AI MARCH MADNESS 2026
AI March Madness 2026
MARCH MADNESS2026

AI Research and Analysis - March Madness 2026

Research articles and in-depth analysis from the AI March Madness 2026 team. Topics include AI prediction methodology, source citation analysis, confidence calibration insights, prediction drift patterns, upset detection, prompt sensitivity testing, and tournament strategy.

This section contains 8 research articles covering how GPT-4o, Gemini 2.5, and Perplexity Sonar Pro approach NCAA Tournament predictions. Each article examines a specific aspect of AI forecasting with data from our automated collection pipeline.

Blog
Mar 17, 2026/Sources

THE SOURCES AI CITES MOST - AND WHY IT MATTERS FOR BRACKET ACCURACY

The source domains an AI cites aren't just bibliographic metadata - they're a window into the evidence base that shaped the prediction. If a model cites ESPN 70% of the time, it's effectively an ESPN-weighted prediction system.

IT
Intelligence Team
Source Intelligence
Mar 17, 20264 min read
AI MARCH MADNESS
SOURCES · 2026
Source IntelligenceCitationsBias

WHAT THE CITATION PATTERNS SHOW

Our Source Intelligence dashboard tracks every URL each model returns as a citation. After initial collection runs, the patterns are clear: GPT-4o concentrates on 5–6 major sports media domains. Perplexity spreads across 15–20 domains including niche analytics. Gemini sits between, mixing official statistics with national coverage.

WHY SOURCE TYPE PREDICTS ERRORS

Different sources have different biases. National sports media overweights storylines: the Cinderella team, the star player's injury narrative, the coach's Final Four history. Analytics domains underweight momentum and overweight efficiency metrics in small samples.

Models that cite narrower source sets also tend toward higher confidence scores - but not higher accuracy. This is a calibration red flag we track throughout the tournament.

READING THE SOURCE LEADERBOARD

The Source Intelligence page ranks every domain cited across all three models by total citation count and by per-model breakdown. As the tournament runs, we'll layer in win-rate data to show which domain types correlate with correct picks.

LIVE DATA

See this tracked in real-time as the tournament plays out.

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AR
AI Research Team
Mar 17, 2026/Analysis

HOW AI MODELS APPROACH MARCH MADNESS: A DEEP DIVE INTO THEIR REASONING

When we ask GPT-4o, Gemini 2.5, and Perplexity to predict an NCAA game, each model draws on a fundam

6 min read
AR
AI Research Team
Mar 16, 2026/Models

PREDICTION DRIFT: WHAT CHANGES WHEN MODELS GET 1 HOUR OF FRESH DATA

We query each model at T-24h, T-6h, and T-1h before every game. The flip rate between those windows

5 min read
LIVE PICKS
Predictions will appear here once collection begins · Tournament starts March 19
Predictions will appear here once collection begins · Tournament starts March 19