Hitting Metrics Correlated with Winning

MLB 2024

Author

Patrick Berry

1)Introduction

I want to explore which offensive statistics in baseball have the biggest impact on helping teams win games and which ones are most closely linked to having a high batting average. This analysis will focus only on the offensive side of the game—things like hitting, getting on base, and base running—while leaving out the defensive side, such as pitching and fielding. I understand that winning a baseball game depends on both offense and defense, but for this project, I’m specifically interested in what parts of hitting and base running contribute the most to team success.

2) How

The data for this analysis was scraped from baseball reference.com. Baseball-Reference.com is a comprehensive online database that provides detailed statistics, historical records, and player profiles for Major League Baseball (MLB) and other leagues. It is widely used by fans, analysts, and researchers for its in-depth data on team performance, individual player stats, game logs, and advanced metrics.

I will scrape data from Baseball Reference containing team hitting statistics from the 2024 MLB season. After importing the data into R, I will manually add a column with each team’s win percentage for that season by going to MLB.com and looking at teams records for the year 2024. My goal is to analyze how various offensive metrics—such as Batting Average (AVG), On-Base Percentage (OBP), Strikeouts (SO), and Home Runs (HR)—correlate with team success. I will use scatter plots to visually explore the relationship between these offensive statistics and win percentage.

3) Analysis

[1] "Correlation between Home Runs and Win Percentage: 0.672"

The correlation between Home Runs and Win Percentage is 0.672, indicating a moderately strong positive relationship. Teams that hit more home runs generally tend to win more games, making home run production a key offensive factor worth tracking.

[1] "Correlation between Batting Average and Win Percentage: 0.617"

The correlation between Batting Average and Win Percentage is 0.617, indicating a moderately strong positive relationship. This suggests that teams with higher batting averages tend to perform better overall, though the impact is slightly less pronounced than with home runs.

[1] "Correlation between On-Base Percentage and Win Percentage: 0.789"

The correlation between On-Base Percentage and Win Percentage is 0.789, indicating a strong positive relationship. This suggests that consistently getting on base is a significant driver of team success, more so than batting average or home runs alone.

cor_batage <- cor(as.numeric(batting_table$BatAge), batting_table$Win_Pct, use = “complete.obs”)

print(paste(“Correlation between the teams batters ages and Win Percentage:”, round(cor_batage, 3)))

[1] "Correlation between the team's batters' ages and Win Percentage: 0.309"

The correlation between a team’s average batter age and Win Percentage is 0.309, indicating a weak positive relationship. This suggests that while older lineups may have a slight edge, age alone is not a strong predictor of team success. It does show that older teams tend to be better than younger teams, at least in 2024.

[1] "Correlation between Strikeouts and Win Percentage: -0.305"

The correlation between Strikeouts and Win Percentage is -0.305, indicating a weak negative relationship. Teams that strike out more tend to win slightly less often, but the connection is not particularly strong. This makes sense because teams could often strikeout but also hit for power and hit lots of doubles and home runs.

[1] "Correlation between Stolen Bases and Win Percentage: 0.1"

The correlation between Stolen Bases and Win Percentage is 0.1, indicating a very weak positive relationship. This suggests that while stealing bases may offer some advantage, it has minimal overall impact on a team’s win percentage.

[1] "Correlation between Slugging Percentage and Win Percentage: 0.749"

The correlation between Slugging Percentage and Win Percentage is 0.749, indicating a strong positive relationship. This suggests that teams with higher slugging percentages—those generating more extra-base hits—tend to perform significantly better in terms of winning games.

[1] "Correlation between BatAge and Win Percentage: 0.309"

The correlation between BatAge and Win Percentage is 0.309, indicating a weak positive relationship. This suggests that teams with slightly older batting lineups may perform marginally better, but age alone is not a strong indicator of winning. Older teams do tend to play better, and this could be attributed to being more experienced.

4) Conclusion

This analysis highlights several key insights about the offensive factors that contribute to team success in Major League Baseball. Among the offensive statistics examined, On-Base Percentage (OBP) and Slugging Percentage (SLG) showed the strongest positive correlations with winning, indicating that teams that consistently get on base and hit for power are most likely to succeed. Overall though, OBP had the highest correlation with winning.

