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 your college basketball wagers. If you gamble at any regularity, then you are likely to drop some of the moment.
But history indicates that you can raise your probability of winning by utilizing the forecasts systems available online.
KenPom and Sagarin are both??math-based ranks systems, which provide a hierarchy for many 353 Division I basketball teams and also predict the margin of victory for every single game.
The KenPom rankings are highly influential in regards to gambling on college basketball. From the words of founder Ken Pomeroy,”[t]he intention of the system is to demonstrate how powerful a team would be whether it performed tonight, independent of accidents or emotional things.” Without going too far down the rabbit hole, his position system incorporates data like shooting percent, margin of victory, and strength of program, finally calculating defensive, offensive, and total”performance” numbers for many teams in Division I. Higher-ranked teams have been predicted to beat lower-ranked teams on a neutral court. Nevertheless, the predictive portion of the site — which you can efficiently get without a membership ??– also factors in home-court benefit, therefore KenPom will frequently predict that a lower-ranked group will win, depending on where the game is played.
For basketball bettors, KenPom produced a windfall in its days. It was more precise than the sportsbooks at predicting how a game could turn out and certain bettors caught on. Of course, it wasn’t long until the sportsbooks understood this and began using KenPom, themselves, even when setting their odds.
Today, it is unusual to observe a point spread which deviates from the KenPom forecasts by over a point or 2,?? unless?? there is a significant injury or suspension . More on that later.
The Sagarin rankings aim to do the same matter as the KenPom ranks, but use another formula, one that doesn’t (seem to) factor in stats such as shooting percent (though the algorithm is proprietary and, consequently, not entirely transparent).
The base of the Sagarin-rankings page (linked to above) lists the Division I Football matches for that day together with three different spreads,??branded COMBO, ELO, and BLUE, which can be based on three different calculations.
UPDATE: The Sagarin Ratings have experienced some changes. All of the Sagarin predictions used as of the 2018-19 season would be the”Rating” predictions, that’s the newest version of the”COMBO” forecasts.
Frequently, the KenPom and also Sagarin predictions are carefully aligned, but on busy college basketball times, bettors could nearly always find one or two games which have substantially different predicted outcomes. If there is a substantial difference between the KenPom spread along with the Sagarin spread, sportsbooks have a tendency to side with KenPom, however, often shade their lines??somewhat in another direction.
For example, if Miami hosted Florida State on Jan. 7, 2018, KenPom needed a predicted spread of Miami -3.5, Sagarin needed a COMBO spread of Miami -0.08, along with the lineup in Bovada closed at Miami -2.5. (The game finished in an 80-74 Miami win/cover.)
We saw something like your Arizona State in Utah match on exactly the exact identical day. KenPom’d ASU -2; Sagarin’d ASU -5.4; along with the disperse wound up being ASU -3.0. (The game finished in an 80-77 push)
In a relatively modest (but growing) sample size, our experience is that the KenPom rankings are more accurate in such scenarios. We’re currently tracking (mostly) power-conference games in the 2018 season where Sagarin and KenPom differ on the predicted outcome.
The are supplied at the very bottom of the page. In brief, the outcomes were as follows:
On all games monitored,?? KenPom’s predicted outcome was closer to the true outcome than Sagarin on 71?? of 121?? games. As a percent…
When the actual point spread fell somewhere in between the KenPom and also Sagarin forecasts, KenPom was accurate on 35?? of 62?? games.?? As a percentage…
But when the true point spread was either higher or lower than the??KenPom and Sagarin predictions, the actual spread was closer to the last results than the two metrics on 35?? of 64?? games. As a percent…
1 limitation of KenPom and Sagarin is they do not, normally, accounts for injuries. If a star player goes down, the calculations to get his team aren’t amended. KenPom and Sagarin both assume that the group carrying the floor tomorrow will be just like the group that took the floor a week and a month.
That’s not all bad news for bettors. Even though sportsbooks are very good at staying up-to-date with harm news and factoring it into their oddsthey miss things from time to time, and they’ll not (immediately) have empirical proof which they may use to correct the spread. They, like bettors, will basically have to guess at how the lack of a star player will impact his group, and they’re not always good at this.
In the very first game of this 2017-18 SEC conference schedule, subsequently no. 5 Texas A&M was traveling to Alabama to confront a 9-3 Crimson Tide team. The Aggies was struck hard by the injury bug and’d lately played closer-than-expected games. Finally beginning to get somewhat healthier, they were little 1.5-point road favorites heading into Alabama. That disperse matched up with the lineup at KenPom, which called a 72-70 Texas A&M triumph.
At 16 or so hours before the game, word came down that leading scorer DJ Hogg would not match up, along with third-leading scorer Admon Gilder. It is uncertain whether the spread was put before news of this Hogg accident, but it’s apparent you can still get Alabama as a 1.5-point home underdog for a while after the news came out.
At some point, the line was corrected to a pick’em game that, to many onlookers, still undervalued Alabama and overvalued the decimated Aggies. (I put a $50 wager about the Tide and laughed all the way into a 79-57 Alabama win.)
Another noteworthy example comes from the 2017-18 Notre Dame team. Whenever the Irish dropped leading scorer Bonzie Colson overdue at 2017, sportsbooks initially shifted the spreads?? way a lot towards Notre Dame’s competitions, calling the apocalypse to the Irish. In their first match without Colson (against NC State), the KenPom prediction of ND -12 was slashed in half an hour, however Notre Dame romped to some 30-point win.
When they moved to Syracuse next time out, the KenPom lineup of ND -1 turned to some 6.5-point disperse in favour of the Orange. The Irish coated with convenience, winning 51-49 straight-up. Sportsbooks had?? no clue what the team was about to look like with no star and ended up overreacting. There was great reason to believe the Irish would be substantially worse since Colson wasn’t only their leading scorer (with a wide margin) but also their top rebounder and just real interior existence.
However, there was also reason to believe the Irish would be okay since Mike Bray teams are basically always?? alright.
Bettors won’t have to capitalize on situations like these daily. But if you pay attention to injury news and use the metrics accessible, you may have the ability to reap the rewards. Teams’ Twitter accounts are a good means to keep tabs on injury information, as are game previews on local blogs. National websites such as CBS Sports and ESPN do not have the resources to cover most of 353 teams closely.
For complete transparency, below is the set of results we monitored once comparing the truth of both KenPom and also Sagarin versus the true point-spread at Bovada along with the last results.

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