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Tim Chartier, Chief Academic Officer, Tresata at MLconf ATL 2017

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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.

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Tim Chartier, Chief Academic Officer, Tresata at MLconf ATL 2017

  1. 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. 2. who am i
  3. 3. math + data = impact
  4. 4. transforming teams
  5. 5. impact: game prep
  6. 6. impact: game time “It kind of blew us away… it really opened our eyes...” – Matt McKillop, NYT
  7. 7. 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%
  8. 8. transforming fans
  9. 9. transforming athletes
  10. 10. injury prevention
  11. 11. current research
  12. 12. current customers
  13. 13. data → coachable moments
  14. 14. got data?
  15. 15. Goal: lay strong ML foundation
  16. 16. Goal 1: enrich data
  17. 17. 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?
  18. 18. 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
  19. 19. scalable viz
  20. 20. Goal 2: improve insight
  21. 21. stage 1: identify
  22. 22. can we (automatically) detect phenomenon?
  23. 23. stage 2: contextualize
  24. 24. football impact data
  25. 25. linebacker offensive line impact by position
  26. 26. quarterbacks and kickers impact by position
  27. 27. fatigue → poor form
  28. 28. Davidson women’s soccer
  29. 29. training load → coachable moments LOAD LIMIT < 1200
  30. 30. stage 3: predict
  31. 31. time to learn
  32. 32. time to learn
  33. 33. Goal 3: consumable & understandable
  34. 34. the right stuff (right insight & right amount)
  35. 35. sports analytics keys •coachable •consumable •understandable (informed opinion)
  36. 36. machine learning to stay fit
  37. 37. questions

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