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GEOVISUALIZING SPATIO-TEMPORAL PATTERNS IN TENNIS

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.

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GEOVISUALIZING
SPATIO-TEMPORAL
PATTERNS IN TENNIS:
An Alternative Approach to
Post-match Analysis
@damiendemaj : Geospatial Product Engineer
THE PROBLEM
A typical summary of tennis fails to
answer important questions about a
match.
WHERE?
WHEN?
MAPS?
Geospatial and visual analysis.
A real opportunity in tennis.
Within sport but outside of
tennis, some spatio-temporal
research has been completed.
In tennis however,
spatio-temporal analytics is a
relatively new area of study.

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GEOVISUALIZING SPATIO-TEMPORAL PATTERNS IN TENNIS

  • 1. GEOVISUALIZING SPATIO-TEMPORAL PATTERNS IN TENNIS: An Alternative Approach to Post-match Analysis @damiendemaj : Geospatial Product Engineer
  • 2. THE PROBLEM A typical summary of tennis fails to answer important questions about a match.
  • 4. Geospatial and visual analysis. A real opportunity in tennis.
  • 5. Within sport but outside of tennis, some spatio-temporal research has been completed.
  • 6. In tennis however, spatio-temporal analytics is a relatively new area of study.
  • 7. THE SERVE The most important shot in tennis?
  • 8. Unpredictable differences in serve location makes your opponents life a lot more difficult. “ ”
  • 9. The results of tennis matches are often determined by a small number of important points. “ ”
  • 10. QUESTION Which player served with more spatio-temporal variation at important points?
  • 11. WHO? Roger Federer v Andy Murray © Getty Images   © Getty Images  
  • 13. DATA 1706 attributed spatial points Source: 3D GIS & streaming video
  • 15. METHOD Plot x,y serve bounces in GIS 78 pts for Federer 86 pts for Murray
  • 16. PART 1 of 4 Identify the visual structure of each serve pattern using K. Means algorithm tool in ArcGIS.
  • 17. K. MEANS Algorithm Looks for natural clusters in the data.
  • 18. K. MEANS Algorithm Allows user to define similarity of serves by attribute (direction of serve) and number of groups. Federer = 11 Murray = 10
  • 19. W B T T B W RESULT 1 of 4 Expected clusters in data. Classify data:Wide – Body – T
  • 20. 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 #
  • 21. Q: How do we measure spatial variation between serve locations?
  • 22. 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
  • 23. LARGE MEAN EUCLIDEAN distance = more spatial serve variation Small mean Euclidean distance = less spatial serve variation
  • 24. RESULT 2 of 4 Federer served with greater spatial variation than Murray
  • 25. PART 3 of 4 Tag the most ‘important’ serves Most important points in tennis: 30-40 and 40-Ad Source: Morris 1977, [21]  
  • 27. RESULT 3 of 4 Murray served with more spatial variation at the most important points than Federer.
  • 28. PART 4 of 4 Overlay successful serves onto important points to determine visual relationship.
  • 29. RESULT 4 of 4 Murray had more success on his serve at important points than Federer.
  • 30. METHOD SUMMARY 1.Visual analytics 2. Introduced K Means Algorithm 3. Euclidean distances 4. Feature overlay
  • 31. WRAP UP GIS provided an effective means to geovisualize spatio-temporal sports data. Reveal potential new patterns within sport.
  • 32. NEXT STEP… Real-time authoritative data More data variables Integrate sports professionals
