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

1. Like A Rock:
Exploring How a Catcher's
Movement Affects Framing
Rob Arthur
@No_Little_Plans
fivethirtyeight.com
Dan Turkenkopf
@dturkenk

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. 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. 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. Measuring Movement
Source: Bloomberg Video
.05s .05s
The Catch

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. Catcher Framing Is All About Movement
http://fivethirtyeight.com/features/buster-poseys-pitch-framing-makes-him-a-potential-mvp/

8. Comparing Across Ballparks Is Difficult (for now)

9. Different Viewing Angles Create
A Parallax Problem
en.wikipedia.org

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. Good And Bad Catchers Have Different Patterns
Posey, Flowers, Zunino,
Castro
Ruiz, James McCann,
Nick Hundley

12. Bad Framers Do Not Pause

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. 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. 6/25/2013 - J.P. Arencibia catching: 93% strike chance
(Source: Baseball Prospectus/Pitch Info)

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. 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. 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. 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. 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. 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. 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. Questions?
Rob: @No_Little_Plans
Dan: @dturkenk

24. Appendix