{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:50:11Z","timestamp":1778896211381,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T00:00:00Z","timestamp":1591315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes\u2019 sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model\u2019s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15\u201320 km\/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.<\/jats:p>","DOI":"10.3390\/s20113213","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T05:16:14Z","timestamp":1591679774000},"page":"3213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Predicting Wins, Losses and Attributes\u2019 Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2053-7644","authenticated-orcid":false,"given":"Amr","family":"Hassan","sequence":"first","affiliation":[{"name":"Department of Sports Training, Faculty of Sports Education, Mansoura University, Mansoura 35516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8292-3113","authenticated-orcid":false,"given":"Abdel-Rahman","family":"Akl","sequence":"additional","affiliation":[{"name":"Faculty of Physical Education-Abo Qir, Alexandria University, Alexandria 21913, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7582-5133","authenticated-orcid":false,"given":"Ibrahim","family":"Hassan","sequence":"additional","affiliation":[{"name":"Faculty of Physical Education, Zagazig University, Zagazig 44519, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7484-1345","authenticated-orcid":false,"given":"Caroline","family":"Sunderland","sequence":"additional","affiliation":[{"name":"Department of Sport Science, Sport, Health and Performance Enhancement Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.2478\/hukin-2013-0060","article-title":"Activity Profiles of Soccer Players during the 2010 world cup","volume":"38","author":"Clemente","year":"2013","journal-title":"J. Hum. Kinet."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1080\/24748668.2015.11868778","article-title":"General network analysis of national soccer teams in FIFA World Cup 2014","volume":"15","author":"Clemente","year":"2015","journal-title":"Int. J. Perform. Anal. Sport"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1080\/02640414.2015.1022578","article-title":"Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup","volume":"33","author":"Hongyou","year":"2015","journal-title":"J. Sports Sci."},{"key":"ref_4","first-page":"1","article-title":"Running Performance of Soccer Players During Matches in the 2018 FIFA World Cup: Differences Among Confederations","volume":"10","author":"Tuo","year":"2019","journal-title":"Front. Psychol. Front. Psychol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1080\/02640414.2015.1117121","article-title":"Technical performance and match-to-match variation in elite football teams","volume":"34","author":"Liu","year":"2016","journal-title":"J. Sports Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40279-016-0562-5","article-title":"Current Approaches to Tactical Performance Analyses in Soccer Using Position Data","volume":"47","author":"Memmert","year":"2017","journal-title":"Sports Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Redwood-Brown, A.J., O\u2019Donoghue, P.G., Nevill, A.M., Saward, C., and Sunderland, C. (2019). Effects of playing position, pitch location, opposition ability and team ability on the technical performance of elite soccer players in different score line states. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211707"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1186\/s40064-016-3108-2","article-title":"Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science","volume":"5","author":"Rein","year":"2016","journal-title":"Springerplus"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1080\/02640414.2014.887850","article-title":"The influence of situational variables on ball possession in the English Premier League","volume":"32","author":"Bradley","year":"2014","journal-title":"J. Sports Sci. J. Sports Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1080\/02640414.2012.712715","article-title":"The effect of playing tactics and situational variables on achieving score-box possessions in a professional soccer team","volume":"30","author":"Rey","year":"2012","journal-title":"J. Sports Sci."},{"key":"ref_11","first-page":"86","article-title":"Passing Success Percentages and Ball Possession Rates of Successful Teams in 2014 FIFA World Cup","volume":"3","year":"2015","journal-title":"Int. J. Sci. Cult. Sport"},{"key":"ref_12","unstructured":"Liu, H., and G\u00f3mez, M.-A. (2014, January 13\u201315). Relationships between match performance indicators and match outcome in 2014 Brazil FIFA world cup. Proceedings of the VIII Congreso Internacional de la Asociacion Espanola de Ciencias del Deporte, C\u00e1ceres, Spain."