A toolbox for volleyball data analytics: a case study on the italian women’s league

Abstract Data analytics plays a central role in volleyball, offering new ways to enhance team performance and inform match strategies. This paper presents an open-source Python-based toolbox that extends the PyDataVolley library, enabling advanced processing, visualization, and analysis of scouting data. The toolbox includes Machine Learning Clustering algorithms, Multi-Criteria Decision Analysis approaches, and Markov Chain models, and is validated using datasets from the 2023–2024 and 2024–2025 seasons of the Italian Women’s Serie A2 Championship. A case study on the 2025 Italian Cup final between Consolini Volley and Trentino Volley highlights the practical impact of the toolbox. Throughout the 2024–2025 season, Consolini Volley consistently showed superior performance metrics compared to Trentino Volley. However, in the final match, Trentino Volley implemented a targeted tactical strategy informed by the insights from the toolbox, which effectively challenged Consolini’s gameplay. As a result, Consolini Volley exhibited a general drop across all main performance indicators during the final.