The Haka network: Evaluating rugby team performance with dynamic graph analysis

Paolo Cintia, Michele Coscia, Luca Pappalardo

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Résumé

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.

langue originaleAnglais
titreProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditeurInstitute of Electrical and Electronics Engineers Inc.
Pages1095-1102
Nombre de pages8
ISBN (Electronique)9781509028467
Les DOIs
Etat de la publicationPublié - 21 nov. 2016
Evénement2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, États-Unis
Durée: 18 août 201621 août 2016

Une conférence

Une conférence2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Pays/TerritoireÉtats-Unis
La villeSan Francisco
période18/08/1621/08/16

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