Evidence map›Paper›PMID 35898983›Full record

ArticleFrontiers in psychology2022

Clustering Performances in Elite Basketball Matches According to the Anthropometric Features of the Line-ups Based on Big Data Technology.

Xiao Xu, Mingxin Zhang, Qing Yi

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 26% of its field
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

2 citing papers in PubMed, 7 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Xiao XuChina Basketball College, Beijing Sport University, Beijing, China.
Mingxin ZhangSchool of Physical Education and Sport Training, Shanghai University of Sport, Shanghai, China.
Qing YiSchool of Physical Education and Sport Training, Shanghai University of Sport, Shanghai, China.
Shanghai University of Sport · CNBeijing Sport University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aims of this study were: 1) to conduct a descriptive analysis of the anthropometric features of the line-ups of strong teams (top 16) in the 2019 FIBA Basketball World Cup; 2) to group the line-ups mentioned above into different clusters based on their average height, weight, and body mass index (BMI); and 3) to explore the performance variables that discriminate between various line-up clusters. The play-by-play statistics were collected from 104 team objects in 67 games and 525 line-ups were analyzed using two-step cluster and discriminant analysis. Line-ups were classified into four groups: low average height and weight with middle BMI (LowH-LowW-MiddleBMI); high average height and low average weight with low BMI (HighH-LowW-LowBMI); low average height and high average weight with high BMI (LowH-HighW-HighBMI); high average height and weight with middle BMI (HighH-HighW-MiddleBMI). The results of the discriminant analysis demonstrated that LowH-LowW-MiddleBMI line-ups had the least time played and the lowest offensive rating, but the best offensive rebounds, turnovers, and fastest game pace performance; HighH-LowW-LowBMI line-ups demonstrated the best defensive rating but performed poorly with a low value of assists and a high value of turnovers; the LowH-HighW-HighBMI group achieved the best time played statistics but had the lowest number of free throws made; the HighH-HighW-MiddleBMI group had a higher number of assists and a higher offensive rating and 2-point field goal performance, while also achieving the lowest number of offensive rebounds and ball possessions. These results provide novel insights for coaches and performance analysts to better understand the technical characteristics of different line-ups in elite basketball competitions.

Indexed as

anthropometric featuresbig data technologyclustering performancesFIBA Basketball World Cupline-upmatch analysis

Identifiers

PMID35898983
PMCPMC9309682
OpenAlexW4285013467

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.