Modelagem Estatística para Previsão de Pontuações no Fantasy Football: Uma Abordagem Baseada em Dados Históricos da NFL

class: center, middle, inverse, title-slide

.title[
# Modelagem Estatística para Previsão de Pontuações no Fantasy Football: Uma Abordagem Baseada em Dados Históricos da NFL
]
.subtitle[
## Laboratório B
]
.author[
### Aluno: Felipe Cordeiro Melo
]
.author[
### Orientador: Dra. Amanda Merian Freitas Mendes
]

---

# Introdução

## NFL

A National Football League (NFL) é a principal liga de futebol americano do mundo. É composta por 32 times que competem ao longo de uma temporada de 23 semanas, culminando no jogo final, o *Super Bowl*.

### O jogo

O jogo é disputado por duas equipes, cada uma composta por 11 jogadores. Os times se alternam entre ataque e defesa, de acordo com a posse de bola. O principal objetivo do time que está no ataque é avançar com a bola até a end zone adversária para marcar um *Touchdown*, que representa a maior pontuação do jogo. Para isso, a equipe ofensiva dispõe de quatro tentativas (chamadas de descidas) para avançar pelo menos 10 jardas. Caso consiga, recebe uma nova sequência de quatro jogadas, e assim sucessivamente, até marcar o *Touchdown* Se não alcançar as 10 jardas dentro das quatro tentativas, a posse da bola é entregue ao time adversário.

---

# Introdução

## NFL

### Entretenimento

A NFL é considerada a maior liga esportiva dos Estados Unidos, com uma receita anual de aproximadamente US$ 23 bilhões em 2024 (Sports Business Journal, 2024). Seu principal evento, o *Super Bowl*, é também o de maior audiência do país, alcançando uma média de 127,7 milhões de espectadores (GE, 2025).

Segundo Almeida, et al. (2015), devido ao sucesso da liga e à sua temporada relativamente curta, surgem outras formas de os torcedores acompanharem e se engajarem com a NFL. Uma das mais populares é o *Fantasy Football*.

---

# Introdução

## Fantasy Football

O *Fantasy Football* é uma competição virtual em que os participantes se reúnem em uma liga e cada um montará seu time com jogadores reais da NFL. Os jogadores pontuam de acordo com seu desempenho em campo, e a cada semana os times se enfrentam, o que pontuar mais saí como vencedor do confronto. O calendário, é parecido com o da NFL, algumas semanas de temporada regular, e no final os *playoffs* em confrontos mata-mata, até restar somente um, o campeão.

### Montagem dos times

Os times são formados por meio de um *draft*, ou seja, todos os jogadores da liga estão disponíveis para seleção, e cada equipe escolhe um atleta por rodada, seguindo uma ordem previamente definida, geralmente aleatória. O formato mais comum é o *Snake*, no qual a ordem de seleção se inverte a cada nova rodada (por exemplo, o último time a escolher na primeira rodada será o primeiro na segunda). Vale destacar que, em uma mesma liga, cada jogador pode pertencer a apenas um time.

---
# Introdução

## Fantasy Football

### Posições

Cada time, será composto de 15 jogadores, dentre esses, 9 titulares, que serão das posições:

- 1 *Quarterback* (QB)
- 2 *Running Backs* (RB)
- 2 *Wide Receivers* (WR)
- 1 *Tight End* (TE)
- 1 *Flex* (RB/WR)
- 1 *Kicker* (K)
- 1 Defesa completa

Os outros 6 reservas podem ser de qualquer posição disponível.

---
# Introdução

## Justificativa

A realização desta pesquisa se justifica por sua representatividade no diálogo relacionado ao uso de estatísticas avançadas no Fantasy Football, tema que, apesar do crescente uso de modelagens preditivas na NFL, ainda carece de abordagens sistemáticas e objetivas na literatura científica.

A análise de bases de pesquisas (Web of Science (2025) e Scopus (2025)) demonstrou a ausência de estudos que modelassem, de forma integrada, a pontuação anual de todas as posições ofensivas, incluindo jogadores calouros, evidenciando uma lacuna investigativa ainda não explorada nesse campo de estudo.

## Objetivo

Estimar a pontuação anual de atletas da NFL em todas as posições ofensivas contempladas pelo *Fantasy Football*, adotando abordagens distintas para jogadores calouros, oriundos do *College Football*, e para atletas já atuantes na liga profissional.

---

# Revisão de Literaura

- Abadzic, Cheun e Patel (2024)
  - Produziu um *ranking* das 12 maiores pontuações para 2024, utilizou regressão *Ridge* e *Ridge* Bayesiana, regularização *Elastic Net*, *Random Forests* e *Gradient Boosting*, com os melhores resultados sendo com o *Random Forest*, porém, não considerou jogadores calouros e limitou à análise para somente as Top 12 predições.

- Morgan et al. (2019)
  - Buscou identificar jogadores subvalorizados no *Fantasy*, que são aqueles selecionados após a quarta rodada de um *draft* do *Fantasy*, mas que terminam no Top 20 de sua posição. Nesse caso os autores incorporaram jogadores calouros, sem dados na NFL. Os resultados foram bem satisfatórios utilizando *Lasso*, *Random Forests* e *XGBoost*, porém, o estudo foi limitado a RBs e WRs.

---

# Materiais e Métodos

## Dados do College

- Extraído com o pacote `cfbfastR`;
 - Estatísticas históricas e informação dos jogadores desde 2004;
 - Dados faltantes para algumas universidades menores.

## Dados da NFL

- Extraído com o pacote `nflfastR`;
 - Estatísticas históricas e informação dos jogadores desde 1999;
 - Mais completo e com estatísticas adicionais em relação aos dados de *College*.

## Dados de Draft

- Extraído com o pacote `nflreadr`;
 - Dados históricos do *Draft* da NFL.

---
# Materiais e Métodos

## Variável Resposta

A variável resposta é composto por 3 formas principais de pontuar no *Fantasy*:

`$$\text{Pontuação de Passe} = 0,04 \times \text{jardas passadas} + 4 \times \text{passes para touchdown} - 2 \times \text{passes interceptados}$$`

`$$\text{Pontuação de Corrida} = 0,1 \times \text{jardas corridas} + 6 \times \text{corridas para touchdown} - 2 \times \text{fumbles sofridos}$$`

`$$\text{Pontuação de Recepção} = 0,1 \times \text{jardas recebidas} + 6 \times \text{recepções para touchdown} - 2 \times \text{fumbles sofridos}$$`

Dessa forma, a variável resposta é obtida por:

`$$\textbf{fantasy_points} = \text{Pontuação de Passe} + \text{Pontuação de Corrida} + \text{Pontuação de Recepção}$$`

---

# Materiais e Métodos

## Recursos Computacionais

- *Software* `R` versão 4.4.3;
 - Principais pacotes utilizados: `nflfastR`, `cfbreadR`, `cfbfastR`, `tidyverse` , `ranger` e `mlr`;
 - Dados armazenados no `DBeaver`, versão 25.0.5 e tratados com consultas escritas na linguagem `MySQL`.
---

# Materiais e Métodos

- Modelagem por posição e experiência dos atletas;
- Divisão em dados de treino (70%) e teste (30%).

## Processamento dos dados

- *College*
 - Dados de *College* agregados por atleta - estatística da carreira do jogador;
 - Adiciona as colunas de pontuação no ano de calouro;
 - Adiciona colunas de características pessoais dos atletas no ano de calouro;
 - Cria a coluna de `draft_round_group` e `conference_group`

- NFL
 - Ajusta a variável resposta para utilizar a do ano seguinte ao das covariáveis.

Para ambas as bases, foram feitas então a separação por posição e também foram filtrados os jogadores que estavam com a pontuação nula. Dessa forma foram geradas 8 bases de dados, 2 para cada posição, para calouros e veteranos.

---

# Materiais e Métodos

## Modelagem em Duas Etapas

Muitos atletas que estão na NFL acabam não pontuando no Fantasy, como por exemplo, jogadores que são reservas durante toda a temporada, esse comportamento acaba gerando inflação de zeros na resposta. Para contornar esse problema, foi adotado o método de **Modelagem em Duas Etapas**, proposto por Cragg (1971). Nessa abordagem, fazemos duas modelagens, a primeira, um modelo de classificação, para predizer se um jogador pontuará ou não, e a segunda um modelo de regressão, predizendo a pontuação.

Adotou-se o critério de 25% de valor mínimo de zeros na resposta para adotar esse método, quando era encontrado, a base de treino era utilizada com todas as linhas no modelo de classificação, mas no modelo de regressão eram utilizados somente quando a resposta era maior que 0.

