Dr Stephanie Kovalchik

Dr Stephanie Kovalchik

BSc Cal Tech, MSc UCLA, PhD UCLA

Research Fellow, Institute of Sport, Exercise & Active Living (ISEAL) & Senior Sport Scientist, Tennis Australia

Stephanie is currently a Research Fellow within ISEAL and holds a joint appointment at Tennis Australia, where she works as a data scientist for the Game Intelligence Group.

Stephanie’s area of expertise is statistics. She received her PhD from UCLA, where she focused on multi-level modelling, prediction, and risk assessment. Stephanie has held appointments as a statistical researcher at the National Cancer Institute and the RAND Corporation, where she developed new statistical methods for handling complex health science data.

While working in the health sciences, Stephanie was also conducting quantitative research in tennis. In her current role at ISEAL and Tennis Australia, she is working on advancing tennis analytics. She is involved with various projects using data analysis to help understand performance in tennis and has particular interest in identifying patterns of performance, patterns of development and measuring the mental side of tennis. 

Recent publications

A full list of Dr. Kovalchik’s publications can be found on Google Author.

Kovalchik, S. A. (In press) Is there a Pythagorean theorem for winning in tennis? Journal of Quantitative Analysis in Sports.

Kovalchik, S. A. (2014). The older they rise the younger they fall: age and performance trends in men’s professional tennis from 1991 to 2012. Journal of Quantitative Analysis in Sports, 10(2), 99-107.

Kovalchik, S. A., & Pfeiffer, R. M. (2014). Population-based absolute risk estimation with survey data. Lifetime data analysis, 20(2), 252-275.

Kovalchik, S. A., & Stefani, R. (2013). Longitudinal analyses of Olympic athletics and swimming events find no gender gap in performance improvement. Journal of Quantitative Analysis in Sports, 9(1), 15-24.

Kovalchik, S. A., Varadhan, R., Fetterman, B., Poitras, N. E., Wacholder, S., & Katki, H. A. (2013). A general binomial regression model to estimate standardized risk differences from binary response data. Statistics in Medicine, 32(5), 808-821.

Professional memberships

  • American Statistical Association
  • Mathsport International 

Areas of expertise

  • Measurement theory in sport
  • Performance analysis
  • Prediction
  • Sports performance analytics
  • Statistical modelling

Contact details

+61 3 9919 4052