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A Machine Learning Approach to Analyze Home Advantage during COVID-19 Pandemic Period with Regards to Margin of Victory and to Different Tournaments in Professional Rugby Union Competitions

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  • Alexandru Nicolae Ungureanu

    (Department of Medical Science, University of Turin, 10143 Turin, Italy
    NeuroMuscularFunction|Research Group, School of Exercise & Sport Sciences, University of Turin, 10143 Turin, Italy)

  • Corrado Lupo

    (Department of Medical Science, University of Turin, 10143 Turin, Italy
    NeuroMuscularFunction|Research Group, School of Exercise & Sport Sciences, University of Turin, 10143 Turin, Italy)

  • Paolo Riccardo Brustio

    (NeuroMuscularFunction|Research Group, School of Exercise & Sport Sciences, University of Turin, 10143 Turin, Italy
    Department of Neuroscience, Biomedicine and Movement, University of Verona, 37131 Verona, Italy)

Abstract

Home advantage (HA) is the tendency for sporting teams to perform better at their home ground than away from home, it is also influenced by the crowd support, and its existence has been well established in a wide range of team sports including rugby union. Among all the HA determinants, the positive contribute of the crowd support on the game outcome can be analyzed in the unique pandemic situation of COVID-19. Therefore, the aim of the present study was to analyze the HA of professional high-level rugby club competition from a complex dynamical system perspective before and during the COVID-19 pandemic. HA was analyzed in northern and southern hemisphere rugby tournaments with (2013–2019) and without (2020/21) crowd support by the means of the exhaustive chi-square automatic interaction detection (CHAID) decision trees (DT). HA was mitigated by the crowd absence especially in closed games, although differences between tournaments emerged. Both for northern and southern hemisphere, the effect of playing without the crowd support had a negative impact on the home team advantage. These findings evidenced that in ghost games, where differences in the final score were less than a converted try (7 points), HA has disappeared.

Suggested Citation

  • Alexandru Nicolae Ungureanu & Corrado Lupo & Paolo Riccardo Brustio, 2021. "A Machine Learning Approach to Analyze Home Advantage during COVID-19 Pandemic Period with Regards to Margin of Victory and to Different Tournaments in Professional Rugby Union Competitions," IJERPH, MDPI, vol. 18(23), pages 1-8, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12711-:d:693427
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    References listed on IDEAS

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    Cited by:

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