Computational Intelligence in Sports: A Systematic Literature Review
This review addresses the need for structured knowledge in sports data mining, which is incremental as it synthesizes existing research rather than introducing new methods.
The paper conducted a systematic literature review of 31 studies from 2010 to 2018 on sports data mining, analyzing themes, databases, algorithms, and research opportunities to provide a better understanding of its potentials.
Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.