How well do Elo-based ratings predict professional tennis matches?
-
Leighton Vaughan Williams
,
Chunping Liu
Abstract
This paper examines the performance of five different measures for forecasting men’s and women’s professional tennis matches. We use data derived from every match played at the 2018 and 2019 Wimbledon tennis championships, the 2019 French Open, the 2019 US Open, and the 2020 Australian Open. We look at the betting odds, the official tennis rankings, the standard Elo ratings, surface-specific Elo ratings, and weighted composites of these ratings, including and excluding the betting odds. The performance indicators used are prediction accuracy, calibration, model discrimination, Brier score, and expected return. We find that the betting odds perform relatively well across these tournaments, while standard Elo (especially for women’s tennis) and surface-adjusted Elo (especially for men’s tennis) also perform well on a range of indicators. For all but the hard-court surfaces, a forecasting model which incorporates the betting odds tends also to perform well on some indicators. We find that the official ranking system proved to be a relatively poor measure of likely performance compared to betting odds and Elo-related methods. Our results add weight to the case for a wider use of Elo-based approaches within sports forecasting, as well as arguably within the player rankings methodologies.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
Angelini, G., and L. De Angelis. 2019. “Efficiency of Online Football Betting Markets.” International Journal of Forecasting 35 (2): 712–21, https://doi.org/10.1016/j.ijforecast.2018.07.008.Search in Google Scholar
Asch, P., B. G. Malkiel, and R. E. Quandt. 1984. “Market Efficiency in Racetrack Betting.” Journal of Business 57: 165–75, https://doi.org/10.1086/296257.Search in Google Scholar
ATP (Association of Tennis Professionals). ATP rankings. www.apttour.com/en/rankings/singles/ (accessed February 2, 2020).Search in Google Scholar
Bolton, R. N., and R. G. Chapman. 1986. “Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races.” Management Science 32 (8): 1040–60, https://doi.org/10.1287/mnsc.32.8.1040.Search in Google Scholar
Brier, G. W. 1950. “Verification of Forecasts Expressed in Terms of Probability.” Monthly Weather Review 78 (1): 1–3https://doi.org/10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2.10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2Search in Google Scholar
Carbone, J., T. Corke, and F. Moisiadis. 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 27 (9): 26–30.Search in Google Scholar
Clarke, S., S. Kovalchik, and M. Ingram. 2017. “Adjusting Bookmaker’s Odds to Allow for Overround.” American Journal of Sports Science 5 (6): 45, https://doi.org/10.11648/j.ajss.20170506.12.Search in Google Scholar
Dingle, N., W. Knottenbelt, and D. Spanias. 2012. “On the (Page) Ranking of Professional Tennis Players.” In European Workshop on Performance Engineering, 237–47. Berlin, Heidelberg: Springer.10.1007/978-3-642-36781-6_17Search in Google Scholar
Easton, S., and K. Uylangco. 2010. “Forecasting Outcomes in Tennis Matches Using Within-Match Betting Markets.” International Journal of Forecasting 26 (3): 564–75, https://doi.org/10.1016/j.ijforecast.2009.10.004.Search in Google Scholar
Elo, A. E. 1978. The Rating of Chessplayers, Past and Present. New York: Arco Pub.Search in Google Scholar
Flashscore. 2018. https://www.flashscore.com/tennis/ (accessed July 15, 2018).Search in Google Scholar
Flashscore. 2019. https://www.flashscore.com/tennis/ (accessed October 15, 2019).Search in Google Scholar
