Let’s get one important part of information out of the way straight from the hop: there is no magic formula for winning all your college basketball wagers. If you gamble at any regularity, then you are going to get rid of some of the moment.
But history suggests that you can improve your odds of winning by using the predictions systems available online.
KenPom and also Sagarin are equally math-based rankings systems, which give a hierarchy for all 353 Division I basketball teams and also predict the margin of success for every match.
The KenPom rankings are highly influential when it comes to gambling on college basketball. In the words of founder Ken Pomeroy,”[t]he intention of the system would be to show how powerful a team could be if it performed tonight, either independent of accidents or psychological elements.” Without going too far down the rabbit hole, his position system incorporates data like shooting percentage, margin of victory, and strength of program, ultimately calculating offensive, defensive, and complete”performance” amounts for many teams at Division I. Higher-ranked teams are called to conquer lower-ranked teams on a neutral court. But the predictive part of the site — which you can effectively access without a subscription — additionally variables in home-court advantage, therefore KenPom will often predict a lower-ranked staff will win, based on where the match is played.
KenPom produced a windfall for basketball bettors. It was more precise than the sportsbooks at predicting the way the game would turn out and specific bettors captured on. Naturally, it was not long until the sportsbooks recognized this and began using KenPom, themselves, even when placing their chances.
These days, it is unusual to see a point spread that deviates from the KenPom forecasts by over a point or 2,?? unless?? there is a substantial injury or suspension at play. More on this later.
The Sagarin ranks aim to do exactly the identical thing as the KenPom ranks, but use a different formulation, one that doesn’t (seem to) factor in stats like shooting percent (although the algorithm is proprietary and, consequently, not entirely transparent).
The bottom of the Sagarin-rankings page (linked to above) lists the Division I basketball games for this day together with three unique spreads,??titled??COMBO, ELO, and BLUE, which can be based on three different calculations.
UPDATE: The Sagarin Ratings have undergone??some changes. All of the Sagarin predictions used as of this 2018-19 season will be the”Rating” forecasts, that’s the new variant of this”COMBO” forecasts.
Often, the KenPom and Sagarin predictions are tightly aligned, but on active school baseball times, bettors could almost always find one or two games that have substantially different predicted outcomes. When there’s a significant gap between the KenPom spread along with the Sagarin spread, sportsbooks have a tendency to side with KenPom, however frequently shade their traces a little ?? in the other direction.
For instance, when Miami hosted Florida State on Jan. 7, 2018, KenPom needed a predicted spread of Miami -3.5, Sagarin had a COMBO disperse of Miami -0.08, and the lineup at Bovada closed at Miami -2.5. (The match finished in an 80-74 Miami win/cover.)
We saw something similar for your Arizona State at Utah game on exactly the exact same day. KenPom’d ASU -2; Sagarin’d ASU -5.4; and the spread wound up being ASU -3.0. (The game ended in an 80-77 push.)
In a comparatively modest (but growing) sample size, our experience is that the KenPom positions are somewhat more accurate in such scenarios. We are currently tracking (mostly) power-conference games from the 2018 season where Sagarin and KenPom disagree on the predicted outcome.
The complete results/data are supplied at the exact bottom of the page. The outcomes were as follows:
On all games tracked,?? KenPom’s predicted outcome was nearer to the actual outcome than Sagarin on 71?? of 121?? games. As a percentage…
When the true point spread dropped somewhere in between the KenPom and Sagarin forecasts, KenPom was more accurate on 35?? of 62?? games.?? As a percent…
But once the actual point spread was higher or lower than the??KenPom and also Sagarin predictions, the actual spread was nearer to the last results than both metrics on 35?? of 64?? games. As a percent…
1 restriction of KenPom and Sagarin is they don’t, generally, account for harms. When a star player goes down, the calculations to get his team are not amended. KenPom and Sagarin both assume that the team taking the floor tomorrow will be just like the group that took the floor last week and last month.
That is not all bad news for bettors. Even though sportsbooks are extremely good at staying up-to-date with trauma news and turning it in their oddsthey miss things from time to time, and they’ll not (immediately) have empirical evidence that they may use to correct the spread. They, for example bettors, will basically have to guess at how the loss of a celebrity player will affect his group, and they’re not always good at this.
In the first game of this 2017-18 SEC conference program, afterward 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’d recently played closer-than-expected games. Finally beginning to get somewhat fitter, they were little 1.5-point road favorites going into Alabama. That spread matched up with all the line at KenPom, that predicted that the 72-70 Texas A&M win.
At 16 or so hours prior to the game, word came that leading scorer DJ Hogg wouldn’t suit up, along with third-leading scorer Admon Gilder. It’s uncertain if the spread was put before news of the Hogg accident, but it is apparent you may still get Alabama as a 1.5-point home underdog for some time after the news came out.
Finally, the point was corrected to a pick’em game that, to most onlookers, nevertheless 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 in the 2017-18 Notre Dame team. When the Irish dropped leading scorer Bonzie Colson late at 2017, sportsbooks initially shifted the spreads?? way a lot towards Notre Dame’s opponents, calling the apocalypse for the Irish. In their first match without Colson (against NC State), the KenPom prediction of ND -12 was shrunk in half, yet Notre Dame romped into a 30-point win.
When they moved to Syracuse second time outside, the KenPom lineup of ND -1 turned to some 6.5-point disperse in favor of the Orange. The Irish coated with convenience, winning 51-49 straight-up. Sportsbooks had?? no idea?? what the team was about to look like without its celebrity and ended up overreacting. There was good reason to think that the Irish would be substantially worse since Colson wasn’t only their top scorer (with a wide margin) but also their top rebounder and only real interior presence.
However, there was reason to believe the Irish will be fine because??Mike Bray clubs are basically always?? ok.
Bettors won’t get to capitalize on situations like these daily. But if you focus on harm news and apply the metrics available, you might be able to reap the benefits. Teams’ Twitter accounts are a fantastic method to keep an eye on injury information, as are game previews on nearby blogs. National websites like CBS Sports and ESPN don’t have the funds to pay all 353 teams carefully.
For absolute transparency, here’s the list of outcomes we monitored once comparing the truth of KenPom and Sagarin versus the actual point-spread at Bovada and the last results.

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