Project: AI/ML Based NFL Scouting and Draft Simulation

Objective

To develop a comprehensive AI/ML-based scouting report for NFL draft prospects and simulate the NFL draft. The AI model will analyze team rosters to identify strengths and weaknesses, evaluate and rank upcoming college players, and recommend the optimal player to draft based on a combination of these factors.

Project Outline

1. Data Collection

   • Collect data on NFL teams’ rosters, including player statistics and positions.
     • Sources:
       • NFL’s official website
       • Sports reference websites
       • APIs like SportsRadar or MySportsFeeds
   • Gather data on college players declared for the NFL draft.
     • Sources:
       • College football’s official website
       • Sports reference websites

2. Data Analysis

   • Analyze NFL teams' rosters to identify strengths and weaknesses.
     • Use statistical analysis and machine learning algorithms.
   • Evaluate college players based on their performances and various metrics.
     • Develop a ranking algorithm based on players' statistics and other quantifiable data.

3. AI/ML Model Development

   • Develop an AI/ML model that can analyze and predict the “superstar factor” of college players.
     • Tools & Libraries:
       • Python
       • Scikit-learn
       • TensorFlow or PyTorch
   • Train the model using historical data of drafted players and their subsequent NFL performances.

4. Draft Simulation

   • Use the AI/ML model to simulate the NFL draft.
     • Tools:
       • Simulation libraries in Python
       • Custom algorithms to simulate different team’s drafting strategies

5. Web Application Development

   • Develop a web application to visualize the results.
     • Tools:
       • JavaScript
       • D3.js or Chart.js for visualizations
       • Flask or Django for the backend if needed

Step-by-Step Instructions

1: Initial Setup and Data Collection

1. Create a GitHub repository for the project.
   • Include a README file with a basic project description and objectives.
2. Start collecting data on NFL teams and college players.
   • Use web scraping or APIs to gather the data.
   • Organize the data in a structured format suitable for analysis.

2: Data Cleaning and Preprocessing

1. Clean and preprocess the collected data.
   • Handle missing values, outliers, and normalize the data if necessary.
2. Perform exploratory data analysis to understand the dataset’s characteristics.
   • Use visualizations to explore relationships, trends, and patterns.

3: Feature Engineering and Model Selection

1. Identify and engineer features that will be used for training the AI/ML model.
2. Select an appropriate machine learning algorithm for predicting the “superstar factor.”

4: Model Training and Evaluation

1. Train the model using the cleaned and processed dataset.
2. Evaluate the model’s performance using appropriate metrics.
   • Refine the model if necessary.

Weekly Updates

• Every Monday, provide a detailed update on the progress.
  • Include accomplishments, challenges faced, and next steps.
  • Use visual aids where necessary to enhance understanding.

GitHub Repository Management

• Regularly update the GitHub repository with the latest code, data, and documentation.