Data-Loving Gamblers Bet on Tennis by the Numbers

Gamblers ignore personalities and focus on the data

Data-Loving Gamblers Bet on Tennis by the Numbers
Gamblers ignore personalities and focus on the data

For someone who says he bets millions of dollars on tennis, Elihu Feustel doesn’t watch many matches. “Which one is Granollers?” he says, referring to Marcel Granollers, a Spaniard ranked 35th in the world. “Is he the one that’s good on clay courts?”

Feustel, who is from South Bend, Ind., says he doesn’t need to recognize the players on the men’s ATP World Tour to make money. He relies on an algorithm he created using data from 260,000 matches to place about 30 bets a day during tournaments, including the Australian Open, which started on Jan. 13. For the record, Granollers prefers clay courts, according to his men’s tour profile, and lost his first-round match with Marin Cilić of Croatia in five sets on the second day of play at the Australian Open.

Professional gamblers and investment funds are using computer models to win money from more casual bettors, according to Scott Ferguson, a former education officer—he taught novices how to wager—at Betfair Group, an Internet gambling company in London. The company, which lets people wager against each other online, matched almost £50 million ($82 million) of bets on the 2012 Australian Open final in which Novak Djokovic beat Rafael Nadal in five sets.

Such quantitative analysts, or quants, focus on tennis in the same way their counterparts at hedge funds analyze the movements of stocks, bonds, and other assets. Tennis is an “attractive” sport to create an algorithm for because there are only two players in a singles match and statistics are freely available, according to William Knottenbelt, an associate professor of computing at London’s Imperial College. He co-wrote a tennis algorithm that he says would have made an average 3.8 percent return on bets on 2,173 ATP matches in 2011.

Feustel, a full-time gambler, says he puts in a 60-hour week checking and improving his model. He works with a programmer who trawls the Internet for data such as serve speed and break-point conversions, then plugs that information into the model, which comes up with what the model considers “fair” betting prices for scheduled games. If those odds diverge from market prices, Feustel says, his trader—who lives outside the U.S., where online sports gambling is largely illegal—will bet as much as the market will allow at bookmakers including Pinnacle Sports on the Caribbean island of Curaçao. That can be about $30,000 on a match in later tournament rounds.

Although bookies have used mathematics for decades to set odds, more gamblers have started applying computerized data to betting in the past 10 years, says Warwick Bartlett, chief executive officer of Isle of Man-based Global Betting & Gaming Consultancy. There may be as many as 20 professional tennis gamblers betting on a similar scale to Feustel, according to Ferguson.

Tennis authorities oppose gambling because of the risk that lower-ranked players “who travel around in a Volkswagen Kombi and stay in hostels” might be lured into fixing games, Ferguson says. Spain’s Guillermo Olaso, ranked No. 254 in the world, was banned for five years for trying to manipulate the outcomes of matches in 2010, according to a Dec. 23 statement from the Tennis Integrity Unit, a London-based joint venture of the International Tennis Federation, ATP, WTA, and Grand Slam tournament organizers. The ITF, ATP, and women’s WTA Tour didn’t respond to requests for comment. Olaso denies attempting to fix games and is appealing his ban to the Court of Arbitration for Sport in Switzerland, according to his lawyer.

There are a handful of funds that raise money from investors to make sports bets. Priomha Capital, a sports-betting investment fund with A$5 million ($4.2 million) in assets, makes about A$100 million worth of bets a year, according to CEO Brendan Poots. The Melbourne-based fund uses computer models to bet on soccer, cricket, horse racing, golf, and tennis, and it had an annual return of about 28 percent last year, Poots says.

Feustel declines to say how much he has made during the two and a half years he has used his computerized tennis model. He aims for a 2 percent return on the total amount he bets each year. His total bets can greatly exceed his initial stake since he can bet the same money over and over again. He says he’s “not really interested” in tennis, watching less than one hour a year, and would recognize only a handful of top players, such as Roger Federer and Maria Sharapova. He’s devoted to his algorithm, though. “You never bet against the model,” he says.

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