Istituto di Scienza e Tecnologie dell'Informazione     
Cintia P., Pappalardo L., Coscia M. The Haka network: evaluating rugby team performance with dynamic graph analysis. In: ASONAM 2016 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (San Francisco, Ca, USA, 18-21 August 2016). Proceedings, pp. 1095 - 1102. IEEE, 2016.
Real world events are intrinsically dynamic and analytic techniques have to take into account this dynamism. This aspect is particularly important on complex network analysis when relations are channels for interaction events between actors. Sensing technologies open the possibility of doing so for sport networks, enabling the analysis of team performance in a standard environment and rules. Useful applications are directly related for improving playing quality, but can also shed light on all forms of team efforts that are relevant for work teams, large firms with coordination and collaboration issues and, as a consequence, economic development. In this paper, we consider dynamics over networks representing the interaction between rugby players during a match. We build a pass network and we introduce the concept of disruption network, building a multilayer structure. We perform both a global and a micro-level analysis on game sequences. When deploying our dynamic graph analysis framework on data from 18 rugby matches, we discover that structural features that make networks resilient to disruptions are a good predictor of a team's performance, both at the global and at the local level. Using our features, we are able to predict the outcome of the match with a precision comparable to state of the art bookmaking.
URL: http://ieeexplore.ieee.org/abstract/document/7752377/?reload=true
DOI: 10.1109/ASONAM.2016.7752377
Subject Data mining
Network science
Sports analytics
H.2.8 DATABASE MANAGEMENT. Database Applications

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