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.

THE SERVE The most important shot in tennis?

Unpredictable differences in serve location makes your opponents life a lot more difficult.

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The results of tennis matches are often determined by a small number of important points.

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QUESTION Which player served with more

spatio-temporal variation at important points?

WHO? Roger Federer v Andy Murray

© Getty Images   © Getty Images  

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

DATA 1706 attributed spatial points Source: 3D GIS & streaming video

METHOD Plot x,y serve bounces in GIS 78 pts for Federer 86 pts for Murray

PART 1 of 4 Identify the visual structure of each serve pattern using K. Means algorithm tool in ArcGIS.

K. MEANS Algorithm

Looks for natural clusters in the data.

K. MEANS Algorithm Allows user to define similarity of serves by attribute (direction of serve) and number of groups. Federer = 11 Murray = 10

W B T

T B W

RESULT 1 of 4 Expected clusters in data. Classify data: Wide – Body – T

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 #

Q: How do we measure spatial variation between serve locations?

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

LARGE MEAN EUCLIDEAN distance = more spatial serve variation Small mean Euclidean distance = less spatial serve variation

RESULT 2 of 4 Federer served with greater spatial variation than Murray

PART 3 of 4 Tag the most ‘important’ serves Most important points in tennis: 30-40 and 40-Ad

Source: Morris 1977, [21]  

SELECT Important points only Recalculate euclidean distance

RESULT 3 of 4 Murray served with more spatial variation at the most important points than Federer.

PART 4 of 4 Overlay successful serves onto important points to determine visual relationship.

RESULT 4 of 4 Murray had more success on his serve at important points than Federer.

METHOD SUMMARY

1. Visual analytics

2. Introduced K Means Algorithm

3. Euclidean distances

4. Feature overlay

WRAP UP GIS provided an effective means to geovisualize spatio-temporal sports data. Reveal potential new patterns within sport.

NEXT STEP… Real-time authoritative data More data variables Integrate sports professionals

OTHER TECH OPPORTUNITIES…

GOOGLE GLASS in sport

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QUESTIONS? @damiendemaj gamesetmap.com

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