Article was published on the Intel Corporation Website by Dean Evans, a Technology writer.

Key Takeaway

Big Data analytics in tennis has been revitalizing the sports experience for athletes, commentators, coaches, spectators, and fans through providing comprehensive match statistics, performance indicators, valuable insights and more.

Article Overview

For years, advanced camera technologies and Hawk-Eye ball tracking mechanisms in tennis have provided a wealth of data for analysts in excruciating detail: from the number of winners hit by players to the exact placement of each ball in the court. These statistics have allowed for real-time analysis, enhancing the experience of both the commentators and the fans watching. To put things in perspective, IBM’s Watson Discovery Service analyzed 53,713,514 data points to churn out match stats and findings.

Data science in tennis isn’t just offering a different viewpoint on matches as to what happened, but clues and indications as to why it happened. Player career Elo ratings, calculated by Tennis Australia’s Game Insight Group (GIG), use data analysis to predict victories and outcomes at tournaments.

Here’s Player Elo Ratings described by Stephanie Kovalchik, a Tennis Data Scientist at GIG:

Tennis player Elo ratings measure a player’s strength at any particular time accounting for a player’s past results and the strength of their opponents and are one of the most predictive measures of what a player is likely to do in the future.

Predicting the 2018 Australian Open

GIG conducted simulations for the 2018 Australian Open 5000 times, including in other factors such as injury ratings and previous hard court surface results, and estimated the grand slam winning percentages for the top tennis favorites.

Player Winning Forecast (%)

Roger.Federer Novak.Djokovic Rafael.Nadal
38.9 20 8.4

Roger Federer was the 2018 Australian Open Champion.

Roger Federer holding his trophy.

Roger Federer holding his trophy.

This image was taken from India Today.

AI: Your New Tennis Coach

Big data analytics can pave the way for new coaching regimens. Tracking player metrics such as serve percentages, return placement, foot work patterns and more can help coaches asses player performance and understand strengths and areas of improvement for future games. The Women’s Tennis Association has on-court coaching rule where coaches can discuss and game-plan with their players during the match. Progress from SAP Tennis Analytics has given coaches the chance to view live match analysis and use this insights to help players adjust their game strategy before the match has even ended. . This image was taken from Venture Beat.

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What Do I Think?

I was fascinated by this article because I am a huge tennis fan– particularly Roger Federer. I remember watching Australian Open in 2018 and thinking, “Wow, Federer is looking good.” And indeed, my pick for the title coincided with the AI’s model as well. Fan experiences aside, I think data science has so much potential in every field but to a whole other level in sports. In a time where competition is rapidly increasing in the sports field, it’s advantageous for coaches and players alike to capitalize on any resource they can get their hands on. What better resource is there than ML/AI models that have analyzed your every move? Data science is making sports more digestible and insightful for players and more exciting and fresh for us spectators.

Roger Federer Tennis Data

2018 includes the Australian Open title that the analysis by the GIG predicted.