Analyzing predictive approaches in martial arts research - Yrui Tropin; Leonid Podrigalo; Natalya Boychenko; Olha Podrihalo; Oleksandr Volodchenko; Denis Volskyi; Mariia Roztorhui (IP Iermakov S.S., 2023)
Background and Study Aim. Predicting the results of martial arts competitions is an important task that attracts the attention of both sports analysts and fans of these sports. The objective of this study is to perform an analytical examination of publications on martial arts prediction, with the aim of identifying the primary research directions in this field. Materials and Methods. the bibliometric analysis of PubMed database data was used to create a sample of studies at 18.05.2023. The keywords "prediction", "martial arts" were used for the search. A total of 151 publications were found. The first publication was dated 1983. VOSviewer 1.6.19 program was used: keyword analysis method and direct citation analysis with the construction of bibliometric maps, the visualization of cluster density, weights - citations. Results. 51 journals from 21 countries were identified. The unconditional leader among the countries is the United States (16 journals). Between 1983 and May 18, 2023, 741 scientific works were found. The analysis involved 67 authors whose link strength was more than 0. Eight clusters were identified. They were characterized by the presence of 271 links with total link strength of 276. The number of items in the clusters did not have a significant difference; this can be explained by the popularity of all directions in the research. The authors of the seventh and eighth clusters had the most publications. To visualize the network 63 items (keywords) were selected. They were grouped into 4 clusters. The network includes 951 links; the total link strength is 4027. The most popular studies are highlighted. These studies include the following keywords: "humans", "martial arts", "female", "male", "athletes", "young adult", "middle aged". Conclusions. The analysis of the bibliometric maps revealed the tendencies of scientific research and highlighted the priority areas. The relevance of the problem of prediction in martial arts is confirmed. An increase in the number of publications in PubMed database over the past decade has been observed. The main areas of research include martial arts, health, sports training, and humans. Most publications focus on utilizing artificial intelligence and machine learning techniques for predicting competition outcomes. Additionally, they explore the application of analytical tools to uncover patterns in data and identify critical factors that impact competition results. Modern technologies and the availability of big data open up new possibilities for predicting competitive success in martial arts.