Rosternomics
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July 10, 2013

PITWSN

PIT won this trade +$0.0M surplus PIT won this trade +0 WAR
PITPIT Neal Huntington net +$0.0M net +0.0
received −$0.8M−$0.8M ± $24M expected surplus · +$0.0M realized received 0.2 ± 3 expected · 0.0 realized WAR
receives — most valuable first
Brian BocockSS·28y·R/R
−$0.8M−$0.8M± $24M exp surplusrealized +$0.0M 0.2± 3 exp WARrealized 0.0
Prior
#266 overall draft pick — at the league baseline → 0.21/yr
Evidence
recent form -2.6/yr over 0.1 season
Talent
0.09/yr blended
Horizon
2.0 control yrs
WSNWSN Michael Rizzo net +$0.0M net +0.0
received +$4.0M−$0.8M ± $39M expected surplus · +$0.0M realized received 0.7 ± 5 expected · 0.0 realized WAR
receives — most valuable first
Brian JerolomanC·28y·L/R
+$4.0M−$0.8M± $39M exp surplusrealized +$0.0M 0.7± 5 exp WARrealized 0.0
Prior
#180 overall draft pick — at the league baseline → 0.21/yr
Evidence
no MLB track record — leans on pedigree
Talent
0.21/yr blended
Horizon
6.0 control yrs × 0.59 age decline

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 →