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By Sascha Paruk
Updated:
Let’s get one important piece of information out of the way right from the jump: there is no magic formula for winning all your college basketball wagers. If you bet with any regularity, you are going to lose some of the time.
But history indicates that you can increase your chances of winning by utilizing the predictions systems available online.
KenPom and Sagarin are both math-based rankings systems, which provide a hierarchy for all 353 Division I basketball teams and predict the margin of victory for every game.
The KenPom ratings are highly influential when it comes to betting on college basketball. In the words of creator Ken Pomeroy, “[t]he purpose of this system is to show how strong a team would be if it played tonight, independent of injuries or emotional factors.” Without going too far down the rabbit hole, his ranking system incorporates statistics like shooting percentage, margin of victory, and strength of schedule, ultimately calculating offensive, defensive, and overall “efficiency” numbers for all teams in Division I. Higher-ranked teams are predicted to beat lower-ranked teams on a neutral court. But the predictive part of the site also factors in home-court advantage, so KenPom will frequently predict that a lower-ranked team will win, depending on where the game is played.
In its younger days, KenPom created a windfall for basketball bettors. It was more accurate than the sportsbooks at predicting how a game would turn out (particularly in terms of totals) and certain bettors caught on. Of course, it wasn’t long before the sportsbooks realized this and started using KenPom, themselves, when setting their odds.
These days, it’s uncommon to see a point spread at reputable college basketball betting sites that deviates from the KenPom predictions by more than a point or two, unless there’s a significant injury or suspension at play. More on that later.
The Sagarin rankings aim to do the same thing as the KenPom ratings, but use a different formula, one that doesn’t (appear to) factor in stats like shooting percentage (though the algorithm is proprietary and, thus, not entirely transparent).
The bottom of the Sagarin-rankings page (linked to above) lists the Division I basketball games for that day along with three different spreads, titled COMBO, ELO, and BLUE, which are based on three slightly different calculations.
UPDATE: The Sagarin Ratings have undergone some changes recently. All of the Sagarin predictions used as of the 2018-19 season are the “Rating” predictions, which is the new version of the “COMBO” predictions.
Often, the KenPom and Sagarin predictions are closely aligned, but on busy college basketball days, bettors can almost always find one or two games that have significantly different predicted outcomes. When there is a significant difference between the KenPom spread and the Sagarin spread, sportsbooks tend to side with KenPom, but often shade their lines a little in the other direction. For all the latest lines, be sure to check out this page that displays real-time college basketball odds.
For instance, when Miami hosted Florida State on Jan. 7, 2018, KenPom had a predicted spread of Miami -3.5, Sagarin had a COMBO spread of Miami -0.08, and the line at sportsbooks closed at Miami -2.5. (The game ended in an 80-74 Miami win/cover, for the record.)
We saw something similar for the Arizona State at Utah game on the same day. KenPom had ASU -2; Sagarin had ASU -5.4; and the spread wound up being ASU -3.0. (The game ended in an 80-77 push.)
In a relatively small (but growing) sample size, our experience is that the KenPom rankings are more accurate in these situations. We are currently tracking (mostly) power-conference games from the 2018-19 season in which Sagarin and KenPom differ on the predicted outcome.
The full results/data are provided at the very bottom of this page. In brief, the results were as follows:
On all games tracked, KenPom’s predicted outcome was closer to the actual outcome than Sagarin on 71 of 121 games. As a percentage …
When the actual point spread fell somewhere in between the KenPom and Sagarin predictions, KenPom was more accurate on 35 of 62 games. As a percentage …
However, when the actual point spread was either higher or lower than both the KenPom and Sagarin predictions, the actual spread was closer to the final outcome than both metrics on 35 of 64 games. As a percentage …
As mentioned, we are still looking at a small sample size, yet the advantage is significant and we can draw a couple of tentative conclusions:
One limitation of KenPom and Sagarin is that they do not, generally, account for injuries. When a star player goes down, the calculations for his team are not amended. KenPom and Sagarin both assume that the team taking the floor tomorrow will be the same as the team that took the floor last week and last month.
That’s not all bad news for bettors. While sportsbooks are very good at staying up-to-date with injury news and factoring it into their odds, they miss things from time to time, and they will not (immediately) have empirical evidence which they can use to adjust the spread. They, like bettors, will basically have to guess at how the loss of a star player will impact his team, and they are not always great at this.
In the first game of the 2017-18 SEC conference schedule, No. 5 Texas A&M was traveling to Alabama to face a 9-3 Crimson Tide team. The Aggies had been hit hard by the injury bug and had recently played some closer-than-expected games. Finally starting to get a little healthier, they were small 1.5-point road favorites heading into Alabama. That spread matched up with the line at KenPom, which predicted a 72-70 Texas A&M win.
At least 16 or so hours before the game, word came down that leading scorer DJ Hogg would not suit up, along with third-leading scorer Admon Gilder. It’s unclear if the spread was set before news of the Hogg injury, but it is clear that you could still get Alabama as a 1.5-point home underdog for a while after the news came out.
Eventually, the line was adjusted to a pick’em game which, to most onlookers, still undervalued Alabama and overvalued the decimated Aggies. (I personally put a $50 wager on the Tide and laughed all the way to a 79-57 Alabama win.)
Another notable example comes from the 2017-18 Notre Dame team. When the Irish lost leading scorer Bonzie Colson late in 2017, sportsbooks initially shifted the spreads way too far towards Notre Dame’s opponents, predicting the apocalypse for the Irish. In their first game without Colson (against NC State), the KenPom prediction of ND -12 was slashed in half, yet Notre Dame romped to a 30-point win.
When they went to Syracuse next time out, the KenPom line of ND -1 turned into a 6.5-point spread in favor of the Orange. Again, the Irish covered with ease, winning 51-49 straight-up. Sportsbooks had no idea what the team was going to look like without its star and wound up overreacting. There was good reason to think the Irish would be significantly worse since Colson was not only their leading scorer (by a wide margin) but also their leading rebounder and only real interior presence.
However, there was also reason to think the Irish would be okay because Mike Brey teams are pretty much always ok.
Bettors won’t get to capitalize on situations like these every day. But if you pay attention to injury news and use the metrics available, you may be able to reap the rewards. Teams’ Twitter accounts are a good way to keep track of injury news, as are game previews on local blogs. National sites like CBS Sports and ESPN don’t have the resources to cover all 353 teams closely.
For complete transparency, below is the list of results we tracked when comparing the accuracy of KenPom and Sagarin versus the actual point-spread at a certain online sportsbook and the final results.
Curious about other basketball ranking systems? Check out our in-depth guide to the NCAA Evaluation Tool, also known as NET rankings.
Want to learn more about the best strategies you can use to your advantage when you’re betting on college basketball? Check out the rest of our guides on sports betting strategy; we cover the best sports betting strategies that apply to everything oddsmakers dish out lines on!
Managing Editor
Sascha has been working in the sports-betting industry since 2014, and quickly paired his strong writing skills with a burgeoning knowledge of probability and statistics. He holds an undergraduate degree in linguistics and a Juris Doctor from the University of British Columbia.
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