Full Time Result Prediction using Ensemble Techniques

Mrigank Vashist, Vasudha Bahl, Amita Goel, Nidhi Sengar
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引用次数: 0

Abstract

Sports Analytics is a growing industry and one of the best real-word applications of Data Science. In this paper, the interest of author and machine learning capabilities were combined to develop a result predictor for football matches. The model proposed is capable of predicting result of any English Premier League Match at the half-time with 75% accuracy. The full-time result predictor is a system based on ensemble of powerful classification algorithms which can predict the odds of winning and draw of both home team and away team on the basis of goals scored at the half time and the current standings in the league. The model learns from the past records of the league and the results of different models are compared in the last section of the paper. Keywords— Data Science, comparative models, result prediction, football analysis
使用集成技术的全时结果预测
体育分析是一个不断发展的行业,也是数据科学的最佳实际应用之一。本文将笔者的兴趣与机器学习能力相结合,开发了一个足球比赛的结果预测器。该模型能够以75%的准确率预测任何一场英超半场比赛的结果。全时比赛结果预测器是一个基于强大的分类算法集合的系统,它可以根据半场进球和当前联赛排名来预测主客场球队的胜率和平局率。该模型借鉴了以往的联赛记录,并在论文的最后部分对不同模型的结果进行了比较。关键词:数据科学,比较模型,结果预测,足球分析
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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