Evidence map›Paper›PMID 39975982›Full record

ReviewFrontiers in sports and active living2024

The principles of tactical formation identification in association football (soccer) - a survey.

Hadi Sotudeh

Abstract readReview
In one paragraph

Review in Frontiers in sports and active living, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Hadi SotudehSocial Networks Lab, Department of Humanities, Social and Political Sciences, ETH Zürich, Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper reviews the principles employed to identify team tactical formations in association football, covering over two decades of research based on event and tracking data. It first defines formations and discusses their history and importance. It then introduces the preprocessing and team/position-level principles. Preprocessing includes match segments and normalized locations followed by data representation using various options, such as average locations, hand-engineered features, and graphs for the team-level and relative locations, distributions, and images for the position-level approaches. Either of them is later followed by applying templates or clustering. Among the limitations for future research to address is the reliance on spatial rather than temporal aggregation, which bases formation identification on newly introduced coordinates that may not be available in raw tracking data. Assuming a fixed number of outfield players (e.g., 10) fails to address scenarios with fewer players due to red cards or injuries. Additionally, accounting for phases of play is crucial to provide more practical context and reduce noise by excluding irrelevant segments, such as set pieces. The existing formation templates do not support arrangments with more or fewer players in each horizontal line (e.g., 6-3-1). On the other hand, clustering forces new observations to be described with previously learned clusters, preventing the possibility of discovering emerging formations. Lastly, alternative evaluation methods should have been explored more rigorously, in the absence of ground truth labels. Overall, this study identifies assumptions, consequences, and drawbacks associated with formation identification principles to structure the body of knowledge and establish a foundation for the future.

Indexed as

footballformationpositionshapesoccer

Identifiers

PMID39975982
PMCPMC11836022

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.