A sophisticated March Madness optimization tool that utilizes Monte Carlo simulations and statistical efficiency data to generate winning tournament brackets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bracket-oracle
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bracket-oracle using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Bracket Oracle?
Bracket Oracle is a specialized technical framework designed to provide a competitive edge in March Madness tournament pools. Part of the Openclaw Skills ecosystem, it synthesizes live efficiency data from Bart Torvik and KenPom to power a robust simulation engine. By running over 10,000 tournament outcomes, it helps users move beyond gut feelings to data-backed decisions.
This skill is particularly valuable for its pool-aware strategy engine, which adapts its recommendations based on the size of your competition. Whether you are playing in a small family pool or a massive national contest, Bracket Oracle leverages historical tournament data from 2015 to 2025 to calibrate its models and maximize your probability of finishing in the top percentile.
Bracket Oracle Use Cases
- Optimizing brackets for small family or office pools using chalk and balanced strategies.
- Generating high-variance contrarian brackets for mega-pools with over 200 entries to stand out from the crowd.
- Analyzing public pick percentages from major platforms like ESPN to identify undervalued teams.
- Customizing tournament simulations to account for specific scoring systems like upset bonuses or seed-weighted points.
How Bracket Oracle Works
- The system initiates a data pull from the Torvik T-Rank API to retrieve the latest efficiency ratings and four-factor statistics.
- A composite score is calculated by weighting metrics such as Adjusted Efficiency Margin (AdjEM) based on their historical predictive power.
- The Monte Carlo simulator executes thousands of tournament runs using a calibrated log5 win probability model.
- The optimization engine generates candidate brackets and compares them against simulated opponent brackets derived from public ownership data.
- The final output provides the user with an optimal bracket and a detailed explanation for each pick.
Bracket Oracle Setup
To integrate this tool into your workflow via Openclaw Skills, first install the necessary Python dependencies:
pip install requests
# Optional: pip install kenpompy for premium KenPom data access
If you have a KenPom subscription, configure your credentials as environment variables:
export KENPOM_EMAIL="[email protected]"
export KENPOM_PASSWORD="your_password"
To verify the installation and generate current rankings, run the following command:
python3 -c "from core.adjustments import generate_adjusted_rankings; [print(r) for r in generate_adjusted_rankings(2026)[:25]]"
Bracket Oracle Data Schema & Taxonomy
Bracket Oracle organizes its logic and data sources into a modular architecture for easy extension within Openclaw Skills:
| Directory/File | Responsibility |
|---|---|
| core/models.py | Core data structures for Teams, Brackets, and Pool definitions. |
| core/data.py | Pipelines for Torvik (free) and KenPom (premium) data ingestion. |
| core/calibration.py | Historical database of tournament results from 2015-2025 for model tuning. |
| core/simulator.py | The Monte Carlo engine utilizing the log5 win probability model. |
| core/optimizer.py | Strategy logic for pool-aware bracket generation. |
Bracket Oracle Advanced Features
- Custom Adjustment Layers: Developers can extend the core logic to add modifiers for injuries, recent form, or coach tournament history.
- Multi-Strategy Engine: Supports Chalk, Balanced, Contrarian, and Chaos strategies tailored to pool size.
- Log5 Probability Calibration: Uses a specific k-factor of 0.0325, meticulously calibrated to modern NCAA tournament data.
- Public Pick Analysis: Automatically discounts picks with high public ownership to increase your bracket's unique value in large pools.
SKILL.md
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