I made a little spreadsheet that I plan to update to look at the probabilities generated at kenpom.com for NCAA Tournament games. It includes the bracket shown in the pic below, a simulator for hypothetical games and an easy way to update it using summary data from kenpom. As for kenpom, Ken Pomeroy generates ratings for each team based on their offensive and defensive efficiency, adjusted for the caliber of their opponents, which can then be turned into probabilities of victory or final score estimates. Think of it as a more relevant version of the RPI.

You can download the spreadsheet here.

And here are the current results if the favorite wins in every match-up, with games color-coded in grades of green (clear favorite) through red (no favorite):

What’s most notable is that certain match-ups you wouldn’t expect to be close have evenly-matched teams. Clemson, despite playing in the play-in game, could be very dangerous for West Virginia. Belmont could be a difficult out for Wisconsin. In fact, once we get to the 5–12 matchups, the probability of the higher-seeded team winning drops off a cliff:

Also, the four play-in games were also not projected to be tremendously balanced, with one team having a probability of winning of 66‒74% in each match-up. Of the four, there was one “upset,” VCU over USC.

Estimates

kenpom includes estimates for scheduled games, which means I had to create an estimate as around half of all tourney match-ups are hypothetical at this point. His formula includes some adjustment for home and away games that I haven’t looked into, but since tourney games are all technically neutral-site, from what I gather the victory % estimate is:

Victory Probability

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def yourVictoryPct
  yourRating * (1 - opponentRating) /
    (yourRating + opponentRating - 2 * yourRating * opponentRating)
end

where rating is a value from 0 to 1.

Possessions

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def possessions
  yourTempo * opponentTempo / d1AverageTempo
end

where tempo is an estimate of possessions per game. d1Average indicates an average for all of Division I basketball.

Score

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def score
  yourOffense * opponentDefense * d1AverageOffense / 1000000 * possessions
end

where offense and defense are kenpom’s adjusted offensive and defensive efficiency ratings, and I adjust the losing team’s score such that it is no greater than the winning team’s score minus 1.