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Publication in Springer Optimization and Its Applications
Publication in Springer Optimization and Its Applications
Indexed by Scopus
Indexed by Scopus
Inclusion of book chapters is free of charge
Inclusion of book chapters is free of charge
Submission of Full Chapter: August 15th, 2026
Extended to October 15th, 2026 (Firm Deadline)
Submission to the following link: https://cmt3.research.microsoft.com/GenAIFoot2026
SCOPE
While football has universally been celebrated as a game of raw passion and physical skill, it is rapidly evolving into a highly sophisticated, data-driven ecosystem. This technological transformation arrives at a critical juncture. Modern professional football faces grueling competitive schedules and intense financial pressures that place unprecedented physical and economic demands on clubs. To navigate this high-stakes landscape, relying on traditional intuition is no longer viable. Success requires the systemic integration of data across all levels of the sport, ranging from macro-organizational governance, sustainable development, and optimizing the public health legacies of major international tournament hosting cycles, down to the granular management of squad performance and league economics.
In this highly competitive context, this book provides a groundbreaking framework for evidence-based decision intelligence. The volume showcases how generative artificial intelligence, advanced optimization techniques, multi-criteria decision frameworks based on hierarchical prioritization, and fuzzy logic translate real-time match analytics into precise tactical innovations, even under conditions of severe in-game uncertainty. Extending this innovation, the book explores the emerging frontier of spatial computing and immersive environments, detailing how virtual reality and augmented reality systems revolutionize tactical visualization, interactive training simulations, and community fitness practices. By constructing real-time, highly granular virtual representations of players and teams, coaching and medical staff can dynamically track fatigue, predict biomechanical injury risks, and personalize recovery regimens to safeguard long-term athlete well-being.
By combining systematic data mapping, advanced algorithm design, generative AI models, and empirical case studies spanning diverse global football contexts, including elite European leagues and rapidly modernizing sports landscapes in many countries, this book bridges the gap between cutting-edge computational theory and practical, on-pitch strategy. It serves as an indispensable reference for sports data analysts, tactical coaches, club executives, sports scientists, policymakers, and advanced students in computer science, machine learning, and sports management. This volume does not merely catalog technological tools; it provides the definitive roadmap for a smart, optimized, and resilient future for the beautiful game.
TOPICS
This book targets a diverse audience of researchers, academics, sports scientists, and football industry professionals across disciplines, providing a platform to share and exchange innovative ideas, methodologies, theoretical frameworks, and practical applications. It aims to address the complex challenges of modern football performance, tactical innovation, and sports economics by leveraging the transformative potential of Generative AI and mathematical optimization techniques.
The tentative structure of the book (but are not limited to the following Parts) is mentioned below:
Part 1: Foundational AI, Knowledge Mapping, and Bibliometrics
Bibliometric synthesis and meta-analysis of AI applications in sports science and football performance.
Knowledge mapping, trend forecasting, and historical evolution of data-driven innovation in football.
Methodological frameworks combining systematic literature reviews with data analytics in sports economics.
Part 2: Tactical Innovation and Mathematical Optimization
Generative AI-driven mathematical models for real-time tactical adjustments and match strategy.
Multi-Criteria Decision-Making (MCDM) frameworks (e.g., Analytic Hierarchy Process) applied to match performance analysis.
Algorithmic design of competitive indices evaluating league intensity, ranking proximity, and seasonal stakes.
Mathematical optimization for set-piece design, formation shifts, and opponent profiling.
Part 3: Talent Identification, Recruitment, and Football Economics
Generative AI architectures for advanced scouting and talent identification pipelines.
Decision Intelligence (DI) and graph-based modeling for optimal player selection and squad building.
Data-driven valuation, transfer market forecasting, and wage structure optimization using GenAI.
Predictive modeling for youth academy graduate success rates.
Part 4: Digital Twins and Immersive Technologies
Integrating Generative AI with Digital Twins for real-time, holistic player and team simulation.
Predictive biomechanical modeling and digital twins for injury prevention and load management.
Virtual Reality (VR) and Augmented Reality (AR) immersive training environments driven by generative spatial computing.
Simulating match dynamics and stadium environments using high-fidelity digital replications.
Part 5: Advanced Computational Intelligence and Soft Computing
Intuitionistic Fuzzy Logic and fuzzy intelligence frameworks for handling uncertainty in performance data.
Hybrid AI models combining machine learning, deep learning, and soft computing for next-generation analytics.
Explainable AI (XAI) models for translating complex mathematical optimization outputs into actionable coaching insights.
Part 6: Ethics, Governance, and Future Directions
Ethical Implications of Generative AI in Football Management
Data Privacy, Security, and Ownership in Football Management Systems
Socio-technical determinants, cultural factors, and barriers to AI adoption in emerging and developing football leagues.
Transparency and Explainability of AI Models in Football Management Decisions
The Future of Football Management
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