Rosternomics
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April 1, 2018

STLLAD

STL won this trade +$0.8M surplus STL won this trade +0.1 WAR
STLSTL John Mozeliak net +$0.8M net +0.1
received +$0.0M+$0.0M ± $12M expected surplus · +$0.0M realized received 0.2 ± 2 expected · 0.0 realized WAR
Playoff odds: this deal moved STL's 2018 odds 11% → 11% (+0.3 pts) — how trade timing is graded ↗
receives — most valuable first
Johan Mieses
+$0.0M+$0.0M± $12M exp surplusrealized +$0.0M 0.2± 2 exp WARrealized 0.0
Unidentified minor-league throw-in — valued at the ~0.2 WAR base rate (most produce nothing)
LADLAD Andrew Friedman net −$0.8M net -0.1
received −$3.2M−$3.2M ± $53M expected surplus · −$0.8M realized received 0.3 ± 7 expected · -0.1 realized WAR
Playoff odds: this deal moved LAD's 2018 odds 95% → 95% (-0.2 pts) — how trade timing is graded ↗
receives — most valuable first
Breyvic Valera2B/3B·26y·B/R
−$3.2M−$3.2M± $53M exp surplusrealized −$0.8M 0.3± 7 exp WARrealized -0.1
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form -5.5/yr over 0.0 season
Talent
0.06/yr blended
Horizon
5.5 control yrs × 0.91 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 →