Rosternomics
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March 30, 2016

SEASDP

SEA won this trade +$8.8M surplus SEA won this trade +2.7 WAR
SEASEA Jerry Dipoto net +$8.8M net +2.7
received +$1.6M+$1.6M ± $18M expected surplus · +$8.8M realized received 0.9 ± 2 expected · 2.7 realized WAR
Playoff odds: this deal moved SEA's 2016 odds 28% → 30% (+1.7 pts) — how trade timing is graded ↗
receives — most valuable first
Nick VincentP·30y·R/R
+$1.6M+$1.6M± $18M exp surplusrealized +$8.8M 0.9± 2 exp WARrealized 2.7
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 0.6/yr over 2.3 seasons
Talent
0.45/yr blended
Horizon
4.0 control yrs × 0.51 age decline
SDPSDP AJ Preller net −$8.8M net -2.7
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 SDP's 2016 odds 0% → 0% (-0 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 →