Friday 23 October 2015

How to Beat the House at Roulette

There have been plenty of "can't lose" systems put forward for winning at roulette over the years but one of the few men to actually pull off the feat has broken his decades-long silence to spill the beans behind his system.

Physicist J. Doyne Farmer made waves more than 30 wears ago when as a graduate student he devised a portable, wearable computer that would tilt the roulette odds firmly in the player's favor -- although technological limitations at the time limited the wins he was able to book at a Nevada casino.

Farmer devoted several years in the late 1970s developing a physics-based approach to beating roulette, which measured and analyzed several factors as the roulette ball rolled around the edge of the rim and fell before bouncing around and finally settling on a number.

Farmer discovered that the first part of that equation -- how the ball rolled around the rim of the wheel and fell -- could be predicted with some accuracy, giving a significant edge in predicting what general range of numbers the ball might settle into after chaotically bouncing around during the second part of the equation, which is otherwise almost impossible to predict on its own.

Farmer's decision to speak out after all the years was spurred on by a paper published about very similar research from Michael Small from the University of Western Australia in Perth and Michael Tse from Hong Kong Polytechnic University, which was recently submitted to the journal Chaos.

Small and Tse used a very similar approach to Farmer's and calculated that a player armed with the system and a small computer or smartphone could gain as much as a 18% edge over the house -- significant enough to lead to very large profits if successfully implemented.

Before gamblers rejoice too much, casinos aren't exactly helpless to the approach, as the amount of time the roulette ball is allowed to spin before falling can be changed and using even a small and hidden computer for the necessary calculations isn't exactly the subtlest of approaches.

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