Aljalal M, Aldosari SA, Molinas M, AlSharabi K, Alturki FA (2022) Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques. Sci Rep. 12(1):Article 1. https://doi.org/10.1038/s41598-022-26644-7
Google Scholar
Bastida Castillo A, Gómez Carmona CD, De la cruz sánchez E, Pino Ortega J (2018) Accuracy, intra- and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time–motion analyses in soccer. Eur J Sport Sci 18(4):450–457. https://doi.org/10.1080/17461391.2018.1427796
Google Scholar
Bastida-Castillo A, Gómez-Carmona CD, De La Cruz Sánchez E, Pino-Ortega J (2019) Comparing accuracy between global positioning systems and ultra-wideband-based position tracking systems used for tactical analyses in soccer. Eur J Sport Sci 19(9):1157–1165. https://doi.org/10.1080/17461391.2019.1584248
Google Scholar
Bialkowski A, Lucey P, Carr P, Matthews I, Sridharan S, Fookes C (2016) Discovering team structures in soccer from spatiotemporal data. IEEE Trans Knowl Data Eng 28(10):2596–2605. https://doi.org/10.1109/TKDE.2016.2581158
Google Scholar
Bilek G, Ulas E (2019) Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators. Int J Perform Anal Sport 19(6):930–941. https://doi.org/10.1080/24748668.2019.1684773
Google Scholar
Borg GA (1982) Psychophysical bases of perceived exertion. Med Sci Sports Exerc 14(5):377–381
Google Scholar
Brinkschulte M, Wunderlich F, Furley P, Memmert D (2023) The obligation to succeed when it matters the most–The influence of skill and pressure on the success in football penalty kicks. Psychol Sport Exerc 65:102369. https://doi.org/10.1016/j.psychsport.2022.102369
Google Scholar
Campbell PG, Stewart IB, Sirotic AC, Drovandi C, Foy BH, Minett GM (2021) Analysing the predictive capacity and dose-response of wellness in load monitoring. J Sports Sci 39(12):1339–1347. https://doi.org/10.1080/02640414.2020.1870303
Google Scholar
Cao S (2024) Passing path predicts shooting outcome in football. Sci Rep. 14(1):9572. https://doi.org/10.1038/s41598-024-60183-7
Google Scholar
Castellano J, Blanco-Villaseñor A, Álvarez D (2011) Contextual variables and time-motion analysis in soccer. Int J Sports Med 32(06):415–421. https://doi.org/10.1055/s-0031-1271771
Google Scholar
Castiglioni P (2010) What is wrong in Katz’s method? Comments on: “A note on fractal dimensions of biomedical waveforms. Comput Biol Med 40(11):950–952. https://doi.org/10.1016/j.compbiomed.2010.10.001
Google Scholar
Catapult | Sports Technology | Unleash Potential. (n.d.). Catapult. Retrieved 4 March 2025, from https://www.catapult.com/
Chawla S, Estephan J, Gudmundsson J, Horton M (2017) Classification of passes in football matches using spatiotemporal data. ACM Trans Spat Algorithms Syst 3(2):6:1-6:30. https://doi.org/10.1145/3105576
Google Scholar
Chen S, Yu L, Ren J, Xie X, Li X, Xu Y, Zhao G, Li P, Yang F, Ren Y, Liaw PK (2016) Self-similar random process and chaotic behavior in serrated flow of high entropy alloys. Sci Rep. 6(1):29798. https://doi.org/10.1038/srep29798
Google Scholar
Clauset A, Larremore DB, Sinatra R (2017) Data-driven predictions in the science of science. Science 355(6324):477–480. https://doi.org/10.1126/science.aal4217
Google Scholar
Dhanya E, Sunitha R, Pradhan N (2015) Power spectral scaling and wavelet entropy as measures in understanding neural complexity. 2015 Annual IEEE India Conference (INDICON), 1–6. https://doi.org/10.1109/INDICON.2015.7469613
Dick U, Brefeld U (2019) Learning to rate player positioning in soccer. Big Data 7(1):71–82. https://doi.org/10.1089/big.2018.0054
Google Scholar
Eguiraun H, López-de-Ipiña K, Martinez I (2014) Application of entropy and fractal dimension analyses to the pattern recognition of contaminated fish responses in aquaculture. Entropy 16(11):Article 11. https://doi.org/10.3390/e16116133
