ArticleScientific reports2026
A decision aid approach for sports performance evaluation and player activity tracking using fermatean fuzzy Z-information.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fermatean fuzzy [Formula: see text] numbers introduce a new decision-making framework which demonstrates greater capacity to handle uncertainty than traditional fuzzy set theory. The research study introduces a set of operational laws together with neutral aggregation operators which function as tools for handling Fermatean fuzzy [Formula: see text] numbers. The operators function as essential tools for multi-criteria decision-making because they enable assessment of complex systems which need simultaneous evaluation of multiple criteria. The research establishes new operational laws which extend the use of Fermatean fuzzy [Formula: see text] numbers to situations which demand flexible decision-making systems. The study investigates how to use Fermatean fuzzy [Formula: see text] numbers for assessing soccer player performance. The research investigates how well FF[Formula: see text]N Neutrality Aggregation Operators that evaluate goal scoring, assists, tackles, and passing accuracy can provide accurate performance rankings. The aggregation process uses expert evaluations which assess both positive and negative contributions to create an unbiased football player ranking system. The case study demonstrates how Fermatean fuzzy [Formula: see text] numbers can be used to make actual decisions. The process compares rankings produced by FF[Formula: see text] Neutrality Aggregation Operators with WASPAS rankings to demonstrate how Fermatean fuzzy [Formula: see text] numbers help solve complex decision-making situations which need to manage uncertainty and multiple factors for optimal outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.