A Statistical Model of Serve Return Impact Patterns in Professional Tennis with Stephanie Kovalchik

Open Source Sports

In this episode we talk to Stephanie Kovalchik about her paper 'A Statistical Model of Serve Return Impact Patterns in Professional Tennis' (co-authored with Jim Albert). Stephanie is a Staff Data Scientist at Zelus Analytics, where she works on advanced performance valuation for multiple pro sports. Before joining Zelus, Stephanie led data science innovation for the Game Insight Group of Tennis Australia, building first-of-a-kind metrics and real-time applications with tracking data. Stephanie is the founder of the tennis analytics blog "On the T" and tweets @StatsOnTheT.

For additional references mentioned in the show:

• ATP Tour Second Screen
• Stephanie's article in Harvard Data Science Review: Why Tennis Is Still Not Ready to Play Moneyball
• Grand Slam R package: courtvisionr
• Stephanie's GitHub with various resources for accessing tennis data: https://github.com/skoval
• Stan tutorials: https://mc-stan.org/users/documentation/tutorials
• Register now for the Carnegie Mellon Sports Analytics Conference: https://www.stat.cmu.edu/cmsac/conference/2022/
• Check out the Big Data Derby now on Kaggle