Google Scholar
Errekagorri I, Castellano J, Echeazarra I, Lago-Peñas C (2020) The effects of the Video Assistant Referee system (VAR) on the playing time, technical-tactical and physical performance in elite soccer. Int J Perform Anal Sport 20(5):808–817. https://doi.org/10.1080/24748668.2020.1788350
Google Scholar
Eusebio P, Prieto-González P, Marcelino R (2024) An analysis of transition-resulted goal scoring patterns in football leagues: A comparison of the first 5 rounds and the last 5 rounds prior midway of the season. BMC Sports Sci Med Rehabil 16(1):60. https://doi.org/10.1186/s13102-024-00854-0
Google Scholar
García-Aliaga A, Marquina M, Coterón J, Rodríguez-González A, Luengo-Sánchez S (2021) In-game behaviour analysis of football players using machine learning techniques based on player statistics. Int J Sports Sci Coaching 16(1):148–157. https://doi.org/10.1177/1747954120959762
Google Scholar
Gásquez R, Royuela V (2016) The determinants of international football success: a panel data analysis of the Elo Rating. Soc Sci Q 97(2):125–141. https://doi.org/10.1111/ssqu.12262
Google Scholar
Gisbert-Pérez J, García-Naveira A, Martí-Vilar M, Acebes-Sánchez J (2024) Key structure and processes in esports teams: a systematic review. Curr Psychol 43(23):20355–20374. https://doi.org/10.1007/s12144-024-05858-0
Google Scholar
Goes FR, Brink MS, Elferink-Gemser MT, Kempe M, Lemmink KAPM (2021) The tactics of successful attacks in professional association football: large-scale spatiotemporal analysis of dynamic subgroups using position tracking data. J Sports Sci 39(5):523–532. https://doi.org/10.1080/02640414.2020.1834689
Google Scholar
Goes FR, Kempe M, Meerhoff LA, Lemmink KA (2019) Not every pass can be an assist: a data-driven model to measure pass effectiveness in professional soccer matches. Big Data 7(1):57–70. https://doi.org/10.1089/big.2018.0067
Google Scholar
Haller N, Kranzinger S, Kranzinger C, Blumkaitis JC, Strepp T, Simon P, Tomaskovic A, O’Brien J, Düring M, Stöggl T (2023) Predicting injury and illness with machine learning in elite youth soccer: a comprehensive monitoring approach over 3 Months. J Sports Sci Med 22(3):476–487
Google Scholar
Herold M, Goes F, Nopp S, Bauer P, Thompson C, Meyer T (2019) Machine learning in men’s professional football: current applications and future directions for improving attacking play. Int J Sports Sci Coaching 14(6):798–817. https://doi.org/10.1177/1747954119879350
Google Scholar
Hewitt JH, Karakuş O (2023) A machine learning approach for player and position adjusted expected goals in football (soccer). Frankl Open 4:100034. https://doi.org/10.1016/j.fraope.2023.100034
Google Scholar
Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Phys D Nonlinear Phenom 31(2):277–283. https://doi.org/10.1016/0167-2789(88)90081-4
Google Scholar
Holder U, Ehrmann T, König A (2022) Monitoring experts: insights from the introduction of video assistant referee (VAR) in elite football. J Bus Econ 92(2):285–308. https://doi.org/10.1007/s11573-021-01058-5
Google Scholar
Huerta EB, Caporal RM, Arjona MA, Hernández JCH (2013) Recursive feature elimination based on linear discriminant analysis for molecular selection and classification of diseases. In: Huang D.S., Jo K. H., Zhou Y.-Q, Han K. (eds.), Intelligent Computing Theories and Technology. pp 244–251. Springer. https://doi.org/10.1007/978-3-642-39482-9_28
Iván-Baragaño I, Ardá A, Losada JL, Maneiro, R (2025) Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: A machine learning approach. Front Psychol 16. https://doi.org/10.3389/fpsyg.2025.1516417
