Assessing team strategy using spatiotemporal data

Patrick Lucey, Dean Oliver, Peter Carr, Joe Roth, Iain Matthews
2013 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13  
The Moneyball [14] revolution coincided with a shift in the way professional sporting organizations handle and utilize data in terms of decision making processes. Due to the demand for better sports analytics and the improvement in sensor technology, there has been a plethora of ball and player tracking information generated within professional sports for analytical purposes. However, due to the continuous nature of the data and the lack of associated highlevel labels to describe it -this rich
more » ... et of information has had very limited use especially in the analysis of a team's tactics and strategy. In this paper, we give an overview of the types of analysis currently performed mostly with handlabeled event data and highlight the problems associated with the influx of spatiotemporal data. By way of example, we present an approach which uses an entire season of ball tracking data from the English Premier League (2010-2011 season) to reinforce the common held belief that teams should aim to "win home games and draw away ones". We do this by: i) forming a representation of team behavior by chunking the incoming spatiotemporal signal into a series of quantized bins, and ii) generate an expectation model of team behavior based on a code-book of past performances. We show that home advantage in soccer is partly due to the conservative strategy of the away team. We also show that our approach can flag anomalous team behavior which has many potential applications.
doi:10.1145/2487575.2488191 dblp:conf/kdd/LuceyOCRM13 fatcat:evol4xgkgzegff5lby57hyswym