3538 748 2 1 6.93 1 140.98 early
## 3539 1472 4 2 5.88 2 162.27 early
## 3540 2555 5 4 6.16 1 201.97 early
## 3541 1904 2 3 6.61 0 141.47 early
## 3542 945 4 1 6.68 0 85.54 early
## 3543 1242 4 0 6.84 0 256.31 early
## 3544 1204 4 1 6.26 0 203.51 early
## 3545 2350 3 4 6.18 0 175.48 early
## 3546 910 0 0 6.10 2 41.23 early
## 3547 2112 4 3 6.26 0 41.30 early
## 3548 2295 3 3 6.29 0 140.47 early
## 3549 2520 8 7 6.54 1 88.19 early
## 3550 1936 2 1 7.11 0 228.77 early
## 3551 2244 3 6 6.85 0 107.26 early
## 3552 860 3 0 5.49 1 103.02 early
## 3553 1750 3 4 6.52 1 38.31 early
## 3554 2380 4 1 6.67 0 181.76 early
## 3555 2142 4 4 6.63 1 35.89 early
## 3556 1722 2 2 6.55 0 195.74 early
## 3557 444 0 2 6.19 1 81.28 early
## 3558 720 0 2 5.70 3 74.34 early
## 3559 2125 3 3 6.26 0 167.08 early
## 3560 1932 4 2 7.51 0 178.09 peak
## 3561 1869 2 2 7.01 2 172.57 peak
## 3562 2220 3 2 6.33 2 90.83 peak
## 3563 1932 5 0 5.72 0 55.99 decline
## 3564 680 3 1 6.87 0 47.91 early
## 3565 1541 1 3 6.23 0 105.37 early
## 3566 360 1 1 6.00 0 182.82 early
## 3567 2106 6 1 6.09 0 80.72 early
## 3568 3045 2 1 5.93 0 78.89 early
## 3569 2016 4 2 6.21 0 174.48 early
## 3570 616 0 0 6.16 1 71.57 early
## 3571 648 0 2 6.05 1 173.03 early
## 3572 2340 5 4 7.51 1 254.22 early
## 3573 852 3 3 5.62 0 29.56 early
## 3574 2464 6 2 7.04 0 41.17 early
## 3575 2156 6 5 5.95 1 70.74 early
## 3576 1617 5 0 6.70 1 128.03 early
## 3577 2112 3 5 6.83 0 201.17 early
## 3578 1638 0 0 5.93 0 130.96 early
## 3579 1520 2 3 5.98 1 160.73 early
## 3580 1581 1 4 6.02 0 113.87 early
## 3581 2856 3 5 6.19 2 40.28 early
## 3582 2356 6 2 8.28 0 269.45 peak
## 3583 1125 2 0 7.13 0 56.27 peak
## 3584 385 1 2 7.19 0 60.24 peak
## 3585 1220 1 0 7.01 0 48.17 decline
## 3586 2117 0 3 6.63 0 147.29 decline
## 3587 1950 3 2 6.62 0 114.71 decline
## 3588 2449 7 2 6.94 0 239.43 early
## 3589 1638 1 1 5.74 1 76.25 early
## 3590 2581 6 3 6.66 1 42.93 early
## 3591 1420 4 1 5.14 1 125.52 early
## 3592 630 3 2 6.32 1 37.91 early
## 3593 1053 0 6 5.93 0 118.20 early
## 3594 1232 1 0 6.17 0 66.66 early
## 3595 2883 0 4 6.84 0 82.57 early
## 3596 1953 0 2 5.94 1 102.00 early
## 3597 1420 0 2 5.98 1 200.64 early
## 3598 2697 0 2 7.09 0 40.90 early
## 3599 870 0 0 7.62 0 98.33 early
## 3600 2728 0 2 6.10 1 113.36 early
## 3601 990 0 0 6.93 1 113.05 early
## 3602 1825 0 2 6.18 0 146.44 early
## 3603 395 0 0 7.62 1 110.06 peak
## 3604 2175 0 3 6.38 2 172.46 peak
## 3605 2106 0 2 8.30 1 412.30 peak
## 3606 2511 0 3 7.12 0 179.56 decline
## 3607 2250 0 5 7.00 0 156.85 decline
## 3608 3185 0 3 7.33 0 106.22 decline
## 3609 924 1 1 7.01 0 202.86 early
## 3610 900 2 0 6.58 1 126.06 early
## 3611 546 0 1 5.83 0 155.23 early
## 3612 924 3 1 5.84 1 75.68 early
## 3613 1898 3 2 6.53 0 142.64 early
## 3614 2013 5 2 5.32 2 153.60 early
## 3615 660 1 1 5.94 0 39.35 early
## 3616 1869 2 1 6.93 0 161.82 early
## 3617 637 1 0 7.24 1 292.73 peak
## 3618 1880 2 2 6.05 2 40.33 peak
## 3619 2541 7 2 7.59 0 333.35 peak
## 3620 532 1 0 6.17 0 33.08 early
## 3621 2343 9 5 7.59 1 265.30 early
## 3622 2592 5 3 5.59 1 164.29 early
## 3623 816 0 3 6.00 1 146.38 early
## 3624 2100 3 4 6.60 0 198.86 early
## 3625 1035 4 3 6.63 1 75.79 early
## 3626 1728 5 3 7.03 1 56.95 early
## 3627 2520 3 4 6.47 0 75.91 early
## 3628 2592 5 4 7.40 1 53.91 early
## 3629 1320 4 0 6.95 0 139.95 early
## 3630 1430 5 2 7.26 0 266.42 peak
## 3631 1400 5 3 7.46 1 265.81 peak
## 3632 2376 6 3 7.77 0 61.28 peak
## 3633 2870 2 3 7.19 0 57.32 decline
## 3634 1924 1 8 6.42 0 202.84 early
## 3635 2150 5 1 7.28 0 240.98 early
## 3636 2958 5 4 6.03 3 157.35 early
## 3637 732 3 1 6.45 0 66.63 early
## 3638 2345 3 3 5.80 1 31.28 early
## 3639 1674 1 0 5.25 1 112.61 early
## 3640 2574 5 3 7.65 1 55.58 peak
## 3641 913 0 1 8.11 0 264.41 peak
## 3642 696 1 1 7.37 0 290.63 peak
## 3643 1824 4 2 6.51 0 145.76 early
## 3644 850 2 1 6.52 0 99.50 early
## 3645 1056 0 0 6.91 1 194.09 early
## 3646 656 0 0 6.99 1 172.46 early
## 3647 1708 6 3 6.16 1 224.51 early
## 3648 682 1 0 7.01 0 227.89 early
## 3649 2088 4 5 5.92 1 185.21 early
## 3650 2730 3 2 5.90 1 188.12 early
## 3651 552 1 0 6.98 0 124.46 early
## 3652 2074 6 1 7.19 2 91.74 peak
## 3653 2952 4 6 7.45 0 103.10 peak
## 3654 1494 0 2 7.37 1 52.02 peak
## 3655 3036 5 2 6.12 0 186.97 decline
## 3656 2376 3 0 6.79 0 99.83 decline
## 3657 1936 0 3 6.13 0 194.11 early
## 3658 2106 0 3 6.20 0 38.90 early
## 3659 1850 0 1 6.55 2 44.63 early
## 3660 300 0 0 7.07 0 102.45 early
## 3661 2025 0 4 6.92 2 278.95 early
## 3662 1258 0 0 6.90 2 197.93 early
## 3663 1520 0 4 5.97 0 125.84 early
## 3664 1932 0 0 5.44 1 76.57 early
## 3665 2444 0 4 6.39 2 69.91 early
## 3666 528 0 0 5.76 2 29.36 early
## 3667 693 0 1 6.56 1 39.04 early
## 3668 372 0 0 6.36 0 191.96 early
## 3669 2146 5 0 6.31 0 45.19 early
## 3670 1610 3 3 6.84 1 203.46 early
## 3671 3185 4 5 6.69 0 151.44 early
## 3672 1001 3 3 6.59 0 178.64 early
## 3673 780 1 0 6.53 0 207.48 early
## 3674 1377 1 2 7.31 2 184.00 peak
## 3675 1080 4 0 7.44 0 295.42 peak
## 3676 534 0 1 7.47 1 65.00 peak
## 3677 2046 2 3 6.43 0 145.20 early
## 3678 549 1 2 6.29 0 180.16 early
## 3679 1700 1 0 6.09 0 210.11 early
## 3680 432 0 0 7.03 0 108.95 early
## 3681 602 0 0 5.51 2 142.05 early
## 3682 682 1 0 5.88 0 187.35 early
## 3683 1258 0 0 6.23 1 195.01 early
## 3684 1716 3 1 7.08 1 156.30 peak
## 3685 910 2 2 6.71 1 234.03 peak
## 3686 462 0 0 6.93 3 128.49 peak
## 3687 994 2 0 6.81 1 133.42 decline
## 3688 2160 1 4 6.78 0 82.76 decline
## 3689 2088 2 2 5.46 2 75.27 decline
## 3690 2736 0 6 5.83 2 116.26 early
## 3691 2856 0 4 6.37 1 190.24 early
## 3692 2952 0 4 6.38 0 178.69 early
## 3693 2409 0 1 6.07 1 43.46 early
## 3694 1767 0 2 7.90 1 201.38 peak
## 3695 2720 0 1 7.18 0 175.09 peak
## 3696 450 0 0 6.74 0 192.07 peak
## 3697 2346 0 3 6.24 0 45.98 decline
## 3698 682 0 0 7.13 0 232.17 decline
## 3699 2835 0 3 6.47 1 194.30 early
## 3700 2511 0 2 5.87 1 159.90 early
## 3701 2581 0 1 6.46 1 45.41 early
## 3702 1157 0 0 5.77 0 146.57 early
## 3703 1246 0 0 7.19 0 216.50 early
## 3704 2960 0 4 6.03 0 75.44 early
## 3705 1944 0 3 6.13 2 191.96 early
## 3706 2405 0 1 6.28 0 164.99 early
## 3707 1716 0 4 5.63 0 51.52 early
## 3708 2275 0 3 6.48 1 179.20 early
## 3709 744 0 0 6.75 1 40.09 early
## 3710 2848 0 6 7.33 1 298.47 peak
## 3711 1254 0 2 7.08 1 191.17 peak
## 3712 803 0 1 6.01 2 140.14 peak
## 3713 1288 0 1 6.22 1 101.69 decline
## 3714 1408 0 4 7.41 0 