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Model-based estimation of baseball batting metrics.

Lahiru Wickramasinghe1, Alexandre Leblanc1, Saman Muthukumarana1

  • 1Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada.

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Summary

This study introduces a weighted likelihood approach to model baseball batting performance, improving player metric estimation by sharing data across all players. This method enhances statistical inference for individual batter analysis.

Keywords:
Dirichlet processMAMSE weightsWeighted likelihoodbaseballmultinomial distributionsparse data

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Area of Science:

  • Sports Analytics
  • Statistical Modeling
  • Baseball Performance Metrics

Background:

  • Traditional baseball batting metrics often analyze players in isolation.
  • Improving the accuracy of player statistics is crucial for performance evaluation and comparison.
  • Information sharing across players can enhance statistical inference.

Purpose of the Study:

  • To introduce and evaluate a weighted likelihood approach for modeling baseball batting outcomes.
  • To compare this new approach with a semi-parametric Bayesian method using the Dirichlet process.
  • To estimate commonly used baseball batting metrics with improved accuracy.

Main Methods:

  • Utilized a weighted likelihood approach with Minimum Averaged Mean Squared Error (MAMSE) weights.
  • Implemented a semi-parametric Bayesian approach based on the Dirichlet process for comparison.
  • Applied methodologies to 2018 Major League Baseball (MLB) batters data.

Main Results:

  • The weighted likelihood approach allows for effective information sharing among players.
  • This sharing of data leads to improved statistical inference for individual batters.
  • Both introduced methods were demonstrated and compared using real-world MLB data.

Conclusions:

  • The weighted likelihood approach offers a robust method for modeling baseball batting performance.
  • Information sharing across players significantly enhances the estimation of batting metrics.
  • The study provides a valuable framework for advanced sports analytics and statistical modeling in baseball.