1 / 4

Unveiling Key Soccer Players Through Network Analysis

Discover how identifying key players in soccer teams using network analysis can revolutionize performance evaluation and player valuation. This research ranks players by influence and uncovers the most vital player groups. The methodology involves analyzing real-time match data, such as passing networks, to determine the central player crucial for a team's success. By exploring the inter-centrality metric, the most influential player can be unveiled, offering insights for player improvement and business decisions. This innovative approach can extend to various applications beyond sports, making it a valuable tool in different fields.

ulla
Download Presentation

Unveiling Key Soccer Players Through Network Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Key Players and Key Groups in Teams: A Network Approach Using MCFC SoccerData Ying Zhu Electrical Engineering Department

  2. Objectives Question:Whoisthemostinfluentialplayer,i.e.,thekeyplayer,inasoccerteam? • A naive answer: the player with the most goals. • A better answer: in modern soccer • A player cannot score without a good sequence of passes. • The player most central to the passes are often the key player. Objectives of Our Research • Find the key player using real-time match data. • Rank players by their influence. • Find the most influential , or key , player group Significance: • Can be used for player performance evaluation and improvement. • Can be used for to determine player valuation for business. • Techniques can be used in other applications (e.g., military, finance, crime fighting)

  3. 2 15 10 3 1 Methodology 4 • Datarepresentation: • Onereal-time playermatchdata(Manchester City (MC) vs. Bolton Wanderers (BW); Date: 08/21/2011, Result: Manchester City won) • Representasapassingnetwork(successful passescompleted by each team as two passing network matrixes, MC and BW ). • Forexample: 4 nodes adjacency matrix: • Passing network data interpretation: rows: passes made; columns: passes received • Mathematical modeling: • Keyplayer:ifthekeyplayerisremoved,thetotalteamperformancesuffersthemost • Howtofindthekeyplayer:the key player has the highest inter-centrality • Ability weights: 5 8 7 6 10 8 5

  4. Results

More Related