Random forest model identifies serve strength as a key predictor of tennis match outcome

Abstract

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

[Retracted] Risk Management of Sports Venues and Olympic Sports Cooperation Spirit under Complex Environment

“…In order to ensure the success of sports competitions, Gao and Kowalczyk identify and evaluate various risks that may arise during the planning and holding of sporting events. Based on their findings, they then optimize and combine different risk management technologies to effectively control these risks and handle the fallout from losses brought on by these risks [ 15 ]. The argument made by Krishna et al is that contemporary sporting events can no longer be adequately served by empirical risk management behaviour.…”

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

“…By reviewing the literature, this paper found that only a few scholars have studied table tennis doubles and mixed doubles, most of which are the technical and tactical analysis of pairs of mixed doubles players. Although the evaluation methods of table tennis skills and tactics are increasing in the world [6][7][8][9][10][11][12][13][14], they are all based on the three-stage index evaluation method proposed by the Chinese scholar Wu Huanqun. In addition, there are some documents on the construction of other network projects, such as technical and tactical analysis models such as tennis [15][16][17][18][19].…”

Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning

“…In this study, we employed two widely recognized predictive models, RF and MLR, commonly utilized in the field of sports analytics for their efficacy in predictive modeling [44,45]. Our evaluation, utilizing R2 values and MSE as performance metrics for each exercise, aligns with prior studies in sports analytics [46,47], indicating the widespread pursuit of effective predictive models in various athletic disciplines [48,49].…”