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.

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Analysis and intelligence on how GPT-4o, Gemini 2.5, and Perplexity predict NCAA games.

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AI Research Team

Mar 17, 2026/AnalysisFEATURED

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 fundamentally different evidence base. Understanding those differences is the first step to auditing their accuracy.

6 min readREAD FULL ANALYSIS

Intelligence Team

Mar 17, 2026/Sources

The Sources AI Cites Most - And Why It Matters for Bracket Accuracy

Citation patterns across 3 models reveal sharp divergence: Perplexity leans heavily on team analytics sites, w...

4 min read

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 tells a st...

5 min read

Methodology Team

Mar 15, 2026/Methodology

Calibration: Why 80% Confidence Should Mean Right 80% of the Time

A model that says 85% confident on every pick regardless of the matchup is useless. Calibration scores reveal ...

7 min read

AI Research Team

Mar 17, 2026/Tournament

First Four Preview: AI Consensus and Where the Models Disagree

The 2026 First Four tips off March 19. All three models have submitted predictions. Here's where they agree, w...

4 min read

Intelligence Team

Mar 12, 2026/Sources

Perplexity vs GPT-4o: How Much Source Overlap Is There?

Both models search the web in real time, but their citation domains barely overlap. This source divergence is ...

5 min read

Data Team

Mar 14, 2026/Analysis

Upset Detection: Which AI Is Most Likely to Call the Chaos Early

March Madness is defined by 12-over-5 upsets that destroy brackets. We analyzed which models show higher confi...

5 min read

Methodology Team

Mar 13, 2026/Methodology

Prompt Sensitivity: The Same Game Question, Five Different Phrasings

We test whether rephrasing a prediction request changes the model's pick. A model that flips on synonymous pro...

6 min read