|
时空足球比赛数据中战术的自动发现
Automatic Discovery of Tactics in Spatio‑Temporal Soccer Match Data |
|
| |
| 课程网址: |
http://videolectures.net/kdd2018_decroos_automatic_discovery/
|
| 主讲教师: |
Tom Decroos
|
| 开课单位: |
KU鲁汶
|
| 开课时间: |
2018-11-23
|
| 课程语种: |
英语
|
| 中文简介: |
如今，运动队正在从训练和比赛中收集大量数据。这些团队越来越有兴趣利用这些数据来获得竞争优势。最流行的新数据类型之一是来自匹配的事件流数据。这些数据能够进行更高级的描述性分析，并有可能更深入地调查对手的战术。由于数据和游戏策略的复杂性，目前大多数战术分析都是由人类亲自查看视频和侦察比赛进行的。因此，这是一个耗时且乏味的过程。本文探讨了从职业足球比赛中收集的事件流数据中自动检测战术的问题。我们强调了这些数据和这一问题造成的几个重要挑战。我们描述了一种数据驱动的方法，用于识别运动模式，该模式考虑了表示潜在进攻战术的空间和时间信息。我们对2015/2016赛季英超联赛的方法进行了评估，并能够确定每支球队与进球、角球和定位球相关的有趣策略。
|
| 课程简介: |
Sports teams are nowadays collecting huge amounts of data from training sessions and matches. The teams are becoming increasingly interested in exploiting these data to gain a competitive advantage over their competitors. One of the most prevalent types of new data is event stream data from matches. These data enable more advanced descriptive analysis as well as the potential to investigate an opponent’s tactics in greater depth. Due to the complexity of both the data and game strategy, most tactical analyses are currently performed by humans reviewing video and scouting matches in person. As a result, this is a time-consuming and tedious process. This paper explores the problem of automatic tactics detection from event-stream data collected from professional soccer matches. We highlight several important challenges that these data and this problem setting pose. We describe a data-driven approach for identifying patterns of movement that account for both spatial and temporal information which represent potential offensive tactics. We evaluate our approach on the 2015/2016 season of the English Premier League and are able to identify interesting strategies per team related to goal kicks, corners and set pieces.
|
| 关 键 词: |
流行的新数据类型; 游戏策略的复杂性; 潜在进攻战术; 匹配的事件流数据
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2023-01-24：cyh
|
| 最后编审: |
2023-01-24：cyh
|
| 阅读次数: |
32
|
|
图 书 馆