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Investigating improved evaluation models for sports analytics

研究改进的运动分析评估模型

基本信息

批准号：
522343-2018
负责人：
Ahmed, SyedEjaz
金额：
$ 1.82万
依托单位：
Brock University
依托单位国家：
加拿大
项目类别：
Engage Grants Program
财政年份：
2018
资助国家：
加拿大
起止时间：
2018-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=643578
关键词：
Investigating improved evaluation models sports

项目摘要

Stathletes is a Canadian company, founded with the purpose of developing a comprehensive statistical database to drive winning**in hockey through real, actionable data. We provide our customers with professional hockeys deepest performance data and**analytics, by collecting hundreds to thousands of microdata from a single game to identify specific movements that players make**that may be key indicators for performance measurements.**Statistical methods of predicting future goals in hockey is challenging because there are relatively low scoring rates compared to**sports, such as basketball. Particularly when analyzing team and player performance in hockey with less than a seasons worth of**data, there are limitations in our ability to properly judge and predict performance. In order to remain competitive in the emerging**market of data analytics, Stathletes technologies must keep pace with novel statistical methods developed by leading**researchers in the field.**In collaboration with Brock University, we would like to develop improved expected goals and player selection models for**application to our extensive database of performance measures. This will enhance the value of our proprietary data assets and**ensure that we maintain superior predictive capabilities over our competitors.

Stathletes是一家加拿大公司，成立的目的是开发一个全面的统计数据库，通过真实的、可操作的数据来推动曲棍球的胜利。我们为我们的客户提供专业曲棍球最深入的性能数据和**分析，从一场比赛中收集成百上千的微观数据，以确定球员做出的特定动作**，这可能是衡量成绩的关键指标。**预测曲棍球未来目标的统计方法具有挑战性，因为与**运动(如篮球)相比，曲棍球的得分率相对较低。尤其是在用不到一个赛季的**数据来分析球队和球员在曲棍球中的表现时，我们正确判断和预测表现的能力是有限的。为了在新兴的**数据分析市场保持竞争力，Stathletes技术必须与该领域领先的**研究人员开发的新统计方法保持同步。**我们希望与布罗克大学合作，开发改进的预期目标和球员选择模型，以便**应用于我们广泛的绩效衡量数据库。这将提高我们专有数据资产的价值，并**确保我们保持优于竞争对手的预测能力。