The Leverage Paradox:
Rethinking the Value of Elite Relievers

An analysis of 6,314 pitcher-seasons from the past decade

High-leverage relievers sit in a valuation gap between baseball's two primary value metrics. WAR systematically undervalues elite relievers while WPA systematically overvalues them — and the truth lies somewhere in between.

The Core Tension: WAR says high-leverage relievers are barely worth noticing. WPA says they’re the most impactful pitchers in baseball. How should this be reconciled?

Data & Methodology

fWAR — Fangraphs Wins Above Replacement

Measures pitcher value in terms of performance (FIP) and volume (IP). Isolates underlying skill but doesn’t fully account for leverage.

WPA — Win Probability Added

Measures actual impact on win probability, fully incorporating timing and context. But it absorbs managerial usage, sequencing luck, and situational noise the pitcher doesn't control.

pLI — Average Leverage Index

Quantifies the average importance of the game situations in which a pitcher is used. Leverage is determined by game flow and managerial deployment — not by the reliever himself.

Classification, data sources & notes

Pitcher Classification

Starting Pitcher (SP)

Games Started ≥ 5 and Innings Pitched ≥ 20 in a season.

Relief Pitcher (RP)

Games ≥ 5 and Games Started < 3. Pitchers meeting neither definition are excluded.

  • All WAR values are fWAR (Fangraphs WAR), which uses FIP—strikeouts, walks, HBP, and home runs—rather than runs allowed, isolating repeatable pitching skill from fielding and sequencing.
  • Scope: 10 MLB seasons [2016–2025]
  • Minimum 5 IP per season
  • All data from Fangraphs via pybaseball — see the Fangraphs Glossary for stat definitions

Volume and Leverage

Before identifying high-leverage relievers, it’s worth seeing just how differently starters and relievers are deployed. The violin plots below show the distributions of Innings Pitched and pLI across all individual pitcher-seasons — not aggregated careers, but each season as its own data point.

102 SP Median IP
30 RP Median IP
0.94 SP Median pLI
0.89 RP Median pLI
SP vs RP Violin Plots
SPs Eat Innings

SPs dominate the workload — a typical SP season covers 3–4× the innings of a typical RP season, which is precisely why WAR favors them so heavily.

Leverage Concentration

SPs cluster around league-average pLI (~1.0), but RPs show enormous variance. A substantial group consistently operates well above that baseline — the high-leverage arms deployed in the highest-stakes situations.

What Constitutes a High-Leverage Reliever?

The Giovanni Gallegos Line

To move beyond per-season snapshots, we aggregate each pitcher’s career totals across all seasons in the dataset, then apply k-means clustering (k = 3) on career-average pLI to separate relievers into three distinct tiers. Because pLI captures how often a pitcher is deployed in high-stakes situations, this clustering effectively identifies the subset of relievers whose managers trust them with the game on the line — the closers, setup men, and firemen who anchor a bullpen. Under this framework, the cutoff between mid- and high-leverage sits at a pLI of 1.08, corresponding to the 10-year average leverage of pitchers like Giovanny Gallegos, Tyler Ferguson, and Joe Jiménez.

665 Starting Pitchers
1195 All Relievers
272 High-Leverage RPs
1.34 Avg pLI (High-Lev)
Low Leverage: 317 pitchers (avg pLI: 0.36) Mid-Leverage: 606 pitchers (avg pLI: 0.80) High Leverage: 272 pitchers (avg pLI: 1.34)

WAR, WPA and the Elusive Value of Systemic Leverage

At their core, WAR and WPA answer different questions. WAR asks how much total value a player provides over a season, independent of context, while WPA asks who actually swings games in real time. The divergence between these lenses is nowhere more apparent than in the evaluation of elite relievers.

WAR and WPA both measure value, but they rest on different baselines, making direct scalar comparison invalid. Still, contrasting the stories they tell reveals important structural insights.

Consider the asymmetry: Zach Wheeler throws six strong innings in a comfortable lead and shifts win probability by only a few percentage points. Jhoan Duran enters in the eighth with the tying run on third, strikes out two batters, and swings win probability by 30–40% in two minutes. Both performances require real skill — but WPA rewards Duran’s two minutes far more than Wheeler’s six innings, not because Duran is better, but because the situation carried more weight.

