The following tables display our predicted point spreads and point totals for all 2017 first round games, separated by region. The play-in teams are marked with an * and will be updated as needed. As an example of how to interpret the tables, in the second row of the East Region table, our models have Wisconsin (-2.8) beating Virginia Tech by 2.8 points, and Vegas has Wisconsin (-5.5) beating Virginia Tech by 5.5 points. Positive numbers for both spread columns indicate that Team2 is favored. Additionally, our models have the total combined points scored in the Wisconsin-Virginia Tech game as 144.1 while Vegas has 137.
For more on the historical performance of our point spread and point total models, click here.
Continue reading March Madness – 1st Round Betting
The following tables display our predicted probabilities for all 2017 first round games, separated by region. The play-in teams are marked with an * and will be updated as needed. As an example of how to interpret the tables, in the first row of the East Region table, our models give Villanova a 97% chance of beating Mount Saint Mary’s (and, conversely, Mount Saint Mary’s a 3% chance of beating Villanova). We have more content on the way, including 1st round point spreads and totals, as well as a look at how our numbers see the entire tournament field.
For more on our methodology, click here.
Continue reading March Madness – 1st Round Probabilities
In addition to using our models to fill out your bracket, we also have models designed to predict point spreads (i.e., margin of victory) and point totals (i.e., how many total points will be scored) that can be used in comparison to the Vegas spreads/totals for each game.
Continue reading March Madness – 2017 Betting Preview
The following article provides a preview of what our predictions will look like for the upcoming 2017 NCAA tournament. For every game, the models produce a win probability for each team, which we use to fill out the bracket as a whole. Check out all of our predicted brackets dating back to 2001.
Continue reading March Madness – 2017 Bracket Preview
The 2016 tournament had good spots and bad – most notably Michigan State, whom my models ranked as the 1st/2nd best team in the field, but lost in their first game. Thankfully, there were some bright spots as well, led by Villanova, who was picked to win it all in only 2.5% of brackets submitted to ESPN, but my models had right up there with Michigan State as the 1st/2nd best team in the field. The sections below go into detail on some of the bigger hits and misses from the tournament as a whole, as well as the results for a few individual models.
Continue reading March Madness 2016 Recap
2016 was the first year that I used a model to predict point spreads, as my previous models focused on win probabilities (see this article for more details on my win probability models). Aside from using predicted point spreads to fill out a bracket, another use is for comparison to the lines in Vegas. For instance, if my predicted spread for a game favors a team by 10 points, but the Vegas spread has that team favored by only 2 points, the model indicates that team should cover the 2 point spread more often than not.
Including all tournament games, my spreads finished exactly 50% against the Vegas spread (ATS), but if we only consider games in which my spreads differed from Vegas’s by at least 4 points, they finished 15-10 ATS (60%), which means betting $110 on all 25 of those games would result in a profit of $400. The chart below compares my spread, Vegas’s spread, and the actual result for all of the second round games from 2016.
Continue reading March Madness Point Spread Model
Over the past three years, I have put together a series of statistical models that predict NCAA Tournament games, and, building off of each game, the tournament as a whole. The models started as an independent research project I did at St. Olaf College with Dr. Matt Richey. For a given game, the models use each team’s statistics and information from regular season games to predict (1) each team’s win probability for that game or (2) a point spread for that game. I use a handful of different models, so each game will have multiple probabilities/point spreads to consider, and will not always agree with each other.
Continue reading March Madness Modeling Background