Markov Processes for Big Data Hockey Analytics

我们的项目调查冰球的大数据分析。它将可用的最具信息量的数据与 机器学习研究中已知的最强大的统计模型，为加拿大的 最喜欢的游戏。基于体育大数据的分析市场正在迅速增长，主要的国际 获得数百万美元投资的公司。加拿大在体育分析方面进行了出色的研究， 但没有多少研究专业知识被转移到商业产品上。总部设在蒙特利尔的 SportLogiq公司使用先进的计算机视觉从视频中提取有关事件的大量信息 在曲棍球比赛中。他们的信息比任何其他公司提供的信息都要详细 组织。我们的项目将使用机器学习为他们的数据建立一个强大的统计模型，已知 作为一个马尔可夫过程。统计建模的应用包括“Moneyball”类型的球员排名 表现要找到对球队有很好补充作用的球员。

Our project investigates big data analytics for ice hockey. It combines the most informative data available with the most powerful statistical model known from machine learning research to provide analytics for Canada's favorite game. The market for analytics based on big sports data is growing rapidly, with major international companies receiving millions of investment dollars. Canada has produced excellent research in sports analytics, but not much of the research expertise has been transferred to commercial products. The Montreal-based company SportLogiq uses advanced computer vision to extract from video extensive information about events in a hockey match. Their information is more detailed than that provided by any other company or organization. Our project will use machine learning to build a powerful statistical model for their data, known as a Markov process. Applications of statistical modeling include "Moneyball"-type ranking of player performance to find players that complement a team well.