Joey Marciano

Joey Marciano Player Comp Visualizations

library(tidyverse)

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## ✓ readr   1.3.1     ✓ forcats 0.5.0

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## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

milb_data <- read.csv('jm_milb.csv')
mlb_data <- read.csv('jm_mlb.csv')
class_vector <- milb_data$Class
factor_class_vector <- factor(class_vector, ordered = TRUE, levels = c("Rookie", "A", "Adv A", "AA", "AAA"))
milb_data$Class <- factor_class_vector
class_vector <- mlb_data$Class
factor_class_vector <- factor(class_vector, ordered = TRUE, levels = c("Rookie", "A", "Adv A", "AA", "AAA"))
mlb_data$Class <- factor_class_vector

Organizational Comparisons

MLB Comparisons

ggplot(mlb_data, aes(Class, ERA, color=Name))+geom_point() +ggtitle('Class vs. ERA')

ggplot(mlb_data, aes(Class, SO9, color=Name))+geom_point() +ggtitle('Class vs SO9')

ggplot(mlb_data, aes(Class, HR9, color=Name))+geom_point() +ggtitle('Class vs. HR9')

MilB Comparisons

ggplot(milb_data, aes(Age, Class, color = Name)) + geom_point() + ggtitle('Age vs Minor League Level') + xlab('Age') + ylab('Level')

ggplot(milb_data, aes(Age, ERA, color = Name)) + geom_point() + ggtitle('Age vs ERA')

ggplot(milb_data, aes(Age, SO9, color = Name)) + geom_point() + ggtitle('Age vs SO9')

ggplot(milb_data, aes(Age, HR9, color = Name)) + geom_point() + ggtitle('Age vs HR9')

Machine Learning Projections

Based off Joey Marciano’s 2019 results, we project his chances of pitching in MLB at 25%.