Latest MLB Pythagorean Win % 2026 – Updated Weekly

What Pythagorean Win % is (and why bettors use it)

In baseball betting, the final score often obscures the true quality of a team. While the win-loss column is the ultimate arbiter of the standings, it is frequently influenced by “noise”. One-run games, extra-inning variance, and bullpen sequencing. Pythagorean Win Percentage is a formula designed to filter out that noise by focusing on a team’s fundamental efficiency: Run production and run prevention.

The metric, originally developed by Bill James, uses the relationship between runs scored (RS) and runs allowed (RA) to determine how many games a team should have won. The standard formula – $RS^{2} / (RS^{2} + RA^{2}), provides an expected winning percentage that often serves as a better predictor of future performance than a team’s current record. For bettors, this is the “RS² / RA²” baseline that identifies which teams are playing sustainable baseball and which are living on borrowed time.

Bettors prioritize this metric because run differential stabilizes much faster than win-loss records. If a team has a massive run differential but a mediocre record, they are typically “unlucky” and prime candidates for a positive correction.

Conversely, a team with a negative run differential and a winning record is “overperforming” and likely due for regression. By monitoring the gap between actual and Pythagorean records, you can identify value in MLB power ratings before the market fully adjusts.

Last Season MLB Pythagorean Results

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Actual MLB Record vs. Pythagorean Record (2026 TBD)

Updated: March 24, 2026

Team	W-L	Run Diff	Pyth W%	Expected W-L	Luck (Actual +/-)
LA Dodgers	0-0	+0	.000	0-0	0
ATL Braves	0-0	+0	.000	0-0	0
NY Yankees	0-0	+0	.000	0-0	0
PHI Phillies	0-0	+0	.000	0-0	0
HOU Astros	0-0	+0	.000	0-0	0
[Remaining 25 teams sorted by Luck descending]	…	…	…	…	…

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Teams Overperforming Their Pythagorean Record

As we move into the 2026 season, keeping a close eye on the “luck” column is essential for identifying overvalued teams. When a team’s actual win total significantly outpaces their run differential, markets may not have priced in regression yet. These teams often see their odds inflated due to “clutch” narratives that rarely hold up over a 162-game grind.

If you believe a team is currently overperforming their underlying metrics, you can find opportunities to fade them in the season-long markets. Check the current Kalshi division winner and World Series odds contracts to see if the market is still paying a premium for teams due for a downward correction.

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Teams Underperforming (bounce-back candidates)

The “unlucky” teams, those with a high Pythagorean win percentage but a poor actual record, are the classic baseball regression teams for 2026. These teams are typically losing a disproportionate number of close games, a trend that almost always reverts to the mean over time. These are the “buy-low” targets that savvy bettors look for when the public has soured on a high-quality roster.

If you think a team is poised to bounce back based on their superior run differential, here’s what the market is pricing. You can trade on these recovery trajectories through Kalshi’s MLB prediction markets, which offer a direct way to capitalize on expected win-loss adjustments.

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Historical Accuracy: How Predictive is Pythagorean W% Mid-Season?

Historical data show that Pythagorean winning percentage is a “depth signal” that has earned its reputation as a premier predictive tool. On average, the correlation between mid-season Pythagorean record and second-half winning percentage is significantly higher than the correlation between first-half actual record and second-half results. By the time teams reach the 40-game mark, the run differential standings provide a much clearer picture of the true power hierarchy than the traditional MLB standings.

Month	Correlation: Actual W% to Final Record	Correlation: Pyth W% to Final Record
April	0.42	0.58
May	0.55	0.69
June	0.71	0.82
July	0.84	0.91

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Pythagorean Win % vs. FanGraphs Projected Standings

Comparing Pythagorean data against FanGraphs projected standings is a vital exercise in model validation. While Pythagorean W% looks at what has already happened on the diamond, FanGraphs projections are forward-looking based on individual player talent and depth charts.

When both models agree that a team is overachieving, it creates a high-conviction signal for regression. In instances where they disagree, it often highlights teams with elite bullpens or specific defensive shifts that the Pythagorean formula might not fully capture.