Identifying Unsustainable Performance in NFL Data
Regression to the mean is the statistical phenomenon where extreme performances tend to move back toward average over time. In NFL betting, identifying regression candidates creates significant edges.
Some statistics are highly variable (high luck component), while others are stable (skill-based). Luck-driven outliers will regress; skill-driven outliers persist.
These stats have low year-over-year correlation and high variance. Extreme values will likely regress:
These stats have high year-over-year correlation. Extreme values are more likely to persist:
| Statistic | YoY Correlation | Why It's Stable |
|---|---|---|
| EPA per Play | ~0.55-0.65 | Reflects true offensive/defensive quality |
| Success Rate | ~0.50-0.60 | Measures consistent execution |
| Yards per Play | ~0.45-0.55 | Fundamental efficiency metric |
| Pass Block Win Rate | ~0.50+ | OL skill is consistent |
| Completion % Over Expected | ~0.45-0.55 | QB accuracy is a stable skill |
When a team has strong EPA, success rate, or CPOE, these indicate genuine quality. Unlike turnover differential, these metrics predict future performance.
Estimate where a stat will regress based on sample size and league average:
Scenario: Team starts 5-1, +10 turnover differential, 70% RZ TD rate
Market Perception: "This team is elite, one of the best in the league"
Reality: Running extremely hot on high-variance stats
Action: Fade this team against the spread, especially vs quality opponents. Their record is inflated by luck.
Scenario: Team is 3-5, -8 turnover differential, strong EPA numbers
Market Perception: "Team can't close games, something wrong"
Reality: Fundamentally solid, experiencing bad luck
Action: Buy this team against the spread. Record will improve as turnovers regress.
Key Adjustments:
Watch for these signs that a team is due for regression:
High Regression (Luck):
Low Regression (Skill):