Big Data no Futebol Análise Tática e Performance - Revisão Sistemática

Full Terms Conditions of access and use can be found at httpswwwtandfonlinecomactionjournalInformationjournalCodetejs20 European Journal of Sport Science ISSN Print Online Journal homepage httpswwwtandfonlinecomloitejs20 Unlocking the potential of big data to support tactical performance analysis in professional soccer A systematic review FR Goes LA Meerhoff MJO Bueno DM Rodrigues FA Moura MS Brink MT ElferinkGemser AJ Knobbe SA Cunha RS Torres KAPM Lemmink To cite this article FR Goes LA Meerhoff MJO Bueno DM Rodrigues FA Moura MS Brink MT ElferinkGemser AJ Knobbe SA Cunha RS Torres KAPM Lemmink 2021 Unlocking the potential of big data to support tactical performance analysis in professional soccer A systematic review European Journal of Sport Science 214 481496 DOI 1010801746139120201747552 To link to this article httpsdoiorg1010801746139120201747552 2020 The Authors Published by Informa UK Limited trading as Taylor Francis Group View supplementary material Published online 16 Apr 2020 Submit your article to this journal Article views 17943 View related articles View Crossmark data Citing articles 14 View citing articles REVIEW Unlocking the potential of big data to support tactical performance analysis in professional soccer A systematic review FR GOES 1 LA MEERHOFF 2 MJO BUENO5 DM RODRIGUES3 FA MOURA 5 MS BRINK1 MT ELFERINKGEMSER1 AJ KNOBBE2 SA CUNHA 4 RS TORRES3 KAPM LEMMINK1 1Center for Human Movement Sciences University of Groningen University Medical Center Groningen UMCG Groningen The Netherlands 2Leiden Institute of Advanced Computer Sciences LIACS Leiden University Leiden The Netherlands 3Institute of Computing IC University of Campinas Campinas Brazil 4Sport Sciences Department DCE University of Campinas Campinas Brazil 5Sport Sciences Department State University of Londrina Londrina Brazil Abstract In professional soccer increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports By joining forces with computer science solutions to these challenges could be achieved helping sports science to find new insights as is happening in other scientific domains We aim to bring multiple domains together in the context of analysing tactical behaviour in soccer using position tracking data A systematic literature search for studies employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases resulting in 2338 identified studies and finally the inclusion of 73 papers Each domain clearly contributes to the analysis of tactical behaviour albeit in sometimes radically different ways Accordingly we present a multidisciplinary framework where each domains contributions to feature construction modelling and interpretation can be situated We discuss a set of key challenges concerning the data analytics process specifically feature construction spatial and temporal aggregation Moreover we discuss how these challenges could be resolved through multidisciplinary collaboration which is pivotal in unlocking the potential of position tracking data in sports analytics Keywords Football big data tactical analysis team sport performance analysis Highlights Over the recent years there has been a considerable growth in studies on tactical behaviour using position tracking data especially in the domains of sports science and computer science Yet both domains have contributed distinctly different studies with the first being more focused on developing theories and practical implications and the latter more on developing techniques Considerable opportunities exist for collaboration between sports science and computer science in the study of tactics in soccer especially when using position tracking data Collaborations between the domains of sports science and computer science benefit from a stronger dialogue yielding a cyclical collaboration We have proposed a framework that could serve as the foundation for the combination of sports science and computer science expertise in tactical analysis in soccer 1 Introduction Increasingly large amounts of data are collected in professional soccer for the purpose of match analysis Player positions are tracked continuously during practice and competition using stateoftheart track ing systems Rein Memmert 2016 Due to recent technological innovations there has been a particular 2020 The Authors Published by Informa UK Limited trading as Taylor Francis Group This is an Open Access article distributed under the terms of the Creative Commons AttributionNonCommercialNoDerivatives License httpcreativecommonsorglicensesbyncnd40 which permits noncommercial reuse distribution and reproduction in any medium provided the original work is properly cited and is not altered transformed or built upon in any way Correspondence FR Goes Center for Human Movement Sciences University of Groningen UMCG Antonius Deusinglaan 1 9713 AV Groningen The Netherlands Email frgoesumcgnl European Journal of Sport Science 2021 Vol 21 No 4 481496 httpsdoiorg1010801746139120201747552 increase in systems and devices that collect and provide position tracking data These innovations have been embraced and widely adopted by pro fessional sports organizations and the use of data is broadly considered as a potential gamechanger in professional sports Rein Memmert 2016 However there is still a lot to be gained as the avail ability of data has increased much more rapidly than the scientific advancements required to valorise data in the domain of soccer Rein Memmert 2016 One of the more interesting opportunities provided by the availability of position tracking data in soccer is the study and analysis of tactical behaviour Tactical behaviour is an important determinant of perform ance in team sports like soccer and refers to how a team manages its spatial positioning over time to achieve a shared goal ie scoring while interacting with the opponent under constraints of the con ditions of play Gréhaigne Godbout Bouthier 1999 Rein Memmert 2016 In the past the analysis of tactical behaviour has mostly been based on manually annotated data and observation by experts Rein Memmert 2016 As these assess ments mainly describe what happens with the ball they only provided insights into the who and what and albeit with poor accuracy the where and when of onball behaviour Vilar Araujo Davids Travassos 2012 However as tactical behaviour is the result of the interaction between all players on and off the ball Gréhaigne et al 1999 Rein Memmert 2016 truly analysing the mechanisms behind it requires accurate data on all 22 players and the ball Therefore position track ing data provides the opportunity to accurately study the mechanisms behind tactical behaviour in soccer However despite its potential in the analysis of tacti cal behaviour so far it has mainly been used to deter mine player activity profiles to monitor player loading and subsequently prescribe training loads Sarmento et al 2014 The large amounts of position tracking data chal lenge the data management and analytics methods native to sports Gandomi Haider 2015 and unlocking its potential in the study of tactical behav iour requires solving these challenges first Rein Memmert 2016 Although data can be considered big based on the three Vs volume variety and vel ocity Gandomi Haider 2015 there are no univer sal benchmarks for these dimensions Whether a dataset is considered big or not heavily depends on the interplay between these dimensions and is gener ally considered to be domain specific Gandomi Haider 2015 One could consider data big when it exceeds the threeV tipping point the point where traditional data management and analysis methods become inadequate Gandomi Haider 2015 The overall process of deriving information from position tracking data can be divided into two components data management and data analytics Gandomi Haider 2015 Labrinidis Jagadish 2012 These components can each be divided further into various sub processes each associated with their own challenges Gandomi Haider 2015 Labrinidis Jagadish 2012 Challenges to the data management component have been thoroughly addressed in previous reviews Manafi fard Ebadi and Moghaddam 2017 for example provide a detailed review on the strengths and weak nesses of optical tracking systems and what could be done when it comes to preprocessing to improve data collection with these systems in the future Man afifard et al 2017 In other examples Stein et al 2017 and Rein and Memmert 2016 both specific to soccer and Gandomi and Haider 2015 in general have addressed the various data streams that need to be brought together in the analysis and how this poses a challenge to data management systems commonly employed in soccer Gandomi Haider 2015 Rein Memmert 2016 Stein et al 