Juego de Posición

VIEW_01
VIEW_02
VIEW_03

> CORE_MODULES (MAIN_FEATURE)

  • [+] PPO Engine: Utilizes the Proximal Policy Optimization (PPO) algorithm for stable and efficient agent training.
  • [+] Tactical Reward Function: A custom logic layer that dynamically evaluates space based on Expected Threat (xT) and defensive pressure.
  • [+] Pitch Rendering: Generates high-fidelity tactical visualizations and heatmaps using the mplsoccer library.

PROJECT_STATS

  • CATEGORY: AI/ML
  • ROLE: Machine Learning Engineer
  • TIMELINE: 2 Days
  • COMPLETION: 100%

MISSION_DOSSIER

> THE_OBJECTIVE

Developing a Reinforcement Learning-based spatial optimizer to analyze and automate collective football tactics, specifically modeling the "Juego de Posición" philosophy utilized by Manchester City. The system bridges real-world IoT sensor data with advanced AI architectures.

> SYSTEM_LOGIC

  • Training RL agents to find the optimal off-ball positioning in a highly dynamic spatial environment.
  • Maximizing the Expected Threat (xT) metric while simultaneously minimizing the risk of pass interceptions.
  • Processing and extracting raw player coordinate data using Metrica Sports.

> MISSION_IMPACT

Achieved successful synchronization of AI agents, providing a scalable framework for automating tactical analysis and evaluating complex spatial intelligence in professional sports.

LAUNCH_DEMO

> EQUIPMENT_LOADOUT

PYTHON 3 PPO (RL) METRICA SPORTS MPLSOCCER PANDAS

> SYSTEM_ARCHITECTURE

This project integrates advanced deep learning models with a high-performance, optimized interface. The workflow begins with the extraction of raw spatial telemetry data, which is subsequently processed by intelligent AI agents to generate strategic tactical decisions in real-time.

> RACE_INCIDENTS & PIT_STOPS

INCIDENT (CHALLENGE) PIT STOP (SOLUTION) RESULT
Defining abstract tactical concepts (like "good positioning") for an AI. Engineered a custom mathematical reward function using Expected Threat (xT) and interception probability. ACCURATE
Processing vast amounts of dynamic, real-world player coordinate data. Leveraged Metrica Sports IoT datasets and optimized state extraction pipelines in Python. OPTIMIZED
Agent synchronization in chaotic, multi-agent environments. Implemented Proximal Policy Optimization (PPO) to prevent massive policy updates and ensure smooth learning curves. STABLE