A range of innovative performance analysis metrics have been applied in recent years to investigate aspects of football using tempo-spatial and network analyses. These approaches have gained traction within some professional teams to quantify and assess features of collective behaviour. However, metrics employed are rarely created from, or clearly link to, domain expertise and as a result coaches may be hesitant of their value. Therefore, the aim of this study was to identify coach perceptions of spatial temporal and network metrics and identify the feasibility of an iterative and collaborative process to developing metrics.
Methods
Two rounds of semi-structured interviews were conducted with three Scottish youth international UEFA Pro License coaches (age: 47.0 ± 2.7 years) with a focus on aligning metrics with concepts and principles of play. An iterative approach was used centring around spatial-temporal and network metrics and their adaptation. Reflexive thematic analyses were conducted with final metrics categorized as resonant (accurately describing concept or principles of play), relevant (appropriate but with limitations that need improvement), or hesitant (skeptical of usefulness).
Results
Across the ten recognized principles of play, nine metrics were identified and adapted to varying degrees. Resonant metrics included: network intensity (mobility), distance between defenders (discipline), triangles (support), team length and distance between deepest defender and goal line (depth).
Conclusion
Coaches recognize principles of play within complex collective behaviour metrics and should be encouraged to collaborate with analysts to develop support systems that may prove to be more valuable and usable.
GoesFRMeerhoffLABuenoMJ, et al.Unlocking the potential of big data to support tactical performance analysis in professional soccer: a systematic review. Eur J Sport Sci2021; 21: 481–496.
2.
SarmentoHMarcelinoRAngueraMT, et al.Match analysis in football: a systematic review. J Sports Sci2014; 32: 1831–1834.
3.
BrechotMFleppR. Dealing with randomness in match outcomes: how to rethink performance evaluation in European club football using expected goals. J Sports Econ2020; 21: 335–362.
4.
GréhaigneJFGodboutP. Opposition and dynamics at the core of game-play analysis in soccer. Int J Phys Educ2021; 58: 10–20.
5.
WrightCAtkinsSJonesB, et al.The role of performance analysts within the coaching process: performance analysts survey ‘the role of performance analysts in elite football club settings.’Int J Perform Anal Sport2013; 13: 240–261.
6.
HeroldMKempeMBauerP, et al.Attacking key performance indicators in soccer: current practice and perceptions from the elite to youth academy level. J Sports Sci Med2021; 20: 158–169.
7.
ClementeFMMartinsFMMendesRS, et al.A systemic overview of football game: the principles behind the game. J Hum Sport Exerc2014; 9: 656–667.
8.
CostaITGargantaJGrecoPJ, et al.System of tactical assessment in Soccer (FUT-SAT): development and preliminary validation. System2011; 7: 69–83.
9.
GudmundssonJWolleT. Football analysis using spatio-temporal tools. In: International Conference on Advances in Geographic Information Systems Redondo Beach, CA, USA, 6 November 2012, pp. 566–569.
10.
CorsieMCraigTSwintonPA, et al.Spatial-temporal metrics to assess collective behavior in football: a systematic review and assessment of research quality and applicability. J Athl Enhanc2021; 10: 1–25.
11.
Bandola-GillJArthurMLengRI. What is co-production? Conceptualising and understanding co-production of knowledge and policy across different theoretical perspectives. Evid Policy2023; 19: 275–298.
12.
BrandsenTSteenTVerschuereB. Co-production and co-creation: engaging citizens in public services. 1st ed. New York, NY: Routledge, 2018, pp.9.
13.
ThabrewHFlemingTHetrickS, et al.Co-design of eHealth interventions with children and young people. Front Psychiatry2018; 9: 481.
14.
WoodsCTRothwellMRuddJ, et al.Representative co-design: utilising a source of experiential knowledge for athlete development and performance preparation. Psychol Sport Exerc2021; 52: 101804.
15.
ShaheenMPradhanS. Sampling in qualitative research. In: GuptaMShaheenMReddyKP (eds) Qualitative techniques for workplace data analysis. Hershey, PA, USA: IGI Global, 2019, pp.25–51.
16.
WellerSCVickersBBernardHR, et al.Open-ended interview questions and saturation. PloS one2018; 13: e0198606.
17.
MeliaK. Conducting an interview. Nurse Res2000; 7: 75–89.
18.
LowBCoutinhoDGonçalvesB, et al.A systematic review of collective tactical behaviours in football using positional data. Sports Med2020; 50: 343–385.
19.
DonaldsonALloydDGGabbeBJ, et al.Scientific evidence is just the starting point: a generalizable process for developing sports injury prevention interventions. J Sport Health Sci2016; 5: 334–341.
20.
BraunVClarkeV. Reflecting on reflexive thematic analysis. Qual Res Sport Exerc Health2019; 11: 589–597.
21.
PitneyWAParkerJ. Qualitative research in physical activity and the health professions. Champaign, IL, USA: Human Kinetics, 2009.
22.
PodlogLEklundRC. High-level athletes’ perceptions of success in returning to sport following injury. Psychol Sport Exerc2009; 10: 535–544.
23.
PrickettP. Football’s Principles of Play. London: Hawksmoor Publishing, 2021.
24.
da CostaITda SilvaJMGrecoPJ, et al.Tactical principles of Soccer: concepts and application. Mot Rev de Educ Fis2009; 15: 657–668.
25.
BaptistaJTravassosBGonçalvesB, et al.Exploring the effects of playing formations on tactical behavior and external workload during football small-sided games. J Strength Cond Res2020; 34: 2024–2030.
26.
OlthofSBFrenckenWGLemminkKA. When something is at stake: differences in soccer performance in 11 vs. 11 during official matches and training games. J Strength Cond Res2019; 33: 167–173.
