How To Build A Tennis Simulation

Did you know that tennis is the fourth most popular sport at DraftKings? Tennis ranks behind football, basketball, and baseball. With its year-round availability, exciting matches, and global tournaments, it’s not hard to see why it’s such a popular sport amongst fans.

For most sports on the Sports Intelligence team, the Sports Data Science team uses a Monte Carlo simulation model for the prediction engines. Whether you are a seasoned sports bettor with your own models or new to the world of mathematically predicting sporting outcomes, this Data Science article provides valuable insights into the world of sports modeling from the bookmaker's perspective.

Why Do We Use Simulations?

Why do we prefer to build the model as a simulation over a calculation? There are ​​ways to use simple algebra to calculate the probabilities of winning games, sets, and matches in tennis. However, at DraftKings, we focus on a Monte-Carlo simulation approach to modeling tennis rather than the more traditional calculation-based approach that many other companies in the industry employ. This approach gives us the opportunity to deliver the most extensible and accurate product. It also allows more flexibility than a traditional calculation-based model to leverage machine learning models and build more advanced momentum-related features.

If you want to learn more about our approach, we’ll walk through the process of building a Monte Carlo simulation model and how it applies to our other sports models at DraftKings in this article.