The secret of playing football: Brazil versus the Netherlands

• Grant number: 16/50250-1
• Support Opportunities: Research Projects - Thematic Grants
• Start date: August 01, 2017
• End date: July 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Netherlands Organisation for Scientific Research (NWO)
• Principal Investigator: Sergio Augusto Cunha
• Grantee: Sergio Augusto Cunha
• Principal researcher abroad: Koen Lemmink
• Institution abroad: University of Groningen , Netherlands
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• City of the host institution: Campinas
• Principal investigators: Claudio Alexandre Gobatto ; Fúlvia de Barros Manchado Gobatto
• Associated research grant(s): 18/15178-3 - Human-computer collaboration for the generation of soccer strategies using multi-agent simulations, AP.R SPRINT; 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system, AP.EMU
• Associated scholarship(s): 19/20894-2 - Maximal tethered running applied to soccer players and active subjects in two scenarios: investigations about mechanical power and oxygenation in more and less active muscles by complex network model, BP.DD; 19/16253-1 - Unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of playing style and playing strategies, BP.PD; 19/10666-2 - Can pre-activation of inspiratory muscles potentiates the force, velocity and power of soccer players? Analysis using mechanical and physiological signals by computational model, BP.DD; + associated scholarships 19/02286-5 - Metabolomic and proteomic analysis in soccer players: relationships with aerobic and anaerobic capacities, BP.DD; 18/19007-9 - Find dribbling time series in a physical-tactical-technical scenario during official professional matches, BE.PQ; 17/24005-2 - Temporal relational reasoning with neural networks, BP.DR - associated scholarships

Abstract

The scientific challenge is about unravelling the secret of Brazilian and Dutch soccer by capturing successful elements of game play of both countries by combining expertise from data science, computer science and sport science. Suggested features from the literature, as well as several novel ones, will be considered and filtered on how they capture success in soccer. A manageable set of features will then be obtained from the various available datasets. Subsequently, the same features will be used to compare playing styles between the two countries. Features of game play will be approached from two different angles. The first angle (spearheaded by the Brazilian Computer Science partner) concerns features that capture the dynamics of game play and characterize aspects of formation on the pitch. The second angle (Lead by the Dutch data science partner) will focus on how an attack is built up, and how key events (shots on goal, transitions from defenders to midfielders) can help to characterize this. For the comparison between countries, data will be collected in four different age categories in Brazil and the Netherlands during official games, in order to compare game play between both countries. Data will be collected by means of the Local Position Measurement System (LPM), for reasons of accuracy and consistency. The applied science part of this proposal is focusing on bridging the gap between fundamental science and soccer practice, i.e. coaches, trainers, clubs and federations. The outcomes of the fundamental part will be implemented in a coach-cockpit, a software application which trainers and coaches can use to: 1) decide upon their strategy before a game; 2) analyses player and team behavior during a game enabling to adjust the strategy accordingly; 3) choose and or design training forms to improve player and team behavior. (AU)

Scientific publications

(64)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

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CORDOVA NEIRA, MANUEL ALBERTO; MENDES JUNIOR, PEDRO RIBEIRO; ROCHA, ANDERSON; TORRES, RICARDO DA SILVA. Data-Fusion Techniques for Open-Set Recognition Problems. IEEE ACCESS, v. 6, p. 21242-U24, 2018. (14/50715-9, 17/20945-0, 16/50250-1, 13/50155-0, 15/24494-8, 14/12236-1, 17/12646-3, 13/50169-1)

WILSON, ROBBIE S.; SMITH, NICHOLAS M. A.; PEREIRA SANTIAGO, PAULO ROBERTO; CAMATA, THIAGO; RAMOS, SOLANGE DE PAULA; CAETANO, FABIO GIULIANO; CUNHA, SERGIO AUGUSTO; SANDES DE SOUZA, ANA PAULA; MOURA, FELIPE ARRUDA. Predicting the defensive performance of individual players in one vs. one soccer games. PLoS One, v. 13, n. 12, DEC 31 2018. (16/50250-1, 17/20945-0)

PEREIRA, VANESSA H.; GOBATTO, CLAUDIO A.; LEWIS, THEODORE G.; RIBEIRO, LUIZ F. P.; BECK, WLADIMIR R.; DOS REIS, IVAN G. M.; SOUSA, FILIPE A. B.; MANCHADO-GOBATTO, FULVIA B.. Computational and Complex Network Modeling for Analysis of Sprinter Athletes' Performance in Track Field Tests. FRONTIERS IN PHYSIOLOGY, v. 9, JUL 6 2018. (09/08535-5, 16/50250-1, 12/06355-2)

