PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A combinatorial approach in predicting the outcome of tennis matches

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets: pre-match betting, which is conducted before the match starts, and live betting, which allows placing bets during the sporting event. Betting systems rely on setting sports odds, something historically done by domain experts. Setting odds for live betting represents a challenge due to the need to follow events in real-time and react accordingly. In tennis, hierarchical models often stand out as a popular choice when trying to predict the outcome of the match. These models commonly leverage a recursive approach that aims to predict the winner or the final score starting at any point in the match. However, recursive expressions inherently contain computational complexity which hinders the efficiency of methods relying on them. This paper proposes a more resource-effective alternative in the form of a combinatorial approach based on a binomial distribution. The resulting accuracy of the combinatorial approach is identical to that of the recursive approach while being vastly more efficient when considering the execution time, making it a superior choice for live betting in this domain.
Rocznik
Strony
525--538
Opis fizyczny
Bibliogr. 55 poz., rys., tab.
Twórcy
  • Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
  • Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
autor
  • Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
Bibliografia
  • [1] Albright, S.C. (1993). A statistical analysis of hitting streaks in baseball, Journal of the American Statistical Association 88(424): 1175–1183.
  • [2] Attali, Y. (2013). Perceived hotness affects behavior of basketball players and coaches, Psychological Science 24(7): 1151–1156.
  • [3] Baker, R.D. and McHale, I.G. (2014). A dynamic paired comparisons model: Who is the greatest tennis player?, European Journal of Operational Research 236(2): 677–684.
  • [4] Baker, R.D. and McHale, I.G. (2017). An empirical Bayes model for time-varying paired comparisons ratings: Who is the greatest women’s tennis player?, European Journal of Operational Research 258(1): 328–333.
  • [5] Ballı, S. and Korukoğlu, S. (2014). Development of a fuzzy decision support framework for complex multi-attribute decision problems: A case study for the selection of skilful asketball players, Expert Systems 31(1): 56–69.
  • [6] Barnett, T. and Brown, A. (2012). The Mathematics of Tennis, http://www.strategicgames.com.au/.
  • [7] Barnett, T., Brown, A. and Clarke, S. (2006). Developing a model that reflects outcomes of tennis matches, Proceedings of the 8th Australasian Conference on Mathematics and Computers in Sport, Coolangatta, Australia, pp. 3–5.
  • [8] Barnett, T. and Clarke, S.R. (2005). Combining player statistics to predict outcomes of tennis matches, IMA Journal of Management Mathematics 16(2): 113–120.
  • [9] Barnett, T.J. and Clarke, S.R. (2002). Using Microsoft Excel to model a tennis match, 6th Conference on Mathematics and Computers in Sport, Queensland, Australia, pp. 63–68.
  • [10] Boulier, B.L. and Stekler, H.O. (1999). Are sports seedings good predictors? An evaluation, International Journal of Forecasting 15(1): 83–91.
  • [11] Boulier, B.L. and Stekler, H.O. (2003). Predicting the outcomes of national football league games, International Journal of Forecasting 19(2): 257–270.
  • [12] Bradley, R.A. and Terry, M.E. (1952). Rank analysis of incomplete block designs. I: The method of paired comparisons, Biometrika 39(3/4): 324–345.
  • [13] Carbone, J., Corke, T. and Moisiadis, F. (2016). The rugby league prediction model: Using an Elo-based approach to predict the outcome of National Rugby League (NRL) matches, International Educational Scientific Research Journal 2(5): 26–30.
  • [14] Carrari, A., Ferrante, M. and Fonseca, G. (2017). A new Markovian model for tennis matches, Electronic Journal of Applied Statistical Analysis 10(3): 693–711.
  • [15] Chang, J.C. (2019). Predictive Bayesian selection of multistep Markov chains, applied to the detection of the hot hand and other statistical dependencies in free throws, Royal Society Open Science 6(3): 182174.
  • [16] Clarke, S.R. and Dyte, D. (2000). Using official ratings to simulate major tennis tournaments, International Transactions in Operational Research 7(6): 585–594.
  • [17] Croucher, J.S. (1986). The conditional probability of winning games of tennis, Research Quarterly for Exercise and Sport 57(1): 23–26.
  • [18] Dadelo, S., Turskis, Z., Zavadskas, E.K. and Dadeliene, R. (2014). Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set, Expert Systems with Applications 41(14): 6106–6113.
  • [19] Dangauthier, P., Herbrich, R., Minka, T. and Graepel, T. (2007). Trueskill through time: Revisiting the history of chess, Advances in Neural Information Processing Systems 20: 337–344.
  • [20] Dietl, H. and Nesseler, C. (2017). Momentum in tennis: Controlling the match, UZH Business Working Paper Series, University of Zurich, Zurich.
  • [21] EGBA (2020). EU Market: Gambling is becoming more and more an online activity, https://www.egba.eu/eu-market/.
  • [22] Elo, A.E. (1978). The Rating of Chessplayers, Past and Present, Arco Pub., New York.
  • [23] Gilovich, T., Vallone, R. and Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences, Cognitive Psychology 17(3): 295–314.
  • [24] Glickman, M.E. (1999). Parameter estimation in large dynamic paired comparison experiments, Journal of the Royal Statistical Society: Series C (Applied Statistics) 48(3): 377–394.
  • [25] Glickman, M.E. (2001). Dynamic paired comparison models with stochastic variances, Journal of Applied Statistics 28(6): 673–689.
