It is time to fill out those NCAA Tournament brackets with the arrival of March Madness
FILE - Staff members for the NCAA place the names of the teams in the Sweet 16 on a bracket in the media workroom before practices at the East Regional of the NCAA college basketball tournament in New York, March 23, 2017. College hoops fans might want to think again before pinning their hopes of a perfect March Madness bracket on artificial intelligence.
Casual fans may spend a few days this week strategically deciding whether to maybe lean on the team with the best mojo — like Sister Jean’s 2018 Loyola-Chicago squad that made the Final Four — or to perhaps ride the hottest-shooting player — like Steph Curry and his breakout 2008 performance that led Davidson to the Sweet Sixteen.
For the 10th straight year, the data science community Kaggle is hosting “Machine Learning Madness.” Traditional bracket competitions are all-or-nothing; participants write one team’s name into each open slot. But “Machine Learning Madness” requires users to submit a percentage reflecting their confidence that a team will advance.
Ford, the Purdue fan who watched last year as the shortest Division I men’s team stunned his Boilermakers in the first round, takes it a different direction. Since 2020, Ford has tried to predict which schools will make the 68-team field. His model wrongly predicted that the 2023 men’s Final Four would include Arizona, Duke and Texas. But it did correctly include UConn. As he adjusts the model with another year’s worth of information, he acknowledged certain human elements that no computer could ever consider.
Chartier has studied brackets since 2009, developing a method that largely relies on home/away records, performance in the second half of the season and the strength of schedule. But he said the NCAA Tournament's historical results provide an unpredictable and small sample size — a challenge for machine learning models, which rely on large sample sizes.
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