Group discussion: Handicapping NCAA Hoops

[Woodson]

Another year of college hoops is approaching. Just like a child preparing to go back to school, one must prepare for College Hoops wagering. This thread will be a an open invitation to discuss handicapping methodology as well as tools used to identify potentially high winning percentage sides and totals. Group discussion is priceless and the goal is to assist one another in continuing to sharpen skills and fatten wallets.

Some of these informational pieces were taken from the internet, google searches, or personally written.

I ask that the thread remain on the subject of methodology and practices only.

More to come...

 

[Woodson]

Understand the teams before wagering. The Blue Ribbon Yearbook is $22 plus shipping. It outlines each team and is used by all serious college hoops analysts. Knowledge is key. Understanding SOS (strength of Schedule) as well as Power Rankings and you are well on your way to a profitable season. Use the Pomeroy Ratings to your advantage as well.

Tools and Guides

1. Blue Ribbon Yearbook
2. Understanding SOS and PR
3. Using the Pomeroy Ratings

 

[bleedingpurple]

What was the system that was great in the second half of last year? Something like take the team who lost by 18 points or more in the first meeting between same two teams..

 

[Woodson]

Line movements against a high percentage public play
If you see 93% on a side and the line moves +1 point in the other direction, it constitutes a no play on that side or a half unit bet against the public immediately. You will never go broke playing this system...

 

[Woodson]

What was the system that was great in the second half of last year? Something like take the team who lost by 18 points or more in the first meeting between same two teams..

College Hoops Prospectus (a must read for the college hoops fan) has a stat about conference games where the road team is blown out (18+) and still has a rematch left at home. The SU win% is over 55% and that doesn't take into account the spread.

 

[Woodson]

When two teams meet for a 3rd Game with Team A winning both previously

When two teams meet for a 3rd Game with Team A winning both previously

Taking Bleedingpurple's point a step further, and quoting Nickelback from a couple years ago.

When two teams meet for a third game with team A winning the first two meetings, the play is on Team B.

Many of you may already be familiar with this "rule" that comes around this time of the year:

If a team faces a "roughly" similar opponent that they have lost to the first two times they have met them in a given year, the third time is usually the charm. Its very difficult to beat a team three straight times in one season unless they are double digit dogs every single time you play them. My favorite example is Michigan/Illinois in 1989 when Michigan played a better Illinois team in the Final Four for the third time that year and won despite losing both conference games and won the National Championship two days later.

The above is an example, there have been other examples more recently but for the sake of discussion, I'm not updating the examples.

 

[Woodson]

Handicapping a Game: The Breakdown

Handicapping a Game: The Breakdown

Taken last year from the internet


REWIND: XAVIER @ DAYTON (Feb 11)

(I chose Xavier to cover -3, they lost by 13)

1. Pulled down the lines from Greek, Bookmaker and Bodog (my three books) and compared it to what the Sagarin Predictor model showed for the game. Predictor showed Xavier winning by four, even with Dayton having home-court advantage. Edge: Xavier

2. Analayzed the schedules of each team and asked four questions:

1. How many times had Xavier beaten a team of Dayton's caliber by four points
2. How many times did a team of Dayton's caliber finish a game within four points of Xavier
3. How many times had Dayton NOT lost by more than four points (either at home or on the road) to a team of Xavier's caliber and
4. How many times did Dayton play a very tight game against a team much worse than Xavier? To answer these questions, I used the Sagarin ratings of every team that Xavier and Dayton had faced. Dayton was ranked 60 in Sagarin. Xavier was ranked 13. The results: Xavier = 3, Dayton = 4.
Edge: Dayton
​

3. Then I flipped over to Statfox and analyzed ATS trends in these categories: home/away (as applicable to this game), Tuesday nights, versus conference opponents, versus teams with a winning record, versus good defensive teams giving up < 64 ppg, February games, and last five games. The results yielded these ATS win percentages for each team in the above categories: Xavier 35-17 (67%), Dayton 21-33 (38%). Edge: Xavier

4. Then I looked at strength of schedule. Dayton's strength of schedule was around 260, Xavier's was around 48. Against teams ranked in the Sagarin top 50, Dayton was 1-0 and Xavier was 4-2. Dayton had only faced one team in the Sagarin top 50, so they were actually an unproven team. Clearly Xavier had much more experience facing solid teams in a nationally televised primetime game (remember the LSU game in Baton Rouge?). Edge: Xavier

