Transparent Predictions Through Machine Learning
We build predictive models that turn basketball data into accurate forecasts. Our XGBoost_TOP17 model combines 17 optimized features and rigorous validation to deliver 67% prediction accuracy.
Our Story
DataProven.bet began with a simple question: Can machine learning predict NBA game outcomes more accurately than traditional methods? The answer led to months of data collection, feature engineering, and systematic model iteration.
Starting with 266 potential features, we used LASSO regularization to identify the 17 most predictive variables. Through rigorous backtesting and time-series cross-validation, we developed our XGBoost_TOP17 model — achieving 66.8% test accuracy with excellent probability calibration.
What sets us apart is our commitment to radical transparency. Most platforms operate as black boxes; we document everything: our LASSO selection process, XGBoost hyperparameters, and even our failed experiments.
Launching January 1, 2026, we're bringing a new standard to sports prediction: one that prioritizes education alongside accuracy and long-term trust over short-term hype.
From 266 candidates to 17 high-impact predictors via LASSO regularization
Six seasons of NBA data used for model development and validation
Every prediction includes methodology, confidence levels, and feature importance
Model Performance Metrics
Our XGBoost_TOP17 model has been extensively tested through backtesting and time-series cross-validation. Here are the key indicators demonstrating our model's reliability:
Note: These metrics represent rigorous backtesting on historical data (2020-2025). Live performance has been tracked daily starting December 22, 2025. Methodology and architecture are fully documented in our educational content.
Our Approach
We believe prediction platforms should educate, not just provide outputs. Our approach combines three core principles:
Radical Transparency
We show our work completely. Every prediction includes confidence levels, feature importance, and key drivers. Our LASSO selection and XGBoost parameters are fully documented and publicly available.
- Complete documentation of all 14 features with exact formulas
- Open discussion of model limitations and edge cases
- Daily performance tracking with full accountability
- Detailed calibration analysis (70% confidence means ~70% win rate)
Education First
Understanding how predictions work is as important as the results. We create content teaching machine learning concepts, XGBoost architecture, and practical NBA analytics.
- Step-by-step tutorials on building prediction models
- Explanations of key concepts (calibration, cross-validation)
- Code examples and practical walkthroughs
- Deep-dives into Dean Oliver's Four Factors and advanced stats
Data-Driven Decisions
We let patterns emerge from data rather than imposing assumptions. Our model uses LASSO for feature selection and time-series validation to prevent overfitting.
- LASSO regularization eliminates 70% of candidate features
- Rolling performance metrics (L5 and L10 windows)
- Rest and fatigue analysis for back-to-back games
- Estimated net rating adjusts for strength of schedule
What We Cover
NBA Game Predictions
Our primary focus is NBA forecasting with calibrated probabilities. We analyze team form, Four Factors efficiency, and rest factors to generate predictions daily at 6:00 AM ET.
- Win probability predictions (home/away)
- Confidence tier classifications (Very High to Low)
- Feature importance breakdown per game
Model Methodology
Complete documentation of our XGBoost_TOP17 architecture and LASSO feature selection. Learn exactly how our predictions are generated—no black boxes.
- LASSO regularization for feature selection
- XGBoost hyperparameter optimization
- Time-series cross-validation techniques
- Calibration curve analysis and Brier scores
Machine Learning Education
Practical tutorials teaching XGBoost implementation, sports analytics, and data science fundamentals. Build your own model with our step-by-step guides.
- Python for sports analytics (pandas, scikit-learn)
- XGBoost from scratch: training to deployment
- Feature engineering for basketball data
- Backtesting strategies and performance metrics
Performance Tracking
Transparent accountability through daily reports. We track every prediction and openly analyze successes, failures, and calibration drift.
- Daily accuracy updates (overall and by tier)
- Calibration curve monitoring
- Week-over-week performance trends
- Model version comparisons and A/B tests
Future Expansion
May 2026: Tennis predictions | Beyond: NBA point spreads, player props and football outcomes
Who's Behind DataProven
Built by Data Scientists, For Analysts
DataProven.bet is built by sports analytics enthusiasts with backgrounds in ML, statistical modeling and software engineering. We're researchers fascinated by the challenge of prediction.
Our team combines expertise in Python, time-series modeling and NBA analytics. We've studied methodologies from FiveThirtyEight and academic research to build a standard-setting platform.
This project represents our commitment to transparency, education and genuine accountability in the sports prediction industry.
Our Values
- Honesty: We share failures alongside successes
- Education: Teaching the 'how' matters as much as the 'what'
- Rigor: Every claim is backed by cross-validation
- Evolution: Continuous improvement through iteration
- Community: Building knowledge together
Prediction is challenging. We don't claim perfection—we claim honesty.
With a rigorous XGBoost model and transparent selection processes, we're building a platform you can trust because we always show our work.
Welcome to data-driven NBA prediction.