Predicting Head-to-Head Games with a Similarity Metric and Genetic Algorithm

摘要

This paper summarizes our approach to predict head to head games using a similarity metric and genetic algorithm. The prediction is performed by simply calculating the distances of any two teams, that are set to play each other, to an ideal team. The nearest team to the ideal team is predicted to win. The approach uses genetic algorithm as an optimization tool to improve the accuracy of the predictions. The optimization is performed by adjusting the ideal team's statistical data. Soccer, basketball, and tennis are the sport disciplines that are used to test the approach described in this paper. We are comparing our predictions to the predictions made by Microsoft's bing.com. Our findings show that this approach appears to do well on team sports, accuracies above 65%, but is less successful for predicting individual sports, accuracies less than 65%. In our future work, we plan to do more testing on team sports as well as studying the effects of the different parameters involved in the genetic algorithm's setup. We also plan to compare our approach to ranking and point based predictions.