---

# Materiais e Métodos

## Modelos Lineares Generalizados

Os **Modelos Lineares Generalizados** (MLG) são modelos propostos por Nelder e Wedderburn (1972), onde é construído uma relação linear entre o preditor linear, representado, por `$\eta$`, e o parâmetro da distribuição da variável resposta, `$\mu$`, por meio de uma função de ligação `$g(\cdot)$`. Essa relação é pode ser expressa da seguinte forma:

`$$g(\mu_i) = \eta_i = \mathbf{x}_i^\top \boldsymbol{\beta}$$`

Para seleção de variáveis adotou-se o algoritmo *stepwise* com Critério de Informação de Akaike (AIC), expresso da seguinte forma:

`$$\text{AIC} = 2k - 2\ln(L)$$`
---

# Materiais e Métodos

## Modelos Lineares Generalizados

### Classificação

Para a classificação com MLG, utilizou-se o **Modelo de Regressão Binomial**, também conhecido como modelo de regressão logística, que segue a seguinte relação:

`$$Y_i|\mathbf{x}_{i} \sim \text{Binomial}(m, \mu_i), i = 1, \dots, n$$`

Como função de ligação utilizou-se as funções logito e probito:

<div style="text-align: center;">
<p style="font-size: 80%; margin-bottom: 5px; text-align: left; font-weight: bold;">Logito</p>
<span>$$g(\mu_i) = \log\left(\frac{\mu}{1 - \mu}\right)$$</span>
</div>

<div style="text-align: center;">
<p style="font-size: 80%; margin-bottom: 5px; text-align: left; font-weight: bold;">Probito</p>
<span>$$g(\mu_i) = \Phi^{-1}(\mu)$$</span>
</div>

</div>

---

# Materiais e Métodos

## Modelos Lineares Generalizados

### Regressão

Para o modelo de regressão, foi utilizado o **Modelo de Regressão Gamma**, que é o ideal para lidar com dados contínuos, positivos e assimétricos, que é o caso para a maioria dos casos enfrentados. Vale ressaltar que esse modelo só foi utilizado quando encontrou-se inflação de zeros na resposta, e portanto, só eram modelados valores positivos para a resposta, que é o adequado.

O modelo é expresso da seguinte forma:

`$$Y_i|\mathbf{x}_{i} \sim \text{Gamma}(\mu_i, \phi) , i = 1, \dots, n$$`

Como função de ligação utilizou-se as funções logarítmica e a inversa:

<div style="text-align: center;">
<p style="font-size: 80%; margin-bottom: 5px; text-align: left; font-weight: bold;">Logarítmica</p>
<span>$$g(\mu_i) = \log(\mu_i)$$</span>
</div>

<div style="text-align: center;">
<p style="font-size: 80%; margin-bottom: 5px; text-align: left; font-weight: bold;">Inversa</p>
<span>$$g(\mu_i) = \frac{1}{\mu_i}$$</span>
</div>

</div>

---

# Materiais e Métodos

## Árvores de Decisão

Modelos de árvores são relativamente simples e, muitas vezes, não atingem alta capacidade preditiva. Foram propostos por Sonquist (1963), e servem como base para métodos mais robustos, como *Random Forest* e *XGBoost*. Todos esses métodos podem ser aplicados tanto a problemas de classificação quanto de regressão.

Um modelo de árvore busca particionar o espaço de predição em regiões disjuntas `$R_{m}$`, de forma que cada observação pertença a apenas uma dessas regiões. O objetivo é que essas regiões englobem subconjuntos do espaço em que as observações da variável resposta sejam o mais homogêneas possível.

Essas segmentações são definidas da seguinte forma:

`$$R_1(j,s) = \{X \mid X_j \leq s\} \quad \text{ e } \quad R_2(j,s) = \{X \mid X_j > s\}$$`

Em que `$X_{j}$` é uma das covariáveis do modelo e `$s$` é o ponto de partição.

---

# Materiais e Métodos

## Árvores de Decisão

Para definir `$X_{j}$` e `$s$`, o algoritmo avalia, entre todas as covariáveis, qual o melhor ponto de partição em cada uma delas. Em seguida, escolhe a variável e o ponto de corte que produzem a divisão mais eficiente, isto é, aquela que gera regiões mais homogêneas entre si. Cada divisão realizada dá origem a um nó na árvore.

Para casos de classificação, o objetivo é minimizar o Índice de Gini:

`$$G = \sum_{k=1}^{K} p_k (1 - p_k) = 1 - \sum_{k=1}^{K} p_k^2$$`

em que `$p_k$` representa a proporção de observações da classe `$k$` em um determinado nó.

Para casos de regressão, o objetivo é minimizar a Soma dos Quadrados dos Resíduos (RSS):

`$$RSS = \sum_{i \in R_m} (y_i - \bar{y}_{R_m})^2$$`
---

# Materiais e Métodos

## Árvores de Decisão

### Seleção de Variáveis

Para a seleção de variáveis utilizou-se o método de *Recursive Feature Elimination* (RFE), proposto por Guyon et al. (2002) e definido pelas seguintes etapas:

1. Treinamento do modelo com o conjunto completo de variáveis;

2. Cálculo das importâncias relativas das covariáveis;

3. Eliminação da variável com menor importância;

4. Reajuste do modelo e cálculo das métricas de avaliação;

5. Repetição do processo até atingir o número mínimo de variáveis.

---

# Materiais e Métodos

## Random Forest

O método de *Random Forest* foi proposto por Breiman (2001) e é baseado no princípio de *ensemble learning*, que combina diversos modelos para atingir um desempenho superior, nesse caso, são combinados diversas árvores de decisão.

Essa combinação é feita a partir de subconjuntos aleatórios da base de dados, então, são ajustadas diversas árvores de decisão com diferentes observações e variáveis para cada uma delas, esse método é conhecido como *bagging*.

---

# Materiais e Métodos

## Random Forest

### Classificação

Para modelos de classificação, a predição final, da observação pertencer a classe 1 é dada por:

`$$\hat{P}(y = 1 \mid x) = \frac{1}{B} \sum_{b=1}^{B} I(\hat{y}^b = 1)$$`

Em que `$B$` representa o número total de árvores do modelo, e `$I(\hat{y}^b = 1)$` é uma função indicadora que vale 1 quando a árvore `$b$` classifica a observação na classe 1, e 0 caso contrário.

### Regressão

Para modelos de regressão, a predição final é dada por:

`$$\hat{y} = \sum_{b=1}^{B}{\frac{\hat{y}^b}{B}}$$`
---

# Materiais e Métodos

## Random Forest

### Hiperparâmetros

Esse algoritmo é controlado por alguns hiperparâmetros que foram tunados utilizando o método de *grid search*, esse hiperparâmetros são:

<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Hiperparâmetro </th>
   <th style="text-align:left;"> Descrição </th>
   <th style="text-align:left;"> Valores </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> mtry </td>
   <td style="text-align:left;"> Proporção de variáveis candidatas sorteadas aleatoriamente em cada divisão. </td>
   <td style="text-align:left;"> 5%, 15%, 25%, 33% e 40% </td>
  </tr>
  <tr>
   <td style="text-align:left;"> sample.fraction </td>
   <td style="text-align:left;"> Proporção de observações sorteadas para cada árvore. </td>
   <td style="text-align:left;"> 50%, 63%, 80% e 90% </td>
  </tr>
  <tr>
   <td style="text-align:left;"> replace </td>
   <td style="text-align:left;"> Define se as observações são sorteadas com ou sem reposição. </td>
   <td style="text-align:left;"> TRUE (com reposição) e FALSE (sem reposição) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> nodesize </td>
   <td style="text-align:left;"> Número mínimo de observações em um nó terminal. </td>
   <td style="text-align:left;"> 1, 3, 5 e 10 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> num.trees </td>
   <td style="text-align:left;"> Número de árvores na floresta. </td>
   <td style="text-align:left;"> 750 e 1000 </td>
  </tr>
</tbody>
</table>

### Seleção de Variáveis

Para seleção de variáveis, foi utilizada a métrica *Mean Decrease in Impurity* (MDI), que contabiliza quantas vezes cada variável é utilizada para dividir os nós das árvores no modelo, a partir disso é computada a redução da impureza e somadas para todas as árvores da floresta.

---

# Materiais e Métodos

## XGBoost

O *XGBoost* (*Extreme Gradient Boosting*) foi introduzido por Chen e Guestrin (2016), e é uma versão otimizada do algoritmo de *Gradient Boosting* proposto por Friedman (2001). A ideia central é construir modelos aditivos compostos por diversas árvores de decisão que, individualmente, são fracas, mas que, combinadas iterativamente, aprendem com os erros dos modelos anteriores. A atualização do modelo ocorre na direção do gradiente da função de perda, otimizando a predição a cada nova iteração.

