Sports Betting

Sports Betting Software Using Neural Networks

Neural networks have proven to be effective at predicting the outcomes of sports events. A neural network is a computerized system that can learn which combinations of inputs (such as a team’s performance statistics) lead to a particular output (such as the probability of the team winning).

NeuroXL Predictor and Clusterizer

NeuroXL Predictor and Clusterizer are both add-ins to Microsoft Excel that harness the power of artificial intelligence for forecasting and clustering tasks. Given historical sports data in Microsoft Excel form, both these software add-ins can be used to predict the outcome of sporting events such as dog racing and thoroughbred horse racing. Both applications are extremely easy to use, and neither requires previous knowledge of artificial intelligence on the part of the user.

Advanced Technology for Sports Betting

Neural networks are extremely well suited to predicting the outcome of sporting events since they have strong pattern matching capabilities, and are adept at working with large multi-variable data sets. Take dog racing, where, in a typical scenario, historical race history for each dog could be analyzed by the network to determine the most likely winner.

Conclusion

NeuroXL Clusterizer and Predictor are both powerful, easy-to-use and affordable solutions for advanced prediction and clustering of sports data. Both are designed as add-ins to Microsoft Excel, are easy to learn and do not require that data be exported out of or imported into Excel. They provide a cost-effective way to harness the power of artificial intelligence for sports betting.

More information

For more information on NeuroXL Clusterizer and Predictor, please visit our home page .

 

News

New versions of NeuroXL Predictor, NeuroXL Clusterizer and NeuroXL Package released: 4.0.6

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July 18, 2016

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Testimonials

I can definitely recommend NeuroXL software to any individual or business that would like to take advantage of the power of artificial neural networks in analyzing complex data.

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Dr. Jean-Michel Jaquet

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