Rosternomics
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July 3, 2022

PITNYY

unsettledToo soon to call — players still accruing.
PITPIT Ben Cherington net +$0.8M net +0.2
received −$3.2M−$3.2M ± $14M expected surplus · +$0.8M realized received 0.0 ± 2 expected · 0.2 realized WAR
Playoff odds: this deal moved PIT's 2022 odds 0% → 0% (+0 pts) — how trade timing is graded ↗
receives — most valuable first
Manny BanuelosP·31y·R/L
−$3.2M−$3.2M± $14M exp surplusrealized +$0.8M 0.0± 2 exp WARrealized 0.2
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form -0.4/yr over 0.5 season
Talent
0.04/yr blended
Horizon
3.9 control yrs × 0.26 age decline
NYYNYY Brian Cashman net −$0.8M net -0.2
received +$0.0M+$0.0M ± $0M expected surplus · +$0.0M realized received 0.0 ± 0 expected · 0.0 realized WAR
Playoff odds: this deal moved NYY's 2022 odds 96% → 96% (-0.2 pts) — how trade timing is graded ↗
receives — most valuable first
cash / PTBNL
+$0.0M+$0.0M± $0M exp surplusrealized +$0.0M 0.0± 0 exp WARrealized 0.0
Cash or player to be named — no projection

Each player is valued on what he was expected to produce at the time of the trade, versus what he actually produced for his new team.

Expected WAR blends a player's pedigree (Baseball America rank / draft slot, or a baseline) with his recent track record, projected over the years of team control acquired. The ± band is the uncertainty — wide for unproven prospects, tight for established veterans. Surplus values that production at the FA market price of a win (~$8M/WAR) minus salary — so cost-controlled players carry large surplus and expensive ones little, even at the same WAR. Who won is descriptive, not a skill claim: ~99% of a trade's outcome is unforeseeable at the time.

Historically these expected values are unbiased and land within ±2 WAR of reality 75% of the time — yet the side the model favors actually comes out ahead only 53% of the time. The grade is a calibrated bet, not a prediction. Why trades are an efficient market →