Nesse método, temos que o modelo vai se atualizando com base no gradiente da função de perda definida por `$L(y, \gamma)$`. Para o caso do *XGBoost*, objetivo é minimizar a seguinte função objetivo:

`$$\mathcal{L}^{(t)} = \sum_{i=1}^{n} L(y_i, \hat{y}_i^{(t-1)} + f_t(x_i)) + \Omega(f_t)$$`

em que `$L(y, \hat{y})$` representa a função de perda, `$f_t(x)$` é a nova árvore ajustada na iteração `$t$`, e `$\Omega(f_t)$` é o termo de regularização que penaliza a complexidade da árvore.

---

# Materiais e Métodos

## XGBoost

### Hiperparâmetros

Esse algoritmo é controlado por alguns hiperparâmetros que foram tunados utilizando o método de *random search*, esse hiperparâmetros são:

<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Hiperparâmetro </th>
   <th style="text-align:left;"> Descrição </th>
   <th style="text-align:left;"> Intervalo </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> max_depth </td>
   <td style="text-align:left;"> Profundidade máxima da árvore. </td>
   <td style="text-align:left;"> 2 a 10 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> eta </td>
   <td style="text-align:left;"> Taxa de aprendizado (learning rate). </td>
   <td style="text-align:left;"> 0,01 a 0,3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> nrounds </td>
   <td style="text-align:left;"> Número de iterações (boosting rounds). </td>
   <td style="text-align:left;"> 50 a 200 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> subsample </td>
   <td style="text-align:left;"> Proporção de amostras usadas por árvore. </td>
   <td style="text-align:left;"> 0,5 a 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> colsample_bytree </td>
   <td style="text-align:left;"> Proporção de variáveis usadas por árvore. </td>
   <td style="text-align:left;"> 0,5 a 1 </td>
  </tr>
</tbody>
</table>

### Seleção de Variáveis

Para seleção de variáveis, foi utilizada a métrica *Gain* que é definido como a redução na função de perda regularizada provocada por uma divisão
(split).

---

# Resultados

## Quarterbacks

Distribuição da resposta para *Quarterbacks*:

</div>

---

# Resultados

## Quarterbacks Calouros

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `draft_round_group`;
- `conference_group`;
- `seasons_played`;
- variáveis de passe (`passing_att`, `passing_td`, `passing_int`);
- variáveis de corrida (`rushing_car`, `rushing_yds`, `rushing_td`).

---

# Resultados

## Quarterbacks Calouros

### Classificação

<div style="display: flex; justify-content: center; gap: 40px; align-items: center;">
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Resultado dos modelos candidatos</p>
<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Modelo </th>
   <th style="text-align:right;"> Acurácia </th>
   <th style="text-align:right;"> N° de Variáveis </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> modelo_log_logito </td>
   <td style="text-align:right;"> 0,7864 </td>
   <td style="text-align:right;"> 15 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_log_logito_stepwise </td>
   <td style="text-align:right;"> 0,8058 </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_log_probito </td>
   <td style="text-align:right;"> 0,7864 </td>
   <td style="text-align:right;"> 15 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_log_probito_stepwise </td>
   <td style="text-align:right;"> 0,8058 </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf </td>
   <td style="text-align:right;"> 0,7767 </td>
   <td style="text-align:right;"> 17 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf_rfe </td>
   <td style="text-align:right;"> 0,8155 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg </td>
   <td style="text-align:right;"> 0,8447 </td>
   <td style="text-align:right;"> 17 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;"> modelo_xg_rfe </td>
   <td style="text-align:right;font-weight: bold;"> 0,8447 </td>
   <td style="text-align:right;font-weight: bold;"> 5 </td>
  </tr>
</tbody>
</table>
</div>
<div style="width: 1px; background-color: #000; align-self: stretch;"></div>
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Importância das variáveis pela métrica Gain</p>
<img role="img" 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style="width: 100%;" />
</div>
</div>

---

# Resultados

## Quarterbacks Calouros

### Classificação

Matriz de confusão do modelo:

- Acurácia = 84,5%
- Sensibilidade = 68,3%
- Especificidade = 95,16%

---

# Resultados

## Quarterbacks Calouros

### Regressão

---

# Resultados

## Quarterbacks Calouros

### Regressão

Equação do modelo:

$$
`\begin{aligned}
\mathbb{E}(Y_i) = \exp \big(& 4,8447 - 0,9801 \cdot I(\text{draft_round_group} = \text{'Early Rounds'}) \\
& - 1,3835 \cdot I(\text{draft_round_group} = \text{'Mid Rounds'}) \\
& - 1,5287 \cdot I(\text{draft_round_group} = \text{'Late Rounds'}) \\
& - 1,8895 \cdot I(\text{draft_round_group} = \text{'Undrafted'}) \\
& + 0,00136 \cdot \text{passing_att} - 0,0453 \cdot \text{passing_int} \big)
\end{aligned}`
$$
---

# Resultados

## Quarterbacks Calouros

### Análise de Resíduos

---

# Resultados

## Quarterbacks Veteranos

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `years_exp`;
- `games`;
- variáveis de passe (`passing_td`, `passing_int`, `sacks_suffered`);
- variáveis de corrida (`rushing_yds`, `rushing_td`).

---

# Resultados

## Quarterbacks Veteranos

### Classificação

---

# Quarterbacks Veteranos

## Classificação

Matriz de confusão do modelo:

- Acurácia = 74,4%
- Sensibilidade = 89,9%
- Especificidade = 38,5%

---

# Resultados

## Quarterbacks Veteranos

### Regressão

# Quarterbacks Veteranos

## Regressão

### Análise de Resíduos

---

# Resultados

## Running Backs

Distribuição da resposta para *Running Backs*:

</div>

---

# Resultados

## Running Backs Calouros

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `draft_round_group`;
- `conference_group`;
- `seasons_played`;
- variáveis de corrida (`rushing_car`, `rushing_td`);
- variáveis de recepção (`receiving_rec`, `receiving_td`)

---

# Resultados

## Running Backs Calouros

### Classificação

---

# Resultados

## Running Backs Calouros

### Classificação

Equação do modelo:

$$
`\begin{aligned}
\frac{\mathbb{P}(Y_i = 1)}{1 - \mathbb{P}(Y_i = 1)} = \exp \big(& 6,3634  - 0,2118 \cdot \text{age} \\
& + 16,0840 \cdot I(\text{draft_round_group} = \text{'Early Rounds'}) \\
& + 0,3895 \cdot I(\text{draft_round_group} = \text{'Mid Rounds'}) \\
& - 1,5030 \cdot I(\text{draft_round_group} = \text{'Late Rounds'}) \\
& - 1,8520 \cdot I(\text{draft_round_group} = \text{'Undrafted'}) \\
& + 0,0112 \cdot \text{rushing_car} + 0,0132 \cdot \text{receiving_rec} \\
& - 0,1046 \cdot \text{receiving_td} \big)
\end{aligned}`
$$
---
# Resultados

## Running Backs Calouros

### Classificação

Matriz de confusão do modelo:

- Acurácia = 80,0%
- Sensibilidade = 92,3%
- Especificidade = 50,9%

---

# Resultados

## Running Backs Calouros

### Regressão

<div style="display: flex; justify-content: center; gap: 40px; align-items: center;">
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Resultado dos modelos candidatos</p>
<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Modelo </th>
   <th style="text-align:right;"> MAE </th>
   <th style="text-align:right;"> N° de Variáveis </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> modelo_gamma_log </td>
   <td style="text-align:right;"> 54,31 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_log_stepwise </td>
   <td style="text-align:right;"> 43,26 </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_inverse </td>
   <td style="text-align:right;"> 72,47 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_inverse_stepwise </td>
   <td style="text-align:right;"> 83,10 </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf1 </td>
   <td style="text-align:right;"> 40,43 </td>
   <td style="text-align:right;"> 15 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;"> modelo_rf_rfe </td>
   <td style="text-align:right;font-weight: bold;"> 40,82 </td>
   <td style="text-align:right;font-weight: bold;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg1 </td>
   <td style="text-align:right;"> 40,52 </td>
   <td style="text-align:right;"> 15 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg_rfe </td>
   <td style="text-align:right;"> 41,70 </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
</tbody>
</table>
</div>
<div style="width: 1px; background-color: #000; align-self: stretch;"></div>
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Importância das variáveis pela métrica MDI</p>
<img role="img" 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style="width: 100%;" />
</div>
</div>

---

# Resultados

## Running Backs Calouros

### Análise de Resíduos

---

# Resultados

## Running Backs Veteranos

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `years_exp`;
- `games`;
- variáveis de corrida (`rushing_car`, `rushing_td`);
- variáveis de recepção (`targets`, `receiving_td`, `receiving_yards_after_catch`, `receiving_first_downs`).

