Time Series Analysis in Sports
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Nov 28, 2025
Time Series Analysis for Sports Performance
Sports data is inherently temporal - players age, teams evolve, and performance fluctuates across seasons. Time series analysis helps us understand trends, seasonality, and make forecasts about future performance.
Key Concepts
- Trend: Long-term direction (player aging curves)
- Seasonality: Regular patterns (hot/cold streaks, monthly performance)
- Stationarity: Statistical properties constant over time
- Autocorrelation: How past values predict future values
Common Methods
| Method | Best For | Sports Application |
|---|---|---|
| ARIMA | Univariate forecasting | Team win totals, player stats |
| Prophet | Trends + seasonality | Attendance, viewership |
| Exponential Smoothing | Simple forecasts | Moving averages |
| GARCH | Volatility modeling | Performance variance |
Sports-Specific Considerations
- Aging curves follow predictable patterns by position
- Injuries create structural breaks in performance
- Rule changes can shift league-wide baselines
- Small sample sizes require careful modeling
Key Takeaways
- Always check for stationarity before applying ARIMA
- Prophet handles seasonality and missing data well
- Combine forecasts for better accuracy
- Account for external factors (injuries, trades)
- Quantify uncertainty with prediction intervals
Discussion
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