Every March, millions fill out NCAA tournament brackets with confidence, relying on stats, trends, and gut feelings. For many, it’s a tradition. For others, a carefully calculated gamble. But for some, it’s just a chaotic mess of random picks. This year, I’m leaning into the latter—with a twist: I’m letting AI do the heavy lifting.
Last year, a ChatGPT-generated bracket nearly won my pool, proving that even casual observers can get lucky with the right algorithm. The experiment wasn’t about expertise; it was about testing whether AI could bridge the gap between cluelessness and competitiveness. The results were…surprising. So, naturally, I’m running it back.
The 2026 tournament bracket is set, and once again, I’m turning to ChatGPT for assistance. I don’t pretend to know every roster or injury update, but that’s part of the fun. The question isn’t whether AI can replace human analysis entirely, but whether it can improve a bracket for someone who primarily follows the NBA.
The Logic Behind the AI Approach
Predicting March Madness perfectly is statistically impossible. Even with perfect knowledge, a flawless bracket has odds of 1 in 120.2 billion – lower than being attacked by a shark. This inherent randomness is what makes the tournament so engaging, and why office pools thrive.
ChatGPT excels at identifying structure: higher seeds usually advance, certain mid-seeds are common upset candidates, and overdoing early upsets is a mistake. Where AI falters is in capturing the unpredictable chaos that defines March Madness: hot shooting nights, injuries, coaching decisions, and sheer momentum shifts.
However, if AI can create a bracket that survives the early rounds and avoids obvious blunders, it might outperform the average pool filled with random guesses. OpenAI itself acknowledges the tool’s potential: “ChatGPT can be a fun and useful tool for anyone filling out their brackets… it’s free to use and can break down team stats, compare matchups, and help you think through different approaches.”
How the AI Built the Bracket
Last year’s success came from a structured prompt designed to mimic how analysts evaluate matchups. The goal isn’t perfection, but a realistic bracket that could actually win a pool. For 2026, I used the following prompt:
“You are helping me fill out a March Madness bracket. Use historical tournament trends, team seeding, and general basketball analytics to suggest winners for each matchup. Avoid unrealistic brackets with too many early upsets, but include a few plausible ones, including possible dark-horse and Cinderella teams. The goal is to build a bracket that could realistically win a pool. Use this as a guide: https://www.ncaa.com/march-madness-live/bracket “
The aggressiveness of upset picks should align with pool size. Larger pools benefit from riskier choices, while smaller pools demand a more conservative approach.
The 2026 Bracket: AI’s Predictions
Below is the full bracket generated by ChatGPT (using version 5.4), including the First Four, projected favorites, and a few strategically placed upsets. I will track its accuracy throughout the tournament.
First Four
- (11) Texas over (11) NC State
- (16) Howard over (16) UMBC
- (11) SMU over (11) Miami (OH)
- (16) Lehigh over (16) Prairie View
East Region
- Round of 64: (1) Duke over (16) Siena, (9) TCU over (8) Ohio State, (5) St. John’s over (12) Northern Iowa, (4) Kansas over (13) California Baptist, (6) Louisville over (11) South Florida, (3) Michigan State over (14) North Dakota State, (7) UCLA over (10) UCF, (2) UConn over (15) Furman
- Round of 32: (1) Duke over (9) TCU, (5) St. John’s over (4) Kansas, (3) Michigan State over (6) Louisville, (2) UConn over (7) UCLA
- Sweet 16: (1) Duke over (5) St. John’s, (2) UConn over (3) Michigan State
- Elite Eight: (1) Duke over (2) UConn
West Region
- Round of 64: (1) Arizona over (16) LIU, (9) Utah State over (8) Villanova, (5) Wisconsin over (12) High Point, (4) Arkansas over (13) Hawaii, (6) BYU over (11) Texas, (3) Gonzaga over (14) Kennesaw State, (7) Miami (FL) over (10) Missouri, (2) Purdue over (15) Queens
- Round of 32: (1) Arizona over (9) Utah State, (4) Arkansas over (5) Wisconsin, (3) Gonzaga over (6) BYU, (2) Purdue over (7) Miami (FL)
- Sweet 16: (1) Arizona over (4) Arkansas, (2) Purdue over (3) Gonzaga
- Elite Eight: (1) Arizona over (2) Purdue
South Region
- Round of 64: (1) Florida over (16) Lehigh, (8) Clemson over (9) Iowa, (5) Vanderbilt over (12) McNeese, (4) Nebraska over (13) Troy, (6) North Carolina over (11) VCU, (3) Illinois over (14) Penn, (7) Saint Mary’s over (10) Texas A&M, (2) Houston over (15) Idaho
- Round of 32: (1) Florida over (8) Clemson, (4) Nebraska over (5) Vanderbilt, (3) Illinois over (6) North Carolina, (2) Houston over (7) Saint Mary’s
- Sweet 16: (1) Florida over (4) Nebraska, (2) Houston over (3) Illinois
- Elite Eight: (1) Florida over (2) Houston
Midwest Region
- Round of 64: (1) Michigan over (16) Howard, (8) Georgia over (9) Saint Louis, (5) Texas Tech over (12) Akron, (4) Alabama over (13) Hofstra, (6) Tennessee over (11) SMU, (3) Virginia over (14) Wright State, (10) Santa Clara over (7) Kentucky, (2) Iowa State over (15) Tennessee State
- Round of 32: (1) Michigan over (8) Georgia, (4) Alabama over (5) Texas Tech, (3) Virginia over (6) Tennessee, (2) Iowa State over (10) Santa Clara
- Sweet 16: (1) Michigan over (4) Alabama, (2) Iowa State over (3) Virginia
- Elite Eight: (1) Michigan over (2) Iowa State
Final Four: (1) Duke over (1) Florida, (1) Arizona over (1) Michigan
National Championship: (1) Duke over (1) Arizona
The bracket leans heavily on No. 1 seeds, with Duke projected to win it all. This isn’t necessarily a bold prediction, but it’s a reflection of the AI’s conservative approach. Whether it holds up remains to be seen.
The experiment continues.