Season-Level View

Each dot below is a single pitcher-season. SPs spread horizontally (high WAR, moderate WPA) — their volume drives skill-based value but dilutes game-context impact. High-leverage RPs spread vertically (low WAR, wide WPA range) — their value is concentrated in high-stakes moments but inflated by sequencing and managerial deployment. The gap between the two clusters is the valuation no-man’s-land where neither metric tells the full story.

Cumulative Value: 2016–2025

Zooming out from individual seasons to aggregate totals over the full decade sharpens the contrast. The bar chart and leaderboard below show cumulative WAR and WPA for all SPs vs. the high-leverage reliever tier identified above.

222% HL RPs as % of SP WPA
23% HL RPs as % of SP WAR
WPA vs WAR Bar Chart

By WPA, high-leverage relievers outproduce all starting pitchers combined. By WAR, they’re worth a fraction.

Top 50 Pitchers by Aggregate WPA

A single leaderboard combining SPs and high-leverage RPs, sorted by total WPA. It’s telling that relievers appear so prominently on this list — alongside aces who threw roughly five times as many innings. This reinforces that elite high-leverage performance of individual relievers persists well beyond short-term variance.

#PlayerRoleWPAWARIPpLISeasons
1Max ScherzerSP24.740.91492.20.9610
2Justin VerlanderSP22.134.91449.40.968
3Jacob deGromSP21.737.51196.50.959
4Josh HaderRP21.513.4510.01.849
5Kenley JansenRP20.314.7585.01.8510
6Clayton KershawSP20.031.91242.00.9410
7Gerrit ColeSP18.836.41489.50.949
8Chris SaleSP18.335.61192.20.968
9Zack WheelerSP17.537.61441.60.969
10Raisel IglesiasRP16.213.2639.51.6610
11Aroldis ChapmanRP15.014.2500.01.8010
12Blake SnellSP14.726.41156.60.9910
13Devin WilliamsRP14.39.0295.81.877
14Edwin DiazRP14.015.4517.01.979
15Max FriedSP13.323.61051.10.968
16Corbin BurnesSP12.622.6917.60.977
17Shane BieberSP12.421.1869.70.987
18David RobertsonRP12.37.9435.11.739
19Brandon WoodruffSP11.217.4700.80.957
20Felipe VazquezRP10.57.3282.11.624
21Blake TreinenRP10.48.5455.21.589
22Mike ClevingerSP10.314.2791.80.938
23Aaron NolaSP10.136.71635.90.9510
24Scott BarlowRP10.15.9454.41.348
25Emmanuel ClaseRP10.09.9357.91.806
26Framber ValdezSP9.920.41078.80.978
27Ranger SuarezSP9.915.5741.60.966
28Kevin GausmanSP9.733.91635.10.9510
29Corey KluberSP9.721.9967.40.927
30Zack BrittonRP9.64.3242.51.676
31Shohei OhtaniSP9.513.9526.31.015
32Jhoan DuranRP9.55.9253.41.904
33Jordan RomanoRP9.43.4270.71.817
34Zac GallenSP9.417.61007.10.987
35Zack GreinkeSP9.321.61292.80.968
36Felix BautistaRP9.34.8160.41.873
37Kyle HendricksSP9.322.41482.80.9310
38Nathan EovaldiSP9.218.81085.90.959
39Tarik SkubalSP9.119.3765.50.916
40Kirby YatesRP9.17.4389.91.248
41Liam HendriksRP9.112.0408.71.359
42Trevor BauerSP9.018.8934.40.976
43Logan WebbSP9.024.31060.71.017
44Paul SkenesSP8.910.8320.20.972
45Freddy PeraltaSP8.817.8928.90.958
46Andrew MillerRP8.85.5273.51.416
47Seth LugoSP8.614.8990.91.1310
48Tony WatsonRP8.52.4328.51.526
49Stephen StrasburgSP8.217.9682.50.965
50Hyun-Jin RyuSP8.212.3705.50.947

Conclusion

Yet if these contextual factors were purely noise, their effects would largely cancel out over large samples. Instead, over a decade of league-wide data, high-leverage relievers consistently dominate aggregate WPA, reflecting a durable structural feature of the game: leverage is systematically concentrated into a small number of bullpen innings. This is made explicit in pLI data, where elite relievers operate at roughly double the average leverage of starters, placing them in the most consequential moments of nearly every game.

Starters and offenses establish win probability baseline; high-leverage relievers resolve it. By controlling the final allocation of wins, elite relievers occupy a structurally distinct role that makes their true value meaningfully larger than WAR suggests, even if the precise magnitude remains debatable.