Rosternomics
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August 28, 2018

PHINYM

PHI won this trade +$0.8M surplus PHI won this trade +0.3 WAR
PHIPHI Matt Klentak net +$0.8M net +0.3
received −$0.8M−$0.8M ± $0M expected surplus · +$0.8M realized received 0.0 ± 0 expected · 0.3 realized WAR
Playoff odds: this deal moved PHI's 2018 odds 9% → 10% (+0.5 pts) — how trade timing is graded ↗
receives — most valuable first
José BautistaOF·38y·R/R
−$0.8M−$0.8M± $0M exp surplusrealized +$0.8M 0.0± 0 exp WARrealized 0.3
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 2.5/yr over 2.6 seasons
Talent
1.79/yr blended
Horizon
0.0 control yr
NYMNYM Sandy Alderson net −$0.8M net -0.3
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 NYM's 2018 odds 16% → 15% (-0.9 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 →