Rosternomics
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July 22, 2017

TBRLAD

TBR won this trade +$1.6M surplus TBR won this trade +1.1 WAR
TBRTBR Erik Neander net +$1.6M net +1.1
received +$1.6M+$1.6M ± $12M expected surplus · +$1.6M realized received 0.6 ± 2 expected · 1.1 realized WAR
Playoff odds: this deal moved TBR's 2017 odds 15% → 17% (+1.9 pts) — how trade timing is graded ↗
receives — most valuable first
Sergio RomoP·34y·R/R
+$1.6M+$1.6M± $12M exp surplusrealized +$1.6M 0.6± 2 exp WARrealized 1.1
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 0.7/yr over 2.8 seasons
Talent
0.58/yr blended
Horizon
1.0 control yr
LADLAD Andrew Friedman net −$1.6M net -1.1
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 LAD's 2017 odds 92% → 91% (-1.1 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 →