5. Then I looked at how both teams performed in their most recent game. Xavier had lost on the road to Duquesne by four points, which was actually due to Duquesne shooting a FREAKISH 81% in the first half. Meanwhile, Dayton got blown out by a much poorer Charlotte team. Edge: Xavier

6. Then I averaged the PF/PA for both teams in their last five games only. I compared these results with the strength of schedule of those five teams (using Sagarin). The PF/PA breakdown showed the teams at dead even, HOWEVER, the SOS of Dayton's previous five opponents was only 135 whereas for Xavier it was 101. Edge: Xavier

7. Then I read the Yahoo Team Reports for both teams in order to pick up on rumors, illness, grade issues, etc...I learned that Xavier's freshmen point guard could have problems against Dayton's full-court press. I leaned that Xavier's 7" tall Freeze was having ankle issues. I also learned that Dayton was concerned about not being able to get inside the paint against Xavier's superior size, especially since Dayton's FG% had been poor in their recent games. I learned that Dayton was 0-6 vs Xavier in their previous 6 matchups and that Xavier's average MOV in those games was more than 14 points. Edge: even, leaning Xavier

I read all of these things and figured: which of these teams has the edge, which is more "proven"?

Xavier -3

Then I cracked a couple of beers, got comfortable, and watched Dayton destroy my play and research. That's gambling! You use as much research as you can, place the best bet you can, and then hope to win 60% of the time. When you lose, at least you can look at yourself in the mirror and know that you did your homework and didn't fall for a sucker play. The key is money management. Money Management means you will always come out on top in the long run!

 

[Woodson]

Totals: Understanding how they are derived

Totals: Understanding how they are derived

Taken from another site recently regarding understanding how to analyze totals:

The #1 key stats are adjusted tempo, adj defensive, and adj offensive.

Tempo is HUGE for totals... emphasis on possessions per game.

Adj Offensive eff is points per possession, with the opponents defensive rankings adjusted into it.

Adj Defensive eff is points given up per possession with the opponents offensive rankings adjusted into it.

For example, looking at last years stats... heres a basic of how you can come up with a total:

Lets say VMI played UNC

VMI tempo: 80.9 possessions per game
UNC tempo: 73.9 possessions per game

I would say there is about 78 possessions in this game.

VMI's offfensive: 106.3, or 1.063 points per possession
UNC Defense: 89.6 or 0.896 points per possession.

Based on 78 possessions and an avg of .975 ppp, VMI scores 76 points.

UNC offense: 124.2 or 1.242 points per possession
VMI defense: 108.2 or 1.082 points per possession

UNC scores: 91

The projected score in a neutral court would be 91-76 UNC

Bad example obviously as UNC would have likely put up 100+, but a good starting point.

I also wanna say that the totals will be very sharp once February comes... Using kenpom's stats will be MOST successful for totals in December and January.

There are other ways where using kenpom's stats could be huge in picking sides. The keys will be looking at what defense the teams play, and looking at each others offensive strengths. What teams are MOST reliant on 3's and what teams force the most 3's.

The teams that give up the most % of 3's will play a heavy zone.

http://kenpom.com/tmleaders.php?c=OppF3GRate

As you can tell by this, its quite obvious without even watching these teams that SIU-Edwardsville and Drexel both played tight man-to-man, while Chattanooga and Air Force both played zone.

You just have to look for situations where a great 3-point shooting team thats reliant on is playing a team that gives up a lot of 3-point attempts.

AN example from 2008-2009 was Wisconsin Green Bay against Valparaiso.

Valpo played a zone defense, and gave up the 320th most 3-pointers, at almost 40% of all attempts.

Wisconsin Green Bay took 36% of there shots behind 3 and hit 40% of them, so they would be considered a solid 3-point dependent and reliable team.

Bad matchup for Valparaiso.

Wisconsin Green Bay won both meetings... 83-76 and 76-61.

Wisc-GB covered both and both games went well over the total.

You just have to use the stats given smartly.

