Markov Processes for Big Data Hockey Analytics

我们的项目研究冰球的大数据分析。它结合了最翔实的数据， 机器学习研究中已知的最强大的统计模型，为加拿大的 最喜欢的游戏基于大体育数据的分析市场正在迅速增长，主要国际 公司获得数百万美元的投资。加拿大在体育分析方面进行了出色的研究， 但没有太多的研究专门知识被转移到商业产品中。这家总部位于蒙特利尔的 SportLogiq公司使用先进的计算机视觉从视频中提取有关事件的广泛信息 在曲棍球比赛中。他们的信息比任何其他公司提供的信息更详细， organization.我们的项目将使用机器学习为他们的数据构建一个强大的统计模型， 一个Markov过程。统计建模的应用包括“点球成金”式的球员排名 找到能很好地补充球队的球员。

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.