2017 Challenges to data analytics on the other hand and specifically the challenge of aggregat ing raw position data into interpretable spatiotem poral features that capture the complex dynamics of tactical behaviour have received considerably less attention so far Contributions from the domain of sports science and the domain of computer science are typically characterized by distinctly different research para digms Research from the domain of sports science on tactical behaviour is generally characterized by deductive reasoning in forming a hypothesis and designing an experimental study Teams are for example considered as complex dynamical systems and hypotheses regarding their behaviour are formu lated based on expectations rooted in such a theoreti cal perspective Araújo et al 2015 Balague Torrents Hristovski Davids Araújo 2013 Seifert Araújo Komar Davids 2017 To study whether soccer teams behave like dynamical systems and to study how manipulating constraints affects the systems behaviour data is typically col lected for a specific research purpose after the research question has been formulated In most sports science contributions this means data is col lected in an experimental setting most frequently a set of manipulated smallsided games which is designed based on the research question and related hypotheses The raw position tracking data is then usually aggregated into features that operationalize the hypotheses and represent group level behaviour such as team centroids or team surface areas Frencken Lemmink Delleman Visscher 2011 482 FR Goes et al Memmert Lemmink Sampaio 2017 A feature like the team centroid reduces the complex behaviour of a group of players into interpretable behaviour by aggregating their movements into a single feature in the case of the centroid representing the average positions at a point in time These aggregated features are then used to study the interaction between groups over time This can be insightful for the development of specific theories However by reducing the teams performance to these aggregated features relevant aspects of the complexity of this behaviour may be overlooked Aggregating the behaviour of 11 players into one feature like the centroid might for example fail to capture the different movements of subunits ie defensive line on the team and thereby fail to fully capture the complexity of tactical behaviour On the other hand contributions from the domain of computer science as well as the application of its techniques also described as data science utilize a distinctly different research paradigm Com puter science concerns the theoretical foundations of computationally retrieving information typically yielding advanced analyses and highlevel represen tations of large and complex data Gudmundsson Horton 2017 For example Knowledge Discovery also referred to as Data Mining is all about identi fying the robustness of patterns that are found without formulating hypotheses about the existence of these patterns Although both sports and compu ter science adopt a deductive approach the type of empirical evidence for these deductions is radically different In sports science experimental research designs typically aim to confirm or reject a hypothesis that was formulated based on theory as discussed in the previous paragraph In computer science new modelling techniques are evaluated by testing the robustness of the generated model This quantifi cation of robustness can then be used to verify whether a discovered pattern was significant How likely is it that this pattern was found by chance In other words whether the technique worked success fully is deduced based on the empirical evidence to quantify the robustness Explorative techniques such as subgroup discovery Grosskreutz Rüping 2009 have the benefit that patterns can be discovered based on how interesting they are for example based on how accurate the pattern is ratio between true positives and false negatives or how many instances it applies to Typically computer science techniques have been developed in the context of large datasets with many possible patterns to explore as it is not always clear which patterns can be expected apriori From position tracking data many features can be derived resulting in a multitude of features Therefore the data mining tools from computer science are wellsuited to deal with the complexity of position tracking data One could argue that unlocking the full potential of big data for sports science and practice requires bringing the two domains and thus two distinctly different paradigms together as their contributions can be regarded complimentary Doing so however requires one to understand the challenges and opportunities of a multidisciplinary interplay between the domains of sports and com puter science Rein Memmert 2016 Several authors have addressed this question in previously published narrative studies Rein and Memmert 2016 have discussed the potential of applying big data in tactical analysis but also discussed how it challenges the methodological approaches native to sports sciences Memmert et al 2017 have applied techniques from both domains to a position tracking dataset of one professional match to illustrate the potential of using contributions from both domains Gudmundsson and Horton 2017 have provided an overview of mostly computer science techniques available in sports for the study of spatiotemporal behaviour Stein et al 2018 have described the entire process from data acquisition to storage to ultimately analysis and interpretation in an attempt to provide an overview of different segments of the process of uti lizing big data for performance analysis Although these studies all refer to challenges as well as the potential of multidisciplinary collaboration none of these studies actually put the contributions from both domains into one framework nor do they discuss the operationalization of such a collaboration The integration of fundamental computer science work into applied settings ie data science has been discussed in other applied domains illustrating the benefits of integrating these techniques in differ ent settings Gandomi and Haider 2015 have dis cussed the challenges and opportunities of applying big data in general while more specific examples of integrating computer science techniques in specific settings outside of sports include forecasting and pattern mining of financial timeseries in economics Cao Tay 2003 development of individual video recommendation systems in media and enter tainment Davidson 2010 and spatiotemporal analysis of geographical data in geographic and earth sciences Peuquet Duan 1995 These examples illustrate that application of techniques from computer science can support analysis and innovation in other areas With the current review we aim to outline a framework that integrates contri butions from the domains of sports science and com puter science in the study and analysis of tactical Unlocking the potential of big data to support tactical performance analysis 483 behaviour in soccer using position tracking data and discuss the additional insights that can be gained from this integration We specifically focus on the identification of challenges and opportunities with regard to the utilization of expertise from the domains of sports science and computer science as both domains benefit from a conceptual model that outlines where each domain complements the other in analysing tactical behaviour in soccer using pos itional tracking data 2 Methods 21 Literature search A systematic review of the available literature was conducted according to PRISMA Preferred Report ing Items for Systematic reviews and Metaanalyses guidelines Moher et al 2015 A literature search was conducted on 14 June 2019 to identify studies that report the use of position tracking data to analyse tactical behaviour in soccer Figure 2 Specifically the following electronic databases were searched Science Direct Dimensions Computer Science Bibliographies PubMed Scopus ACM Digital Library IEEE Xplore Titles andor abstracts of all records in an elec tronic database were searched for the combination of the following search terms soccer OR football AND tactic OR strateg OR formation OR inter player OR interteam OR spatiotemporal NOT robo Furthermore additional studies to consider were identified by manually searching the reference lists of included papers 22 Study selection To be considered for this review studies had to concern tactical behaviour and meet the inclusion cri teria outlined in Table I For the purpose of this review tactical behaviour was defined as how a team or individual manages its spatial position over time to achieve a shared goal ie scoring while adapting to