VolleyViz

🏁 VolleyViz: A volleyball performance analysis system using ML + NCAA stats to simulate games 🏐

VolleyViz is a volleyball performance analysis system that leverages NCAA college stats and machine learning to simulate games. Whether you're a fan wanting to engage with the game on a deeper level or a coach looking to analyze matchups, VolleyViz offers an interactive and user-friendly web app to visualize and explore team and player stats.

🌟 Features

• Game Simulation: Use machine learning models to simulate volleyball games based on NCAA stats.
• Interactive Dashboard: Explore team and player statistics in a visually engaging way.
• Matchup Visualization: Visualize potential matchups and see how teams are forecasted to perform against each other.
• User-Friendly Interface: Designed for both fans and coaches with easy-to-navigate features.

🗺️ Navigation

VolleyViz consists of several key pages to help you engage with and analyze volleyball stats:

Rankings Page: View and explore team rankings based on various metrics.

Team Selection Page: Select teams to view detailed statistics and compare their performance.

Stat Dashboard Page: Access a comprehensive dashboard displaying team and player stats.

Matchup Output Page

a. Matchup Page: Visualize and analyze simulated matchups between teams.

b. Win Prediction Component: Predicts the winning team and the forecasted number of sets.

c. Forecasted Statline: Provides a detailed statline for the matchup as well as a computed metric for comparing the teams' performance.

d. Offensive and Defensive Advice: Offers strategic advice for the losing team to improve both offensive and defensive play.

💻 Technologies

• Frontend: React.js
• Backend: Flask
• Data Processing: Pandas
• Machine Learning: Scikit-learn, Keras

🤝 Contributing

Below are the key roles and team members involved in this project:

• Project Manager: Arnav Akula
• Project Members: Ruba Thekkath, Ankita Khatri, Nikhil Karthikeyan
• Media Member(s): Lauren Lee
• Business Member(s): Akshay Raj

🎯 Areas of Improvement

1. Tailoring to UC Davis Volleyball Teams: Customizing the application specifically for UC Davis volleyball teams to enable coaches to use it as a training tool.

2. Optimizing Stat Prediction Model: Reducing the computational cost and memory footprint of our stat prediction model to improve performance and scalability.

3. Enhancing Simulation Aspect: Expanding upon the game simulation feature to make it more engaging and interactive for volleyball fanatics.