Rosternomics
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April 10, 2024

CLETOR

unsettledToo soon to call — players still accruing.
CLECLE Chris Antonetti net +$0.8M net +0.1
received −$0.8M−$1.6M ± $18M expected surplus · +$0.8M realized received 0.0 ± 2 expected · 0.1 realized WAR
Playoff odds: this deal moved CLE's 2024 odds 20% → 21% (+0.4 pts) — how trade timing is graded ↗
receives — most valuable first
Wes ParsonsP·32y·R/R
−$0.8M−$1.6M± $18M exp surplusrealized +$0.8M 0.0± 2 exp WARrealized 0.1
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form -1.3/yr over 0.3 season
Talent
-0.05/yr blended
Horizon
1.5 control yr
TORTOR Ross Atkins net −$0.8M 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 TOR's 2024 odds 9% → 8% (-0.2 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 →