Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior

Fernando Martins, Ricardo Gomes, Vasco Lopes, Frutuoso Silva and Rui Mendes
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Fernando Martins: Instituto Politécnico de Coimbra, ESEC, UNICID-ASSERT, 3030-329 Coimbra, Portugal
Ricardo Gomes: Instituto Politécnico de Coimbra, ESEC, UNICID-ASSERT, 3030-329 Coimbra, Portugal
Vasco Lopes: Department of Informatics, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
Frutuoso Silva: Instituto de Telecomunicações, Delegação da Covilhã, 6201-001 Covilhã, Portugal
Rui Mendes: Instituto Politécnico de Coimbra, ESEC, UNICID-ASSERT, 3030-329 Coimbra, Portugal

Mathematics, 2020, vol. 8, issue 9, 1-12

Abstract: Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game.

Keywords: social network analysis; entropy; Markov chain; football (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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