Flashscore. 2020. https://www.flashscore.com/tennis/ (accessed May 28, 2020).Search in Google Scholar
Graham, I., and H. Stott. 2008. “Predicting Bookmaker Odds and Efficiency for UK Football.” Applied Economics 40 (1): 99–109, https://doi.org/10.1080/00036840701728799.Search in Google Scholar
Hvattum, L. M., and H. Arntzen. 2010. “Using Elo Ratings for Match Result Prediction in Association Football.” International Journal of Forecasting 26 (3): 460–70, https://doi.org/10.1016/j.ijforecast.2009.10.002.Search in Google Scholar
Irons, D. J., S. Buckley, and T. Paulden. 2014. “Developing an Improved Tennis Ranking System.” Journal of Quantitative Analysis in Sports 10 (2): 109–18, https://doi.org/10.1515/jqas-2013-0101.Search in Google Scholar
Kovalchik, S. A. 2016. “Searching for the GOAT of Tennis Win Prediction.” Journal of Quantitative Analysis in Sports 12 (3): 127–38, https://doi.org/10.1515/jqas-2015-0059.Search in Google Scholar
Kovalchik, S. A., and M. Reid. 2019. “A Calibration Method with Dynamic Updates for Within-Match Forecasting of Wins in Tennis.” International Journal of Forecasting 35 (2): 756–66, https://doi.org/10.1016/j.ijforecast.2017.11.008.Search in Google Scholar
Leitner, C., A. Zeileis, and K. Hornik. 2009. “Is Federer Stronger in a Tournament without Nadal? An Evaluation of Odds and Seedings for Wimbledon 2009.” Austrian Journal of Statistics 38 (4): 277–86.10.17713/ajs.v38i4.280Search in Google Scholar
Leitner, C., A. Zeileis, and K. Hornik. 2010. “Forecasting Sports Tournaments by Ratings of (Prob)abilities: A Comparison for EURO 2008.” International Journal of Forecasting 26 (3): 471–81, https://doi.org/10.1016/j.ijforecast.2009.10.001.Search in Google Scholar
Mangaroska, K., B. Vesin, and M. Giannakos. 2019. Elo-Rating Method: Towards Adaptive Assessment in E-Learning, 1–3. Los Alamitos: IEEE.10.1109/ICALT.2019.00116Search in Google Scholar
Morris, B., C. Bialik, and J. Boice. 2016. How we’re Forecasting the 2016 U.S. Open. FiveThirtEight.Com. August 28. https://fivethirtyeight.com/features/how-were-forecasting-the-2016-us-open/ (accessed October 15, 2019).Search in Google Scholar
Oddschecker. 2018. www.oddschecker.com (accessed July 15, 2018).Search in Google Scholar
Oddschecker. 2019. www.oddschecker.com (accessed September 8, 2019).Search in Google Scholar
Oddschecker. 2020. www.oddschecker.com (accessed February 2, 2020).Search in Google Scholar
Pencina, M. J., R. B. D’Agostino, and R. S. Vasan. 2008. “Evaluating the Added Predictive Ability of a New Marker: From Area under the Roc Curve to Reclassification and beyond.” Statistics in Medicine 27 (2): 157–72, https://doi.org/10.1002/sim.2929.Search in Google Scholar
Pope, P. F., and D. A. Peel. 1989. “Information, Prices and Efficiency in a Fixed-Odds Betting Market.” Economica: 323–34, https://doi.org/10.2307/2554281.Search in Google Scholar
Reade, J. J., C. Singleton, and L. Vaughan Williams. 2020. “Betting Markets for English Premier League Results and Scorelines: Evaluating a Forecasting Model.” Economic Issues 25 (1): 85–106.Search in Google Scholar
Reid, M., D. McMurtrie, and M. Crespo. 2010. “The Relationship between Match Statistics and Top 100 Ranking in Professional Men’s Tennis.” International Journal of Performance Analysis in Sport 10 (2): 131–8, https://doi.org/10.1080/24748668.2010.11868509.Search in Google Scholar
Ryall, R., and A. Bedford. 2010. “An Optimized Ratings-Based Model for Forecasting Australian Rules Football.” International Journal of Forecasting 26 (3): 511–7, https://doi.org/10.1016/j.ijforecast.2010.01.001.Search in Google Scholar
