Rosternomics
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January 20, 2012

CLECOL

CLE won this trade +$0.8M surplus CLE won this trade +0.0 WAR
CLECLE Chris Antonetti net +$0.8M net +0.0
received +$2.4M+$2.4M ± $12M expected surplus · +$0.0M realized received 0.9 ± 2 expected · 0.0 realized WAR
receives — most valuable first
Kevin SloweyP·28y·R/R
+$2.4M+$2.4M± $12M exp surplusrealized +$0.0M 0.9± 2 exp WARrealized 0.0
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 1.3/yr over 2.4 seasons
Talent
0.92/yr blended
Horizon
1.0 control yr
COLCOL Daniel O'Dowd net −$0.8M net +0.0
received +$1.6M+$1.6M ± $50M expected surplus · −$0.8M realized received 1.1 ± 6 expected · 0.0 realized WAR
receives — most valuable first
Zach PutnamP·25y·R/R
+$1.6M+$1.6M± $50M exp surplusrealized −$0.8M 1.1± 6 exp WARrealized -0.0
Prior
#171 overall draft pick — at the league baseline → 0.21/yr
Evidence
recent form 0.2/yr over 0.5 season
Talent
0.20/yr blended
Horizon
5.5 control yrs

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 →