Case Study 1: Measuring Defensive Impact with Tracking Data
Introduction
Traditional defensive statistics (steals, blocks) capture only a fraction of defensive value. This case study examines how tracking data revolutionizes defensive player evaluation using the example of a premier NBA defender.
The Defensive Tracking Revolution
Traditional Limitations
Box score defense: STL, BLK, DRB - Steals: 1.5 per game - Blocks: 0.8 per game - DRB: 4.2 per game
Problem: These stats barely correlate with defensive impact.
Tracking Metrics Available
Contest Data: - Shots contested: 12.5 per game - Opponent FG% on contested shots: 38% - Altered shots: 8.2 per game
Matchup Data: - Primary matchup PPP: 0.82 (league avg: 0.95) - Iso defense PPP: 0.75 - Post defense PPP: 0.88
Positioning Data: - Average distance to assignment: 4.5 feet - Help defense rotations: 8 per game - Closeout speed: 14.2 mph avg
Case: Elite Perimeter Defender
Player Profile
Position: Wing (SF/SG) Height: 6'7", Wingspan: 7'0" Traditional Stats: 1.2 STL, 0.5 BLK
Tracking Evaluation
Ball Pressure: - Forces 15% more turnovers in matchups - Opponent shooting -8% vs. expected
Versatility: - Defended all 5 positions - Effective against PG-PF (positions 1-4)
Help Defense: - 9.5 help rotations per game (elite) - Successful rotation rate: 78%
Value Quantification
Estimated defensive value: - Matchup value: +1.5 points/game - Help defense: +0.8 points/game - Total: +2.3 points/game (~0.85 wins over 82 games)
Comparison to Traditional Metrics
| Metric Type | Value Captured |
|---|---|
| Steals | 15% |
| Blocks | 5% |
| DBPM | 40% |
| Tracking-based | 80%+ |
Conclusions
Tracking data captures defensive value invisible to box scores: - Shot contestation quality - Positional versatility - Help defense contribution - Ball pressure effects
Discussion Questions
- How should teams weight tracking-based defense vs. traditional metrics?
- What defensive skills remain uncaptured even by tracking data?
- How might opponents adjust to a defender identified as elite by tracking?