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1080\/02640414.2015.1114660","article-title":"The effects of ball possession status on physical and technical indicators during the 2014 FIFA World Cup Finals","volume":"34","author":"Ribeiro","year":"2016","journal-title":"J. Sports Sci."},{"key":"ref_14","first-page":"1338","article-title":"Technical and physical analysis of the 2014 FIFA World Cup Brazil: Winners vs. losers","volume":"57","author":"Rumpf","year":"2017","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1080\/02640414.2013.853130","article-title":"Analysis of football game-related statistics using multivariate techniques","volume":"32","author":"Moura","year":"2014","journal-title":"J. Sports Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.3389\/fpsyg.2018.01900","article-title":"Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game","volume":"9","author":"Busquets","year":"2018","journal-title":"Front. Psychol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, J.H., Garrido, D., Herrera-Diestra, J.L., Busquets, J., Sevilla-Escoboza, R., and Buld\u00fa, J.M. (2020). Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective. Entropy, 22.","DOI":"10.3390\/e22020172"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s40279-017-0695-1","article-title":"Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice","volume":"47","author":"Ribeiro","year":"2017","journal-title":"Sports Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1080\/02640414.2016.1183804","article-title":"Evaluation of tactical training in team handball by means of artificial neural networks","volume":"35","author":"Hassan","year":"2017","journal-title":"J. Sports Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2478\/v10078-012-0015-7","article-title":"The use of match statistics that discriminate between successful and unsuccessful soccer teams","volume":"31","author":"Castellano","year":"2012","journal-title":"J. Hum. Kinet."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1080\/24748668.2014.11868767","article-title":"Performance indicators that predict success in an English Professional League One Soccer Team","volume":"14","author":"Harrop","year":"2014","journal-title":"Int. J. Perform. Anal. Sport"},{"key":"ref_22","first-page":"288","article-title":"Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League","volume":"9","author":"Dellal","year":"2010","journal-title":"J. Sports Sci. Med."},{"key":"ref_23","first-page":"358","article-title":"Performance indicators related to points scoring and winning in international rugby sevens","volume":"13","author":"Higham","year":"2014","journal-title":"J. Sports Sci. Med."},{"key":"ref_24","unstructured":"Principe, J.C., Euliano, N.R., and Lefebvre, W.C. (2000). Neural and Adaptive Systems: Fundamentals Through Simulations, Wiley."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Barron, D., Ball, G., Robins, M., and Sunderland, C. (2018). Artificial neural networks and player recruitment in professional soccer. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205818"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1515\/ijcss-2017-0003","article-title":"Network structure of UEFA Champions League teams: Association with classical notational variables and variance between different levels of success","volume":"16","author":"Clemente","year":"2017","journal-title":"Int. J. Comput. Sci. Sport"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, K.Y., and Chang, W.L. (2010, January 18\u201323). A neural network method for prediction of 2006 World Cup Football Game. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596458"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/02640410802442007","article-title":"Game creativity analysis using neural networks","volume":"27","author":"Memmert","year":"2009","journal-title":"J. Sports Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"812","DOI":"10.7763\/IJCTE.2013.V5.802","article-title":"Football Result Prediction with Bayesian Network in Spanish League-Barcelona Team","volume":"5","author":"Owramipur","year":"2013","journal-title":"Ijcte Int. J. Comput. Theory Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Perl, J., and Memmert, D. (2016). Soccer analyses by means of artificial neural networks, automatic pass recognition and Voronoi-cells: An approach of measuring tactical success. Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer.","DOI":"10.1007\/978-3-319-24560-7_10"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1757-899X\/226\/1\/012099","article-title":"Predicting Football Matches Results using Bayesian Networks for English Premier League (EPL)","volume":"226","author":"Razali","year":"2017","journal-title":"Iop Conf. Ser. Mater. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Q., Yao, Y., Zhu, H., Hu, W., and Shen, Z. (2015, January 10\u201313). Discerning tactical patterns for professional soccer teams: An enhanced topic model with applications. