NBA Prediction
End-to-end ML pipeline predicting NBA game winners with 62.1% accuracy (2024 season) using gradient boosting and real-time data integration
(2024-2025 NBA Season and Playoffs)
The Challenge
Build an end-to-end machine learning pipeline to predict the winners of NBA games:
- - Trains a model to predict game winners
- - Retrieves new data daily
- - Performs feature engineering on the data
- - Predict the probabilty of the home team winning
- - Display the results online
My Solution
Constructed a modular system that uses:
- - XGBoost, Optuna, and Neptune.ai for model training and experiment tracking
- - Github Actions, Selenium, and ScrapingAnt to scrape new games daily from NBA.com
- - Pandas to engineer features and store these
- - XGBoost and Sklearn to predict and calibrate winning probability
- - Streamlit to deploy online
Technical Highlights
Key innovations and technical achievements in this project
Advanced Modeling
XGBoost and LightGBM with Optuna hyperparameter tuning for optimal performance and Scikit-learn to calibrate probabilities
Feature Engineering
Created engineered features such as win streaks, losing streaks, home/away streaks, rollling averages for various time periods, and head-to-head stats
Data Pipeline
Automated ETL pipeline with error handling, data validation, and daily NBA.com scraping
Model Validation
Cross-validation and test set evaluation using stratified K-Folds and time-series K-Folds along with SHAP for interpretability and comparing feature importances between train and validation sets
Experiment Tracking
Neptune.ai integration for comprehensive experiment logging and model comparison
Production Ready
GitHub Actions CI/CD, Streamlit deployment with monitoring dashboard
Technology Stack
Tools and frameworks used throughout the project lifecycle
🐍 Core ML/Data
🚀 ML Models and Experimentation
📊 Data & Scraping
⚙️ MLOps and Deployment
Results & Impact
Season Results (2024-2025 NBA Season and Playoffs)
(As you can see, the model has issues with playoffs. The 7 day rolling averages have bigger swings during playoffs as teams play each other multiple times in a short span. This is an area of improvement for future iterations of the model.)
Lessons and Future Plans
This was my first big ML project and I learned a lot. So much in fact, that I am in the process of completely re-doing the project from scratch. I plan to write more about this at a certain point, but there are a couple of big takeaways.
Key Issues
Accuracy
My model had achieved an accuracy of 62.1% for the 2024 season. The baseline of "home team always wins" had an accuracy of 54.6%, but better models often achieve closer to 65%.
Last season, at least one expert at nflpickwatch.com achieved an accuracy of around 69.3%. Close to 100 got over 65%.
Reliability and Extensibility
Third-party services like Hopsworks, Neptune, and Streamlit at times would just stop working. Sometimes they would start back on their own, and sometimes I had to create a workaround. I abandoned Hopsworks altogether, and for Streamlit, I had to create a "light" dashboard-only repo.
Solutions
More Data, Feature Engineering, and Experimentation
Feature engineering is the key to improving the performance of any ML model. More advanced statistics are available on the NBA.com website that will be scraped to aid in the feature engineering, and a better evaluation framework will be used to facilitate faster experimentation to figure out which features work best.
Modular, OOP Architecture
A finer-grained, OOP approach with abstract interfaces and dependency injection makes it easier to swap-out problematic components and to add new capabilities. Alternates and fallbacks can be more easily incorporated into the pipeline.
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