03557ntm a22005057i 4500 000723912 CZ-PrVSE 20250621144446.0 m d cr n|||||||||| 250621s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Čurila, Igor ISIS:162115 dis Predikce optimálních rozestavení hráčů ve volejbalu pomocí strojového učení eng Predicting successful player alignments in volleyball using machine learning / Igor Čurila 2025 ?? stran : digital, PDF soubor Vedoucí práce: Ondřej Sokol Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 This bachelor thesis investigates the application of machine learning techniques to analyze and predict the effectiveness of defensive formations in volleyball. Using a manually annotated dataset of 300 plays from professional volleyball matches, three different machine learning models-logistic regression, random forest, and XGBoost-were developed and evaluated. Among these, the XGBoost model achieved the highest accuracy, but all models demonstrated solid performance, suggesting that even with a relatively small and structured dataset, meaningful predictions can be made. The study emphasizes the importance of feature selection and data preparation, focusing on variables available before the attack, such as defender positions, attack type, set quality, and block details. Feature importance analysis revealed that the type of attack, block effectiveness, and especially the position of defender 6 are crucial factors influencing defensive success. The results show that optimal defensive positioning is not static but should adapt dynamically depending on the specific game situation, such as whether the block touches the ball. Although the dataset was limited in size and scope, this research demonstrates that machine learning can provide valuable tactical insights for coaches and players. With larger datasets and automated data collection methods, these approaches have the potential to support real-time decision-making and strategy development in volleyball, ultimately contributing to improved team performance and a deeper understanding of the game. Způsob přístupu: Internet data analytics [obor bakal. práce] bakalářské práce fd132403 czenas bachelor's theses eczenas volleyball machine learning sports analytics defensive formations Sokol, Ondřej ISIS:85564 ths Zouhar, Jan, 1982- vse2009539908 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/91132/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/91132 Hlavní práce https://insis.vse.cz/zp/91132/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/91132/posudek/oponent/86801 Oponentura https://insis.vse.cz/zp/91132/podrobnosti dc:identifier NEPOSILAT VSKP vse91132 250617 91132