NPB Pythagorean Expectation 2026: True Strength and Schedule Difficulty

Central League

Largest gap vs expectation Yakult +5.5 wins

Pacific League

Largest gap vs expectation ORIX +5.8 wins

end of Aug 16, 2026 · npbc.media

As of Aug 16, 2026, the largest gap between the Pythagorean expectation and the actual record belongs to Yakult (+5.5 wins) in the Central League and ORIX (+5.8 wins) in the Pacific League. A positive gap means overperforming the run differential; a negative one, underperforming.

The standings show results, but runs scored and allowed are known to reflect a team's underlying strength more faithfully. This page shows the gap between the Pythagorean expected record and the actual one, the difficulty of each club's remaining schedule, and the biggest over- and underperformances in history - a view of where each club really stands beyond the win-loss table.

Pythagorean expectation

Expected records from runs scored and allowed, prorated over decisions and compared with the actual record. Ties drop out of the denominator, as in the official NPB percentage.

Central League Gap vs expectation end of Aug 16, 2026 npbc.media ← Underperforming (actual < expected) Overperforming (actual > expected) → Hanshin −1.8 Yomiuri +2.5 Yakult +5.5 DeNA −4.7 Chunichi −7.6 Hiroshima +0.7
Pacific League Gap vs expectation end of Aug 16, 2026 npbc.media ← Underperforming (actual < expected) Overperforming (actual > expected) → SoftBank −3.4 Seibu +2.5 Nippon-Ham −1.0 ORIX +5.8 Lotte +3.9 Rakuten −0.2

Central League

Team Actual RS/RA Pythagorean pct Expected W-L Diff
Hanshin 59-46-1 402 / 338 .579 60.8-44.2 −1.8
Yomiuri 57-48-2 361 / 346 .519 54.5-50.5 +2.5
Yakult 49-55-1 333 / 399 .418 43.5-60.5 +5.5
DeNA 48-56-3 416 / 410 .507 52.7-51.3 −4.7
Chunichi 46-62-1 369 / 372 .496 53.6-54.4 −7.6
Hiroshima 40-58-4 301 / 375 .401 39.3-58.7 +0.7

Pacific League

Team Actual RS/RA Pythagorean pct Expected W-L Diff
SoftBank 67-39-1 517 / 356 .664 70.4-35.6 −3.4
Seibu 60-47-3 393 / 362 .537 57.5-49.5 +2.5
Nippon-Ham 62-49-0 458 / 395 .567 63.0-48.0 −1.0
ORIX 53-54-2 390 / 444 .441 47.2-59.8 +5.8
Lotte 48-53-3 366 / 421 .436 44.1-56.9 +3.9
Rakuten 41-63-1 340 / 428 .396 41.2-62.8 −0.2

Remaining schedule difficulty

The average current winning percentage of remaining opponents, weighted by games remaining against each. Higher means a tougher run-in. Home/away, travel, schedule density and recent form are not considered.

Central League

Difficulty rank Team Left Avg. opponent pct
1 Hiroshima 41 .494
2 DeNA 36 .493
3 Yakult 38 .482
4 Chunichi 34 .480
5 Hanshin 37 .459
6 Yomiuri 36 .456

Pacific League

Difficulty rank Team Left Avg. opponent pct
1 ORIX 34 .535
2 Rakuten 38 .533
3 Lotte 39 .519
4 Nippon-Ham 32 .515
5 SoftBank 36 .500
6 Seibu 33 .494

Biggest overperformers in history: top 10 (2005-2025)

Reference values based on archived game results (runs) and official final standings. Club names are shown as they were at the time; each year links to that season's pennant race page.

