Rosternomics
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June 22, 2016

SEATOR

SEA won this trade +$3.2M surplus SEA won this trade +1.2 WAR
SEASEA Jerry Dipoto net +$3.2M net +1.2
received +$0.0M+$0.0M ± $16M expected surplus · +$3.2M realized received 0.5 ± 2 expected · 1.2 realized WAR
Playoff odds: this deal moved SEA's 2016 odds 31% → 30% (-0.9 pts) — how trade timing is graded ↗
receives — most valuable first
Wade LeBlancP·32y·L/L
+$0.0M+$0.0M± $16M exp surplusrealized +$3.2M 0.5± 2 exp WARrealized 1.2
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 0.4/yr over 1.5 season
Talent
0.33/yr blended
Horizon
3.0 control yrs × 0.49 age decline
TORTOR Ross Atkins net −$3.2M net -1.2
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 TOR's 2016 odds 43% → 44% (+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 →