论文标题
预测二十20个板球比赛的结果：机器学习方法
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach
论文作者
论文摘要
Twenty20板球，有时是二十20，经常缩写为T20，是板球的一小部分。在一场二十二十比赛中，两支球员组成的两支球队都有一局，最多仅限20分。此版本的板球尤其是不可预测的，这是它最近在近期越来越受欢迎的原因之一。但是，在本文中，我们尝试了四种不同的机器学习方法来预测T20板球比赛的结果。具体来说，我们要考虑：以前的竞争团队参与者的绩效统计，从知名的板球统计网站获得的玩家的评分，使用类似的性能统计数据聚集了玩家的玩家，并使用基于ELO的基于ELO的评分玩家的方法提出了一种新颖的方法。我们通过使用不同的ML算法，包括逻辑回归，支持向量机，贝叶斯网络，决策树，随机森林来比较这些特征工程方法的性能。
Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.