Joseph A, Fenton NE, Neil M (2006) Predicting football results using Bayesian nets and other machine learning techniques. Knowl -Based Syst 19(7):544–553. https://doi.org/10.1016/j.knosys.2006.04.011
Google Scholar
Katz MJ (1988) Fractals and the analysis of waveforms. Comput Biol Med 18(3):145–156. https://doi.org/10.1016/0010-4825(88)90041-8
Google Scholar
Kim H, Kim CJ, Jeong M, Lee J, Yoon J, Ko, S-K (2023) Cost-efficient and bias-robust sports player tracking by integrating GPS and Video. In: Brefeld U, Davis J, Van Haaren J, & Zimmermann A (eds.), Machine Learning and Data Mining for Sports Analytics pp 74–86. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-27527-2_6
Knauf K, Memmert D, Brefeld U (2016) Spatio-temporal convolution kernels. Mach Learn 102(2):247–273. https://doi.org/10.1007/s10994-015-5520-1
Google Scholar
Kristiansen E, Ivarsson A, Solstad BE, Roberts GC (2019) Motivational processes affecting the perception of organizational and media stressors among professional football players: a longitudinal mixed methods research study. Psychol Sport Exerc 43:172–182. https://doi.org/10.1016/j.psychsport.2019.02.009
Google Scholar
Krumer A (2020) Pressure versus ability: evidence from penalty shoot-outs between teams from different divisions. J Behav Exp Econ 89:101578. https://doi.org/10.1016/j.socec.2020.101578
Google Scholar
Kulkarni S(2024) AI and Euro 2024: VAR is shaking up football — and it’s not going away. Nature 630(8017):538–539. https://doi.org/10.1038/d41586-024-01764-4
Google Scholar
Lago-Peñas C, Gómez-Ruano M, Megías-Navarro D, Pollard R (2016) Home advantage in football: examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 16(2):411–421. https://doi.org/10.1080/24748668.2016.11868897
Google Scholar
Leitner MC, Richlan F (2021) Analysis System for Emotional Behavior in Football (ASEB-F): matches of FC Red Bull Salzburg without supporters during the COVID-19 pandemic. Humanities Soc Sci Commun 8(1):1–11. https://doi.org/10.1057/s41599-020-00699-1
Google Scholar
Liga F 2023/24. (n.d.). Página web oficial de LALIGA | LALIGA. Retrieved 2 May 2024, from https://www.laliga.com/en-GB/futbol-femenino
Lopategui IG, Castellano Paulis J, Echeazarra Escudero I (2021) Physical demands and internal response in football sessions according to tactical periodization Int J Sports Physiol Perform 16(6):858–864. https://doi.org/10.1123/ijspp.2019-0829
Google Scholar
López-de-Ipiña K, Alonso J-B, Travieso CM, Solé-Casals J, Egiraun H, Faundez-Zanuy M, Ezeiza A, Barroso N, Ecay-Torres M, Martinez-Lage P, de Lizardui UM (2013) On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis. Sensors 13(5):Article 5. https://doi.org/10.3390/s130506730
Google Scholar
Low B, Coutinho D, Gonçalves B, Rein R, Memmert D, Sampaio J (2020) A systematic review of collective tactical behaviours in football using positional data. Sports Med 50(2):343–385. https://doi.org/10.1007/s40279-019-01194-7
Google Scholar
Malina RM, Martinho DV, Valente-dos-Santos J, Coelho-e-Silva MJ, Kozieł SM (2021) Growth and maturity status of female soccer players: a narrative review. Int J Environ Res Public Health 18(4):Article 4. https://doi.org/10.3390/ijerph18041448
Google Scholar
Mandelbrot B (1967) How Long Is the Coast of Britain? Statistical self-similarity and fractional dimension. Science 156(3775):636–638. https://doi.org/10.1126/science.156.3775.636
Google Scholar
Mandelbrot BB, Wheeler JA (1983) The fractal geometry of nature. Am J Phys 51(3):286–287. https://doi.org/10.1119/1.13295
Google Scholar
Mead J, O’Hare A, McMenemy P (2023) Expected goals in football: Improving model performance and demonstrating value. PLOS ONE 18(4):e0282295. https://doi.org/10.1371/journal.pone.0282295
