Evidence map›Paper›PMID 37908810›Full record

ArticleFrontiers in psychology2023

Soccer's AI transformation: deep learning's analysis of soccer's pandemic research evolution.

Jea Woog Lee, Sangmin Song, YoungBin Kim, Seung-Bo Park, Doug Hyun Han

Abstract read
In one paragraph

Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jea Woog LeeIntelligent Information Processing Lab, Chung-Ang University, Seoul, Republic of Korea.
Sangmin SongDepartment of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea.
YoungBin KimGraduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Republic of Korea.
Seung-Bo ParkGraduate School of Sports Medicine, CHA University, Seongnam-si, Republic of Korea.
Doug Hyun HanDepartment of Psychiatry, Chung Ang University Hospital, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This paper aims to identify and compare changes in trends and research interests in soccer articles from before and during the COVID-19 pandemic. Methods: We compared research interests and trends in soccer-related journal articles published before COVID-19 (2018-2020) and during the COVID-19 pandemic (2021-2022) using Bidirectional Encoder Representations from Transformers (BERT) topic modeling. Results: In both periods, we categorized the social sciences into psychology, sociology, business, and technology, with some interdisciplinary research topics identified, and we identified changes during the COVID-19 pandemic period, including a new approach to home advantage. Furthermore, Sports science and sports medicine had a vast array of subject areas and topics, but some similar themes emerged in both periods and found changes before and during COVID-19. These changes can be broadly categorized into (a) Social Sciences and Technology; (b) Performance training approaches; (c) injury part of body. With training topics being more prominent than match performance during the pandemic; and changes within injuries, with the lower limbs becoming more prominent than the head during the pandemic. Conclusion: Now that the pandemic has ended, soccer environments and routines have returned to pre-pandemic levels, but the environment that have changed during the pandemic provide an opportunity for researchers and practitioners in the field of soccer to detect post-pandemic changes and identify trends and future directions for research.

Indexed as

BERTCOVID-19data sciencefootballpandemicresearch trendsoccertopic modeling

Identifiers

PMID37908810
PMCPMC10613686

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.