and interacting with the opponent under constraints of the conditions of play Gré haigne et al 1999 We operationalized this by searching for studies that at least included data and analysis on the interactions in space and time on the interteam as well as intrateam level The first author conducted the first selection based on titles and abstracts conducted by the first author Any study that clearly not met the inclusion criteria was excluded at this stage When a confident decision based on the title and abstract could not be made the study was included for fulltext analysis Next the eli gibility for inclusion was assessed based on analysis of fulltext papers by the first author of this review The final selection was then validated by at least one of the coauthors Any ambiguities regarding the inclusion of papers of the review until consensus was reached 23 Data extraction All included studies were classified as sports science 1 or computer science 2 based on the journal or conference they were published in as well as the associated keywords Next information on data col lection was extracted To review the contributions of all studies to the components of feature construc tion and modelling analysis Figure 1 we extracted data on the spatial aggregation features window selection and techniques applied for analy sis Furthermore data was extracted on the link with match performance the problem definition or aim of the study and the inclusion of a theoretical definition of tactical behaviour to review the inter pretability of all included studies Finally all findings were categorized and put into a single framework Figure 2 that will serve as the context for the dis cussion of our findings and as a proposed structure for the utilization of expertise from the domains of sports science and computer science in the study and analysis of tactical behaviour All data extraction Table I In and exclusion criteria for the systematic literature search Inclusion criteria Exclusion criteria Published in the last 15 years Fulltext publication English Published as a peerreview journal or conference paper Tactical analysis based on position tracking data LPM GPS or Optical Tracking Data collected in matches or SSGs Data collected in soccer No fulltext available in English Analysis based only on notational data Data collected in futsal Data available for only one team Data available of less than two players Notes LPM Local Position Measurement system with Radio Frequency Identification RFID Frencken et al 2010 GPS Global Positioning System SSGs Smallsided games 484 FR Goes et al was based on fulltext assessment by the first author of this review Data extraction tables Supplementary Data were developed based on consensus between all authors 3 Results The initial database search returned 2290 records to be considered for inclusion An additional 48 papers were identified based on manual inspection of the reference lists of already included papers see Identi fication in Figure 1 As a result a total of 2338 records were screened based on title and abstract of which 146 were considered for fulltext assessment see Screening in Figure 1 After fulltext assess ment 73 records were excluded because they did not meet our inclusion criteria see Eligibility in Figure 1 The remaining 73 records Aguiar Gon çalves Botelho Lemmink Sampaio 2015 Andrienko et al 2017 Aquino et al 2016a 2016b Baptista et al 2018 Batista et al 2019 Barnabé Volossovitch Duarte Ferreira Davids 2016 Bartlett Button Robins DuttMazumder Kennedy 2012 Bialkowski et al 2014a 2014b 2014c 2016 Castellano Fernandez Echeazarra Barreira Garganta 2017 Chawla Estephan Gud mundsson Horton 2017 Clemente Couceiro Martins Mendes Figueiredo 2013a 2013b 2014 Couceiro Clemente Martins Machado 2014 Coutinho et al 2017 2018 Duarte et al 2012 2013a 2013b Fernandez Bornn 2018 Fig ueira Gonçalves Masiulis Sampaio 2018 Filetti Ruscello DOttavio Fanelli 2017 Folgado Gon çalves Abade Sampaio 2014a Frencken et al 2011 Frencken De Poel Visscher Lemmink Figure 1 Flowchart of systematic literature search conform PRISMA guidelines where the number of included studies during each of the stages of the search process is shown The main reasons for exclusion based on fulltext assessment as well as the number of included studies are shown at the bottom Unlocking the potential of big data to support tactical performance analysis 485 2012 Frencken van der Plaats Visscher Lemmink 2013 Frias Duarte 2014 Gonçalves Figueira Maçãs Sampaio 2014 Gonçalves et al 2017a 2017b Gonçalves Marcelino Torres Ronda Torrents Sampaio 2016 Grunz Memmert Perl 2012 Gudmundsson Wolle 2010 Janetzko et al 2014 Janetzko Stein Sacha Schreck 2016 Knauf Memmert Brefeld 2016 Link Lang Seidenschwarz 2016 Machado et al 2017 Memmert et al 2017 Memmert Raabe Schwab Rein 2019 Moura Barreto Martins Anido De Barros Cunha 2012 Moura et al 2013 2016 Olthof Frencken Lemmink 2015 2018 2019 Power Ruiz Wei Lucey 2017 Ramos Lopes Marques Araújo 2017 Rein Raabe Memmert 2017 Ric et al 2017 Sampaio Lago Gonçalves Macas Leite 2014 Sampaio Macas 2012 Siegle Lames 2013 Silva et al 2014a 2014b 2015 2016a 2016b Spearman Basye Dick Hotovy Pop 2017 Stein et al 2015 2016 Travassos Gonçalves Marcelino Monteiro Sampaio 2014 Vilar Araujo Davids BarYam 2013 2014a 2014b Wei Sha Lucey Morgan Sridharan 2013 Yue Broich Seifriz Mester 2008a 2008b Zhang Beernaerts Zhang de Weghe 2016 were Figure 2 Conceptual framework for the combination of sports science translucent red bars and computer science translucent blue bars expertise in the study of tactical behaviour in soccer Based on the results from the current systematic review Bars with percentage represent the relative occurrence of a certain method or feature within a domain Abbreviations SSG SmallSided Games LPM Local Position Measurement 486 FR Goes et al included for analysis in the review Of the included papers 54 74 were qualified as sports science papers and 19 26 as computer science papers Below we will describe the results of our systema tic analysis of the literature We examine various cat egories including Problem Definition Data Collection Spatial Aggregation Temporal Aggrega tion and Modelling Interpretation We analyse the included studies numerically by describing how often various categories occur Moreover we sum marize the different categories in a visual framework where we combine the expertise from sports and computerscience domains see Figure 2 This figure will be used as a guide to explain the body of literature that encompasses the study of tactical be haviour Full details and data extracted from the included studies can be found in the supplementary data 31 Problem definition In most included sports science studies research questions were driven by theoretical or practical domain expertise from for example physiology be havioural science or psychology Studies frequently aimed for practical implications and study designs and data collection result from the research question When looking at the problem definitions and aims of the included sports science papers 63 studied the effect of an intervention on tactical behaviour as is illustrated by the work of Olthof et al 2018 2019 who studied the effect of manipulating pitch sizes on tactical behaviour in different age groups and the work of Gonçalves et al 2016 2017a 2017b who studied the effect of numerical imbalance between teams on tactical behaviour Gonçalves et al 2017a 2017b Ric et al 2017 Twenty percent studied a variablemethod to quantify tactical behaviour as is illustrated by the work of Link et al 2016 who conceptualized a new feature called dan gerousity to quantify offensive impact Finally 17 studied the relationship between variables see Problem Definition in Figure 2 as for example illustrated in the work of Rein et al 2017 who studied the relation between pass effectiveness quan tified by the change in space control and number of outplayed defenders and success in 103 Bundesliga games Rein et al 2017 In most included computer science studies on the other hand research questions were driven by theor etical and methodological domain expertise from for example computer sciences mathematics or data science These studies frequently aimed for new methodological approaches and techniques rather than practical implications Furthermore in many cases the design could be considered datadriven rather than formulating hypotheses based on theory and collecting data in an experimental setup to test these hypotheses studies used large sets of available data and generated hypotheses from the data When looking at the problem definitions of these studies 5 studied the effect of an intervention or constraint as there is the work by Bialkowski et al 2014a 2014b 2014c studying