Neural Network Applications for Sports Betting and Fantasy Leagues

Neural networks provide significant benefits in many applications. They are actively being used for such applications as sports betting and fantasy leagues

Below we have listed some of the common applications of neural networks for sports betting and fantasy leagues. If you are currently using neural networks in your sports betting or fantasy leagues, we would love to hear about it.

Sample Applications

Locate common characteristics in large amounts of data (stat mining).
Predict probability of an outcome (odds setting, bet sizing, fantasy player ranking).
Forecast progression of results over time (momentum mentality, player aging).
Group potential assets or players based on similarities (identify similar winners, similar players).

Locate common characteristics in large amounts of data

Detecting common characteristics in large amounts of business data is a type of classification problem. Neural networks can be used to solve classification problems, typically through Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) type networks.

Examples of classification applications in sports betting include the prediction of sporting events. For example, data from sport statistics can be used to predict the outcome of sporting events such as football games.

Sample Study: Prediction of NFL Football Games

In this sample study, the neural network model was able to predict 75% correct outcomes for weeks 14 and 15 of the NFL season. Through the first 13 weeks of the 2003 NFL Football season, ESPN.com forecasters on average had only correctly picked 63% of the total games. Neural Network Prediction of NFL Football Games (2003) Joshua Kahn Locate this paper on Google Scholar!

Our NeuroSolutions product is an excellent resource for classification applications. For an interactive example of classification in NeuroSolutions for Excel, download the free evaluation version and view the demo called “Testing Classifiers” in the Help menu.

Predict probability of an outcome

Forecasting results based on existing data is a type of function approximation problem. Neural networks can be used to solve function approximation problems, typically through Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and CANFIS (Co-Active Neuro-Fuzzy Inference System) type networks.

Examples of function approximation applications in sports betting include the prediction of races. For example, data from sport statistics can be used to predict the outcome of sporting events such as horse and greyhound racing.

Sample Study: Neural Network Prediction of Greyhound Racing

In this sample study, the two neural network models were able to out perform the “human experts” in terms of prediction accuracy and monetary payoff. The Backpropagation network out-performed the best expert (and the other experts) in monetary payoffs at the 10% significance level. Locate this paper on Google Scholar!

Our NeuroSolutions product is an excellent resource for function approximation applications. For an interactive example of function approximation in NeuroSolutions, download the free evaluation version of the software and view the demo called “Multi-Layer Perceptron, Basic” in the Help menu.

Forecast trends based on previous data

Forecasting the relationship between multiple factors in sports betting data is a type of time-series prediction problem. Neural networks can be used to solve time-series problems, typically through Time-Lagged Recurrent (TLRN) type network.

Examples of time-series predictions in sports betting include forecasting thoroughbred horse races. For example, data from sporting events can be used in predicting the outcome of horse racing events since they can determine patterns and trends in large multi-variable data sets.

Our NeuroSolutions product is an excellent resource for time-series prediction applications. For an interactive example of time-series prediction in NeuroSolutions, download the free evaluation version of the software and view the demo called “Time Lagged Recurrent Network” in the Help menu.

Group potential assets or players based on similarities

Grouping of sports betting data based on key characteristics is a type of clustering problem. Neural networks can be used to solve clustering problems, typically through Self-Organizing Map (SOM) type network.

Examples of clustering in sports betting include the detection of key characteristics in player and team statistics. For example, data from team statistics can be grouped into common categories to determine the likelihood of a baseball team winning the World Series.

Our NeuroSolutions product is an excellent resource for clustering applications. For an interactive example of clustering in NeuroSolutions, download the free evaluation version of the software and view the demo called “Unsupervised Learning” in the Help menu.