Steamer Projections
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Steamer Projections
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Steamer Projections. The Basics of Projection Systems. Forecasting the upcoming season is essentially the same as determining current ability. Most projection systems are modifications on the same simple system (Marcel “the monkey): Weighs stats from more recent seasons more heavily - PowerPoint PPT Presentation
Transcript of Steamer Projections
Steamer Projections
The Basics of Projection SystemsForecasting the upcoming season is essentially the same as determining current ability.Most projection systems are modifications on the same simple system (Marcel “the monkey):
Weighs stats from more recent seasons more heavily
Regress to the mean
Why regress to the mean?Results = Ability + Luck
Two Examples of Marcel in Action
18.3%23.0%
SteamerAlong with most “fancier” systems:
Uses adjusted minor league statistics in addition to MLB stats.
Adjusts for home ballparks, league, starting v. relieving
What makes Steamer distinct: We use a different system for each component (K%,
BB%, HR%…) We regress to a different “prior” for each player
Projecting Joaquin Benoit’s K% in 2011:4 possible forecasts
28.0%
26.1% 23.7%
24.9%
Actual K%: 26.1%
K/PA for All Pitchers: 1993-2011
HR/PA for All Pitchers: 1993-2011
Regression is Bayes
Distribution of MLB talent
ProjectionLikelihood of player statisticsGiven different levels of talent
K% v. FBV for Starters
K% v. FBV for Relievers
Matt Thornton 2012
24.0% 27.2%
Marcel error v. Fastball Velocity
More regression = Stronger Relationship
It might be working…
Where to go from here?For Pitchers:
Develop a better measure of stuff than fastball velcoity Jeremy Greenhouse: StuffRV based on velocity and
movement Josh Kalk/Brooksbaseball: Similarity Scores based on
pitchf/x
For Hitters: Can something similar be done with hitf/x? Trackman?
Speed off the bat Trajectory