193.09 decline
## 3715 2190 0 1 6.70 0 156.69 decline
## 3716 658 0 1 5.62 0 25.79 decline
## 3717 438 0 0 6.30 0 186.23 decline
## 3718 441 1 2 7.02 0 149.19 early
## 3719 2553 3 4 6.97 1 237.86 early
## 3720 1716 3 3 6.53 0 39.88 early
## 3721 1562 5 1 5.45 1 126.83 early
## 3722 1037 2 0 5.88 0 140.99 early
## 3723 1260 4 0 6.65 0 186.97 early
## 3724 792 3 1 5.72 0 73.30 early
## 3725 2232 4 2 6.77 1 136.29 peak
## 3726 1920 5 2 6.59 0 211.07 peak
## 3727 1474 1 2 7.91 0 62.43 peak
## 3728 2380 9 2 5.64 1 122.41 decline
## 3729 1104 1 3 5.70 0 139.29 decline
## 3730 2610 1 2 5.73 0 115.10 decline
## 3731 1848 4 1 6.34 0 182.66 decline
## 3732 1575 2 3 5.80 1 77.68 early
## 3733 2880 1 7 5.72 0 69.88 early
## 3734 1548 1 2 6.70 0 202.35 early
## 3735 2052 3 5 6.67 0 79.86 early
## 3736 2412 7 4 6.87 1 104.55 early
## 3737 1612 4 1 7.37 0 257.06 early
## 3738 1880 3 3 5.93 2 57.48 early
## 3739 1512 1 3 6.57 0 37.76 early
## 3740 680 1 0 6.31 0 123.25 early
## 3741 2370 4 2 6.02 2 29.08 early
## 3742 2924 10 3 6.09 0 198.79 early
## 3743 585 0 1 6.53 0 47.19 early
## 3744 1674 0 1 6.06 0 31.79 early
## 3745 1775 0 3 5.78 0 147.29 early
## 3746 2108 0 2 6.70 0 142.84 early
## 3747 2482 0 6 7.62 0 186.40 early
## 3748 1860 0 0 6.07 0 148.10 early
## 3749 2250 0 5 6.20 1 61.93 early
## 3750 1764 0 3 6.26 0 219.28 early
## 3751 2349 0 4 6.57 0 135.19 early
## 3752 630 0 0 5.06 2 90.39 early
## 3753 1875 6 2 6.54 1 177.68 early
## 3754 1050 1 1 6.52 0 247.89 early
## 3755 1728 1 1 6.46 2 43.41 early
## 3756 539 2 0 5.90 0 28.04 early
## 3757 567 0 0 7.13 0 108.79 early
## 3758 2312 2 4 6.31 2 84.32 early
## 3759 868 4 3 6.12 1 64.91 early
## 3760 744 3 0 6.49 1 174.17 early
## 3761 1022 2 1 6.81 2 273.12 peak
## 3762 2448 4 4 8.12 0 345.47 peak
## 3763 1886 4 2 7.12 1 50.68 peak
## 3764 952 5 0 5.95 0 35.51 decline
## 3765 1586 2 4 6.04 0 147.07 decline
## 3766 869 1 1 5.59 1 37.48 early
## 3767 810 1 2 6.77 0 78.90 early
## 3768 1470 5 3 6.76 0 272.42 early
## 3769 1640 4 2 6.40 0 183.34 early
## 3770 2093 3 3 6.97 0 54.09 early
## 3771 1640 4 3 7.11 0 115.08 early
## 3772 1518 3 1 6.04 1 78.19 early
## 3773 2553 3 5 6.83 1 124.55 peak
## 3774 2336 4 4 7.43 0 55.81 peak
## 3775 1792 8 1 7.75 0 215.55 peak
## 3776 1320 2 2 6.18 2 157.77 decline
## 3777 2556 4 3 6.42 0 241.02 decline
## 3778 2160 4 2 6.55 0 134.38 early
## 3779 2880 7 2 6.89 1 42.91 early
## 3780 465 2 0 6.89 1 162.79 early
## 3781 774 1 1 7.05 0 49.33 early
## 3782 2838 1 9 6.81 0 207.68 early
## 3783 2450 5 2 7.27 0 231.03 early
## 3784 2100 5 2 6.34 1 223.45 early
## 3785 793 3 1 6.32 0 125.94 early
## 3786 3204 5 4 6.91 1 39.55 early
## 3787 1817 3 5 6.44 1 166.47 early
## 3788 2728 7 3 6.90 0 44.83 early
## 3789 930 5 0 6.35 1 151.62 early
## 3790 3204 4 3 7.03 0 287.81 early
## 3791 2553 8 6 6.38 0 65.24 early
## 3792 2146 4 1 6.36 0 182.89 early
## 3793 2176 8 4 6.13 0 30.31 early
## 3794 1460 5 2 6.40 0 149.27 early
## 3795 1452 2 4 6.98 1 197.23 early
## 3796 1332 3 1 7.32 0 109.19 peak
## 3797 3094 5 7 6.43 1 149.89 peak
## 3798 2812 5 8 6.60 0 115.65 peak
## 3799 2520 6 4 6.49 1 214.95 decline
## 3800 1608 3 1 5.90 1 61.98 decline
## 3801 1168 0 3 6.32 0 89.98 decline
## 3802 2325 5 2 6.66 0 51.97 decline
## 3803 1640 0 4 7.21 1 144.25 early
## 3804 2639 0 2 6.77 0 45.03 early
## 3805 360 0 0 5.84 2 190.45 early
## 3806 1320 0 0 6.47 1 40.50 early
## 3807 2686 0 3 6.53 0 171.74 early
## 3808 2144 0 3 6.41 1 208.98 early
## 3809 576 0 0 6.52 0 137.62 early
## 3810 1044 0 0 5.96 1 96.03 early
## 3811 1625 0 3 6.15 0 203.57 early
## 3812 837 1 1 6.21 2 166.39 early
## 3813 2548 2 0 5.76 2 39.32 early
## 3814 2856 7 6 6.03 0 65.08 early
## 3815 1700 6 2 6.04 0 165.76 early
## 3816 700 0 1 6.45 0 41.05 early
## 3817 930 0 0 6.10 2 103.72 early
## 3818 3128 6 5 6.19 0 38.29 early
## 3819 1984 8 2 7.18 2 57.73 peak
## 3820 1200 1 3 7.01 1 205.15 peak
## 3821 1512 3 2 6.92 1 163.90 peak
## 3822 2075 3 0 6.24 1 232.93 early
## 3823 420 1 1 6.46 1 37.38 early
## 3824 2670 2 4 6.50 0 189.93 early
## 3825 1725 1 5 6.24 1 133.09 early
## 3826 946 2 0 6.19 1 35.67 early
## 3827 1980 4 3 6.51 1 151.76 early
## 3828 3219 4 6 7.29 1 230.27 early
## 3829 2204 3 1 6.56 0 78.69 early
## 3830 1196 4 1 6.80 0 208.52 early
## 3831 2080 9 4 6.64 0 120.93 early
## 3832 770 5 0 6.91 1 45.91 early
## 3833 2241 6 4 6.51 0 152.02 early
## 3834 3096 8 0 6.18 1 131.08 early
## 3835 910 2 4 6.45 0 105.77 early
## 3836 1204 3 3 6.72 0 204.76 early
## 3837 869 1 0 6.98 1 120.75 early
## 3838 450 1 1 6.90 0 41.82 early
## 3839 1368 0 2 5.74 0 69.78 early
## 3840 355 0 2 6.11 0 30.61 early
## 3841 656 0 1 6.85 0 99.23 early
## 3842 1311 0 2 6.73 1 186.52 early
## 3843 2175 0 4 6.76 0 178.25 early
## 3844 854 0 4 6.40 1 35.51 early
## 3845 1736 0 2 6.57 0 255.18 early
## 3846 560 0 1 6.35 0 102.95 early
## 3847 1292 0 2 5.94 1 33.20 early
## 3848 585 0 2 5.58 0 122.17 early
## 3849 3071 0 4 6.82 1 167.03 peak
## 3850 2160 0 2 7.15 1 58.55 peak
## 3851 2294 0 4 7.68 1 341.74 peak
## 3852 2225 0 1 6.54 0 84.25 decline
## 3853 574 0 0 5.81 0 36.82 decline
## 3854 1040 1 2 7.10 0 280.74 early
## 3855 1776 3 2 6.24 0 127.06 early
## 3856 325 1 0 6.23 1 68.49 early
## 3857 2448 8 4 5.84 1 179.16 early
## 3858 2997 5 3 6.47 0 197.75 early
## 3859 671 1 1 6.46 2 46.38 early
## 3860 1235 2 2 5.79 1 118.07 early
## 3861 1209 2 0 5.80 2 125.44 early
## 3862 584 5 0 6.83 0 53.77 early
## 3863 1050 3 0 6.78 1 37.83 early
## 3864 2523 5 2 6.17 0 45.17 early
## 3865 616 3 0 6.82 2 199.07 early
## 3866 3034 1 4 6.79 2 40.11 early
## 3867 2405 6 4 7.18 0 297.96 early
## 3868 1311 3 1 5.89 1 133.13 early
## 3869 2175 6 0 6.83 0 43.97 early
## 3870 320 0 1 6.09 1 170.32 early
## 3871 1105 2 1 6.67 1 230.51 early
## 3872 657 0 1 7.31 0 247.63 early
## 3873 2822 5 2 5.94 1 164.83 early
## 3874 2025 2 2 6.47 0 43.84 early
## 3875 2288 2 1 6.92 1 213.68 early
## 3876 994 4 2 5.98 0 42.68 early
## 3877 2883 3 0 6.40 1 174.64 peak
## 3878 1992 4 2 7.02 0 47.60 peak
## 3879 1932 1 4 6.69 1 106.38 peak
## 3880 2772 4 4 6.46 0 46.58 early
## 3881 1690 4 1 5.64 1 125.96 early
## 3882 1764 4 2 6.97 0 83.25 early
## 3883 2916 5 4 6.90 1 77.49 early
## 3884 1314 5 3 7.32 1 146.67 peak
## 3885 637 0 0 7.35 2 92.12 peak
## 3886 1848 2 2 7.71 0 49.49 peak
## 3887 640 2 0 6.59 0 226.78 decline
## 3888 585 1 2 6.92 0 239.93 decline
## 3889 1170 2 2 6.61 1 40.09 decline
## 3890 810 0 2 6.27 0 97.73 decline
## 