Beyond a Bit Fit An emerging and important avenue of sport analytics is biometric data. From the casual athlete tracking steps and sleep to professional athletes tracking heart rate and impact data, biometric data can improve performance and prevent injury. What can we learn from biometric data? How can it aid athletes and coaches? How can you be a bit fitter by analyzing a body’s data? This talk will discuss the data, analysis and insights available and evolving in sports analytics of biometric data.

1. Beyond a bit fit
Department of Mathematics
and Computer Science
tichartier@davidson.edu
Dr. Tim Chartier
timchartier
Chief Academic Officer
tim@tresata.com

2. who am i

7. math + data = impact

8. transforming teams

9. impact: game prep

10. impact: game time
“It kind of blew us away… it really opened our eyes...”
– Matt McKillop, NYT

11. impact: off-season
Player Poss. TO% OR% EFG% 2P% 3P% A/FGM 3PA/FG
A
Brian
Sullivan
77 14.3% 20.0% 65.6% 67.4% 40.0% 27.9% 24.6%
without 56 23.2% 20.8% 55.3% 42.9% 47.1% 26.3% 44.7%

12. transforming fans

13. transforming athletes

14. injury prevention

15. current
research

16. current customers

17. data → coachable moments

18. got data?

19. Goal: lay strong ML foundation

20. Goal 1: enrich data

21. data → data asset
•Adding right data creates an asset.
•What could we add?
• session type: practice or game
• heart rate
• sleep
• weather
•Question: Can adding data lead to
more coachable insights?

22. small grows to big
•1 athlete’s file ≈ 6 MB per practice or game
•Consider a whole team for every practice and game
over a season.
•Consider looking for trends over multiple seasons.
N(small data) = (big data), for large enough N

23. scalable viz

24. Goal 2: improve insight

25. stage 1: identify

26. can we (automatically)
detect phenomenon?

27. stage 2: contextualize

28. football impact data

29. linebacker offensive line
impact by position

30. quarterbacks and kickers
impact by position

32. fatigue → poor form

33. Davidson women’s soccer

34. training load →
coachable moments
LOAD LIMIT
< 1200

37. stage 3: predict

38. time to learn

39. time to learn

40. Goal 3: consumable &
understandable

42. the right stuff
(right insight & right amount)

43. sports analytics keys
•coachable
•consumable
•understandable (informed opinion)

44. machine learning to stay fit

45. questions