  • [26] Green, B. and Zwiebel, J. (2017). The hot-hand fallacy: Cognitive mistakes or equilibrium adjustments? Evidence from major league baseball, Management Science 64(11): 5315–5348.
  • [27] Hvattum, L.M. and Arntzen, H. (2010). Using Elo ratings for match result prediction in association football, International Journal of Forecasting 26(3): 460–470.
  • [28] Iso-Ahola, S.E. and Mobily, K. (1980). Psychological momentum: A phenomenon and an empirical (unobtrusive) validation of its influence in a competitive sport tournament, Psychological Reports 46(2): 391–401.
  • [29] Keller, J.B. (1984). Probability of a shutout in racquetball, SIAM Review 26(2): 267–268.
  • [30] Klaassen, F.J. and Magnus, J.R. (2001). Are points in tennis independent and identically distributed? Evidence from a dynamic binary panel data model, Journal of the American Statistical Association 96(454): 500–509.
  • [31] Klaassen, F.J. and Magnus, J.R. (2003). Forecasting the winner of a tennis match, European Journal of Operational Research 148(2): 257–267.
  • [32] Knottenbelt, W.J., Spanias, D. and Madurska, A.M. (2012). A common-opponent stochastic model for predicting the outcome of professional tennis matches, Computers & Mathematics with Applications 64(12): 3820–3827.
  • [33] Kovalchik, S.A. (2016). Searching for the goat of tennis win prediction, Journal of Quantitative Analysis in Sports 12(3): 127–138.
  • [34] Kovalchik, S. and Reid, M. (2019). A calibration method with dynamic updates for within-match forecasting of wins in tennis, International Journal of Forecasting 35(2): 756–766.
  • [35] Lebovic, J.H. and Sigelman, L. (2001). The forecasting accuracy and determinants of football rankings, International Journal of Forecasting 17(1): 105–120.
  • [36] Leitner, C., Zeileis, A. and Hornik, K. (2010). Forecasting sports tournaments by ratings of (prob) abilities: A comparison for the Euro 2008, International Journal of Forecasting 26(3): 471–481.
  • [37] Liu, Y. (2001). Random walks in tennis, Missouri Journal of Mathematical Sciences 13(3): 1–9.
  • [38] Martin, D.E. (2006). A recursive algorithm for computing the distribution of the number of successes in higher-order Markovian trials, Computational Statistics & Data Analysis 50(3): 604–610.
  • [39] McHale, I. and Morton, A. (2011). A Bradley–Terry type model for forecasting tennis match results, International Journal of Forecasting 27(2): 619–630.
  • [40] Morris, B., Bialik, C. and Boice, J. (2016). How we’re forecasting the 2016 US Open, https://fivethirtyeight.com/features/how-were-forecasting-the-2016-us-open/.
  • [41] Newton, P.K. and Aslam, K. (2009). Monte Carlo tennis: A stochastic Markov chain model, Journal of Quantitative Analysis in Sports 5(3): 1–44.
  • [42] Newton, P.K. and Keller, J.B. (2005). Probability of winning at tennis. I: Theory and data, Studies in Applied Mathematics 114(3): 241–269.
  • [43] O’Malley, A.J. (2008). Probability formulas and statistical analysis in tennis, Journal of Quantitative Analysis in Sports 4(2): 1–23.
  • [44] Percy, D.F. (2015). Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes, Journal of the Operational Research Society 66(11): 1840–1849.
  • [45] Pollard, G. (1983). An analysis of classical and tie-breaker tennis, Australian Journal of Statistics 25(3): 496–505.
  • [46] Radicchi, F. (2011). Who is the best player ever? A complex network analysis of the history of professional tennis, PloS ONE 6(2): e17249.
  • [47] Renick, J. (1976). Optimal strategy at decision points in singles squash, Research Quarterly. American Alliance for Health, Physical Education and Recreation 47(3): 562–568.
  • [48] Ryall, R. and Bedford, A. (2010). An optimized ratings-based model for forecasting Australian rules football, International Journal of Forecasting 26(3): 511–517.
  • [49] Šarčević, A., Pintar, D., Vranić, M. and Gojsalić, A. (2021). Modeling in-match sports dynamics using the evolving probability method, Applied Sciences 11(10): 4429.
  • [50] Schutz, R.W. (1970). A mathematical model for evaluating scoring systems with specific reference to tennis, Research Quarterly: American Association for Health, Physical Education and Recreation 41(4): 552–561.
  • [51] Silver, N. and Fischer-Baum, R. (2015). How we calculate NBA Elo ratings, https://fivethirtyeight.com/features/how-we-calculate-nba-elo-rating s/.
  • [52] Spanias, D. and Knottenbelt, W. J. (2013). Predicting the outcomes of tennis matches using a low-level point model, IMA Journal of Management Mathematics 24(3): 311–320.
  • [53] Tversky, A. and Gilovich, T. (1989). The cold facts about the “hot hand” in basketball, Chance 2(1): 16–21.
  • [54] Wetzels, R., Tutschkow, D., Dolan, C., Van der Sluis, S., Dutilh, G. and Wagenmakers, E.-J. (2016). A Bayesian test for the hot hand phenomenon, Journal of Mathematical Psychology 72: 200–209.
  • [55] Wozniak, J. (2011). Inferring tennis match progress from in-play betting odds, Project report, Imperial College London, London.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b843585d-5537-464d-8e3d-a2f8e2418fe2
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.