1. Description

This document describes the process of developing a mocked proof of concept (M-POC) for Deriven Sports, a sports analytics platform. The M-POC aims to demonstrate the feasibility of the business idea and the technical implementation. The document outlines the key steps involved in the development of the M-POC with a focus on the technical aspects of the project. It includes, data collection, data processing, model architecture, use cases, and infrastructure requirements. The document also provides an next steps overview for the project after the completion of the M-POC. Also the documentation provides open questions and challenges that need to be addressed in the making of the M-POC and should be (partly) answered in the M-POC.

Therefore we will first define requirements and goals for the M-POC, then we will outline the technical details of the M-POC, and finally, we will discuss the achieved results and the next steps for the project.

2. Goals, Requirements, and Milestones

In this section, we will define the requirements and goals for the M-POC. The requirements will outline the key features and functionalities that the M-POC should demonstrate. The goals will describe the expected outcomes of the M-POC and the milestones will provide a timeline for the development of the M-POC. First the goals are defined and based on that the requirements and milestones are derived.

Goals

DSG Engine: Create a deterministic simulation engine that mimics football events.
LSM‑Lite Model: Develop a lightweight prediction model to forecast the next simulation event.
Real-Time Visualization: Build an interactive web application to display simulation progress and model predictions.
Proof of Feasibility: Demonstrate that our integrated system can drive actionable sports analytics.

Requirements

Simulation Engine (DSG):
- Entities: Simulated players, teams, and coaches.
- Mechanics: Simplified physics for movement and event generation (e.g., passes, shots, tackles).
- Adjustability: Parameter n to control simulation complexity.
Data Architecture:
- Schema: Use MongoDB to store collections like seasons, matches, teams, players, and events.
- Output Format: DSG generates JSON documents conforming to the schema.
Model Training (LSM‑Lite):
- Architecture: Sequential model (RNN or Transformer) built with PyTorch/TensorFlow.
- Data Input: Features derived from DSG data (player positions, ball coordinates, etc.).
- Experiment Tracking: Use MLflow to log metrics and model artifacts, stored in MinIO.
Interactive Platform:
- Backend API: Developed with Python FastAPI, using async calls to MongoDB.
- Frontend Application: Built with Blazor .NET, using SignalR/WebSockets for real-time updates.
Infrastructure:
- Containerization: Dockerize each component; orchestrate with Docker Compose.
- Security: Manage secrets via environment variables and use a cloud KMS for production.

Milestones

Architecture Definition: Finalize the data schema and overall system design.
DSG Engine Implementation: Develop the simulation engine with adjustable complexity.
Data Generation: Run DSG to produce and store mocked match data in MongoDB.
LSM‑Lite Model Development: Build and train the prediction model.
Integrate MLflow/MinIO: Set up experiment tracking and artifact storage.
API Development: Create FastAPI endpoints for simulation control and predictions.
Frontend Development: Develop a Blazor .NET web app for real-time visualization.
Integration & Testing: Combine components for end-to-end testing and performance evaluation.

3. Technical Details

3.1 Architecture

3.1.1 Data Architecture

Our system is built around a robust MongoDB schema that organizes simulation data into several key collections:

Seasons: Contains metadata about each season.
Matches: Each document represents a match and includes details like match ID, season, participating teams, and a sequence of events.
Teams: Stores team profiles.
Players: Holds individual player data.
Events: Records in-game events (e.g., passes, shots, tackles) with timestamps and relevant details.

This design allows us to efficiently store and query simulation data. The deterministic simulated game (DSG) engine uses predefined rules to generate realistic football match events. It outputs structured JSON documents conforming to this schema, with an adjustable complexity parameter (n) to scale simulation detail as needed.

Below is a Mermaid diagram illustrating the high-level data architecture:

3.1.2 Overall Architecture

The overall architecture of the M-POC consists of following main components: the services with DSG engine, the backend, the LSM-Lite model, and the frontend web application. The architecture is designed to be modular and scalable, allowing for easy integration and future enhancements.

3.2 DSG Engine

The DSG engine is a deterministic simulation engine that generates football match events based on predefined rules. It uses a set of parameters to control the complexity and realism of the simulation. The engine is designed to be flexible and can be adjusted to simulate different types of matches, teams, and players. In the following different complexities are defined:

3.2.1 Complexity level 1

Data amount:

3 seasons
10 teams
5 players per team
30 matches per season
3600 events per match (1 event per second)

Movement:

Events:

Dynamics:

3.3 Model Training & Architecture

The core of our predictive capability is a lightweight sequential model (LSM‑Lite) designed to forecast the next event in a match. This model is built using deep learning frameworks such as PyTorch. The training pipeline follows these stages:

Data Ingestion: DSG-generated match data is loaded from MongoDB.
Model Training: The LSM‑Lite model learns from sequential match events, capturing temporal dependencies.
Experiment Tracking: MLflow is used to record training metrics, hyperparameters, and model artifacts.
Artifact Management: Model checkpoints and experiment outputs are stored in MinIO (an S3-compatible object store).

The following diagram summarizes the workflow:

flowchart TD;
    A[Simulation Data in MongoDB] --> B[Data Ingestion]
    B --> C[Model Training]
    C --> D["Experiment Tracking (MLflow)"]
    D --> E["Artifact Storage (MinIO)"]

This structured approach ensures reproducibility, transparency, and scalability in our training process.

3.2.1 LSM-Lite Model

The LSM-Lite model is a lightweight sequential model designed to predict the next event in a football match. It is built using PyTorch based on recent xLSTM architectures.

3.2.2 Fine-Tuning

The LSM-Lite model is fine-tuned to the specific use case of football match event prediction. The model is trained on the DSG-generated data and uses a combination of player positions, ball coordinates, and other relevant features to make predictions. The model is designed to be lightweight and efficient, allowing for real-time predictions during the simulation.

3.3 Web Application

4. Results and Next Steps

Results

DSG Engine: Successfully generates realistic football match events and stores them in MongoDB.
Prediction Model: The LSM‑Lite model is trained on the simulated data and effectively forecasts upcoming match events, with training metrics and model artifacts tracked via MLflow.
Interactive Platform: The combined FastAPI backend and Blazor frontend deliver real-time visualization of simulations and predictions, providing an intuitive user experience.

Next Steps

Validation: Compare model predictions against actual football data to refine and validate performance.
Extension: Develop additional specialized models for use cases such as tactical analysis or injury prediction.
Optimization: Enhance the efficiency of both the simulation engine and the training pipeline to handle larger, more complex datasets.
Feature Enhancement: Improve the Blazor UI by adding detailed dashboards, historical data replays, and interactive controls.
Deployment: Transition to a cloud-based environment with scalable infrastructure, robust monitoring, and enhanced security measures.

5. Conclusion

This roadmap outlines a detailed, step-by-step plan for building the M-POC for Deriven Sports. By integrating a deterministic simulation engine, a lightweight prediction model, and a real-time visualization platform, we lay the foundation for a next-generation sports analytics solution. The system leverages modern technologies—Python FastAPI, MongoDB, deep learning frameworks, MLflow/MinIO, and Blazor .NET—to provide a scalable, reproducible, and production-ready platform that can be expanded and refined in future development phases.

Road to M-POC