Regression to the Mean Calculator
Identify positive and negative regression candidates for smarter sports betting. Calculate expected performance reversion and find buy-low, sell-high opportunities.
Small sample - moderate regression
| Name | Sport | Stat | Current | Expected | Regression | Signal | Conf. |
|---|---|---|---|---|---|---|---|
| Team A | NFL | Red Zone TD% | 75 | 62 | -17% | SELL | 78% |
| Team B | NBA | 3PT% | 28 | 34 | +21% | BUY | 72% |
| Player X | MLB | BABIP | 0.38 | 0.315 | -17% | SELL | 85% |
| Goalie Y | NHL | Save% | 94.2 | 91.5 | -3% | SELL | 68% |
| Team C | NFL | Turnover Diff | -12 | -4 | +67% | BUY | 75% |
| Team D | NBA | FT% | 68 | 75 | +10% | BUY | 70% |
| Pitcher Z | MLB | LOB% | 85 | 74 | -13% | SELL | 80% |
| Team E | NHL | PDO | 104 | 100.5 | -3% | SELL | 82% |
* This is sample data for illustration. Use the calculator above with current stats to find real regression candidates.
Understanding Regression to the Mean in Sports Betting
Regression to the mean is the most powerful yet overlooked concept in sports betting. It's the statistical phenomenon where extreme performances tend to be followed by more moderate ones. Understanding regression helps you identify buy-low opportunities on cold teams/players and sell-high opportunities on hot streaks.
Why Regression Happens
Luck vs Skill Component
Every stat has a luck component. A 95th percentile performance usually means above-average skill PLUS above-average luck. Next time, luck will likely be average, pulling results toward the mean.
Sample Size Reality
Small samples are noisy. A 10-game hot streak could be 80% luck. With more games, true talent emerges and performance gravitates toward the player's actual ability level.
High-Regression Stats by Sport
- NFL: Red Zone TD%, Turnover Differential, FG% in clutch situations, 3rd/4th down conversion rates
- NBA: 3PT% (team and player), FT%, late-game shooting, clutch performance
- MLB: BABIP, LOB%, HR/FB rate, clutch hitting, reliever ERA in small samples
- NHL: Shooting%, Save% (short-term), PDO, Power Play% in small samples
How to Use This for Betting
BUY LOW Strategy
When a team/player is performing well below expected (negative z-score, high positive regression expected), the market often overreacts. Look for value on their upcoming games as they bounce back to normal.
SELL HIGH Strategy
When a team/player is running hot (high z-score, negative regression expected), the market often inflates their lines. Consider fading them or taking the other side as they regress.
Common Regression Betting Mistakes
- ✗ Betting on a 7-0 team just because they're winning (check point differential)
- ✗ Fading a player on a 10-game hitting streak expecting immediate decline
- ✗ Ignoring sample size - 5 games means almost nothing
- ✗ Not distinguishing between high and low-variance stats
- ✓ Using regression alongside other factors, not in isolation
- ✓ Waiting for sufficient sample size before acting
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Responsible Gambling
Gambling should be entertaining, not a way to make money. Only bet what you can afford to lose, and never chase your losses.
- Betting more than you can afford to lose
- Chasing losses with bigger bets
- Lying to others about gambling habits