Sports & Tennis Analytics

Sports Analytics

Through the collection and analyzation of data, sports analytics inform players, teams, coaches and other stakeholders in order to support decision making both during and prior to sporting events.

There are two key aspects of sports analytics — on-field and off-field analytics. On-field analytics deals with improving the on-field performance of teams and players. It digs deep into aspects such as game tactics and player fitness. Off-field analytics deals with the business side of sports. Off-field analytics focuses on helping a sport organization or body surface patterns and insights through data that would help increase ticket and merchandise sales, improve fan engagement, etc. Off-field analytics essentially uses data to help rightsholders take decisions that would lead to higher growth and increased profitability.

As technology has advanced over the last number of years data collection has become more in-depth and can be conducted with relative ease. Advancements in data collection have allowed for sports analytics to grow as well, leading to the development of advanced statistics and machine learning, as well as sport specific technologies that allow for things like game simulations to be conducted by teams prior to play, improve fan acquisition and marketing strategies, and even understand the impact of sponsorship on each team as well as its fans.

Another significant impact sports analytics have had on professional sports is in relation to sport gambling. In depth sports analytics have taken sports gambling to new levels, whether it be fantasy sports leagues or nightly wagers, bettors now have more information at their disposal to help aid decision making. A number of companies and webpages have been developed to help provide fans with up to the minute information for their betting needs

(Excerpts taken verbatim from: https://en.wikipedia.org/wiki/Sports_analytics (11.07.2021)) 

Tennis Analytics

Most Top 20 players on both the ATP (Association of Tennis Professionals) and WTA (Women Tennis Association) tours now make use of analytics, and it is becoming increasingly institutionalized. Across the women's tour, the WTA's partnership with SAP provides coaches with increasingly detailed statistics that can be used during on-court coaching. Grand Slam events ply players with numbers, and Tennis Australia's GIG initiative does extensive data mining. Plus, private companies have sprung up to provide even more video and data to players, federations and college teams.

Reactions from players, though, are still mixed. Novak Djokovic was a pioneer in the use of analytics, starting in 2013 and now going so far as to employ a data analysis consultant. Roger Federer, who appears to have begun only uponhis injury comeback in 2017, remains cautious about its value. Rafael Nadal, despite embracing other technology innovations like racquet sensors, has chosen not to get into the stats game at all.

The beginnings go back to twenty years ago, when a company from Switzerland developed a video analysis software called Dartfish, allowing customized tagging of athletes' movements to assess performance. Originally designed for skiing, the technology first made inroads there and and then caught on in other sports, including tennis. Fittingly, agroup of skiers, cross-training in tennis, brought it to the attention of their teaching pro, Warren Pretorius. The former college player got so mmersed in the technology he took a job with Dartfish, eventually starting his own offshoot, Tennis Analytics, in 2011. Another company, Golden Set Analytics, started in 2012 and employs a range of specialized experts to help it gather and extract performance and even strokes data across 150 players on the ATP Tour. 

Volumes of video and analysis have provided two big insights into the game. There is the now oft-cited observation that most points are decided in four shots or less, and winning more of these usually means winning the contest. This has led to more attention on the first two shots a player hits in a rally—serving and returning "+1"—rather than on lengthy point construction. The second is the importance of winning return points, especially off an opponent's weaker second delivery. That has led to greater interest in the serving preferences of players.

(Excerpts taken verbatim from: https://www.tennis.com/news/articles/the-role-of-analytics-in-tennis-is-on-a-long-slow-rise (11.07.2021)

For – among other aspects – a brief history of statistical thinking in tennis, see also: https://hdsr.mitpress.mit.edu/pub/uy0zl4i1/release/3