ArticleEntropy (Basel, Switzerland)2021
Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in football talent identification: a systematic review.Frontiers in sports and active living · 2026Pooled it
- Position prediction from performance and anthropometric indicators in young footballers: a machine learning approach.Scientific reports · 2026Article
- Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.British journal of sports medicine · 2025Article
- Machine learning application in soccer: a systematic review.Biology of sport · 2023Review
- Pre-processing methods in chest X-ray image classification.PloS one · 2022Article
- A Learn-to-Rank Approach for Predicting Road Cycling Race Outcomes.Frontiers in sports and active living · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundthe machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers.
methodsin this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers.
resultsin the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83).
conclusionthe presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.
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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.