International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
GAMEBUZZ – A Cognitive Sports Event Orchestration Framework R. Danu1, V. Akshaya2, V. Saraswathi3, S. Yuvaraj Kannan4 1 Assistant Professor, Department of AI-DS, SRM Valliammai Engineering College, Tamil Nadu, India. 2,3,4Department of AI-DS, SRM Valliammai Engineering College, Tamil Nadu, India.
---------------------------------------------------------------------***--------------------------------------------------------------------inefficiencies, human errors, and scheduling conflicts that Abstract -The management of multi-sport events,
affect both players and stakeholders. In recent years, the rapid adoption of artificial intelligence (AI) and datadriven techniques has begun to transform this field, offering new opportunities to automate repetitive tasks, ensure fairness in competition, and generate actionable insights [1]. AI-driven predictive modeling has already demonstrated its potential in optimizing athlete selection and enhancing performance evaluation, confirming the feasibility of extending such methods into broader event management workflows.
particularly cricket and badminton tournaments, often faces challenges such as manual scheduling, inefficient coordination, and limited use of historical data for decisionmaking. To address these issues, this paper introduces an AIPowered Multi-Sport Event Management Platform that automates critical aspects of sports administration while enhancing the player and organizer experience. The platform implements role-based authentication, offering dedicated dashboards for players and organizers with tailored functionalities. Players register with sport-specific details, such as batting style in cricket or player type in badminton, while organizers can create events by defining sport, venue, match officials, and available facilities. A major innovation is the AI fixture scheduler, which ensures team eligibility by validating minimum player requirements before generating fair match schedules through RoundRobin algorithms. The platform further incorporates a rules-based highlight generator that processes match statistics to automatically create text-based summaries of significant performances, along with a smart team selector that recommends optimal line-ups based on player roles, historical performance data, and opponent strengths. Both dashboards integrate real-time analytics to provide players with insights into event participation and readiness, while enabling organizers to track the preparedness of registered teams. In its early stage, the system uses rule-based methods and simple ranking algorithms due to the absence of large datasets, but it is designed to scale toward machine learning approaches such as Random Forests and neural networks, as well as natural language processing for chatbot integration, once sufficient data is collected. By establishing a robust data-driven foundation, the platform transforms sports event management into a more efficient, fair, and strategic process, bridging the gap between traditional practices and AI-enabled innovation.
Event scheduling is one of the most critical components of sports administration. Conventional methods rely on manual arrangement or simple fixed schedules, which are prone to clashes and lack adaptability. Technologyenabled scheduling and management systems have been explored to reduce these burdens, proving that automation significantly enhances the efficiency of organizing tournaments [2]. Moreover, systematic reviews highlight that AI is increasingly being adopted in sports analytics, particularly for generating insights from structured data, managing performance records, and supporting strategic decisions for teams and organizers [3]. This shift toward AI-based solutions is particularly relevant in multi-sport contexts, where diverse rules, player requirements, and event structures must be managed simultaneously. The complexity of sports like cricket and badminton exemplifies the need for intelligent, adaptable platforms. Research on event management systems in academic institutions demonstrates how big data frameworks can streamline operations, though many implementations remain generic and lack sport-specific rules validation [4]. For example, in cricket tournaments, where team size requirements and match duration differ substantially from badminton, the application of optimized scheduling techniques such as Tabu Search has shown measurable improvements in fairness and balance [5]. However, such advanced methods often remain theoretical or limited to large-scale tournaments, creating a gap for practical, accessible solutions for smaller organizers.
Key Words: Sports Event Management, Fixture Scheduling, Team Selection, Sports Analytics, Artificial Intelligence, Badminton, Cricket
1.INTRODUCTION Sports event management is a multifaceted domain that requires careful coordination of scheduling, team formation, resource allocation, and participant engagement. Traditionally, these processes have been handled manually by organizers, often leading to
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Beyond scheduling, team composition and lineup optimization are major challenges for coaches and players alike. Recent advancements in reinforcement learning and deep learning models have been applied to fantasy sports
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