Home Runs (HR) and Batting Average (BA) also displayed moderately strong positive relationships with win percentage, reinforcing the importance of both power hitting and consistent contact at the plate. In contrast, Strikeouts (SO) had a weak negative relationship with winning, suggesting that while avoiding strikeouts can help, it is not the sole determinant of success. Similarly, Stolen Bases (SB) showed a very weak positive correlation, implying that while speed can be a small advantage, it is much less critical than power and getting on base.

Average Batter Age (BatAge) had a weak positive relationship with winning percentage, suggesting that experience might provide a slight edge but is not a major driver of team success.

Overall, the data supports the idea that building a strong offensive team in today’s MLB environment depends more on getting on base and producing extra-base hits than on traditional measures like batting average or aggressive base running. Teams aiming to maximize their chances of winning should prioritize players who can consistently reach base and deliver impactful hits.

--- title: "Hitting Metrics Correlated with Winning" # Name of your HTML output subtitle: "MLB 2024" author: "Patrick Berry" # Author name toc: true # Generates an automatic table of contents. format: # Options related to formatting. html: # Options related to HTML output. code-tools: TRUE # Allow the code tools option showing in the output. embed-resources: TRUE # Embeds all components into a single HTML file. execute: # Options related to the execution of code chunks. warning: FALSE # FALSE: Code chunk sarnings are hidden by default. message: FALSE # FALSE: Code chunk messages are hidden by default. echo: FALSE --- # 1)Introduction I want to explore which offensive statistics in baseball have the biggest impact on helping teams win games and which ones are most closely linked to having a high batting average. This analysis will focus only on the offensive side of the game—things like hitting, getting on base, and base running—while leaving out the defensive side, such as pitching and fielding. I understand that winning a baseball game depends on both offense and defense, but for this project, I'm specifically interested in what parts of hitting and base running contribute the most to team success. # 2) How The data for this analysis was scraped from baseball reference.com. Baseball-Reference.com is a comprehensive online database that provides detailed statistics, historical records, and player profiles for Major League Baseball (MLB) and other leagues. It is widely used by fans, analysts, and researchers for its in-depth data on team performance, individual player stats, game logs, and advanced metrics. I will scrape data from Baseball Reference containing team hitting statistics from the 2024 MLB season. After importing the data into R, I will manually add a column with each team's win percentage for that season by going to MLB.com and looking at teams records for the year 2024. My goal is to analyze how various offensive metrics—such as Batting Average (AVG), On-Base Percentage (OBP), Strikeouts (SO), and Home Runs (HR)—correlate with team success. I will use scatter plots to visually explore the relationship between these offensive statistics and win percentage. ```{r} #| label: loading-in-the-libraries #| include: false library(tidyverse) library(rvest) library(polite) library(httr) library(ggplot2) ``` ```{r} #| include: FALSE batting_table <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/berryp1_xavier_edu/EairH7eDvTdCjGeqHAn1OMIBEJiqq8VjyN20w0pJ0vG6tA?download=1") ``` # 3) Analysis ```{r} ggplot(batting_table, aes(x = as.numeric(HR), y = Win_Pct)) + geom_point(color = "steelblue", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Home Runs and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Total Home Runs", y = "Win Percentage" ) + theme_minimal(base_size = 13) correlation <- cor(as.numeric(batting_table$HR), batting_table$Win_Pct, use = "complete.obs") print(paste("Correlation between Home Runs and Win Percentage:", round(correlation, 3))) ``` The correlation between Home Runs and Win Percentage is 0.672, indicating a moderately strong positive relationship. Teams that hit more home runs generally tend to win more games, making home run production a key offensive factor worth tracking. ```{r} ggplot(batting_table, aes(x = as.numeric(BA), y = Win_Pct)) + geom_point(color = "seagreen", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Batting Average and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Team Batting Average", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between Batting Average and Win Percentage:", round(cor(as.numeric(batting_table$BA), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between Batting Average and Win Percentage is 0.617, indicating a moderately strong positive relationship. This suggests that teams with higher batting averages tend to perform better overall, though the impact is slightly less pronounced than with home runs. ```{r} ggplot(batting_table, aes(x = as.numeric(OBP), y = Win_Pct)) + geom_point(color = "darkorange", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between On-Base Percentage and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Team On-Base Percentage (OBP)", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between On-Base Percentage and Win Percentage:", round(cor(as.numeric(batting_table$OBP), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between On-Base Percentage and Win Percentage is 0.789, indicating a strong positive relationship. This suggests that consistently getting on base is a significant