QUANTITATIVE
SUPERIORITY.
The SOAR Method transitions from raw data ingestion via cbbd and cfbd SDKs to a specialized ensemble execution pipeline. We isolate mathematical edges by processing 13+ efficiency variables per matchup.
Multi-Threaded Ingestion
College Basketball
Library: cbbd==1.22.0
Our system utilizes concurrent.futures to perform parallel sweeps of 365+ D1 programs. We ingest real-time game-by-team results to calculate recursive SRS and Adjusted Net Ratings.
College Football
Library: cfbd==5.13.2
The football engine flattens complex API responses via pd.json_normalize. We synthesize Team Combined metrics, merging recruiting talent grades with PPA (Predicted Points Added) efficiency data.
The Edge Algorithm
RANDOM FOREST
REGRESSION ENGINE
By processing 800+ decision trees per game, our model identifies the "True Margin." We look beyond the box score to analyze high-dimensional interactions between team efficiency deltas.
Ensemble Methodology
We use Scikit-Learn ensembles to prevent overfitting on single-game statistical noise.
Vegas Delta Calculation
The system identifies the 'spread-gap'—the delta between market lines and our projected margin.
Train/Test Validation
Models are validated against a 20% test split of historical 2024-2025 season data.
Platinum Verification
Before a prediction is authorized as a Platinum Play, it must survive a secondary Logistic Regression confidence check.
Back-Tested Threshold
65%+
Confidence Interval
95% CI
Computational Superiority
UNMATCHED
TRANSPARENCY.
Daily Recalibration | Python 3.12
SOAR Analytics uses proprietary datasets and scikit-learn ensemble methods. Models are retrained every 24 hours via automated CRON jobs to ensure zero-day data relevance.