一日国际板球比赛结果预测的机器学习技术

Inam Ul Haq, Inzimam Ul Hassan, Hilal Ahmad Shah
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引用次数: 0

摘要

板球是最受欢迎的运动,也是每天观看人数最多的运动。测试赛、一日国际赛(ODI)和二十20国际赛是板球的三种形式。直到最后一局的最后一个球,谁也无法预测谁将赢得这场比赛。机器学习是一个利用现有数据预测未来结果的新领域。这项研究的目标是建立一个模型,在一天的国际比赛开始前预测获胜者。机器学习技术将用于测试和训练数据集,以根据指定的特征预测ODI比赛的获胜者。该模型的数据是从Kaggle收集的,有些数据是从不同的板球网站收集的,因为从Kaggle获得的数据只匹配到2021年7月。预测采用了K-Nearest和XGBoost两种算法,其中K-Nearest Neighbor算法预测准确率为91%,XGBoost算法预测准确率为89%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Techniques for Result Prediction of One Day International (ODI)Cricket Match
Cricket is the most popular sport and most watched now a day. Test matches, One Day Internationals (ODI), and Twenty20 Internationals are the three forms in which it is played. Until the last ball of the last over, no one can predict who would win the match. Machine learning is a new field that uses existing data to predict future results. The goal of this study is to build a model that will predict the winner of a One-Day International Match before it begins. Machine learning techniques will be used on testing and training datasets to predict the winner of ODI match that will be based on the specified features. The data for the model is collected from Kaggle and some of the data are collected from the different cricket websites because the data obtained from Kaggle has only matches up until July 2021. Two algorithms were used for the prediction, K-Nearest and XGBoost, out of these two algorithms prediction accuracy of 91% was obtained by K-Nearest Neighbor Algorithm and prediction accuracy of 89% was obtained by XGBoost Algorithm
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