---

# Resultados

## Running Backs Veteranos

### Regressão

---

# Resultados

## Running Backs Veteranos

### Análise de Resíduos

---

# Resultados

## Wide Receivers

Distribuição da resposta para *Wide Receivers*:

</div>

---

# Resultados

## Wide Receivers Calouros

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `draft_round_group`;
- `conference_group`;
- `seasons_played`;
- variáveis de recepção (`receiving_rec`, `receiving_td`)

---

# Resultados

## Wide Receivers Calouros

### Classificação

---

## Wide Receivers Calouros

### Classificação

Equação do modelo:

$$
`\begin{aligned}
\frac{\mathbb{P}(Y_i = 1)}{1 - \mathbb{P}(Y_i = 1)} = \exp \big(& 8,9078 - 0,0206 \cdot \text{weight} - 0,3787 \cdot \text{seasons_played} \\
& - 0,4137 \cdot I(\text{draft_round_group} = \text{'Early Rounds'}) \\
& - 2,1163 \cdot I(\text{draft_round_group} = \text{'Mid Rounds'}) \\
& - 2,7589 \cdot I(\text{draft_round_group} = \text{'Late Rounds'}) \\
& - 4,0702 \cdot I(\text{draft_round_group} = \text{'Undrafted'}) \\
& + 0,0090 \cdot \text{receiving_rec} - 0,0452 \cdot \text{receiving_td} \big)
\end{aligned}`
$$

---
# Resultados

## Wide Receivers Calouros

### Classificação

Matriz de confusão do modelo:

