Predictive modeling of tennis matches: a review

Predicting the outcome of sporting events has always been a popular area of interest for sports professionals as well as the general public. Although domain experts have traditionally been the main contributors for generating sports predictions, relying solely on human expertise and intuition is not a scalable, time-effective, nor a cost-effective solution. Hence, computers are taking the leading role in predicting the outcomes of sporting events, in terms of estimating the final score or guessing the winner, but also for predicting the occurrence of various events that may happen during the game. Predictive analytics has been effectively applied to a wide range of sports, including popular team sports as well as individual sports. The nature of sport plays a significant role in the adaptation of different predictive techniques based on diverse statistical and mathematical models. Tennis has a strongly defined structure and a rigid scoring system which, along with the sport’s popularity and large and easily accessible datasets, makes tennis match modeling a hot topic in scientific literature. This paper provides an overview of scientific papers on the prediction of tennis match outcomes, from the first regression-based models all the way to complex models based on machine learning.