Abstract
This paper is about predicting the outcome of tennis matches of the Association of Tennis Professionals (ATP) and the Women’s Tennis Association (WTA) using both data and judgments. There are many factors that influence that outcome. An important question is which factors have significant influence on the outcome. We have identified numerous factors and systematically prioritized them subjectively and objectively, so as to improve the accuracy of the prediction. We then used them to predict the win-lose outcome of the 2015 US OPEN tennis matches (63 men and 31 women’s games) before they took place. The tennis match prediction in sports literature thus far reported an accuracy rate of 70%.The accuracy of our proposed model which combines data and judgment reaches 85.1%
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Armstrong JS, Green KC, Graefe A (2015). Golden rule of forecasting: be conservative. Journal of Business Research 68(8): 1717–1731.
Borghesi R (2007). Price biases in a prediction market: NFL contracts in tradesports. The Journal of Prediction Markets 1(3): 233–253.
Boulier BL., Stekler HO (1999). Are sports seedings good predictors? An evaluation. International Journal of Forecasting 15(1): 83–91.
Boulier BL, Stekler HO (2003). Predicting the outcomes of national football league games. International Journal of Forecasting 19(2): 257–270.
Chitnis A, Vaidya O (2014). Performance assessment of tennis players: application of DEA. Procedia - Social and Behavioral Sciences 133: 74–83.
Clarke S (2000). Using official ratings to simulatemajor tennis tournaments. International Transactions in Operational Research 7(6): 585–594.
Del Corral J, Prieto-Rodríguez J (2010). Are differences in ranks good predictors for Grand Slam tennis matches? International Journal of Forecasting 26(3): 551–563.
Easton SA, Uylangco K (2007). An examination of in-play sports betting using one-day cricket matches. The Journal of Prediction Markets 1(2): 93–109.
Easton S, Uylangco K (2010). Forecasting outcomes in tennis matches using within-match betting markets. International Journal of Forecasting 26(3): 564–575.
Forrest D, Simmons R (2000). Forecasting sport: the behaviour and performance of football tipsters. International Journal of Forecasting 16(3): 317–331.
Gil RGR, Levitt SD (2012). Testing the efficiency of markets in the 2002 World Cup. The Journal of Prediction Markets 1(3): 255–270.
Gu W, Saaty TL, Whitaker R (2016). Expert system for ice hockey game prediction: data mining with human judgment. International Journal of Information Technology & Decision Making 15(4): 763–789.
Gu W, Basu M, Chao Z, Wei L (2017). A unified Framework for credit evaluation for internet finance companies: multi-criteria analysis through AHP and DEA. International Journal of Information Technology & Decision Making 16(3): 597–624.
Gu W, Saaty TL, Wei L (2018). Evaluating and optimizing technological innovation efficiency of industrial enterprises based on both data and judgments. International Journal of Information Technology&Decision Making 17(9): 9–43.
Goddard J (2005). Regression models for forecasting goals and match results in association football. International Journal of Forecasting 21(2): 331–340.
Green KC, Armstrong JS (2015). Simple versus complex forecasting: The evidence. Journal of Business Research 68(8): 1678–1685.
Groll A, Abedieh J (2013). Spain retains its title and sets a new record ? Generalized linear mixed models on European football championships. Journal of Quantitative Analysis in Sports 9(1): 51–66.
Groll A, Schauberger G, Tutz G (2015). Brazil or Germany - who will win the trophy? Prediction of the FIFAWorld Cup 2014 based on team-specific regularized poisson regression. Journal of Quantitative Analysis in Sports 11(2): 97–115.
Joseph A, Fenton N. E, Neil M (2006). Predicting football results using Bayesian nets and other machine learning techniques. Knowledge-Based Systems 19(7), 544–553.
Klaassen FJGM, Magnus JR (2003). Forecasting the winner of a tennis match. European Journal of Operational Research 148(2): 257–267.
