Amy Langville, Professor of Mathematics, The College of Charleston in South Carolina & Tim Chartier, Chief Researcher, Tresata at MLconf ATL 2016
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Learning to Play Sports: Sports Analytics is an active and growing field. With large datasets from biometric devices and player tracking equipment, sports teams can benefit from techniques in data analytics and machine learning. This talk will discuss work in the areas of March Madness and game-to-game analysis. With the emergence of algorithms to study such dynamics as player performance and fan engagement, the collection of data also becomes paramount. Professional sports organizations have access to premium technology. This talk will also discuss how such work can be transferred to the college and secondary levels. Machine learning allows cutting edge technology to play from the bench.
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Amy Langville, Professor of Mathematics, The College of Charleston in South Carolina & Tim Chartier, Chief Researcher, Tresata at MLconf ATL 2016
- 1. Learning to Play Sports ML in sports analytics Dr. Tim Chartier Tresata Davidson College tichartier@davidson.edu Dr. Amy Langville College of Charleston Dept. of Math LangvilleA@cofc.edu @timchartier
- 3. Play from bench data availability general interest (a.k.a. cool factor) domain knowledge
- 5. Apply here Picture credit: http://orlandonest.files.wordpress.com/2011/03/2011-march- madness-bracket.gif
- 6. How do we do? • ESPN Tournament Challenge: > 4 million brackets! • 1st round correct choice = 10 points • nth round correct choice = 2*(previous round)
- 7. finding ideal weight 4 prediction
- 8. Method 1: crowd source • 2009 – best bracket – 97% • 2010 – best bracket – 99% • 2014 – national media led to thousands of brackets on: marchmathness.davidson.edu
- 9. Method 2: learn sports • vary parameter weights to optimize ESPN score or prediction rate • subtlety: not all seasons are equally predictive
- 10. Method 3: mad web 10 years, 50,000 games
- 11. Application 2: Cats Stats Analytics for college teams to support coaching.
- 12. sports analytics keys • coachable • consumable • understandable (informed opinion)
- 13. impact: coaching “It kind of blew us away…it really opened our eyes...” – Matt McKillop, NYT
- 14. impact: off-season Player Poss. TO% OR% EFG% 2P% 3P% Brian Sullivan 77 14.3% 20.0% 65.6% 67.4% 40.0% without 56 23.2% 20.8% 55.3% 42.9% 47.1%
- 15. Application 3: Lotsa data
- 17. Filtered for Warriors regular season
- 18. data we have
- 20. MasseyRatings.com column 1 = date of game as measured as days since 1/1/0000 column 2 = date in YYYYMMDD format column 3 = team 1 index column 4 = team 1 home field (1 = home, -1 = away, 0 = neutral) column 5 = team 1 score column 6 = team 2 index column 7 = team 2 home field (1 = home, -1 = away, 0 = neutral) column 8 = team 2 score
- 21. Tresata Data For network analysis, Tresata added: • seed • coach’s Madness history • kenpom.com statistics • every season game (and added game stats) What can we learn from about 50,000 games?
- 22. Data needed • ESPN bracket challenge scores for past years • injuries for every game • score with 2 min or 4 minutes left • learn from Vegas odds • biometric data
- 23. • If we remove a team and it highly affects reranking, what can we learn about such a team for March Madness? • How can Buddy Hield light up March Madness? • Compare Jack Gibbs to Stephen Curry in college play. media ?’s
- 25. New Work How rankable is this dataset?
- 26. Rankability Data Apps Amazon products Netflix movies Financial networks Teams
- 27. Intuitive Ideas one extreme Dominance graph (very rankable) Random graph (less rankable) other extreme
- 28. Inconsistency Uparcs in a rank-ordered graph 5 uparcs
- 29. Inconsistency Uparcs in a rank-ordered graph Minimum Violations Ranking 3 uparcs
- 30. Inconsistency BUT this measure of rankability is tied to the ranking. March Madness 2008 sorted by Massey rating uparcs = 27.2% March Madness 2014 sorted by Massey rating uparcs = 26.9%
- 31. Goal k-cycles Create a rankability measure that is independent of ranking. 2-cycles: 1-2-1 2-1-2 5-cycles: 1-2-3-4-5-1 2-3-4-5-1-2 3-4-5-1-2-3 4-5-1-2-3-4 5-1-2-3-4-5
- 32. Goal k-cycles Create a rankability measure that is independent of ranking. 2-cycles: 1-2-1 2-1-2 5-cycles: 1-2-3-4-5-1 2-3-4-5-1-2 3-4-5-1-2-3 4-5-1-2-3-4 5-1-2-3-4-5 4-paths: 1-2-1-2-1 2-1-2-1-2
- 33. Goal k-cycles Create a rankability measure that is independent of ranking. 2-cycles: 1-2-1 2-1-2 5-cycles: 1-2-3-4-5-1 2-3-4-5-1-2 3-4-5-1-2-3 4-5-1-2-3-4 5-1-2-3-4-5 4-paths: 1-2-1-2-1 2-1-2-1-2
- 34. Future Work If a dataset is not very rankable, which edges should we add to the graph to improve its rankability?
- 35. earn to play sports data questions applications