the impact of homeadvan tage on the dynamic formation of a team on the pitch The majority 84 of computer science con tributions however studied a new technique or model mostly classification or clustering problems like the work by Fernandez and Bornn 2018 who proposed an improved model for measuring space control the work by Andrienko et al 2017 propos ing a new feature to quantify pressure on a player or the work by Bialkowski et al 2014a 2014b 2014c and the work by Grunz et al 2012 proposing new methods to identify patterns and formation in the data Bialkowski et al 2014a 2014b 2014c Grunz et al 2012 Finally 11 studied prediction or prob ability problems as illustrated in the work by Spear man et al 2017 or Chawla et al 2017 who proposed models to predict if a pass would arrive at a teammate or not Chawla et al 2017 Spearman et al 2017 see Problem Definition in Figure 2 32 Data collection The type quality and quantity of data strongly influences the research questions that can be answered within the study of tactical behaviour as well as the approach that can be used see Data Collection in Figure 2 Most studies 64 used optical tracking data as this is the system of choice in many professional competitions As opposed to LPM and GPS systems optical tracking systems typically allow tracking of the ball However they are also known to have a lower accu racy in comparison to wearable tracking devices especially LPM Frencken Lemmink Delleman 2010 Work by Mara Morgan Pumpa and Thompson 2017 revealed optical tracking systems suffer measurement errors in the range of 25 m25 m in measuring covered distance on 20100 m change of direction runs Mara et al 2017 Although these errors could limit the use of optical tracking data for the analysis of physical performance the subsequent errors of 005 m in measuring position still allow for accurate assess ment of tactical behaviour as the error margin is small enough for data to still represent actual pos itions Only a minority 18 of the studies used ball tracking and a much larger part of the Unlocking the potential of big data to support tactical performance analysis 487 studies 42 used the more timeconsuming nota tional event data to study ball events Sensor systems 36 and experimental designs 48 like smallsided games SSGs were exclusively used in sports science studies As sensor systems do not allow ball tracking eventbased analyses are impossible without notational event data Figure 2 33 Spatial aggregation Tracking the X and Y position of 22 players and the ball 1100 times a second results in sizeable amounts of data even for one match as well as a high complexity as the 22 degrees of freedom of the system allow for numerous potential interactions Therefore most studies aggregate raw position data by reducing the spatial positions of all players into spatial features More specifically spatial aggregation refers to the process of constructing features that capture grouplevel behaviour per timeframe and allow one to derive contextual meaning as these fea tures reduce the systems complexity to an interpret able level see Spatial Aggregation in Figure 2 These features can be constructed at the macro level full team as for example in work by Frencken et al 2012 who aggregated the positions of the team into one team cerntroid at the microlevel sub groups of at least two players like in the work by Memmert et al 2017 who aggregated the positions of a subgroup eg defensive line into a line centroid or even at the level of the individual as in the work by Olthof et al 2015 who measured the average dis tance of all players to the team centroid eg stretch index Furthermore combinations of spatial aggre gates can be used to construct composite measures of spatial subgroup interactions as for example presented in the work by Goes Kempe Meerhoff Lemmink 2019 who constructed a measure of pass effectiveness by using line centroids team spread and team surface areas Most sports science studies 84 used some form of spatial aggregation most frequently 57 centroid related features Frencken et al 2011 Yue et al 2008a 2008b fol lowed by team surface areas and spread Moura et al 2012 46 length and width Folgado Lemmink Frencken Sampaio 2014b 30 and space control Rein et al 2017 7 Distribution amongst computer science studies is somewhat similar with 58 of the studies using spatial aggre gates specifically centroid features 32 length and width 11 and space control 11 However as data mining techniques can directly be applied to the positional data without aggregating it into features a small minority of the sports science studies 16 and nearly half of the computer science studies 42 do not use spatial aggregation In these cases patterns in the raw data can for example be detected using unsupervised machine learning techniques like clustering as is illustrated by the work of Grunz et al 2012 Knauf et al 2016 and Machado et al 2017 who all mine pat terns in the data by clustering the raw positions in some way Grunz et al 2012 Knauf et al 2016 Machado et al 2017 Furthermore machine learn ing techniques also allow for the inclusion of many features and studying their nonlinear relationships like there is the work by Power et al 2017 and Spearman et al 2017 who model pass risk and reward and the probability of a pass arriving and include a multitude of features Power et al 2017 Spearman et al 2017 In many of these computer science contributions the algorithm does feature selection automatically The main benefit of this is that instead of creating features based on apriori assumed relationships between entities hidden relationships can be uncovered from the data As fea tures are not created and selected based on expec tations of the user but rather based on their importance in the algorithm they could prove to be a better depiction of patterns in the data 34 Temporal aggregation To extract information statistically compare or model timeseries of either raw data or aggregated spatial features data needs to be aggregated within the temporal domain as well see Temporal Aggrega tion in Figure 2 Temporal aggregation refers to the summation of data over a given timewindow by for example computing the mean value of a given feature We consider three different methods for temporal aggregation first of all data can be aggregated eg averaged over time windows with a fixed size inde pendent of the context of the game Sampaio Macas 2012 In such methodologies for example data is aggregated over the course of a half or full match or another time window with a fixed duration Secondly data can also be aggregated over a window with a fixed size that is linked to match events An example is looking at the 3 s following a pass Goes et al 2019 or the 30 s before a goal Frencken et al 2012 Finally data can be aggregated over windows with a flexible size In these cases windows are always linked to events with variable durations like a sequence of passes or running trajec tories Rein et al 2017 Spearman et al 2017 The majority of sports science studies 60 utilized fixed windows in which they often aggregate spatial data over the course of a full SSG or match while only a minority aggregates over fixed 9 or flexible 488 FR Goes et al 24 eventbased windows However the majority of computer science studies aggregated over fixed 26 or flexible 42 eventbased windows and only a minority 32 aggregated over fixed windows independent of context 35 Modelling interpretation Most included sports science studies utilized statisti cal models and models rooted in the dynamical systems theory like relativephase Palut Zanone 2005 and entropy Pincus 1991 1995 analyses that allow for timeseries analysis These models are generally based on linear relationships and allow comparison of multiple conditions the study of relationships between variables and testing specific hypotheses Furthermore they are interpretable on the level of individual features Most computer science studies on the other hand used methods that are in comparison computationally complex ie require more computations and therefore more processing power like various machine learning approaches These approaches allow the study of non linear complex relationships amongst many different features and the discovery of hidden pat terns in the data but require specific programming skills and often highperformance computing clus ters and can be harder to interpret especially without the methodological domain expertise To be able to interpret the practical impact of a study on behaviour it needs to be clear what tactical behaviour was actually studied and how changing this behaviour impacts performance see Modelling Interpretation in Figure 2 Only 19 explicitly defined tactical behaviour of which