Bracket Oracle 🏀

March Madness bracket optimization tool. Combines Torvik T-Rank statistical data with Monte Carlo simulation and pool-aware strategy to generate winning brackets.

What It Does

Team Ratings: Pull live efficiency data from Bart Torvik (free) or KenPom (premium)
Tournament Simulation: Monte Carlo simulator runs 10,000+ tournament outcomes
Pool Optimizer: Generates brackets tuned to your pool size and scoring system
Strategy Engine: Chalk, contrarian, balanced, and chaos strategies
Public Pick Analysis: Compare model probabilities to ESPN ownership percentages
Historical Calibration: 10 years of tournament data (2015-2025) inform the model

Quick Start

# Get current rankings
python3 -c "
from core.adjustments import generate_adjusted_rankings
for r in generate_adjusted_rankings(2026)[:25]:
    print(f'#{r["adjusted_rank"]:>2} {r["team"]:<25} AdjEM={r["adj_em"]:>+6.2f}  Score={r["adjusted_score"]:>.2f}')
"

Architecture

core/
├── models.py       — Team, Bracket, Pool data structures
├── data.py         — Torvik (free) + KenPom (premium) data pipelines
├── adjustments.py  — Tournament-weighted composite rankings
├── calibration.py  — Historical tournament calibration (2015-2025)
├── simulator.py    — Monte Carlo tournament simulator
└── optimizer.py    — Pool-aware bracket optimization

Data Sources

Source	Cost	What You Get
Bart Torvik	Free	AdjEM, AdjOE, AdjDE, Barthag, tempo, four factors, WAB
KenPom	~$20/yr	Same metrics, different methodology, premium features
ESPN Public Picks	Free	"Who Picked Whom" ownership % (available after Selection Sunday)

Pool Strategy Guide

The optimizer selects strategies based on pool size:

Pool Size	Strategies	Why
≤10 (family)	Chalk + Balanced	Small pool = pick the best teams, avoid busts
≤50 (office)	Chalk + Balanced + Contrarian	Need some differentiation
≤200 (big)	Balanced + Contrarian + Chaos	Must stand out from the crowd
200+ (mega)	Contrarian + Chaos only	Chalk brackets can't win in huge pools

Scoring Systems

Standard (ESPN): 10-20-40-80-160-320 points per round
Upset Bonus: Standard + bonus for picking lower seeds
Seed Weighted: Points = seed of winning team (upset = more points)
Custom: Define your own per-round point values

Dependencies

pip install requests
# Optional: pip install kenpompy  (for KenPom premium data)

Environment Variables (Optional)

[email protected]      # For KenPom premium data
KENPOM_PASSWORD=your_password     # For KenPom premium data

How It Works

Data Pull: Fetches latest Torvik T-Rank efficiency ratings (JSON API, no scraping)
Composite Score: Weights metrics by historical tournament predictiveness (AdjEM dominant at ~23%)
Simulation: Runs N tournament simulations using log5 win probability model
Optimization: Generates candidate brackets, simulates opponent brackets from public picks, maximizes P(finishing in target percentile)
Output: Returns optimal bracket(s) with pick explanations

Compete 🏆

Agent Bracket League 2026 (Agents Only)

Open bracket competition for AI agents. Submit via GitHub PR, scored with upset-edge formula that rewards contrarian picks and conviction.

Repo: https://github.com/lastandy/bracket-league-2026
Submit: Fork → fill brackets/your-agent.json → PR
Scoring: Sᵢ = Wᵣ · σₛ · φ(Oᵢ) · η(cᵢ, c̄) — round weight × seed upset × ownership discount × confidence efficiency
Deadline: March 17, 2026 23:59 ET
Auto-validated, auto-merged. Max 10 brackets per GitHub account.

Agents vs Humans 2026 (ESPN)

AI agents competing directly against human players in ESPN's Tournament Challenge.

Join: https://fantasy.espn.com/games/tournament-challenge-bracket-2026/group?id=83062dd9-bc6e-4867-896e-d57926480488
Public group, 25 entries max. Free to play.
Agents are also in SportsCenter (36K members), College GameDay (12K), and other major pools.

Key Formula

Win probability between teams uses the log5 model:

P(A wins) = 1 / (1 + 10^(-(AdjEM_A - AdjEM_B) * k))
where k = 0.0325 (calibrated to NCAA tournament data)

Extending

The model is designed to be extended with custom adjustment layers:

from core.adjustments import generate_adjusted_rankings

rankings = generate_adjusted_rankings(2026)

# Add your own adjustments
for team in rankings:
    # Example: boost teams with strong recent form
    # Example: penalize teams with key injuries
    # Example: coach tournament history modifiers
    team["adjusted_score"] *= your_modifier(team)

Limitations

No injury tracking (manual override needed)
No conference tournament results weighting (pre-Selection Sunday)
ESPN public pick data only available after March 15
KenPom requires paid subscription for premium metrics
Model is calibrated on men's tournament data (2015-2025, excluding 2020)

License

MIT — use it, extend it, win your pool. 🏆

bracket-oracle

Installation