Influence of the viewers on the performance results of sports teams

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PhD, Associate Professor V.N. Yushkin
Volgograd State Agricultural University, Volgograd

Objective of the study was a theoretical substantiation and description of the calculation of the rating using numerical methods in team sports.
Methods and structure of the study. The formation of rating classifications in team sports was carried out using mathematical modeling using high-level programming languages ​​and numerical calculation methods. The requirements that must be met by the general targets, guidelines that form the rating of teams are determined: taking into account the results of previous performances, the factor of influence of one's field, the number of spectators at the stadium, the potential of teams. The mathematical model was evaluated by the indicator of the convergence of the current rating of the teams participating in the match with the actual result of the game. The analysis of the results of the performance of teams in the matches of the championships of Russia in 1992-2022 was carried out.
Results and conclusions. Three variants of calculation were performed: 1) calculation of a unified system of equations, taking into account the factor of influence of one's own field; 2) calculation with the calculation of the index of the coefficient of influence of spectators on the results of games; 3) calculation of the coefficients of influence of the home field factor and spectators on the results of the games. The system of linear equations has a unique solution if the results of the teams' performance do not have zero uncertainty during the entire period of the competition. The developed rating system is aimed at numerical confirmation of the level of readiness and potential of teams, the accuracy of predicting the performance of teams in the short and long term in all team sports.

Keywords: rating, system, viewers, classification, modeling, numerical method.

References

  1. Polozov A.A., Mikhryakov S.V., Naboichenko E.S., Bozhko E.M. Informatsionnaya model futbola na primere uchastiya sbornoy Rossii na CHM-2018 [Football data processing model in context of Russian national team participation in 2018 World Cup]. Teoriya i praktika fizicheskoy kultury. 2018. No. 1. pp. 75-77.
  2. Polozov A.A., Suvorova E.A., Melnikova A.V., Korelina A.V., Mikhryakov S.V. Prognozirovaniye rezultatov CHM-2018 na osnove novogo algoritma konsolidatsii dannykh [Forecasting of results of the 2018 World Cup on the basis of a new algorithm of consolidation of data]. Uchenye zapiski universiteta imeni P.F. Lesgafta. 2018, No. 4. pp. 263-269.
  3. Yushkin V.N. Mathematical model for determining the rating in a linear formulation [Linear mathematical model to rate influence of home field factor in football]. Teoriya i praktika fizicheskoy kultury. 2022. No. 3. pp. 14-16.
  4. Yushkin V.N. Sistema opredeleniya reytinga [The Ranking System], Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya: Gumanitarnye nauki. 2020. No. 1. pp. 122-126.
  5. Yushkin V.N. Tsifrovaya model reytingovoy otsenki sorevnovatelnoy deyatelnosti [Digital model of rating evaluation of competitive activity]. Uchenye zapiski universiteta imeni P.F. Lesgafta. 2021. No. 10. pp. 428-431.
  6. Boudreaux C.J., Sanders S.D., Walia B. A natural experiment to determine the crowd effect upon home court advantage, Journal of Sports Economics, 2015. Vol. 18. No. 7. pp. 737-749.
  7. Couceiro M.S., Clemente F., Martins F., Machado J. Dynamical stability and predictability of football players: The study of one match, Entropy. Vol. 16. No. 2, 2014. pp. 645-674.
  8. Goumas Chris. Modelling home advantage for individual teams in UEFA Champions League football. Journal of Sport and Health Science, 2017. Vol. 6. No. 3. рp. 321-326.
  9. Karminsky A., Polozov A.A. Handbook of Ratings. Approaches to Ratings in the Economy, Sports, and Society, Springer International Publishing house. London, 2016. 360 p.
  10. Mangan S., Collins K. A rating system for gaelic football teams: factors that influence success. International Journal of Computer Science in Sport, 2016. Vol. 15. Issue 2. pp. 78-90.
  11. Pollard R., Gómez M.A. Components of home advantage in 157 national soccer leagues worldwide. International Journal of Sport and Exercise Psychology, 2014. Vol. 12. No. 3. pp. 218-233.
  12. Principe V., Gavião L.O., Henriques R., Lobo V., Alves Lima G.B., Sant’anna A.P. Multicriteria analysis of football match perfomances: Composition of probabilistic preferences applied to the English premier league 2015/2016 // Pesquisa Operacional, 2017. Vol. 37. No. 2. pp. 333-363.