Silver, N., and R. Fischer-Baum. 2015. How We Calculate NBA Elo ratings. FiveThirtyEight.Com. May 21. Also available at https://fivethirtyeight.com/features/how-we-calculate-nba-elo-ratings/.Search in Google Scholar
Smith, A. 1993. “Estimating Nonlinear Time-series Models Using Simulated Vector Autoregressions.” Journal of Applied Econometrics 8 (S1): 63–84, doi:https://doi.org/10.1002/jae.3950080506.Search in Google Scholar
Snyder, W. W. 1978. “Horse Racing: Testing the Efficient Markets Model.” The Journal of Finance 33: 1109–18, https://doi.org/10.1111/j.1540-6261.1978.tb02051.x.Search in Google Scholar
Sonas, F. 2002. The Sonas Rating Formula: Better Than Elo? Chessbase News. https://chessbase.com/newsdetail.asp?newsid=562Last (accessed May 30, 2020).Search in Google Scholar
Spann, M., and B. Skiera. 2009. “Sports Forecasting: A Comparison of the Forecast Accuracy of Prediction Markets, Betting Odds and Tipsters.” Journal of Forecasting 28 (1): 55–72, https://doi.org/10.1002/for.1091.Search in Google Scholar
Stekler, H., D. Sendor, and R. Verlander. 2010. “Issues in Sports Forecasting.” International Journal of Forecasting 26 (3): 606–21, https://doi.org/10.1016/j.ijforecast.2010.01.003.Search in Google Scholar
Tennis Abstract. 2018–2020a. Current Elo Ratings for the ATP Tour. www.tennisabstract.com/reports/atp_elo_ratings.html (accessed February 2, 2020).Search in Google Scholar
Tennis Abstract. 2018–2020b. Current Elo Ratings for the WTA Tour. www.tennisabstract.com/reports/wta_elo_ratings.html (accessed February 2, 2020).Search in Google Scholar
Tetlock, P. E., and D. Gardner. 2015. Superforecasting: The Art and Science of Prediction. Crown Publishers.Search in Google Scholar
Vaughan Williams, L., and H. Stekler. 2010. “Sports Forecasting.” International Journal of Forecasting 26 (3): 445–7, https://doi.org/10.1016/j.ijforecast.2009.12.005.Search in Google Scholar
Vaughan Williams, L. 2005. Information Efficiency in Financial and Betting Markets. Cambridge: Cambridge University Press.10.1017/CBO9780511493614Search in Google Scholar
Vergin, R. C., and M. Scriabin. 1978. “Winning Strategies for Wagering on National Football League Games.” Management Science 24 (8): 809–18, https://doi.org/10.1287/mnsc.24.8.809.Search in Google Scholar
Walkofmind. 2019. Elo Rating vs. Winning Probabilities. https://www.walkofmind.com/prgramming/chess/elo.htm (accessed October 15, 2019).Search in Google Scholar
WTA (Women’s Tennis Association). Rankings.www.wtatennis.com/rankings (accessed on February 2, 2020).Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research articles
- A Bayesian marked spatial point processes model for basketball shot chart
- How well do Elo-based ratings predict professional tennis matches?
- Algorithmically deconstructing shot locations as a method for shot quality in hockey
- An iterative Markov rating method
- TRAP: a predictive framework for the Assessment of Performance in Trail Running
- The influence of field size, goal size and number of players on the average number of goals scored per game in variants of football and hockey: the Pi-theorem applied to team sports
Articles in the same Issue
- Frontmatter
- Research articles
- A Bayesian marked spatial point processes model for basketball shot chart
- How well do Elo-based ratings predict professional tennis matches?
- Algorithmically deconstructing shot locations as a method for shot quality in hockey
- An iterative Markov rating method
- TRAP: a predictive framework for the Assessment of Performance in Trail Running
- The influence of field size, goal size and number of players on the average number of goals scored per game in variants of football and hockey: the Pi-theorem applied to team sports
