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+# APIs
+## Basketbal APIs
+NBA: https://github.com/swar/nba_api
+Scraping: https://www.basketball-reference.com/
+
+## Baseball APIs
+MLB: https://statsapi.mlb.com/
+
+## Kalshi API
+https://docs.kalshi.com/welcome?redirect=%2F
+
+# Control Flow
+
+## 1. Collect Data
+Get NBA + MLB data from APIs and get injury list too maybe. Use Python for API calls with aiohttp async calls.
+
+## 2. Clean Data and Normalization
+Pandas and Numpy to filter and clean data. 
+
+## 3. Feature Engineering
+Past performance (face-offs), Offensive/defensive ratings, Player availability, Pitch speed/batting stats (for MLB), Kalshi market metrics (price, volume, spread)
+
+Scikit-Learn
+
+## 4. Model Training
+Logistic Regression, Random Forest, Time-series models, Simple NN
+
+PyTorch, Scikit-Learn
+
+## 5. Test Preditctions on Kalshi/Fine-Tuning
+For a few days, put custom parameters and see what our model will predict. Wait for outcome to come and see how our model did
+
+FastAPI, Docker
+
+## 6. Track Record
+Track PnL
+
+PostgreSQL/SQLite or Amazon S3 Bucket
+
+## 7. Evaluate Performance
+Plot different graphs/metrics of wins vs. losses or net money
+
+Pandas/Matplotlib or Streamlit
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