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Characterizing patterns of scoring and ties in competitive sports

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  • Jeon, Gyuhyeon
  • Park, Juyong

Abstract

Sports audiences’ sense of excitement originates from a multitude of factors such as the uncertainty in the outcome of a game and the expectation of winning streaks. The uncertainty factor can be said to be maximized when the game is tied. At the same time, a tie represents the antipode to the ultimate goal-to win-of the contestants and the wishes of their loyal fans. A tie therefore encourages the contestants to continually adapt to the situations and strategize to break it, leading to an even more dynamic and engrossing gameplay. A key to understanding this phenomenon starts from the characteristic dynamics of ties and scoring events in sports games. Here we analyze the complete data from a full season of the National Basketball Association (NBA), the professional basketball league of the United States and Canada, to find the patterns of scoring and ties and how they correlate with the interactive nature of sports, and show how they differ from traditional simple random models based on cruder summary statistics that can show their insufficiencies on fine details of gameplay. Given the social and economic significance of such enterprises, these types of findings will prompt the much-needed developments in detailed modeling of sports based on actual data.

Suggested Citation

  • Jeon, Gyuhyeon & Park, Juyong, 2021. "Characterizing patterns of scoring and ties in competitive sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308426
    DOI: 10.1016/j.physa.2020.125544
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    References listed on IDEAS

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    1. Tao Chen & Qingliang Fan, 2018. "A functional data approach to model score difference process in professional basketball games," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 112-127, January.
    2. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, September.
    3. Jaime Sampaio & Manuel Janeira, 2003. "Statistical analyses of basketball team performance: understanding teams’ wins and losses according to a different index of ball possessions," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 3(1), pages 40-49, April.
    4. Alon Daks & Nishant Desai & Lisa R. Goldberg, 2018. "Do the Golden State Warriors Have Hot Hands?," The Mathematical Intelligencer, Springer, vol. 40(4), pages 1-5, December.
    5. Miguel Angel Gomez & Lorenzo Gasperi & Corrado Lupo, 2016. "Performance analysis of game dynamics during the 4 game quarter of NBA close games," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(1), pages 249-263, April.
    6. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    7. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    8. Martín-González, Juan Manuel & de Saá Guerra, Yves & García-Manso, Juan Manuel & Arriaza, Enrique & Valverde-Estévez, Teresa, 2016. "The Poisson model limits in NBA basketball: Complexity in team sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 182-190.
    9. Leonardo Lamas & Felipe Santana & Matthew Heiner & Carlos Ugrinowitsch & Gilbert Fellingham, 2015. "Modeling the Offensive-Defensive Interaction and Resulting Outcomes in Basketball," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-14, December.
    10. E. Bittner & A. Nußbaumer & W. Janke & M. Weigel, 2009. "Football fever: goal distributions and non-Gaussian statistics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 459-471, February.
    11. Greenhough, J & Birch, P.C & Chapman, S.C & Rowlands, G, 2002. "Football goal distributions and extremal statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 615-624.
    12. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
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