Statistical Methods and Computation for Sports Analytics

随着人们对体育运动的兴趣日益浓厚，数据的可用性在规模和复杂性方面都在不断提高，在体育分析领域开展令人兴奋的工作的机会从未如此之大。体育分析中最广泛和最重要的问题之一是球员评估。在职业体育中，球员评价对奖项、名册选择和工资管理都有影响。玩家评价是本研究提案的主要目标之一。在许多运动中，球员的评价传统上依赖于无数的统计数据，并不是所有的统计数据都能准确地反映球员对这项运动的贡献。今天发生变化的是大量数据的可用性，包括玩家跟踪数据。例如，在一些职业足球联赛中，在90分钟的比赛加上附加时间内，球和场上22名球员的位置每秒被记录10次。这些庞大的数据集使研究人员能够调查这项运动的细微差别，并更准确地描绘球员贡献的整体情况。我计划学习的两项运动是正在发展的（但仍然是次要的）运动泡菜球和世界上最受欢迎的运动足球。在pickleball中，我们有一个来自双打活动的大型数据集，其中合作伙伴定期更换。使用比赛分数作为数据，我们计划提供球员实力的准确排名，其中比赛结果被球员的个人贡献所混淆。具体来说，我们感兴趣的是在何种程度上匹克球是一个强（弱）链接运动;一个强（弱）链接运动是一个更强（弱）的合作伙伴对比赛结果有更大的影响。在足球运动中，我们将开发球员评估方法，利用详细的球员跟踪数据和涉及“无球”活动的足球洞察力。在体育运动中，分析事件数据-即球附近发生的事情-是诱人而方便的。我的意图是考虑什么没有发生，描述为什么它没有发生，并在这些情况下归因于价值。这个想法是复制和加强什么教练/经理推断时，教练密切关注特定球员的活动。分析中的技术方面之一涉及通过聚类方法确定相似的阵型，以便可以根据许多相似阵型的结果为玩家动作分配价值。随着我们的连接到国家和省级加拿大体育组织，研究活动是在提供竞争优势，加拿大队的重要。这些方法也是新颖的，并可应用于自然科学和工程学的其他领域。例如，我们提出的聚类方法涉及一个新的时间方面的意义上说，团队的形成是动态的。

With the growing fascination in sport and the increased availability of data in both size and complexity, the opportunity to do exciting work in sports analytics has never been greater. One of the most widespread and important problems in sports analytics is player evaluation. In professional sport, player evaluation has an impact on awards, roster selection and the management of salaries. Player evaluation is one of the primary objectives in this research proposal. Player evaluation in many sports has traditionally relied on a myriad of statistics, not all of which provide an accurate picture of a player's contribution to the sport. What has changed today is the availability of massive amounts of data including player tracking data. For example, in some leagues of professional soccer, the location of the ball and the 22 players on the field are recorded 10 times per second over 90 minute matches plus added time. These massive datasets allow researchers to investigate nuances of the sport and paint a more accurate picture of the entirety of player contribution. Two sports that I plan on studying are the growing (but still minor) sport of pickleball and the most popular sport in the world, soccer. In pickleball, we have a large dataset from doubles events where the partners regularly change. Using match scores as data, we plan on providing accurate rankings of player strength where match outcomes are confounded by the individual contributions of players. Specifically, we are interested in the degree to which pickleball is a strong (weak) link sport; a strong (weak) link sport is one where the stronger (weaker) partner has a greater influence on the match outcome. In the sport of soccer, we will develop player evaluation methods that take advantage of detailed player tracking data and soccer insight involving activity "off the ball". It is tempting and convenient in sport to analyze event data - that is, what has happened in the vicinity of the ball. The consideration of what did not happen, characterizing why it did not happen and attributing worth in these circumstances is my intention. The idea is to replicate and enhance what a coach/manager infers when the coach closely follows the activities of a particular player. One of the technical aspects in the analysis involves the determination of similar formations via clustering methods so that worth can be assigned to player actions based on the outcomes from many similar formations. With our connections to national and provincial Canadian sports organizations, the research activities are important in providing a competitive advantage to Canadian teams. The methods are also novel and translate to other domains in the natural sciences and engineering. For example, our proposed clustering methods involve a novel temporal aspect in the sense that team formations are dynamic.