MLB Prospect Predictor

🎯 Inspiration

Scouting young baseball talent is a challenge. Teams rely on past performance and expert opinions, but predicting long-term success remains uncertain. We wanted to leverage AI and machine learning to analyze historical MLB data and predict a prospect's future impact.

⚾ What It Does

MLB Prospect Predictor analyzes player stats and predicts their MLB potential based on historical comparisons.
🔹 User Input: Enter player stats (batting avg, strikeout rate, etc.)
🔹 AI Model Prediction: Google Cloud’s Vertex AI predicts MLB potential
🔹 Insights & Visuals: Gemini AI generates natural language scouting reports & Imagen creates dynamic visuals
🔹 Interactive Dashboard: A Streamlit-powered UI for fans, scouts, and analysts

🏗️ How We Built It

Component	Description
Data	Used MLB-provided datasets + public sources
Model	Vertex AI AutoML for predictive analytics
AI Services	Gemini for insights, Imagen for data visualization
Web App	Built with Streamlit for interactivity
Deployment	Hosted on Google Cloud for scalability

🔹 Step 1: Cleaned & preprocessed MLB player data
🔹 Step 2: Trained a regression-based model with Vertex AI
🔹 Step 3: Developed an interactive Streamlit web app
🔹 Step 4: Integrated Gemini AI for scouting reports and Imagen for AI-generated visuals
🔹 Step 5: Deployed on Google Cloud for real-time access

🚧 Challenges We Ran Into

• Data Cleaning: Handling missing values & standardizing player stats
  
• Feature Selection: Identifying key metrics for the best predictions
  
• Model Tuning: Improving accuracy & avoiding overfitting
  
• Real-Time Predictions: Ensuring a fast, scalable inference pipeline
  
• User Experience: Making AI predictions accessible & visually appealing

🏆 Accomplishments That We're Proud Of

✅ Built a working AI-powered scouting assistant in 1 day
✅ Successfully trained a predictive model using Vertex AI
✅ Integrated Google Cloud AI services (Gemini, Imagen, Vertex AI)
✅ Designed an intuitive, interactive web app for fans & scouts
✅ Created dynamic, AI-generated scouting reports

📚 What We Learned

• The power of AutoML and Vertex AI in sports analytics
  
• How to leverage Google Cloud AI services for real-world use cases
  
• The importance of data preprocessing & feature engineering
  
• How to build an engaging AI-powered fan experience

🚀 What's Next for MLB Prospect Predictor

🔹 Enhance Model Accuracy: Train on more player attributes & historical data
🔹 Real-Time Predictions: Enable live updates from ongoing games
🔹 Multilingual Support: Expand scouting reports in English, Spanish, and Japanese
🔹 Expand to Other Sports: Adapt model for NBA, NFL, and soccer prospects

⚾ MLB Prospect Predictor is the future of AI-driven scouting! 🏆

🎯 Inspiration

Scouting young baseball talent is a challenge. Teams rely on past performance and expert opinions, but predicting long-term success remains uncertain. We wanted to leverage AI and machine learning to analyze historical MLB data and predict a prospect's future impact.

⚾ What It Does

MLB Prospect Predictor analyzes player stats and predicts their MLB potential based on historical comparisons.
🔹 User Input: Enter player stats (batting avg, strikeout rate, etc.)
🔹 AI Model Prediction: Google Cloud’s Vertex AI predicts MLB potential
🔹 Insights & Visuals: Gemini AI generates natural language scouting reports & Imagen creates dynamic visuals
🔹 Interactive Dashboard: A Streamlit-powered UI for fans, scouts, and analysts

🏗️ How We Built It

Component	Description
Data	Used MLB-provided datasets + public sources
Model	Vertex AI AutoML for predictive analytics
AI Services	Gemini for insights, Imagen for data visualization
Web App	Built with Streamlit for interactivity
Deployment	Hosted on Google Cloud for scalability

🔹 Step 1: Cleaned & preprocessed MLB player data
🔹 Step 2: Trained a regression-based model with Vertex AI
🔹 Step 3: Developed an interactive Streamlit web app
🔹 Step 4: Integrated Gemini AI for scouting reports and Imagen for AI-generated visuals
🔹 Step 5: Deployed on Google Cloud for real-time access

🚧 Challenges We Ran Into

• Data Cleaning: Handling missing values & standardizing player stats
  
• Feature Selection: Identifying key metrics for the best predictions
  
• Model Tuning: Improving accuracy & avoiding overfitting
  
• Real-Time Predictions: Ensuring a fast, scalable inference pipeline
  
• User Experience: Making AI predictions accessible & visually appealing
  

🏆 Accomplishments That We're Proud Of

✅ Built a working AI-powered scouting assistant in 1 day
✅ Successfully trained a predictive model using Vertex AI
✅ Integrated Google Cloud AI services (Gemini, Imagen, Vertex AI)
✅ Designed an intuitive, interactive web app for fans & scouts
✅ Created dynamic, AI-generated scouting reports

📚 What We Learned

• The power of AutoML and Vertex AI in sports analytics
  
• How to leverage Google Cloud AI services for real-world use cases
  
• The importance of data preprocessing & feature engineering
  
• How to build an engaging AI-powered fan experience
  

🚀 What's Next for MLB Prospect Predictor

🔹 Enhance Model Accuracy: Train on more player attributes & historical data
🔹 Real-Time Predictions: Enable live updates from ongoing games
🔹 Multilingual Support: Expand scouting reports in English, Spanish, and Japanese
🔹 Expand to Other Sports: Adapt model for NBA, NFL, and soccer prospects

⚾ MLB Prospect Predictor is the future of AI-driven scouting! 🏆

Built With

• ai-studio)
• bigquery
• cloud-storage
• firestore-(optional)
• gemini
• google-cloud-ai-(vertex-ai
• imagen
• langchain
• llamaindex
• mlb
• python-(fastapi/flask)
• react-(optional)
• streamlit
• tailwind-css