4.3 Article

Forecasting Tennis Match Results Using the Bradley-Terry Model

Journal

INTERNATIONAL JOURNAL OF PHOTOENERGY
Volume 2022, Issue -, Pages -

Publisher

HINDAWI LTD
DOI: 10.1155/2022/1898132

Keywords

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Funding

  1. Deanship of Scientific Research (DSR), King AbdulAziz University, Jeddah [G: 358-247-1442]
  2. DSR

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Forecasting plays a crucial role in various fields, including decision-making in management, weather forecasting, and betting market activities such as tennis forecasting. The Bradley-Terry model is utilized to predict match outcomes in men's professional tennis matches, showing promising results compared to ATP rankings.
Forecasting has been playing an important role in different fields of life, i.e., in decision-making activities of management, to predict uncertain events within an organization, in weather forecasting, in flood forecasting, etc. Stakeholders involved in betting market take advantage of tennis forecasting directly or indirectly. Winning probability calculated using forecasting models helps the bettors in deciding whether to place a bet or not. Keeping in view the importance of tennis forecasting, the Bradley-Terry model is used to model men's professional tennis for predicting match outcomes in tennis matches of men's singles. Model coefficients are estimated using data from January 2019 to September 2020 of 3439 matches. Ratings for each player are calculated using model coefficients. Player rankings are then calculated using these ratings. Comparison of model rankings with ATP rankings has shown satisfactory results. Winning probability for each player is calculated using model coefficients and ratings. These probability predictions are evaluated against four measures of performance. The results reveal that surface on which a game is played on contributes significantly towards a player's performance. Due to this impact of the surface, our model has produced superior player rankings for certain players who had been ranked very low in official ATP rankings. According to most of the performance measures, the model has shown good results for clay court data which are closely followed by hard court data. To calculate return on investment, model results are compared with the bookmakers' average odds and best available odds. It has been found that return on investment for a fitted model is highest in the case of clay court data in comparison to bookmaker's average odds and best odds.

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