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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/1898132 |
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| author | Aisha Fayomi Rizwana Majeed Ali Algarni Sohail Akhtar Farrukh Jamal Jamal Abdul Nasir |
| author_facet | Aisha Fayomi Rizwana Majeed Ali Algarni Sohail Akhtar Farrukh Jamal Jamal Abdul Nasir |
| author_sort | Aisha Fayomi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0737093d6d984a27bee36eb7d7c924cc |
| institution | OA Journals |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-0737093d6d984a27bee36eb7d7c924cc2025-08-20T02:02:51ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/1898132Forecasting Tennis Match Results Using the Bradley-Terry ModelAisha Fayomi0Rizwana Majeed1Ali Algarni2Sohail Akhtar3Farrukh Jamal4Jamal Abdul Nasir5Faculty of Science Department of StatisticsDepartment of StatisticsFaculty of Science Department of StatisticsDepartment of Mathematics and StatisticsDepartment of StatisticsDepartment of StatisticsForecasting 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.http://dx.doi.org/10.1155/2022/1898132 |
| spellingShingle | Aisha Fayomi Rizwana Majeed Ali Algarni Sohail Akhtar Farrukh Jamal Jamal Abdul Nasir Forecasting Tennis Match Results Using the Bradley-Terry Model International Journal of Photoenergy |
| title | Forecasting Tennis Match Results Using the Bradley-Terry Model |
| title_full | Forecasting Tennis Match Results Using the Bradley-Terry Model |
| title_fullStr | Forecasting Tennis Match Results Using the Bradley-Terry Model |
| title_full_unstemmed | Forecasting Tennis Match Results Using the Bradley-Terry Model |
| title_short | Forecasting Tennis Match Results Using the Bradley-Terry Model |
| title_sort | forecasting tennis match results using the bradley terry model |
| url | http://dx.doi.org/10.1155/2022/1898132 |
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