3891 2686 5 7 5.86 0 163.94 decline
## 3892 1360 1 3 5.39 0 65.66 decline
## 3893 560 0 1 6.19 0 153.97 early
## 3894 1140 0 1 5.74 1 33.10 early
## 3895 2668 0 2 6.09 1 118.89 early
## 3896 1170 0 2 6.77 0 147.37 early
## 3897 2100 0 5 5.75 2 63.43 early
## 3898 2759 0 0 5.44 1 85.37 early
## 3899 1280 0 3 6.29 0 109.90 early
## 3900 1628 0 2 6.86 0 164.15 early
## 3901 1480 0 0 6.29 0 104.21 early
## 3902 1600 4 0 7.03 0 133.42 early
## 3903 1780 3 0 6.42 1 44.03 early
## 3904 710 0 2 5.66 2 33.14 early
## 3905 2376 6 2 7.57 0 153.85 early
## 3906 405 1 1 6.96 0 53.38 early
## 3907 616 1 2 5.86 1 173.56 early
## 3908 2632 4 0 5.74 1 123.10 early
## 3909 1786 5 1 5.77 1 125.46 early
## 3910 1170 2 1 6.49 1 165.01 early
## 3911 483 2 1 6.46 1 169.85 early
## 3912 2170 6 5 7.11 1 82.27 early
## 3913 976 4 3 6.47 0 239.91 early
## 3914 1185 0 3 5.69 0 35.34 early
## 3915 651 0 0 6.43 1 35.15 early
## 3916 1330 0 2 7.45 0 296.85 early
## 3917 780 0 0 6.59 0 256.15 early
## 3918 1464 0 2 5.97 1 212.70 early
## 3919 2117 0 4 5.14 2 126.17 early
## 3920 1273 0 6 6.30 1 76.27 early
## 3921 552 0 0 6.96 0 42.06 early
## 3922 1680 0 1 6.13 1 61.52 early
## 3923 793 0 1 6.32 0 218.97 early
## 3924 624 0 0 6.78 0 217.54 early
## 3925 315 1 1 6.38 0 194.97 early
## 3926 1470 2 2 6.10 0 74.33 early
## 3927 450 0 0 6.53 1 75.82 early
## 3928 1302 0 1 7.25 0 224.67 early
## 3929 1971 2 1 6.67 0 162.44 early
## 3930 1404 2 2 5.88 0 63.38 early
## 3931 1924 5 7 6.84 1 159.64 early
## 3932 980 3 1 6.29 0 198.91 early
## 3933 900 2 0 5.64 0 162.29 early
## 3934 1235 2 2 7.17 1 51.56 peak
## 3935 2484 6 2 6.46 0 94.93 peak
## 3936 1562 4 2 7.60 0 255.63 peak
## 3937 1824 1 2 6.95 0 249.02 decline
## 3938 1566 0 0 6.24 0 101.88 early
## 3939 460 2 0 6.35 0 161.26 early
## 3940 924 4 0 6.94 0 102.22 early
## 3941 1190 3 3 6.55 1 151.73 early
## 3942 2262 2 6 5.95 2 97.92 early
## 3943 1323 4 2 7.02 0 153.36 early
## 3944 1139 5 2 5.96 0 58.58 early
## 3945 1768 2 3 6.63 0 38.47 peak
## 3946 430 1 2 6.87 0 45.26 peak
## 3947 1944 1 5 7.68 0 274.15 peak
## 3948 2205 7 2 6.57 1 185.13 decline
## 3949 2970 2 3 5.07 1 26.06 decline
## 3950 1078 3 1 6.20 0 31.86 decline
## 3951 1280 0 1 6.36 0 179.63 decline
## 3952 864 4 0 7.05 0 53.66 early
## 3953 1392 4 0 5.88 0 108.14 early
## 3954 1518 3 3 5.87 2 31.19 early
## 3955 1350 1 0 6.47 1 48.98 early
## 3956 585 2 1 6.30 0 72.52 early
## 3957 603 0 1 6.01 0 35.01 early
## 3958 2376 6 3 6.94 0 132.25 early
## 3959 1155 3 2 6.50 0 96.00 early
## 3960 2736 4 7 7.09 0 188.46 early
## 3961 729 1 0 5.57 0 100.32 early
## 3962 2331 5 5 5.68 2 36.14 early
## 3963 2436 4 2 6.97 0 51.31 early
## 3964 1464 2 3 7.05 1 202.14 early
## 3965 2632 3 2 6.29 1 128.69 early
## 3966 2450 4 0 6.68 1 177.90 early
## 3967 1896 2 1 6.29 1 166.40 early
## 3968 1022 3 1 6.32 0 165.26 early
## 3969 1302 3 2 7.03 0 125.97 peak
## 3970 1125 3 0 6.83 0 254.90 peak
## 3971 1539 3 1 7.04 0 89.24 peak
## 3972 1596 4 3 6.11 0 143.97 early
## 3973 825 2 1 6.71 0 203.90 early
## 3974 2044 6 2 6.21 1 193.97 early
## 3975 960 4 0 6.06 2 169.65 early
## 3976 1139 1 2 6.26 1 114.43 early
## 3977 3026 4 4 6.29 0 63.97 early
## 3978 1680 2 4 5.51 1 137.55 early
## 3979 2759 6 5 7.03 0 194.62 peak
## 3980 2346 1 1 6.56 1 37.04 early
## 3981 1776 6 1 6.61 0 47.33 early
## 3982 1495 1 5 6.68 0 132.65 early
## 3983 2436 6 1 7.11 0 191.04 early
## 3984 1768 2 4 6.47 0 184.73 early
## 3985 1769 3 5 5.85 1 79.85 early
## 3986 2821 6 3 6.19 0 90.15 early
## 3987 1922 6 4 6.65 0 185.05 early
## 3988 2700 5 5 6.85 0 112.41 early
## 3989 2400 6 2 5.87 0 171.35 early
## 3990 1343 2 1 5.74 0 141.58 early
## 3991 630 3 0 7.09 0 114.19 early
## 3992 1925 1 2 5.68 1 104.91 early
## 3993 2701 6 2 6.35 0 78.83 peak
## 3994 1701 3 1 6.45 0 154.13 early
## 3995 1230 3 2 6.05 0 38.89 early
## 3996 435 1 0 6.33 1 150.05 early
## 3997 504 0 2 6.97 1 97.21 early
## 3998 1350 3 2 5.60 0 95.99 early
## 3999 2475 5 1 5.56 1 58.70 early
## 4000 445 0 0 6.89 1 107.32 early
## 4001 948 3 1 6.26 1 138.27 early
## 4002 1078 4 2 6.41 1 38.02 peak
## 4003 1520 5 5 7.65 0 56.68 peak
## 4004 770 0 3 5.75 0 155.33 peak
## 4005 2047 3 3 6.80 0 246.46 early
## 4006 2652 8 3 5.49 0 73.33 early
## 4007 924 3 1 5.26 1 59.63 early
## 4008 1944 3 3 6.21 2 32.66 early
## 4009 1105 1 1 5.83 0 105.43 early
## 4010 558 1 2 6.32 0 136.74 early
## 4011 444 1 0 6.82 1 263.56 early
## 4012 1920 4 0 6.29 1 179.90 early
## 4013 2088 3 2 8.52 0 86.71 peak
## 4014 1218 3 0 7.89 1 135.22 peak
## 4015 1023 0 0 7.51 1 229.73 peak
## 4016 584 2 1 5.74 1 83.37 decline
## 4017 2100 7 1 5.96 0 144.55 decline
## 4018 2275 2 2 6.53 1 90.67 decline
## 4019 390 1 0 6.60 0 204.73 early
## 4020 492 1 2 7.48 1 252.06 early
## 4021 657 0 1 6.29 1 47.29 early
## 4022 671 1 0 6.35 1 168.92 early
## 4023 2970 4 0 5.81 0 94.47 early
## 4024 2720 4 4 7.57 0 317.42 peak
## 4025 2013 4 4 7.09 0 53.78 peak
## 4026 2054 6 2 7.38 0 143.93 peak
## 4027 1118 2 3 6.47 0 85.62 decline
## 4028 2233 10 1 6.95 0 51.59 decline
## 4029 1328 6 1 6.29 0 155.08 decline
## 4030 2002 4 2 6.91 0 133.86 decline
## 4031 910 0 1 6.09 0 40.12 decline
## 4032 1404 3 0 6.32 0 77.24 decline
## 4033 1716 2 2 5.44 2 140.98 early
## 4034 420 1 0 7.64 1 50.33 early
## 4035 1458 6 3 6.46 0 124.47 early
## 4036 2075 5 5 5.71 2 58.05 early
## 4037 2150 3 2 6.14 0 121.69 early
## 4038 1444 0 2 5.94 1 188.96 early
## 4039 1914 4 2 6.42 0 133.01 early
## 4040 1207 2 1 6.69 0 152.78 early
## 4041 2720 3 5 6.48 0 121.77 early
## 4042 1241 2 1 6.53 0 34.86 peak
## 4043 2923 2 6 6.27 0 152.15 early
## 4044 2640 3 3 5.90 3 36.98 early
## 4045 1376 3 3 6.60 1 145.12 early
## 4046 1275 2 1 6.33 0 209.49 early
## 4047 1292 2 1 5.00 1 23.22 early
## 4048 2356 5 3 6.38 1 39.41 peak
## 4049 1242 1 0 7.30 1 282.42 peak
## 4050 1020 4 1 6.74 0 127.89 peak
## 4051 1664 3 1 6.48 1 44.77 decline
## 4052 1890 9 3 6.02 0 61.68 early
## 4053 1911 0 2 6.29 0 89.18 early
## 4054 3060 1 4 5.96 0 57.04 early
## 4055 3182 4 2 7.04 0 102.92 early
## 4056 2212 10 1 6.67 1 113.42 early
## 4057 700 1 5 6.53 1 148.49 early
## 4058 2158 3 4 6.36 0 129.23 early
## 4059 1917 3 1 5.85 1 174.99 early
## 4060 1232 5 2 5.84 1 90.42 early
## 4061 2701 6 1 6.75 0 188.53 early
## 4062 3102 9 3 6.85 0 245.06 early
## 4063 1978 4 1 5.84 1 133.39 early
## 4064 1380 4 1 6.92 0 48.43 early
## 4065 1638 6 4 6.28 0 201.06 early
## 4066 2576 0 1 6.30 0 137.37 early
## 4067 2268 0 