2024 Salary-Per-Win Visualization

My experiment tested the correlation between total team salary and wins in Major League Baseball. Understanding this relationship is crucial for MLB teams, as it can guide them in spending more efficiently to maximize success on the field. To explore this, I posed the research question: Is there a correlation between the total salary of MLB rosters and their wins per season? By analyzing this data, I aim to provide insights that could help teams allocate resources more efficiently and effectively when constructing rosters.

Experiment Setup

Variables 

Independent Variable: Team Salary 

Dependent Variable: Number of Wins 

Controls: All data is from 2024 season 

Setup & Methods 

Type of Study: Correlation 

Graph: Scatterplot 

Purpose: To determine if spending more money results in more wins generated for teams 

Materials and Setup (including controlled variables): Data containing teams’ salary and win/loss records for 2024 season; Excel to analyze and format data Measurement protocols: Create a formula titled, “salary-per-win,” which equals total-team-salary divided by the number of wins 

Procedure for collecting data: Collect salary and win/loss data for each MLB team from trusted sources (Spotrac, Baseball Reference).

Findings

The R² value of 0.0629 indicates a weak correlation between salary-per-win and total wins, suggesting that changes in spending on salary-per-win alone cannot reliably predict changes in a team’s win total. In conclusion, while there appears to be little to no correlation between salary-per-win and wins, several other factors should be considered for a more comprehensive analysis. For instance, injuries can significantly impact team performance by limiting the availability of key players. Additionally, factors such as team depth, opponent strength, management strategies, and the timing of investments all play critical roles in determining a team’s success. These variables suggest that while salary is one aspect of team performance, understanding the full picture requires analyzing multiple influences that extend beyond payroll alone.