Rosternomics
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March 28, 2017

BALPHI

BAL won this trade +$0.8M surplus BAL won this trade +0.2 WAR
BALBAL Dan Duquette net +$0.8M net +0.2
received +$4.0M+$4.0M ± $47M expected surplus · +$0.8M realized received 1.7 ± 6 expected · 0.2 realized WAR
Playoff odds: this deal moved BAL's 2017 odds 1% → 1% (+0 pts) — how trade timing is graded ↗
receives — most valuable first
Alec AsherP·26y·R/R
+$4.0M+$4.0M± $47M exp surplusrealized +$0.8M 1.7± 6 exp WARrealized 0.2
Prior
#156 overall draft pick — at the league baseline → 0.21/yr
Evidence
recent form 0.5/yr over 0.6 season
Talent
0.32/yr blended
Horizon
5.5 control yrs × 0.96 age decline
PHIPHI Matt Klentak net −$0.8M net -0.2
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 PHI's 2017 odds 2% → 2% (-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 →