Tennis Quant’s System Doubles Money Without Knowing Players

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Marcel Granollers of Spain in his first round match during day two of the 2014 Australian Open at Melbourne Park on Jan. 14, 2014. Close

Marcel Granollers of Spain in his first round match during day two of the 2014... Read More

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Photographer: Chris Hyde/Getty Images

Marcel Granollers of Spain in his first round match during day two of the 2014 Australian Open at Melbourne Park on Jan. 14, 2014.

For someone who says he bets millions of dollars on tennis a year, sports gambler Elihu Feustel doesn’t watch many matches.

“Which one is Granollers?” Feustel says, referring to Marcel Granollers, a Spaniard ranked 35th in the world. “Is he the one that’s good on clay courts?”

Feustel, from South Bend, Indiana, says he doesn’t need to pay attention to who the players on the men’s ATP World Tour are to double his money. He relies on an algorithm he created using data from 260,000 matches to make about 30 bets a day on Grand Slams such as the Australian Open, which started Jan. 13.

Gamblers and investment funds are increasingly vying for profits from tennis by using computer models to win money from more casual bettors, according to Scott Ferguson, a former Betfair Group Plc (BET) education officer. Such quantitative analysts, or so-called quants, are focusing on tennis in the same way their counterparts are employed by hedge funds to predict moves for stocks, bonds and other assets.

Betfair, a London-based company that enables bettors to wager against each other online, matched almost 50 million pounds ($82 million) of bets on the 2012 final in which Novak Djokovic beat Rafael Nadal. Djokovic is an 8-11 favorite to win a fourth straight title in Melbourne with U.K. bookmaker William Hill Plc, meaning a successful $11 wager would return $8 plus the original stake.

Granollers prefers clay courts, according to his men’s tour profile, and lost his first-round match with Marin Cilic of Croatia in five sets on the second day of play on the hard courts of this year’s Australian Open.

60-Hour Weeks

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 a 3.8 percent return on bets on 2,173 ATP matches in 2011.

Feustel, who says he puts in a 60-hour week checking and improving his model, works with a computer programmer and trader. The programmer trawls the Internet for data such as serve speed and break-point conversions. That’s plugged into the model which comes up with “fair” betting prices for scheduled games.

If those odds diverge from market prices, Feustel says, his trader -- who lives outside the U.S. -- will gamble as much as the market will allow at bookmakers including Pinnacle Sports, based on the Caribbean island of Curacao. That can be about $30,000 on a match result in later tournament rounds.

Computerized Moves

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

Tennis authorities would rather gambling not take place because of the risk of lower-ranked players “who travel around in a Volkswagen Kombi and stay in hostels” being lured into fixing games, Ferguson said. The International Tennis Federation, ATP and women’s WTA Tour didn’t respond to a request for comment on algorithm-driven tennis betting.

Spain’s Guillermo Olaso, the world No. 254, was banned for five years for trying to manipulate outcome of matches in 2010, the London-based Tennis Integrity Unit said in a Dec. 23 statement. The unit is a joint venture between the ITF, ATP, WTA and Grand Slam tournament organizers.

Higher Income

Feustel declined to say how much he has made using his computerized tennis model, which he has employed for 2 ½ years. He said he aims for a 2 percent return on the total amount he bets each year, implying that wagers totaling $5 million would bring $100,000 in profit. The annual amount bet can greatly exceed the starting sum as money is constantly re-bet. The 43-year-old said it is possible to double his starting capital in a year through compounding.

“That sounds like being a little too optimistic considering that I should imagine the usual trade-off between risk and return applies in sports,” Knottenbelt said in an e-mail.

After paying his partners their cut, Feustel still earns considerably more than in his previous job as a commercial lawyer, he says. A lawyer in Indiana might command a salary of between $50,000 and $100,000, he says.

Bigger Risks

It’s “very possible” for Feustel to double his money in a year if his bankroll is less than $500,000 as it may be easier to take bigger risks with a small amount, according to Brendan Poots, CEO of Australia’s Priomha Capital Pty. Ltd., a sports-betting investment fund.

Priomha wagers about A$100 million ($90.3 million) a year, turning over A$5 million of funds under management about twice a month, Poots said by phone. The Melbourne-based fund uses computer models to bet on soccer, cricket, horse racing, golf and tennis and had an annual return of about 28 percent last year, Poots said. The fund’s 2012 report showed a 7.56 percent return. Priomha takes a more cautious approach than Feustel because it manages clients’ money, hedging bets to offset potential losses, Poots added.

Quantitative funds operating in financial markets gained 11.5 percent on average last year and have produced an annualized return of 6.6 percent since the end of 2008, according to Chicago-based Hedge Fund Research Inc.

Market Efficiency

For tennis betting it’s “very hard” to get an annual return of more than 5 percent on investment, according to Dan Weston, whose tennisratings.co.uk website charges subscribers for insight. He said his service using a model would have returned 6.1 percent on the amount bet since August.

“The market is fairly efficient,” Weston, 34, said. “Casual bettors aren’t stupid.”

Weston, a University of Kent accounting graduate based in Preston, England, said his model processes data such as the record players have of recovering from a break-point deficit, and their accumulated fatigue from matches.

That helps in situations like that of Peng Shuai, who was the overwhelming 1-5 favorite to beat Storm Sanders in the first round of an event on Jan. 7 in Hobart, Australia, after a slog to a final against Li Na in Shenzhen, China, three days earlier. Peng lost 6-2, 6-2 to Sanders, who was only in the tournament because another player withdrew.

Players’ Fatigue

Weston says his data show certain players tire more quickly than others as a match progresses. American Jack Sock has an “awful” record in the third set, Weston said, adding he advises against betting on the world No. 95 winning in five-set Grand Slam matches.

Feustel says he’s “not really interested” in tennis, watching less than one hour a year, and would only recognize a handful of players including Roger Federer and Maria Sharapova.

He’s devoted to his algorithm, though.

“You never bet against the model,” Feustel says.

To contact the reporter on this story: Alex Duff in Madrid at aduff4@bloomberg.net

To contact the editor responsible for this story: Christopher Elser at celser@bloomberg.net

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