Machine Learning Expert for MLB Home Run Prediction Model

I am looking for a sports analytics expert with MLB betting experience to build an automated predictive model for home run predictions. The model should analyze MLB player & pitcher data and return the best home run picks daily.

Scope of Work:
✅ Data Collection (Automated Scraping/API Calls)

Pull pitcher and batter data from Gameday Insights, Baseball Reference, RotoWire.
Scrape pitcher HR tendencies, batter vs. pitch mix performance, weather conditions.
✅ Feature Engineering & Data Processing

Process & weight variables (e.g., HR rate, lineup position, pitcher HR tendencies).
Develop a scalable feature selection method.
✅ ML Model Development

Build ML algorithm (Logistic Regression, XGBoost, or Random Forest).
Tune hyperparameters for highest success rate.
✅ Daily Prediction Output

Deliver daily home run probability scores for players.
Provide automated report/dashboard output.
✅ Deliverables

Fully documented Python script with source code.
Automated system that runs predictions daily.
Basic dashboard or spreadsheet output.
Preferred Qualifications:
✅ Previous experience in sports betting models
✅ Knowledge of MLB data sources & APIs
✅ Portfolio with predictive modeling experience


Data Formats:
- All data will be accessible via APIs.

Ideal Skills:
- Proficiency in sports analytics, particularly in baseball.
- Experience with predictive modeling and MLB betting.
- Familiarity with the specified metrics and data sources.
- Ability to work with APIs.

Please keep the bids competitive and include previous relevant work.