Who Will Win Wimbledon? Serving Up Some Data
It’s been a fun year in the world of professional tennis. From the men’s tour, we’ve witnessed Novak Djokovik winning in Australia, Rafael Nadal winning in France, and Roger Federer rounding out the continued domination of the Big Three. With the emergence of a new generation on the women’s tour, it’s been exciting to watch Naomi Osaka win in Australia and Ashleigh Barty win in France. Next up, Wimbledon!
Will these trends continue? Will the Big Three continue their dominance or will we see the next generation of tennis champions finally break through on the men’s tour? Will we see a first-time slam winner at Wimbledon, a continued streak by Osaka and Barty, or a triumphant return by Serena Williams? We can’t wait to find out who has the best chance of winning at Wimbledon, so we took matters (data) into our own hands.
Much like our approaches for March Madness and the Stanley Cup Finals, we decided to simulate both the men’s and women’s draws for Wimbledon. We started with the result of every match (and set scores) for ATP and WTA tour matches from 2010 through 2018. Using this data, we built a historical dataset containing past results, current Elo scores (both overall and surface specific), and tournament information and then used DataRobot to determine the best model and predict the probability that a player would win a set.
Once we had built this prediction model, we could take the draw of any tournament and simulate the results 100,000 times to find out how often each player would win with that particular draw.
With the draw complete, we know the 128 men and women who will compete in the 2019 tournament. Based on our simulations, the top 10 women most likely to win Wimbledon is given in the table below, with Serena Williams the favorite with 22% chance of winning.
Probability of Winning
Similarly, the top 10 men most likely to win Wimbledon is given in the table below, with Novak Djokovic the favorite with 39% chance of winning followed by Roger Federer with a 32% change of winning. We were surprised to see Nadal with only a 10% chance of winning, but after examining the difficulty of his draw, we can see why his chances are suppressed.
Probability of Winning
In our simulations, Serena Williams looks like a favorite to win Wimbledon for her eighth time, but after her, there are a cluster of other players with a reasonable chance of winning Wimbledon (for example Naomi Osaka falls just outside the top 10, but still has a 3% chance to win). On the Men’s side, Novak Djokovic is the slight favorite to capture his fifth Wimbledon title and Roger Federer only slightly behind him in his quest to win his ninth title. Unlike the Women’s side, our simulation shows a strong preference for these two, with even Rafael Nadal a distant third.
All eyes will be on Wimbledon as seasoned champions and rookies step out onto the grass to face off in the Grand Slam tournament. Approaching the game from a data perspective adds another layer of excitement, and machine learning even more so, bringing spectators even closer to the sport.
Interested in more Sports Analytics? DataRobot works with professional teams across sports globally. Visit our Sports Analytics solutions page for more content and insights.
About the Author:
Andrew Engel is General Manager for Sports and Gaming at DataRobot. He works with DataRobot customers across sports and casinos, including several Major League Baseball, National Basketball League and National Hockey League teams. He has been working as a data scientist and leading teams of data scientists for over 10 years in a wide variety of domains from fraud prediction to marketing analytics. Andrew received his Ph.D. in Systems and Industrial Engineering with a focus on optimization and stochastic modeling. He has worked for Towson University, SAS Institute, the US Navy, Websense (now ForcePoint), Stics, and HP before joining DataRobot in February of 2016.
Andrew Engel is General Manager for Sports and Gaming at DataRobot. He works with DataRobot customers across sports and casinos, including several Major League Baseball, National Basketball League and National Hockey League teams. He has been working as a data scientist and leading teams of data scientists for over ten years in a wide variety of domains from fraud prediction to marketing analytics. Andrew received his Ph.D. in Systems and Industrial Engineering with a focus on optimization and stochastic modeling. He has worked for Towson University, SAS Institute, the US Navy, Websense (now ForcePoint), Stics, and HP before joining DataRobot in February of 2016.
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