Machine Learning on Sports Prediction
How can we forget Paul the Octopus who predicted the winner of the world cup in 2010 which had gone viral globally? The Octopus used the flags of both the teams, which were presented in front of him in a box before a game, and the winning team would be selected the box from which Paul would eat first. Paul had 12 correct predictions out of 14.
The result of any given sports game is unpredictable, yet we humans try and predict results either on the basis of statistical analysis, or simply a hunch. Predicting the winner of any a game is part of the game viewing experience and has been a crucial selling point for the betting industry since the beginning. Another interesting method to predict results of games has recently surfaced: Prediction by using Machine Learning, in which the winner of the game or the tournament is predicted by companies who've used several different algorithms to predict these results.
Machine learning is one of the clever methodologies that has shown promising results in the field of classification and prediction. One of the growing areas that requires high predictive accuracy is sports forecasting due to the large sums of money involved in betting. In addition, club managers and owners look for classification patterns so that they can understand and articulate the strategies needed to win matches.
Also See: Linear Regression For Sports Prediction
For example, in a 2008 study, scientists attempted to predict performance in four different sports: NFL (Rugby League), AFL (Australian Rugby), Super Rugby ( football federation) and top UK rugby league (EPL) using 2002 data. Their artificial neural network (ANN) has 20 nodes in input layer, 10 nodes in hidden layer and 1 button in the output layer (20-10-1). Features used for all sports are the same, and attributes related to specific events during a rugby or soccer match are not considered. The average efficiency of the ANN algorithm in predicting the results is about 67.5%, compared to experts' prediction, the accuracy is about 60–65%.
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An example of an ANN structure with 4 input buttons in input layer, 5 hidden buttons in hidden layer and one output node in output layer. (4-5-1) |
ANN was also used to predict race results. The study author used data from 100 races at the Aqueduct Track, held in New York in January 2010. An ANN is used for each horse in the race and the output is complete time. city of that horse. Eight functions are used for input nodes in each ANN. These are horse weight, race type, coach, jockey, number of horses in the race, race distance, race condition and weather. This optimal network architecture (8-2-1), in terms of square average error, consists of four layers: one input layer (with eight input nodes), two hidden layers, and one output layer ( with horse completion time). Five different learning algorithms were applied to the data: Gradient Descent (BP), Momentum Parameter Gradient Descent (BPM), Levenberg-Marquadt (LM), and Conjugate Gradient Descent (CGD). Found, the BPM (parametric 0.7 pulses) and BP algorithms were most effective in predicting the winner of the race, with 77% accuracy. However, the disadvantage of BP is the long learning time (LM has the shortest learning time).
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