only one study Janetzko et al 2014 was classified as a computer science study Analysing the extracted definitions three common elements were identified Tactical performancebehaviour refers to 1 the dynamic positioning and organisation in space and time of a team and its players on the pitch in interaction with and adapting to the movement of the ball 2 move ment of the opponents and conditions of play 3 and constitutes more than just the sum of individual parts As according to these criteria tactical behaviour is emergent it cannot be studied by breaking down the behaviour of a team into 11 individual parts and analysing them separately as behaviour is the result of interaction Furthermore only 30 used match performance indicators eg outcome shots on goal in their study of tactical behaviour Most 86 investigated the link between tactical features and match performance using performance indi cators related to shots or goals Interestingly there is little consensus on the relation of most tactical fea tures with performance outcome On the one hand studies that investigated the link between oftenused tactical features like the teamcentroid did not find a clear relationship with offensive events and per formance Bartlett et al 2012 Frencken et al 2012 On the other hand authors who used more complex tactical features like the team surface area or spread Moura et al 2012 2016 or composite features related to passing Rein et al 2017 Spear man et al 2017 did report some relationship with performance These rather inconsistent reports on the effect of tactical features on performance as well as the large variety of possible tactical features to analyse highlight how difficult it is to uncover and interpret consistent and generalizable patterns in tactics 4 Discussion With this review we aimed to put the contributions of sports and computer science to the analysis of tactical behaviour in soccer using position tracking data into perspective Both domains contributed significantly to the study of tactical behaviour and provide a set of unique approaches towards analytics Our results show that there are considerable differences in meth odology We propose that both domains benefit from a cyclical collaboration and embedding each others domain expertise Therefore we provide a frame work for optimizing this collaboration by linking the contributions from both domains to different parts of the analytical process that entails the analysis of tac tical behaviour using position tracking data Figure 2 Our framework could support the field of sports ana lytics and specifically the analysis of tactical behaviour and result in a better translation to practice We have argued in our introduction that research from sports science and research from computer science is characterized by distinctly different and to some extent contrasting research paradigms Our results have revealed that this was also true for research specifically concerning the study of tactical behaviour using position tracking data The sports science studies we have included in this review were predominantly characterized by deductive reasoning in which hypotheses were formed based on theory and tested in mostly experimental settings This is clearly illustrated by many of the included sports science works like those by Aguiar et al 2015 Bap tista et al 2018 Coutinho et al 2017 2018 Duarte et al 2012 Frencken et al 2011 2013 or Olthof et al 2015 2018 who all presented a theoretical framework to study and understand tacti cal behaviour that is rooted in the dynamical systems theory Aguiar et al 2015 Baptista et al 2018 2019 Coutinho et al 2017 2018 Duarte et al 2012 Frencken et al 2012 2013 Olthof et al 2015 Unlocking the potential of big data to support tactical performance analysis 489 2018 and specifically designed experimental setups with smallsided games to analyse behaviour against the backdrop of this framework The aims of these sports science studies are generally focused on advan cing our understanding of tactical behaviour and applying the findings in practice to for example improve training design or talent identification and development This is illustrated in studies like those by Gonçalves et al 2016 2017a 2017b who studied the impact of numerical imbalance and spatial constraints on tactical behaviour in small sided games to optimize training design Gonçalves et al 2016 2017a 2017b Or the work by Olthof et al 2015 2018 2019 who studied the impact of field size on tactical behaviour in smallsided games and compared that behaviour to behaviour seen in a real match to find out what design would be the best format to improve match performance The included computer science studies on the other hand provide a very different perspective The studies we included from this domain generally do not present any theoretical context to explain tac tical behaviour nor do they contain hypotheses about what this behaviour would look like or how teams or players would react to certain manipulations or stimuli We would like to argue that based on our findings this is not necessarily a shortcoming but rather a matter of a different aim and perspective Rather than aiming for an increased understanding and practical implications in sport the computer science studies we included were typically focussed on advancing methodology and computational tech niques for data processing modelling and extraction of information by means of inductive designs that centre on data mining feature extraction and visual analysis This is illustrated by for example the work of Bialkowski et al 2014a 2014b 2014c 2016 and Wei et al 2013 who presented new methods to detect formations and identify positional roles based on data based on large observational dataset collected in competition Or the work of Stein et al 2015 2016 and Janetzko et al 2014 2016 who presented a data visualization and exploration tech niques that aim to optimize the workflow of video analysts in professional soccer organizations Janetzko et al 2014 2016 Stein et al 2015 2016 Or the work of Chawla et al 2017 who pre sented a model to accurately classify successful and nonsuccessful passes based on data None of these works extensively discus practical applications explain the findings based on a theoretical under standing of tactical behaviour or advance our under standing of behaviour have experimental designs or result in direct practical implications on the level of training and performance However this is by design as these contributions all aimed to propose new techniques features and data processing and visualization routines instead The distinct difference in contributions from both domains to the research on tactical behaviour is also confirmed by other recent review studies on similar topics In systematic reviews characteristic for sports science like those by Sarmento et al 2014 and Ometto et al 2018 the focus is on how position tracking data can be used to analyse performance and monitor loading or how to manipulate small sided games to change behaviour On the other hand in typical computer science survey papers like the one by Perin et al 2018 Gudmundsson and Horton 2017 and Stein et al 2017 the focus is more technical discussing topics from data manage ment to visualization and how to develop analytical tools Given the fundamental differences in expertise and methodology collaboration between both domains can therefore be regarded a key challenge Most studies included in this review fit well into one end of the sports science computer science spectrum and collaborations between domains are still relatively sparse However we have also included multiple studies that gravitate towards the middle of the spectrum and illustrate the added benefit of a synergy between both domains The studies by Link et al 2016 Rein et al 2017 and Goes et al 2019 are examples of sports science work that uti lizes observational designs in which large datasets were collected in competition and used for the devel opment and validation of new features that assess some aspect of performance Goes et al 2019 Link et al 2016 Rein et al 2017 Although in these studies most involved scientists had a back ground in sports science at least some of them also had a background computer science helping them applying computer science techniques for data pro cessing visualization and analytics coming from domains like mathematics data mining and machine learning and information processing Despite their methodology these studies were still classified as sports science as their aim was not necessarily the sole development of a new approach or technique but rather the validation of these approaches by studying their relation to successful performance and applying the approach