I made a little spreadsheet that I plan to update to look at the probabilities generated at "kenpom.com":kenpom 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":summary. 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":download.
And "here are the current results":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:
h4. Higher Seed Win %
|_. Match-up |_. Win% |
| #1 vs. #16 |>. 98% |
| #2 vs. #15 |>. 91% |
| #3 vs. #14 |>. 88% |
| #4 vs. #13 |>. 82% |
| #5 vs. #12 |>. 56% |
| #6 vs. #11 |>. 55% |
| #7 vs. #10 |>. 54% |
| #8 vs. #9 |>. 51% |
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.
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:
h4. Victory Probability
bc. yourVictoryPct = yourRating * (1 - opponentRating) /
(yourRating + opponentRating - 2 * yourRating * opponentRating)
where @rating@ is a value from 0 to 1.
bc. possessions = yourTempo * opponentTempo / d1AverageTempo
where @tempo@ is an estimate of possessions per game. @d1Average@ indicates an average for all of Division I basketball.
bc. score = yourOffense * opponentDefense * d1AverageOffense / 1000000 * possessions
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.