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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Tynykulova, Assemgul
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Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods Kassymova, Akmaral; Aibatullin, Tolegen; Yelezhanova, Shynar; Konyrkhanova, Assem; Mukhanbetkaliyeva, Ainur; Tynykulova, Assemgul; Makhazhanova, Ulzhan; Azieva, Gulmira
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10458

Abstract

This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning
Generating data for predicting court decisions in Kazakhstan using machine learning Ignatovich, Artyom; Yessengeldina, Anar; Baidullayeva, Gulzhakhan; Ussipbekova, Dinara; Jakhanova, Baktykul; Saduakassova, Gulmira; Serimbetov, Bulat; Tynykulova, Assemgul
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10490

Abstract

This study presents the development of a synthetic dataset and machine learning models for predicting court decisions in Kazakhstan. The dataset contains 100,000 cases generated from the Code of the Republic of Kazakhstan, covering both administrative and criminal offenses. Each record includes attributes such as the age of the accused, offense type and severity, and mitigating or aggravating factors. Regression models were applied to estimate offense severity, level of guilt, and likelihood of penalties, while classification models predicted the offense category, relevant law articles, and sentencing type. Predictions addressed both general outcomes—classifying cases as criminal or administrative—and specific judicial decisions, including fines, imprisonment terms, and other penalties. Classification models achieved 92% accuracy in determining offense category and sentencing type, and regression models reached a root mean squared error (RMSE) of 0.12 for offense severity. Using synthetic data preserves confidentiality while enabling pattern discovery for decision support. The results demonstrate the potential of artificial intelligence (AI) to improve sentencing prediction, prioritize case processing, and enhance transparency in Kazakhstan’s judicial system. Beyond transparency in decision support, the proposed approach also shows potential in crime prevention, workload optimization, and fostering digital transformation within judicial operations.
Co-Authors Aibatullin, Tolegen Alimova, Zhanar Azieva, Gulmira Baidullayeva, Gulzhakhan Baitemirov, Madiyar Balabayeva, Zulfiya Bapiyev, Ideyat Bayegizova, Aigulim Ignatovich, Artyom Jakhanova, Baktykul Kaldarova, Mira Kassymova, Akmaral Kedelbayeva, Kamilya Khamitova, Zhainagul Konyrkhanova, Assem Makhazhanova, Ulzhan Maxutova, Natalya Mukhamedrakhimova, Galiya Mukhanbetkaliyeva, Ainur Mukhanova, Ayagoz Mukhomedyarova, Ainagul Saduakassova, Gulmira Serimbetov, Bulat Smailova, Ulmeken Tanirbergenov, Adilbek Tasbolatuly, Nurbolat Tussupov, Jamalbek Tynykulov, Marat Ussipbekova, Dinara Yelezhanova, Shynar Yersultanova, Zauresh Yessengeldina, Anar Zhunussova, Kamila