Cricket Squad Analysis Using Multiple Random Forest Regression

Nigel Rodrigues, Nelson Sequeira, Stephen Rodrigues, Varsha Shrivastava
{"title":"Cricket Squad Analysis Using Multiple Random Forest Regression","authors":"Nigel Rodrigues, Nelson Sequeira, Stephen Rodrigues, Varsha Shrivastava","doi":"10.1109/ICAIT47043.2019.8987367","DOIUrl":null,"url":null,"abstract":"In the game of cricket, analyzing the performance of a player is very crucial so as to have a well-balanced squad. Different tours demand various combinations of players as the conditions differ from stadium to stadium. Thus, the selectors have to consider various attributes of a player along with certain other attributes like experience of the player, performance in a particular condition and many more attributes. Such information can be obtained from the player’s career record. This paper covers the concepts of Multiple Random Forest Regression to be used to predict the value of the attributes of the batsmen and the bowlers in the given match, which will help in selecting the players for the given tour.The model will be used for the ODI format of the game. The past record of a player against a particular opposition is used as the dataset to train the model. The touring team, the opposition and the venue of the match are taken as input by the model. A rank-wise list of all the batsmen and bowlers is generated based on the input fields which can be used by the selectors to select the team as per the desired combination.","PeriodicalId":221994,"journal":{"name":"2019 1st International Conference on Advances in Information Technology (ICAIT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 1st International Conference on Advances in Information Technology (ICAIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIT47043.2019.8987367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In the game of cricket, analyzing the performance of a player is very crucial so as to have a well-balanced squad. Different tours demand various combinations of players as the conditions differ from stadium to stadium. Thus, the selectors have to consider various attributes of a player along with certain other attributes like experience of the player, performance in a particular condition and many more attributes. Such information can be obtained from the player’s career record. This paper covers the concepts of Multiple Random Forest Regression to be used to predict the value of the attributes of the batsmen and the bowlers in the given match, which will help in selecting the players for the given tour.The model will be used for the ODI format of the game. The past record of a player against a particular opposition is used as the dataset to train the model. The touring team, the opposition and the venue of the match are taken as input by the model. A rank-wise list of all the batsmen and bowlers is generated based on the input fields which can be used by the selectors to select the team as per the desired combination.
用多元随机森林回归分析板球队
在板球比赛中,分析球员的表现是非常重要的,这样才能有一个平衡的球队。不同的巡回赛需要不同的球员组合,因为每个体育场的条件都不同。因此,选择器必须考虑球员的各种属性以及某些其他属性,如球员的经验,特定条件下的表现以及更多属性。这些信息可以从球员的职业生涯记录中获得。本文介绍了多元随机森林回归的概念,用于预测给定比赛中击球手和投球手的属性值,这将有助于为给定的巡回赛选择球员。该模型将用于游戏的ODI格式。一个球员过去与一个特定对手的比赛记录被用作训练模型的数据集。模型输入的是参赛队、对手和比赛场地。根据输入字段生成所有击球手和投球手的排名列表,选择器可以使用该列表根据所需的组合选择球队。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信