Volume 2, Issue 3, September 2017, Page: 86-98
Using Soft Computing Techniques for Prediction of Winners in Tennis Matches
Mateus de Araujo Fernandes, Federal Institute of Education, Science and Technology in Sergipe, Aracaju/SE, Brazil
Received: Feb. 24, 2017;       Accepted: Mar. 20, 2017;       Published: Apr. 10, 2017
DOI: 10.11648/j.mlr.20170203.12      View  1787      Downloads  78
Abstract
The forecast of winners in sports brings valuable information for both organizers, media and audience, and this is particularly important in tennis, where the results of a round in a tournament determine which matches will occur in the next round. With that in mind, this work presents a study of the main factors influencing matches predictability and, from this analysis, a new hybrid approach is proposed to calculate the chances of victory of each of the competitors before the start of a match. A Fuzzy Inference System, with its ability to reproduce knowledge of an expert among mixed information, a Neural Network, with the capability of features extraction from examples, and a Strength Equation with optimized weighting factors are the techniques employed. These predictors have as inputs data from previous performances of the players, which in this case try to capture their short, medium and long-term performances, as well as their affinity for the different types of surfaces. Subsequently the results from these predictors are combined by a voting system. The results are encouraging, showing significant gains when comparing to the use of the ATP ranking.
Keywords
Artificial Intelligence, Forecast, Soft Computing
To cite this article
Mateus de Araujo Fernandes, Using Soft Computing Techniques for Prediction of Winners in Tennis Matches, Machine Learning Research. Vol. 2, No. 3, 2017, pp. 86-98. doi: 10.11648/j.mlr.20170203.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
ATP. Official site of men’s professional tennis. 2015. Available online at: . Last accessed: November 1, 2015.
[2]
ITF. International tennis federation. 2015. Available online at: . Last accessed: November 1, 2015.
[3]
FORBES. The world's highest-paid athletes. 2015. Available online at: . Last accessed: November 1, 2015.
[4]
GONZÁLEZ-DÍAZ, J.; GOSSNERB, O.; ROGERS, B. W. Performing best when it matters most: Evidence from professional tennis. Journal of Economic Behavior & Organization, n. 84, p. 767– 781, 2012. ISSN 0167-2681.
[5]
FERRAUTI, A. et al. Diagnostic of footwork characteristics and running speed demands in tennis on different ground surfaces. Sport Orthopädie Traumatologie, n. 29, p. 172–179, 2013. Available online at: . Last accessed: November 1, 2015.
[6]
KLAASEN, F.; MAGNUS, J. R. Forecasting the winner of a tennis match. European Journal of Operational Research, n. 148, p. 257–267, 2003. ISSN 0377-2217.
[7]
MCHALE, I.; MORTON, A. A Bradley-Terry type model for forecasting tennis match results. International Journal of Forecasting, n. 27, p. 619–630, 2011. ISSN 0169-2070.
[8]
CLOWES, S.; COHEN, G.; TOMLJANOVIC, L. Dynamic evaluation of conditional probabilities of winning a tennis match. In: AUSTRALIAN CONFERENCE ON MATHEMATICS AND COMPUTERS IN SPORT, 6. Proceedings… Gold Coast, Australia: 6M&CS, 2002. Available online at: . Last accessed: November 1, 2015.
[9]
KNOTTENBELT, W. J.; SPANIAS, D.; MADURSKA, A. M. A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers and Mathematics with Applications, n. 64, p. 3820–3827, 2012. ISSN 0898-1221. Available online at: . Last accessed: November 1, 2015.
[10]
CLARKE, S. R.; DYTE, D. Using official ratings to simulate major tennis tournaments. International Transactions in Operational Research, n. 7, p. 585–594, 2000. ISSN 1475-3995.
[11]
KLAASSEN, F.; MAGNUS, J. Are points in tennis independent and identically distributed? Evidence from a dynamic binary panel data model. Journal of the American Statistical Association, n. 96, p. 500–509, 2001.
[12]
DEL CORRAL, J.; PRIETO-RODRIGUEZ, J. Are differences in ranks good predictors for Grand Slam tennis matches? International Journal of Forecasting, n. 26, p. 551–563, 2010. ISSN 0169-2070.
[13]
SCHEIBEHENNE, B.; BRODER, A. Predicting Wimbledon 2005 tennis results by mere player name recognition. International Journal of Forecasting, n. 23, p. 415–426, 2007. ISSN 0169-2070.
[14]
TENNIS DATA. Tennis results and betting odds data. 2015. Available online at: . Last accessed: November 1, 2015.
[15]
HOLDER, R. L.; NEVILL, A. M. Modelling performance at international tennis and golf tournaments: is there a home advantage? The Statistician, n. 46, p. 551–559, 1997.
[16]
BARNETT, T.; POLLARD, G. How the tennis court surface affects player performance and injuries. Medicine and Science in Tennis, n. 12, v. 1, p. 34-37, 2007. ISSN 1567-2352.
[17]
WEISSTEIN, E. W. Correlation Coefficient. 2015. Available online at: . Last accessed: November 1, 2015.
[18]
ZADEH, L. Fuzzy Sets. Information and Control, n. 8: p. 338-353, 1965. Available online at: . Last accessed: November 1, 2015.
[19]
JANG, J.-S.; SUN, C.-T.; MIZUTANI, E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ, USA: Prentice-Hall, 1997.
[20]
FERNANDES, M. A. Classificação de alvos utilizando atributos cinemáticos. Master’s Degree Dissertation, ITA, São José dos Campos, Brazil, 2009.
[21]
SUGENO, M. et al. (Ed.). Industrial Applications of Fuzzy Control. New York, NY, USA: Elsevier Science Pub. Co., 1985.
[22]
BRAGA, A. P.; CARVALHO, A.; LUDERMIR, T. Redes Neurais Artificiais – Teoria e Aplicações. Rio de Janeiro, RJ, Brazil: LTC, 2000.
[23]
CYBENKO, G. Approximation by superpositions of a sigmoidal function. Mathematics of Controls, Signals, and Systems, Springer Verlag, n. 2, p. 303-314, 1989.
[24]
HAYKIN, S. Neural Networks – A Comprehensive Foundation. Upper Saddle River, NJ, USA: Prentice Hall, 1998.
[25]
FERNANDES, M. A. Inteligência computacional aplicada à previsão de vencedores em partidas de tênis. Revista Brasileira de Computação Aplicada, v. 8, n. 2, p. 82–98, 2016. ISSN 2176-6649.
[26]
ARRUDA, M. L. Poisson, Bayes, Futebol e DeFinetti. Master’s Degree Dissertation, USP, São Paulo, Brazil, 2000.
[27]
LIMA, B. N. B. et al. Probabilidades no esporte. TRIM: revista de investigación multidisciplinar, Universidad de Valladolid, n. 5, p. 39-53, 2012. Available online at: . Last accessed: November 1, 2015.
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