- Acurácia = 76,9%
- Sensibilidade = 87,5%
- Especificidade = 48,0%

---

# Resultados

## Wide Receivers Calouros

### Regressão

<div style="display: flex; justify-content: center; gap: 40px; align-items: center;">
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Resultado dos modelos candidatos</p>
<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Modelo </th>
   <th style="text-align:right;"> MAE </th>
   <th style="text-align:right;"> N° de Variáveis </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> modelo_gamma_log </td>
   <td style="text-align:right;"> 38,27 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_log_stepwise </td>
   <td style="text-align:right;"> 29,65 </td>
   <td style="text-align:right;"> 7 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_inverse </td>
   <td style="text-align:right;"> 30,06 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_inverse_stepwise </td>
   <td style="text-align:right;"> 32,54 </td>
   <td style="text-align:right;"> 5 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf1 </td>
   <td style="text-align:right;"> 31,47 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf_rfe </td>
   <td style="text-align:right;"> 31,17 </td>
   <td style="text-align:right;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg1 </td>
   <td style="text-align:right;"> 30,09 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;"> modelo_xg_rfe </td>
   <td style="text-align:right;font-weight: bold;"> 29,37 </td>
   <td style="text-align:right;font-weight: bold;"> 4 </td>
  </tr>
</tbody>
</table>
</div>
<div style="width: 1px; background-color: #000; align-self: stretch;"></div>
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Importância das variáveis pela métrica Gain</p>
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65jd9Pc6aSTTvr5z3+ekZFRlyY7nzmXXHJJve56L/Tiiy9u3bo1tr7//vvfdtttOTk5dWmSmZl5zTXXHH300cmeDgCaR9+YWOTSDfW+ZvpHK7ZV/7J1Vqh1Vot/F71FrEx+TsYp++dXr8xZU7pqS5yPaQEAAADQYC3+rS4AAPZON9xwQ1FRUYNPnzhx4q7tGKu76667unXr1oCGI0eOPOGEE2LrNUXoYt19991x64MHD7766qvrGLZLUAouS4IGDBiQmZkZW//www8b3HP79u2zZs2KrZ9zzjkN7plEd9xxR4cOHep1yrBhw+L+/U6dOrXuTdasWTNjxozYelFR0c9//vO65JWrO+ecc+q+Le5eqKys7PHHH4+t5+Tk3HbbbVlZ0decrUUoFLrmmmu6dOmSvOkAoNkc1Dk6Frm+tKq+TeasKY2qdMyL83qyZWkRK3P6QQVRlfvfL05ifwAAAAACgUCLf6sLAIC90KBBg+q122WUzZs3x70o+aWXXrrvvvs2uO1PfvKTd999t7KysnpxyZIly5Yt22O2deXKlXEvRt+mTZvrr78+Wdewrl0KLkvisrKyvvOd78Q+rmeeeWb48OEN6xk3FpmVldXg/W6T6LzzzmvAX1YwGLzkkkt+97vfRdVXrly5ffv23NzcujSJ++QJBALjxo1rWNz5Zz/72QcffBD1zGGnd955JxwOx9avvvrqOv59VZeVlfXrX//6iiuuSMZoTa20tHTjxo3NPUXzi0QizT0CQEro1T67+pcVVZGqcL3/D7l2a/TLjw6tM5dvjPOpqhYk9VcmGAj89OjO1StVkcg7i7YkpXl1q1atSnpPANJGVVVtH6goLi6u7wdiASDpav9ptWrVKj+taNG6du3a3CPsFQRGAQBoeS677LJETn/ppZdii4WFhSNHjkykbW5u7sUXX/zwww9H1d9+++0LL7yw9nOfeuqpuPXrrruujpeWTlwKLktSxA2MLlmyZN26dR07dmxAw2eeeSa2OHLkyLhbmTalrKysMWPGNOzcww8/PG59xYoVBx544B5Pr6ysjPv8OfbYY3v27NmwkXJzcy+88MJHH320YaensUgk8uSTT8bWu3Xr1uAk/b777nv00Ue///77iY3WDMLhcEWFK9UC8H96tdvtlfPqBl3NfOOO6N+9dWr5O4ym/soc2KlVl7a77ZL+1Kfry6uS/4kIrxwAaDAfagUg9flpBdSFS9IDANDCHHDAAd27d2/w6eFw+Pnnn4+t//CHP0x8I8/jjz8+tvjyyy/XvvdbeXn5m2++GVvfd999DzvssARHqqMUXJZk6dmzZ2FhYWz9rbfeakC31atXr1y5MrY+YsSIBnRLrlGjRmVnZ+/5uHiys7MPOOCA2PqSJUvqcvqiRYvivg81duzYhs2z02mnnZbI6elq3bp1a9euja1fcskliXx2/IILLkhgKABofsFAIC97txeuq7c2JBq4uSw6Ftkmp2W/i94iVuYHh0e/Yp/06fpkNQcAAABgl5b9VhcAAHuh//zP/0zk9CVLlpSWlsbWjznmmETa7lRQUBC7HeO2bdtqv2Ly3Llz49YvuuiiJrt0SAouS7IEg8HRo0fH1p9//vm4F/Wu3RtvvBFb7Ny5c48ePRoyXFLFDebW3VFHHRVbjJuOjTVz5szYYn5+flFRUSIj5ebmDhkyJJEOaWn27NmxxVAoNGjQoETa1hStBoCWIhTzynndtoZsrLK9PPolYpvsjAbOlBpSf2VaZYbGHNqheuXbzRUL1+1ISnMAAAAAqhMYBQCghTnkkEMSOf2jjz6KLR566KEN3poxStzU3eLFi2s5ZdasWXHrAwcOTMpIdZGCy5JEw4cPjy1u3rx50aJF9eoTDodfeOGF2Pr3vve9Jov21iQzMzPB0GrXrl1ji6tWrarLuXEDoyeeeGLiyxL3724v9+GHH8YWhwwZkpWVFVuvu2AwePrppyfSAQCaV0bMW91llQ3Zz74qZhf8Vlkt+1301F+ZE/ZrG9r9ZeP97xc3xcUIAAAAAPY+LfutLgAA9jbZ2dkdOnTY83E1mzFjRmzx6KOPTqRndb17944tLl26tJZT3n333dji0KFDE4x/1UsKLksSFRQU9O/fP7b+8ssv16vPggUL4u7DetxxxzVwsuTZb7/9QqGE/n0Xd3fJuuwCW1lZGffK9YMHD05knp369euXeJN0EolEPvnkk9h63Ex2fR122GGJNwGA1FEVbkjmMPakjGb+ZFDypdrK/PTozlGVVxZsSk5rAAAAAHYnMAoAQEvSu3fvRPYsrKqqWrhwYWw9iaG0Ll26xBZr2cmyvLx8/fr1sfWhQ4cma6Q9SsFlSbq4V6V/4403ysvL695kypQpscWBAwe2bdu24ZMlyX777Zdgh7iPYsuWLXs8saZjErwe/U4dO3ZMvEk6KS0tjfuk7du3b+LNk/JXBgCpIyPUkH84xJ5UlXZ7XabUynQvyD6oU6vqlde+3rylrCoJrQEAAACIITAKAEBL0rNnz0RO37x5c9x63L0VGyY3Nze2WFxcXNPxNe3gmHj+r+5ScFmSbuDAgbEbcIbD4U8//bSOHcrKyqZNmxZbHzVqVKLDJUPiUb/MzMzYYmVl5R5PjJt4DtSQQK2vzMzMnJycxPukjQ0bNsStt2/fPvHmrVu3TrwJADSXqnB0JSezIbHIjJjPp+2oiGndoqT4yow5NPplzKOz1ibeFgAAAIC44vxGEAAAUlaPHj0SOb2kpCRu/dJLL92ZJoxE/m+TnF1/CIfj/BI09rDaj6/pfgOBwLp16+LWO3ToUNMpSZeCy5J0WVlZp5122tSpU6Pqzz333JFHHlmXDh9++GFsMTMz89BDD03CfAnLy8tLsEODr2i/aVP8C4ZmZ2cnMM6/7bvvvgsWLEhKqzRQUzw3bia7voLBYJcuXVavXp14KwBoeuFI9HaXHXIb8u53blb0i6Jt5S07MJrKK5MRCv5w8G47ym8uq/pk5fYE2wIAAABQE4FRAABakgR30atpK83S0tJE2u5RLdf13rp1a9x64vm/ukvBZWkMp59+emxgdN68eRs3bmzXrt0eT3/22Wfj9oy7MWfTa8Yx4j6HO3fuHIzZg6phunfvLjC6y/btcfITubm5GRkZSenfq1evlhUYDQaDyXrsLVokEombywfYq0QCgdKKcOtqocYubbIa0Kdtq+ifLFvLW/bl0VN5ZYb2zGu9ew71kQ/XhpNxpfuaeOUAQC3C4XAk5oMWu4RCoWS91wEADeanFZC4lPjVJgAA1FFWVkN+tblLEycUd6nlut41hTWbMv+XgsvSGHr16tW+ffvYK3pPnz79zDPPrP3ckpKSRYsWxdZPO+20pM2XmGZ8DyhuhLFNmzbJ6t+U4enUF/e7tVOnTsnqX1hYmKxWTSM3Nzcpu6u2dNu3b69pv2qAvcrSjeUHdWq168tu+Q35t0O7mFjk2m1N+qq1MaTsylw2NPplzPNzNibYs3bdu3dv1P4AtGgbNmyo5V2yLl26pMjHhgHYm61du7aWzT66d+8uMArsUQOvOQgAAM2iwVfN3ilusq15lZfMEQRLAAAgAElEQVSXx60n+EjrJQWXpTEEg8HRo0fH1p977rlaPo+701tvvRVbLCws7NmzZ3KGa8niBn9zcnKS1T/BmHiaKSsriy22atUqttgw2dnZyWoFAE1vyfrdflBmZQQz6v97sk5tooMg67e3+MBoaq5Mh9zMY3rt9imjT1ZuX7O1IpGeAAAAANROYBQAgL1IVVXKXU1yj1HFJpCCy9JIhg8fHlssKSlZvnx5LWdFIpHJkyfH1kePHu2juoEaLusZN9fYMBUVQgMAQJ0sWLcjqtIht97XHz+4c+uoyrqWHxhNzZU5s1+7qMoDHxQn0hAAAACAPRIYBQBgL5KCWxXWtBFjOBxushlScFkaSfv27fv16xdbf/XVV2s5a/HixZs3b46tx42f7oXiXhA8idvW7iU74NZR3B1Ad+yIjoA0WE17HgNAi/BlcfTPxKJ29d48e0iPvOpfllaEt5c33SvzRpKCKxMMBH581G7Xo68MR95dsrXBDQEAAACoi+iLyAAAQBpr27ZtbDEnJ+fvf/970w+zU35+ftx6ZWVlk10bOgWXpfGMHj36d7/7XVRx6tSpP/zhDzMz4//76OWXX44t9u/fv6CgIPnztUBt2rSJLa5evToSiSRlB9aVK1cm3iRtxP1uXbduXbL6l5SUJKsVADS9r0uiY5H9Orf++Nt6fPgkIxjs17lV9crM5duSMFlzS8GVOaRL68Lc3V5+/+8nJRVVzX/5BQAAAID0ZodRAAD2InGzVmVlZc14Tfa4YbtA0+6qmILL0ngOP/zwUCj630EVFRVz5syJe3xFRcW//vWv2Pro0aOTP1zLVFPoOVl7VS5ZsiQpfdJDXl5ebHHbtm3J+m5dvnx5UvoAQLNYsakiqhJ70fPa9SmM3v7/7UVbEpopNaTgylw0uGNU5clP1yfSEAAAAIC6EBgFAGAv0r59+7j1uBccbxodO0b/onSn9eub7telKbgsjSc7O/vkk0+Orb/44otxj//000/D4ehLbYZCoYEDByZ/uJapsLAwbn3r1iRcUbSysrK0tDTxPmmjQ4cOcetJiZhHIhH7uQLQolWFI9MX75ZiHNQ9Ny+7Hu+Bjzgg+pMwM5elw0XSU21lcrNDZx+8W2J16YbyRevLGtwQAAAAgDoSGAUAYC9SUzrz22+/beJJdmnXLv7WPt98802TzZCCy9KoRo4cGVucNWvWtm1xrqr53HPPxRZPO+20rKys5E/WMtW0w+iyZcsSb96UyekWoaZ498aNGxNvvmNH9MVqAaDF+fucDVGVY3rF39E/VjAQ+P7hu30SprQinDYpxpRamdj46f0fFDe4GwAAAAB1JzAKAMBepFWrVnEv6PzFF180/TA7ZWdnx90ycNasWU02QwouS6Pq3bt33IzjjBkzoiqbNm2aO3du7JGnn356o0zWMmVmZhYVF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y9vW0uFhYUXXnjhQw891LbTM1mCM1ggI+/WTJjEUq+hoWHr1q3p7oJUG9T9K2/ptVvbsmTmph3RK332aodlL9Pb6pXH9OnTJW/Xwx88u7wNcdV2MqxXp/vOHrRqS8OP/rL807XRS6gmzswAHY60KACZz98rApD5tm3blu4WICECo6nhr6AAAGi1oUOHDhgwoM2nRyKRadOmxdYvuuiixP9M/7jjjostPvfcc03Nrl9VX1//+uuvx9b32WefQw45JMGWWigDL0tSlJeXl5aWxtbfeOONNoy2du3a1atXx9ZPPfXUNoyWdBMnTszPz9/7cfHk5+cPHTo0tr5s2bKWnL5kyZK4SwxOnjy5bf3sdNpppyVyemZKcAYLZOTdmgmTGKRGMBAoyv/KjbZ2W1tSmFvqolOYXQqS/FOy9LY6tGenS8f02vXwkY+rFq5vbrWktBhQnPfMBUN+cFRvC18AAAAAAKSGwCgAAK121llnJXL6smXLamvjLCV19NFHJzLsTt26dYtdjrGmpmbTpk3NnDVv3ry49e9///sp27wjAy9LUgSDwUmTJsXWp02bFolEWjvaa6+9Flvs3bt3WVlZW5pLtrhRv5Y76qijYotxA7KxZs+eHVssLi7e6z7mzSssLBw9enQiI2SgBGewQEberZkwiUFqhGLe0htq4sTl92p7ffT3oC75OW3saQ/S2GpOKHj/2V9uRr+ptvE3b61pw0unxlVj+/znSf3T3QUAAAAAwNdC+jdtBACgwxk5cmQip3/44YexxYMPPrjNSzNGOeqooz777LOo4tKlS5vZxeCDDz6IWx81alRSWmqJDLwsyTJu3Lj7778/qrhly5YlS5YMGTKk5eNEIpFnn302tn7OOedkQiQuNzc3wdxqv379Yotr1rQo4hM3MHrCCSckfmXGjRu3pxukg0pwBgtk5N2aCZMYpEZOzN8+14Xbslp2Y8yqvZ3ykvxn1Wls9bIje5V3/3JG+uFflrftpVtreXX9s/OrP1xZs2JT/eYdjbUNTZGmpmAwUJAb6lGYe0CvTuceXHLifsWxJ154WOnSjXX/7/9WpaBJAAAAAICvMyuMAgDQOvn5+T169EhkhHfffTe2+I1vfCORMXc3ePDg2OLy5cubOeXtt9+OLY4ZMyYvLy9ZXe1VBl6WZOnWrduIESNi688991yrxlm0aFHcZR2PPfbYNnaWVPvtt1+C25GXlpbGFluyCmw4HI67c/3hhx+eSD87DR8+PPFBMkfiM1ggI+/WTJjEIF0aI22JQsaelNP+f3qQmlYHds+/amyfXQ9fWLR59oqaNrxuy1VtD//slVVH37vg5D8suvvdyvcqalZtadhWH2lsamoKBCJNgdqGyKrN9a8t3nLZ1OVj7lkwY0Gcb20/P7n/7iFXAAAAAADag8AoAACtM3jw4ETWLGxsbFy8eHFsPYmhtL59+8YWlyxZsqfj6+vrN27cGFsfM2ZMslraqwy8LMkVd1f61157rb6+vuWDTJ8+PbY4atSorl27tr2z5Nlvv/0SHCHuF7J169a9nrinYxLcj36nnj17Jj5I5khwBgtk5N2aCZMYpFFOqC03dexJje2/BGcKWg0FA/ftthl9Xbjp+pdWteFFW+XxTzY+/snG9TXhlhy8cXv4qhkr/m1anBD8r05LaKFuAAAAAAD2SmAUAIDWKS8vT+T0LVu2xK3HXVuxbQoLC2OLlZWVezp+Tys4Jp7/a7kMvCzJNWrUqNjVNyORyCeffNLCEerq6mbOnBlbnzhxYqLNJUni6czc3NzYYji89/xN3LBgYA8J1NbKzc0tKChIfJwMkeAMFsjIuzUTJjFImcZIdKUgty0pzJyY7PiOhpihE5OWVi84rOfQnp12PfyP51dsrWtsw4u2t1cXb/nRXyuiimPKiwZaZBQAAAAAoD3F+X0kAAA0o6wsocWfqqqq4tYvvfTSnYHCpqZ/rJq16x+RSJxUROxhzR+/p9cNBAIbNmyIW0983+qWy8DLklx5eXmnnXba888/H1WfMmXKkUce2ZIR3n///dhibm7uwQcfnIT+kqGoqCjBEdq8o/3mzZvj1vPzkxO72WeffRYtWpSUodIuwRkskJF3ayZMYpAykabo1TV7FLblp1uFedFTbk19kgOjqW+1X9e8/zyx366HH6yseXFR/G8QmeCFRZunL9h05vDuuxfPGlHyP++sS1dLAAAAAABZT2AUAIDWKSkpSeT0PS3OV1tbm8iwe9XMvt7btm2LW088/9dyGXhZku7000+PDYzOnz9/06ZN3bt3j3vK7p5++um4Y8ZdlTMt0thJ3Pdw7969E9x7fZcBAwZkTWA0wRkskJF3ayZMYpAyTYFAbUOk824Zyr5d8towTtdOOVGVbfVJXokzxa2GgoF7zvpyM/pIU+DHf63Y8971GeG/Zq6NCox+Z6TAKAAAAABAO8qU360CANBR5OW1JeuwSyoTirtrZl/vPcW/Upn/y8DLknSDBg0qKSmprq6Oqs+aNevMM89s/tyqqqolS5bE1k877bSk9ZewZKUz22D79u2xxS5duiRr/GzKHSY4gwUy8m7NhEksXTp37tyvX7+9H5ftduzYETu7ZrHlm+oP6PXlruv9i9tyX3ePSWGur0n+98RUtjrpoB4H9e286+HNr61uj68ouVZvaZhfWXtg7y/b7l+c1yk3tCOc6GqvZgbIQNu2bWvmc1RpaWmyVscHgDbbvHlz3J+x7NS7d++cnOgP5wCQYtXV1Tt27NjTs3379k3jryqAjiL7f3sEAEBytXnX7J2a+alrutTX18etJ/iVtkoGXpakCwaDkyZNevDBB6PqU6ZMGT9+fPM/wnjjjTdii6WlpeXl5clsscOKGyUsKChI1viJhywzR+L3dQberZkwiaVLKBT6OnyZe9XQ0JDuFlJq2ca63VOYeTnBnGCwMWb/9+b16hL9M7GN25Mfr0xZq3k5wZtPHbDr4ecbdjz2SVUrm02Pt5Zs3T0wGggEehTmrN6SaGA0m75zQdZoPmGTm5vrzgUg7fb63err8JeZAGS45n+ZkpeXJzAK7JUPtQAApFRjY5L3e01cUytzG+0hAy9Lexg3blxsYLSqqmrFihUDBw7c01lNTU1Tp06NrU+aNMkPPnaK+/uMurq6ZI3/dcuiNS8D79ZMmMQglRZt2HHasG67V3oU5rR2Nc2okGIgENjQDoHRlLWaEwyGdvuWOLRnp3k/GdnC8XND0d9M51w1ImpeGXvfwg3ts17pgsroJTGKC3JWB3zfAQAAAABoF9bhAAAgpTJw2Zg9LcQYiSS6ulXLZeBlaQ8lJSXDhw+Prb/00kvNnLV06dK4O26PGzcuaZ11cIWFhbHFJC6EmYFraqZRBt6tmTCJQSotjIkYDuze6l2MR5cV7f6wtiGyvT75t0waW80NBVv4v9hzc4J7PyZZtsV8Lfk5/hoEAAAAAKC9WGEUAICU6tq1a2yxoKDgmWeeSX0zOxUXF8eth8Ph/PxWpzraJgMvSzuZNGnSzTffHFV8/vnnL7rooj3t6vXcc8/FFkeMGNGtW7fY+tdTly5dYotr165tampKyiKsq1evTnyQrJGBd2smTGKQSp9XRacwh/fu/NGqVkTbc4LB4b077V6ZvaImCZ3F6ECtpktRfvRfs9c3WjUZAAAAAKC9WGEUAICUipu1qqurS+Muz3HDdoHUrqqYgZelnRx66KGhUPR/hjQ0NMydOzfu8Q0NDa+88kpsfdKkSclvrsPaU16wvr4+KeMvW7YsKeNkhwy8WzNhEoNUWrk5er/yM4d3b9UI+5ZGr8v75pKtCfW0Bx2o1XQZ3qtTVGVrXbZ9+AEAAAAAyBwCowAApFRJSUncetw9x1OjZ8+ecesbN25MWQ8ZeFnaSX5+/kknnRRb/8tf/hL3+E8++SR2W+1QKDRq1KjkN9dhlZaWxq1v27Yt8cHD4XBtbW3i42SNDLxbM2ESg1RqjDTNWvqV0ORhAwpjF6psxqlDo3P2syuSMGHG6kCtpsvYwdEp/I21AqMAAAAAAO3FlvQAAKTUnoJNq1at2lMMq7117x5/ra8vvvhi3333TU0PGXhZ2s/48eNjFw394IMPampqioqKoupTpkyJHeG0007Ly8trr/46oD2tMFpRUbGnLGnLCR1GycC7NRMmMUixZ+ZWRwUNjx7U5ZXPW5TbDgYC3zv0K3NjbUNkyca6ZPa3m9S0uiMcGfrbOW3r8Bcn9496lRG3z03NvvB9u+Yd1Lfz7pX1NeHahug/FAEAAAAAIFmsMAoAQEp16tQpNhQYCATmzGljyiFx+fn5PXr0iK1/8MEHKeshAy9L+xk8eHDcgOO7774bVdm8efO8efNijzz99NPbpbMOKzc3d+DAgbH1jz/+OPHBFyxYkPgg2SQD79ZMmMQgxWYujV5l8z/G9Q227NzR5UW9ir7yF9R//rgq0m7xyMxvNTYamoqsaCAQCASuOrZPVGXa3OpUvTgAAAAAwNeRwCgAAKk2duzY2OLLL7+c+k52idvSe++919DQkN4e0ntZ2kkoFJo0aVJs/emnn46qzJo1K/awkpKSQYMGtUtnHdmYMWNii2+++WZTU6Kxn5kzZyY4QvbJwLs1EyYxSKWtdY0zFmzavTK4R8Fpw7rt9cScUPDXp5VFFR/9pCqZzX1VB2o1xU7Yr+vEkdELM0+bJzAKAAAAANCOBEYBAEi1o446Kra4fv36VatWpb6ZnY488sjYYiQSSeWSgRl4WdrPcccdF1tcvXr1mjVrdj1samqKjZAGAoFJkyYFgy1cmu1rJG5gtLq6esWKFYkMW1tbO3v27ERGyEoZeLdmwiQGKXb72+uiKr87o7xf17zmz7r+hH7l3fN3r7z8+ZbVW1oUrc4JBRf8n5FR/8vMVlPm0P6FJ+zXNdT6b8tHD+rywMR9oopz1tYurqpLSmMAAAAAAMQlMAoAQKodfPDBcetPPvlkijvZ5cADDwyF4nw2fuSRRxJfoLGFMvCytJ/S0tKhQ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style="width: 100%;" />
</div>
</div>

