YOUR
BRACKET
IS WRONG

The model has seen 40 years of March Madness. It knows which favorites collapse, which underdogs are built for this moment, and exactly where your bracket is about to get embarrassed.

GET MY BRACKET PICKS

Trusted by serious bracket players. Powered by real machine learning.

0.0%
Temporal Accuracy
0
Games Analyzed
0
Years of Data
BracketIQ Model Output
2026 Tournament Analysis
READY
CV AUC0.808
Temporal Accuracy80.6%
Top SignalArchetype Matchup
Signal Importance47.6%
Training Data1985–2025
Teams Profiled1,279
OOS Validation Yrs6 / 6 passed
Awaiting Selection Sunday · Mar 15, 2026
40 years of NCAA tournament data
XGBoost + Random Forest + Logistic Regression ensemble
8 team archetypes clustered via k-means
Validated on 6 real tournament years out of sample
Self-improving after every settled result
40 years of NCAA tournament data
XGBoost + Random Forest + Logistic Regression ensemble
8 team archetypes clustered via k-means
Validated on 6 real tournament years out of sample
Self-improving after every settled result

PICK YOUR EDGE

From casual bracket filler to prediction market trader. There is a level for everyone.

THE QUICK HITS
$27

Upset Special

The five upsets the bracket field is sleeping on. Ranked by model confidence. Each pick comes with the historical rhyme game that matches the archetype pattern and three specific data bullets that explain exactly why the favorite is about to have a very bad Thursday.

  • Top 5 upset picks ranked by confidence
  • Historical rhyme game for each pick
  • Archetype matchup label
  • Specific vulnerability factor for the favorite
  • Confidence score 1–10
THE TRADER
$197

Market Edge Package

Built for prediction market traders on Kalshi and Polymarket. Everything in the Full Bracket plus daily model updates throughout the tournament as results change the probability landscape.

  • Everything in Full Bracket Intelligence
  • Daily tournament update PDFs — model recalibrates after each day
  • Kalshi market signals — specific contracts with edge scores
  • First half market recommendations
  • Live upset alerts via email when model detects mispriced markets
  • Post-tournament model accuracy report
WAITLIST
COURSE WAITLIST
$97

Build Your Own Bot

The full story of how BracketIQ was built in 72 hours using Claude Code with zero prior coding experience. Every prompt. Every bug. Every breakthrough. From first API call to live trading engine.

  • Full video course — every build session documented
  • All prompts used to build the system
  • Complete codebase access
  • Weather trading bot included
  • NCAA tournament engine included
  • Private community access
  • Lifetime updates

THIS IS NOT A GUT PICK

Here is exactly what the model does and why it is different from every bracket prediction you have seen.

Data Foundation

40 Years of Tournament DNA

Every NCAA tournament game from 1985 to 2025 scraped, cleaned, and stored. Box scores. Play by play. Coaching records. Recruiting data. Venue altitude. Days of rest. 2,488 games of signal.

1,279 Profiles Built

Team DNA Profiles

1,279 team-year profiles built across 15 dimensions. How a team performs in the last 5 minutes when tied. How their shooting changes when trailing by 10. Whether their coach gets better or worse at halftime. All of it quantified.

47.6% Feature Importance

8 Team Archetypes

Every tournament team clustered into one of 8 archetypes — Comeback Specialists, Lead Protectors, Boom-Bust Three Point teams, Grind Halfcourt teams, and more. Archetype matchup is the single most predictive feature in the model. Style eats seed.

CV AUC 0.808

Upset Detection Engine

XGBoost model trained on 2,488 tournament matchups with 15 features. Calibrated against actual historical upset frequencies by seed. Caught its own data leakage during training and eliminated it. The model checks its own work.

80.6% Mean Accuracy

6 Years of Out-of-Sample Validation

Trained on 1985–2018 data only. Tested on 2019, 2021, 2022, 2023, 2024, and 2025 tournaments separately — years the model never saw. 80.6% mean accuracy across all six. Consistent. Not a fluke.

Live During Tournament

Self-Improving Architecture

After every tournament game settles, the model analyzes what it got right and wrong, adjusts feature weights automatically, and runs A/B tests on new model versions before deploying them. It gets sharper every day of the tournament.

STRAIGHT FROM THE MODEL

This is what it looks like when the engine runs.

bracketiq — ncaa_bot.py

REAL QUESTIONS

No. Most bracket sites use season stats and gut feel. BracketIQ uses 40 years of tournament-specific data, team archetype clustering, and a model validated on six real out-of-sample tournament years. The difference is we show our work and our work has an 80.6% track record.

It means we trained the model on old data and tested it on recent tournaments it had never seen. Any model can memorize data it trained on. The real test is whether it predicts data it never saw. Ours does. 80.6% mean accuracy across 2019, 2021, 2022, 2023, 2024, and 2025.

Immediately after Selection Sunday on March 15 when the bracket is announced. The model runs the full 68-team analysis and the PDF is generated and delivered within hours. No waiting around.

The $197 Market Edge package is specifically built for Kalshi and Polymarket traders with specific contract signals and edge scores. The $27 and $99 products are bracket intelligence — they show probabilities and upset picks but do not constitute betting advice. Always trade responsibly.

THE BRACKET FIELD
IS ALREADY WRONG

68 teams. 63 games. One model that has seen it all before.

PDF delivered after Selection Sunday March 15 · Instant email delivery · No fluff, just the model