Angielski
Basketball Data Science: From Match Events to Board Decisions is a practical and strategic guide to the use of data analytics, artificial intelligence, performance metrics, and executive dashboards in modern basketball organisations.
Professional basketball now produces vast amounts of data: shots, passes, screens, rebounds, turnovers, fouls, possessions, tracking signals, video tags, player workloads, scouting reports, ticketing patterns, sponsorship evidence, and fan engagement indicators. The challenge is no longer data collection alone. The real challenge is turning basketball data into better decisions.
This book explains how raw match events become structured datasets, how player and team performance can be evaluated, how tactical patterns can be detected, how predictive models can support coaching and scouting, and how analytics can reach the boardroom through KPIs, risk signals, scenario planning, and governance.
Written for coaches, analysts, executives, sporting directors, scouts, students, consultants, and sports business professionals, the book covers the full basketball analytics value chain. It begins with data foundations, including event taxonomy, data sources, data quality control, and basketball data dictionaries. It then examines possession-based analysis, player performance metrics, line-up analytics, shot selection models, and tactical pattern detection.
The book also introduces predictive and AI-assisted methods, including match outcome forecasting, player development models, injury risk analytics, scouting algorithms, recruitment filters, transfer risk scoring, real-time dashboards, video intelligence, and automated tactical reports. The final part translates basketball metrics into executive decisions on talent, budget, roster construction, commercial value, sponsorship, fan behaviour, governance, model risk, and board-level control.
Unlike books that focus only on statistics or coaching tactics, this volume connects the court with management, finance, risk, strategy, and commercial decision-making. It shows how basketball data science can support roster planning, salary allocation, player development, injury prevention, tactical preparation, fan engagement, and strategic governance.
Basketball Data Science is designed for readers who want to understand how analytics can improve the way basketball clubs think, compete, invest, and grow. It is a structured guide for anyone interested in sports analytics, basketball intelligence, AI in sport, sports management, performance analysis, scouting, and data-driven decision-making.
