Rosternomics
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August 5, 2018

OAKWSN

WSN won this trade +$0.8M surplus OAK won this trade +0.5 WAR
OAKOAK David Forst net −$0.8M net +0.5
received −$3.2M−$3.2M ± $12M expected surplus · −$0.8M realized received 0.1 ± 2 expected · 0.5 realized WAR
Playoff odds: this deal moved OAK's 2018 odds 56% → 59% (+2.7 pts) — how trade timing is graded ↗
receives — most valuable first
Shawn KelleyP·34y·R/R
−$3.2M−$3.2M± $12M exp surplusrealized −$0.8M 0.1± 2 exp WARrealized 0.5
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 0.1/yr over 2.8 seasons
Talent
0.13/yr blended
Horizon
1.0 control yr
WSNWSN Michael Rizzo net +$0.8M net -0.5
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 WSN's 2018 odds 49% → 47% (-2.8 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 →