NCAAM Model Recap

There are so many words that I could use to describe the entire experience I had with my NCAAM model. It was humbling to have nearly 1,000 people donate to Doctors Without Borders, raising $25,378 in the process. It was fulfilling to have the model finish 77-49-2 (60.94%) for +34.43 units (13.76% ROI) and provide an incredible return to those who contributed their money to a good cause. I decided to take a look at how donators fared given the day they contributed, here is a look at those splits:

  • If you donated on 2/12: 56-37-2 (60.00%) for +21.34u (11.52% ROI)
  • If you donated on 2/13: 55-36-2 (60.22%) for +22.53u (12.40% ROI)
  • If you donated on 2/27: 28-16-0 (63.64%) for +16.27u (19.74% ROI)
  • If you donated on 2/28: 25-14-0 (64.10%) for +15.29u (21.67% ROI)

Looking at the above splits from the perspective of a $100 unit bettor, donators had a profit yield that was 75 to 110 times larger than the $20 minimum donation. In other words, you would have to be a 25 cents per unit bettor to not have made enough profit to cover the minimum donation. You get the idea, so I’ll move on some of the model’s key performance splits.

Home/road and favorite/underdog splits

If you recall, the NFL model from last season performed best with underdogs and with road teams. Interestingly enough, the same was largely true with the NCAAM model. Here’s a full look at those splits:

Like with the NFL (and any sport), I attribute this to the psychological tendency for bettors (especially “square” bettors) to favor favorites and home teams. Because of this, oddsmakers typically shade their lines in order to capture more value on that action. The effect is very small on a game-to-game basis, but provides opportunity and value over the course of a season. Next lets take a look at when we combine home/road and favorite/underdog splits:

Obviously combining the larger edges on road and underdog teams into a singular road underdogs split very clearly demonstrates where the model found the most opportunity and success. Road underdogs accounted for 60% of all model plays and those plays hit at a 64.47% rate, generating a 25.55 unit profit (17.38% ROI). The other splits contain far too small of sample sizes to make any definitive statements, but are there for you to interpret any particular way you’d like.

Unit size splits

If there’s one split of the model’s performance that was most concerning, it would certainly be how the model performed as the disagreement level grew. One sign of a good model is that the model generates profit at a higher rate as the size of disagreement increases. Given that the model had a 13.76% ROI on all model plays but a 9.08% ROI on model plays that were two or more units is admittedly a bit concerning. The only counter I can offer is that flipping two or three of the results in that split from losses to wins does make that split more profitable than the overall ROI. Either way, it will obviously be something I keep an eye on for future seasons.

Frequently backed/opposed teams

There were certainly teams that the model deemed undervalued and overvalued and took repeated action on or against. Of the teams that the model backs three or more times (Binghamton, Columbia, New Hampshire, Notre Dame, Portland, San Diego State, and William & Mary), the model went 12-9 for +5.82 units. Of the teams that the model played against three or more times (Brown, Drake, Florida Atlantic, Fresno State, Louisville, Murray State, Saint Mary’s, Stony Brook, Utah State, VCU, Virginia, and Virginia Tech), the model went 29-21-1 for +9.80 units. I thought the positive results for both were a good sign for the model as it signals that the model is proficient on some level of identifying which teams are overvalued and undervalued in the market.

Conference splits

It goes without saying that these splits have way too small of sample sizes to make any definitive conclusions, but I included it just for fun and it was a split I got asked to include on several occasions. The Ivy League obviously stands out as the model’s best conference with a 6-1 record for +6.96 units. Outside of that, I thought the model’s performance with the Power 5 conferences would be interesting to look at given that those games get the most attention in terms of handle. The Big 12, SEC, and Pac 12 combined for a 5-0 record for +8.75 units whereas the Big 10 and ACC went a combined 9-9 for -1.99 units. 

Takeaways and closing thoughts

First and foremost, I think it’s safe to say that I should have put more time and attention into the NCAAM model. For the entire first month of sharing plays, I only checked lines once in the morning and that was it. It comes as no surprise that once I started checking lines more frequently, including looking at overnight lines, the model found more opportunity and more success.

In the past, I’ve typically been very hesitant to take any action any earlier than February. I don’t think I’d go as far to say that I would use the model for the non-conference play that starts the season, but I definitely think there is an opportunity to use the model in December and January.

All in all, I’m incredibly satisfied with what the NCAAM model accomplished. The profit it was able to generate was of course a great feat, but nothing I’ve done in my life comes close to the collective contribution we were able to generate for Doctors Without Borders. I don’t think there has been a day since that I haven’t thought about those two donation periods and the rush of happiness I experienced as donation after donation came in. Thank you all again for making that happen.

Until next time.

A Model-Recommended First Round Upset for Your Consideration

Every March, 68 of the 353 nation’s best college basketball teams travel around the country to compete in the most exciting opening round of any tournament in any sport. There will be more games played and as many teams sent home in the first round as there are in the rest of the entire tournament combined. Millions of brackets across the country are busted before the second round begins, and much of that comes to the chaotic nature of the March Madness tournament and the difficulty of predicting first round upsets. However, one upset candidate stands above the rest this year: #12 Oregon vs. #5 Wisconsin.

This matchup is definitely the juiciest upset candidate for several reasons. The first is that Oregon +1.5 is officially a model play, meaning that my numbers give validation to the upset potential. Looking deeper into the numbers, this game should be a very ugly and low-scoring affair. Oregon ranks 18th in KenPom defensive efficiency while Wisconsin ranks 3rd. Offensive sets will be largely ineffective, as Oregon ranks in the 91st percentile in non-transition effective field goal percentage allowed whereas Wisconsin ranks in the 99th percentile. However, in the transition game, Oregon certainly has the advantage with their 71st percentile transition eFG% compared to Wisconsin’s 47th percentile ranking. Given that Oregon ranks above average in percentage of shots taken in transition whereas Wisconsin could literally not rank any worse, this is definitely an area where Oregon can exploit an edge.

Transition eFG%

% of Shots in Transition


That isn’t the only place where Oregon can find some wiggle room. Wisconsin’s struggles at the free throw line are well-documented, but it really would be a disservice on my part if I didn’t touch on it briefly. Wisconsin as a team is shooting 64.4% at the charity stripe. To put into perspective how awful that is, there are only 23 other teams in all of college basketball who sport a worse percentage. Wisconsin also ranks outside the top 300 in offensive rebound percentage. Considering we are expecting a low-scoring environment, getting the most out of your free throw opportunities as well as generating second-chance opportunities on the glass become even more valuable. Oregon happens to rank 133rd in free throw percentage and 123rd in offensive rebound percentage, meaning these are yet a few more areas where Oregon can exploit some matchup edges.

Another reason why Oregon is a fantastic upset candidate is that they provide great contrarian value. As someone who lives in Wisconsin and went to UW Madison, I tend to always try to take a contrarian stance against the deep Wisconsin runs that are littered all throughout my bracket pools. This admittedly worked against me from 2014 to 2017 as the Badgers fielded some incredible tournament teams. Nevertheless, a combination of Wisconsin’s name recognition value and their recent performance in tournaments (outside of their absence last year) as well as the large gap in seeds seems to have enamored the vast majority of the public. As of the time of this writing, Oregon is only being chosen to win this first round matchup by 37% of all CBS users. This comes despite the fact that neither team has been favored by more than two points since the line has opened. That to me, along with all of the considerations mentioned above, sounds like a great way to generate some value by backing an Oregon upset.