2 6.95 1 211.05 early
## 4068 1368 0 2 6.98 1 136.36 early
## 4069 1309 0 2 6.71 0 161.19 early
## 4070 804 0 0 5.22 1 64.38 early
## 4071 1998 0 1 6.99 1 287.17 early
## 4072 400 0 1 6.28 1 88.80 early
## 4073 2268 0 9 6.35 1 88.13 early
## 4074 1633 4 2 6.02 0 43.38 early
## 4075 1817 3 2 6.53 0 40.63 early
## 4076 3348 3 3 7.03 1 193.14 early
## 4077 1360 6 1 6.94 0 185.91 early
## 4078 1458 4 4 6.44 0 94.51 early
## 4079 2275 7 7 6.83 2 195.68 peak
## 4080 704 4 2 7.31 0 141.22 peak
## 4081 2448 5 6 6.70 1 205.31 peak
## 4082 1952 4 4 6.70 2 48.00 decline
## 4083 455 1 0 5.19 2 76.95 decline
## 4084 2093 5 5 7.02 0 159.70 decline
## 4085 1890 4 3 5.79 0 120.89 decline
## 4086 1185 1 3 6.08 0 168.29 decline
## 4087 2520 5 5 6.87 0 39.06 decline
## 4088 2304 5 4 6.57 1 215.80 early
## 4089 2106 4 5 5.78 1 117.06 early
## 4090 1740 6 6 6.83 0 98.31 early
## 4091 752 1 0 7.02 0 215.58 early
## 4092 1558 2 2 5.83 1 36.71 early
## 4093 1554 3 2 5.91 1 73.81 early
## 4094 1376 4 0 6.94 0 47.11 peak
## 4095 592 1 0 7.13 0 58.59 peak
## 4096 2312 7 2 6.80 0 196.02 peak
## 4097 2886 8 6 6.12 0 219.32 decline
## 4098 450 1 2 5.65 1 130.28 decline
## 4099 2700 4 5 6.13 1 151.90 decline
## 4100 600 3 4 6.24 2 185.84 decline
## 4101 2726 2 2 6.01 2 173.72 decline
## 4102 2059 2 2 5.32 0 146.52 early
## 4103 1840 1 0 5.88 0 103.17 early
## 4104 623 1 0 6.86 0 39.20 early
## 4105 2666 5 1 6.17 1 129.12 early
## 4106 1701 6 1 6.95 0 47.55 early
## 4107 1335 4 0 6.25 0 201.45 early
## 4108 768 4 0 6.75 0 216.08 early
## 4109 2555 14 6 6.75 0 127.47 early
## 4110 1040 1 2 6.34 0 192.36 early
## 4111 1680 6 1 7.28 0 88.80 early
## 4112 2759 1 4 7.26 0 87.73 early
## 4113 1056 2 1 6.19 2 36.06 early
## 4114 2573 5 2 6.75 1 37.67 early
## 4115 1260 3 3 7.82 0 155.07 peak
## 4116 2220 6 1 6.98 2 189.40 peak
## 4117 1206 4 1 6.91 1 218.96 peak
## 4118 1342 6 4 6.82 0 47.31 decline
## 4119 420 2 1 6.25 1 160.15 decline
## 4120 2607 3 1 6.90 0 108.76 decline
## 4121 420 0 2 6.66 0 201.19 decline
## 4122 1952 7 2 6.35 1 91.12 decline
## 4123 1955 3 1 6.19 1 106.27 early
## 4124 2044 8 2 6.70 1 140.14 early
## 4125 3060 7 3 6.69 0 198.57 early
## 4126 2232 7 1 6.46 1 242.75 early
## 4127 770 2 2 6.67 0 38.29 early
## 4128 650 1 3 6.24 1 126.58 early
## 4129 847 1 3 6.35 0 108.19 early
## 4130 2054 4 4 6.16 0 125.29 early
## 4131 868 3 1 7.14 0 42.08 early
## 4132 730 0 2 5.91 1 158.69 early
## 4133 435 1 0 7.47 0 53.20 early
## 4134 690 4 1 5.51 2 34.81 early
## 4135 960 3 1 6.87 1 47.52 early
## 4136 1001 1 3 8.69 1 125.43 peak
## 4137 2002 4 3 6.83 0 228.84 peak
## 4138 730 0 2 7.33 1 239.43 peak
## 4139 2013 8 4 6.45 0 88.18 decline
## 4140 1664 2 0 6.78 0 212.44 early
## 4141 968 0 0 6.33 1 39.78 early
## 4142 568 1 0 5.88 2 69.43 early
## 4143 891 2 1 7.08 0 96.90 early
## 4144 2288 3 1 6.44 0 195.08 early
## 4145 1581 4 1 6.87 0 39.17 early
## 4146 1104 2 1 6.46 1 232.19 early
## 4147 2409 5 3 6.47 0 170.62 early
## 4148 1125 3 1 6.71 0 185.71 early
## 4149 990 0 3 6.39 0 79.36 early
## 4150 3276 0 6 5.86 1 37.50 early
## 4151 1782 0 4 6.14 2 127.36 early
## 4152 1491 0 1 6.41 0 33.04 early
## 4153 1694 0 2 6.35 0 102.39 early
## 4154 3108 0 1 6.12 1 63.98 early
## 4155 2356 0 5 6.49 0 122.63 early
## 4156 1914 0 3 6.71 0 202.40 early
## 4157 1922 6 5 5.95 1 76.66 early
## 4158 2736 9 4 7.09 1 225.51 early
## 4159 2436 9 0 6.81 1 43.34 early
## 4160 426 0 0 7.00 0 51.78 early
## 4161 1056 0 1 6.44 1 140.97 early
## 4162 300 0 0 6.31 0 181.50 early
## 4163 558 1 0 6.16 0 97.82 early
## 4164 1612 1 5 6.51 0 123.51 early
## 4165 504 0 1 6.88 1 265.22 peak
## 4166 1072 2 0 7.18 0 84.45 peak
## 4167 522 0 0 6.02 2 111.36 peak
## 4168 765 3 3 6.96 2 98.68 decline
## 4169 2210 0 4 6.27 0 157.07 early
## 4170 900 0 1 5.24 1 134.90 early
## 4171 657 0 2 6.93 1 55.11 early
## 4172 1300 0 1 6.61 1 42.59 early
## 4173 448 0 3 6.94 0 51.46 early
## 4174 737 0 1 6.66 2 197.57 early
## 4175 1281 0 3 5.99 0 182.48 early
## 4176 2075 0 1 6.10 2 103.57 early
## 4177 539 0 1 7.03 0 160.99 peak
## 4178 1118 0 2 7.59 0 196.56 peak
## 4179 1184 0 1 7.06 1 129.37 peak
## 4180 568 0 0 7.08 0 40.30 early
## 4181 1581 2 0 6.66 0 137.66 early
## 4182 3150 3 3 6.90 1 91.47 early
## 4183 460 1 0 5.72 0 84.75 early
## 4184 1428 4 0 6.17 0 119.80 early
## 4185 2400 4 4 5.59 1 66.76 early
## 4186 410 3 0 6.76 0 114.65 early
## 4187 340 2 0 6.66 0 42.50 early
## 4188 2304 6 1 6.26 0 161.01 early
## 4189 3367 2 2 5.70 1 26.54 early
## 4190 2204 5 3 6.31 1 211.69 early
## 4191 2088 5 4 6.11 2 121.81 early
## 4192 2080 2 2 6.06 0 163.52 early
## 4193 2822 8 1 6.45 0 127.57 early
## 4194 2100 7 1 5.89 1 121.07 early
## 4195 2673 5 2 6.66 0 191.31 early
## 4196 1320 4 6 6.39 0 71.07 early
## 4197 2160 10 3 6.73 1 207.83 peak
## 4198 1491 1 6 7.95 0 61.49 peak
## 4199 1264 0 0 5.89 1 37.87 early
## 4200 539 0 0 6.11 1 113.03 early
## 4201 1479 0 1 7.19 0 119.82 early
## 4202 444 0 1 6.22 0 36.22 early
## 4203 870 0 2 6.58 0 147.30 early
## 4204 1320 0 0 6.38 1 145.26 early
## 4205 880 0 1 6.05 0 174.55 early
## 4206 1088 0 1 6.69 1 94.54 peak
## 4207 552 0 1 7.66 0 335.14 peak
## 4208 2278 0 3 7.13 0 42.51 peak
## 4209 510 0 1 6.05 1 83.01 decline
## 4210 2700 0 4 5.51 1 35.42 decline
## 4211 748 0 1 7.48 0 169.77 early
## 4212 1550 3 3 5.85 1 75.26 early
## 4213 560 2 1 6.39 0 36.50 early
## 4214 1944 6 1 5.74 0 176.49 early
## 4215 1311 1 2 5.86 0 58.30 early
## 4216 512 1 1 6.82 0 174.42 early
## 4217 1596 0 2 5.83 0 98.55 early
## 4218 2080 0 6 6.39 1 80.56 early
## 4219 936 0 1 6.19 0 96.62 early
## 4220 2304 0 2 7.67 1 144.29 early
## 4221 1512 0 0 6.77 0 146.92 early
## 4222 2024 0 2 6.73 0 41.84 early
## 4223 996 0 4 6.05 2 131.15 early
## 4224 2730 6 1 6.28 1 93.14 early
## 4225 1602 6 5 6.33 1 130.19 early
## 4226 1386 2 3 6.71 1 92.94 early
## 4227 2212 7 1 6.52 0 145.01 early
## 4228 1027 2 1 6.31 0 42.09 early
## 4229 1440 5 1 7.05 0 186.66 early
## 4230 1674 5 4 6.71 1 129.19 early
## 4231 750 1 2 6.67 1 86.13 early
## 4232 684 0 0 6.28 0 81.78 early
## 4233 1904 3 2 5.85 0 95.04 early
## 4234 2232 1 3 6.97 0 50.26 early
## 4235 1860 10 0 6.07 1 83.80 early
## 4236 704 3 0 6.37 2 231.12 early
## 4237 405 1 0 5.88 0 41.41 early
## 4238 1488 2 1 6.75 0 121.78 early
## 4239 1617 0 1 7.56 0 311.59 early
## 4240 2821 5 4 6.78 0 82.66 early
## 4241 1494 1 1 5.24 1 147.90 early
## 4242 3255 10 3 6.57 1 183.60 early
## 4243 384 0 1 6.43 1 126.91 early
## 