For example, Bet gives an odds of 2. But things are not always nice and simple. In reality, to maximize profit, bookmakers employ teams of data scientists to analyze decades of sports data and develop highly accurate models for predicting the outcome of sports events and giving odds to their advantage.
That extra 2. To get the real probabilities, we need to correct for the profit by dividing through by For a perfectly efficient bookmaker, these are the probabilities of each outcome. The expected profit is the same if I had betted for Man United:.
And — you guessed it — if I bet on a draw, I expect to get back 97 cents. This understanding does not stop me from trying to exploit any potential inefficiencies in the market. At first, I devise the general bet strategies. Implementing the Kelly Criterion is quite simple in R:.
However, if we aggregate all the odds from many different betting houses, we should get a better reflection of how bookmakers view the probability of an event, Arsenal defeating Man United for example:. Obviously, there are inherent risks in this optimal Poisson model. Both Merson and the Poisson-process model and me!!!
All in the same weekend!!! Before you clone my Github repo and raise capital for your sports hedge fund, I should make it clear that there are no guarantees. If anything, this article is a toy example of what you could potentially do. But the bookmakers have made it extremely difficult for anyone to gain sustainable profits. If there are still a lot of people placing a bet at 4. Chances are that by the time the code infers the most optimal odds, it has been changed.
Furthermore, if you do start to make a regular profit, bookmakers can simply thank you for your business, pay out your winnings and cancel your account. This is what has happened to a research group from the University of Tokyo . A few months after we began to place bets with actual money bookmakers started to severely limit our accounts. If you enjoy this article, you may also enjoy my other article about interesting statistical facts and rules of thumbs.
For other deep dive analyses:. The entire code for this project can be found on my Github profile. Bell System Technical Journal. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look. Get started. Open in app. Sign in. Editors' Picks Features Explore Contribute.
Tuan Nguyen Doan. The algorithm against an expert One of the difficulties of testing an algorithm is to find a good benchmark for its performance. Neither is it a recommendation to bet or gamble. Please be aware that sports betting is not legal in several states in the USA. Building your own book recommendation engine in Python. Written by Tuan Nguyen Doan.
Sign up for The Daily Pick. Get this newsletter. Casino executives neglect sports books because taking bets on athletic events seems like a risky proposition. They like guests to play craps, slots, and baccarat, games in which a favorable outcome for the house is all but guaranteed. From the point of view of casino owners, the result of a sporting event is incredibly uncertain, and they have no control over it. Experienced sports bettors, known as smart-money gamblers, can win far more steadily than someone playing roulette or pai gow poker.
And a major upset can require a huge payout. For all these reasons, many casinos have decided that it's best to minimize their risks by posting odds that stay in line with those of the other casinos in town, keeping betting limits low, and discouraging wagers by expert gamblers. We do trades all day with Goldman, Deutsche, Citibank.
You think those guys are stupid? Amaitis' scrappiness and his willingness to use technology to open untapped markets fit right in with the ethos of Cantor Fitzgerald. It is, after all, the firm that in became the first to do fully electronic US bond market trades with customers.
The swagger even survived September 11, , when its New York headquarters on floors through of One World Trade Center was destroyed. More than two-thirds of its New York staff perished, and the company's primary data center was decimated. But a skeleton crew of Cantor staffers was able to get back online and resume trading just 48 hours after the attack.
You win. The Action A pass seems likely. Not a bad time to bet big on a turnover. The Resolution The Vikings don't turn over the ball, but they do end the drive with a punt. The Resolution Oof. After an epic drive, Vikings kicker Ryan Longwell completes a yard field goal. The Play Minnesota has the ball on its own 18 and is again making risky pass attempts. The Resolution Big risk, big reward! The Play The Vikings have only 79 seconds to go 64 yards if they want to take the lead.
The Resolution A yard touchdown pass from Favre with 40 seconds to go. Translation: Ka-ching! Cantor's latest innovation is the Midas algorithm, which is constantly being refined and fed reams of new statistical data. Even with Midas, however, sporting events are too volatile for Cantor to always end up on the right side of all the wagers it takes. But that's OK: The point isn't to nail the outcome of every contest; that's a sucker's game.
There's only one sure thing in sports gambling: the standard commission, known as the vigorish, that casinos charge when they take bets. When your wins are effectively balancing out your losses, the vigorish starts to add up. In this light, Cantor's business model begins to look more like E-Trade than a conventional sports book. Cantor will make that money by taking far more bets than are made at present, enough that the vigorish will dwarf the income other casinos now make with their smaller and safer sports books.
Which would you rather have? Vegas has never seen this level of technological firepower, but Amaitis insists that it's just the beginning. He likens the Midas program's abilities to Wall Street's first foray into computerization nearly 40 years ago, before people realized how the increased speed of trading opened up many exciting—or frightening, depending on your point of view—new investment opportunities. This year, Cantor was accepting bets on the outcome of NFL games months before the season started.