 

[bleedingpurple]

http://www.blueribbonyearbookonline.com/

http://kenpom.com/rate.php

Sorry if I can't post these sites.. Thought it would help

 

[axp59]

Invaluable thread Woodson. While CBB isn't my forte, I can impart with a basic approach to college action.
Pick a conference. Hopefully one that can hold your interest. There are two sides to this coin. The major conferences will have an abundance of information but will also capture the focus of those setting the numbers not to mention all the other action seekers. The lesser conferences will generate value but information will require some digging. The dynamics of the high profile conferences and the lesser conferences make up the economy of the board. The action on the big names yields value for those lesser known. For those interested on this subject, I'm sure I have a post or two from the past.
I personally don't mind doing the research. Find that obscure fan site. There you'll find valuable information like how some players still aren't feeling well after eating the pizza on the team bus on the ride home from the last game. How a starting player isn't in a great mood since being busted cheating on his girlfriend.
As Woodson has already mentioned, knowledge is key. Forget about finding that blockbuster inside information that will make your action a lock. There isn't and will never be any lock. Find that little tidbit that levels the field to your advantage and overcomes the juice. To make money, you merely have to break the hump from 52.38% (even) to 52.39% (profit).

Best of luck.

Thanks for the welcome back Woodson. It was a great summer.

 

[Woodson]

Author: Tom Stryker - Friday, February 02, 2007

A few weeks ago, I wrote an NBA article telling you about how good teams have a tendency to bounce back after a bad performance. When I applied a similar theory to the college ranks, I expected profitable results. Well, I got some great numbers but was extremely surprised with my discovery. Let's take a look at a college basketball system that I call "Erase This Bad Chalk."

The concept for this technical situation is really quite simple. There are so many teams that hit the road favored and lay an egg. I wanted to see how these teams responded in a typical bounce-back situation especially when they're at home. Here's what I found:

Play AGAINST any college basketball home favorite of -7 or more provided they lost straight up priced as a road favorite of -7 or more last.

Record Since 1990 - 77-41-2 ATS for 65.2 percent

The knee-jerk reaction would be to take this piece of home chalk coming off an embarrassing road loss. But, as you can see from the results, that's exactly what you DON'T want to do! There are a few parameters that work within this system to really make it pop.

If our host is an elite team and carries a won/loss percentage of .700 or better, this situation for our road team improves to a blistering 56-24 ATS for 70.0 percent. That goes against the norm too. One would immediately think that a good college basketball team would respond after a crushing loss but they don't! They lay another egg!

Momentum plays a big part in college basketball handicapping and we can make this system even better if our guest enters off a straight up win of four points or more. In this set, the record of this angle tightens up to a magnificent 32-5 ATS for 86.4 percent! Now that is making money gentlemen!

There is one final parameter that I can add to this system that will make it hit perfection. With our 32-5 ATS in hand, this technical situation zips to a SPOTLESS 21-0-1 ATS for 100% provided our home team is at game 17 of the season or later. By eliminating the early non-conference action and placing our host in competitive conference play we've tightened this system up beautifully and got a jaw-dropping result.

Based on Wednesday's results, there is one potential play on Saturday that would fit the general 77-41-2 ATS situation. Provided Xavier is favored by -7 or more against Charlotte, the Musketeers will be locked into this "play against" situation. Be sure to keep an eye on this technical system over the next few weeks. I guarantee you the 21-0-1 ATS system will pop shortly.

 

[Woodson]

College Basketball AOPR Betting System

Long before college football used the BCS ratings and its now famous Strength of Schedule factor, sports bettors were doing the exact same thing. But instead of calling it Strength of Schedule, sports bettors referred to it as Average Opponent Power Rating, or AOPR.
AOPR is simply a way of looking at the opposition a team has played. Teams with higher AOPR figures have played tougher competition than teams with lower AOPR numbers.


A power rating is a numerical rating of a team, which in theory, is a way to compare that team to another team. If Duke has a power rating of 95 points and Wake Forrest has a power rating of 88 points, we would conclude that Duke is seven points better than Wake Forrest.

To find the AOPR for any team, merely add up the power rating of each opponent and divide by the number of games.


If Duke had played the following teams with the following power ratings:
Georgia Tech (83)
North Carolina (92)
Clemson (89)

Duke's AOPR would be 88, as (83+92+88=264 and 264/3 = 88).


As that involves a fair amount of work, many sports bettors turn to USA Today's Jeff Sagarin and his power ratings, which happen to include a listing called SCHEDL, which stands for Strength of Schedule, which is the exact same thing as AOPR. The one difference is that Sagarin's Strength of Schedule numbers are influenced by his power ratings, although his ratings are likely to be as good as any that you'll find elsewhere.


