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Abstract
Effective management of a soccer team requires informed decision-making that profoundly influences team success, particularly in the dynamic and uncertain environment of the sport. To navigate the complexities, coaches have increasingly adopted data-driven techniques, resulting in an abundance of analyses that can lead to information overload. Therefore, acquiring a comprehensive understanding of existing soccer performance analysis and leveraging relevant insights become crucial. This study encompasses 3,263 papers from Scopus published between 1978 and 2022. Our research employs a two-layer bibliometric analysis approach, involving the construction of a broader keyword co-occurrence network and subsequent nested keyword co-occurrence networks to identify and analyze the main research themes and their sub-themes within the field. Through this nested bibliometric approach, we develop a two-layer hierarchical taxonomy comprising seven overarching research themes and 40 corresponding sub-themes, thereby unveiling the intellectual structure of the literature in soccer performance analysis. Furthermore, our methodology allows us to delve deeper into the data collection process, enabling us to measure the proportion of articles effectively included in the keyword co-occurrence network. By employing this nested bibliometric approach, we evaluate the prevalence of multi-thematic papers in the field, revealing a paucity of truly multi-thematic studies. This research contributes to a deeper understanding of the intellectual structure of soccer performance analysis, providing valuable insights for both practitioners and researchers to advance knowledge in the field and foster a more comprehensive comprehension of soccer performance analysis.
Original language | English |
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Pages (from-to) | 7501-7534 |
Number of pages | 34 |
Journal | Scientometrics |
Volume | 129 |
Issue number | 12 |
DOIs | |
Publication status | Published - 14 Nov 2024 |
Keywords
- Soccer
- Performance analysis
- Bibliometric analysis
- Science mapping
- Taxonomy
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Development of a Data-Intensive Recommendation Framework for Player Selection in Sport Management
Vermaut, B. (PI), Faulkner, S. (Supervisor) & Burnay, C. (Co-supervisor)
1/10/22 → 1/10/25
Project: PHD