Presentation for the PitchFX data hackathon on using pitching data from the MLB, building a machine learning model around it, and building the foundation for a better pitcher scouting report.

1. PitchFX Hackathon
Team R (No pirate jokes please)

2. Exploratory Data Analysis

9. How to Win

15. What Happens to a Pitch?

20. How to Win

24. Machine Learning Method
5 Pitchers X 9 Batters
Cluster 4-7 pitches
Decision Tree
Classification Model
X

28. Top Bang for the Buck
Nick Tepesch FF Josh Hamilton: -0.58
Robbie Ross SL Howie Kendrick: -0.46
Nick Tepesch FF Albert Pujols: -0.45

29. Further Research

30. Questions/Comments:
frankdevans@yahoo.com
Code:
github.com/frankdevans
Slides:
slideshare.net/frankdevans

31. –Me at midnight last night 3 RedBulls in
“Who needs sleep when you have Data Science”