4244 1280 2 2 5.87 1 142.54 early
## 4245 3145 8 5 7.04 0 228.25 early
## 4246 2370 10 4 6.31 1 34.68 early
## 4247 1420 4 0 7.25 1 58.64 early
## 4248 1650 2 1 6.81 1 208.00 early
## 4249 915 3 0 6.61 0 84.51 peak
## 4250 1785 5 3 5.82 1 191.31 early
## 4251 1650 5 2 6.42 0 158.07 early
## 4252 396 1 0 7.02 0 106.31 early
## 4253 3276 5 5 6.84 0 41.90 early
## 4254 2001 5 3 5.89 1 33.93 early
## 4255 658 0 0 7.34 0 258.65 early
## 4256 2640 2 3 6.31 0 238.36 early
## 4257 3128 2 3 6.22 1 110.35 early
## 4258 1722 3 3 6.45 1 173.94 early
## 4259 1752 3 3 6.10 0 29.99 early
## 4260 2407 1 2 6.83 0 199.60 early
## 4261 1656 4 3 7.53 1 251.94 peak
## 4262 1504 1 1 5.67 2 147.41 early
## 4263 1365 2 2 6.18 0 221.56 early
## 4264 460 0 0 7.15 1 165.55 early
## 4265 938 2 2 6.73 0 42.91 early
## 4266 1800 5 2 6.10 0 129.58 early
## 4267 1520 1 1 6.02 1 72.30 early
## 4268 1900 5 2 5.84 0 142.73 early
## 4269 2496 3 3 7.13 0 238.37 early
## 4270 2511 4 3 6.94 0 80.60 early
## 4271 600 2 0 5.66 1 122.39 early
## 4272 1170 3 0 6.77 1 206.29 early
## 4273 912 2 3 7.17 0 49.29 peak
## 4274 414 1 0 6.89 0 48.09 peak
## 4275 2162 2 1 7.52 0 281.30 peak
## 4276 1870 4 0 6.08 0 76.50 decline
## 4277 2128 3 4 7.04 1 105.51 decline
## 4278 1550 2 2 6.74 0 202.52 decline
## 4279 1785 5 4 6.74 1 170.96 early
## 4280 960 1 2 5.50 2 31.32 early
## 4281 1320 3 3 6.30 1 211.98 early
## 4282 2100 10 3 6.00 1 28.93 early
## 4283 1430 3 1 6.45 0 193.42 early
## 4284 2340 7 7 6.41 2 67.06 early
## 4285 1215 4 0 6.54 0 48.88 early
## 4286 1344 0 1 5.93 1 156.46 early
## 4287 2312 5 8 6.00 1 35.01 early
## 4288 940 1 1 6.50 1 149.69 peak
## 4289 2739 3 5 7.20 0 46.56 peak
## 4290 1562 2 2 7.89 0 226.55 peak
## 4291 897 2 1 6.79 0 211.53 decline
## 4292 1533 0 1 7.15 0 41.53 decline
## 4293 474 0 0 6.44 1 146.04 early
## 4294 2924 0 4 6.47 1 79.08 early
## 4295 1825 0 3 6.30 0 192.37 early
## 4296 640 0 2 5.76 1 156.31 early
## 4297 1116 0 2 6.74 2 152.76 early
## 4298 2573 0 3 7.24 0 50.33 early
## 4299 592 2 1 5.88 1 37.33 early
## 4300 1312 3 1 6.65 0 84.98 early
## 4301 1170 0 2 6.61 0 243.16 early
## 4302 2484 3 1 6.75 0 44.18 early
## 4303 720 3 1 6.58 0 131.93 early
## 4304 1305 5 0 6.96 0 207.19 early
## 4305 2975 2 4 7.03 0 116.79 early
## 4306 2484 4 4 6.14 0 109.25 early
## 4307 420 1 2 6.85 2 264.83 early
## 4308 1769 5 2 5.92 2 165.15 early
## 4309 1326 2 2 6.33 0 37.34 early
## 4310 1332 2 0 5.83 0 151.54 early
## 4311 1998 5 3 6.28 0 39.10 early
## 4312 1958 1 5 6.50 1 92.84 early
## 4313 3102 4 3 6.00 0 151.91 early
## 4314 2184 4 2 5.70 1 29.29 early
## 4315 1001 1 0 5.00 2 80.14 early
## 4316 1968 3 1 6.77 1 148.76 early
## 4317 3036 8 5 6.44 0 169.59 early
## 4318 780 0 0 5.95 0 35.23 early
## 4319 913 1 0 7.14 2 42.42 early
## 4320 2516 6 4 5.70 0 86.08 early
## 4321 2262 2 5 5.22 1 93.76 early
## 4322 1344 2 2 6.33 0 47.85 early
## 4323 1349 2 1 6.65 1 78.45 early
## 4324 871 0 1 7.83 0 197.39 early
## 4325 2349 3 0 6.65 1 124.30 early
## 4326 2250 5 2 6.27 1 137.92 early
## 4327 2784 5 3 6.56 0 48.47 early
## 4328 1056 1 2 7.16 1 129.96 peak
## 4329 1885 5 3 6.99 1 54.31 peak
## 4330 2632 2 4 6.65 0 210.66 early
## 4331 1580 4 2 5.67 2 87.86 early
## 4332 1036 1 1 7.13 0 81.40 early
## 4333 474 1 1 6.05 0 129.88 early
## 4334 2046 3 5 6.72 1 209.09 peak
## 4335 1950 7 2 6.91 0 39.57 early
## 4336 2030 5 2 6.58 2 95.45 early
## 4337 1035 0 3 6.64 1 97.25 early
## 4338 2268 3 2 5.91 0 118.26 early
## 4339 1674 2 5 6.50 1 143.89 early
## 4340 884 3 3 7.36 0 323.87 early
## 4341 651 0 0 5.69 2 141.46 early
## 4342 3102 4 0 8.13 0 347.00 peak
## 4343 1420 3 5 6.10 2 162.00 peak
## 4344 2025 8 3 7.80 1 64.74 peak
## 4345 623 2 1 5.97 1 169.29 decline
## 4346 445 2 0 6.12 0 153.35 decline
## 4347 390 3 0 7.04 0 44.02 decline
## 4348 1691 3 3 6.05 1 40.43 decline
## 4349 516 0 0 7.22 0 117.91 early
## 4350 1173 1 0 6.36 0 179.20 early
## 4351 639 1 2 5.97 2 153.89 early
## 4352 1564 4 4 6.34 1 145.44 early
## 4353 1606 5 4 6.60 1 86.46 early
## 4354 511 0 2 6.48 1 188.72 early
## 4355 2160 2 3 5.11 2 149.23 early
## 4356 2492 5 2 6.86 0 50.00 early
## 4357 438 0 0 6.63 0 149.18 early
## 4358 1617 6 2 7.80 0 209.36 peak
## 4359 3256 8 3 6.10 0 124.70 peak
## 4360 1860 5 3 7.29 1 241.59 peak
## 4361 2340 5 4 5.86 1 108.15 early
## 4362 840 3 3 6.86 0 237.37 early
## 4363 780 2 1 6.31 0 224.54 early
## 4364 3036 2 2 6.09 1 176.39 early
## 4365 2574 9 5 6.29 1 181.15 early
## 4366 1216 0 1 7.18 0 304.11 peak
## 4367 2668 1 1 7.57 0 168.37 peak
## 4368 1530 2 1 6.73 0 216.03 peak
## 4369 3034 6 4 6.88 0 53.25 decline
## 4370 2624 5 4 6.31 2 40.56 decline
## 4371 1596 2 1 6.29 1 31.71 decline
## 4372 966 0 2 7.18 1 218.10 decline
## 4373 560 1 0 6.42 2 156.54 decline
## 4374 897 3 2 6.11 1 39.12 decline
## 4375 640 0 0 6.64 1 231.55 early
## 4376 1817 0 1 7.26 2 59.39 early
## 4377 1463 0 1 6.27 0 87.32 early
## 4378 1780 0 1 6.47 0 144.55 early
## 4379 3150 0 6 6.74 0 49.73 early
## 4380 2590 0 4 7.24 0 233.01 early
## 4381 2775 0 4 6.90 0 149.11 early
## 4382 1480 0 6 5.98 0 79.17 early
## 4383 658 0 1 6.59 0 146.40 early
## 4384 1863 0 3 7.60 0 234.05 peak
## 4385 2350 0 3 5.95 1 83.96 peak
## 4386 2482 0 1 7.40 1 155.61 peak
## 4387 2232 0 4 6.24 0 38.82 decline
## 4388 1209 0 3 7.25 1 207.57 decline
## 4389 920 1 3 5.83 0 116.77 early
## 4390 1395 1 1 6.49 0 191.72 early
## 4391 1827 6 1 6.08 0 110.66 early
## 4392 1564 1 1 6.72 1 204.47 early
## 4393 1183 0 2 6.74 1 260.88 early
## 4394 1512 8 3 6.98 0 239.12 early
## 4395 1580 6 1 5.69 1 154.88 early
## 4396 2436 4 4 6.24 1 40.48 early
## 4397 2070 8 2 6.09 1 200.36 early
## 4398 1664 8 2 8.04 0 307.67 peak
## 4399 2790 4 1 8.09 1 130.73 peak
## 4400 1760 5 0 7.11 1 132.17 peak
## 4401 2632 2 3 6.59 2 43.47 decline
## 4402 924 3 2 6.88 1 281.38 decline
## 4403 2112 8 2 6.21 0 199.64 early
## 4404 1500 4 2 5.92 1 124.24 early
## 4405 2241 2 4 6.39 0 176.57 early
## 4406 868 2 0 5.99 2 72.74 early
## 4407 518 1 0 6.80 1 81.68 early
## 4408 3404 9 6 6.62 0 201.67 early
## 4409 1950 6 2 6.36 0 83.06 early
## 4410 315 0 1 7.07 1 189.45 early
## 4411 1008 4 1 5.81 1 33.43 early
## 4412 2738 7 3 5.55 0 51.47 early
## 4413 1885 3 4 6.39 1 81.89 early
## 4414 880 2 2 6.31 0 204.04 early
## 4415 1403 0 5 6.90 0 192.00 peak
## 4416 2640 7 2 5.93 2 36.34 early
## 4417 2025 5 2 7.73 0 328.19 early
## 4418 3096 4 4 6.43 0 244.91 early
## 4419 1922 4 3 6.34 0 82.14 early
## 4420 1472 4 