But Amaitis wishes he could take more granular bets before kickoff. After all, more opportunities to bet means more commissions for Cantor. He dreams of being able to offer programmed sports betting that will allow gamblers to put in bid orders, just as you would with an electronic stock purchase.
Before the game begins, you'll be able to set your account so that a bet will automatically be placed if, for example, a team is ever a six-point underdog. You'd also be able to set it up to place a bet on the other side, say, if that number drops from six to four. Once your bids are in, you won't even have to watch the game. The ideas that led to Midas were born almost 15 years ago, over a lavish dinner at Cliveden House, an elegant manor outside London. Amaitis was supping with Andrew Garrood, a mathematician turned derivatives trader he'd just met in a private suite at the Ascot Racecourse.
They were both there to watch a leg of the Royal Ascot, one of the most important horse-racing events in the UK. But they spent much of their time discussing the resemblance between brokers and bookies. The two must have made for quite a contrast. Garrood is a genteel Englishman with an upper-crust accent. The street-smart Cantor exec, then head of the company's European brokerage operation, was deemed coarse and uncouth by London standards.
He was the target of a harassment suit by a former employee, and the British tabloids dubbed him the Brooklyn Bruiser. But they were both excited by the idea of a Cantor-backed sports-betting enterprise. The ubiquity of gambling in the UK was an eye-opener for Amaitis. There are legal betting shops on nearly every corner, and having a punt on a footy match—a wager on a soccer game—is as common as having a pint at the pub. Garrood knew enough about gambling to tell Amaitis that it had a great deal of overlap with the sort of work he did trading derivatives for Japan's Sanwa Bank.
The two chewed over the idea with their meal. By March , he was on the Cantor payroll. Garrood was tasked with gathering the data and creating software that could calculate the in-running odds for an online sports-wagering operation. He developed the algorithms, and an IT team turned them into code. In , the UK site Cantor Spreadfair was launched. It was a peer-to-peer sports gambling site, allowing users to set their own bets, which would be taken by other players or by Cantor.
The site allowed for spread betting, a volatile form of gambling in which the payoffs are based on how closely you predict the final point difference between two teams. They also set up a site called Cantor Index for "financial fixed odds"—allowing people to bet on the moment-to-moment changes in the stock market—as well as financial spread betting. Such activities are illegal in the US but not in the UK. Although Cantor's UK gaming scheme was a moderate success, Amaitis viewed it as a proof of concept.
He used it to pitch Vegas casinos on the prospect of allowing him to lease their sports books and introduce in-running to American gamblers. Discussions went on for a year and a half, but I knew I wanted to bring Cantor to M.
Amaitis also presented his UK setup to the Nevada Gaming Control Board as evidence that the wireless component of his master plan was sound and secure. We always blocked them. As the ink dried on the deal, Garrood had about 10 months to create the algorithms for a computer system that could establish airtight betting lines and calculate odds on the fly. Inefficiencies, he knew, would be rooted out by bettors and cost Cantor dearly.
Working out of a skyscraper in the Canary Wharf business district of London, Garrood found that he was able to port some elements of Cantor's British betting site into the nascent Midas, but he would need all-new information on which to base his algorithms. After all, Americans wouldn't want to bet on rugby or cricket.
This was the first time many in Vegas noticed the fledgling Cantor operation; LVSC had been the city's oddsmaker of choice since , and now Cantor owned its vast library of research and stats. Garrood worked with a staff of two to build its algorithms, find patterns, and categorize the data. If the Patriots are playing the Colts, and the Patriots want to go for it on fourth and two from their own yard line with two minutes left in the fourth quarter, people think it's ridiculous.
It doesn't happen that often, but it does happen"—and it did happen when the teams faced off last November. And you can put a price on it. In a town where most sports books still rely on back-of-the-envelope calculations and teams of guys in low tech war rooms, Garrood's mathematics give Cantor a level of comfort in its odds and point spreads that other Vegas bookmaking operations simply can't match.
He also learned some things in the course of Midas' creation that go against the conventional wisdom among Vegas bookies and bettors. For example, when a star player is injured, a casino will sometimes move the line on a game by a point or two.
But decades of stats tell Garrood that a star—who, let's face it, will be replaced by another high- caliber professional athlete—is usually worth a negligible amount in the spread. So is the belief that a team like the Miami Dolphins can't win when they play in cold climates. These are the sorts of misconceptions that can help Cantor make money.
If you're being given 2-to-1 odds that the Dolphins will make a first down in Green Bay, and you have 30 seconds in which to decide whether that is a worthwhile bet, you will be basing your decision on what happened over the past couple of plays—a woefully unreliable sample—or on a widely held belief that a team from the sunny South will fare poorly in the frigid North. You'll be betting with your gut; Cantor bets with Midas.
