Intelligent Systems in the Gambling Industry Kieran O’Neill 25/03/10

Introduction Motivation Sports Betting Victor the Predictor MAIT Accuscore Casinos Table-Eye 21 Conclusion

Motivation Predicting an outcome is difficult –Requires time and dedication –Often requires luck Try to predict more favorable odds –Minimize risk Casinos and Bookmakers –Need to secure their profit –Need to detect cheaters

Victor The Predictor Neural Network 30 Input Features 3 Outputs – Win/Loss/Draw Single Hidden Layer Back-propagation Learning

MAIT Neural Network Neuron Activation - Sigmoid Function 83.3% in Rugby World Cup % in English Premier League

Accuscore Clients –ESPN, Yahoo! Sports 67% Accuracy in 08/09 NFL Season Evolutionary Algorithms Simulates each game

Casino Fraud Lavish casinos built on losers Casino games generally have “House Advantage” –Blackjack has adjustable house advantage –Card-counting methods lower advantage Maximize Profit –Lower staff numbers –Reduce croupier errors –Comprehensive Benefits “comps” –Understand players betting patterns

TableEye 21 Machine Vision Detects –Dealt Card and Suit –Dealer Errors –Card Counting Bets Placed with RFID Betting Patterns Comps for Players

Conclusion Neural Networks have drawbacks –Models based on teams not players Accuscore dominates market for now Machine Vision implemented in casinos –Very accurate feature detection Need to reduce cost of implementation