SportsStats: AI-Powered Sports Statistics Application

SportsStats is a web application that allows users to query and retrieve Major League Baseball (MLB) statistics using natural language processing (NLP). The project leverages AI/ML technologies to transform user queries into SQL queries and provides data visualizations for better insights.

Table of Contents

• Project Overview
• Features
• Tech Stack
• Testing

Project Overview

SportsStats enables users to retrieve MLB statistics by entering natural language queries. The application processes the input using an AI-powered NLP model to convert it into SQL queries, which are then executed to return relevant statistics from a database. It also provides visualized insights, making it easy for users to interpret sports data.

Key Goals:

• Allow users to query MLB statistics naturally (e.g., "What were Aaron Judge’s stats in 2023?").
• Provide clean, interactive data visualizations for easier interpretation.
• Ensure a seamless user experience with features like user accounts and saved query history.

Features

• Natural Language Querying: Retrieve sports statistics using conversational queries.
• AI-Driven Query Processing: AI/ML models convert natural language into SQL queries for data retrieval.
• Real-Time Data Visualizations: Charts and graphs show user-requested sports statistics.
• User Account Support: Create accounts, log in, and access previous queries.

Tech Stack

Frontend:

• Next.js: Framework for server-side rendering and React-based UI.
• TypeScript: Strong typing for better code reliability.
• SWC: Next.js’s default compiler, used for transforming code.

Backend:

• Node.js: Backend runtime for handling API requests.
• PostgreSQL: Database used to store and query MLB statistics.
• AWS RDS: Managed database service for PostgreSQL.
• AWS Lambda: Serverless compute for processing user queries.

CI/CD and Hosting:

• Amazon Amplify: Used to automate the build and deployment process for production.

Testing

SportsStats employs Jest for unit and integration testing, using SWC to transpile TypeScript files quickly.

• Test Suite: Comprehensive tests covering UI components, backend functionality, and API interactions.

• Test Coverage: Focuses on key features, including natural language query translation, data retrieval, and rendering of statistics.

  Running Tests

  Tests are run as part of the continuous integration process with GitHub and Amplify to ensure code quality and reliability.

• Unit & Integration Tests: Test individual components and integrations across the system.

• Mocking APIs: Third-party API calls (e.g., MLB API) are mocked to test system behavior without external dependencies.

Deployment

The application is deployed using Amazon Amplify, which automates the build, deployment, and hosting processes for the production environment.

Key Deployment Details:

• CI/CD: Every push to the production branch triggers a new build and deployment.
• Amplify Build Pipeline: Amplify automates the creation of optimized production builds and handles environment variables for database connections and API endpoints.