Learning Bayesian Statistics BITESIZE | How to Think Causally About Your Models?
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Sep 10, 2025 In this discussion, Ron Yurko, an expert in sports analytics, shares insights on the complexities of modeling player contributions in soccer and football. He highlights the significance of understanding replacement levels and introduces the Going Deep framework for analyzing player performance. They touch on the challenges of teaching Bayesian modeling, particularly how students struggle with model writing. The conversation underscores the importance of using advanced tracking data for better predictions and the necessity of viewing entire distributions in utility function modeling.
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Celebrate Model Deployments
- Celebrate deployed models even if imperfect to avoid perpetual tinkering.
- Balance seeing flaws with acknowledging momentary wins to sustain progress.
Model Positions, Not Players Alone
- Soccer and American football require position-specific models because contributions are continuous and interdependent.
- Estimating a single universal player effect won't capture role-specific interactions and team context.
Value Depends On Personnel Mix
- Model from personnel packages to capture how a player's value depends on team usage.
- Estimating player effects conditional on personnel mix reveals how trades or scheme changes alter value.