Evidence map›Paper›PMID 34886435›Full record

ArticleInternational journal of environmental research and public health2021

A Machine Learning Approach to Analyze Home Advantage during COVID-19 Pandemic Period with Regards to Margin of Victory and to Different Tournaments in Professional Rugby Union Competitions.

Alexandru Nicolae Ungureanu, Corrado Lupo, Paolo Riccardo Brustio

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. 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
1.2field-weighted citation impact, top 12% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  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

3 authors at 2 institutions in 1 country.

Alexandru Nicolae UngureanuDepartment of Medical Science, University of Turin, 10143 Turin, Italy.ORCID 0000-0002-8780-1737
Corrado LupoDepartment of Medical Science, University of Turin, 10143 Turin, Italy.ORCID 0000-0003-1546-8573
Paolo Riccardo BrustioNeuroMuscularFunction|Research Group, School of Exercise & Sport Sciences, University of Turin, 10143 Turin, Italy.ORCID 0000-0001-8422-5132
University of Turin · ITUniversity of Verona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Home advantage (HA) is the tendency for sporting teams to perform better at their home ground than away from home, it is also influenced by the crowd support, and its existence has been well established in a wide range of team sports including rugby union. Among all the HA determinants, the positive contribute of the crowd support on the game outcome can be analyzed in the unique pandemic situation of COVID-19. Therefore, the aim of the present study was to analyze the HA of professional high-level rugby club competition from a complex dynamical system perspective before and during the COVID-19 pandemic. HA was analyzed in northern and southern hemisphere rugby tournaments with (2013-2019) and without (2020/21) crowd support by the means of the exhaustive chi-square automatic interaction detection (CHAID) decision trees (DT). HA was mitigated by the crowd absence especially in closed games, although differences between tournaments emerged. Both for northern and southern hemisphere, the effect of playing without the crowd support had a negative impact on the home team advantage. These findings evidenced that in ghost games, where differences in the final score were less than a converted try (7 points), HA has disappeared.

Indexed as

COVID-19HumansMachine LearningPandemicsRugbySARS-CoV-2COVID-19decision treeshome advantagemargin of victoryrugby union

Identifiers

PMID34886435
PMCPMC8656876
OpenAlexW3217034710

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.