MELBOURNE, January 16, 2017
Tennis Australia’s elite team of data, analytics and sport science experts have been crunching the numbers and the results may surprise you.
The Tennis Australia Game Insight Group or GIG is headed up by leading sports scientist and former High Performance Manager Dr Machar Reid whocoached Greg Rusedski and Li Na before turning his focus to the science behind the sport.
Using several years of advanced tracking data from the Australian Open and the latest analytics software and hardware, the team has one task – change the way players and fans engage in tennis. This includes improvements to tournament and prize money structure, racquet and string selection and developing a new way to describe a player’s performance.
With the recruitment of leading American data scientist, Dr Stephanie Kovalchik, the GIG has been substantially bolstered recently.
So who is the hardest working player in the Australian Open main draw? Well, based on GIG’s calculations it is Andy Murray on the men’s side and Barbora Strycova on the women’s tour.
Novak Djokovic is the fastest man with a peak speed of 36 kph. To put that in perspective Usain Bolt reached a peak speed of 44.70 kph during his world record 100 metre sprint in 2009. Simona Halep is the fastest woman on the circuit with a peak speed of 23 kph which is quicker than Marin Cilic andDominic Thiem.
This year’s Australian Open analysis will include point-by-point assessment of the changing probability of a player winning a match or Win Prediction.
Win Prediction in the women’s field has Serena Williams with a sizeable lead over Angelique Kerber and Simona Halep to become Australian Open 2017 champion ahead of their first round matches.
Win Prediction on the men’s side has Novak Djokovic taking home his record breaking seventh Australian Open singles trophy followed by Andy Murray andKei Nishikori.
Another innovation set to debut at this year’s Australian Open assesses a player’s performance in pressure situations. GIG’s Clutch Index is a next-level assessment of match statistics designed to demonstrate who played better when it mattered most.
“Outside of break point conversion, traditional tennis statistics tend to identify and treat every point the same way,” Dr Kovalchik explains.
“This fails to provide a complete view of a player’s performance under pressure. Hitting an ace while 30-40 down at 4-4 in the deciding set has a very different meaning to hitting an ace at 40-0 in the first game of the match – in the former, I’m under pressure and showing real clutch performance, while in the latter, I’m not.”
According to Dr Reid the rise of analytics in tennis is long overdue.
“For the last 40 years, tennis matches have been described in a particular way – first serve percentage, second serve percentage, unforced errors, forced errors, et cetera,” Dr Reid said.
“It represented a logical starting point, but we haven’t really progressed since. If you were to compare that to our contemporaries – big professional sports like baseball and basketball – they have really shifted the dial. Their vocabulary has evolved, and their understanding of the game has probably left tennis a couple of decades behind.”
In 2016, GIG published a paper identifying the shots and patterns of the top players on the circuit. The team was awarded the grand prize at the leading annual sports conference, the MIT Sloan Sports Analytics Conference.
The paper detailed how among the big four, Roger Federer and Novak Djokovic’s games were the most similar, while Andy Murray’s was more like that of David Ferrer, Jo-Wilfried Tsonga and Lleyton Hewitt. Less surprisingly, Rafael Nadal’s game made him an outlier.
“It showed that we could compete with the world’s most popular sports in the analytics space,” Dr Reid said.
“Credit to Tennis Australia – they’ve invested in something that’s not really been explored before in tennis.
“It’s not a risk-free proposition but it’s progressive and innovative. The players and coaches have hopefully benefitted at various times, and the intent is for others within the sport to do likewise.
“There’s an opportunity, as other sports have done, to capitalise on the step change we’re experiencing in data and technology. This journey is in its absolute infancy for us a sport, but it will help the game – and more importantly the players – in more ways than many of us can imagine is possible right now.”