Rosternomics
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July 31, 2019

PHIPIT

PIT won this trade +$3.2M surplus PHI won this trade +0.4 WAR
PHIPHI Matt Klentak net −$3.2M net +0.4
received +$8.0M+$8.0M ± $12M expected surplus · −$3.2M realized received 1.6 ± 2 expected · 0.4 realized WAR
Playoff odds: this deal moved PHI's 2019 odds 3% → 3% (+0.3 pts) — how trade timing is graded ↗
receives — most valuable first
Corey DickersonOF·30y·L/R
+$8.0M+$8.0M± $12M exp surplusrealized −$3.2M 1.6± 2 exp WARrealized 0.4
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 2.4/yr over 2.3 seasons
Talent
1.65/yr blended
Horizon
1.0 control yr
PITPIT Neal Huntington net +$3.2M net -0.4
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 PIT's 2019 odds 1% → 1% (-0.1 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 →