Which Advanced Metric Should Bettors Use: KenPom or Sagarin?

Let’s get one important bit of advice out of the way straight from the jump: there is not any magic formula for winning all of your college basketball wagers. If you bet with any regularity, you’re going to get rid of some of the time.
But history suggests that you can boost your chances of winning by utilizing the forecasts systems available online.
KenPom and also Sagarin are both??math-based rankings systems, which give a hierarchy for many 353 Division I basketball teams and also predict the margin of victory for each match.
The KenPom ranks are highly influential in regards to betting on college soccer. In the words of founder Ken Pomeroy,”[t]he purpose of this system would be to demonstrate how strong a group would be whether it played tonight, independent of accidents or emotional factors.” Without going too far down the rabbit hole, his ranking system incorporates statistics like shooting percentage, margin of success, and strength of schedule, finally calculating offensive, defensive, and general”performance” numbers for all teams in Division I. Higher-ranked teams are predicted to beat lower-ranked teams on a neutral court. Nevertheless, the predictive area of the site — which you can efficiently get here without a subscription — additionally variables in home-court advantage, therefore KenPom will often predict that a lower-ranked group will win, depending on where the game is played.
KenPom made a windfall. It was more precise than the sportsbooks at forecasting how a game could turn out and particular bettors captured on. Of course, it was not long before the sportsbooks realized this and began using KenPom, themselves, when setting their odds.
These days, it’s rare to observe a point spread that deviates in the KenPom predictions by over a point or 2,?? unless?? there is a significant harm or suspension . More on that later.
The Sagarin rankings aim to do the same thing as the KenPom rankings, but use another formula, one which does not (appear to) factor in stats like shooting percent (though the algorithm is proprietary and, thus, not completely translucent ).
The bottom of the Sagarin-rankings page (linked to above) lists the Division I basketball matches for that day together with three unique ranges,??titled??COMBO, ELO, and BLUE, which can be predicated on three slightly different calculations.
UPDATE: The Sagarin Ratings have undergone??a few changes lately. All the Sagarin predictions utilized as of the 2018-19 season would be the”Rating” predictions, that’s the new version of this”COMBO” predictions.
Often, both the KenPom and also Sagarin predictions are closely coordinated, but on active college baseball times, bettors can almost always find one or two games that have substantially different predicted outcomes. When there is a significant difference between the KenPom spread and the Sagarin disperse, sportsbooks tend to side with KenPom, but frequently shade their traces a little ?? from the other direction.
For instance, when Miami hosted Florida State on Jan. 7, 2018, KenPom had a predicted spread of Miami -3.5, Sagarin had a COMBO disperse of Miami -0.08, along with the line at Bovada closed at Miami -2.5. (The match finished in an 80-74 Miami win/cover.)
We saw something like your Arizona State at Utah game on the identical day. KenPom’d 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 comparatively modest (but growing) sample size, our experience is the KenPom positions are more accurate in these situations. We are currently tracking (largely ) power-conference games in the 2018 period in which Sagarin and KenPom disagree on the predicted result.
The full results/data are provided at the bottom of the page. In brief, the results 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 percentage…
When the actual point spread dropped somewhere between the KenPom and also Sagarin forecasts, KenPom was accurate on 35?? of 62?? games.?? As a percentage…
However, when the actual point spread was higher or lower than both the??KenPom and also Sagarin predictions, the actual spread was closer to the last outcome than the two metrics on 35?? of 64?? games. As a percent…
1 restriction of KenPom and also Sagarin is they don’t, generally, account for harms. If a star player goes down, the calculations for his group are not amended. KenPom and Sagarin both presume that the team taking the ground tomorrow is going to be just like the team that took the floor a week and last month.
That is not bad news for bettors. While sportsbooks are extremely good at staying up-to-date with trauma news and turning 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 basically have to guess at how the lack of a celebrity player will affect his team, and they are sometimes not good at this.
In the first game of the 2017-18 SEC convention schedule, afterward no. 5 Texas A&M has been traveling to Alabama to confront a 9-3 Crimson Tide team. The Aggies had been struck hard by the injury bug and’d recently played some closer-than-expected games. Finally beginning to get a little fitter, they had been little 1.5-point street favorites going into Alabama. That spread matched up with all the lineup at KenPom, that predicted a 72-70 Texas A&M triumph.
At least 16 or so hours before the game, word came that major scorer DJ Hogg wouldn’t suit up, along with third-leading scorer Admon Gilder. It is unclear whether the spread was set before information of this Hogg injury, but it’s clear that you could still get Alabama as a 1.5-point house underdog for some time after the news came out.
At some point, the line was corrected to a pick’em game that, to most onlookers, nevertheless undervalued Alabama and overvalued the decimated Aggies. (I put a $50 wager on the Tide and laughed all the way to your 79-57 Alabama win)
Another notable example comes in the 2017-18 Notre Dame team. As soon as the Irish dropped leading scorer Bonzie Colson overdue in 2017, sportsbooks initially altered the spreads?? way a lot towards Notre Dame’s competitions, predicting the apocalypse for the Irish. In their first game with no Colson (against NC State), the KenPom forecast of ND -12 was slashed in half an hour, nevertheless Notre Dame romped to a 30-point win.
When they moved to Syracuse next time out, the KenPom lineup of ND -1 turned to some 6.5-point spread in favor of the Orange. Again, the Irish covered with ease, winning 51-49 straight-up. Sportsbooks had?? no clue what the team was about to look like with no celebrity and ended up overreacting. There was good reason to believe that the Irish would be considerably worse because Colson was not only their top scorer (by a wide margin) but also their top rebounder and just real interior existence.
However, there was also reason to believe that the Irish will be okay because??Mike Bray teams are pretty much?? always?? okay.
Bettors will not get to capitalize on situations such as these every day. But if you focus on injury news and use the metrics accessible, you may be able to reap the benefits. Teams’ Twitter accounts are a good method to keep an eye on injury information, as are match previews on local blogs. National sites like CBS Sports and ESPN do not have the resources to pay most of 353 teams closely.
For absolute transparency, below is the set of outcomes we monitored when comparing the accuracy of both KenPom and Sagarin versus the actual point-spread at Bovada and the last results.

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