27.
BarnabéLVolossovitchADuarteR, et al.Age-related effects of practice experience on collective behaviours of football players in small-sided games. Hum Mov Sci2016; 48: 74–81.
28.
FolgadoHLemminkKAFrenckenW, et al.Length, width and centroid distance as measures of teams tactical performance in youth football. Eur J Sport Sci2014; 14: 487–492.
29.
MouraFAMartinsLEAnidoRD, et al.Quantitative analysis of Brazilian football players’ organisation on the pitch. Sports Biomech2012; 11: 85–96.
30.
BartlettRButtonCRobinsM, et al.Analysing team coordination patterns from player movement trajectories in soccer: methodological considerations. Int J Perf Anal Sport2012; 12: 398–424.
31.
DuarteRAraújoDFolgadoH, et al.Capturing complex, non-linear team behaviours during competitive football performance. J Syst Sci Complex2013; 26: 62–72.
32.
LinkDLangSSeidenschwarzP. Real time quantification of dangerousity in football using spatiotemporal tracking data. PloS One2016; 11: e0168768.
33.
ShafizadehMDavidsKCorreiaV, et al.Informational constraints on interceptive actions of elite football goalkeepers in 1v1 dyads during competitive performance. J Sports Sci2016; 34: 1596–1601.
34.
SteinerSRauhSRumoM, et al.Outplaying opponents—a differential perspective on passes using position data. Ger J Exerc Sport Res2019; 49: 140–149.
35.
FernandezJBornnL. Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. In: Sloan sports analytics conference Boston, USA, 23–24 February 2018.
36.
ClementeFMCouceiroMSMartinsFML. Soccer teams behaviors: analysis of the team’s distribution in function to. Res J Appl Sci Eng Technol2013; 6: 130–136.
37.
VilarLAraújoDDavidsK, et al.Science of winning soccer: emergent pattern-forming dynamics in association football. J Syst Sci Complex2013; 26: 73–84.
38.
DuarteRAraújoDDavidsK, et al.Interpersonal coordination tendencies shape 1-vs-1 sub-phase performance outcomes in youth soccer. J Sports Sci2012; 30: 871–877.
39.
LeserRMoserBHochT, et al.Expert-oriented modelling of a 1vs1-situation in football. Int J Perform Anal Sport2015; 15: 949–966.
40.
ClementeFMCouceiroMSMartinsFM, et al.Soccer team’s tactical behaviour: measuring territorial domain. Proc Inst Mech Eng P J Sport Eng Technol2015; 229: 58–66.
41.
GrundTU. Network structure and team performance: the case of English Premier League soccer teams. Soc Networks2012; 34: 682–690.
42.
GrundTU. The relational value of network experience in teams: evidence from the English Premier League. Am Behav Sci2016; 60: 1260–1280.
43.
ClementeFMCouceiroMSMartinsFM, et al.Interpersonal dynamics: 1v1 sub-phase at sub-18 football players. J Hum Kinet2013; 36: 181–191.
44.
LaaksoTTravassosBLiukkonenJ, et al.Field location and player roles as constraints on emergent 1-vs-1 interpersonal patterns of play in football. Hum Mov Sci2017; 54: 347–353.
45.
LaaksoTDavidsKLiukkonenJ,et al.Interpersonal dynamics in 2-vs-1 contexts of football: the effects of field location and player roles. Front Psychol2019; 10: 1407.
46.
LeserRHochTTanX, et al.Finding efficient strategies in 3-versus-2 small-sided games of youth soccer players. Kinesiology2019; 51: 110–118.
47.
LeserRMoserBHochT, et al.Expert-oriented modelling of a 1vs1-situation in football. Int J Perf Anal Sport2015; 15: 949–966.
48.
PraçaGMFolgadoHAndradeAG, et al.Influence of additional players on collective tactical behavior in small-sided soccer games. Rev Bras de Cineantropometria e Desempenho Hum2016; 18: 62–71.
49.
TengaAZubillagaACaroO, et al.Explorative study on patterns of game structure in male and female matches from elite Spanish soccer. Int J Perf Anal Sport2015; 15: 411–423.
50.
AquinoRLGonçalvesLGVieiraLH, et al.Biochemical, physical and tactical analysis of a simulated game in young soccer players. J Sports Med Phys Fitness2016; 56: 1554–1561.
51.
ReinRRaabeDMemmertD. Which pass is better? Novel approaches to assess passing effectiveness in elite soccer. Hum Mov Sci2017; 55: 172–181.
52.
SpearmanWBasyeADickG, et al.Physics-based modeling of pass probabilities in soccer. In: MIT Sloan Sports Analytics Conference Boston, MA, USA, 3–4 March 2017.
53.
BrefeldULasekJMairS. Probabilistic movement models and zones of control. Mach Learn2019; 108: 127–147.
54.
FilettiCRuscelloBD’OttavioS, et al.A study of relationships among technical, tactical, physical parameters and final outcomes in elite soccer matches as analyzed by a semiautomatic video tracking system. Percept Mot Skills2017; 124: 601–620.
55.
MemmertDLemminkKAPMSampaioJ. Current approaches to tactical performance analyses in soccer using position data. Sports Med2016; 47: 1–10.
56.
FolgadoHDuarteRMarquesP, et al.The effects of congested fixtures period on tactical and physical performance in elite football. J Sports Sci2015; 33: 1238–1247.
57.
MouraFAvan EmmerikRESantanaJE, et al.Coordination analysis of players’ distribution in football using cross-correlation and vector coding techniques. J Sports Sci2016; 34: 2224–2232.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
