Forecasting Tennis Match Results Using the Bradley-Terry Model

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 forec...

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Bibliographic Details
Published in:International journal of photoenergy 2022-03, Vol.2022, p.1-12
Main Authors: Fayomi, Aisha, Majeed, Rizwana, Algarni, Ali, Akhtar, Sohail, Jamal, Farrukh, Nasir, Jamal Abdul
Format: Article
Language:English
Subjects:
Clay
Coefficients
Decision making
Economic forecasting
Flood forecasting
Flood predictions
Forecasting
Gambling
Mathematical models
Probability
Ratings
Ratings & rankings
Return on investment
Statistical analysis
Tennis
Tournaments & championships
Weather forecasting
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Summary: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.
ISSN:1110-662X
1687-529X
DOI:10.1155/2022/1898132