Raising the bar in sports performance research

Abstract

The number of original manuscripts submitted to the Sports Performance section of the Journal of Sports Sciences increased 34% between 2017 and 2020 (637 vs 854). There are many factors that could be contributing to this rise in submissions, including an increase in the popularity of the Journal of Sports Sciences, "publish or perish" pressure (Brischoux & Angelier, 2015), increased data availability through routine monitoring of athletes (Robertson, 2020), perverse incentives, metrification, and hyper-competition that drives academics and researchers to prioritise quantity over quality (Edwards & Roy, 2017;Moore et al., 2017).The rise in submissions to the Sports Performance section means that the bar has raised for getting published. Unfortunately, the space we have within the Journal is not unlimited, and that space is not being increased by the publisher to accommodate the increase in submissions. We also have a dearth of reviewers, which is exacerbated by some researchers wanting their own papers to receive high-quality reviews but unfortunately not willing to review themselves (Stafford, 2018). The bar to publication has also been raised by the emergence of Open Science (Munafò et al., 2017), a movement that comprises both principles (e.g., transparency, reuse, participation, accountability) and practices (e.g., open publications, data-sharing, citizen science; National Academies of Sciences Engineering and Medicine, 2018). Practices aimed at increasing the methodological rigour of scientific studies that have emerged, such as pre-registration and, more recently, Registered Reports (Abt et al., 2021b;Caldwell et al., 2020), are now offered as submission types in the Journal of Sports Sciences.Given this background, we now set out what we are looking for in manuscripts submitted to the Sports Performance section.

Quantifying Exposure and Intra-Individual Reliability of High-Speed and Sprint Running During Sided-Games Training in Soccer Players: A Systematic Review and Meta-analysis

“…This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [ 61 , 62 ] (items checklist available in the Electronic Supplementary Material [ESM]), alongside the consensus statement for reviews in Exercise, Rehabilitation, Sport medicine and SporTs science (PERSiST) [ 63 ], and was registered [ 64 ] in the Open Science Framework ( https://osf.io/gh792 ) on 4 March, 2021. Two reviewers (ADI, TS) and a senior librarian with ~ 15 years of experience in conducting systematic searches for meta-analyses in sport performance fields independently performed standard and optimized electronic searches using the PubMed/MEDLINE, Scopus, SPORTDiscus and Web of Science Core Collection databases, from inception to 28 April, 2021 (further details in the ESM: https://osf.io/28vap ).…”

“…For effects emerging from the main results and meta-regression analyses, we opted to avoid a dichotomous approach for their interpretation based upon traditional null hypothesis significance testing, which has been extensively criticized [ 83 , 84 ]. Alternatively, we considered the practical implications of all results with an emphasis on the pooled point estimates as well as the lower and upper limits of the interval estimates [ 64 ]. Uncertainty in meta-analysis estimates was expressed using 95% compatibility (confidence) intervals (CIs), representing ranges of values compatible with our models and assumptions.…”

“…Uncertainty in meta-analysis estimates was expressed using 95% compatibility (confidence) intervals (CIs), representing ranges of values compatible with our models and assumptions. We also derived 95% prediction intervals (PIs), which convey the likely range of the true measurement properties in similar future studies [ 64 ].…”

Recommendations for the design of randomized controlled trials in strength and conditioning. Common design and data interpretation

“…However, sport science is plagued by popular beliefs, myths and poor-quality evidence (Gabbett and Blanch, 2019 ). There are many reasons why the quality of articles is sometimes low, for example, the authors' knowledge of research methods or statistics is inadequate (Cleather et al, 2021 ; Sainani et al, 2021 ), the resources invested in the research are limited, or the research was carried out in a hurry, which could be related to many reasons, for instance, several studies are performed by students who have limited time and experience when performing data recording (Abt et al, 2022 ). Consequently, a key question remains: what should we do to improve current scientific evidence and limit the spread of new low-quality evidence?…”

New predictive model of the touchdown times in a high level 110 m hurdles

“…Based on the previous discussion, it is clear that the existing model does not provide accurate predictions. Therefore, a more robust, reliable model established in an appropriate manner, such as LOOCV [18], is required. The present study, therefore, aimed to establish a more robust, reliable statistical model of touchdown times based on the data of elite 110 m hurdlers to precisely predict their typical touchdown times.…”