Analysis of Football Game Performance Based on Social Network

DOI

10.2991/978-94-6463-010-7_81How to use a DOI?

Keywords

Football Team Performance Evaluation; Social Network Analysis; Betweenness; Clustering Algorithm

Abstract

With the advent of big data and network era, the interpersonal relationship is getting closer and closer, and the advantage of teamwork is becoming more and more prominent. By analyzing the team competition rules, combining the team members’ abilities, characteristics, and interactions between team members in previous competitions, team cooperation, and coordination strategy can be further optimized effectively. Based on the data samples recorded in 38 football games, this paper takes the 14th huskies vs. O14 game with a 4-0 result as an example. After data preprocessing, DBSCAN, a clustering algorithm, is introduced to simplify the data set by presenting a scatter graph. Each player’s resident coordinates in each game are obtained, which provides data for social network analysis. We used a Python program to get the overall network analysis. The D6, M1, and F2 players on the Huskies form a triad configuration, the players D3 and D4, the players D1 and M2 in the O14 team form multiple dyadic configurations. To better understand the characteristics of the passing network, the individual network analysis is carried out. Through the study of particular net research degree center degree, it can be found that the Huskies coach’s tactical arrangement was 4-4-2 formation, which was changed into 4-3-3 attacking formation in an actual combat operation. The tactical arrangement of the O14 coach was the same as the essential combat operation, which was 2-3-5 formation and more defensive. The in-depth analysis found that the Huskies’ core was a midfielder, while the O14’s core was not a midfielder. The tempo of the game and the fluidity of the passing network of the O14 team were significantly reduced, which was also the main factor leading to the failure.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yaoyao Qian
AU  - Xianming Wang
PY  - 2022
DA  - 2022/12/02
TI  - Analysis of Football Game Performance Based on Social Network
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 811
EP  - 819
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_81
DO  - 10.2991/978-94-6463-010-7_81
ID  - Qian2022
ER  -