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Dr. Carlos Ruiz Viquez
Dr. Carlos Ruiz Viquez

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**Technical AI Sports Coach Challenge: 'Adaptive Tactical Fo

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Technical AI Sports Coach Challenge: 'Adaptive Tactical Formation Synthesis for Multi-Agent Soccer Teams'

Imagine developing an AI Sports Coach that can synthesize optimal tactical formations for a multi-agent soccer team in real-time, adapting to the opponent's strategy, team dynamics, and player skills. Your task is to design and implement an AI system that can achieve the following:

  1. Input Representation: Utilize a combination of text, numerical, and visual data to describe the team's strategy, opponent's tactics, player skills, and match context.
  2. Adaptive Formation Synthesis: Develop an algorithm that generates and evaluates multiple tactical formations, considering factors such as:
    • Player skill distributions and availability
    • Opponent's strategy and current lineup
    • Match dynamics, such as time elapsed, score, and ball possession
  3. Tactical Formation Evaluation: Design a system to evaluate the generated formations based on performance metrics, including:
    • Expected possession and creation of scoring opportunities
    • Defensive stability and counter-attacking potential
    • Adaptability to opponent's formations
  4. Real-time Updates: Develop your system to receive real-time updates on the match state, player performance, and opponent's strategy, allowing for continuous formation adjustments.
  5. Collaborative Multi-Agent Decision Making: Incorporate a mechanism for decision-making within the team, considering individual player preferences and strengths.
  6. Robustness and Explainability: Ensure that your system is robust to data imperfections, outliers, and missing-values, while providing transparent explanations for its tactical formation recommendations.

Challenge Constraints:

  • Utilize a publicly available soccer dataset (e.g., World Football Match Schedule or Soccer Dataset from Kaggle)
  • Implement your solution using a combination of programming languages and AI frameworks (e.g., Python, TensorFlow, or PyTorch)
  • Develop a demonstrable prototype that showcases the system's capabilities on a real-world scenario

Submission Guidelines:

  • Share your project's repository link and a technical report (max 5 pages) detailing your approach, methodology, and system architecture

Evaluation Criteria:

  • Innovation and originality of the approach
  • Effectiveness and robustness of the system
  • Clarity and quality of the technical report

Deadline: January 15, 2026

Prizes:

  • 1st Place: $5,000 and a feature article in a prominent AI and Sports publication
  • 2nd Place: $2,500 and recognition in the AI and Sports community
  • Best Technical Report: $500 and a certificate for 'Outstanding Contribution to AI and Sports Research

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