RRosternomics
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March 15, 2014

WSNKCR

WSN won this trade +$0.0M surplus WSN won this trade +0.0 WAR
WSNWSN Michael Rizzo net +$0.0M net +0.0
received −$1.6M−$1.6M ± $40M expected surplus · +$0.0M realized received 0.2 ± 5 expected · 0.0 realized WAR
receives — most valuable first
Brandon Laird1B/3B·27y·R/R
−$1.9M−$1.9M± $40M exp surplusrealized +$0.0M 0.2± 5 exp WARrealized 0.0
Prior
#814 overall draft pick — at the league baseline → 0.21/yr
Evidence
recent form -0.7/yr over 0.3 season
Talent
0.05/yr blended
Horizon
4.0 control yrs
KCRKCR Dayton Moore net +$0.0M net +0.0
received +$0.0M+$0.0M ± $0M expected surplus · +$0.0M realized received 0.0 ± 0 expected · 0.0 realized WAR
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 →