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia.","DOI":"10.1145\/2783258.2788577"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., and Malvaldi, M. (2015, January 19\u201321). The harsh rule of the goals: Data-driven performance indicators for football teams. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Paris, France.","DOI":"10.1109\/DSAA.2015.7344823"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wagenaar, M., Okafor, E., Frencken, W., and Wiering, M.A. (2017, January 24\u201326). Using Deep Convolutional Neural Networks to Predict Goal-scoring Opportunities in Soccer. Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), Porto, Portugal.","DOI":"10.5220\/0006194804480455"},{"key":"ref_35","first-page":"2","article-title":"A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games","volume":"22","author":"Sahin","year":"2017","journal-title":"Math. Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1080\/24733938.2017.1283435","article-title":"Linear vs. non-linear classification of winners, drawers and losers at FIFA World Cup 2014","volume":"1","author":"Winter","year":"2017","journal-title":"Sci. Med. Football"},{"key":"ref_37","unstructured":"Eggels, H., Elk, R.V., and Pechenizkiy, M. (2016, January 19\u201323). Explaining Soccer Match Outcomes with Goal Scoring Opportunities Predictive Analytics. Proceedings of the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Riva del Garda, Italy."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Eryarsoy, E., and Delen, D. (2019, January 8\u201311). Predicting the Outcome of a Football Game: A Comparative Analysis of Single and Ensemble Analytics Methods. Proceedings of the 52nd Hawaii International Conference on System Sciences, Wailea, HI, USA.","DOI":"10.24251\/HICSS.2019.136"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wei, X., Sha, L., Lucey, P., Morgan, S., and Sridharan, S. (2013, January 26\u201328). Large-Scale Analysis of Formations in Soccer. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, Australia.","DOI":"10.1109\/DICTA.2013.6691503"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1177\/1471082X18810971","article-title":"Exploring and modelling team performances of the Kaggle European Soccer database","volume":"19","author":"Carpita","year":"2019","journal-title":"Stat. Model."},{"key":"ref_41","unstructured":"Liti, C., Piccialli, V., and Sciandrone, M. (2017, January 26\u201328). Predicting soccer match outcome using machine learning algorithms. Proceedings of the MathSport International 2017 Conference, Padua, Italy."},{"key":"ref_42","first-page":"59","article-title":"The next winner of the 2018 FIFA World Cup will be\u2026: An illustration of the use of statistical simulation to make a prediction in a complex tournament","volume":"9","author":"Correa","year":"2018","journal-title":"Chil. J. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1515\/jqas-2018-0060","article-title":"A hybrid random forest to predict soccer matches in international tournaments","volume":"15","author":"Groll","year":"2019","journal-title":"J. Quant. Anal. Sports"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1080\/24748668.2018.1545180","article-title":"Performance outcomes and their associations with network measures during FIFA World Cup 2018","volume":"18","author":"Clemente","year":"2018","journal-title":"Int. J. Perform. Anal. Sport"},{"key":"ref_45","first-page":"364","article-title":"Football match winner prediction","volume":"10","author":"Gevaria","year":"2015","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1080\/02640414.2019.1648120","article-title":"Technical and physical match performance of teams in the 2018 FIFA World Cup: Effects of two different playing styles","volume":"37","author":"Yi","year":"2019","journal-title":"J. Sports Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Linke, D., Link, D., Lames, M., and Kerherv\u00e9, H.A. (2020). Football-specific validity of TRACABs optical video tracking systems. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230179"},{"key":"ref_48","first-page":"233","article-title":"Analysis of the distances covered by first division brazilian soccer players obtained with an automatic tracking method","volume":"6","author":"Barros","year":"2007","journal-title":"J. Sports Sci. Med."},{"key":"ref_49","unstructured":"NeuroDimension, I. (2019, April 07). NeuroSolutions for Excel, 7.0.1.0. Available online: http:\/\/www.neurosolutions.com\/neurosolutions\/:nDimensional\/\/4LibertySquare\/\/."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, J. (2013). Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation, Springer-Verlag.","DOI":"10.1007\/978-3-642-34816-7"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1061\/(ASCE)0733-9364(2006)132:6(650)","article-title":"Neural Networks for Estimating the Productivity of Concreting Activities","volume":"132","author":"Ezeldin","year":"2006","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.proeng.2016.08.081","article-title":"The influence of input data standardization method on prediction accuracy of artificial neural networks","volume":"153","author":"Anysz","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"297","DOI":"10.20965\/jaciii.2010.p0297","article-title":"Data Cleaning for Classification Using Misclassification Analysis","volume":"14","author":"Jeatrakul","year":"2010","journal-title":"J. Adv. Comput. Intell. Intell. Inform."},{"key":"ref_54","first-page":"136","article-title":"Application of radial basis network model for HIV\/AIDS regimen specifications","volume":"1","author":"Balasubramanie","year":"2009","journal-title":"J. Comput."},{"key":"ref_55","unstructured":"Bullinaria, J.A. (2014). Radial Basis Function Networks: Algorithms, University of Birmingham."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/24748668.2017.1336688","article-title":"The prediction of action positions in team handball by non-linear hybrid neural networks","volume":"17","author":"Hassan","year":"2017","journal-title":"Int. J. Perform. Anal. Sport"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Magoul\u00e8s, F., and Zhao, H.-X. (2016). Data Mining and Machine Learning in Building Energy Analysis, Wiley Online Library.","DOI":"10.1002\/9781118577691"},{"key":"ref_58","first-page":"50","article-title":"Hybrid training algorithm for RBF network","volume":"8","author":"Mashor","year":"2000","journal-title":"Int. J. Comput. Internet Manag."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"35","DOI":"10.11648\/j.ajss.20170505.13","article-title":"An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws","volume":"5","author":"Akl","year":"2017","journal-title":"Am. J. Sports Sci."},{"key":"ref_60","unstructured":"M\u00fchlh\u00e4user, M., Erwin, A., and Alois, F. Constructing Ambient Intelligence. Proceedings of Communications in Computer and Information Science, Springer."},{"key":"ref_61","unstructured":"Khosrow-Pour, D.M. (2013). Bioinformatics: Concepts, Methodologies, Tools, and Applications, Medical Information Science Reference (an imprint of IGI Global). [1st ed.]."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"12","DOI":"10.9790\/3021-04124012020","article-title":"An improved prediction system for football a match result","volume":"4","author":"Igiri","year":"2014","journal-title":"Iosr J. Eng."},{"key":"ref_63","unstructured":"Fan, Z., Kuang, Y., and Lin, X. (2013). Chess game result prediction system. CS 229 Machine Learning Project Report, Stanford University."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/aris.2010.1440440110","article-title":"Sports knowledge management and data mining","volume":"44","author":"Schumaker","year":"2010","journal-title":"Aris Annu. Rev. Inf. Sci. Technol."},{"key":"ref_65","unstructured":"Kahn, J. (2019, April 07). Neural Network Prediction of NFL Football Games. Available online: http:\/\/homepages.cae.wisc.edu\/~ece539\/project\/f03\/kahn.pdf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/07408170490426107","article-title":"Data Mining: Concepts, Models, Methods, and Algorithms M. Kantardzic","volume":"36","author":"Liang","year":"2004","journal-title":"IIE Trans."},{"key":"ref_67","first-page":"7","article-title":"A review of data mining techniques for result prediction in sports","volume":"2","author":"Haghighat","year":"2013","journal-title":"Adv. Comput. Sci. Int. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/24748668.2016.11868892","article-title":"Game style in soccer: What is it and can we quantify it?","volume":"16","author":"Hewitt","year":"2016","journal-title":"Int. J. Performance Anal. Sport"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1080\/02640414.2013.786185","article-title":"The effect of high and low percentage ball possession on physical and technical profiles in English FA Premier League soccer matches","volume":"31","author":"Bradley","year":"2013","journal-title":"J. Sports Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/02640410802512775","article-title":"High-intensity running in English FA Premier League soccer matches","volume":"27","author":"Bradley","year":"2009","journal-title":"J. Sports Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1080\/02640414.2011.561868","article-title":"The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches","volume":"29","author":"Bradley","year":"2011","journal-title":"J. Sports Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:36:04Z","timestamp":1760175364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,5]]},"references-count":71,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113213"],"URL":"https:\/\/doi.org\/10.3390\/s20113213","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,5]]}}}