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Like a Rock: Exploring How a Catcher's Movement Affects Framing

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Saber Seminar presentation on 8/22/2015 in Boston.

Like a Rock: Exploring How a Catcher's Movement Affects Framing

  1. 1. Like A Rock: Exploring How a Catcher's Movement Affects Framing Rob Arthur @No_Little_Plans fivethirtyeight.com Dan Turkenkopf @dturkenk
  2. 2. Catcher Framing Is A Real And Measurable Skill @MetsUmp Called Strike 40% of the time with average catcher Called Strike 30% of the time when Carlos Ruiz is catching Called Strike 50% of the time when Buster Posey is catching
  3. 3. Current Catcher Framing Metrics Measure The Outcome, But Not The Process Catcher Skill Level Hitter Skill Level Foot Speed Plate Discipline Raw Power Reputation With Umps Technique Command of Pitchers ??? PITCHf/x
  4. 4. Video Analysis Can Tell Us How Catcher Framing Works http://grantland.com/features/studying-art-pitch-framing-catchers-such-francisco-cervelli-chris-stewart-jose-molina-others/
  5. 5. Measuring Movement Source: Bloomberg Video .05s .05s The Catch
  6. 6. We Select A Very Particular Set of Pitches Selecting Only: • Called strikes • RHH vs. RHP • At the catcher’s home ballpark Filtering If: • The batter checked his swing • The captured pixels failed to include the catcher • The pitcher’s foot got in the frame
  7. 7. Catcher Framing Is All About Movement http://fivethirtyeight.com/features/buster-poseys-pitch-framing-makes-him-a-potential-mvp/
  8. 8. Comparing Across Ballparks Is Difficult (for now)
  9. 9. Different Viewing Angles Create A Parallax Problem en.wikipedia.org
  10. 10. Normalizing Pixel Movement Normalized movement = ∆ 𝑖 𝐸[∆] In English: The movement in a given frame divided by the average movement across the catch. What this can’t tell us: which catchers are the quietest in their reception. What this can tell us: when, during the reception, a given catcher is quietest (relative to other parts of the reception).
  11. 11. Good And Bad Catchers Have Different Patterns Posey, Flowers, Zunino, Castro Ruiz, James McCann, Nick Hundley
  12. 12. Bad Framers Do Not Pause
  13. 13. Conclusions and Limitations • We can analyze catcher framing with video. • Good catchers display a particular pause soon after receiving the pitch, and steady their gloves just prior. • We are limited by parallax and other issues. • But, Dan is going to fix all of our problems…
  14. 14. We can do more (or at least we think we can) 7/28/2013 – Josh Thole catching: 46% strike chance (Source: Baseball Prospectus/Pitch Info)
  15. 15. 6/25/2013 - J.P. Arencibia catching: 93% strike chance (Source: Baseball Prospectus/Pitch Info)
  16. 16. More Granular Catcher Movements • Identify candidate points to track • Head, shoulders, knees and gloves (knees and gloves) • Measure how those points move across the timeline of the pitch • Correlate the movement to the framing random effects • Add movement into the framing mixed models • Should shrink the per catcher variance • Allows for predictions of framing ability based on video of movement
  17. 17. An Automated Approach • Identify glove position at release • i.e. CommandFX • Identify position of the other important points at release • Determine movement vectors for each • Distance and direction • Simplifying assumption: use start and end points rather than actual paths
  18. 18. This is Hard • Use a set of techniques known as computer vision (CV) • Lots of well-known approaches for identifying and tracking objects in images/videos • But, we have A LOT of complications • Calibration: parallax, distance, t0 • Markerless tracking: how can we identify the points we’re interested in at scale? • What part of the glove do we use to measure glove position? What if the catcher doesn’t set up before the pitch is released? • Etc. • Etc.
  19. 19. The Manual Approach • Choose good pitches • Find the release frame • Draw calibration line on front of home plate • Mark the points we care about • Rinse and repeat for the catch frame and the ump’s first movement frame (estimated) • Overlay them • Figure out the movement vectors
  20. 20. That Thole Frame Job Point Release to Catch (in.) Deg Catch to Ump Mvmt (in.) Deg Release to Ump Mvmt (in.) Deg Right Knee 1.6 58 1.4 -83 1 0 Left Knee 11.9 -7 1 -45 11.1 -4 Right Shoulder 2.8 -60 0.9 -79 2 -52 Left Shoulder 1.5 -21 0.7 0 0.9 -37 Head (center) 0.7 76 0.9 -68 0.5 -18 Glove (center) 7.5 67 5.1 -74 4.8 26
  21. 21. And Then There’s J.P. Point Release to Catch (in.) Deg Catch to Ump Mvmt (in.) Deg Release to Ump Mvmt (in.) Deg Right Knee 3.1 11 1 -53 2.8 30 Left Knee 1.9 77 5.8 39 4.4 24 Right Shoulder 0.2 0 1.7 -45 1.9 -41 Left Shoulder 3.1 -32 2.6 -51 5.6 -41 Head (center) 1.5 -34 4.5 -10 5.9 -16 Glove (center) 19.3 -36 3.3 -76 16.9 -29
  22. 22. How Can We Use This in Player Evaluation? • Expected catcher movement based on a lot of factors • Pitch type • Pitch location • Runners • Etc. • Probably can’t just figure out average movement for a catcher and correlate to framing • Will need to be done on a pitch by pitch basis and summed • MILB expected movement probably not the same as MLB • Largely due to pitcher command
  23. 23. Questions? Rob: @No_Little_Plans Dan: @dturkenk
  24. 24. Appendix
  • RyanFroistad

    Aug. 25, 2015

Saber Seminar presentation on 8/22/2015 in Boston.

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