Google Scholar
Mukherjee S, Huang Y, Neidhardt J, Uzzi B, Contractor N (2019) Prior shared success predicts victory in team competitions. Nat Hum Behav 3(1):74–81. https://doi.org/10.1038/s41562-018-0460-y
Google Scholar
Nieto S, Castellano J, Echeazarra I, Fernández E (2023) Effects on collective behaviour and locomotor and neuromuscular response in young players by varying the length of the pitch in 11-a-side football. Int J Sports Sci Coaching 18(4):1229–1239. https://doi.org/10.1177/17479541221101603
Google Scholar
Okholm Kryger K, Wang A, Mehta R, Impellizzeri FM, Massey A, McCall A (2022) Research on women’s football: a scoping review. Sci Med Footb 6(5):549–558. https://doi.org/10.1080/24733938.2020.1868560
Google Scholar
Olaizola A, Errekagorri I, Lopez-de-Ipina K, Calvo PM, Castellano J (2024) Very high-speed running (VHSR) profile in elite female football: an update. Plos ONE 19(10):e0308618. https://doi.org/10.1371/journal.pone.0308618
Google Scholar
Olaizola A, Errekagorri I, Lopez-de-Ipina K, Calvo PM, Castellano J (2025) Analysis of running performance in the two main Spanish Women’s football leagues: a case study. Int J Sports Sci Coaching, 17479541251320590. https://doi.org/10.1177/17479541251320590
Olaizola A, Errekagorri I, Lopez-de-Ipina K, María Calvo P, Castellano J (2022) Comparison of the external load in training sessions and official matches in female football: a case Report. Int J Environ Res Public Health 19(23):Article 23. https://doi.org/10.3390/ijerph192315820
Google Scholar
Pappalardo L, Cintia P (2018) Quantifying the relation between performance and success in soccer. Adv Complex Syst 21(03n04):1750014. https://doi.org/10.1142/S021952591750014X
Google Scholar
Pino-Ortega J, Oliva-Lozano JM, Gantois P, Nakamura FY, Rico-González M (2022) Comparison of the validity and reliability of local positioning systems against other tracking technologies in team sport: a systematic review. Proc Inst Mech Eng, Part P: J Sports Eng Technol 236(2):73–82. https://doi.org/10.1177/1754337120988236
Google Scholar
Prieto-González P, Martín V, Pacholek M, Sal-de-Rellán A, Marcelino R (2024) Impact of offensive team variables on goal scoring in the first division of the spanish soccer league: A comprehensive 10-year study. Sci Rep 14(1):25231. https://doi.org/10.1038/s41598-024-77199-8
Google Scholar
Python, R (n.d.). Python Machine Learning – Real Python. Retrieved 2 May 2024, from https://realpython.com/tutorials/machine-learning/
Rainio O, Teuho J, Klén R (2024) Evaluation metrics and statistical tests for machine learning. Sci Rep 14(1):6086. https://doi.org/10.1038/s41598-024-56706-x
Google Scholar
Rey-Devesa P, Prudencio J, Benítez C, Bretón M, Plasencia I, León Z, Ortigosa F, Gutiérrez L, Arámbula-Mendoza R, Ibáñez JM (2023) Tracking volcanic explosions using Shannon entropy at Volcán de Colima. Sci Rep 13(1):Article 1. https://doi.org/10.1038/s41598-023-36964-x
Google Scholar
Rico-González M, Los Arcos A, Nakamura FY, Moura FA, Pino-Ortega J (2020) The use of technology and sampling frequency to measure variables of tactical positioning in team sports: a systematic review. Res Sports Med 28(2):279–292. https://doi.org/10.1080/15438627.2019.1660879
Google Scholar
Rico-González M, Los Arcos A, Rojas-Valverde D, Clemente FM, Pino-Ortega J (2020) A survey to assess the quality of the data obtained by radio-frequency technologies and microelectromechanical systems to measure external workload and collective behavior variables in team sports. Sensors 20(8):Article 8. https://doi.org/10.3390/s20082271
Google Scholar