---

# Resultados

## Wide Receivers Calouros

### Análise de Resíduos

---

# Resultados

## Wide Receivers Veteranos

Váriaveis utilizadas:

---

# Resultados

## Wide Receivers Veteranos

### Regressão

---

# Resultados

## Wide Receivers Veteranos

### Análise de Resíduos

---

# Resultados

## Tight Ends

Distribuição da resposta para *Tight Ends*:

</div>

---

# Resultados

## Tight Ends Calouros

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `draft_round_group`;
- `conference_group`;
- `seasons_played`;
- variáveis de recepção (`receiving_rec`, `receiving_td`)

---

# Resultados

## Tight Ends Calouros

### Classificação

<div style="display: flex; justify-content: center; gap: 40px; align-items: center;">
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Resultado dos modelos candidatos</p>
<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Modelo </th>
   <th style="text-align:right;"> Acurácia </th>
   <th style="text-align:right;"> N° de Variáveis </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> modelo_log_logito </td>
   <td style="text-align:right;"> 0,7931 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_log_logito_stepwise </td>
   <td style="text-align:right;"> 0,7586 </td>
   <td style="text-align:right;"> 5 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_log_probito </td>
   <td style="text-align:right;"> 0,7931 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_log_probito_stepwise </td>
   <td style="text-align:right;"> 0,7586 </td>
   <td style="text-align:right;"> 5 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf </td>
   <td style="text-align:right;"> 0,7816 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf_rfe </td>
   <td style="text-align:right;"> 0,7931 </td>
   <td style="text-align:right;"> 7 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg </td>
   <td style="text-align:right;"> 0,8161 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;"> modelo_xg_rfe </td>
   <td style="text-align:right;font-weight: bold;"> 0,8391 </td>
   <td style="text-align:right;font-weight: bold;"> 4 </td>
  </tr>
</tbody>
</table>
</div>
<div style="width: 1px; background-color: #000; align-self: stretch;"></div>
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Importância das variáveis pela métrica Gain</p>
<img role="img" 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AAAsRkwYEDmeFdXV0nmL/en2/so2GUGWxgkLoWzmcIajxg3btzXvva1zEvbtm179dVXK1wP1Kh0mkaAbD6EydmMiTtGKAmNsS9++ctf3nvvvZmXxowZc+ONNxa2HJsPZeK/OAAAABCbY445JnO8VC8m9jbPoEGDSjJ/HwW7zGALg8SlcDZTWON7TjvttNNOOy3z0n/8x39UuBioUUk1jdDYfAiTsxkZd4xQPI3xA73wwgv/+I//mHlp6NCht99+e8FrsflQJgKjAAAAQGwGDx6cOX748OGSzH/w4MHM8Qq/mBjsMoMtDBKXwtlMYY1HW7hwYeb4z3/+81K9fwZxS61pBMXmQ5iczfi4Y4QiaYzv75VXXvn617+eeampqenOO+8cMWJEwZPbfCgTgVEAAAAgNr29mHjgwIGSzL9///7M8d4+9V4mwS4z2MIgcSmczRTWeLTm5uZJkyb1HO/o6HjjjTcqXg7UntSaRlBsPoTJ2YyPO0Yoksb4PrZu3bps2bLMS42NjXfeeeeYMWOKmd/mQ5kIjAIAAACxGTZsWOZ4by8C9tc777yTOd7bi5hlEuwygy0MEpfC2Uxhjd2cd955meO/+93vKlsI1KQEm0Y4bD6EydmMkjtGKIbG2JsdO3Zcd911md9VnMvlbr/99ubm5iKfwuZDmQiMAgAAALEZNWpU5nhra2tJ5m9packcP/bYY0syfx8Fu8xgC4PEpXA2U1hjN3/0R3+UOb558+YKVwK1KMGmEQ6bD2FyNqPkjhGKoTFm2rVr15IlSzo6OjKv3nLLLRMnTiz+WWw+lInAKAAAABCbESNGZI7v27evJPPv3bs3c3zIkCElmb+Pgl1msIVB4lI4mymssZvefuDvzTffrHAlUIsSbBrhsPkQJmczSu4YoRgaY09vv/32okWL2traMq/efPPN06ZNK8kT2XwoE4FRAAAAIDbDhw/PHN+1a1dJ5t+5c2fm+KBBg0oyfx8Fu8xgC4PEpXA2U1hjN8ccc0zm+FtvvVXhSqAWJdg0wmHzIUzOZpTcMUIxNMZuWlpaFi1a9O6772ZevfHGG3v7VuMC2HwoE4FRAAAAIDbDhg3LHN+4cWNJ5v/Nb37Tc3D06NENDQ0lmb+Pgl1msIVB4lI4mymssZtcLvtF/v3791e4EqhFCTaNcNh8CJOzGSV3jFAMjfFo77zzzuLFi3v7RfgbbrhhxowZJXw6mw9lIjAKAAAAxCaXy40fP77n+KZNm4qfvL29PfPnik466aTiJ++XYJcZbGGQuBTOZgpr7KN8Pl/tEqAGaBpVZPMhTM5mUtwxQl9ojO959913Fy9e/Pbbb2devf76608//fTSPqPNhzIRGAUAAAAilPnS3uHDhw8ePFjkzPv27csc/4M/+IMiZy5AsMsMtjBIXApnM4U1Hq2rqytzfMiQIRWuBGpUak0jKDYfwuRsxscdIxRJY6yrqzt48ODixYt3796defXaa6+dNWtWOZ7X5kM5CIwCAAAAETr11FMzx7dv317kzFu3bs0cr8qLicEuM9jCIHEpnM0U1ni0Q4cOZY6PGTOmwpVAjUqtaQTF5kOYnM34uGOEImmMbW1t11133c6dOzOvXn311Z/5zGfK9NQ2H8pBYBQAAACI0LRp0zLHX3755SJnfv755zPHJ06cWOTMBQh2mcEWBolL4WymsMajvfXWW5njY8eOrXAlUKNSaxpBsfkQJmczPu4YoUiJN8bDhw8vXbq0t4Dml770pdmzZ5fv2RPffCgTgVEAAAAgQh/60Ieampp6jj/11FPFTJvP559++ume4xMnTsx8unILdpnBFgaJS+FsprDGo61fvz5z/IQTTqhwJVCjUmsaQbH5ECZnMz7uGKFIKTfG9vb2ZcuW/e53v8u8esUVV1x44YVlLSDlzYfyERgFAAAAIpTL5T772c/2HN++fXtLS0vB0+7YsePgwYM9x8v6Sfr3Eewygy0MEpfC2UxhjUd74oknMsd9Jwr0UWpNIyg2H8LkbMbHHSMUKdnG2NHRsXz58o0bN2ZevfTSS+fOnVvuGpLdfCgrgVEAAAAgTrNmzcocX716dcFz/vSnP80cnzlzZsFzFinYZQZbGCQuhbOZwhqP2Llz59atW3uO53K5j3zkI5WvB2pUOk0jQDYfwuRsxsQdI5REgo2xs7Pzm9/8Zm9fUfz5z39+3rx5lakkwc2HchMYBQAAAOI0bdq0oUOH9hx/+OGHu7q6Cpiwra1t1apVPcenTJnyoQ99qIAJSyLYZQZbGCQuhbOZwhqPuPfeezPHP/GJTwwYMKDCxUDtSqdpBMjmQ5iczZi4Y4SSSK0xdnV13XTTTb/+9a8zr/7VX/3VlVdeWbFiUtt8qACBUQAAACBOuVzusssu6zne2tr6i1/8ooAJH3/88cxXISv2efpMwS4z2MIgcSmczRTWWFdXt27dul/96leZly644IIKFwM1LZGmESabD2FyNqPhjhFKJanG2NXV9d3vfnfNmjWZV//8z/984cKF9fX1Fasnqc2HyqhvaWmpdg0AAAAAZfHuu+9eeumlPV8BHDhw4IMPPjhkyJC+T7Vr164FCxb0HB8+fPiDDz5YwNdyPProo//2b//Wc3zevHkXX3xxv6YKdpnBFgaJC/lslqo3BrjG//zP/zznnHOampr6/tTv46233lq4cGF7e3vPS2PHjr3vvvtyOd8WAf0QYNN4TwlvGvvonXfeyXxLfv78+XPmzCn504W8+ZCykM9mxI3RHSOELJHGmM/nb7/99v/+7//OvPrpT3/62muvrWRa9IiQNx9qkTsAAAAAIFqDBw++4ooreo4fPnz4xhtv7PuPFh0+fPjrX/965qVFixYV9kpiV1dXR5YCfkop2GUGWxgkLuSzWareGOAan3322Xnz5v3v//5vPp/v+6Myvfnmm4sWLcp877+urm7x4sXe+4f+CrBpvKeEN41hCnnzIWUhn82IG6M7RghZCo0xn8/fddddvaVFzzjjjCVLllQ+LVoX9uZDLXITAAAAAMTsc5/7XOanzNevX//d7363s7PzA2c4fPjwDTfcsGPHjp6XJkyY8Kd/+qclqLJowS4z2MIgcSmczQDXePDgwZtuuunqq69+6aWXCgs05PP5Z555ZuHChfv378/8gxkzZpxyyikFzAwE2DTSYfMhTM5mVbhjhJBF3xh/+MMfrlq1KvPSJz7xiaVLl1Yxax795kMlCYwCAAAAMRs4cODy5cszLz3zzDNLly7dvXv3+zx827Zt11xzzfr16zOvLl++PJChksZvAAAM1klEQVTv5Ah2mcEWBolL4WwGu8YdO3b83d/93Re/+MUnn3xy3759fXxUPp/fuHHjV7/61Ztvvrm36MCQIUOWLVtWla97gQgE2zRSYPMhTM5mFbljhDDF3Rh/9KMfPfbYY5mXZs6cuWzZsuqWF/fmQ4XVt7S0VLsGAAAAgPK6//77H3nkkd6uzpo1a/bs2ZMnTx40aFB9fX1XV9eBAwc2b9787//+7y+88EJvj1qyZMk555xTcEkrV6584IEHeo7Pnz9/zpw5hc0Z4DIDLwwSF+DZLHlvDGeNf//3f//iiy/2HG9ubv7kJz/5sY997Ljjjjv22GMbGxuP/ApePp/v6OhobW3duXPnr3/961WrVrW2tr7P/Llc7q677mpubu5vYcDRwmka7ylhY+zs7Lzooos+8M+6urp6ixk1NDR84MOvu+66WbNm9auwIwLcfKAuyLMZcWN0xwg1IcrG2NbW9j79MJfLlTZP+ZGPfOSf/umfCnhggJsPteiDb18AAAAAat38+fNff/31Z599NvPq6tWrV69e3a8JL7zwwgBfSQx2mcEWBolL4WyGv8Y33nhj5cqVK1euPHowl8v16xdIGxsbb731Vu/9Q/HCbxpF6ujoKPfD8/l8YZNHv/lQo6I/myE3xve4Y4SgRN8Ye3qf3HzBExb2wAQ3H8rBF+oCAAAA8auvr1+2bNmZZ55ZktkuueSSBQsWFDlJZ2dn5nhTU1PBcwa4zCOCLQwSF+DZLHlvDHCNfdGvN88mTJhw3333TZ48uXz1QDoCbBrluGkMU4CbD9QFeTbTaYzvzx0jVIvGWEUBbj7UIt8wCgAAACQhl8stXbp04sSJ999/fzGTXHvttWeffXbx9fT2Q0hFTh7aMsMvDBIX2tksR28MbY2l9YUvfGHOnDlHfpMUKInQmkaZbhrDFNrmA0eEdjaTaowl4Y4RSk5jrKLQNh9qkW8YBQAAAFJRX19/ySWX/PM///OUKVMKePjJJ5/8r//6ryV5JbG9vX3Dhg09xy+55JJhw4YVOXk4y6yVwiBx4ZzN8vXGENb4jW9844Ybbvj4xz9ezCTvyeVy559//ooVKy677DLv/UPJhdA0jijrTWOYwtl84GjhnM24G6M7RqghGmMVhbP5UKPqW1paql0DAAAAQEXl8/kNGzY88sgja9as6cvfn3nmmRdffPGkSZNKVcCmTZu+8pWvdBvM5XIPP/zw4MGDS/UsVV9mb4ItDBJX9bNZgd5Y9TXW1dW1t7e/+uqr//d///f000/v2rWrvw8/9dRTP/vZz86cOTO+3xaEAFW9aZS2MXZ2dn7uc58rUWm9+tu//dszzjij+HmqvvlApqqfzUQaoztGqCFxNMa2traLLrqoVCV9oAkTJnz/+98vfp6qbz7UKIFRAAAAIF0HDx7csmXLSy+9tHnz5t///vc7d+48fPhwU1PTcccdd/zxx5944ol/+Id/OGXKlIEDB5b2eVeuXPnAAw90G5w/f/6cOXNK+0RHVGuZtVsYJC6F3hhI/2lra3vzzTdfe+21N954Y+fOnTt37nz99dcPHDjQ1taWy+VGjBgxcuTIMWPGTJ48efz48VOmTBk3blxjY2NZSwIypdAYgxVIxwa60Rgrxh0j1AqNsYrcMUK/CIwCAAAAVNqXv/zl11577eiRxsbGhx9+2Jd/ACnTGwG60RgButEYAbrRGIH+ylW7AAAAAIC0H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style="width: 100%;" />
</div>
</div>

