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Understanding Sabermetrics in Baseball: The Search for Objective Knowledge

This lecture covers an overview of sabermetrics in baseball, focusing on goals such as runs created and additional topics like bunting, pitch framing, and defensive independent pitching tools. It discusses the importance, measurement, and repeatability of metrics and examines the impact of various factors on player contributions. The session also delves into the benefits, weaknesses, and associations of runs created, along with implications for error assessment and predictive power.

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Understanding Sabermetrics in Baseball: The Search for Objective Knowledge

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  1. MA 276: Sports and statisticsLecture 2: Statistics in baseball

  2. Goals • Overview of sabermetrics: What to look for? • Example: Runs created • Additional topics after lecture -Bunting/pitchouts, Pitch framing -Defensive independent pitching Tools • Bivariate tools: scatter plots, r, R-squared • In-sample versus out-of-sample comparisons

  3. What is sabermetrics? ‘Search for objective knowledge about baseball’ -Bill James Ex: Which player on the Red Sox contributed most to his team’s offense? Ex: Which player is your favorite? Ex: Which player deserves the MVP award?

  4. Questions we’ll want to answer 1 – Is the metric important to success? 2 – How well does the metric measure a player’s contribution? 3 – Is the metric repeatable?

  5. Is the metric important to success?What’s “important?” What’s “success?”-Examplesin baseball Stolen bases Batting average Home runs WalksRBIsSlugging percentage

  6. How well does the metric measure a player’s contribution? Stolen bases Batting average Home runs WalksRBIsSlugging percentageWhich are impacted by a player’s teammates?Which are impacted by a player’s ballpark?Which are impacted by a player’s coach? Which are impacted by a player’s era?

  7. Is the metric repeatable? Stolen bases Batting average Home runs WalksRBIsSlugging percentageHow to judge repeatable?Why is repeatability (?) important?How does sample size fit in?

  8. Ex: Runs created Why runs created?

  9. Ex: Runs created • General assumptions & expectations • Different valuations to different types of hits • Hitters only control their performance • -What is assumed here? • Hitters do not control when they hit • Hitters do not control importance of at-bat relative to game’s outcome

  10. Ex: Runs created

  11. Ex: Runs created • Benefits of runs created • Team level accuracy: • - Basic version can predict a team’s run total within a 5% margin of error • Individual talent: • - Reflects individual performance only • Repeatability? • - To be determined in Thursday’s lab.

  12. Ex: Runs created • Weaknesses of runs created • What if clutch exists? • Ballpark dependencies • Opponent dependencies

  13. Ex: Runs created What’s it look like?

  14. Ex: Runs created

  15. Ex: Runs created How do we describe the association between runs created and actual runs?

  16. Ex: Runs created What about the association between team runs and other team variables? Note: What does the select command do?

  17. Ex: Runs created

  18. Ex: Runs created What about runs created against more popular but advanced metrics?

  19. Ex: Runs created

  20. What we’ve shown 1 – Is runs created important to success? -Yes. Strong link to team runs 2 – How well does the metric measure a player’s contribution? -Pretty well. Other advanced formulas exist -Adjustments possible 3 – Is the metric repeatable? -Let’s find out

  21. Ex: Runs created 3 – Is the metric repeatable? Explanatory power vs. Predictive power

  22. Ex: Runs created 3 – Is the metric repeatable?

  23. Ex: Runs created 3 – Is the metric repeatable?

  24. Ex: Runs created Implications: Other tools for assessing error: MSE: MAE:

  25. Additional topics • Bunting, pitchouts • Pitch framing • Defensive independent pitching • 1 – Importance • 2 – Player-specific contributions • 3 – Repeatability

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