Rosternomics
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September 13, 2016

PITSEA

PIT won this trade +$1.6M surplus PIT won this trade +0.5 WAR
PITPIT Neal Huntington net +$1.6M net +0.5
received +$0.0M+$0.0M ± $16M expected surplus · +$1.6M realized received 0.5 ± 2 expected · 0.5 realized WAR
Playoff odds: this deal moved PIT's 2016 odds 8% → 8% (+0.6 pts) — how trade timing is graded ↗
receives — most valuable first
Wade LeBlancP·32y·L/L
+$0.0M+$0.0M± $16M exp surplusrealized +$1.6M 0.5± 2 exp WARrealized 0.5
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form 0.4/yr over 1.5 season
Talent
0.33/yr blended
Horizon
3.0 control yrs × 0.49 age decline
SEASEA Jerry Dipoto net −$1.6M net -0.5
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 SEA's 2016 odds 32% → 30% (-1.6 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 →