---

#Resultados

## Tight Ends Calouros

### Classificação

Matriz de confusão do modelo:

- Acurácia = 83,9%
- Sensibilidade = 87,5%
- Especificidade = 73,9%

---

#Resultados

## Tight Ends Calouros

### Regressão

<div style="display: flex; justify-content: center; gap: 40px; align-items: center;">
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Resultado dos modelos candidatos</p>
<table class="table table-striped table-hover lightable-classic" style="font-size: 14px; color: black; width: auto !important; margin-left: auto; margin-right: auto; color: black; font-family: Cambria; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Modelo </th>
   <th style="text-align:right;"> MAE </th>
   <th style="text-align:right;"> N° de Variáveis </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> modelo_gamma_log </td>
   <td style="text-align:right;"> 20,95 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_log_stepwise </td>
   <td style="text-align:right;"> 18,25 </td>
   <td style="text-align:right;"> 7 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_inverse </td>
   <td style="text-align:right;"> 18,83 </td>
   <td style="text-align:right;"> 11 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_gamma_inverse_stepwise </td>
   <td style="text-align:right;"> 18,56 </td>
   <td style="text-align:right;"> 8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_rf </td>
   <td style="text-align:right;"> 17,46 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;"> modelo_rf_rfe </td>
   <td style="text-align:right;font-weight: bold;"> 17,28 </td>
   <td style="text-align:right;font-weight: bold;"> 12 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg </td>
   <td style="text-align:right;"> 20,06 </td>
   <td style="text-align:right;"> 13 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> modelo_xg_rfe </td>
   <td style="text-align:right;"> 19,32 </td>
   <td style="text-align:right;"> 7 </td>
  </tr>
</tbody>
</table>
</div>
<div style="width: 1px; background-color: #000; align-self: stretch;"></div>
<div style="width: 45%; text-align: center;">
<p style="font-size: 90%; margin-bottom: 8px;">Importância das variáveis pela métrica MDI</p>
<img role="img" 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style="width: 100%;" />
</div>
</div>

---

# Resultados

## Tight Ends Calouros

### Análise de Resíduos

---

# Resultados

## Tight Ends Veteranos

Váriaveis utilizadas:

- `age`;
- `height`;
- `weight`;
- `years_exp`;
- `games`;
- variáveis de recepção (`targets`, `receiving_td`, `receiving_yards_after_catch`, `receiving_first_downs`).

---

# Resultados

## Tight Ends Veteranos

### Regressão

---

# Resultados

## Tight Ends Veteranos

### Análise de Resíduos

<img src="figs/residuos_nfl_te.png" style="display: block; margin: auto; width: 50%;">
---

# Considerações Finais

- Bons resultados, considerando a dificuldade dos dados e a generalização feita;
- Modelos tiveram dificuldades para predizer pontuações de jogadores em temporadas *breakout*;
- Algumas outras variáveis poderiam ser consideradas, como salário e histórico de lesão;
- Pode servir de base para modelos mais complexos e específicos.

---

# Referências

ABADZIC, A.; CHEUN, J.; PATEL, M. Data analysis on predicting the top 12 fantasy football players by position. SMU Data Science Review, v. 8, n. 2, p. 7, 2024.

ALMEIDA, R. B. d.; ALMEIDA, V. M. C. d.; LIMA, D. d. F. P. Comunidades de marca de fantasy sports games: Identificação, engajamento, intenção de continuidade e valor da marca do patrocinador. ReMark - Revista Brasileira de Marketing, v. 14, n. 1, p. 33–48, abr. 2015. Disponível em: <https://periodicos.uninove.br/remark/article/view/12074>.

BREIMAN, L. Random forests. Machine learning, Springer, v. 45, p. 5–32, 2001.

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