STEM / applied
Sports analytics · Undergraduate · Math
Learning objectives from the Sports analytics syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Sports analytics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Sports data fundamentals
- Event data vs aggregate statistics — Event data vs aggregate statistics
- Player and team rating systems — Player and team rating systems
- Sabermetrics — Sabermetrics
- Advanced baseball metrics — Advanced baseball metrics
- Expected goals — Expected goals
- Possession models in soccer/hockey — Possession models in soccer/hockey
- Home-field advantage — Home-field advantage
- Schedule effects — Schedule effects
Modeling and prediction
- Regression models for player performance — Regression models for player performance
- Logistic models for win probability — Logistic models for win probability
- Simulation — Simulation
- Monte Carlo season projections — Monte Carlo season projections
- Ranking systems: Elo — Ranking systems: Elo
- Bradley–Terry (intro) — Bradley–Terry (intro)
- Draft and roster optimization (introduction) — Draft and roster optimization (introduction)
Inference and ethics
- Hypothesis testing with small sports samples — Hypothesis testing with small sports samples
- Bayesian updating for in-game decisions — Bayesian updating for in-game decisions
- Analytics in coaching — Analytics in coaching
- Front-office roles — Front-office roles
- Privacy — Privacy
- Fairness in player tracking data — Fairness in player tracking data
Learning objectives
Click an objective for study materials.
Sports data fundamentals
- Event data vs aggregate statistics — Event data vs aggregate statistics
- Player and team rating systems — Player and team rating systems
- Sabermetrics — Sabermetrics
- Advanced baseball metrics — Advanced baseball metrics
- Expected goals — Expected goals
- Possession models in soccer/hockey — Possession models in soccer/hockey
- Home-field advantage — Home-field advantage
- Schedule effects — Schedule effects
Modeling and prediction
- Regression models for player performance — Regression models for player performance
- Logistic models for win probability — Logistic models for win probability
- Simulation — Simulation
- Monte Carlo season projections — Monte Carlo season projections
- Ranking systems: Elo — Ranking systems: Elo
- Bradley–Terry (intro) — Bradley–Terry (intro)
- Draft and roster optimization (introduction) — Draft and roster optimization (introduction)
Inference and ethics
- Hypothesis testing with small sports samples — Hypothesis testing with small sports samples
- Bayesian updating for in-game decisions — Bayesian updating for in-game decisions
- Analytics in coaching — Analytics in coaching
- Front-office roles — Front-office roles
- Privacy — Privacy
- Fairness in player tracking data — Fairness in player tracking data
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Sports data fundamentals
Event data vs aggregate statistics
Coming soon - Modeling and prediction
Regression models for player performance
Coming soon - Inference and ethics
Hypothesis testing with small sports samples
Coming soon
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$1,162 · Sports analytics · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.