A decade ago, Ed Feng was just a curious sports fan who wanted to win his bracket in March. He enjoyed books like Moneyball and Basketball on Paper but never put the ideas to use for winning a pool. That changed in 2008. After reading an academic paper on Google’s technology, Ed was inspired to apply his Stanford Ph.D. to the ranking of sports teams. From 2002 through 2018, his pre-tournament numbers predicted the winner of 71.2% of March Madness games (792-320).
Virginia Tech as a sleeper in the East region, and why you might still decide to go against the analytics in picking your bracket
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Does any favorite have an easier path to the Final Four? Which region might be the most interesting?
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Duke was the favorite to win the tournament. Then Zion destroyed his shoe and hurt his knee. How do we evaluate this talented group of freshmen?
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Live by the three, die by the three. The data behind whether to go against teams that fire up the threes.
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Kentucky
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To win larger pools, you need to go against the public and think contrarian.
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Gonzaga has the NBA level talent to compete with the best teams in the nation. But how does one account for their weak in-conference schedule?
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Tony Bennett has another terrific team in 2019. But does last year's catastrophic upset as a 1 seed to a 16 seed matter?
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John Beilein’s teams had traditionally featured brilliant offense. So why is this program now a defensive juggernaut, with questions about whether they have enough offense to make a deep run?
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If you get this one thing wrong, you might as well light your entry fee on fire...Pool Size.
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If you want to win your pool, analytics can help, especially with the most important decision of your bracket. Listen now to get ahead on your NCAA tournament bracket planning!
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