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Traditional methods for summarizing tennis matches have long ignored the spatio-temporal component of the match, and often fail to geovisualize patterns by way of map or graphic. This presentation presents alternative approaches to post-match analysis using geospatial data analysis with a Geographical Information System (GIS). A case study focusing on the spatial variation of serving from the London Olympics Gold Medal match, where Andy Murray defeated Roger Federer 6-2, 6-1, 6-4, is conducted. By mapping the relationship between space and time, we were able to visually and statistically quantify that Federer served with more spatial variation during the match. Murray, however, served with greater spatial variation at key points during the match. Results suggest that there is potential to better understand players serve tendencies using spatio-temporal analysis. The importance of such analysis for coaches, players, fans and the media to further explore player tactics and strategies are discussed.

Published in: Sports, Technology, Education
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  1. 1. GEOVISUALIZING SPATIO-TEMPORAL PATTERNS IN TENNIS: An Alternative Approach to Post-match Analysis @damiendemaj : Geospatial Product Engineer
  2. 2. THE PROBLEM A typical summary of tennis fails to answer important questions about a match.
  3. 3. WHERE? WHEN? MAPS?
  4. 4. Geospatial and visual analysis. A real opportunity in tennis.
  5. 5. Within sport but outside of tennis, some spatio-temporal research has been completed.
  6. 6. In tennis however, spatio-temporal analytics is a relatively new area of study.
  7. 7. THE SERVE The most important shot in tennis?
  8. 8. Unpredictable differences in serve location makes your opponents life a lot more difficult. “ ”
  9. 9. The results of tennis matches are often determined by a small number of important points. “ ”
  10. 10. QUESTION Which player served with more spatio-temporal variation at important points?
  11. 11. WHO? Roger Federer v Andy Murray © Getty Images   © Getty Images  
  12. 12. WHERE? Olympic Gold Medal Match, London, UK Sunday Aug 5, 2012
  13. 13. DATA 1706 attributed spatial points Source: 3D GIS & streaming video
  14. 14. METHOD Plot x,y serve bounces in GIS 78 pts for Federer 86 pts for Murray
  15. 15. PART 1 of 4 Identify the visual structure of each serve pattern using K. Means algorithm tool in ArcGIS.
  16. 16. K. MEANS Algorithm Looks for natural clusters in the data.
  17. 17. K. MEANS Algorithm Allows user to define similarity of serves by attribute (direction of serve) and number of groups. Federer = 11 Murray = 10
  18. 18. W B T T B W RESULT 1 of 4 Expected clusters in data. Classify data:Wide – Body – T
  19. 19. PART 2 of 4 Arrange the data into a temporal sequence to see who served with more spatial variation. Temporal sequence = service box, point #, shot #, game #, set #
  20. 20. Q: How do we measure spatial variation between serve locations?
  21. 21. Create EUCLIDEAN LINES p1 (x1,y1) and p2 (x2,y2), p2 (x2,y2) and p3 (x3,y3), p3 (x3,y3) and p4 (x4,y4) etc in each service court location
  22. 22. LARGE MEAN EUCLIDEAN distance = more spatial serve variation Small mean Euclidean distance = less spatial serve variation
  23. 23. RESULT 2 of 4 Federer served with greater spatial variation than Murray
  24. 24. PART 3 of 4 Tag the most ‘important’ serves Most important points in tennis: 30-40 and 40-Ad Source: Morris 1977, [21]  
  25. 25. SELECT Important points only Recalculate euclidean distance
  26. 26. RESULT 3 of 4 Murray served with more spatial variation at the most important points than Federer.
  27. 27. PART 4 of 4 Overlay successful serves onto important points to determine visual relationship.
  28. 28. RESULT 4 of 4 Murray had more success on his serve at important points than Federer.
  29. 29. METHOD SUMMARY 1.Visual analytics 2. Introduced K Means Algorithm 3. Euclidean distances 4. Feature overlay
  30. 30. WRAP UP GIS provided an effective means to geovisualize spatio-temporal sports data. Reveal potential new patterns within sport.
  31. 31. NEXT STEP… Real-time authoritative data More data variables Integrate sports professionals
  33. 33. GOOGLE GLASS in sport
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  35. 35. QUESTIONS? @damiendemaj