Using Machine Learning to Assess and Compare Athletes in Team Sports

被引：0

作者：

Zimmermann, Albrecht ^{[1
]}

机构：

[1] Normandie Univ Caen, Caen, France

来源：

9TH INTERNATIONAL PERFORMANCE ANALYSIS WORKSHOP AND CONFERENCE & 5TH IACSS CONFERENCE | 2022年

关键词：

Sports analytics; Player assessment; Machine learning;

D O I：

10.1007/978-3-030-99333-7_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Using machine learning techniques (ML) to assess action (and derived from it player) quality is a recent and promising alternative to expert-based assessments. The big challenges for these approaches consist of data acquisition, data transformation and augmentation, which often require in-depth knowledge of the sport in question, as well as understanding which ML techniques are well-suited not only for final modeling but also for data preparation.

引用

页码：13 / 17

页数：5

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