Horse racing

Horse Racing Software Using Neural Networks

Neural networks are ideally suited to predicting the outcome of thoroughbred horse races. By providing a neural network with historical information on horses such as speed, horse position during previous races, class, earnings, in-the-money percentages, and postposition in today's and previous races, the network can use its advanced pattern matching capabilities to predict the outcome of future races.

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. Users require no previous knowledge of neural networks to perform clusterings or predictions. For example, to perform predictions, all the user needs to do is specify the historical data in the easy-to-use interface, and set a few parameters. The application does the work of building the neural network and supplying the final prediction.

Advanced Technology for Sports Betting

Neural networks are extremely well suited to predicting the outcome of horse racing events since they can determine patterns and trends in large multi-variable data sets. They can also make predictions when faced with incomplete or non-linear data, which is often the case when dealing with historical horse racing information.

Conclusion

NeuroXL Clusterizer and Predictor are both powerful, easy-to-use and affordable solutions for advanced prediction and clustering of horse racing data. Both are designed as add-ons to Microsoft Excel, are easy to learn and do not require that data be exported out of or imported into Excel.

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: 4.0.5

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July 26, 2014

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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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