Spend enough time at the M Resort sports book and you will hear gamblers talking about Midas in hushed, reverent tones. Invariably, they gesture toward a locked wooden door at the front of the room, where they imagine the supercomputer resides. Listen to them for a few minutes and you get the sense that Midas is a sort of bookie HAL , chomping on cigars, dicing numbers, and dictating point spreads from a jewel-encrusted silicon throne.
In reality, Midas is , lines of code, a piece of predictive software that largely resides in a nondescript office building about 14 miles north of the M. It was here that the final step of Midas' development was overseen by Cantor's CTO, Sunny Tara, who has since left to form his own software company.
Prior to joining Cantor Gaming, the diminutive engineer had been the senior director of enterprise architecture and services at Harrah's Entertainment, the largest gaming company in the world. At Cantor, he and his team of about 15 took the statistical matrices Garrood created and made them into a fully functional piece of enterprise software.
Dressed in jeans and a light blue button-down shirt, dark hair combed neatly, Tara speaks with the patient earnestness of a comp-sci professor addressing undergrads. He makes it sound so simple. But it was a logistical nightmare. The code had to accommodate apples and oranges. But we were able to leverage what Andrew already had in England and what Cantor already had for its financial markets. The heart of the operation exists inside a 2,square-foot back room that I was not allowed to enter, where there are racks of servers and a handful of Panasonic flatscreens on the wall being monitored by a control-room staff.
It's connected to the M Resort and other Cantor facilities by a dedicated megabit Ethernet circuit. Though the score is tied at this moment, Midas believes that the Braves are a favorite to win. Then the pitch comes in—an mph changeup— and Hinske blasts it out of the park for a home run. Midas finds out about this a split second after the spectators at Turner Field.
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Who would you place your bets on? Sports websites such as NHL. So Team C should have a higher chance of beating Team A this time. But when there are many teams and games, we need a systematic way to analyze the data. How do we incorporate the details like goal differences from the past games to get a better ranking of teams? The home team usually benefits over the visiting team.
With this extra piece of information, which team do you think has a higher chance of winning now? To answer the questions above, we build a statistical model using NHL data downloaded from Hockey Reference website. You can modify for other sports as well. You can read the detailed description of the system here.
Or just implement it by following the three steps below. The data has rows, which includes game results between Oct 2nd, , and Jan 3rd, For example, the row below records the game on Dec 6th, The Montreal Canadiens visiting team played against the New York Rangers home team with a final score of 2—1. It is greater than 0 when the home team wins and less than 0 when the home team loses while being 0 when two teams tie.
Row 0 has column Vancouver Canucks of value 1 and other columns of value 0. It shows that the visiting team in this particular game is the Vancouver Canucks. For example, row 4 says Anaheim Ducks home team played against Arizona Coyotes visiting team. And Anaheim Ducks home team won the game by one goal. In this way, the final dataset includes information on both goal differences and the home advantage factor. We use the ridge regression model as a demonstration. It is a linear regression model with an additional term as the penalty.
According to this model, Colorado Avalanche is the best team with the highest rating. My favorite Toronto Maple Leafs is approved as a good team by the model as well! The statistical method does seem more sophisticated than traditional methods. But how do the performances compare? As we talked about in the earlier section of this article, this is a fundamental statistic that often appears on sports websites.
This is a complicated method that contains information about goal difference and home advantage as well. The method with ridge regression would consider this because it looks at all the teams and all the games together. Use the example at the beginning again. Team A home team is going to play Team C visiting team.
We use the below statistic to predict the result:. To compare these methods, we use cross-validation for evaluation. Because the result of the model only improves and becomes better than other methods, as the season progresses when more data is available.
You could add variables considering the recent schedule of the teams. Did the team play games or rest within the last few days? Did the team travel a lot outside the home location? So the recent games should be more informative compared to the earlier ones. Adding an indicator for that would help. We used the ridge regression model as an example. As an experienced sports fan, you must have valuable knowledge. Combing both the statistical methods and your experience is crucial to making better predictions.
I follow your blog and I love your posts! I hope you keep doing it! Thank you. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. The favorite team is expected to win Bad bets include , , etc.
The supplied algorithm will output a point system or percentage system accompanying its prediction. The results are returned in a csv file. The parameter algorithm will solely calculate wins vs losses for a ranking system. The ranking sytem can be points or percentage based. The results are returned in a txt file. Each parameter is respective to the variables. The results will be output to a txt file ".
Ideal to have a bell curve type distribution of total games from 1 most games to 10 least games. Level 10 should not have more games won than level 9. These will be the denominators for the variables. If level 10 isn't rached, the max level will be adjusted.
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