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Record W4387412085 · doi:10.2478/ijcss-2023-0011

A comparison of tournament systems for the men’s World Handball Championship

2023· article· en· W4387412085 on OpenAlexaboutno aff
Peter O’ Donoghue, Hafrún Kristjánsdóttir, Kristján Halldórsson, Sveinn Þorgeirsson

Bibliographic record

VenueInternational Journal of Computer Science in Sport · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTournamentChampionshipNinthRanking (information retrieval)Quarter (Canadian coin)World championshipOperations managementOperations researchMathematicsAdvertisingEngineeringComputer scienceGeographyArtificial intelligenceBusinessCombinatorics

Abstract

fetched live from OpenAlex

Abstract The men’s Handball World Championship commences with eight round robin groups of four teams before the “main round” of four groups of six teams. These groups of six each include the top three teams from pairs of initial groups. The tournament draw uses pots of eight which risks two teams in the top four appearing in the same group of the main round. A further issue is that teams finishing between third and sixth in the main round groups are awarded tournament places between ninth and 24th without any further matches. Therefore, the purpose of this investigation was to compare the current tournament system with alternatives using pots of four teams in the draw, and / or adding a knockout stage to place teams from ninth to 24th. These four tournament systems were simulated 100,000 times, using underlying regression models for the goals scored based on their World ranking points. Introducing pots of four increased the chance of reaching the quarter-finals for teams ranked one to four and nine to 12 by 1.3% and 1.6% respectively. It is recommended that the draw uses pots of four teams associated with pairs of initial groups that lead to common main draw groups.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.086
GPT teacher head0.334
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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