No gut feelings. No office pools.
Just math, history, and chaos.
Nine AI models. Nine brackets. One picked the national champion. The machine that trained itself got it right.
“Film room intelligence at machine speed.”
Evaluates every matchup like a head scout — coaching experience, roster age, injuries, clutch performance, travel distance, and momentum. The eye test, automated.
Score
1030
Accuracy
75%
Rank
#4
Champion
Duke
“10,000 simulations. Zero feelings.”
Ingests efficiency ratings, assigns win probabilities, simulates the tournament 10,000 times. The bracket is whatever outcome happens most. No feelings, no narratives.
Score
1050
Accuracy
73%
Rank
#2
Champion
Duke
“Every team has a twin. History already played this game.”
Finds every 2026 team's closest statistical twin from past tournaments (2010–2025). Uses what actually happened to that twin as the prediction.
Score
1040
Accuracy
73%
Rank
#3
Champion
Duke
“Your bracket is too safe. This one isn't.”
Doesn't ask who's better. Asks what could go wrong for the favorite. Engineered to find upsets. The bracket everyone screenshots.
Score
710
Accuracy
67%
Rank
#5
Champion
Gonzaga
“Every other model predicts games. This one plays the scoring system.”
Doesn't optimize for correct picks — optimizes for ESPN bracket points. A correct champion is worth 32 first-round picks. This model exploits the scoring system by maximizing expected value across the full bracket path.
Score
700
Accuracy
65%
Rank
#6
Champion
Illinois
“21 strategies tested. 14 years backtested. One bracket.”
An autonomous research loop that tested 21 bracket strategies against 15 years of tournament data, trained an ML ensemble on 827 games, then used Monte Carlo optimization to find the single bracket that maximizes expected ESPN points. No human picks — just data, models, and math.
Score
1310
Accuracy
75%
Rank
#1
Champion
Michigan