Sports analytics
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Sports data fundamentals
- Event data vs aggregate statistics
- Player and team rating systems
- Sabermetrics and advanced baseball metrics
- Expected goals and possession models in soccer/hockey
- Home-field advantage and schedule effects
Modeling and prediction
- Regression models for player performance
- Logistic models for win probability
- Simulation and Monte Carlo season projections
- Ranking systems: Elo and Bradley–Terry (intro)
- Draft and roster optimization (introduction)
Inference and ethics
- Hypothesis testing with small sports samples
- Bayesian updating for in-game decisions
- Analytics in coaching and front-office roles
- Privacy and fairness in player tracking data
STEM / applied
Applied sports analytics projects
- Scraping or using public sports APIs
- Building dashboards for team or fantasy analysis
- Tracking data visualization (intro)
- Case studies from MLB, NBA, NFL, or soccer leagues
- Communicating analytics to coaches and fans
- Reproducible pipelines for weekly model updates
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Popular elective in statistics and data science programs. Combines regression, simulation, and domain-specific metrics.