Knottenbelt WJ, Spanias D, Madurska AM (2012). A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers & Mathematics with Applications 64(12): 3820–3827.
Lebovic JH, Sigelman L (2001). The forecasting accuracy and determinants of football rankings. International Journal of Forecasting 17(1): 105–120.
Lessmann S, Sung M, Johnson JEV, Ma T (2012). A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction. European Journal of Operational Research 218(1): 163–174.
May JH, Shang J, Tjader YC, Vargas LG (2013). A new methodology for sensitivity and stability analysis of analytic network models. European Journal of Operational Research 224(1): 180–188.
McHale I, Morton A (2011). A Bradley-Terry type model for forecasting tennis match results. International Journal of Forecasting 27(2): 619–630.
Min B, Kim J, Choe C, Eom H, Bob McKay R. I (2008). A compound framework for sports results prediction: A football case study. Knowledge-Based Systems 21(7): 551–562.
Reid M, Crespo M, Lay B, Berry J (2007). Skill acquisition in tennis: research and current practice. Journal of Science and Medicine in Sport 10(1): 1–10.
Reid M, Schneiker K (2008). Strength and conditioning in tennis: current research and practice. Journal of Science and Medicine in Sport 11(3): 248–256.
Saaty T.L (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
Saaty T.L (1982). Decision making for leaders: the analytical hierarchy process for decisions in a complex world. European Journal of Operational Research 42(1): 107–109.
Saaty T. L. (1996). Decision Making with Dependence and Feedback: The Analytic Net-work Process. Pittsburgh RWS Publication. 200l.
Saaty T.L (2005). The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. International Series in Operations Research & Management Science 78: 345–405.
Saaty T.L (2013). The modern science of multicriteria decision making and its practical applications: the ahp/anp approach. Operations Research 61(5): 1101–1118.
Saaty T.L, Shang JS (2011). An innovative orders-ofmagnitude approach to ahp-based mutli-criteria decision making: prioritizing divergent intangible humane acts. European Journal of Operational Research 214(3): 703–715.
Shang J, Ergu D (2016). New concepts and applications of ahp and anp: a tribute to professor Tom Saaty on his 90th birthday. International Journal of Information Technology & Decision Making 15(4):729–731.
Scheibehenne B., Bröder A (2007). Predicting Wimbledon 2005 tennis results by mere player name recognition. International Journal of Forecasting 23(3): 415–426.
Song C, Boulier BL, Stekler HO(2007). The comparative accuracy of judgmental and model forecasts of American football games. International Journal of Forecasting 23(3): 405–413.
Tjader Y, May JH, Shang J, Vargas LG, Gao N (2014). Firm-level outsourcing decision making: A balanced scorecard-based analytic network process model. International Journal of Production Economics 147: 614–623.
Leitner C, Zeileis A, Hornik K (2010). Forecasting sports tournaments by ratings of probabilities: A comparison for the EURO 2008. International Journal of Forecasting 26(3): 471–481.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant Number 71702009, 71531013, 71729001) and Fundamental Research Funds for the Central Universities (FRF-BR-16-005A). Also, the authors sincerely thank the referees for their much practical help to improve the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Wei Gu is an associate professor in the Donlinks School of Economics and Management, University of Science and Technology Beijing. He received his PhD (2009) from University of Science and Technology Beijing. His research interests include decision making, marketing and big data.
Thomas L. Saaty is an distinguished professor in the University of Pittsburgh. He has made contributions in the fields of operations research (parametric linear programming, epidemics and the spread of biological agents, queuing theory, and behavioral mathematics as it relates to operations), arms control and disarmament, and urban design. He has written more than 35 books and 350 papers on mathematics, operations research, and decision making.
Rights and permissions
About this article
Cite this article
Gu, W., Saaty, T.L. Predicting the Outcome of a Tennis Tournament: Based on Both Data and Judgments. J. Syst. Sci. Syst. Eng. 28, 317–343 (2019). https://doi.org/10.1007/s11518-018-5395-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11518-018-5395-3