for the purpose of performance analysis The work by Goes et al 2019 for example resulted in a new metric to quantify the effectiveness of a pass that was con structed using clustering techniques and then applied for player evaluation purposes while the work by Rein et al 2017 was focussed on applying multiple metrics that assess pass effectiveness by studying their relation to offensive performance As we identified several sports science studies that utilized techniques from other domains to advance 490 FR Goes et al their research we also identified multiple computer science studies that did the same The studies by Power et al 2017 Spearman et al 2017 Andrienko et al 2017 and Fernandez and Bornn 2018 can all be regarded as examples of studies that predominantly involved expertise from computer and data science but who also involved domain expertise from sports science Andrienko et al 2017 Fernandez and Bornn 2018 Power et al 2017 Spearman et al 2017 These studies focussed on feature development and modelling as they con structed models for the assessment of pass risk and reward pressure space control and pass probability Different to the sports science examples mentioned before the scope of these studies was methodologi cal yet they typically validated their approach and its assumed relation to performance based on domain expertise and provided several examples of practical use cases based on data collected in compe tition These examples from sports science and com puter science studies that utilize expertise from other domains illustrate the additional benefits that can be gained and can in some ways be regarded as tem plates for future collaborations The included studies are illustrative of collabor ations between the domains of computer science and sports science suggest contributions from both domains are compliant rather than concomitant We therefore propose that collaboration between sports science and computer science in the process of studying tactical behaviour using position tracking data should be a cyclical rather than a parallel one Sports science tests theory and translates practical problems into research questions By applying tech niques from computer science to sports science research designs one could come to different answers to research questions These answers might differ in the sense that sports scientists could assess different aspects of performance but they could also differ in the sense that these methods allow for a more indepth answer The other way around research questions deduced from theory and obser vation by sports science can be used by computer science to define the scope of their search for and development of appropriate technologies to derive information from position tracking data Computer science provides the tools to gain indepth knowledge and enables sports science to test increasingly complex hypotheses and ask new questions As both domains bring relevant expertise in relation to con ducting and interpreting tactical analyses we propose that impactful analytics relies on the combi nation of expertise from both domains The quality ie accuracy sampling frequency inclusion of ball data and quantity of available data have a big impact on most types of research and cannot be ignored in any discussion of sports ana lytics Due to technological advancements lowers costs and growing interest Rein Memmert 2016 we have seen an increase in the availability and quality of data in soccer similar to big data devel opments in other areas providing numerous oppor tunities Gandomi Haider 2015 like opponent analysis scouting and performance optimization on a team and individual level However based on our results these opportunities only seem to be seized to a limited extent Most sports science studies are characterized by experimental setups in which small samples of data are collected in a specific popu lation to answer a predetermined research question Olthof et al 2015 Travassos et al 2014 Although this kind of research has allowed us to draw general inferences about what drives tactical behaviour of groups the small sample sizes and highly specific cir cumstances that are often different from a real match also limit the use of findings from these studies in reallife tactical analysis As tactical behaviour is highly dependent on the context Gréhaigne et al 1999 Rein Memmert 2016 larger reallife datasets collected in actual competitive matches in combination with methodology that enables capturing complex pat terns might allow one to draw conclusions about per formance with a stronger ecological validity Of course causation and correlation should not be con fused but with large enough datasets the discovered patterns carry some weight and at the very least provide a good basis for developing new theories that can be further examined in more controlled settings On the other hand handling and analysing much larger datasets challenges backend processes ie storing preprocessing and querying and analytics ie aggregation and feature construction that are not typically addressed by sports science research and can thus be regarded a key challenge The domain of computer science typically focuses on tech nological developments within these processes and collaboration could advance the ability of sports science to work with increasingly large datasets As illustrated by the results in this review the majority of sports science studies utilizes lowlevel simple to compute and high reduction of complex ity spatial features like the team centroid Folgado et al 2014 Yue et al 2008a 2008b that aim to capture grouplevel behaviour in one feature The computation of these features is relatively easy and their computational cost is low yet as illustrated by the results they have limited value Features like the team centroid have often been developed to study tactics in smallsided games but seem incap able of fully capturing the complex dynamics of an 11aside match Goes et al 2019 Combining computer science expertise on for example data Unlocking the potential of big data to support tactical performance analysis 491 mining and machine learning with sports science theory provides many opportunities to innovate in this aspect A potential example could be applying the work of Bialkowski et al 2014a 2014b 2014c 2016 that has resulted in methodology to automati cally and dynamically identify formations and pos itional roles Applying this method in sports science research like that of Memmert et al 2017 Goes et al 2019 or Siegle and Lames 2013 who all use line centroids in which the lines are based on manual annotation of fixed positional roles could lead to different answers and new insights The other way around applying the theoretical framework of dynamical systems theory that is presented in for example the sports science work by Frencken et al 2012 2013 to feature construction in computer science work like that on quantifying pressure by Andrienko et al 2017 could lead to advanced methods that use coupling between features and movement synchrony of players to quantify pressure defensive strategies and offball performance of offensive players These are typical examples of cycli cal collaboration The outcome of a collaboration like this would for example allow one to innovate the way we analyse the performance of a team during the game to support decisionmaking by the coach in neartime to analyse the opponent before the match by studying patterns that characterise their successful attacks or to identify specific patterns to emphasize and train in the own team Ultimately spatial features no matter their com plexity hold little meaning when aggregated over a full match and temporal aggregation is essential to place spatial behaviour in a temporal context Gré haigne et al 1999 Rein Memmert 2016 Most included sports science studies aggregated over fixed windows independent of gamecontext like a match or half Duarte et al 2013a 2013b Gonçalves et al 2017a 2017b which limits interpretability We argue that deriving meaning from spatial features requires the use of eventbased timewindows which is more common in computer science studies Andrienko et al 2017 Chawla et al 2017 Fernandez and Bornn 2018 as using eventbased timewindows allows one to draw conclusions about for example a pass dribble or setpiece On such a small timescale it is much easier to find structural patterns than on the level of the entire game This in turn would allow one to answer questions like what defines an effective attack or successful dribble Although this might seem like another opportunity for sports science to implement existing computer science expertise this one is less straightforward than spatial aggregation and adequate temporal aggregation