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On one hand, you want to pick good teams, the "favorites," because those teams seem more likely to win.
In my heart, I always know which teams will win, or at least which teams I want to root for. To find the best way to build our own brackets, we need to first build a mathematical model for simulating the tournament. Suppose we model the tournament by replacing basketball games with coin flips, except with coins that don't land evenly heads or tails but rather are weighted to reflect each game's actual odds. But there are plenty of other methods: Las Vegas betting odds give a point spread for each game, and based on those teams' scoring averages, you can convert the point spread into a probability of winning.
Further complicating the situation, the rules of your office or friends' pool probably mean that picking correctly in later-round games earns more points than early-round picks.
In one of the first analytic papers on this subject, Kaplan and Garstka gave an algorithm for deciding which picks are expected to score the highest.
This doesn't sound like something a human would do, and in fact it is best implemented by a computer. To this point our model is ignoring an important fact: the goal of picking your bracket is not to achieve a high score, but to win a pool against other people.
In a psychological experiment, McCrea and Hirt found evidence that pool participants pursue "probability matching": if a collection of games (say, the 5-12 matchups) has historically produced an upset one-third of the time, people will attempt to predict upsets in about one-third of those games in their brackets.
This brings us back to what makes this problem interesting: you need to pick teams that win, but not the same teams as everyone else a€“ so you come out on top in your pool. Let's assume people make their picks the same way we modeled the games, by flipping biased coins for each game in the bracket. Soon Americans nationwide will begin poring over NCAA men's basketball tournament brackets in their annual attempt at glory -- and maybe even a little cash -- in winning the ubiquitous, albeit illegal, office pool. That whirlwind of cubicle activity greeting office drones reporting to work this week is not a frenzy to finish last week's sales reports.

Each year, millions of people lose billions of dollars in NCAA March Madness basketball pools. A research team co-led by a scientist at New Zealand's University of Otago has sequenced the first complete mitochondrial genome of a 2500-year-old Phoenician dubbed the "Young Man of Byrsa" or "Ariche". This may sound like a familiar kind of riddle: How many brilliant mathematicians does it take to come up with and prove the Kelmans-Seymour Conjecture? Check out the Rafflecopter widget below, do as many of the Daemon LOVE activities as you can between now and November 29th at 11:59pm EST for a chance of winning Oblivion. On the other hand, you want to pick some weaker teams, the "underdogs," so your bracket will stand out from the rest and win the pool. As an academic, though, I'd rather squeeze all the fun out of it by overanalyzing the situation.
For example, when Baylor plays Yale on Thursday, instead of playing the game, we just flip a coin that gives the higher-seeded Baylor a greater chance of winning. Their method builds a list of 64 brackets backwards, round-by-round, starting each one with a different team as the winner.
The brackets produced tend to be "chalk" a€“ in which higher-ranked teams are most likely to win a€“ but do not always select the higher seed. In fact, people do no better than random chance at making such predictions, and so hurt their overall chances in the pool. Each year, large, free, Internet pools publish data on player behavior, and they publish it before your brackets are due on Thursday morning. The brackets produced tend to be very conservative in the first two rounds, include one or two surprises in the Final Four, and a strong but not heavily favored champion. Serious modeling and data analysis quail before the absurdity of predicting such a notoriously unpredictable event. We'd need to flip one of these coins for every first-round game, every potential second-round game, and for each possible matchup in the tournament.

For example, Duke's bracket starts with just Duke, and adds one round at a time, doubling in size but always keeping Duke as the winner.
And Kaplan and Garstka did observe that their algorithm did better than just automatically picking the high seeds. Last year, 48 percent of their players had selected prohibitive favorite Kentucky as champion. Nobody I know actually picks their bracket this way, but it turns out that real (human-picked) brackets and randomized brackets have nearly the same score distribution. After a decade of study, the only things we really know are that the tournament is madness and that your friend whose picks are based on mascots will probably win your pool. Basically, we want to find how much of a Daemon Black fan you are, and there are a bunch of ways to do that, like sharing your fave scene from the Lux Series or the reasons that you love Daemon on social media. Each coin must be weighted in a way that models the actual game, so its probabilities must be determined by the specific matchup.
Picking the correct champion is important, but if everyone else has the same opinion then you need to pick a bunch of other games well, too. According to the computers, these picks increase the chances of winning a big Internet pool by a factor of 100 to 1,000. And your probability of winning is very low indeed a€“ even with a boost from math and computer analytics.

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