Abstract
Machine learning and digitization tools are exponentially increasing in these last years and their applications are reflected in different areas of our life: in particular, this article has the aim to focus on football (i.e. soccer for Americans), the most practised sport in the world. Due to needing of professional teams, an- alytics tools in football are becoming a crucial point, in order to help technical staff, scouting and clubs management in policy evaluation and to optimize strate- gic decisions. In this article we propose an original bibliometric analysis about football analytics in the decade 2010-2020, thanks the powerful R package Bibliometrix and the well-known bibliometric database SCOPUS. The main goal is to understand better what already exist in football analytics literature and what not, in order to suggest future researchers to find new topics or to refine existing tools. Furthermore, our intention is to show some results starting from the sources production distribution, then focus on the most productive research groups and their countries, discover the most dynamic authors and highlight topics trend thanks keywords, during these last ten years. Finally, three relevant articles that summaries the most important themes are presented.
Machine learning and digitization tools are exponentially increasing in
these last years and their applications are re
ected in dierent areas of our
life: in particular, this article has the aim to focus on football (i.e. soc-
cer for Americans), the most practised sport in the world. Due to needing
of professional teams, an- alytics tools in football are becoming a crucial
point, in order to help technical sta, scouting and clubs management in
policy evaluation and to optimize strate- gic decisions. In this article we pro-
pose an original bibliometric analysis about football analytics in the decade
2010-2020, thanks the powerful R package Bibliometrix and the well-known
bibliometric database SCOPUS. The main goal is to understand better what
already exist in football analytics literature and what not, in order to sug-
gest future researchers to nd new topics or to rene existing tools. Fur-
thermore, our intention is to show some results starting from the sources
production distribution, then focus on the most productive research groups
and their countries, discover the most dynamic authors and highlight topics
trend thanks keywords, during these last ten years. Finally, three relevant
articles that summaries the most important themes are presented.
Aria, M. and Cuccurullo, C. (2017). bibliometrix: An r-tool for comprehensive science
mapping analysis. Journal of Informetrics, 11(4):959{975.
Belore, P., Ascione, A., and Di Palma, D. (2019). Technology and sport for health
promotion: A bibliometric analysis. Journal of Human Sport and Exercise, 10(4):932{
Bransen, L. and Van Haaren, J. (2018). Measuring football players' on-the-ball contri-
butions from passes during games. In International Workshop on Machine Learning
and Data Mining for Sports Analytics, pages 3{15. Springer.
Canova, L. and Canepa, C. (2016). La scienza dei goal: numeri e statistica applicati allo
sport piu bello del mondo. La scienza dei goal, pages 1{174.
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., and Herrera, F. (2011). An
approach for detecting, quantifying, and visualizing the evolution of a research eld:
A practical application to the fuzzy sets theory eld. Journal of informetrics, 5(1):146{
Lopez-Carril, S., Escamilla-Fajardo, P., Gonzalez-Serrano, M. H., Ratten, V., and
Gonzalez-Garca, R. J. (2020). The rise of social media in sport: a bibliometric analy-
sis. International Journal of Innovation and Technology Management, 17(06):2050041.
Pappalardo, L., Cintia, P., Ferragina, P., Massucco, E., Pedreschi, D., and Giannotti,
F. (2019). Playerank: data-driven performance evaluation and player ranking in soc-
cer via a machine learning approach. ACM Transactions on Intelligent Systems and
Technology (TIST), 10(5):1{27.
Stein, M., Seebacher, D., Marcelino, R., Schreck, T., Grossniklaus, M., Keim, D. A.,
and Janetzko, H. (2019). Where to go: Computational and visual what-if analyses in
soccer. Journal of sports sciences, 37(24):2774{2782.
Vigneshwaran, G. and Kalidasan, R. (2018). Study of publications output on sports
science{a bibliometric analysis. Ganesar College of Arts and Science, pages 256{260.
Email this article 
