RUDN Journal of Engineering ResearchRUDN Journal of Engineering ResearchВестник Российского университета дружбы народов. Серия: Инженерные исследования2312-81432312-8151Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)4036110.22363/2312-8143-2024-25-2-162-172MURUAKArticlesСтатьиResearch ArticleThe Role of Convolutional Neural Networks in Cricket Performance AnalysisРоль сверточных нейронных сетей в анализе результатов игры в крикетhttps://orcid.org/0009-0008-1193-4681RanasingheNaduni K.РанасингхеНадуни Кешани

Master student of the Department of Mechanics and Control Processes, Academy of Engineering

магистрант департамента механики и процессов управления, инженерная академия

1032225220@rudn.ruhttps://orcid.org/0000-0002-8824-12412920-9463KruglovaLarisa V.КругловаЛариса Владимировна

Candidate of Technical Sciences, Associate Professor of the Department of Mechanics and Control Processes, Academy of Engineering

кандидат технических наук, доцент департамента механики и процессов управления, инженерная академия

kruglova-lv@rudn.ruRUDN UniversityРоссийский университет дружбы народов30072024252VOL 25, NO2 (2024)ТОМ 25, №2 (2024)16217211082024Copyright ©; 2024, Ranasinghe N.K., Kruglova L.V.Copyright ©; 2024, Ранасингхе Н.К., Круглова Л.В.2024Ranasinghe N.K., Kruglova L.V.Ранасингхе Н.К., Круглова Л.В.https://creativecommons.org/licenses/by-nc/4.0/legalcodehttps://journals.rudn.ru/engineering-researches/article/view/40361

Significant insights have arisen from an extensive review of the current literature, highlighting the importance of Convolutional Neural Networks (CNNs) in cricket performance analysis and mapping new directions for future research. Despite difficulties such as limited availability of data, processing difficulty, and interpretability issues, incorporating CNNs into cricket statistics is a potential effort made possible by advances in machine learning and deep learning methods. Instructors, players, and data analysts can use CNNs to better comprehend the game, extract meaningful information from video data, and improve decision-making processes. Key findings show that CNNs are effective tools for a variety of cricket analysis tasks involving batting, bowling, fielding, and player tracking. The use of CNNs represents an advancement in cricket analysis, promising to open up new aspects of performance and usher in a data-driven era of cricket genius. Augmenting data, the use of parallelization, explainable AI, and concerns about ethics, provide opportunities to address current challenges can be identified as future advances in sports analysis with CNNs. Embracing technological advancements and mapping out future research directions are critical steps towards realizing this revolutionary potential.

Обширный обзор современной литературы позволил сделать важные выводы, подчеркнув важность сверточных нейронных сетей (СНС) для анализа результатов игры в крикет и наметив новые направления для будущих исследований. Несмотря на такие трудности, как ограниченная доступность данных, трудности с обработкой и интерпретируемостью, включение СНС в статистику по крикету, - это потенциальная возможность, появившаяся благодаря достижениям в области машинного обучения и методов глубокого обучения. Инструкторы, игроки и аналитики данных могут использовать СНС для лучшего понимания игры, извлечения значимой информации из видеоданных и улучшения процессов принятия решений. Основные результаты показывают, что СНС являются эффективными инструментами для решения различных задач анализа крикета, связанных с отбиванием, боулингом, филдингом и отслеживанием игроков. Применение СНС представляет собой прогресс в анализе крикета, обещающий открыть новые аспекты производительности и ознаменовать эру совершенного крикета, основанного на данных. Расширение данных, использование распараллеливания, поддающийся объяснению искусственный интеллект и следование этическим принципам предоставляют возможности решения существующих проблем и определяют будущие успехи в области спортивного анализа с СНС. Внедрение технологических достижений и определение направлений перспективных исследований являются важными шагами на пути к реализации этого революционного потенциала.

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