PALUCCI VIEIRA, LUIZ H.; BARBIERI, FABIO A.; KELLIS, ELEFTHERIOS; OLIVEIRA, LUCAS; AQUINO, RODRIGO; CUNHA, SERGIO; BEDO, BRUNO; MANECHINI, JOAO; SANTIAGO, PAULO. Organisation of instep kicking in young U11 to U20 soccer players. SCIENCE AND MEDICINE IN FOOTBALL, v. 5, n. 2, p. 111-120, APR 3 2021. (16/50250-1, 18/02965-7)

CORDOVA, MANUEL; PINTO, ALLAN; HELLEVIK, CHRISTINA CARROZZO; ALALIYAT, SALEH ABDEL-AFOU; HAMEED, IBRAHIM A.; PEDRINI, HELIO; TORRES, RICARDO DA S.. itter Detection with Deep Learning: A Comparative Stud. ENSOR, v. 22, n. 2, JAN 2022. (15/24494-8, 19/17729-0, 19/16253-1, 14/12236-1, 16/50250-1, 17/20945-0, 19/22262-3)

GUIMARAES PEDRONETTE, DANIEL CARLOS; VALEM, LUCAS PASCOTTI; ALMEIDA, JURANDY; TONES, RICARDO DA S.. Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking. IEEE Transactions on Image Processing, v. 28, n. 12, p. 5824-5838, DEC 2019. (14/50715-9, 16/50250-1, 17/25908-6, 17/20945-0, 14/12236-1, 16/06441-7, 18/15597-6, 13/50155-0, 17/02091-4, 15/24494-8)

FERREIRA GOMES, LUIZ ALBERTO; TORRES, RICARDO DA SILVA; CORTES, MARIO LTICIO. Bug report severity level prediction in open source software: A survey and research opportunities. INFORMATION AND SOFTWARE TECHNOLOGY, v. 115, p. 58-78, NOV 2019. (14/50715-9, 16/50250-1, 13/50169-1, 14/12236-1, 17/20945-0, 13/50155-0, 15/24494-8)

ESMAEL, AGNALDO APARECIDO; DOS SANTOS, JEFERSSON ALEX; TORRES, RICARDO DA SILVA. On the ensemble of multiscale object-based classifiers for aerial images: a comparative study. MULTIMEDIA TOOLS AND APPLICATIONS, v. 77, n. 19, p. 24565-24592, OCT 2018. (14/50715-9, 16/50250-1, 13/50169-1, 14/12236-1, 17/20945-0, 13/50155-0, 15/24494-8)

OLIVEIRA, ALBERTO; OAKLEY, ERIC; TORRES, RICARDO DA SILVA; ROCHA, ANDERSON. Relevance prediction in similarity-search systems using extreme value theory. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 60, p. 236-249, APR 2019. (14/50715-9, 16/50250-1, 17/20945-0, 14/12236-1, 17/12646-3, 13/50155-0, 15/24494-8)

PALUCCI VIEIRA, LUIZ H.; KALVA-FILHO, CARLOS A.; SANTINELLI, FELIPE B.; CLEMENTE, FILIPE M.; CUNHA, SERGIO A.; SCHIMIDT, V, CAROLINE; BARBIERI, FABIO A.. Lateral Preference and Inter-limb Asymmetry in Completing Technical Tasks During Official Professional Futsal Matches: The Role of Playing Position and Opponent Quality. FRONTIERS IN PSYCHOLOGY, v. 12, AUG 19 2021. (17/20945-0, 16/50250-1, 13/08302-6)

CORDOVA, MANUEL; PINTO, ALLAN; PEDRINI, HELIO; TORRES, RICARDO DA SILVA. Pelee-Text plus plus : A Tiny Neural Network for Scene Text Detection. IEEE ACCESS, v. 8, p. 223172-223188, 2020. (17/20945-0, 19/17729-0, 16/50250-1, 19/22262-3, 19/16253-1, 14/12236-1, 15/24494-8)

PALUCCI VIEIRA, LUIZ HENRIQUE; ARINS, FRANCIMARA BUDAL; GUGLIELMO, LUIZ GUILHERME ANTONACCI; DE LUCAS, RICARDO DANTAS; CARMINATTI, LORIVAL JOSE; SANTIAGO, PAULO ROBERTO PEREIRA. Game Running Performance and Fitness in Women's Futsal. INTERNATIONAL JOURNAL OF SPORTS MEDICINE, v. 42, n. 01, p. 74-81, JAN 2021. (16/50250-1, 13/08302-6)

SEGUNDO, MAURICIO PAMPLONA; PINTO, ALLAN; MINETTO, RODRIGO; TORRES, RICARDO DA SILVA; SARKAR, SUDEEP. Measuring Economic Activity From Space: A Case Study Using Flying Airplanes and COVID-19. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v. 14, p. 7213-7224, 2021. (19/22262-3, 16/50250-1, 14/12236-1, 15/24494-8, 19/16253-1, 19/17729-0)