Year Team Record RS/RA Expected wins Diff
2015 Hanshin Tigers 70-71-2 465 / 550 59.8 +10.3
2020 Chunichi Dragons 60-55-5 429 / 489 50.6 +9.4
2007 Hanshin Tigers 74-66-4 518 / 561 64.9 +9.1
2012 Chunichi Dragons 75-53-16 423 / 405 66.5 +8.5
2011 Tokyo Yakult Swallows 70-59-15 484 / 504 62.1 +7.9
2024 Tohoku Rakuten Golden Eagles 67-72-4 492 / 579 59.2 +7.8
2024 Chunichi Dragons 60-75-8 373 / 478 52.4 +7.6
2022 Yokohama DeNA BayStars 73-68-2 497 / 534 65.9 +7.1
2025 Tohoku Rakuten Golden Eagles 67-74-2 446 / 526 59.9 +7.1
2022 Chunichi Dragons 66-75-2 414 / 495 59.1 +6.9

Biggest underperformers in history: bottom 10 (2005-2025)

Year Team Record RS/RA Expected wins Diff
2022 Hanshin Tigers 68-71-4 489 / 428 77.9 −9.9
2023 Tokyo Yakult Swallows 57-83-3 534 / 567 66.2 −9.2
2007 Tokyo Yakult Swallows 60-84-0 596 / 623 69.1 −9.1
2021 Fukuoka SoftBank Hawks 60-62-21 564 / 493 68.5 −8.5
2013 Fukuoka SoftBank Hawks 73-69-2 660 / 562 81.4 −8.4
2005 Chiba Lotte Marines 84-49-3 740 / 479 91.7 −7.6
2008 Tohoku Rakuten Golden Eagles 65-76-3 627 / 607 72.6 −7.6
2008 Yokohama BayStars 48-94-2 552 / 706 55.3 −7.3
2018 Yomiuri Giants 67-71-5 625 / 575 74.3 −7.2
2023 Hokkaido Nippon-Ham Fighters 60-82-1 464 / 496 66.7 −6.7

What the Pythagorean expectation is

The Pythagorean expectation is an empirical formula devised by Bill James, the father of sabermetrics: it estimates a team's expected winning percentage from the ratio of runs scored to runs allowed. The original insight - winning percentage roughly tracks runs squared over the sum of runs squared and runs allowed squared - gave the formula its theorem-like name; in practice the exponent is tuned slightly away from 2.

The expected records on this page multiply the Pythagorean percentage by the number of decisions. Ties drop out of the denominator, exactly as in the official NPB percentage, so expected wins plus expected losses always equal actual wins plus losses.

Reading the gap: it is not all luck

Overperforming the expectation reflects an ability to win close games - bullpen quality, late-game management - as well as the bounce of one-run games. Underperformance often comes from a mix of blowout wins and narrow losses. Not all of the gap is luck, but extreme gaps are known to regress toward the mean in following seasons.

The historical rankings at the bottom of the page give a sense of scale: gaps around plus or minus ten wins are among the largest ever recorded.

Strength of schedule: the same games left can weigh differently

Down the stretch, who you still have to play matters as much as how many games remain. The difficulty shown here (SoS, strength of schedule) is the average current winning percentage of remaining opponents, weighted by the number of games left against each. Higher means more strong opponents ahead.

It is a deliberately simple definition: home/away splits, travel, schedule density and recent form are not considered. Note that the odds board's simulation already bakes in the remaining matchups themselves.

Frequently asked questions

What is the Pythagorean winning percentage?

An empirical formula by Bill James that estimates a team's expected winning percentage from runs scored and allowed; its theorem-like shape gave it the name.

Why do actual records differ from expected records?

Because the same run totals can be distributed differently - winning the close ones versus piling up blowouts. Bullpen quality, late-game management and one-run-game fortune all show up in the gap, and extreme gaps tend to regress the following season.

How do ties affect the expected records?

They do not. Expected records are prorated over decisions only, so the number of ties leaves them unchanged - the same convention as the official NPB winning percentage, where ties drop out of the denominator.

How is the schedule difficulty (SoS) computed?

It is the weighted average of remaining opponents' current winning percentage, with games remaining against each opponent as weights. Home/away, travel, schedule density and recent form are not considered.

Sources and references

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