Rico-González M, Pino-Ortega J, Méndez A, Clemente F, Baca A (2022) Machine learning application in soccer: a systematic review. Biol Sport 40(1):249–263. https://doi.org/10.5114/biolsport.2023.112970
Google Scholar
Robertson S, Duthie GM, Ball K, Spencer B, Serpiello FR, Haycraft J, Evans N, Billingham J, Aughey RJ (2023) Challenges and considerations in determining the quality of electronic performance & tracking systems for team sports. Front Sports Active Living, 5. https://doi.org/10.3389/fspor.2023.1266522
Rosso OA, Blanco S, Yordanova J, Kolev V, Figliola A, Schürmann M, Basar E (2001) Wavelet entropy: a new tool for analysis of short duration brain electrical signals J Neurosci Methods 105(1):65–75. https://doi.org/10.1016/S0165-0270(00)00356-3
Santos R, Ribeiro J, Davids K, Garganta J (2023) Sports teams as collective homeostatic systems: exploiting self-organising tendencies in competition. N. Ideas Psychol 71:101048. https://doi.org/10.1016/j.newideapsych.2023.101048
Google Scholar
Seshadri DR, Li RT, Voos JE, Rowbottom JR, Alfes CM, Zorman CA, Drummond CK (2019) Wearable sensors for monitoring the internal and external workload of the athlete. Npj Digital Med 2(1):1–18. https://doi.org/10.1038/s41746-019-0149-2
Google Scholar
Sklearn.feature_selection.RFECV. (n.d.). Scikit-Learn. Retrieved 2 May 2024, from
Sklearn.model_selection.LeaveOneOut. (n.d.). Scikit-Learn. Retrieved 2 May 2024, from
Theodoropoulos JS, Bettle J, Kosy JD (2020) The use of GPS and inertial devices for player monitoring in team sports: a review of current and future applications. Orthopedic Rev 12(1). https://doi.org/10.4081/or.2020.7863
Torres-Ronda L, Beanland E, Whitehead S, Sweeting A, Clubb J (2022) Tracking systems in team sports: a narrative review of applications of the data and sport specific analysis. Sports Med Open 8(1):15. https://doi.org/10.1186/s40798-022-00408-z
Google Scholar
Tossici G, Zurloni V, Nitri A (2024) Stress and sport performance: a PNEI multidisciplinary approach. Front Psychol 15. https://doi.org/10.3389/fpsyg.2024.1358771
Tsonis AA, Elsner JB (1992) Nonlinear prediction as a way of distinguishing chaos from random fractal sequences. Nature 358(6383):217–220. https://doi.org/10.1038/358217a0
Google Scholar
Vélez-Páez JL, Baldeón-Rojas L, Cañadas Herrera C, Montalvo MP, Jara FE, Aguayo-Moscoso S, Tercero-Martínez W, Saltos L, Jiménez-Alulima G, Guerrero V, Pérez-Galarza J (2023) Receiver operating characteristic (ROC) to determine cut-off points of clinical and biomolecular markers to discriminate mortality in severe COVID-19 living at high altitude. BMC Pulm Med 23(1):393. https://doi.org/10.1186/s12890-023-02691-2
Google Scholar
Viol A, Palhano-Fontes F, Onias H, de Araujo DB, Viswanathan GM (2017) Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca. Sci Rep 7(1):Article 1. https://doi.org/10.1038/s41598-017-06854-0
Google Scholar
Wang Z, Veličković P, Hennes D, Tomašev N, Prince L, Kaisers M, Bachrach Y, Elie R, Wenliang LK, Piccinini F, Spearman W, Graham I, Connor J, Yang Y, Recasens A, Khan M, Beauguerlange N, Sprechmann P, Moreno P, Tuyls K (2024) TacticAI: an AI assistant for football tactics. Nat Commun 15(1):1906. https://doi.org/10.1038/s41467-024-45965-x
Google Scholar
Zhang B, Zhang Y, Jiang X (2022) Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm. Sci Rep 12(1):Article 1. https://doi.org/10.1038/s41598-022-13498-2
Google Scholar
Zheng-you H, Xiaoqing C, Guoming L (2006) Wavelet entropy measure definition and its application for transmission line fault detection and identification; (Part I: Definition and Methodology). 2006 International Conference on Power System Technology, 1–6. https://doi.org/10.1109/ICPST.2006.321939