FADEL, SAMUEL G.; MAIR, SEBASTIAN; DA SILVA TORRES, RICARDO; BREFELD, ULF. Contextual movement models based on normalizing flows. AStA-Advances in Statistical Analysis, AUG 2021. (17/24005-2, 16/50250-1, 15/24494-8, 17/20945-0, 18/19350-5, 19/17729-0)

VARGAS MUNOZ, JAVIER A.; DIAS, ZANONI; TORRES, RICARDO DA SILVA. A genetic programming approach for searching on nearest neighbors graphs. MULTIMEDIA TOOLS AND APPLICATIONS, v. 81, n. 16, p. 24-pg., 2022-03-18. (14/12236-1, 17/20945-0, 13/50155-0, 13/50169-1, 17/12646-3, 15/11937-9, 16/50250-1, 15/24494-8, 17/16246-0, 14/50715-9)

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GUTIERREZ-CASTILLA, NICOLAS; TORRES, RICARDO DA S.; FALCAO, ALEXANDRE X.; KOZERKE, SEBASTIAN; SCHWITTER, JURG; MASCI, PIER-GIORGIO; MONTOYA-ZEGARRA, JAVIER A.; IEEE. Long-Range Decoder Skip Connections: Exploiting Multi-Context Information for Cardiac Image Segmentation. 2019 32ND SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 8-pg., 2019-01-01. (14/12236-1, 16/50250-1, 17/20945-0, 15/24494-8, 13/50155-0)

PEREIRA, ERICO M.; TORRES, RICARDO DA S.; DOS SANTOS, JEFERSSON A.. A genetic algorithm approach for image representation learning through color quantization. MULTIMEDIA TOOLS AND APPLICATIONS, v. 80, n. 10, p. 15315-15350, APR 2021. (13/50169-1, 16/50250-1, 15/24494-8, 17/20945-0, 13/50155-0, 14/50715-9, 14/12236-1)

DE OLIVEIRA BUENO, MURILO JOSE; CAETANO, FABIO GIULIANO; DE SOUZA, NICOLAU MELO; CUNHA, SERGIO AUGUSTO; MOURA, FELIPE ARRUDA. Variability in tactical behavior of futsal teams from different categories. PLoS One, v. 15, n. 3, MAR 17 2020. (18/19007-9, 16/50250-1, 17/20945-0)

FERREIRA GOMES, LUIZ ALBERTO; TORRES, RICARDO DA SILVA; CORTES, MARIO LUCIO. On the prediction of long-lived bugs: An analysis and comparative study using FLOSS projects. INFORMATION AND SOFTWARE TECHNOLOGY, v. 132, APR 2021. (14/50715-9, 16/50250-1, 14/12236-1, 15/24494-8, 17/20945-0, 13/50155-0)

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PINTO, ALLAN; CORDOVA, MANUEL A.; DECKER, LUIS G. L.; FLORES-CAMPANA, JOSE L.; SOUZA, MARCOS R.; DOS SANTOS, ANDREZA A.; CONCEICAO, JHONATAS S.; GAGLIARDI, HENRIQUE F.; LUVIZON, DIOGO C.; TORRES, RICARDO DA S.; et al. PARALLAX MOTION EFFECT GENERATION THROUGH INSTANCE SEGMENTATION AND DEPTH ESTIMATION. 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), v. N/A, p. 5-pg., 2020-01-01. (19/16253-1, 16/50250-1, 17/12646-3)

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CAETANO, FABIO GIULIANO; DA SILVA, VITOR PANULA; TORRES, RICARDO DA SILVA; ANIDO, RICARDO DE OLIVEIRA; CUNHA, SERGIO AUGUSTO; MOURA, FELIPE ARRUDA. Analysis of Match Dynamics of Different Soccer Competition Levels Based on The Player Dyads. Journal of Human Kinetics, v. 70, n. 1, p. 173-182, NOV 2019. (16/50250-1, 17/20945-0)

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MAROSTEGAN, ANITA B.; GOBATTO, CLAUDIO A.; RASTEIRO, FELIPE M.; HARTZ, CHARLINI S.; MORENO, MARLENE A.; MANCHADO-GOBATTO, FULVIA B.. Effects of different inspiratory muscle warm-up loads on mechanical, physiological and muscle oxygenation responses during high-intensity running and recovery. SCIENTIFIC REPORTS, v. 12, n. 1, p. 13-pg., 2022-07-02. (19/20894-2, 19/10666-2, 16/50250-1, 12/06355-2, 18/05821-6, 09/08535-5)

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