How the AI Bracket Predicted Michigan Before the Tournament Started

When we published our 2026 March Madness bracket the morning after Selection Sunday, the headline pick was Michigan to win it all. The model gave Michigan the highest championship probability of any team in the field.

Three weeks later, Michigan beat Connecticut 69-63 to win the national title.

The Final Score

The AI bracket finished with 1,410 points — beating all four human entries in our public bracket challenge:

| Rank | Entry | Points | |------|-------|--------| | 🤖 | AI Bracket | 1,410 | | 1 | Josmckenna | 1,010 | | 2 | The Dude | 780 | | 3 | BracketTrends | 680 | | 4 | LCS | 650 |

Through the Elite Eight, the AI correctly predicted 44 of 60 completed games — a 73.3% accuracy rate.

Round-by-Round Score

| Round | Points | |-------|-------:| | Round of 64 | 270 | | Round of 32 | 220 | | Sweet 16 | 200 | | Elite 8 | 240 | | Final Four | 160 | | Championship | 320 | | Total | 1,410 |

The Championship round was worth 320 points alone — and the AI was the only entry in our challenge that got it right.

What the Model Got Right

Michigan as champion. Predicted before R1 tipped off. Hit.

Connecticut to the Final Four. UConn was a 2-seed. The model saw their efficiency profile and put them in the semifinals. They beat Illinois 71-62 to make the championship game.

The Final Four picks. Two of four Final Four teams correct (Michigan + Connecticut). The AI also picked Houston, who made the Sweet 16 before losing, and Duke, who made the Elite Eight before losing.

Round of 64. Strongest round of the tournament — the model nailed most first-round matchups, including a few mild upset calls based on tempo and efficiency mismatches.

What the Model Got Wrong

Houston to the championship. The AI had Houston as a Final Four team. They lost in the Sweet 16. This was the biggest miss — Houston's efficiency numbers looked elite all season, but they ran into a hot opponent.

Duke to the Final Four. Duke made the Elite Eight before falling. The model overweighted their late-season form.

Some Sweet 16 teams. A few seed-line favorites the model trusted got bounced in earlier rounds. This is the cost of trusting the numbers — sometimes the favorites lose.

How the Model Works

Our bracket model is built on three pieces:

1. Team strength ratings. We compute proprietary efficiency metrics for every D1 team, updated daily during the regular season. These factor in offensive and defensive efficiency, tempo, and strength of schedule.

2. Monte Carlo simulation. For every possible matchup in the bracket, we simulate the game 10,000 times using the team strength ratings plus tempo-adjusted possession estimates. This gives us a probability distribution for every team to advance to every round.

3. EP-optimal picking. For the bracket challenge, we don't just pick the favorite in every game — we pick the team that maximizes expected points given the bracket scoring structure. This means upsets get picked when their probability times the round point value exceeds the favorite's expected value. It's the same principle that drives our daily betting picks: edge over certainty.

The full bracket and all advancement probabilities are still live at /march-madness-bracket.

What's Next

The 2026 NCAA tournament is over. Our daily picks pipeline rolls into the NHL and NBA playoffs starting April 18, plus full coverage of the Masters this weekend.

If you want to see how the model performs on every pick — every win, every loss, every dollar — that's all on our track record page. 800+ picks tracked publicly, all the math, no cherry-picking.

Bracket data current as of April 8, 2026. AI bracket built immediately after Selection Sunday on March 15 and locked before R1 tipped off on March 19.