Collective behaviour monitoring in football using spatial temporal and network analysis: application and evaluations.

Analysis is an important part of understanding and exploiting performance of football teams. Traditional approaches of analysis have centred around events that may not fully incorporate the highly dynamic nature of matches. To circumvent this weakness, applications of collective behaviour metrics applying spatial temporal and social network analyses to data in football have been trending over the last 10 years. The aims of this PhD were to: 1) establish the strengths and limitations of current research investigating collective behaviour in football applying novel analytical procedures; 2) investigate the credibility of present methods informing coaching practice; and 3) provide guidance for practitioners in implementing complex analytical procedures with current data collection methods. These aims were achieved through the completion of five interlinked studies. The first two studies comprised systematic reviews evaluating the quality of previous research investigating collective behaviours. The first systematic review focussed on spatial temporal metrics and the second systematic review focussed on social network analysis metrics. In addition to standard review procedures, both systematic reviews included analyses of author quotes regarding the metrics used within each study. These included description and conceptualisation of each metric, along with practical applications and measurements of reliability. The first systematic review identified several limitations in the current literature base of spatial temporal metrics investigating collective behaviour in football. These included a lack of conceptualisation of the metrics used, assumptions of metric reliability, frequent use of broad and non-actionable practical recommendations, failure to justify sample sizes and a bias towards including males. Similar findings were found in the social network analysis systematic review where authors also seldom conceptualised metrics, provided vague practical applications and often failed to justify sample size. Literature including social network analysis were also inconsistent with the metric calculations and nearly all studies investigated elite male matches. The third study in this PhD attempted to quantify the reliability of spatial temporal metrics by simulating expected error values on top of real-world data. Through fitting linear mixed effects models on signal to noise ratios, metrics were established to be reliable where positioning systems are accurate to 0.5 m or less. In situations where positioning systems errors were approached 2 m, only some were considered to produce reliable values, (e.g. team centroid), whereas metrics using distances and numerical relations were considered to produce unreliable values. After assessing the literature and reliability, the PhD focussed on implementation of common and reliable metrics, leading into the final study of the PhD which employed an iterative design comprising multiple interviews to investigate coach perceptions of collective behaviour metrics. A thematic analysis identified themes that closely resembled the 10 traditional principles of play in football, further establishing their validity. Moreover, coaches reacted positively to presented measurements, most notable network intensity, distance between defenders, triads, team length, and team depth. Coaches stated they trained players with the concepts these measurements represent as a central focus. The PhD work was concluded with a final chapter set as pedagogical support for practitioners wishing to implement these techniques providing a guide to measuring the tactical concepts discussed within this thesis. Collectively, this PhD highlights that novel collective behaviour metrics have a place in current performance analysis systems in football. Additionally, a methodology is presented for practitioners to apply to their own teams and generate specific metrics relevant to the teams own tactical principles

Translating novel collective behaviour measures to concepts and principles of play as understood by football coaches.

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. 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). 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). 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

Reliability of spatial-temporal metrics used to assess collective behaviours in football: an in-silico experiment.

The purpose of this study was to investigate the reliability of spatial-temporal measurements applied within collective behaviour research in football. In-silico experiments were conducted introducing positional errors (0.5, 2 and 4 m) representative of commercial tracking systems to match data from the 2020 European Championship qualifiers. Ratios of the natural variance ("signal") of spatial-temporal metrics obtained throughout sections of each game relative to the variance created by positional errors ("noise") were taken to calculate reliability. The effects of error magnitude and time of analysis (1, 5 and 15 mins; length of attack: 20 s) were assessed and compared using Cohen's f2 effect size. Error magnitude was found to exert greater influence on reliability (f2 = 0.15 to 0.81) compared with both standard time of analysis (f2 = 0.03 to 0.08) and length of attacks (f2 = 0.15 to 0.32). the results demonstrate that technologies generating positional errors of 0.5 m or less should be expected to produce spatial-temporal metrics with high reliability. However, technologies that generate errors of 2 m or greater may produce unreliable values, particularly when analyses are conducted over discrete events such as attacks, which although critical, are often short in duration

The effect of bio-banding on academy soccer player passing networks: Implications of relative pitch size

The primary aims of this study were to examine the effects of bio-banding players on passing networks created during 4v4 small-sided games (SSGs), while also examining the interaction of pitch size using passing network analysis compared to a coach-based scoring system of player performance. Using a repeated measures design, 32 players from two English Championship soccer clubs contested mixed maturity and bio-banded SSGs. Each week, a different pitch size was used: Week 1) small (36.1 m2 per player); week 2) medium (72.0 m2 per player); week 3) large (108.8 m2 per player); and week 4) expansive (144.50 m2 per player). All players contested 12 maturity (mis)matched and 12 mixed maturity SSGs. Technical-tactical outcome measures were collected automatically using a foot-mounted device containing an inertial measurement unit (IMU) and the Game Technical Scoring Chart (GTSC) was used to subjectively quantify the technical performance of players. Passing data collected from the IMUs were used to construct passing networks. Mixed effect models were used with statistical inferences made using generalized likelihood ratio tests, accompanied by Cohen’s local f2 to quantify the effect magnitude of each independent variable (game type, pitch size and maturation). Consistent trends were identified with mean values for all passing network and coach-based scoring metrics indicating better performance and more effective collective behaviours for early compared with late maturation players. Network metrics established differences (f2 = 0.00 to 0.05) primarily for early maturation players indicating that they became more integral to passing and team dynamics when playing in a mixed-maturation team. However, coach-based scoring was unable to identify differences across bio-banding game types (f2 = 0.00 to 0.02). Pitch size had the largest effect on metrics captured at the team level (f2 = 0.24 to 0.27) with smaller pitch areas leading to increased technical actions. The results of this study suggest that the use of passing networks may provide additional insight into the effects of interventions such as bio-banding and that the number of early-maturing players should be considered when using mixed-maturity playing formats to help to minimize late-maturing players over-relying on their early-maturing counterparts during match-play