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.
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
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Winning big points is critical to a players success
Grouping Analysis tool in ArcGIS
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.
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.
Overlay Euclidean lines between important points, then add average distances between each player (on click).
Overlay Euclidean lines between important points, then add average distances between each player (on click).