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Steamer Projections

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

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Steamer Projections

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  1. Steamer Projections

  2. 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 • Regress to the mean Why regress to the mean? Results = Ability + Luck

  3. Two Examples of Marcel in Action 23.0% 18.3%

  4. Steamer Along 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

  5. Projecting Joaquin Benoit’s K% in 2011:4 possible forecasts 26.1% 23.7% 28.0% 24.9% Actual K%: 26.1%

  6. K/PA for All Pitchers: 1993-2011

  7. HR/PA for All Pitchers: 1993-2011

  8. Regression is Bayes Likelihood of player statistics Given different levels of talent Projection Distribution of MLB talent

  9. K% v. FBV for Starters

  10. K% v. FBV for Relievers

  11. Matt Thornton 2012 27.2% 24.0%

  12. Marcel error v. Fastball Velocity

  13. More regression = Stronger Relationship

  14. It might be working…

  15. 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

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