Rosternomics
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July 18, 2022

SFGCLE

unsettledToo soon to call — players still accruing.
SFGSFG Farhan Zaidi net +$1.6M net +0.4
received −$3.2M−$3.2M ± $31M expected surplus · +$1.6M realized received 0.0 ± 4 expected · 0.4 realized WAR
Playoff odds: this deal moved SFG's 2022 odds 21% → 22% (+1.5 pts) — how trade timing is graded ↗
receives — most valuable first
Alex YoungP·29y·L/L
−$3.2M−$3.2M± $31M exp surplusrealized +$1.6M 0.0± 4 exp WARrealized 0.4
Prior
league baseline (track record outweighs draft pedigree) → 0.21/yr
Evidence
recent form -0.5/yr over 2.1 seasons
Talent
-0.26/yr blended
Horizon
4.0 control yrs
CLECLE Chris Antonetti net −$1.6M 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 CLE's 2022 odds 48% → 46% (-2.3 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 →