Automating Hockey Analytics with AI, LLMs, and Mage AI

Project Objective:

This project aimed to build a fully automated hockey analytics pipeline, leveraging AI, Machine Learning (ML), and Large Language Models (LLMs) alongside tools like Scrapy, Minio, and Mage AI. By integrating advanced technologies, the system automates data collection, transformation, and visualization, providing interactive dashboards that deliver actionable insights. The pipeline is now deployed on Heroku for real-time accessibility.

How It Started:

Hockey analytics relies heavily on timely and accurate data processing. However, manual workflows for scraping, transforming, and visualizing data are inefficient and prone to errors. This project sought to address these challenges by:

1. Using Scrapy to scrape hockey statistics from online sources, managed and scheduled by Mage AI.
2. Incorporating AI and Machine Learning to clean, standardize, and categorize data.
3. Leveraging LLMs to extract meaningful insights from unstructured text data (e.g., game summaries, player reports).
4. Visualizing data with Dash, Plotly, and Rill for interactive dashboards.
5. Deploying the entire system on Heroku for easy sharing and scalability.

The goal was to create a system that combines cutting-edge AI tools with modern data engineering techniques for a seamless end-to-end workflow.

What Was Built:

The project established a comprehensive pipeline with the following components:

• Data Collection:

  • Web scraping hockey statistics using Scrapy, orchestrated by Mage AI.
  • AI and LLM-based models were integrated to process unstructured text data, such as player bios and game commentary.

• Data Storage:

  • Minio was used for secure and scalable storage of raw and intermediate data.
  • S3-compatible APIs simplified integration with Python tools and workflows.

• Data Transformation:

  • DuckDB handled lightweight SQL transformations, allowing efficient data structuring.
  • Machine Learning models cleaned and enriched the data by detecting anomalies and filling missing values.

• Insights and Categorization:

  • NLP-powered LLMs categorized player statistics and extracted themes from text data.
  • Sentiment analysis was applied to fan feedback and commentary, adding a new layer of insights.

• Dashboards and Visualization:

  • Dash and Plotly were used to create dynamic dashboards showcasing player performance, trends, and predictions.
  • Rill provided additional dashboards for high-level summaries and regional breakdowns.

• Deployment:

  • The entire pipeline was deployed on Heroku, making the system accessible for stakeholders and ensuring scalability.

How It Works Today:

1. Data Collection:

   • Scrapy scrapes hockey statistics and other related data from multiple sources.
   • Mage AI schedules and monitors scraping workflows, ensuring reliability.

2. AI and LLM Integration:

   • AI models preprocess and clean data, filling gaps and standardizing formats.
   • LLMs analyze unstructured text data, extracting insights like player highlights and game summaries.

3. Data Transformation:

   • Data is ingested into DuckDB, where lightweight transformations standardize the structure.
   • ML models enrich the data, preparing it for advanced analytics and visualizations.

4. Dashboards:

   • Interactive dashboards built with Dash and Plotly provide in-depth analysis of player performance and trends.
   • Rill offers complementary dashboards for macro-level insights and summaries.

5. Cloud Deployment:

   • The system is deployed on Heroku, allowing users to access real-time updates and insights from anywhere.

Outcome:

This enhanced system delivers a fully automated solution for hockey analytics with the following benefits:

• AI-Driven Workflows: Integration of AI and LLMs enables advanced data processing and insights extraction.
• Automated Pipeline: Mage AI orchestrates the entire pipeline, reducing manual intervention.
• Scalability: Minio and Heroku ensure the system can handle growing data volumes and user demands.
• Interactive Dashboards: Dash and Rill provide stakeholders with detailed and actionable visualizations.
• Improved Decision-Making: The combination of structured data, ML insights, and NLP-generated themes allows for better understanding and decision-making.

Step-by-Step Guide Prompt:

To replicate or extend this project, use the following prompt as a guide:

“Design a scalable and automated hockey analytics pipeline. The solution should include: (1) Web scraping hockey statistics with Scrapy, managed through Mage AI workflows, (2) Storing raw data in Minio for object storage, (3) Loading and transforming the data in DuckDB to set formats and structure, (4) Building interactive dashboards using Dash and Plotly, with additional perspectives using Rill, (5) Applying Machine Learning models for data enrichment and gap-filling, and (6) Using LLMs for analyzing unstructured text data, extracting insights, and categorizing themes. Deploy the pipeline on Heroku for real-time accessibility and scalability.”

Step-by-Step Workflow:

1. Web Scraping:

   • Set up Scrapy to scrape hockey statistics and related data.
   • Schedule and monitor scraping workflows with Mage AI.

2. Data Storage:

   • Store raw data in Minio, using organized folders by source and date.

3. AI and LLM Integration:

   • Preprocess and clean data using AI models.
   • Use LLMs for NLP tasks like extracting themes, categorizing text, and analyzing sentiment.

4. Data Transformation:

   • Load data into DuckDB for lightweight SQL transformations.
   • Apply ML models to enrich data and detect anomalies.

5. Visualization:

   • Build dashboards with Dash and Plotly for in-depth analysis.
   • Use Rill for high-level summaries and trend analysis.

6. Deployment:

   • Deploy the pipeline on Heroku for easy access and scalability.
   • Use Mage AI’s monitoring tools to ensure reliability.