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Sport Analytics Innovation Summit


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An overview of the Sports Analytics Innovation Summit held in Boston, September 2013

Published in: Sports, Technology

Sport Analytics Innovation Summit

  1. 1. Sports Analytics Innovation Summit Winning in Sports Through Performance Analysis & Data Analytics September 12 & 13, Boston 2013
  2. 2. Who went? Coaches, General Managers, Sports Analysts, Technologists
  3. 3. Key questions How do you engage Coaches? How do you create value based on the Data?
  4. 4. Two tracks On Field Analytics Off Field Analytics
  5. 5. The pursuit of innovation in the English Premier League Tony Strudwick Head of Sports Science at Manchester United
  6. 6. The pursuit of innovation in the English Premier League Tony Strudwick Head of Sports Science at Manchester United
  7. 7. Psychology of Sport
  8. 8. Analytics may not capture a player's main strengths or weaknesses
  9. 9. For example ... One players greatest skill may be to motivate other players. If players are not physically or mentally ready to perform then data is a waste of time.
  10. 10. Other Problems ...
  11. 11. In the last 20 years, sport science has been oversubscribed yet has underdelivered.
  12. 12. Most coaches feel sport science brings no value to their team. The problem is perceived as the inability of data practitioners to communicate actionable metrics.
  13. 13. Analytics do not always explain human psychological principles because ... Humans are not rational Humans are risk adverse Under pressure humans will fail
  14. 14. Analytics must drive decisions and actions or else they're worthless. Need more graphical representations, not excel spreadsheets Emphasis on real time apps, real time data & analysis, real time decisions Catapult Team Tracking System
  15. 15. Decisions that are now well supported by analytics ... Managing training of new players Analytics & reports for chief execs
  16. 16. Display relevant relationships between these variables: Age Group Position Ave body Mass Cumulative minutes trained
  17. 17. Example 49% of squad is 29+ years old Higher number of injuries coming from this group During November, December, January intensity training goes down, injury goes up
  18. 18. Goals of social media to analysis Using search for the next Olympic team Remain injury free Increase player availability and individual performance Troy Flanagan Maintain high performance over 45 games Performance Director, US Ski & Snowbaord Assn. per season, 4 games per week
  19. 19. Using social media to search for the next Questions? Olympic team Troy Flanagan Performance Director, US Ski & Snowbaord Assn.
  20. 20. Using social media to search for the next Olympic team Troy Flanagan Performance Director, US Ski & Snowbaord Assn.
  21. 21. Goal of program is to transfer ex-gymnasts into aerial skiing for the 2018 Olympics 3 years to reach the podium ...
  22. 22. The US Ski Team created a Facebook app through for finding talent
  23. 23. Kids submit their best tricks to win an invitation to tryout camp If theres not a tangible reward people won't participate
  24. 24. Using Visual Analytics in Performance Analysis Questions? Kirk Goldsbery Visiting scholar at Harvard, now at ESPN
  25. 25. Using Visual Analytics in Performance Analysis Kirk Goldsbery Visiting scholar at Harvard, now at ESPN
  26. 26. What are Analytics?
  27. 27. Analytics are Reasoning Artifacts … things we use to make decisions. New Data, New Analytics, Same reasoning
  28. 28. Maps Maps show spatial structure and patterns Maps provoke spatial reasoning Maps work for all sports
  29. 29. We’re visual creatures and when we see something attractive we want to consume it It takes time to make something that people want to consume. If you were to ask Faulkner how he writes … he doesn't just write, he considers how to frame the story first
  30. 30. How do you harness spatial science? Sports are spatial Sports are visual Analytics are not spatial or visual
  31. 31. Spatial Analysis Visualize patterns and quantify information Visual Analytics Translate raw game data into useful information
  32. 32. Example All LeBron James shots for the last 5 seasons Spatial map of shooting patterns Good for engaging the athlete Good for finding players that are similar or different
