Key Takeaways: Introduction to College Football Analytics
One-Page Summary
What Is Sports Analytics?
Analytics = Systematic data analysis to find patterns that inform decisions
Analytics differs from traditional statistics by adding: - Context (a 5-yard gain means different things on 3rd-and-3 vs. 3rd-and-10) - Expectations (was that performance better or worse than expected?) - Prediction (what will happen next?) - Attribution (who deserves credit or blame?)
The Evolution
| Era | Approach | Limitation |
|---|---|---|
| Pre-2000s | Box score statistics | No context |
| 2000s | Rate statistics, efficiency | Limited prediction |
| 2010s+ | EPA, Win Probability, ML | Computational power enabled sophistication |
Key milestone: Moneyball (2003) showed analytics could find undervalued players.
Core Concepts
Expected Points (EP) - Average points scored from a field position - Opponent's 5-yard line ≈ +6 EP - Own 20-yard line ≈ +0.5 EP
Expected Points Added (EPA) - Change in EP from one play - EPA = EP(after) - EP(before) - Common currency for comparing all plays
Win Probability (WP) - Chance of winning from current game state - Depends on: score, time, field position, down/distance
Win Probability Added (WPA) - Change in WP from one play - Measures clutch performance
How Programs Use Analytics
| Application | Example |
|---|---|
| Opponent Analysis | Identify tendencies by down/distance |
| Self-Evaluation | Compare performance to expectations |
| In-Game Decisions | Fourth down, two-point conversions |
| Recruiting | Predict prospect development |
| Player Development | Track improvement over time |
The Analytics Workflow
┌──────────────────┐
│ 1. QUESTION │ → What do we want to know?
├──────────────────┤
│ 2. DATA │ → What information do we need?
├──────────────────┤
│ 3. PROCESSING │ → Clean, transform, integrate
├──────────────────┤
│ 4. ANALYSIS │ → Apply appropriate methods
├──────────────────┤
│ 5. COMMUNICATION │ → Make findings actionable
└──────────────────┘
Analytics vs. Traditional Scouting
| Traditional Scouting | Analytics |
|---|---|
| Expert observation | Systematic data analysis |
| Qualitative insights | Quantitative evidence |
| Deep on individuals | Broad across many |
| Captures intangibles | Captures measurables |
Best practice: Integrate both approaches
Ethical Considerations
- Privacy: Respect player data rights
- Honesty: Present findings accurately, with uncertainty
- Credit: Acknowledge sources and contributions
- Humanity: Remember data represents real people
Key Terms Quick Reference
| Term | Definition |
|---|---|
| Analytics | Systematic data analysis for decision support |
| EPA | Expected Points Added—value of a play |
| WP | Win Probability—chance of winning from game state |
| WPA | Win Probability Added—clutch impact of a play |
| Play-by-play | Detailed record of every play |
| Context-aware | Accounting for game situation |
Decision Checklist
Before starting an analytics project, ask:
- [ ] Is my question specific and answerable?
- [ ] Does relevant data exist?
- [ ] Will the answer inform a decision?
- [ ] Have I considered ethical implications?
- [ ] Do I understand the limitations of my data?
What to Remember
- Analytics complements, doesn't replace, football knowledge
- Context matters—same statistic can mean different things
- Expected value thinking enables better decisions
- The analytics revolution is changing football at all levels
- Ethical responsibility accompanies analytical capability
Looking Ahead
Chapter 2 introduces the data landscape of college football: - Where data comes from - How to access it - What its limitations are - How to start building your data toolkit