RRosternomics
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NEW Model v1.2 — updated trade values. See what changed →

Rosternomics — MLB Trade Values, Calculator & GM WAR

Build trades, evaluate GMs, and visualize the economics of baseball.

Trade Calculator

Build any hypothetical MLB trade — drop in players from any roster, add cash, and get each side's expected surplus and WAR from the Bayesian model that grades every real trade since 1985.

Trade Calculator →

Or look up a real one: BOS won the biggest deal of 2026 by +$4M →

Active GM WAR leaders

Wins/season above a replacement GM — an average front office on the same budget — org-adjusted & shrunk. How it's built →

ExecutiveTeamsYrsGM WARTitles
1 Andrew Friedman LAD TBR 21 +9.3 3
2 Erik Neander TBR 10 +9.1 0
3 Brandon Gomes LAD 5 +5.9 2
4 Mike Chernoff CLE 11 +5.9 0
5 Matt Arnold MIL 6 +4.9 0
6 Chris Antonetti CLE 16 +4.2 0

Full active leaderboard → · all-time →

The league, every season

All 30 front offices in one view — who runs each team, total payroll, the year-over-year spend change, competitive regime, record & postseason — for any year since 1985. Leading 2026 so far:

TeamHead of baseball opsRegimeRecordPayrollΔ Pay
1 Dodgers Andrew Friedman CONTEND 65–38 $350M +18M
2 Brewers Matt Arnold WINDOW 64–38 $113M +5M
3 Braves Alex Anthopoulos FORCED-SPEND 60–42 $226M +37M
4 Rays Erik Neander WINDOW 59–43 $74M +13M
5 Yankees Brian Cashman CONTEND 57–45 $272M +26M
6 Cubs Jed Hoyer CONTEND 57–45 $212M +11M

Open the full league view →

Graphs

Every team's competitive story, traced from the data.

Roster Value Distribution Roster Value Distribution Every roster slot vs. the league — depth vs. stars, any team & season. Competitive Regimes Competitive Regimes Every team's orbit through payroll × talent-above-budget. Run Environment Run Environment How offense and scoring have shifted across eras.

All graphs →

All-time leaders — GM WAR

Career org-adjusted skill — the headline number, over a full tenure. How it's built →

ExecutiveTeamsSeasonsGM WARTitles
1 Andrew Friedman LAD TBR 21 +9.3 3
2 Erik Neander TBR 10 +9.1 0
3 Pat Gillick BAL PHI SEA TOR 20 +7.0 3
4 John Schuerholz ATL KCR 23 +6.5 2
5 Brandon Gomes LAD 5 +5.9 2
6 Jeff Luhnow HOU 9 +5.9 1

See the full GM leaderboard →

All-time leaders — Scouting Directors (DVOS)

Draft Value Over Slot: career fWAR each pick produced above what its draft slot historically returns. Hit DVOS (drafting position players above slot) is the reliable position skill.

Scouting DirectorTeamsDraftsDVOSHit DVOS
1 Roy Clark ATL 10 +194.2 +204.5
2 Mike Arbuckle PHI 8 +148.8 +188.1
3 Brian Sabean NYY SFG 6 +133.5 +107.7
4 Logan White LAD 7 +130.8 +118.8
5 John Barr BAL NYM SFG 11 +129.7 +143.1
6 Sandy Johnson TEX 11 +124.4 +56.8

See the full scouting-director leaderboard →

Biggest Draft Steals of All Time

The 10 picks whose career WAR most exceeded what their draft slot × position × age cohort historically produces. Settled cohorts only (drafts ≤ 2018). The patron saint is Mike Trout — #25 overall in 2009.

PlayerYearPickTeamPosSchoolCareer WARDVOS
1 Albert Pujols 1999 #402 STL IF HS 88 +87
2 Barry Bonds 1985 #6 PIT OF COLL 110 +87
3 Jeff Bagwell 1989 #109 BOS IF COLL 80 +78
4 Mike Trout 2009 #25 ANA OF HS 85 +77
5 Mike Mussina 1990 #20 BAL P COLL 70 +64
6 Randy Johnson 1985 #36 WSN P COLL 66 +63
7 Mookie Betts 2011 #172 BOS IF HS 63 +62
8 Clayton Kershaw 2006 #7 LAD P HS 71 +61
9 Mike Piazza 1988 #1390 LAD C COLL 59 +59
10 Aaron Judge 2013 #32 NYY OF COLL 64 +59

See the full draft database →

Metrics

Every number on the site, built in the open — the model, the formula, and a worked example for each.

GM WAR — wins above a replacement GM The headline front-office metric: how many wins a general manager adds per season above an average front office on the same budget, with the franchise he inherited and the luck of a short sample stripped out. WAB — Wins Above Budget How many games a team won above what its payroll predicted — a resource-controlled, era-neutral scorecard for front-office performance. TAB — Talent Above Budget How much roster talent a team assembled above what its payroll predicted — the roster-construction half of front-office skill. Acquisition Leverage Each acquisition's fair share of the playoff-odds boost its season's deals bought over the roster already on hand. Graded once, at the deal — did the GM buy wins where they actually move the needle? Acquisition Reliance How much of a team's contention is carried by acquired (FA/trade) talent rather than home-grown — counted every season that talent stays on the roster. Surplus $M — Dollar Value Above Cost The market dollar value of a player's production minus what he was actually paid — how much free money a front office extracted from its roster. WAR Surplus — Era-Neutral Value The same surplus idea, expressed in wins instead of dollars — so a 1990 bargain and a 2025 bargain are directly comparable. DVOS — Draft Value Over Slot The scouting-director analog of WAB: how much fWAR a director's picks produced above what their draft slots historically return. Plus the surprising structure of where draft skill is — and isn't — real. Grading Trades — and why nobody can pick winners Our trade grades are calibrated and unbiased — and they still can't tell you who will win a trade, because trades are an efficient market and ~92% of the outcome is unforeseeable at the time. Model Versions — what changed, and when A running changelog for the trade-grading model. We'd rather show our work than silently move the numbers.
Rosternomics — quantifying front-office skill, attribution-clean. Follow @rosternomics on X. Decision-makers on record back to 1933; resource-controlled metrics (WAR & payroll) cover 1985–2026.
Data: FanGraphs (WAR), Baseball-Reference & the Chadwick Bureau register (rosters, IDs), the Lahman Database, Baseball America prospect rankings (via The Baseball Cube), and public payroll records. Transaction and trade data was obtained free of charge from and is copyrighted by Retrosheet. Executive birth/death data via Wikidata (CC0); bios are original. Emoji graphics by OpenMoji (CC BY-SA 4.0).
All figures are franchise-level outcomes credited to the decision-maker in the acquisition year — not solely attributable to one executive.