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Tennis is a popular sport worldwide, boasting millions of fans and numerousnational and international tournaments. Like many sports, tennis has benefittedfrom 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 useof sports records to predict tennis match outcomes prior to match start. Wecompiled, cleaned, and used the largest database of tennis match information todate to predict match outcome using fairly simple machine learning methods.Using such methods allows for rapid fit and prediction times to readilyincorporate new data and make real-time predictions. We were able to predictmatch outcomes with upwards of 80% accuracy, much greater than predictionsusing betting odds alone, and identify serve strength as a key predictor ofmatch outcome. By combining prediction accuracies from three models, we wereable to nearly recreate a probability distribution based on average bettingodds from betting companies, which indicates that betting companies are usingsimilar information to assign odds to matches. These results demonstrate thecapability of relatively simple machine learning models to quite accuratelypredict tennis match outcomes.

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