Which Advanced Metric Should Bettors Use: KenPom or Sagarin?

Let’s get just one important part of advice from the way straight from the jump: there is not any magic formula for winning all of your school basketball wagers. If you bet at any regularity, then you’re going to lose some of the time.
But history suggests that you can increase your chances of winning by using the forecasts systems readily available online.
KenPom and also Sagarin are equally math-based rankings systems, which give a hierarchy for many 353 Division I basketball teams and also predict the margin of victory for every match.
The KenPom rankings are highly influential when it comes to betting on college soccer. From the words of creator Ken Pomeroy,”[t]he intention of the system is to demonstrate how powerful a team would be if it played tonight, independent of injuries or psychological factors.” Without going too far down the rabbit hole, his ranking system incorporates statistics like shooting percent, margin of victory, and strength of schedule, ultimately calculating defensive, offensive, and complete”performance” numbers for many teams at Division I. Higher-ranked teams are called to beat lower-ranked teams on a neutral court. But the predictive part of the website — that you can effectively access without a membership ??– also variables in home-court advantage, so KenPom will often predict a lower-ranked group will win, based on where the match is played.
KenPom made a windfall. It had been more precise than the sportsbooks at predicting how a game could turn out and certain bettors caught on. Naturally, it wasn’t long until the sportsbooks realized this and started using KenPom, themselves, when placing their odds.
Today, it’s uncommon to observe a point spread that deviates in the KenPom predictions by over a point or two,?? unless?? there’s a significant harm or suspension at play. More on this later.
The Sagarin rankings aim to do exactly the identical thing as the KenPom ranks, but use another formulation, one that doesn’t (appear to) factor in stats like shooting percentage (though the algorithm is both proprietary and, consequently, not entirely transparent).
The bottom of the Sagarin-rankings webpage (related to above) lists the Division I basketball games for this day together with three different spreads,??titled??COMBO, ELO, and BLUE, which can be predicated on three somewhat different calculations.
UPDATE: The Sagarin Ratings have experienced some changes lately. All the Sagarin predictions used as of the 2018-19 season are the”Rating” predictions, that’s the new version of this”COMBO” forecasts.
Often, both the KenPom and also Sagarin predictions are tightly coordinated, but on busy college baseball days, bettors can almost always find one or two games which have significantly different predicted outcomes. If there’s a substantial difference between the KenPom spread and the Sagarin disperse, sportsbooks have a tendency to side with KenPom, however often shade their lines??somewhat from another direction.
For instance, when Miami hosted Florida State on Jan. 7, 2018, KenPom had a predicted spread of Miami -3.5, Sagarin needed a COMBO disperse of Miami -0.08, along with the line in Bovada closed at Miami -2.5. (The game finished in an 80-74 Miami win/cover.)
We saw something similar for your Arizona State in Utah game on exactly the exact same day. KenPom had ASU -2; Sagarin’d ASU -5.4; and the disperse wound up being ASU -3.0. (The game ended in an 80-77 push.)
In a relatively modest (but growing) sample size, our experience is the KenPom ranks are somewhat more accurate in these situations. We’re currently tracking (largely ) power-conference games from the 2018 season where Sagarin and KenPom disagree on the predicted result.
The complete results/data are provided at the exact bottom of the page. In brief, the outcomes were as follows:
On all games tracked,?? KenPom’s predicted result was closer to the actual results 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 accurate on 35?? of 62?? games.?? As a percentage…
But when the true point spread was higher or lower than the??KenPom and also Sagarin predictions, the true spread was nearer to the final results than the two metrics about 35?? of 64?? games. As a percentage…
One limitation of KenPom and Sagarin is that they do not, generally, account for injuries. When a star player goes down, the calculations because of his group aren’t amended. KenPom and Sagarin both assume that the team taking the floor tomorrow is going to be the same as the team that took the floor last week and a month.
That’s not all bad news for bettors. Even though sportsbooks are very good at staying up-to-date with injury news and devoting it in their chances , they miss things from time to time, and they will not (immediately) have empirical evidence that they may use to adjust the spread. They, for example bettors, will essentially have to guess at how the lack of a superstar player will impact his team, and they are not always great at this.
From the very first game of this 2017-18 SEC conference program, afterward no. 5 Texas A&M has been traveling to Alabama to face a 9-3 Crimson Tide team. The Aggies had been hit hard by the injury bug and had lately played some closer-than-expected games. Finally starting to get a little fitter, they had been small 1.5-point road favorites going into Alabama. That spread matched up with the line at KenPom, that predicted that the 72-70 Texas A&M triumph.
At least 16 or so hours prior to the match, word came that leading scorer DJ Hogg would not match up, together with third-leading scorer Admon Gilder. It’s uncertain if the spread was set before information of the Hogg injury, but it’s clear that you can still get Alabama as a 1.5-point home underdog for a while after the information came out.
Eventually, the point was corrected to a pick’em game which, to many onlookers, nevertheless undervalued Alabama and overvalued the decimated Aggies. (I put a $50 wager on the Tide and laughed all the way into your 79-57 Alabama win.)
Another notable example comes from the 2017-18 Notre Dame team. Whenever the Irish dropped leading scorer Bonzie Colson late at 2017, sportsbooks initially altered the spreads?? way a lot towards Notre Dame’s competitions, predicting the apocalypse for the Irish. In their first game without Colson (against NC State), the KenPom prediction of ND -12 was shrunk in half an hour, nevertheless Notre Dame romped into some 30-point win.
When they moved to Syracuse second time out, the KenPom lineup of ND -1 turned into a 6.5-point disperse in favor of the Orange. The Irish covered with simplicity, winning 51-49 straight-up. Sportsbooks had?? no clue what the team was about to look like without its star and ended up overreacting. There was good reason to believe the Irish would be considerably worse because Colson wasn’t only their top scorer (with a wide margin) but also their leading rebounder and just real interior existence.
But, there was reason to believe that the Irish would be fine since Mike Bray clubs are pretty much?? always?? okay.
Bettors will not get to capitalize on situations like these daily. But if you look closely at harm news and use the metrics accessible, you might be able to reap the rewards. Teams’ Twitter accounts are a good way to keep track of injury news, as are game previews on nearby sites. National sites such as CBS Sports and ESPN do not have the resources to pay most of 353 teams carefully.
For complete transparency, here’s the set of results we tracked once comparing the truth of both KenPom and Sagarin versus the actual point-spread in Bovada and the final outcomes.

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