[Luke Granto] – Data Science, Physics, and Sports Analytics

Physics student at RIT | Python programmer | NFL/fantasy analytics enthusiast

Welcome! I blend physics, analytical modeling, and programming—specializing in sports prediction, data visualization, and educational/scientific Python projects.

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Featured Projects

1. NFL Game Outcome Predictor

• Summary: Machine learning model trained on NFL data predicts home team win probabilities, visualized by color-coded bar chart.
• Skills: Python, data science, scikit-learn, Matplotlib
• Result:
  
• Code: GitHub: NFL Predictor
• Details:
  • Random forest model trained on simulated NFL stats.
  • Output probabilities visualized vs. actual outcomes.
  • Demonstrates end-to-end sports analytics workflow.

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2. Physics: Projectile Motion Visualizer

• Summary: Simulates and visualizes classic projectile motion with adjustable velocity and angle—a staple physics problem.
• Skills: Python, physics, Matplotlib
• Result:
  
• Code: GitHub: Projectile Motion
• Details:
  • Models kinematic equations.
  • Plots trajectory curve for any initial condition.
  • Useful for teaching or lab report visualizations.

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3. Fantasy Football Points Simulation

• Summary: Simulates and visualizes weekly fantasy points for QBs, RBs, and WRs; shows scoring trends and outliers by position.
• Skills: Python, simulation, data visualization
• Result:
  
• Code: GitHub: Fantasy Simulation
• Details:
  • Generates 10-player samples for each position group.
  • Boxplot highlights relative value/variance for each group.

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About Me

• Education: Physics undergrad at RIT
• Focus: Data analytics, physics, NFL stats, Python apps
• Contact: lmg1645@rit.edu | LinkedIn | GitHub

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Want to collaborate? Let’s talk: lukegranto04@gmail.com