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.

3. WHERE?
WHEN?
MAPS?

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

12. WHERE?
Olympic Gold
Medal Match,
London, UK
Sunday Aug 5, 2012

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]

26. SELECT
Important points only
Recalculate euclidean distance

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

33. OTHER TECH
OPPORTUNITIES…

34. GOOGLE GLASS in sport

38. REFERENCES[1] M.J. Smith et el,“Geospatial Analysis, a comprehensive guide to principles, techniques and software tools”, Matador, 2007.
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[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,
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[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

39. QUESTIONS?
@damiendemaj
gamesetmap.com
esri.com