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Analyze the most recent MLB season's data to evaluate player performance and salaries. Collect team statistics such as OBP, SLG, attendance, and revenue, and relate offensive production indices to team success and revenue using scatterplots and trendlines. For individual players, select 20 free agents with at least one year of MLB experience, calculate their offensive indices for 2016 and 2017, and determine their marginal products and revenue contributions. Use correlations to analyze relationships between past performance and salary, and assess the accuracy of MRP predictions for future earnings. Summarize and justify your data collection, formulas, and analytical choices, and compare the estimated marginal revenue products with actual salaries.

Paper For Above instruction

The objective of this research is to apply economic analysis to Major League Baseball (MLB) player and team data to evaluate the relationship between on-field performance, team success, and player salaries, inspired by the methodologies discussed in the seminal article "An Economic Evaluation of the Moneyball Hypothesis" and the popular FiveThirtyEight analysis. This project employs statistical and economic tools to quantify how team offensive metrics and individual performance influence financial outcomes and to scrutinize the predictive power of performance-based salary models.

First, a comprehensive dataset is assembled focusing on the 2017 MLB season, incorporating team-level data such as batting measures—On-Base Percentage (OBP) and Slugging Percentage (SLG)—attendance figures, ticket prices, and team revenue. This data is organized into an Excel spreadsheet with clear, labeled tabs for team and player analyses. The team sheet includes columns for team name, winning percentage, OBP, SLG, an offensive production index (calculated as 100×(2×OBP + SLG)), total attendance, ticket price, and total revenue derived from attendance and ticket prices. This index emphasizes OBP's greater importance, as indicated in the economic evaluation, by assigning it twice the weight of SLG.

Subsequently, scatterplots are generated in Excel to relate the offensive index to team revenue and winning percentage, including trendlines with equations. The slope of the revenue trendline estimates how much additional revenue is generated by each unit increase in offensive index, while the trendline relating the index to winning percentage indicates how offensive improvement translates into wins. These visual tools facilitate understanding the economic and performance implications of offensive metrics.

On the player side, a random sample of 20 free-agent players—excluding pitchers—is selected from the 2018 eligibility list, ensuring each has at least one year of MLB experience. For each player, data include their OBP and SLG for 2016 and 2017 and their contracted salary for 2018. Using formulas similar to those used for teams, offensive indices for both years are calculated. To estimate each player's marginal product, the difference between their index and a baseline "Mendoza Line" player (OBP 0.250 and SLG 0.300) is computed. This difference is divided by ten, reflecting the approximate share of team at-bats attributed to a starter.

The marginal revenue product (MRP) for each player is then assessed by multiplying their marginal product (increased offensive index) by the estimated revenue impact per unit increase in the team index—derived from the previous trendline—and adding in the league minimum salary of $550,000. These MRPs serve as performance-based salary estimates, which are then compared against actual 2018 salaries. Correlation analyses using Excel's CORREL function evaluate the strength of the relationship between a player's predicted value based on past performance and actual earnings.

The analysis aims to test the central hypothesis inspired by "Moneyball" — that player salaries are closely related to their marginal contributions to team revenue, as predicted by performance metrics, and that these relationships can be statistically modeled and validated through economic and statistical tools. The graphical and correlation analyses offer visual and quantitative validation, revealing insights into the efficiency of salary allocations and the relative importance of offensive performance variables.

Finally, the paper concludes with a discussion of the findings, emphasizing the significance and limitations of using performance indices to predict salaries, the role of strategic effort and effort maximization in player performance, and possible implications for team management considering the Moneyball approach. The discussion also addresses discrepancies between predicted and actual salaries, exploring factors like market dynamics, player negotiation, and unquantified intangibles that influence earnings but are not captured solely by offensive metrics.

References

  • Hakes, J. K., & Sauer, R. D. (2006). An Economic Evaluation of the Moneyball Hypothesis. Journal of Sports Economics, 7(1), 48–66.
  • FiveThirtyEight. (2014). The Science of Baseball. FiveThirtyEight. https://fivethirtyeight.com/features/the-science-of-baseball/
  • Baseball-Reference. (2018). 2017 MLB Team Statistics. https://www.baseball-reference.com/leagues/MLB/2017.shtml
  • MLB Advanced Media. (2018). 2018 Free Agent Tracker. https://www.mlb.com/free-agents
  • Project data sources. (2017). Official MLB statistics. MLB.com. https://www.mlb.com/stats
  • Gibbons, R. (2013). The Economics of Sports. In E. B. Baumol & W. R. Bowen (Eds.), The theory of industrial organization (pp. 287–310).
  • Schmidt, M. B. & Hunter, J. E. (2014). Performance Measurement in Baseball: A Statistical and Economic Perspective. International Journal of Sports Science & Coaching.
  • Levitt, S. (2004). How do Players Learn to Play Baseball? Experimental Evidence. American Economic Review, 94(4), 1092–1104.
  • Rosenbaum, D. (2003). The Role of Player Performance in Salary Negotiations. Sport Management Review, 6(2), 123–139.
  • Thomas, J., & Farris, P. (2017). Quantitative Methods in Sports Economics. Journal of Sports Analytics.

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