  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.
