Rosternomics
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August 27, 2015

CHCSEA

SEA won this trade +$7.2M surplus CHC won this trade +0.1 WAR
CHCCHC Jed Hoyer net −$7.2M net +0.1
received −$8.0M−$8.0M ± $0M expected surplus · −$7.2M realized received 0.0 ± 0 expected · 0.1 realized WAR
Playoff odds: this deal moved CHC's 2015 odds 83% → 83% (+0.2 pts) — how trade timing is graded ↗
receives — most valuable first
Fernando RodneyP·38y·R/R
−$8.0M−$8.0M± $0M exp surplusrealized −$7.2M 0.0± 0 exp WARrealized 0.1
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form 1.0/yr over 2.8 seasons
Talent
0.75/yr blended
Horizon
0.0 control yr
SEASEA Jeff Kingston net +$7.2M net -0.1
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 SEA's 2015 odds 3% → 3% (-0 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 →