Special Issue "Data Analytics in Sports Sciences: Changing the Game"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editor

Dr. Filipe Manuel Clemente
E-Mail Website
Guest Editor
Polytechnic Institute of Viana do Castelo, School of Sport and Leisure, 4960-320 Melgaço, Portugal
Interests: football; soccer; match analysis; performance analysis; network analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, many monitoring instruments are used daily in sports sciences to track information about the training load, sports performance, well-being, readiness, and lifestyle of athletes. This information generates an enormous amount of data that, without the correct process, will not provide useful information for coaches and sports scientists working with athletes. For this reason, data analytics and statistics have increased in popularity in sports sciences, namely, by applying new methods that help to quickly understand the most determinant information and generate useful insights for the practice.

The use of non-linear statistics, artificial intelligence, Bayesian statistics, and machine learning is not often reported on in a sports sciences context. However, there is still a need for more applications and scientific research about how to properly use these methods, techniques, and approaches to consistently better understand their usability in sports. Trying to push forward innovative approaches, this Special Issue calls for original articles, systematic reviews, and meta-analyses that can meaningfully contribute to the field of data analytics in sports using data treatment and data processing approaches. Specific articles bridging the gap between science and practice are particularly welcome.

Dr. Filipe Manuel Clemente
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-linear statistics
  • big data
  • data analytics
  • machine learning
  • sports sciences
  • Bayesian statistics

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Effects of Match Location, Quality of Opposition and Match Outcome on Match Running Performance in a Portuguese Professional Football Team
Entropy 2021, 23(8), 973; https://0-doi-org.brum.beds.ac.uk/10.3390/e23080973 - 29 Jul 2021
Cited by 1 | Viewed by 567
Abstract
The aim of this study was to analyze the effects of match location, quality of opposition and match outcome on match running performance according to playing position in a Portuguese professional football team. Twenty-three male professional football players were monitored from eighteen Portuguese [...] Read more.
The aim of this study was to analyze the effects of match location, quality of opposition and match outcome on match running performance according to playing position in a Portuguese professional football team. Twenty-three male professional football players were monitored from eighteen Portuguese Football League matches during the 2019–2020 season. Global positioning system technology (GPS) was used to collect time-motion data. The match running performance was obtained from five playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM) and forwards (FW). Match running performance was analyzed within specific position and contextual factors using one-way analysis of variance (ANOVA) for repeated measures, standardized (Cohen) differences and smallest worthwhile change. CM and WM players covered significantly greater total distance (F = 15.45, p = 0.000, η2 = 0.334) and average speed (F = 12.79, p < 0.001, η2 = 0.294). WM and FB players covered higher distances at high-speed running (F = 16.93, p = 0.000, η2 = 0.355) and sprinting (F = 13.49; p < 0.001, η2 = 0.305). WM players covered the highest number of accelerations (F = 4.69, p < 0.001, η2 = 0.132) and decelerations (F = 12.21, p < 0.001, η2 = 0.284). The match running performance was influenced by match location (d = 0.06–2.04; CI: −0.42–2.31; SWC = 0.01–1.10), quality of opposition (d = 0.13–2.14; CI: –0.02–2.60; SWC = 0.01–1.55) and match outcome (d = 0.01–2.49; CI: −0.01–2.31; SWC = 0.01–0.35). Contextual factors influenced the match running performance with differential effects between playing positions. This study provides the first report about the contextual influence on match running performance in a Portuguese professional football team. Future research should also integrate tactical and technical key indicators when analyzing the match-related contextual influence on match running performance. Full article
(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)
Show Figures

Figure 1

Article
Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions
Entropy 2021, 23(8), 952; https://0-doi-org.brum.beds.ac.uk/10.3390/e23080952 - 25 Jul 2021
Viewed by 656
Abstract
Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early [...] Read more.
Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer. Full article
(This article belongs to the Special Issue Data Analytics in Sports Sciences: Changing the Game)
Show Figures

Figure 1

Back to TopTop