can be regarded as a key challenge As timeseries analysis is typically challenging for most machine learning techniques Fu 2011 and sport and behavioural sciences actually have a lot of expertise in timeseries analysis one could argue innovation here would definitively be on the brink of interaction between both domains Despite the often underlined potential Memmert et al 2017 Rein Memmert 2016 Stein et al 2017 of position tracking data to study tactical be haviour in sports and specifically in soccer the application is still relatively limited Rein Memmert 2016 Folgado et al 2014 Our results demonstrated the contributions to this topic have increased substantially over the recent years and already resulted in an indepth understanding of tactics in soccer However so far these studies have had little practical impact and the potential of pos ition tracking data does not seem to be fully utilized so far We argue that changing this requires domain expertise from sports science as well as computer science embedded within a multidisciplinary approach which is a key challenge for sports ana lytics It also requires a clear link between method ology findings and reallife performance ie answering the question how does this help meis this related to winning the game asked by prac tioners Understanding behaviour therefore requires an approach that at least evaluates a certain aspect within the context of others as well as answers the key performance question how does changing this behaviour impact our performance With this systematic review we provided an evalu ation of contributions from sports science and compu ter science to the study of position tracking data for the purpose of tactical analysis in soccer and we have shown how an interplay between both domains could results in innovative contributions to the field of sports analytics One major limitation of the current review is its narrow scope as we largely ignored essen tial components of the data analytics process like data acquisition storage management visualization as well as ethics and privacy issues Perin et al 2018 Stein et al 2017 However doing so allowed us to discuss the opportunities for position tracking data to impact tactical behaviour whereas previous reports have merely touched upon its potential This has resulted in the discussion of a set of challenges con cerning the data analytics process specifically feature construction spatial and temporal aggregation that could be resolved by multidisciplinary collaboration which is pivotal in unlocking the potential of position tracking data in sports analytics 5 Conclusion With this review we have shown the considerable opportunities for collaboration between sports 492 FR Goes et al science and computer science to study tactics in soccer particularly when using position tracking data Our systematic review highlights that sports and computer science research on tactical behaviour contains distinctly different contributions We pro posed a framework that could serve as the foundation for the combination of sports science and computer science expertise in tactical analysis It has become clear that the collaborations between both domains benefit from a stronger dialogue yielding a cyclical collaboration sports science identifies problems and tests theory hypotheses computer science develops robust techniques to solve such problems and sports science in turn adjusts theories and derives practical implications from data by implementing them Acknowledgements This work was supported by grants of the Nether lands Organization for Scientific Research and FAPESP project title The Secret of Playing Foot ball Brazil vs The Netherlands Disclosure statement No potential conflict of interest was reported by the authors Supplemental data Supplemental data for this article can be accessed here httpsdoiorg1010801746139120201747552 Funding This work was supported by grants of the Netherlands Organiz ation for Scientific Research 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and correlation analyses Studies in Applied Mathematics 121 245261 Zhang P Beernaerts J Zhang L de Weghe N 2016 Visual exploration of match performance based on football movement data using the continuous triangular model Applied Geography 76 113 496 FR Goes et al RESENHA DO ARTIGO Unlocking the potential of big data to support tactical performance analysis in professional soccer A systematic review Tradução Desbloquear o potencial do big data para apoiar análise de desempenho tático em profissionais futebol uma revisão sistemática Segue abaixo uma resenha sobre o artigo Unlocking the potential of big data to support tactical performance analysis in professional soccer A systematic review de autoria de FR Goes LA Meerhoff MJO Bueno DM Rodrigues FA Moura MS Brink MT Elferink Gemser AJ Knobbe SA Cunha RS Torres KAPM Lemmink publicado no Jornal Europeu de Ciência do Esporte 2021 Vol 21 No 4 481496 O artigo trata sobre a análise de desempenho no futebol profissional e como ferramentas tecnológicas podem auxiliar na construção dos dados para facilitar o gerenciamento das informações O objetivo do artigo é reunir vários dados para criar conclusões sobre o comportamento tático realizando analises sobre as posições dos jogadores Como fonte de base foram utilizados sete bancos de dados eletrônicos resultando em 2338 estudos identificados Além disso foram usados diversos artigos com a finalidade de proporcionar um melhor entendimento sobre o tema contribuindo com informações teóricas de outros estudos relacionados Essas junções de informações servem para dar sustentabilidade a pesquisa uma vez que apresenta uma rica e variada fonte de referências teóricas Cada domínio analisado contribui para os resultados que serão apresentados a seguir possibilitando chegar a conclusões sobre o comportamento tático dos jogadores profissionais que foram analisados Foram considerados na análise fatores de características espaciais e temporais Também foram colocados em pautas os desafios que serão necessários para potencializar os resultados na análise esportiva O artigo apresenta como palavras chaves o futebol big data análise tática esporte coletivo análise de desempenho Todas essas palavras foram escolhidas de forma pontual pois concentram a base de toda a pesquisa relatada no artigo O artigo apresenta como destaque a evolução na análise dos dados referente ao posicionamento dos jogadores de futebol associando o crescimento ao avanço da informática Os domínios analisados serviram para nortear tanto as teorias quanto as suas aplicações práticas Através da ciência da computação o rastreamento por posição pode oferecer mais informações contribuindo para melhores análises esportivas A introdução do artigo é composta por uma rica e variada analise do tema baseada em referências de diversos autores apresentando conceitos definições e exemplos facilitando o entendimento do leitor nos eventos que serão apresentados posteriormente Destaca inicialmente a enorme quantidade de dados coletados nas partidas de futebol que servem de parâmetros para criar resultados e interpretações afim de melhorar o desempenho das equipes e também facilitar a tomada de decisões da equipe técnica e dos gestores da equipe O avanço tecnológico foi o responsável pelo crescimento nas técnicas e métodos para rastrear as posições dos jogadores durante a partida de futebol Quanto mais acesso à tecnologia maior será a tendência de sucesso na análise dos resultados Os grandes times de futebol acabam se diferenciando dos demais devido a estrutura responsável pela tecnologia que serve como base para construir uma análise esportiva eficaz e eficiente buscando inovar e aprimorar as técnicas já existentes no mercado Com isso aumenta a preparação e interpretação sobre o comportamento tático do jogador o que é primordial para o desenvolvimento tanto da equipe quanto do próprio jogador O artigo ressalta que antes do avanço tecnológico a coleta de dados era realizada de forma manual o que dificultava o sucesso e a eficiência do processo Sendo assim é possível analisar de forma mais eficiente o deslocamento do jogador com e sem bola ao longo da partida possibilitando criar estratégias para resolver problemas ou aprimorar técnicas e métodos esportivos com a finalidade de melhorar os resultados Existem uma enorme quantidade de dados o que poderia afetar a análise correta do processo com um grande volume de informações Sendo assim é necessário além de analisar gerenciar os dados obtidos e ainda dividido em subprocessos para facilitar a obtenção dos resultados Existem alguns desafios durante o processo que já foram apreciados em outras pesquisas semelhantes Praticamente todos os desafios anteriores citam a grande quantidade dados como