Which Advanced Metric Should Bettors Use: KenPom or Sagarin?

Let’s get just one important item of advice out of the way straight from the hop: there is not any magic formula for winning all of your school basketball wagers. If you bet at any regularity, then you are going to lose some of this time.
But history suggests that you can raise your chances of winning by utilizing the predictions systems readily available online.
KenPom and Sagarin are both??math-based rankings systems, which offer a hierarchy for all 353 Division I basketball teams and forecast the margin of victory for each and each game.
The KenPom ranks are highly influential when it comes to betting on college soccer. From the words of creator Ken Pomeroy,”[t]he purpose of the system is to show how strong a group could be whether it played tonight, independent of accidents or psychological things.” Without going too far down the rabbit hole, his ranking system incorporates data like shooting percentage, margin of success, and power of schedule, ultimately calculating offensive, defensive, and total”performance” amounts for many teams in Division I. Higher-ranked teams have been predicted to beat lower-ranked teams on a neutral court. But the predictive portion of the site — that you can effectively get here without a membership ??– also variables in home-court benefit, therefore KenPom will often predict a lower-ranked group will win, depending on where the game is played.
For basketball bettors, KenPom produced a windfall in its times. It was more precise than the sportsbooks at predicting the way the game could turn out and certain bettors captured on. Obviously, it wasn’t long before the sportsbooks realized this and began using KenPom, themselves, when placing their odds.
These days, it is rare to find a point spread which deviates in the KenPom forecasts by over a point or two,?? unless?? there is a significant harm or suspension . More on this later.
The Sagarin positions aim to do the identical factor as the KenPom rankings, but use another formula, one that does not (appear to) variable in stats like shooting percent (although the algorithm is both proprietary and, consequently, not entirely transparent).
The base of the Sagarin-rankings webpage (linked to above) lists the Division I basketball games for that day along with three distinct spreads,??branded COMBO, ELO, and BLUE, which are predicated on three slightly different calculations.
UPDATE: The Sagarin Ratings have experienced some changes. All of the Sagarin predictions utilized as of this 2018-19 season will be the”Rating” forecasts, which is the new variant of this”COMBO” forecasts.
Frequently, both the KenPom and also Sagarin predictions are closely coordinated, but on active college baseball days, bettors can almost always find a couple of games that have significantly different predicted outcomes. If there is a substantial difference between the KenPom spread along with the Sagarin spread, sportsbooks tend to side with KenPom, however, often shade their traces a little ?? from the other direction.
For instance, if Miami hosted Florida State on Jan. 7, 2018, KenPom had a predicted spread of Miami -3.5, Sagarin had a COMBO distribute of Miami -0.08, and the line at Bovada closed at Miami -2.5. (The match ended in a 80-74 Miami win/cover.)
We saw something like the Arizona State in Utah match on precisely exactly the exact identical day. KenPom had ASU -2; Sagarin’d ASU -5.4; along with the spread wound up being ASU -3.0. (The game ended in an 80-77 push.)
In a relatively modest (but increasing ) sample size, our experience is that the KenPom rankings are more accurate in such situations. We are tracking (mostly) power-conference games in the 2018 season in which Sagarin and KenPom disagree on the predicted result.
The are provided at the bottom of this page. The outcomes were as follows:
On all games tracked,?? KenPom’s predicted result was nearer to the true outcome than Sagarin on 71?? of 121?? games. As a percent…
When the true point spread dropped somewhere in between the KenPom and Sagarin forecasts, KenPom was accurate on 35?? of 62?? games.?? As a percent…
However, once the actual point spread was either higher or lower than the??KenPom and also Sagarin forecasts, the true spread was closer to the final outcome than both metrics about 35?? of 64?? games. As a percent…
One limit of KenPom and Sagarin is they do not, generally, accounts for harms. After a star player goes down, the calculations to get his team aren’t amended. KenPom and Sagarin both assume that the team carrying the floor tomorrow will be the same as the team that took the ground last week and last month.
That’s not bad news for bettors. Even though sportsbooks are very good at staying up-to-date with trauma news and factoring it in their odds, they miss things from time to time, and they will not (immediately) have empirical proof that they can use to correct the spread. They, like bettors, will basically have to guess at how the loss of a superstar player will impact his group, and they’re not always good at this.
From the first game of the 2017-18 SEC convention program, then no. 5 Texas A&M has been traveling to Alabama to face a 9-3 Crimson Tide team. The Aggies was struck hard by the injury bug and had recently played closer-than-expected games. Finally starting to get a little fitter, they have been little 1.5-point street favorites heading into Alabama. That disperse matched up with all the lineup at KenPom, which predicted a 72-70 Texas A&M triumph.
At 16 or so hours before the match, word came that major scorer DJ Hogg wouldn’t match up, together with third-leading scorer Admon Gilder. It’s uncertain whether the spread was put before news of this Hogg injury, but it is apparent you could still get Alabama as a 1.5-point home underdog for a while after the news came out.
Eventually, the point was corrected to a select’em game which, to many onlookers, nonetheless undervalued Alabama and overvalued the decimated Aggies. (I put a $50 bet on the Tide and laughed all the way to your 79-57 Alabama win)
Another notable example comes from the 2017-18 Notre Dame team. When the Irish lost leading scorer Bonzie Colson overdue at 2017, sportsbooks initially shifted the spreads?? way a lot towards Notre Dame’s opponents, calling the apocalypse for the Irish. In their first game with no Colson (against NC State), the KenPom prediction of ND -12 was slashed in half, however Notre Dame romped to some 30-point win.
When they went to Syracuse second time outside, the KenPom lineup of ND -1 turned to some 6.5-point disperse in favor of the Orange. Again, the Irish covered with ease, winning 51-49 straight-up. Sportsbooks had?? no idea?? what the team was definitely going to look like with no star and ended up overreacting. There was good reason to believe that the Irish would be significantly worse because Colson was not only their top scorer (by a wide margin) but also their top rebounder and just real interior presence.
But, there was reason to think that the Irish would be okay because??Mike Bray teams are basically always?? ok.
Bettors won’t have to capitalize on situations like these every day. But should you pay attention to harm news and use the metrics accessible, you might be able to reap the rewards. Teams’ Twitter accounts are a fantastic method to keep track of injury information, as are match previews on neighborhood sites. National sites such as CBS Sports and ESPN do not have the funds to pay all 353 teams closely.
For absolute transparency, below is the list of results we tracked once comparing the truth of KenPom and also Sagarin versus the true point-spread in Bovada and the last results.

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