Which Advanced Metric Should Bettors Use: KenPom or Sagarin?

Let’s get one important part of information from the way right from the hop: there is no magic formula for winning all of your college basketball wagers. If you bet with any regularity, then you’re going to lose some of this moment.
But history suggests you could increase your odds of winning by using the forecasts systems available online.
KenPom and also Sagarin are equally math-based ranks systems, which offer a hierarchy for many 353 Division I basketball teams and also predict the margin of success for each game.
The KenPom ranks are highly influential in regards to gambling on college basketball. From the words of creator Ken Pomeroy,”[t]he intention of the system is to show how strong a team would be whether it played tonight, either independent of injuries or emotional factors.” Without going too far down the rabbit hole, his ranking system incorporates data like shooting percent, margin of victory, and power of program, finally calculating offensive, defensive, and general”efficiency” amounts for many teams at Division I. Higher-ranked teams have been called to conquer lower-ranked teams on a neutral court. Nevertheless, the predictive portion of the website — that you can effectively access without a membership ??– also factors in home-court advantage, so KenPom will frequently predict a lower-ranked team will win, based on where the game is played.
In its younger times, KenPom made 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 wasn’t long until the sportsbooks recognized this and started using KenPom, themselves, when placing their chances.
These days, it’s uncommon to observe a point spread that deviates from the KenPom predictions by more than a point or two,?? unless?? there’s a significant injury or suspension . More on that later.
The Sagarin rankings aim to do exactly the same matter as the KenPom rankings, but use another formula, one that does not (seem to) variable in stats like shooting percent (though the algorithm is proprietary and, consequently, not entirely transparent).
The base of the Sagarin-rankings webpage (related to above) lists the Division I Football matches for this day together with three distinct spreads,??branded COMBO, ELO, and BLUE, which are based on three different calculations.
UPDATE: The Sagarin Ratings have undergone??a few changes lately. All the Sagarin predictions used as of this 2018-19 season are the”Rating” predictions, which is the newest variant of this”COMBO” predictions.
Frequently, the KenPom and also Sagarin predictions are tightly coordinated, but on busy school basketball days, bettors can almost always find a couple of games that have considerably different predicted outcomes. If there is a significant gap between the KenPom spread and the Sagarin spread, sportsbooks tend to side with KenPom, but often shade their lines??somewhat in the other direction.
For instance, if Miami hosted Florida State on Jan. 7, 2018, KenPom needed a predicted spread of Miami -3.5, Sagarin needed a COMBO distribute of Miami -0.08, along with the line at Bovada closed at Miami -2.5. (The match finished in a 80-74 Miami win/cover.)
We saw something similar for the Arizona State at Utah match on exactly the exact identical day. KenPom had ASU -2; Sagarin had ASU -5.4; and the disperse wound up being ASU -3.0. (The game finished in an 80-77 push)
In a comparatively modest (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 in the 2018 year where Sagarin and KenPom disagree on the predicted result.
The entire results/data are supplied at the very bottom of the page. In Summary, the outcomes were as follows:
On all games monitored,?? KenPom’s predicted result was closer to the true results than Sagarin on 71?? of 121?? games. As a percent…
When the actual point spread dropped somewhere in between the KenPom and also Sagarin forecasts, KenPom was more accurate on 35?? of 62?? games.?? As a percent…
However, once the true point spread was either higher or lower than both the??KenPom and Sagarin predictions, the true spread was closer to the final outcome than the two metrics on 35?? of 64?? games. As a percent…
1 restriction of KenPom and Sagarin is they do not, generally, account for harms. After a star player goes down, the calculations to get his team aren’t amended. KenPom and Sagarin both presume that the team taking the ground tomorrow will be just like the team that took the ground last week and last month.
That is not all bad news for bettors. While sportsbooks are very good at staying up-to-date with injury news and devoting it in their odds, they miss things from time to time, and they will not (immediately) have empirical evidence which they may use to adjust the spread. They, like bettors, will basically have to guess how the loss of a superstar player will affect his group, and they’re not always great at this.
From the first game of this 2017-18 SEC conference schedule, then no. 5 Texas A&M was traveling to Alabama to face a 9-3 Crimson Tide team. The Aggies had been struck hard by the injury bug and’d lately played closer-than-expected games. Finally starting to get somewhat healthier, they have been small 1.5-point street favorites heading into Alabama. That spread matched up with the line at KenPom, that called a 72-70 Texas A&M win.
At least 16 or so hours before the match, word came down that top scorer DJ Hogg would not suit up, along with third-leading scorer Admon Gilder. It’s unclear whether the spread was set before information of this Hogg accident, but it is clear that you may still get Alabama as a 1.5-point house underdog for some time after the news came out.
At some point, the point was adjusted to a pick’em game which, to many onlookers, still undervalued Alabama and overvalued the decimated Aggies. (I put a $50 wager about the Tide and laughed all the way to a 79-57 Alabama win.)
Another notable example comes from the 2017-18 Notre Dame team. As soon as the Irish lost leading scorer Bonzie Colson late at 2017, sportsbooks initially altered the spreads?? way a lot towards Notre Dame’s opponents, predicting the apocalypse for the Irish. In their first match without Colson (against NC State), the KenPom prediction of ND -12 was slashed in half, nevertheless Notre Dame romped into a 30-point win.
When they moved to Syracuse second time out, the KenPom line of ND -1 turned to some 6.5-point spread in favor of the Orange. Again, the Irish covered with simplicity, winning 51-49 straight-up. Sportsbooks had?? no clue what the group was going to look like with no celebrity and ended up overreacting. There was great reason to believe that the Irish would be significantly worse because Colson wasn’t only their top scorer (with a wide margin) but also their leading rebounder and just real interior existence.
However, there was reason to think that the Irish will be fine because??Mike Bray teams are pretty much?? always?? ok.
Bettors will not get to capitalize on situations like these every day. But should you pay attention to injury news and use the metrics available, you might have the ability to reap the benefits. Teams’ Twitter accounts are a fantastic means to keep an eye on harm news, as are match previews on nearby sites. National sites such as CBS Sports and ESPN do not have the resources 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 actual point-spread in Bovada and the last outcomes.

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