how does betting systems capture sport data

Following a series of several small wins, the trio were surprised to find that their accounts had been limited, restricting how much they could bet to as little as $1.25.Ī spokesman for William Hill, one of the bookmakers used by the team, said betting is sometimes restricted “in a small number of cases”.

“We were excited but it’s worth mentioning – you need to spend a lot of time to do it.”īut their streak was cut short.

But Kaunitz and his wife did get to splash out on a nice dinner in Tokyo, he says. There were some administrative costs, such as the expense of running servers to keep track of the odds in real time. After five months the team had made a profit of $957.50 – a return of 8.5 per cent. They kept this up for five months, placing $50 bets around 30 times a week.Īnd they were winning. When a favourable opportunity arose, a member of the team would email Kaunitz and his wife, one of whom then placed a bet. They developed an online tool that would apply their odds-averaging formula to upcoming football matches. Then the team decided to try betting for real. Making bets randomly resulted in a loss of 3.32 per cent. When they applied their strategy in a simulation, they made a return of 3.5 per cent. The team used the historical data to work out the optimal distance from the mean odds – the one that would give a positive payout for the largest number of games. But 5 to 1 offers higher returns should the outcome materialise.

Mean odds of 2 to 1 suggest the bookies collectively think this reflects fair odds for that outcome. “What’s the probability of Barcelona winning against Real Madrid, for example.” “Using the odds that are published, you can get a very good estimate of an event occurring,” says team member Javier Kreiner, a data scientist at transport start-up CargoX in São Paulo, Brazil.

How does betting systems capture sport data