Special Issue "Big Data Management Through Multivariate Data Analysis Techniques in Sport"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Sport and Health".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Markel Rico-González
E-Mail Website
Guest Editor
Department of Physical Education and Sport, University of the Basque Country, UPV/EHU, 01007 Vitoria-Gasteiz, Spain
Interests: healthcare; applied technology; performance analysis in sport; team sports; training load management
Special Issues, Collections and Topics in MDPI journals
Dr. José Pino-Ortega
E-Mail Website1 Website2
Guest Editor
1 Department of Physical Activity and Sport, Faculty of Sport Science, University of Murcia, 30720 Murcia, Spain
2 Faculty of Sports Sciences, BioVetMed & SportSci Research Group, University of Murcia, 30100 Murcia, Spain
Interests: electronic performance and tracking systems; technology; local positioning systems; global positioning systems; team sports performance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sport has experienced accelerated growth and evolution in technological developments, and this is impacting daily work in the area of sports sciences, from researchers to practitioners. The inclusion of electronic performance and tracking systems has been developed to capture up to a thousand data per second in an amount of up to 400 variables. This Special Issue on “Big Data Management through Multivariate Data Analysis Techniques in Sportrepresents a supreme challenge to the sports scientist or technical, medical, and administrative staff when trying to identify those variables which provide the most relevant information about a player’s performance to make decisions based on this scientific evidence. Therefore, it may drive a change within sport training processes and big data management through multivariate data analysis methods.

Considering the relevance of this issue in sport training, the aim of this Special Issue is to publish high-quality original investigations, narrative, and systematic reviews in the field. We look forward to receiving contributions related (but not limited) to the following topics:

  • Big data
  • Data processing
  • Principal component analysis (PCA)
  • Factor analysis
  • Data reductionist methods
  • Exploratory factor analysis (EFA)

Dr. Markel Rico-González
Dr. José Pino-Ortega
Guest Editors

Manuscript Submission Information

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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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly 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 2300 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

  • Principal components
  • Training load
  • Sport
  • Statistics
  • Sport performance
  • Game-analysis

Published Papers (6 papers)

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Research

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Article
The Influence of Individual Set-Pieces in Elite Rink Hockey Match Outcomes
Int. J. Environ. Res. Public Health 2021, 18(23), 12368; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182312368 (registering DOI) - 24 Nov 2021
Viewed by 289
Abstract
The main objective of this study was to analyze the influence of individual set-pieces (Free Direct Hits and Penalties) in elite rink hockey match outcomes in different game situations. A sample of 161 matches played between high-standard teams during ten consecutive seasons (2009–2010 [...] Read more.
The main objective of this study was to analyze the influence of individual set-pieces (Free Direct Hits and Penalties) in elite rink hockey match outcomes in different game situations. A sample of 161 matches played between high-standard teams during ten consecutive seasons (2009–2010 to 2018–2019) were analyzed using a binary logistic regression. The full evaluated model was composed of an explanatory variable (set-pieces scored) and five potential confounding and interaction variables (match location, match level, match importance, extra time, and balanced score). However, the final model only included one significant interaction variable (balanced score). The results showed that scoring more individual set-pieces than the opponent was associated with victory (OR = 6.1; 95% CI: 3.7, 10.0) and was more relevant in unbalanced matches (OR = 19.5; 95% CI: 8.6, 44.3) than in balanced matches (OR = 2.3; 95% CI: 1.2, 4.5). These findings indicate that individual set-pieces are strongly associated with match outcomes in matches played between high-standard teams. Therefore, it is important for teams to excel in this aspect, and it is suggested that these data can encourage coaches to reinforce the systematic practice of individual set-pieces in their training programs. Additionally, it is suggested that teams have specialist players in this kind of action to mainly participate in these specific match moments. Full article
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Article
Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study
Int. J. Environ. Res. Public Health 2021, 18(8), 4256; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18084256 - 16 Apr 2021
Cited by 1 | Viewed by 956
Abstract
The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study [...] Read more.
The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players’ data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (≥15 years). Featured variables were taken from the higher correlation coefficients (<0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model’s parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93–0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87–0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players. Full article
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Article
Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis
Int. J. Environ. Res. Public Health 2021, 18(6), 3176; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18063176 - 19 Mar 2021
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Abstract
The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to [...] Read more.
The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a greater number of successful passes and in the execution of a greater number of dynamic offensive transitions. The bottom teams were characterized by executing more defensive than offensive actions, showing fewer number of goals and a greater ball possession time in the final third of the field. Goals, ball possession time in the final third of the field, number of effective shots and crosses are the main discriminating performance factors of football. This information allows us to increase knowledge about the key performance indicators (KPI) in football. Full article
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Article
What Is the Relevance in the Passing Action between the Passer and the Receiver in Soccer? Study of Elite Soccer in La Liga
Int. J. Environ. Res. Public Health 2020, 17(24), 9396; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17249396 - 15 Dec 2020
Cited by 1 | Viewed by 1018
Abstract
Soccer is a high-complexity sport in which 22 players interact simultaneously in a common space. The ball-holder interacts with their teammates by passing actions, establishing a unique communication among them in the development of the game in its offensive phase. The main aim [...] Read more.
Soccer is a high-complexity sport in which 22 players interact simultaneously in a common space. The ball-holder interacts with their teammates by passing actions, establishing a unique communication among them in the development of the game in its offensive phase. The main aim of the present study was to analyze the pass action according to the trajectory of the ball receiver and the space for receiving the ball in terms of success at the end of play. Twenty La Liga 2018/2019 matches of two elite teams were analyzed. A system of notational analysis was used to create 11 categories based on context, timing and pass analysis. The data were analyzed using chi-squared analysis. The results showed that the main performance indicators were the efficiency of the pass, the zone of the field, the trajectory of the receiver and the reception space of the ball, which presented a moderate association with the end of play (p < 0.001). We concluded that receiving the ball on approach and in separation increased the probability of success by 5% and 7%, respectively, and a diagonal run increased the probability by 7%. Moreover, the combined analysis of these variables would improve the team performance. Full article
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Review