  • 38. REFERENCES[1] M.J. Smith et el,“Geospatial Analysis, a comprehensive guide to principles, techniques and software tools”, Matador, 2007. [2] A. Mitchell,“The Esri guide to GIS Analysis”, Esri Press, 1999. [3] J. Bertin,“Semiology of Graphics: Diagrams, Networks, Maps”, Esri Press, 2nd Edition, 2010. [4] Franc J.G.M. Klaassen and Jan R. Magnus,“Forecasting the winner of a tennis match”, European Journal of Operational Research, no. 148, pp. 257-267, Sept. 2003. [5] J.K Vis et el,“Tennis Patterns: Player, Match and Beyond”, In 22nd Benelux Conference on Artificial Intelligence (BNAIC 2010), Luxembourg, 25-26 October 2010. [6] T. Barnett and S.R. Clarke,“Combining player statistics to predict outcomes of tennis matches”, IMA Journal of Management and Mathematics, vol. 16, pp. 113-120, 2005. [7] F. Radicchi,“Who is the best player ever? A complex network analysis of the history of professional tennis”, PLoS ONE 6(2): e17249. doi: 10.1371/journal.pone.0017249. [8] T. Barnett and S.R. Clarke,“Using Microsoft Excel to model a tennis match”, In Proceedings 6th Australian Conference on Mathematics and Computers in Sport, Bond University, pp. 63-68, 2002. [9] B. Schroeder,“A methodology for pattern discovery in tennis rallys using the adaptive framework ANIMA”, In Second International Workshop on Knowledge Discovery from Data Streams (IWKDDS), 2005. [10] A.Terroba et el,“Tactical analysis modeling through data mining, Pattern discovery in racket sports”, In International Conference on Knowledge Discovery and Information Retreival (KDIR 2010), 2010. [11] A. Moore et el,“Sport and Time Geography: a good match?”, Presented at the 15th Annual Colloquium of the Spatial Information Research Centre (SIRC 2003: Land, Place and Space), 2003. [12] A. Gatrell and P. Gould,“A micro-geography of team games: graphical explorations of structural relations”, Area, 11, 275-278. [13] K. Goldsberry,“CourtVision: New Visual and Spatial Analytics for the NBA”, In Proceedings MIT Sloan Sports Analytics Conference, 2012. [14] United States Tennis Association,“Tennis tactics, winning patterns of play”, Human Kinetics, 1st Edition, 1996. [15] G. E. Parker,“Percentage Play in Tennis”, In Mathematics and Sports Theme Articles, http://www.mathaware.org/mam/2010/essays/ [16] Hawk-Eye Innovations, http://www.hawkeyeinnovations.co.uk/ [17] J Ren,“Tracking the soccer ball using multiple fixed cameras”, Computer Vision and Image Understanding, vol. 113, pp. 633-642, 2009. [18] J.R.Wang and N. Parameswaran,“Survey of Sports Video Analysis: Research Issues and Applications”, In Proceedings of the Pan-Sydney area workshop on Visualization, pp.87-90, 2005. [19] J. A. Hartigan and M. A.Wong,“Algorithm AS 136: A K-Means Clustering Algorithm”, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, No. 1, pp. 100-108, 1979. [20] ArcGIS Resources Help 10.1, http://resources.arcgis.com/en/help/main/10.1/index.html – /Grouping_Analysis/005p00000051000000/ [21] C. Morris,“The most important points in tennis”, In Optimal Strategies in Sports, vol 5 in Studies and Management Science and Systems, , North-Holland Publishing, Amsterdam, pp. 131-140, 1977. [22] C.D. Lloyd,“Spatial data analysis, an introduction to for GIS users”, Oxford University Press, 1st edition, NewYork, 2010 [23] M. Lames,“Modeling the interaction in games sports – relative phase and moving correlations”, Journal of Sports Science and Medicine, vol 5, pp. 556-560, 2006

Editor's Notes

  1. Winning big points is critical to a players success
  2. Grouping Analysis tool in ArcGIS
  3. Federer’s spatial serve cluster in the ad court on the left side of the net was the most spread of all his clusters. However, he served out wide with great accuracy into the deuce court on the left side of the net by hugging the line 9 times out 10 (Figure 5). Murray’s clusters appeared to be grouped overall more tightly in each of the service boxes. He showed a clear bias by serving down the T in the deuce court on the right side of the net. Visually there appeared to be no other significant differences between each player’s patterns of serve. 
  4. Morris (1977) defines the importance of a point for winning a game (IPG) as the probability that the server wins the game given he wins the next point minus the probability that the server wins the game given he loses the next point. Assumes the server has a 0.62 probability of winning a point on serve.
  5. Overlay Euclidean lines between important points, then add average distances between each player (on click).
  6. Overlay Euclidean lines between important points, then add average distances between each player (on click).
  7. Sergey Brin, co-founder Google