Rosternomics
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July 12, 2019

CLESDP

CLE won this trade +$6.4M surplus CLE won this trade +1.3 WAR
CLECLE Chris Antonetti net +$6.4M net +1.3
received +$2.4M+$2.4M ± $38M expected surplus · +$6.4M realized received 1.1 ± 5 expected · 1.3 realized WAR
Playoff odds: this deal moved CLE's 2019 odds 70% → 70% (+0.3 pts) — how trade timing is graded ↗
receives — most valuable first
Phil MatonP·26y·R/R
+$2.4M+$2.4M± $38M exp surplusrealized +$6.4M 1.1± 5 exp WARrealized 1.3
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 0.2/yr over 1.6 season
Talent
0.22/yr blended
Horizon
5.0 control yrs
SDPSDP AJ Preller net −$6.4M net -1.3
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 SDP's 2019 odds 3% → 3% (-0 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 →