| Title | Discerning tactical patterns for professional soccer teams: An enhanced topic model with applications |
| Authors | Wang, Qing Zhu, Hengshu Hu, Wei Shen, Zhiyong Yao, Yuan |
| Affiliation | School of Mathematical Science, Peking University, China Big Data Lab., Baidu Research, China |
| Issue Date | 2015 |
| Publisher | 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 |
| Citation | 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015.Sydney, NSW, Australia,2015/8/10,2015-August(2197-2206). |
| Abstract | Analyzing team tactics plays an important role in the professional soccer industry. Recently, the progressing ability to track the mobility of ball and players makes it possible to accumulate extensive match logs, which open a venue for better tactical analysis. However, traditional methods for tactical analysis largely rely on the knowledge and manual labor of domain experts. To this end, in this paper we propose an unsupervised approach to automatically discerning the typical tactics, i.e., tactical patterns, of soccer teams through mining the historical match logs. To be specific, we first develop a novel model named Team Tactic Topic Model (T3M) for learning the latent tactical patterns, which can model the locations and passing relations of players simultaneously. Furthermore, we demonstrate several potential applications enabled by the proposed T3M, such as automatic tactical pattern discovery, pass segment annotation, and spatial analysis of player roles. Finally, we implement an intelligent demo system to empirically evaluate our approach based on the data collected from La Liga 2013-2014. Indeed, by visualizing the results obtained from T3M, we can successfully observe many meaningful tactical patterns and interesting discoveries, such as using which tactics a team is more likely to score a goal and how a team's playing tactic changes in sequential matches across a season. ? 2015 ACM. |
| URI | http://hdl.handle.net/20.500.11897/436750 |
| ISSN | 9781450336642 |
| DOI | 10.1145/2783258.2788577 |
| Indexed | EI |
| Appears in Collections: | 数学科学学院 |
