player hr salary
1 Aaron Judge 58 40000000
2 Shohei Ohtani 44 46000000
3 Matt Olson 41 20000000
4 Ronald Acuna Jr. 41 17000000
5 Kyle Schwarber 38 19000000
6 Luis Robert 38 6000000
7 Julio Rodriguez 37 800000
8 Juan Soto 35 31000000
9 Pete Alonso 34 14500000
10 Rafael Devers 33 31300000Do Home Runs Affect MLB Player Salaries?
Introduction to the Data
This project investigates whether MLB players who hit more home runs tend to earn higher salaries. Home runs are one of the most important offensive statistics in baseball and are often associated with player value and power hitting ability. This analysis explores whether teams financially reward players based on offensive production.
Seasons are indexed by their ending year. Accordingly, the 2024 season denotes the full 2023–2024 MLB campaign.
Question #1: Who are the top 10 home run hitters, and what are their salaries?
Example: Aaron Judge hit 58 HR in 2024 and earned $40,000,000
The top 10 home run hitters in the 2024 season paint a clear picture of how power hitting and payroll intersect — but not always in the way you might expect. Aaron Judge leads the list with 58 home runs and a $40 million salary, which is about as clean a reward-for-production story as baseball offers. Shohei Ohtani follows at 44 HR and $46 million, making him the highest paid player in the dataset despite not leading in home runs — a reflection of his two-way value rather than power output alone. What stands out further down the list is how many elite home run hitters are still on pre-arbitration deals. Julio Rodriguez hit 37 home runs and earned just $800,000, and Adolis Garcia hit 33 home runs and earned $6 million — both cases where team-controlled contracts mean production and pay are completely decoupled. This immediately signals that the relationship between home runs and salary is real, but far from perfect.
Question #2: What does the distribution of home runs look like across all players?
The home run distribution is heavily right-skewed, which is exactly what you would expect from a dataset that includes both pitchers and position players. The vast majority of players cluster between 0 and 15 home runs, with pitchers anchoring the left tail at zero and a long right tail stretching out toward the 40s and 50s where only a handful of elite power hitters live. This skew is important context for interpreting any correlation with salary — because most players hit very few home runs, the relationship is largely being driven by a small group of sluggers at the top end of the distribution rather than a clean linear trend across the whole roster.
Question #3: What does the distribution of salaries look like across all players?
MLB salaries follow an even more extreme right skew than home runs, which reflects the well-documented structure of baseball’s pay system. The overwhelming majority of players — particularly those in their first three years of service time — earn at or near the league minimum of $700,000, creating a massive spike on the left side of the distribution. From there, salaries thin out dramatically as you move right, with only a small number of players earning north of $30 million. This distribution reinforces why raw correlation between home runs and salary can be misleading: a significant portion of the dataset consists of young players earning near-minimum wages regardless of how productive they are, simply because the collective bargaining agreement controls their pay until they reach arbitration eligibility.
Question #4: Is there a relationship between home runs and salary?
The scatterplot reveals a positive but moderate relationship between home runs and salary — the trend line slopes upward, confirming that players who hit more home runs do tend to earn more, but the scatter around that line is wide enough to make clear that home runs alone are not driving contracts. The most visually striking feature of the plot is the cluster of players at zero home runs who span a huge salary range, from league minimum pitchers all the way up to max-contract starters like Gerrit Cole and Max Scherzer. On the right side of the plot, the high-HR players are mostly clustered in the mid-to-high salary range, but outliers like Julio Rodriguez and Bobby Witt Jr. pull the trend line down by posting elite power numbers at rookie-scale salaries. The correlation is real — it is just heavily distorted by service time and contract structure.
Question #5: Do players who hit more home runs than average earn significantly more?
Players who hit above the median home run total do earn measurably more on average than those below it, and the bar chart makes that gap visually clear. However, the magnitude of the difference is tempered by the same pre-arbitration problem that surfaced in the scatterplot the below median group contains a large number of pitchers and young position players earning near-minimum salaries, which drags the average down and makes the above-median group look more financially rewarded than they might actually be relative to their specific peers. Strip out the pre-arb players and the gap between the two groups would likely narrow considerably, suggesting that the salary premium for home run production is most visible and most meaningful at the veteran and free agent level, where the market is actually free to reward offensive output.
Overall Takeaways and Reasoning
The reason I chose this topic is straightforward home runs are the most celebrated offensive statistic in baseball, and I wanted to know whether teams actually put their money where the highlight reels are. What the data shows is that the relationship exists, but it is significantly muddied by baseball’s unique compensation structure. In most labor markets, high production leads to high pay almost immediately. In MLB, a player can hit 37 home runs and earn $800,000 in the same season, simply because the rules say so.
My biggest takeaway is that any honest analysis of home runs and salary has to account for service time without that context, the correlation looks weaker than it actually is at the free agent level, where teams are genuinely competing to pay for power. The pre-arbitration players in this dataset are essentially noise when it comes to measuring whether the market rewards home runs, because the market isn’t allowed to price them freely yet.
For a next step, I would be interested in isolating free agents and arbitration eligible players specifically and re-running this analysis on that subset. My prediction is that the correlation would be substantially stronger, and the scatterplot would look much cleaner which would actually make a stronger case that teams do value home run production, just not until the rules allow them to show it.