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Review
A Systematic Review of Methods and Criteria Standard Proposal for the Use of Principal Component Analysis in Team’s Sports Science
Int. J. Environ. Res. Public Health 2020, 17(23), 8712; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17238712 - 24 Nov 2020
Cited by 13 | Viewed by 1124
Abstract
The availability of critical information about training and competition is fundamental on performance. Principal components analysis (PCA) is widely used in sports as a multivariate technique to manage big data from different technological assessments. This systematic review aimed to explore the methods reported [...] Read more.
The availability of critical information about training and competition is fundamental on performance. Principal components analysis (PCA) is widely used in sports as a multivariate technique to manage big data from different technological assessments. This systematic review aimed to explore the methods reported and statistical criteria used in team’s sports science and to propose a criteria standard to report PCA in further applications. A systematic electronic search was developed through four electronic databases and a total of 45 studies were included in the review for final analysis. Inclusion criteria: (i) of the studies we looked at, 22.22% performed factorability processes with different retention criteria (r > 0.4–0.7); (ii) 21 studies confirmed sample adequacy using Kaiser-Meyer-Olkim (KMO > 5–8) and 22 reported Bartlett’s sphericity; (iii) factor retention was considered if eigenvalues >1–1.5 (n = 29); (iv) 23 studies reported loading retention (>0.4–0.7); and (v) used VariMax as the rotation method (48.9%). A lack of consistency and serious voids in reporting of essential methodological information was found. Twenty-one items were selected to provide a standard quality criterion to report methods sections when using PCA. These evidence-based criteria will lead to a better understanding and applicability of the results and future study replications. Full article
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Other

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Systematic Review
Training Design, Performance Analysis, and Talent Identification—A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby
Int. J. Environ. Res. Public Health 2021, 18(5), 2642; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052642 - 05 Mar 2021
Cited by 7 | Viewed by 1816
Abstract
Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify [...] Read more.
Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify determinant variables in soccer, basketball and rugby using exploratory factor analysis for, training design, performance analysis and talent identification. Three electronic databases (PubMed, Web of Science, SPORTDiscus) were systematically searched and 34 studies were finally included in the qualitative synthesis. Through PCA, data sets were reduced by 75.07%, and 3.9 ± 2.53 factors were retained that explained 80 ± 0.14% of the total variance. All team sports should be analyzed or trained based on the high level of aerobic capacity combined with adequate levels of power and strength to perform repeated high-intensity actions in a very short time, which differ between team sports. Accelerations and decelerations are mainly significant in soccer, jumps and landings are crucial in basketball, and impacts are primarily identified in rugby. Besides, from these team sports, primary information about different technical/tactical variables was extracted such as (a) soccer: occupied space, ball controls, passes, and shots; (b) basketball: throws, rebounds, and turnovers; or (c) rugby: possession game pace and team formation. Regarding talent identification, both anthropometrics and some physical capacity measures are relevant in soccer and basketball. Although overall, since these variables have been identified in different investigations, further studies should perform PCA on data sets that involve variables from different dimensions (technical, tactical, conditional). Full article
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