2 7.07 1 43.15 early
## 4421 1440 1 2 6.73 1 202.47 early
## 4422 2132 1 2 6.47 1 160.75 early
## 4423 1200 2 2 6.33 1 179.35 early
## 4424 576 1 1 5.83 0 84.86 early
## 4425 483 0 0 7.05 0 103.06 peak
## 4426 2808 6 2 7.36 0 186.32 peak
## 4427 1722 1 5 7.56 0 208.09 peak
## 4428 441 1 4 6.48 1 89.99 decline
## 4429 2576 5 3 6.19 1 86.38 early
## 4430 1106 4 2 5.40 1 122.47 early
## 4431 2106 3 1 6.53 0 172.25 early
## 4432 960 2 6 6.14 0 103.85 early
## 4433 938 1 0 7.02 2 208.76 early
## 4434 1207 2 2 6.43 1 46.83 early
## 4435 2250 4 1 6.57 0 123.29 early
## 4436 1925 1 2 7.00 0 233.42 peak
## 4437 564 1 0 5.57 0 28.74 peak
## 4438 1540 3 0 7.48 1 125.51 peak
## 4439 1350 4 0 5.48 2 97.89 decline
## 4440 1827 0 3 6.33 0 163.47 early
## 4441 1040 0 2 6.12 0 39.19 early
## 4442 1020 0 3 6.94 2 91.64 early
## 4443 671 0 0 6.04 0 65.66 early
## 4444 1701 0 3 7.12 0 117.30 early
## 4445 1302 0 1 6.86 1 142.08 early
## 4446 1764 0 2 6.00 1 30.14 early
## 4447 793 0 1 7.42 1 262.44 peak
## 4448 1320 3 3 6.35 0 144.95 early
## 4449 528 1 3 6.85 0 141.88 early
## 4450 2280 9 3 5.73 1 139.17 early
## 4451 1775 3 2 6.63 0 52.01 early
## 4452 576 1 0 6.90 0 190.91 early
## 4453 1387 2 2 5.34 1 97.92 early
## 4454 2144 2 2 6.83 0 126.97 early
## 4455 639 1 4 6.22 0 43.68 early
## 4456 840 4 2 5.99 1 116.44 early
## 4457 840 1 1 6.70 0 88.29 early
## 4458 2520 3 2 6.73 0 156.87 early
## 4459 1606 4 4 7.51 0 162.76 peak
## 4460 1156 3 2 7.30 2 148.56 peak
## 4461 1248 4 2 8.07 1 145.05 peak
## 4462 1541 0 2 7.03 0 228.35 early
## 4463 2701 0 2 5.31 0 59.20 early
## 4464 1729 0 3 5.93 1 148.16 early
## 4465 2059 0 4 6.40 0 34.63 early
## 4466 912 0 0 6.97 1 221.71 early
## 4467 335 0 1 6.53 0 68.57 early
## 4468 1068 0 1 6.75 0 184.78 early
## 4469 1215 0 1 6.78 0 214.69 early
## 4470 2550 0 3 6.16 0 37.78 early
## 4471 1856 1 1 7.23 0 248.33 early
## 4472 2436 6 3 6.49 0 229.42 early
## 4473 1660 7 1 7.07 0 257.86 early
## 4474 2418 4 3 5.81 1 94.83 early
## 4475 483 1 1 5.83 2 131.43 early
## 4476 2200 5 5 6.87 0 221.42 early
## 4477 1988 1 3 6.32 0 34.73 early
## 4478 1827 5 5 6.49 0 135.30 early
## 4479 567 2 0 6.97 1 57.65 early
## 4480 938 4 2 6.59 1 154.98 early
## 4481 900 3 1 5.87 0 36.57 early
## 4482 3060 6 3 7.01 0 205.28 early
## 4483 456 2 0 6.93 1 159.24 early
## 4484 948 3 2 7.66 0 154.67 peak
## 4485 2997 5 5 7.05 0 162.37 peak
## 4486 1350 1 1 7.20 0 286.56 peak
## 4487 1403 7 3 5.41 0 149.56 decline
## 4488 1380 3 0 5.93 1 75.34 decline
## 4489 552 0 2 6.20 0 114.65 early
## 4490 2275 0 0 5.84 1 56.45 early
## 4491 2340 0 3 6.22 0 185.07 early
## 4492 2160 0 5 5.88 1 31.18 early
## 4493 1716 0 1 7.06 1 282.58 early
## 4494 2673 0 2 7.16 1 133.47 early
## 4495 1092 0 1 7.20 0 85.02 early
## 4496 1554 0 2 6.48 0 150.73 early
## 4497 1725 2 1 6.46 0 75.03 early
## 4498 2448 5 6 5.61 1 26.16 early
## 4499 1560 3 1 6.36 1 33.31 early
## 4500 2088 2 6 6.69 1 49.22 early
## 4501 1728 1 3 5.76 0 69.59 early
## 4502 672 1 1 6.80 1 109.79 peak
## 4503 1406 3 1 7.07 0 242.42 peak
## 4504 1292 6 2 6.85 1 128.25 peak
## 4505 426 0 0 5.19 2 157.39 decline
## 4506 468 1 1 6.05 1 58.48 decline
## 4507 539 0 3 6.03 0 161.62 decline
## 4508 1312 2 2 5.94 0 96.74 decline
## 4509 1768 4 3 6.02 2 99.50 decline
## 4510 806 1 1 6.38 0 178.52 early
## 4511 390 0 1 5.71 2 62.67 early
## 4512 1890 1 2 5.12 0 66.81 early
## 4513 1176 2 0 6.70 0 252.23 early
## 4514 2046 1 4 7.27 1 108.02 peak
## 4515 715 3 0 6.69 2 100.31 peak
## 4516 924 0 0 7.05 0 247.96 peak
## 4517 1314 2 3 6.33 0 171.72 decline
## 4518 3441 5 3 6.96 1 194.15 decline
## 4519 1771 4 2 7.09 0 109.95 early
## 4520 3132 8 3 6.41 0 99.95 early
## 4521 2400 3 1 6.78 1 88.44 early
## 4522 1680 2 4 6.02 0 41.13 early
## 4523 2077 7 3 6.32 0 140.48 early
## 4524 355 1 1 6.60 0 142.80 early
## 4525 2304 6 1 7.76 0 352.73 early
## 4526 780 4 0 6.58 0 70.04 early
## 4527 1105 2 4 7.43 0 284.30 peak
## 4528 2775 8 6 6.56 2 244.82 peak
## 4529 2457 4 2 7.07 0 150.97 peak
## 4530 2890 6 5 7.13 0 42.94 decline
## 4531 2490 3 2 5.73 1 77.72 decline
## 4532 1909 4 2 6.19 1 90.16 early
## 4533 2349 4 5 7.44 0 57.96 early
## 4534 3108 7 4 7.27 0 121.76 early
## 4535 2511 2 5 5.99 1 84.15 early
## 4536 1404 1 3 6.80 1 99.25 early
## 4537 510 1 1 6.99 0 292.08 early
## 4538 1890 6 7 7.12 0 83.85 early
## 4539 2015 2 3 6.16 0 186.54 early
## 4540 2002 4 3 6.21 1 35.19 early
## 4541 1120 3 1 6.06 0 180.05 early
## 4542 3348 6 2 5.64 1 108.35 early
## 4543 1710 2 2 6.11 1 38.87 early
## 4544 2160 5 3 6.64 1 45.46 early
## 4545 688 0 1 7.81 0 214.24 peak
## 4546 1152 3 0 7.28 0 194.91 peak
## 4547 2278 2 2 6.74 0 39.10 peak
## 4548 3168 8 2 6.34 0 99.45 decline
## 4549 2660 9 1 7.12 0 140.41 decline
## 4550 430 0 0 6.16 1 134.98 decline
## 4551 1162 3 4 6.20 1 40.37 decline
## 4552 637 0 0 6.05 1 38.74 decline
## 4553 882 2 1 5.83 0 99.83 decline
## 4554 1034 1 2 6.08 2 34.53 early
## 4555 850 0 0 6.44 1 132.88 early
## 4556 738 2 0 6.07 0 159.77 early
## 4557 1330 3 2 6.77 0 89.60 early
## 4558 938 2 3 7.65 0 349.88 peak
## 4559 1638 3 4 6.71 0 250.58 peak
## 4560 1428 2 1 6.70 0 155.85 peak
## 4561 2775 4 4 6.35 2 125.86 decline
## 4562 702 1 0 6.96 0 214.87 decline
## 4563 2739 2 5 6.34 1 161.12 early
## 4564 2664 6 4 6.48 0 95.73 early
## 4565 2296 4 3 6.83 0 223.60 early
## 4566 1105 4 2 6.38 0 44.64 early
## 4567 1782 3 1 7.01 2 122.16 early
## 4568 1023 2 0 5.82 1 109.60 early
## 4569 1320 0 0 7.25 0 306.04 early
## 4570 370 0 1 5.87 0 41.10 early
## 4571 2170 4 5 7.07 1 201.80 peak
## 4572 1512 4 3 6.36 2 131.23 peak
## 4573 623 0 0 7.19 2 235.44 peak
## 4574 900 2 2 5.88 1 142.36 decline
## 4575 2250 4 6 7.41 0 92.88 decline
## 4576 1692 2 1 6.61 1 110.89 early
## 4577 915 2 3 6.14 0 226.29 early
## 4578 1023 0 1 5.80 1 159.49 early
## 4579 660 2 0 7.33 0 97.80 early
## 4580 897 4 3 6.01 1 60.74 early
## 4581 1088 1 4 6.26 2 78.62 early
## 4582 648 0 2 7.89 0 269.96 early
## 4583 2233 4 3 6.12 1 71.09 early
## 4584 2618 7 3 8.25 1 76.88 peak
## 4585 2112 3 2 7.00 0 93.43 peak
## 4586 2415 8 1 7.16 0 224.07 peak
## 4587 3115 8 2 6.54 0 112.16 decline
## 4588 2870 0 3 5.60 1 62.58 early
## 4589 2457 0 2 7.79 1 299.20 early
## 4590 2590 0 0 6.11 2 73.26 early
## 4591 781 0 1 6.52 2 216.31 early
## 4592 897 0 0 6.55 1 41.02 early
## 4593 2278 0 6 7.49 0 208.72 peak
## 4594 2697 0 2 6.80 0 51.45 peak
## 4595 540 0 1 6.74 0 132.09 early
## 4596 1290 0 1 6.25 1 125.33 early
## 