Rosternomics
← Trade Database
Share on 𝕏
August 31, 2016

BALPIT

BAL won this trade +$0.0M surplus BAL won this trade +0.0 WAR
BALBAL Dan Duquette net +$0.0M net +0.0
received +$8.0M+$8.0M ± $39M expected surplus · +$0.0M realized received 2.2 ± 5 expected · 0.0 realized WAR
receives — most valuable first
Kyle LobsteinP·27y·L/L
+$8.0M+$8.0M± $39M exp surplusrealized +$0.0M 2.2± 5 exp WARrealized 0.0
Prior
#47 overall draft pick — at the league baseline → 0.21/yr
Evidence
recent form 0.7/yr over 1.2 season
Talent
0.46/yr blended
Horizon
5.0 control yrs × 0.94 age decline
PITPIT Neal Huntington net +$0.0M net +0.0
received −$2.4M−$2.4M ± $14M expected surplus · +$0.0M realized received 0.1 ± 2 expected · 0.0 realized WAR
receives — most valuable first
Zach PhillipsP·30y·L/L
−$2.4M−$2.4M± $14M exp surplusrealized +$0.0M 0.1± 2 exp WARrealized 0.0
Prior
#681 overall draft pick — at the league baseline → 0.21/yr
Evidence
recent form -0.1/yr over 0.3 season
Talent
0.14/yr blended
Horizon
4.4 control yrs × 0.23 age decline

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 →