Rosternomics
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May 12, 2022

SEASFG

unsettledToo soon to call — players still accruing.
SEASEA Jerry Dipoto net +$2.4M net +0.6
received −$3.2M−$3.2M ± $42M expected surplus · +$2.4M realized received 0.0 ± 5 expected · 0.6 realized WAR
Playoff odds: this deal moved SEA's 2022 odds 28% → 28% (-0.3 pts) — how trade timing is graded ↗
receives — most valuable first
Mike Ford1B/DH·30y·L/R
−$3.2M−$3.2M± $42M exp surplusrealized +$2.4M 0.0± 5 exp WARrealized 0.6
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form -0.7/yr over 0.4 season
Talent
-0.01/yr blended
Horizon
4.4 control yrs
SFGSFG Farhan Zaidi net −$2.4M net -0.6
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 SFG's 2022 odds 22% → 22% (+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 →