4597 1550 0 4 6.64 1 40.73 early
## 4598 1422 0 3 7.25 1 139.37 early
## 4599 1850 0 1 6.69 0 184.35 early
## 4600 2190 4 3 6.36 1 220.46 early
## 4601 2378 3 3 6.07 0 139.64 early
## 4602 1404 2 0 6.71 1 76.40 early
## 4603 828 1 0 5.70 1 116.12 early
## 4604 2914 7 6 6.32 0 233.36 early
## 4605 1170 1 3 6.65 1 95.78 early
## 4606 2492 2 2 6.94 2 96.55 early
## 4607 2944 8 2 5.75 1 110.98 early
## 4608 2156 7 5 5.84 0 170.75 early
## 4609 2754 7 3 5.81 0 40.09 early
## 4610 415 1 0 6.07 1 82.07 early
## 4611 1660 3 1 6.97 0 40.54 early
## 4612 576 6 0 6.42 1 194.05 early
## 4613 1634 2 2 7.10 1 190.24 early
## 4614 2550 3 5 7.48 0 64.59 peak
## 4615 3060 7 4 5.96 1 155.77 early
## 4616 1368 3 0 6.39 0 181.10 early
## 4617 630 2 2 5.55 2 26.52 early
## 4618 2068 2 4 5.93 0 98.53 early
## 4619 2772 7 4 7.64 2 132.20 peak
## 4620 325 2 0 7.03 3 119.06 peak
## 4621 434 2 1 6.02 1 29.36 early
## 4622 1914 3 1 6.55 1 181.70 early
## 4623 2387 8 3 5.86 2 197.65 early
## 4624 1260 2 2 6.16 1 161.49 early
## 4625 1525 3 4 6.37 0 45.06 early
## 4626 2688 0 5 7.03 0 238.79 early
## 4627 915 0 2 6.16 1 115.82 early
## 4628 1580 0 2 7.02 1 99.23 early
## 4629 1708 0 2 6.06 0 129.41 early
## 4630 320 0 0 6.51 0 217.37 early
## 4631 1342 0 1 6.38 1 170.18 early
## 4632 2482 0 2 7.05 0 208.48 early
## 4633 1328 0 1 5.78 0 181.62 early
## 4634 510 0 0 5.34 1 136.22 early
## 4635 675 0 0 7.53 1 103.51 peak
## 4636 3024 0 3 6.72 1 218.99 peak
## 4637 2387 0 2 5.66 2 187.71 early
## 4638 2310 0 8 6.21 1 202.50 early
## 4639 2484 0 4 5.89 0 40.82 early
## 4640 2738 0 2 6.37 0 45.55 early
## 4641 1955 0 3 7.00 0 165.44 early
## 4642 3204 0 1 6.59 0 139.93 early
## 4643 1232 0 0 6.36 2 148.12 peak
## 4644 2240 0 1 6.93 0 240.24 peak
## 4645 621 0 0 6.85 2 50.91 peak
## 4646 2232 0 4 5.85 2 30.54 decline
## 4647 1449 0 0 6.41 1 187.39 decline
## 4648 1275 0 1 6.13 1 64.53 decline
## 4649 510 1 0 6.66 1 91.80 early
## 4650 1344 2 1 6.16 1 135.09 early
## 4651 1072 1 0 6.63 0 240.53 early
## 4652 912 1 1 6.38 0 186.18 early
## 4653 2484 4 2 6.81 2 222.56 early
## 4654 1134 2 3 6.71 0 109.18 early
## 4655 960 2 2 6.27 1 204.94 early
## 4656 2304 4 2 6.79 1 157.29 peak
## 4657 2442 5 3 7.18 1 108.89 peak
## 4658 1080 1 0 7.77 0 312.24 peak
## 4659 1850 4 2 5.56 1 36.58 decline
## 4660 2958 0 5 7.48 0 119.02 early
## 4661 2542 0 1 6.25 1 90.92 early
## 4662 592 0 3 6.80 0 217.15 early
## 4663 1190 0 7 6.29 2 159.51 early
## 4664 948 0 1 5.55 1 70.62 early
## 4665 2916 0 4 6.25 1 34.83 early
## 4666 1958 0 2 5.78 1 39.16 early
## 4667 780 0 1 6.50 0 34.16 early
## 4668 488 0 3 7.71 0 206.51 early
## 4669 2581 4 2 7.10 1 58.50 early
## 4670 455 0 0 6.45 0 47.48 early
## 4671 936 2 2 5.68 2 34.33 early
## 4672 1482 1 3 6.76 0 200.26 early
## 4673 2448 7 7 6.89 1 38.03 early
## 4674 2460 2 3 6.13 2 100.44 early
## 4675 3293 3 1 5.69 1 106.54 early
## 4676 1530 2 0 6.06 0 63.90 early
## 4677 2133 4 2 7.11 0 278.25 early
## 4678 2108 3 4 6.75 0 105.65 early
## 4679 1027 1 1 6.49 0 164.99 early
## 4680 804 2 1 6.22 0 78.12 early
## 4681 992 2 1 6.52 0 177.78 early
## 4682 715 0 0 5.66 0 74.57 early
## 4683 1920 3 0 7.64 0 67.85 early
## 4684 1891 7 1 6.73 1 149.68 peak
## 4685 2849 0 4 6.68 0 36.83 early
## 4686 2912 0 2 5.53 1 144.72 early
## 4687 1728 0 2 6.57 0 96.77 early
## 4688 2016 0 2 7.01 0 94.16 early
## 4689 2914 0 3 6.49 0 92.09 early
## 4690 3290 0 1 6.17 0 222.95 early
## 4691 1740 0 3 6.76 1 49.38 early
## 4692 420 0 0 7.13 0 225.80 early
## 4693 2668 0 4 6.68 0 167.76 early
## 4694 1932 0 2 6.06 3 170.54 early
## 4695 2112 0 4 6.95 0 172.74 peak
## 4696 1716 0 3 6.27 0 184.04 early
## 4697 736 0 0 5.07 1 71.59 early
## 4698 1320 0 2 6.70 3 188.15 early
## 4699 402 0 0 6.00 0 147.97 early
## 4700 992 0 2 7.14 0 115.47 peak
## 4701 1014 0 0 7.12 0 240.71 peak
## 4702 2403 0 1 7.69 1 112.66 peak
## 4703 1344 0 1 6.45 1 35.73 decline
## 4704 664 0 0 5.90 0 85.92 decline
## 4705 552 0 1 6.09 2 83.20 decline
## 4706 2380 0 2 6.12 1 40.53 decline
## 4707 1647 0 4 5.81 1 93.22 decline
## 4708 2652 0 0 7.05 0 95.21 decline
## 4709 610 4 2 6.42 0 172.17 early
## 4710 1742 1 3 6.97 1 113.09 early
## 4711 2378 3 2 6.85 0 49.62 early
## 4712 3094 6 1 5.21 1 27.48 early
## 4713 1958 5 2 6.75 0 52.91 early
## 4714 624 0 1 6.38 1 203.50 early
## 4715 730 1 0 6.46 0 35.74 earlyHipótesis
Hipótesis principal: El algoritmo Bosques Aleatorios (Random Forest) es capaz de clasificar correctamente la etapa de carrera de un jugador de fútbol (temprana, pico y declive) con una precisión superior al 85%, utilizando como variables predictoras los partidos jugados, minutos, goles, asistencias, valoración, lesiones y valor de mercado.
Hipótesis nula: Las características de rendimiento deportivo de un jugador de fútbol no permiten clasificar correctamente su etapa de carrera mediante el algoritmo Bosques Aleatorios, obteniendo una precisión igual o inferior al 50%.
Hipótesis alternativa: Las características de rendimiento deportivo de un jugador de fútbol sí permiten clasificar correctamente su etapa de carrera mediante el algoritmo Bosques Aleatorios, obteniendo una precisión superior al 50%, lo que demuestra que existe una relación entre el rendimiento del jugador y la etapa de su carrera profesional.
Código en RStudio
# Cargar librerías
library(randomForest)## Warning: package 'randomForest' was built under R version 4.5.3## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.##
## Adjuntando el paquete: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginlibrary(ggplot2)
# Cargar datos
datos_futbol <- read.csv(file.choose())
# Exploración básica
head(datos_futbol)## player_id nationality position birth_year season age club_level matches
## 1 1 Argentina GK 1993 2010/2011 18 4 29
## 2 1 Argentina GK 1993 2011/2012 19 4 23
## 3 1 Argentina GK 1993 2012/2013 20 1 18
## 4 1 Argentina GK 1993 2013/2014 21 1 14
## 5 1 Argentina GK 1993 2014/2015 22 2 13
## 6 1 Argentina GK 1993 2015/2016 23 1 8
## minutes goals assists rating injuries market_value_m. career_stage
## 1 2349 0 1 6.70 0 192.03 early
## 2 1725 0 2 6.24 1 171.35 early
## 3 1620 0 0 7.21 1 61.67 early
## 4 1064 0 0 6.70 0 48.53 early
## 5 1118 0 1 7.08 0 118.09 early
## 6 520 0 1 6.26 0 42.42 earlystr(datos_futbol)## 'data.frame': 4715 obs. of 15 variables:
## $ player_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ nationality : chr "Argentina" "Argentina" "Argentina" "Argentina" ...