um possível problema para ser solucionado ou melhorado O artigo aponta que em média as pesquisas realizadas no domínio da ciência do esporte no que diz respeito ao comportamento tático são realizadas através do método dedutivo onde são levadas hipóteses e posteriormente realizado um experimento para aprovar ou negar a hipótese Entretanto analisar um time de futebol pode ser muito complexo devido as diversas possibilidades de movimentos e posições dos jogadores O desenvolvimento das ciências dos dados através da ciência da computação permite a criação de novas hipóteses e testes contribuindo para o avanço e crescimento do gerenciamento e análise dos dados relacionados as posições dos jogadores profissionais Isso permite chegar ao tema base do artigo onde seria possível desbloquear todo o potencial do big data para atividades esportivas Os autores nesse ponto se apoiam em outros estudos para relatar os desafios enfrentados para conseguir elevar todo o potencial dos dados apurados O big data pode ser usado para realizar análises táticas de um time de futebol Seria então aplicada técnicas de ambos os domínios para rastrear as posições dos jogadores em campo A utilização do big data seria importante para realizar uma análise de desempenho mais completa explorando todas as características e possibilidades possíveis de acordo com o rastreio dos movimentos dos jogadores Experiências em operações de dados em outras áreas como a geográfica ou econômica podem servir como exemplos para explorar ao máximo as potencialidades do big data nas atividades esportivas A metodologia do artigo é baseada em diversos autores todos devidamente referenciados ao longo do texto e ao fim da pesquisa Também foram utilizados no artigo pesquisas de dados eletrônicos A seleção dos estudos foi realizada com base nos dados e informações relacionados a análise tática e comportamental dos jogadores durante as partidas As extrações dos dados foram baseadas em elementos classificados como ciência da computação ou ciência do esporte Posteriormente foram extraídos os dados para realizar as análises pertinentes ao tema e objetivo do artigo Sobre os resultados apresentados foram considerados 2290 registros para inclusão Entretanto 48 registros foram retirados do processo por que já estavam incluídos entre a totalidade Sendo assim considerase para a análise do resultado 2238 registros Destes 146 foram considerados para avaliação de texto completo no qual posteriormente foram excluídos 73 por não estar de acordo com os parâmetros estabelecidos Com relação aos incluídos 74 eram referentes a ciência dos esportes e 26 a ciência da computação Com relação a definição do problema as coletas dos dados serão responsáveis pelo resultado da pesquisa Com relação aos artigos incluídos 63 estudaram o efeito de uma intervenção no comportamento tático Sobre o estudo da variávelmétodo para quantificar táticas comportamento foram 20 e com relação ao estudo das variáveis 17 Analisando sobre o olhar referente as definições do problema 5 estudaram o efeito de uma intervenção ou restrição Entretanto 84 estudou uma nova técnica ou modelo e 11 estudaram problemas de predição ou probabilidade Com relação a coleta dos dados 64 utilizou dados de rastreamento óptico visto que é o sistema de escolha em muitas competições profissionais Sobre o rastreamento de bola foram 18 e 42 usaram os dados de eventos notacionais mais demorados para estudar eventos de bola Sobre a agregação espacial foram rastreadas as posições X e Y referente a 22 jogadores além de analisar a bola de 1100 vezes por segundo Neste caso são consideradas as posições espaciais dos jogadores A agregação temporal teve sua importância uma vez que eram somados os dados em uma determinada janela de tempo Além disso os dados também podem ser agregados numa janela com um tamanho fixo vinculado aos eventos da partida e também os dados podem ser agregados em janelas com tamanho flexível No que diz respeito a modelagem e interpretação foram utilizados modelos estatísticos e modelos enraizados na dinâmica teoria de sistemas como fase relativa e análises de entropia com objetivo de poder realizar as análises de cunho temporal Isso também permite o estudo de relações complexas não lineares e a possibilidade de conseguir descobrir padrões ocultos nos dados apresentados Para chegar aos resultados sobre o comportamento dos jogadores todas as possibilidades que afetem o seu comportamento devem ser estudadas e analisadas visando identificar o desempenho do jogador A discursão do artigo apresenta as contribuições que a ciência da computação e a ciência do esporte podem fornecer elementos importantes para a análise do comportamento tático no futebol profissional através dos dados de rastreamento por posição do jogador Foi detectado que existem diferenças consideráveis na metodologia Os autores afirmaram que ambos os domínios beneficiem uma colaboração cíclica e incorporando uns aos outros experiência de domínio Ao longo da pesquisa foram confirmadas as afirmações que ciência do esporte e pesquisa da informática ciência é caracterizada por características distintamente diferentes e até certo ponto paradigmas de pesquisa contrastantes segundo palavras dos autores O objetivo da pesquisa era avançar a compreensão do comportamento tático e aplicar as descobertas na prática Isso irá gerar um conjunto de opções para que aumente o desempenho dos jogadores durante a partida de futebol Entretanto com relação a ciência da computação não apresentam nenhum contexto teórico para explicar o comportamento tático nem contêm hipóteses sobre como seria o comportamento dos jogadores ou das equipes analisadas e como eles reagiriam a outros cenários de manipulação e estímulos Sobre esse resultado especifico os autores destacam que não se trata de algo negativo ou deficiente mas apenas uma visão em uma perspectiva diferente As diferenças apresentadas entre a metodologia teórica e a prática através de experiências causam um grande desafio para gerenciar a análise do comportamento tático dos jogadores Entretanto estimase que no futuro possa haver uma maior interação entre a parte teórica e parte prática A ciência do esporte estuda a teoria e aplica a prática Já a ciência da computação analisando todas as informações e com isso chegando a resultados distintos Isso possibilita análises diferentes visando possibilidades que aumentem o desempenho do jogador durante as partidas Ambos apresentam qualidades importantes para o gerenciamento e análise do comportamento dos jogadores Nos tempos atuais com a evolução da tecnologia o desenvolvimento da análise tática dos jogadores de futebol se assemelha ao big data de outras áreas profissionais Os autores também ressaltam a importância de observar que o comportamento tático dos jogadores depende do contexto da partida e da situação do jogo envolvendo assim variáveis distintas ao longo do jogo e mudando também em relação a características específicas da partida As partidas de futebol não são iguais e isso gera uma complexidade na análise do comportamento tático dos jogadores Portanto o artigo conclui sua pesquisa sobre o tema confirmando ser benéfica a relação entre ciência do esporte e ciência da computação para conseguir dados e informações importantes e necessárias para o desenvolvimento da análise e gerenciamento do comportamento dos jogadores de futebol O rastreamento de posição dos jogadores aparece com uma ferramenta importante para o desenvolvimento do processo Concluise que a ciência do esporte e a ciência da computação apresentam resultados distintos nas suas observações Entretanto são primordiais para aumentar o desempenho dos jogadores possibilitando ao treinador que tenha mais opções para auxiliar no seu processo de tomada de decisão Atualmente grandes times se concentram em informações estatísticas para efetuar contrações renovações de contrato além de esquemas táticos desenvolvidos para explorar o máximo de cada jogador Essa pesquisa é rica em detalhes técnicos e teóricos sendo amplamente recomendada para todos os profissionais esportivos que se interessem em ferramentas tecnológicas e teóricas para aumentar a capacidade de resposta e desempenho dos atletas Além disso a pesquisa serve como base de estudo para estudantes da área esportiva e também da área de econometria Os autores são convincentes sobre seus estudos e afirmações demonstrando domínio e conhecimento sobre o assunto REFERÊNCIA BIBLIOGRÁFICA FR Goes LA Meerhoff MJO Bueno DM Rodrigues FA Moura MS Brink MT ElferinkGemser AJ Knobbe SA Cunha RS Torres KAPM Lemmink 2021 Unlocking the potential of big data to support tactical performance analysis in professional soccer A systematic review European Journal of Sport Science 214 481496 DOI 1010801746139120201747552