Let’s get 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 gamble with any regularity, you are going to eliminate some of this moment.
But history suggests you could raise your odds of winning by utilizing the predictions systems available online.
KenPom and Sagarin are both??math-based ranks systems, which give 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 basketball. From the words of creator Ken Pomeroy, »[t]he purpose of the system would be to show how strong a group could be if it performed tonight, either independent of injuries or psychological factors. » Without going too far down the rabbit hole, his ranking system incorporates statistics like shooting percentage, margin of victory, and power of schedule, finally calculating offensive, defensive, and complete »efficiency » amounts for all teams at Division I. Higher-ranked teams have been called to beat lower-ranked teams on a neutral court. But the predictive part of the website — that you can effectively get without a subscription — additionally factors in home-court advantage, so KenPom will frequently predict that a lower-ranked team will win, based on where the game is played.
For basketball bettors, KenPom made a windfall in its younger days. It had been more accurate than the sportsbooks at predicting how a game would turn out and certain bettors captured on. Needless to say, it wasn’t long until the sportsbooks understood this and started using KenPom, themselves, when placing their chances.
Today, it is rare to find that a point spread which 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 ranks aim to do exactly the same factor as the KenPom ranks, but use another formula, one which doesn’t (seem to) variable in stats such as shooting percentage (though the algorithm is both 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 along with three unique spreads,??branded COMBO, ELO, and BLUE, which are predicated on three somewhat different calculations.
UPDATE: The Sagarin Ratings have experienced some changes. All the Sagarin predictions used as of this 2018-19 season would be the »Rating » predictions, which is the new variant of the »COMBO » predictions.
Often, the KenPom and also Sagarin predictions are tightly aligned, but on active school basketball times, bettors could almost always find a couple of games which have significantly different predicted results. When there is a substantial gap between the KenPom spread along with the Sagarin disperse, sportsbooks have a tendency to side with KenPom, however, often shade their lines??a little ?? from another direction.
For example, when Miami hosted Florida State on Jan. 7, 2018, KenPom needed a predicted spread of Miami -3.5, Sagarin needed a COMBO disperse of Miami -0.08, and the lineup at Bovada closed at Miami -2.5. (The game ended in a 80-74 Miami win/cover.)
We saw something similar for your Arizona State at Utah game on the exact same day. KenPom had ASU -2; Sagarin had ASU -5.4; along with the spread wound up being ASU -3.0. (The game ended in an 80-77 push.)
In a comparatively small (but increasing ) sample size, our experience is that the KenPom ranks are more accurate in these scenarios. We are tracking (mostly) power-conference games from the 2018 season where Sagarin and KenPom disagree on the predicted outcome.
The are supplied at the very bottom of this page. In brief, the results 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 fell somewhere between the KenPom and Sagarin predictions, KenPom was more accurate on 35?? of 62?? games.?? As a percent…
However, once the actual point spread was either higher or lower than the??KenPom and Sagarin forecasts, the actual spread was closer to the last results than both metrics about 35?? of 64?? games. As a percentage…
1 limitation of KenPom and Sagarin is that they don’t, generally, account for injuries. When a star player goes down, the calculations to get his group are not amended. KenPom and Sagarin both assume that the team carrying the ground tomorrow is going to be the same as the group that took the ground 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 factoring it into their chances they miss things from time to time, and they’ll not (immediately) have empirical proof that they may use to adjust the spread. They, for example bettors, will basically have to guess at how the loss of a celebrity player will impact his group, and they are sometimes not great at this.
From the very first game of the 2017-18 SEC conference program, afterward no. 5 Texas A&M has been traveling to Alabama to confront a 9-3 Crimson Tide team. The Aggies had been hit hard by the injury bug and’d recently played closer-than-expected games. Finally beginning to get somewhat healthier, they were small 1.5-point street favorites heading into Alabama. That disperse matched up with all the line at KenPom, that predicted that the 72-70 Texas A&M triumph.
At 16 or so hours before the game, word came that leading scorer DJ Hogg wouldn’t suit up, along with third-leading scorer Admon Gilder. It is unclear whether the spread was put before news of the Hogg accident, but it is apparent you may still get Alabama as a 1.5-point home underdog for some time after the news came out.
Eventually, the line was adjusted to a pick’em game which, to most onlookers, nevertheless undervalued Alabama and overvalued the decimated Aggies. (I put a $50 wager about the Tide and laughed all the way to your 79-57 Alabama win)
Another noteworthy example comes from the 2017-18 Notre Dame team. As soon as the Irish lost leading scorer Bonzie Colson late at 2017, sportsbooks initially shifted the spreads?? way too far 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, yet Notre Dame romped into some 30-point win.
When they went to Syracuse next time out, the KenPom lineup of ND -1 turned into a 6.5-point disperse in favor of the Orange. The Irish coated with convenience, winning 51-49 straight-up. Sportsbooks had?? no clue what the group was about to look like with no star and wound up overreacting. There was good reason to think the Irish would be substantially worse since Colson wasn’t only their leading scorer (by a wide margin) but also their top rebounder and just real interior presence.
However, there was also reason to think the Irish would be fine because??Mike Bray clubs are basically always?? ok.
Bettors won’t have to capitalize on situations such as these daily. But should you look closely at injury news and use the metrics accessible, you may have the ability to reap the rewards. Teams’ Twitter accounts are a fantastic way to keep track of injury information, as are game previews on neighborhood blogs. National websites like CBS Sports and ESPN don’t have the resources to cover all 353 teams closely.
For absolute transparency, here’s the list of results we monitored when comparing the accuracy of KenPom and also Sagarin versus the actual point-spread at Bovada and the final outcomes.
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