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Record W4214497905 · doi:10.1080/24733938.2022.2046286

A holistic analysis of collective behaviour and team performance in Australian Football via structural equation modelling

2022· article· en· W4214497905 on OpenAlexaboutno aff
William B. Sheehan, Rhys Tribolet, Andrew R. Novak, Job Fransen, Mark L. Watsford

Bibliographic record

VenueScience and Medicine in Football · 2022
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsFootballQuarter (Canadian coin)LeaguePsychologyStructural equation modelingSocial psychologyApplied psychologyPolitical scienceGeographyMathematicsStatistics

Abstract

fetched live from OpenAlex

Despite awareness of the importance of quantifying technical, tactical, and physical characteristics of match play, few studies have examined the structural relationship of these aspects in professional sport. Accordingly, this study concurrently examined these components in relation to quarter outcome (n = 272) in Australian Football. The study followed a retrospective longitudinal case study design where one teams’ cooperative passing network, skill counts, physical loads, and spatiotemporal behaviours during official Australian Football League games were collected from a period spanning four seasons (2016–2019). A principal components analysis (PCA) and structural equation modelling were used to explore the structural relationships between components and examine the influence on quarter outcome as determined by the point differential (quarter margin). Scoring opportunity and ball movement had direct associations with quarter margin, while unpredictability, uncontested behaviour and physical behaviour did not. Negative associations between uncontested behaviour and scoring opportunity suggest that elevated high-pressure success and a lack of synchrony may positively influence scoring opportunity, a determinant of quarter margin. Further, negative associations between physical behaviour and ball movement suggest that with less physical work, a team’s collective ability to transfer possession between teammates is facilitated, offering an interesting dichotomy between skill and physical demands of Australian Football. While hundreds of different metrics are available, the present study was the first to concurrently examine the influence of a variety of match play components on performance outcomes in Australian Football. These results may provide direction for coaches and practitioners when contemplating practice design, tactical strategies, or the development of behaviour through specific training exercises. Game plans and training drills that focus on optimising attacking and low-pressure ball movement coupled with high levels of mutual interaction between teammates may be beneficial for performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.336
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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