developing and validating customized machine learning and data analytics methods for mining cricket

板球被认为是世界上第二受欢迎的运动。过多的可用板球数据和人工智能技术的发展创造了对板球数据分析的巨大需求。在过去的二十年里，人工智能在板球领域的应用急剧增加。这个博士项目是开发和验证定制的机器学习和数据分析方法，以挖掘与板球相关的数据，以支持人才培养和表现概况的决策。该项目将与英格兰和威尔士的国家板球管理机构英格兰和威尔士板球委员会(ECB)合作完成。ECB还将提供几年来详细的国内和国际板球比赛数据、板球运动员(纵向)表现数据和球探数据。手头的数据是巨大的，因此，成功的学生要回答的另一个问题是使用众多数据集中的哪个，来自哪个时期，以什么粒度。本项目将集中在两个主要问题/研究差距上：1.什么是英格兰板球运动员发展的驱动因素？解决这个问题将涉及使用统计分析和机器学习(例如，特征重要性、降维、聚类)来探索不同运动员角色的人口统计和发展运动史(例如，佩斯保龄球手，击球手和守门员)，他们拥有不同的专业水平，以评估这些因素对英格兰球员发展的影响。对驱动因素有更好的理解(有数据支持)，将有助于培养人才和定制训练方案。哪些数据和性能指标可以用来预测不同形式的板球(如测试、ODI、IT20)的成功？解决这个问题将涉及使用功能工程和预测方法来确定第一类(县)游戏中的性能指标，这些性能指标可以预测英语球员(潜在的海外球员)在国际水平的测试、One Day International和T20I格式的成功。最终，人们可能想要根据第二季度确定的表现指标来影响发展的驱动因素(第一季度)，以便为板球运动员提供个性化的职业道路。成功的学生将能够在两个广泛的主题领域内开发研究计划。该项目将与英格兰和威尔士的国家板球管理机构英格兰和威尔士板球委员会(ECB)合作完成。

Cricket is considered the world's second most popular sport. The plethora of available cricket data and the development of AI technologies have created a massive demand for cricket data analytics. The applications of AI in the cricket domain have increased dramatically during the last two decades.This PhD project is about developing and validating customized machine learning and data analytics methods for mining cricket-related data to support decision-making in talent nurturing and performance profiling.The project will be done in collaboration with the England and Wales Cricket Board (ECB), which is the national governing body of cricket in England and Wales. ECB will also provide access to several years of detailed national and international cricket match data, (longitudinal) performance data of cricket players, and scouting data. The data available to hand is of huge volume, and hence another question to be answered by the successful student is which of the many datasets to use, from which periods, and at what granularity.This project will focus on two main questions/research gaps:1. What are the driving factors for the development of England's cricket players?Addressing this question will involve using statistical analysis and machine learning (e.g. feature importance, dimensionality reduction, clustering) to explore the demographic and developmental sporting history of different player roles (e.g. pace bowlers, batters and wicket keepers) with differing expertise levels to assess the impact these factors have on the development of England's players. Having an improved understanding (that is backed up by the data) of the driving factors, will be useful for nurturing talent and customize training regimes.2. What data and performance metrics can be used to predict success for different formats of cricket (e.g. Test, ODI, iT20)?Addressing this question will involve using feature engineering and prediction methods to identify performance metrics in the First Class (county) game that predict success at International level in Test, One Day International and T20I) formats for English players (potential to explore overseas players). Ultimately, one may want to influence the driving factors for development (Q1) according to the performance metrics identified in Q2 to provide cricket players with a personalized career path.The successful student would be able to develop the research programme within the scope of the two broad topic areas.The project will be done in collaboration with the England and Wales Cricket Board (ECB), which is the national governing body of cricket in England and Wales.