  33. 33. Visualizations can handle big data As strategic devices As communicative devices
  34. 34. Two different approaches to visualizations Exploratory Confirmatory Depends on audience
  35. 35. Sensors lead to quantitative spatial research questions However, provoking spatial reasoning may lead to more questions than it answers Find a question to try to answer and attack it
  36. 36. Staying Connected: The Rise in Fitness Data Questions? Chris Glode GM, MapMyFitness
  37. 37. Staying Connected: The Rise in Fitness Data Chris Glode GM, MapMyFitness
  38. 38. MapMyRun Team of 100 split between Austin & Denver Connected Fitness - social, fun, simple, effective, and rewarding
  39. 39. Users 40% Aspirational 45% Recreational 15% Fanatical
  40. 40. 160 million workouts logged in 2013 Team working toward less friction in the app experience
  41. 41. Growth driven by Smart phones Wireless technology & reduced friction seamless data download Cloud computing Wearables… reduction in hardware costs Obesity epidemic in US
  42. 42. People use MapMyRun to “outsource their willpower” Friends in the system keep other users more active via notifications
  43. 43. Techniques for engagement: Games: people keep coming back for competition Games are considered a “jedi mind trick” by MapMyRun, effectively manipulating users to return Route art: motivated users who were otherwise uninterested in social
  44. 44. Practical applications of fitness data Corporate wellness Trainer driven programming, tailored to the individual based on real, recent and new fitness data. Total accountability
  45. 45. Opportunity via tons of information mined on the geospatial front Example: advertising to women along certain routes, etc.
  46. 46. Future Woven wearables Advanced activity detection Ubiquity of incentives to track fitness iOS7 - Support for passive all day activity tracking in background when app is inactive
  47. 47. Using GIS to study Spatio-Temporal Questions? Patterns in Sport Damien Demaj Geospatial Product Engineer at Esri
  48. 48. Using GIS to study Spatio-Temporal Patterns in Sport Damien Demaj Geospatial Product Engineer at Esri
  49. 49. Space and time go hand in hand in sport
  50. 50. Mapping a tennis match
  51. 51. Example Who: Federer v Murray Data: 1706 spatial points 3D GIS & streaming video
  52. 52. The serve: the most important shot Speed & spin: the most important metrics Variation is key: mapping unpredictability is important
  53. 53. Approach 1. K Means algorithm - looks for natural clusters in the data, balls that are close in space but also have a similar attribute 2. Create euclidean lines & calculate large mean distance 3. Tag the most important points in the match 4. Add a feature overlay – Pseudo Realism – putting players back in their environment
  54. 54. Smart Soccer Qaisar Hassonjee Questions? VP Innovation, adidas Nelson Rodriquez MLS EVP Competition & Game Operations
  55. 55. Smart Soccer Qaisar Hassonjee VP Innovation, adidas Nelson Rodriquez MLS EVP Competition & Game Operations
  56. 56. How is technology enabling sports analytics?
  57. 57. Wearables Multifunctions, always connected, smart/ aware devices that measure me Enabled by advances in sensor technology, algorithms, data science Everyone from startups to established brands are developing tools What are you looking for? What kind of sensors to develop, ease of use
  58. 58. Lots of fear in the industry... but lots of copycats once something works
  59. 59. The adidas Team System Open platform… more sensors over time can be added to the platform Measures Heart rate, Speed, Distance, Location, Acceleration 100 shirts 30 pods 4 ipads
  60. 60. 19 MLS clubs are now using the system - bell curve of adoption
  61. 61. iPad App Most people don’t look at all 20-30 parameters, just the top 2-3 How is the information actionable? How is pre-season trianing improving the fitness of my athletes?
  62. 62. Results Athletes appreciate and want to use the technology Tool can extend careers and improve performance Injuries are down 2% this year
  63. 63. Future Drop the tech into the academy programs & build a national data set of kids & analytics using the system Commercial opportunities, super fan stuff, fantasy teams, etc.
  64. 64. USA Volleyball Questions? Anton Willert Technical Coordinator/Tem Manager US Men’s National Team
  65. 65. Thanks for visualizing