Random forest model identifies serve strength as a key predictor of tennis match outcome
This work provides a practical tool for bettors and betting companies to predict tennis match outcomes more accurately than using betting odds alone, though it is incremental in applying existing methods to new data.
The researchers tackled the problem of predicting tennis match outcomes before matches start by compiling the largest tennis match database to date and using simple machine learning methods, achieving over 80% accuracy and identifying serve strength as a key predictor while showing that their predictions align with betting company odds.
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.