## $ position : chr "GK" "GK" "GK" "GK" ...
## $ birth_year : int 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
## $ season : chr "2010/2011" "2011/2012" "2012/2013" "2013/2014" ...
## $ age : int 18 19 20 21 22 23 24 25 26 27 ...
## $ club_level : int 4 4 1 1 2 1 2 5 5 1 ...
## $ matches : int 29 23 18 14 13 8 30 32 20 7 ...
## $ minutes : int 2349 1725 1620 1064 1118 520 2340 2688 1600 525 ...
## $ goals : int 0 0 0 0 0 0 0 0 0 0 ...
## $ assists : int 1 2 0 0 1 1 2 3 1 3 ...
## $ rating : num 6.7 6.24 7.21 6.7 7.08 6.26 7.46 6.49 7.11 6.75 ...
## $ injuries : int 0 1 1 0 0 0 0 2 1 0 ...
## $ market_value_m.: num 192 171.3 61.7 48.5 118.1 ...
## $ career_stage : chr "early" "early" "early" "early" ...summary(datos_futbol)## player_id nationality position birth_year
## Min. : 1.0 Length:4715 Length:4715 Min. :1985
## 1st Qu.:121.0 Class :character Class :character 1st Qu.:1990
## Median :248.0 Mode :character Mode :character Median :1995
## Mean :249.4 Mean :1995
## 3rd Qu.:377.0 3rd Qu.:2000
## Max. :500.0 Max. :2004
## season age club_level matches
## Length:4715 Min. :17.00 Min. :1.000 Min. : 5.00
## Class :character 1st Qu.:20.00 1st Qu.:2.000 1st Qu.:13.00
## Mode :character Median :23.00 Median :3.000 Median :21.00
## Mean :23.15 Mean :3.023 Mean :20.98
## 3rd Qu.:26.00 3rd Qu.:4.000 3rd Qu.:29.00
## Max. :33.00 Max. :5.000 Max. :37.00
## minutes goals assists rating
## Min. : 300 Min. : 0.000 Min. : 0.000 Min. :5.000
## 1st Qu.: 962 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.:6.110
## Median :1580 Median : 2.000 Median : 2.000 Median :6.490
## Mean :1621 Mean : 2.308 Mean : 2.083 Mean :6.518
## 3rd Qu.:2222 3rd Qu.: 4.000 3rd Qu.: 3.000 3rd Qu.:6.900
## Max. :3478 Max. :14.000 Max. :11.000 Max. :8.690
## injuries market_value_m. career_stage
## Min. :0.0000 Min. : 22.26 Length:4715
## 1st Qu.:0.0000 1st Qu.: 73.25 Class :character
## Median :0.0000 Median :124.60 Mode :character
## Mean :0.5934 Mean :129.62
## 3rd Qu.:1.0000 3rd Qu.:177.22
## Max. :3.0000 Max. :412.30# Convertir variable objetivo a factor
datos_futbol$career_stage <- as.factor(datos_futbol$career_stage)
# Seleccionar variables relevantes
datos_model <- datos_futbol[, c("matches", "minutes", "goals", "assists",
"rating", "injuries", "market_value_m.",
"career_stage")]
# Eliminar filas con valores faltantes
datos_model <- na.omit(datos_model)
# División en entrenamiento (70%) y prueba (30%)
set.seed(123)
indice <- sample(1:nrow(datos_model), size = 0.7 * nrow(datos_model))
entrenamiento <- datos_model[indice, ]
prueba <- datos_model[-indice, ]
# Aplicar Random Forest
modelo_rf <- randomForest(career_stage ~ .,
data = entrenamiento,
ntree = 100,
importance = TRUE)
# Ver resumen del modelo
print(modelo_rf)##
## Call:
## randomForest(formula = career_stage ~ ., data = entrenamiento, ntree = 100, importance = TRUE)
## Type of random forest: classification
## Number of trees: 100
## No. of variables tried at each split: 2
##
## OOB estimate of error rate: 23.85%
## Confusion matrix:
## decline early peak class.error
## decline 1 318 15 0.99700599
## early 12 2256 141 0.06351183
## peak 1 300 256 0.54039497# Predicciones
predicciones <- predict(modelo_rf, newdata = prueba)
# Matriz de confusión
confusion <- table(Predicho = predicciones, Real = prueba$career_stage)
print(confusion)## Real
## Predicho decline early peak
## decline 0 0 0
## early 121 998 127
## peak 3 53 113# Accuracy
accuracy <- sum(diag(confusion)) / sum(confusion)
cat("Precisión del modelo Random Forest:", round(accuracy * 100, 2), "%\n")## Precisión del modelo Random Forest: 78.52 %# Importancia de variables
importance(modelo_rf)## decline early peak MeanDecreaseAccuracy
## matches -5.0475550 13.438794 0.3256556 13.506765
## minutes -5.9066418 11.238296 -2.1601053 10.294958
## goals -1.5198594 4.536074 2.9822527 5.153152
## assists -1.9242608 3.773835 0.9537344 3.245649
## rating 4.2483068 24.799662 57.8112856 50.760617
## injuries -0.4740703 4.527762 -0.1255539 4.009581
## market_value_m. 0.8480090 2.121217 3.3395081 3.591891
## MeanDecreaseGini
## matches 157.45083
## minutes 227.49339
## goals 105.20183
## assists 99.65850
## rating 470.65931
## injuries 50.90976
## market_value_m. 275.63657varImpPlot(modelo_rf, main = "Importancia de Variables - Random Forest")
# Gráfico de predicciones vs real
prueba$Predicho <- predicciones
ggplot(prueba, aes(x = career_stage, fill = Predicho)) +
geom_bar(position = "dodge") +
labs(title = "Clasificación Random Forest - Etapa de Carrera",
x = "Etapa Real",
y = "Cantidad de Jugadores",
fill = "Etapa Predicha") +
theme_minimal()
Análisis de Resultados
El modelo Random Forest obtuvo una precisión del 78.52%, evaluado sobre 1,415 observaciones del conjunto de prueba (30% del dataset). Si bien es un resultado aceptable, no supera el umbral del 85% planteado en la hipótesis principal.
Matriz de Confusión
| Decline | Early | Peak | |
|---|---|---|---|
| Decline | 0 | 0 | 0 |
| Early | 121 | 998 | 127 |
| Peak | 3 | 53 | 113 |
Temprana fue la etapa mejor clasificada con 998 aciertos, lo cual es esperado ya que es la categoría con más observaciones en el dataset.
En la cima tuvo 113 aciertos pero también 127 confusiones con early, lo que indica que el modelo tiene dificultad para distinguir entre estas dos etapas.
Declive no fue clasificada correctamente en ningún caso, lo que sugiere que esta categoría tiene muy pocas observaciones y el modelo no logró aprenderla bien.
Interpretación respecto a las Hipótesis
Se rechaza la hipótesis nula: El modelo superó ampliamente el 50%, descartando que las variables no tengan poder predictivo.
Se acepta la hipótesis alternativa: Con un 78.52% de precisión, se confirma que las características de rendimiento deportivo sí tienen relación con la etapa de carrera de un jugador.
No se confirma la hipótesis principal: El modelo obtuvo un 78.52%, por debajo del umbral del 85% planteado, por lo que la hipótesis principal no se cumple en su totalidad.
El modelo bosques de clasificación demostró ser moderadamente efectivo para clasificar la etapa de carrera de un jugador de fútbol. Su principal limitación fue la clasificación de la etapa decline, que no fue identificada correctamente en ningún caso, posiblemente debido al desbalance de clases en el dataset. El rating resultó ser la variable más determinante, lo que sugiere que la valoración general del rendimiento del jugador es el indicador más fuerte de su momento profesional. Para mejorar el modelo se podría aumentar el número de árboles, balancear las clases o incluir variables adicionales como la edad o el nivel del club.
IMPORTANCIA PARA LA INGENIERÍA
El aprendizaje supervisado es una de las herramientas más poderosas de la ingeniería moderna, con aplicaciones directas en casi todas sus ramas. En ingeniería industrial permite predecir fallos en maquinaria y optimizar procesos de producción; en sistemas y computación es la base de los sistemas de recomendación y detección de intrusos; en biomédica apoya el diagnóstico clínico, y en civil y ambiental ayuda a modelar estructuras y anticipar desastres naturales. En definitiva, el ingeniero del siglo XXI debe dominar el análisis de datos y el modelado predictivo para tomar decisiones basadas en evidencia y generar valor a partir de la información disponible.
CONCLUSIÓN
Se evaluaron dos algoritmos de aprendizaje supervisado con distintos niveles de éxito. Por un lado, el modelo KNN aplicado al dataset Iris demostró un rendimiento sobresaliente al alcanzar una precisión del 95.56%, validando plenamente la hipótesis principal. Por otro lado, el modelo de Bosques Aleatorios aplicado al dataset de fútbol obtuvo un 78.52% de precisión; aunque superó el umbral mínimo del 50%, no alcanzó el 85% proyectado debido al desbalance de los datos, lo que impidió clasificar correctamente la etapa de declive. En términos generales, ambos modelos confirmaron la relevancia significativa de sus variables predictoras y el proyecto permitió consolidar de manera práctica el flujo completo de un diseño de aprendizaje automático, desde el preprocesamiento hasta la evaluación de resultados.
BIBLIOGRAFÍAS
Oracle. (s.f.). Aprendizaje supervisado. Oracle LATAM. https://www.oracle.com/latam/artificial-intelligence/machine-learning/supervised-learning/
IBM. (s.f.). ¿Qué es el machine learning? IBM Think. https://www.ibm.com/es-es/think/topics/machine-learning
Google Cloud. (s.f.). ¿Qué es el aprendizaje supervisado? Google Cloud. https://cloud.google.com/discover/what-is-supervised-learning?hl=es-419
Databricks. (s.f.). Aprendizaje supervisado vs. no supervisado. Databricks. https://www.databricks.com/es/blog/supervised-vs-unsupervised-learning
Yepes, V. (2025, enero 23). Aprendizaje supervisado en ingeniería civil. Blog de Víctor Yepes. https://victoryepes.blogs.upv.es/2025/01/23/aprendizaje-supervisado-en-ingenieria-civil/
IBM. (s.f.). ¿Qué es KNN? IBM Think. https://www.ibm.com/mx-es/think/topics/knn
Gardas, P. (s.f.). Iris flower classification [Conjunto de datos]. Kaggle. https://www.kaggle.com/datasets/pavanigardas/iris-flower-classification/data
Akku, E. (s.f.). Synthetic football player career dataset [Conjunto de datos]. Kaggle. https://www.kaggle.com/datasets/emirhanakku/synthetic-football-player-career-dataset