Analysis of football research trends using text network analysis

TL;DR

This study analyzes trends in football research over 30 years using keyword network analysis to identify shifts in focus.

Contribution

The novel use of text network analysis reveals evolving research priorities in football studies.

Findings

Injury, performance, and club were the most frequently mentioned keywords in football research.

The keyword 'cup' gained the highest eigenvector centrality in the 2010s, indicating a shift in research focus.

Research on football itself has increased recently, despite longstanding focus on injuries.

Abstract

This study was aimed to identify football research trends in various periods. A total of 30,946 football papers were collected from a representative academic database and search engine, the ‘Web of Science’. Keyword refinement included filtering nouns, establishing synonyms and thesaurus, and excluding conjunctions, and the Cyram’s Netminer 4.0 software was used for network analysis. A centrality analysis was conducted by extracting the words corresponding to the top 2% of the main research topics to obtain the degree and eigenvector centralities. The most frequently mentioned research keywords were injury, performance, and club. Keyword performance showed the highest degree centrality (0.294) and keyword world and cup showed the highest eigenvector centrality (0.710). The keyword with the highest eigenvector degree changed from injury in the 1990s and world in the 2000s to cup since the 2010s. Although various studies on football injuries have been conducted, research on the sport itself has recently been conducted. This study provides fundamental information on football trends from research published over the past 30 years.

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