If you're gonna cheat, cheat smart. Like an Oxford maths professor, who has revealed how he used the world's first wearable computer to beat the roulette tables of Las Vegas back in the 1970s.
In an interview with New Scientist, Oxford mathematician Doyne Farmer explains that, as a graduate student, he used a small computer secreted about his person to shift the odds in his favour. Or, cheat, depending on how you look at it.
The reason he's come clean? Two other researchers—Michael Small and Michael Tse—have recently published research just like his from the 70s. New Scientists explains what the new research, which you can read in full here, reveals:
"Their model divides the game into two parts: what happens while the ball rolls around the rim of the wheel and then falls, which is highly predictable, and what happens after the ball starts bouncing around, which is chaotic and hard to predict. Because the first part is predictable, Small and Tse were able to calculate roughly where the ball would begin its erratic bouncing and therefore in which part of the wheel it was more likely to land.
"Using a subtle counting device similar to Farmer's, the pair was able to predict in which half of the wheel the ball would fall in 13 out of 22 trials. In three trials, the model predicted the exact pocket. That is equivalent to taking the odds from 2.7 per cent in the house's favour (on European roulette wheels) to 18 per cent in the player's favour. That is a very small number of trials, so they then confirmed their technique via 700 trials using an automated camera system, which would be too conspicuous to use in a casino."
Farmer, however, is going to publish his own research, which used calculations based on air resistance rather than Small and Tse's model which uses rim friction.
And, before you ask: no, you probably can't use this trick these days. The researchers reckon casinos are aware of the ploy, and can get round it by closing bets before the wheel has rotated enough times for sufficient measurements to be taken. Dammit. [New Scientist]
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