driver of team success, more so than batting average or home runs alone. cor_batage \<- cor(as.numeric(batting_table\$BatAge), batting_table\$Win_Pct, use = "complete.obs") print(paste("Correlation between the teams batters ages and Win Percentage:", round(cor_batage, 3))) ```{r} ggplot(batting_table, aes(x = as.numeric(BatAge), y = Win_Pct)) + geom_point(color = "lightgreen", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Average Batter Age and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Average Batter Age (BatAge)", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between the team's batters' ages and Win Percentage:", round(cor(as.numeric(batting_table$BatAge), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between a team’s average batter age and Win Percentage is 0.309, indicating a weak positive relationship. This suggests that while older lineups may have a slight edge, age alone is not a strong predictor of team success. It does show that older teams tend to be better than younger teams, at least in 2024. ```{r} # Create scatter plot for SO vs Win Percentage ggplot(batting_table, aes(x = as.numeric(SO), y = Win_Pct)) + geom_point(color = "firebrick", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Strikeouts and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Team Strikeouts (SO)", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between Strikeouts and Win Percentage:", round(cor(as.numeric(batting_table$SO), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between Strikeouts and Win Percentage is -0.305, indicating a weak negative relationship. Teams that strike out more tend to win slightly less often, but the connection is not particularly strong. This makes sense because teams could often strikeout but also hit for power and hit lots of doubles and home runs. ```{r} ggplot(batting_table, aes(x = as.numeric(SB), y = Win_Pct)) + geom_point(color = "purple", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Stolen Bases and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Team Stolen Bases (SB)", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between Stolen Bases and Win Percentage:", round(cor(as.numeric(batting_table$SB), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between Stolen Bases and Win Percentage is 0.1, indicating a very weak positive relationship. This suggests that while stealing bases may offer some advantage, it has minimal overall impact on a team's win percentage. ```{r} ggplot(batting_table, aes(x = as.numeric(SLG), y = Win_Pct)) + geom_point(color = "pink", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Slugging Percentage and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Team Slugging Percentage (SLG)", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between Slugging Percentage and Win Percentage:", round(cor(as.numeric(batting_table$SLG), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between Slugging Percentage and Win Percentage is 0.749, indicating a strong positive relationship. This suggests that teams with higher slugging percentages—those generating more extra-base hits—tend to perform significantly better in terms of winning games. ```{r} ggplot(batting_table, aes(x = as.numeric(BatAge), y = Win_Pct)) + geom_point(color = "goldenrod", size = 3, alpha = 0.8) + geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 1) + labs( title = "Relationship Between Average Batter Age and Win Percentage", subtitle = "MLB 2024 Season — Team-Level Data", x = "Average Batter Age (BatAge)", y = "Win Percentage" ) + theme_minimal(base_size = 13) # Calculate correlation print(paste( "Correlation between BatAge and Win Percentage:", round(cor(as.numeric(batting_table$BatAge), batting_table$Win_Pct, use = "complete.obs"), 3) )) ``` The correlation between BatAge and Win Percentage is 0.309, indicating a weak positive relationship. This suggests that teams with slightly older batting lineups may perform marginally better, but age alone is not a strong indicator of winning. Older teams do tend to play better, and this could be attributed to being more experienced. # 4) Conclusion This analysis highlights several key insights about the offensive factors that contribute to team success in Major League Baseball. Among the offensive statistics examined, On-Base Percentage (OBP) and Slugging Percentage (SLG) showed the strongest positive correlations with winning, indicating that teams that consistently get on base and hit for power are most likely to succeed. Overall though, OBP had the highest correlation with winning. Home Runs (HR) and Batting Average (BA) also displayed moderately strong positive relationships with win percentage, reinforcing the importance of both power hitting and consistent contact at the plate. In contrast, Strikeouts (SO) had a weak negative relationship with winning, suggesting that while avoiding strikeouts can help, it is not the sole determinant of success. Similarly, Stolen Bases (SB) showed a very weak positive correlation, implying that while speed can be a small advantage, it is much less critical than power and getting on base. Average Batter Age (BatAge) had a weak positive relationship with winning percentage, suggesting that experience might provide a slight edge but is not a major driver of team success. Overall, the data supports the idea that building a strong offensive team in today's MLB environment depends more on getting on base and producing extra-base hits than on traditional measures like batting average or aggressive base running. Teams aiming to maximize their chances of winning should prioritize players who can consistently reach base and deliver impactful hits.