Rosternomics
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February 8, 2017

TBRSEA

SEA won this trade +$1.6M surplus TBR won this trade +0.0 WAR
TBRTBR Erik Neander net −$1.6M net +0.0
received +$8.0M+$8.0M ± $34M expected surplus · −$1.6M realized received 2.2 ± 4 expected · 0.0 realized WAR
Playoff odds: this deal moved TBR's 2017 odds 14% → 17% (+2.9 pts) — how trade timing is graded ↗
receives — most valuable first
Jesús SucreC·29y·R/R
+$8.0M+$8.0M± $34M exp surplusrealized −$1.6M 2.2± 4 exp WARrealized 0.0
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form 1.8/yr over 0.4 season
Talent
0.62/yr blended
Horizon
4.0 control yrs × 0.87 age decline
SEASEA Jerry Dipoto net +$1.6M net +0.0
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 SEA's 2017 odds 12% → 10% (-2.1 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 →