Several other sports betting publications will also list AOPR figures for those who lack the time to calculate their own power ratings and AOPR figures.


The other numbers that a bettor will need are the average points for and average points allowed by each team. These are available in several places, including ************ edit link to rival site:admin under college basketball match-ups, which will list the points scored and points allowed by each team.


Once we have our AOPR numbers and our points scored and points allowed, we're ready to begin.


Using the Method

For demonstration purposes, we'll use a game of Bradley at Drake. Bradley has an AOPR of 85 points and scored 68 points and allows 64. Drake has an AOPR of 82 points and scores 76 points and allows 74.

Our first step is to divide the higher AOPR by the lower AOPR. In this case, we divide Bradley's 85 by Drake's 82 and get a figure of 1.037. What this means is that Bradley has played a schedule that 3.7-percent more difficult than the teams Drake has played.

The second step is to take each team's offensive points for and divide that by the median points scored in college basketball (another method longtime readers will be familiar with), which is 71 points. Therefore, Bradley's 68 divided by 71 is .958, while Drake's 76 points divided by 71 points is 1.070.

Because Bradley has played the more difficult schedule, we will increase Bradley's offensive rating by the 3.7-percent from above, which gives us a new figure of .993.


We'll also decrease Drake's figure by the same 3.7-percent and get a new figure of 1.032.

Next, we'll take Bradley's figure of .993 and multiply that by Drake's points allowed, which is 74 and get a predicted score of 73.48 points, which rounded becomes 73 points.

Doing the same for Drake will show 1.032 multiplied by Bradley's points allowed of 64 points and will give us a predicted score of 66.04 points, which rounded down becomes 66.


Therefore our predicted score on the game is Bradley 73, Drake 66.


The final step is to add two points to the home team's predicted score and subtract two points from the road team's predicted score to allow for home court advantage.


One More Example

Let's use USC playing at UCLA for this example. The Trojans score 84 points and allow 80 and have an AOPR of 79. UCLA scores 63 points and allows 54 points and has an AOPR of 82 points.
Our first step is to divide the higher AOPR by the lower, which in this case is 82 by 79. This gives us a figure of 1.038, meaning UCLA has played a schedule that is 3.8-percent more difficult than USC.


Our next step is to divide each team's points scored by our median figure of 71. USC receives a figure of 1.183 (84/71) and UCLA receives a figure of .887 (63/71).


Because UCLA has played the more difficult schedule, we'll subract 3.7-percent from USC's figure of 1.183 and get a new figure of 1.141. Next, we'll increase UCLA's figure of .887 by 3.7-percent and get an updated figure of .922.

We'll then multiply USC's updated figure of 1.141 by 54 (UCLA's points allowed) and get a predicted score of 61.61 points, which we'll round up to 62.


For UCLA, we'll multiply .922 by USC's points allowed (80) and get a predicted score of 73.76, which we'll round up to 74. On a neutral court, our prediction on the game would be UCLA 74, USC 62.


If UCLA was the home team, our prediction would be 76-60, while UCLA would be predicted to win by a score of 72-64 if the game was played at USC.


The system does need a minimum of seven or eight games to be played, as there can be large fluctuations in the earliest part of the season, but the system is one that is worth the time requirement.

 

[Woodson]

College Basketball Betting System

One of the oldest college basketball systems around happens to be one of the most successful. It's one that most experienced sports bettors have heard of, but possibly didn't realize its success. But, there is an addition to the popular system, that has also fared pretty well and we'll throw into the equation as well.
The first part of the system is to simply wager on any unranked team that is favored at home over a ranked team. That's all there is to it.

The system went 38-22 in the 2006-07 NCAA basketball season and it currently 12-6 in the 2007-08 season.


The rationale behind the system makes sense, as you're taking a good team, as evident by the fact that they are favored over one of the top 25 teams in the country, who is playing in front of the home fans. The team, along with its fans, tend to get up for these games, wanting to prove they are just as good than the ranked team. You can expect the home team to give its best effort.


The second part of the system, that isn't as widely known, is to wager against these home teams in their next game if they did cover the point spread against the ranked team.


