Rosternomics
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July 7, 2014

CLEPIT

PIT 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 ± $15M expected surplus · −$0.8M realized received 0.8 ± 2 expected · 0.0 realized WAR
receives — most valuable first
Chris DickersonOF·32y·L/L
+$2.4M+$2.4M± $15M exp surplusrealized −$0.8M 0.8± 2 exp WARrealized -0.0
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 1.1/yr over 0.9 season
Talent
0.59/yr blended
Horizon
1.5 control yr × 0.90 age decline
PITPIT Neal Huntington net +$0.8M net +0.0
received +$0.0M+$0.0M ± $0M expected surplus · +$0.0M realized received 0.0 ± 0 expected · 0.0 realized WAR
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 →