The hidden complexities of the game of tennis have always been a challenge in statistical analysis of the game For example the scoring system is very complicated, while the type of playing style differs from player to player, even the surfaces that the game is played on changes throughout the year. As opposed to other games such as baseball, tennis has a variety of factors to consider for “standardizing” based on statistical analysis. The complexity is not as much as in the numbers as is the pressure of “conservatism” on the game, where even players’ “grunt’s” is considered controversial!
It is only as late as 2013 that data analytics came to be used by Novak Djokovic in a fresh approach to winning his matches, followed by Federer as recently as 2017. The slow pace of analytics used in tennis is because it is an individualistic game and not a team sport like baseball and is relatively individualistic in nature.
It is only as late as 2013 that data analytics came to be used by Novak Djokovic in a fresh approach to winning his matches, followed by Federer as recently as 2017. The slow pace of analytics used in tennis is because it is an individualistic game and not a team sport like baseball and is relatively individualistic in nature.
The gradual increase in the use of big data and analytics has led to tennis becoming an easier sport to analyze. The institutionalization of analytics in tennis is seen in the top 20 players on the ATP and WTA tours using these defined analytics. Private data crunching companies too are providing extensive video and data for federations and players to win more matches. WTA partnership with SAP gives coaches detailed, statistical on-court coaching.
The proliferation of data and analysis is changing businesses and tennis analytics services is offered by:
The arrival of data analytics in tennis is both inspiring and reimagining tennis and its extended fields.
The core technology used in tennis analytics is tagging videos of matches to assess performance. Coaches can use the same data to scout for new talent. It is also used in studying opponents in major tournaments.
Lately, this type of analytics that was limited to single-shot data points, patterns in returns, patterns in serves, or player positioning has become more sophisticated with the use of Machine Learning technologies. So, analytics is being used not just to find out where the player’s serve will land, but to data on how rally will evolve based on other factors. Certain specialist data analysts are offering data for variables, like game score, time of day, or court surface, and opponent games.
The use of smart equipment such as Hawk-Eye optical tracking cameras in the top-prize tournaments is also the reason for such analytics to become popular. The gaming industry and media components using one-camera angle for analysis of ball and player tracking at lower than 400 milliseconds latency on a single one-camera angle are some of the advanced levels of analytics possible in tennis.
Applications in tennis analytics will soon evolve into futuristic analysis which is cutting edge, next-generation to quantifying playing as - body language, rituals, and routines, self-talk, or breathing. These are features that players can be trained upon. The use of applications in the analysis of facial expressions, audio of players can help in discerning key issues for inclusion within the automated highlights. The last feature is provided by IBM’s supercomputer Watson for video indexing that drives new metrics. Red Steps is one such feature that traces the number of times, and the rate at which players can change direction.
The new technology platform that is expected to drive tennis data analytics this year is the Coach Advisor. This new platform is an energy system monitoring feature. The data produced by Hawk-Eye camera systems is positional data with measures physiological load or cumulative matches and mechanical intensity or the energy bursts measured in watts. The industry expects this to be drive major changes in the approach of a player’s support team. The method in which the strength and conditioning will change for optimization of performance and mitigation is visible.
In conclusion, data analytics has become the most powerful tool for coaches. The role of coaches is to deliver these information pieces so that the player has the data which makes the difference to their game. All of the numbers and analysis could overwhelm the player, pressurizing them to commit errors. As newer tennis analytics becomes available as in the form of Lvision’s computer vision technology to build metrics from any kind of video feed.
It is ‘advantage’ game Tennis at this point of time, as many data science companies such as Drive Analytics leverage their technical capabilities to offer cutting-edge analytics support. Tennis players, especially junior levels in this sport, have the opportunity to use data generated by companies such as Tennis Analytics which tag match videos by hand and tabulate portfolio for metrics to gain a percentage of points for first or second serve, the probability for the win on opponent’s second service or first service. Thus the It is ‘advantage’ game Tennis at this point of time, as many data science companies such as Drive Analytics leverage their technical capabilities to offer cutting-edge analytics support. Tennis players, especially junior levels in this sport, have the opportunity to use data generated by companies such as Tennis Analytics which tag match videos by hand and tabulate portfolio for metrics to gain a percentage of points for first or second serve, the probability for the win on opponent’s second service or first service. Thus the opportunity for tracking player development and enhancing training based on anthropometric data of every match is tennis analytics core strength.