Unranked teams that do knock-off the ranked teams are ripe for a letdown in their next game, and are often spent emotionally, as well as physically, and many times have trouble getting up for its next opponent. Wagering against these teams has produced a 9-3 record so far in the 2007-08 NCAA season.


There you have it. A simple, easy to use system that takes no more than a few minutes each day to check and is likely to add a few dollars to your betting bankroll over the course of a season.

 

[Bennymac17]

Appreciate the info Woodson.

Best of luck.

 

[scott_baio]

Agree with all - great thread.

Am wondering if anyone has taken the kenpom database and imported it into a database program like access.

Am interested in hearing feedback of such trials, and maybe some things to look for.

thx in advance.

 

[Mr. Poon]

Woodson - great thread. I'll add a couple of quick easy comments here in a bit. Wanted to touch on one of yours though to get clarity.

Unranked Fav at home vs. a ranked team - does anyone have the updated stats over the last 2 years on this? I believe it has not fared as well as it did 3-5 years ago.

 

[Mr. Poon]

A system that I follow that has been real profittable in the Big East is the Under in Monday games in which both teams played the prior Saturday (short rest). I'll update the #'s I've tracked over the last few seasons on this and post later, but it hits at a really nice rate.

It is important to note that these games are the Big Monday games on ESPN when there isn't many other games that night. Due to that the Over gets bet up a point or so during the day on almost all of these. Just an FYI that if you follow this for a season, wait to see if you can get an extra point or so on this.

 

[Mr. Poon]

One more comment on Totals to follow-up on Woodson's post about pace. It is profitable to find teams that can change pace and play different tempos based on their opponent. If you can lock in and know about some of these it will prove beneficial. Early in the season you will see some totals based truly on ppg averages. If you know a team and know how they are going to play a particular opponent, there will be some big opportunities on totals.

 

[JCoverS]

I'll add a few pearls of wisdom. First one is something I have noticed over the years that probably has more meaning in college basketball wagering, due to the larger number of lined teams and the nightly action. It is that the linesmakers can often be "asleep at the wheel" for extended periods on certain teams that are either far exceeding or not meeting the current value being placed on them by the oddsmakers. Pay attention to team ATS streaks of several games or more (winning, as well as losing) and ride that streak. Bet with it and not against it. In my opinion, it takes the linesmakers much more time in this sport to catch onto a "hot" or really "cold" team in regard to their line establishing numbers. Long (10+ game ATS streaks) are more apt to happen in college hoops.

Also, pay attention to ATS numbers split by a team's home vs. road results. This can give you a good indication of teams with strong home/road dichotomies. In college basketball, we all know home court can be a HUGE factor. While this is priced into the line, the fact is that some teams have more extreme variance in their performance at home vs. on the road than normal. Each year, if you keep this in mind, it becomes fairly easy to isolate certain teams you might only consider placing a wager on when they are playing at home, while looking for reasons to fade when on the road. It's really pretty simple, but often overlooked.

Great thread so far, guys...hope my modest contribution adds some value.

The leaves are fallin' and that means college hoops season is just around the corner! :00hour

-JC

 

[spang]

Good thread Woodson

A lot of good entries here from some solid people. I would like to add a couple things.

As solid as Ken Pom and others are in coming up with power numbers keep in mind that this is meant as a supplement and to be used with other factors. Do not base your capping solely on just one method of capping whether it be trends, power ratings or other info. They are all pieces to a puzzle and each should be given their own weight along with a number of other factors. I'm more of a current form guy and give a lot more attention to a teams recent activity than keeping in touch of what happened over the last five years.

Trends from previous years can be useful to know to some extent but its important to realize that each game is an independent event and with each season there is a new cast of characters involved.

Early in a season give an edge to teams that have very little turnover or return their starting five from last year. A good example is my hometown team the Akron Zips. The 2010 Zips return all but one from last years NCAA tourney team. The one open spot was filled by a highly regarded 7' shutdown center. A luxury that the Zips have not enjoyed in the past.

The following can not be stressed enough.

Find a RELIABLE injury site and check it daily. Do not rely on covers or other stat sites for your injury info. Even the best of the injury reports miss a few. Check university web sites and a teams local newspapers for up to date info. In these times there are more suspensions than ever before and not keeping up with this stuff can kill you. In football there are 22 starters and one or two people out may have a minimal impact on the outcome. In basketball one or two guys out can have a monumental effect on the outcome.

Keep informed !