Prediction with Transparency: Offensive Value in Baseball
- CSA Authors
- Dec 10, 2025
- 2 min read
This Version November 5, 2025
Megan Czasonis is a Founding Partner of Cambridge Sports Analytics in Cambridge, MA.
Miles Kee is a data scientist at Cambridge Sports Analytics in Cambridge, MA.
Mark Kritzman is a Founding Partner of Cambridge Sports Analytics in Cambridge, MA, and a senior lecturer at the MIT Sloan School of Management in Cambridge, MA.
David Turkington is a Founding Partner of Cambridge Sports Analytics in Cambridge, MA.
Abstract
Accurate and interpretable prediction of player performance requires analytic methods that account for contextual player-specific information and give visibility into the influence of observations and variables on the formation of the predictions. We describe a novel model-free prediction system called relevance-based prediction (RBP) that addresses these needs, and we show how it enables prediction-specific interpretability that is beyond the reach of model-based approaches such as linear regression analysis or machine learning models. RBP reveals the specific reliability of each prediction before the prediction is made, the importance of each prior player to each prediction, the contribution of each predictive variable to each prediction’s value, and the contribution of each predictive variable to each prediction’s reliability. We illustrate this new prediction system by applying it to predict wRC+ for major league baseball players. The prediction-specific information given by RBP stands in contrast to R-squared, beta, and t-statistics, which only give information about average effects, as we illustrate with specific player examples.
This research was authored by Megan Czasonis, Miles Kee, Mark Kritzman, and David Turkington.
Published as an MIT Sloan Research Paper (No. 7338-25) and available via SSRN.
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