Causal Inference in Sports
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Nov 28, 2025
Causal Inference in Sports Analytics
Understanding cause and effect in sports goes beyond correlation. Causal inference methods help answer questions like: Does the new training program improve performance? What is the true impact of a coaching change?
The Fundamental Problem
We can never observe the same player both receiving and not receiving a treatment. Causal methods help estimate this counterfactual.
Key Methods
| Method | When to Use | Example |
|---|---|---|
| Difference-in-Differences | Treatment at specific time | Rule change impact |
| Regression Discontinuity | Treatment threshold | Draft pick value |
| Propensity Score Matching | Non-random treatment | Surgery vs. rest |
| Instrumental Variables | Unmeasured confounders | Stadium effect on attendance |
Sports Applications
- Impact of pitch clock on game pace
- Effect of rest days on injury risk
- Value of draft position independent of talent
- Coaching change effects on team performance
Key Takeaways
- Correlation is not causation - use proper methods
- Identify your "treatment" and comparison group
- Check for parallel trends in diff-in-diff
- Propensity matching requires common support
- Be explicit about assumptions
Discussion
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GitHub Discussions.
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