NBA 2nd Half Season Betting Systems

With the College Bowl games now in the rear view mirror and a new Super Bowl champion about to be crowned, our focus shifts to the NBA where teams are now engaged in the 2nd half the season.

With that, let’s check your handicapping prowess as we take a look at a handful of handicapping theories that apply to teams playing from Game 42 out during the regular season in the NBA. All results are ATS (Against The Spread) and are since 1990, prior to the start of the current 2009-10 season…

1. FACT OR FICTION: TEAMS OFF BACK-TO-BACK DOUBLE-DIGIT WINS DOMINATE TEAMS OFF BACK-TO-BACK DOUBLE DIGIT LOSSES.

Fiction. The fact of the matter is these teams are just 52-59-1 ATS, including 23-32-1 ATS at home. At home against .367 or greater opponents they dip to 6-14-1 ATS. Worse, at home with a win percentage of less than .677 they are 1-12-1 ATS when facing a .367 or greater foe.

2. FACT OR FICTION: DOUBLE-DIGIT DOGS OFF SU WIN AS A DOUBLE DIGIT-DOG ARE JUST GETTING STARTED.

Fact. That’s confirmed by the fact that these teams are 32-18 ATS, including 15-6 ATS when facing an opponent off a SU and ATS wins. Better yet, put these guys up against an opponent off a SU and ATS win of six or more points and they zoom to 10-1 ATS.

3. FACT OR FICTION: DOGS OFF A SU LOSS AS DOUBLE-DIGIT FAVORITES BOUNCE BACK BIG THE NEXT GAME VERSUS AN OPPONENT OFF A SU AND ATS LOSS.

Fiction. Quite the opposite, considering the fact that they are 9-17 ATS, including 6-15 ATS away.Home or away, they are virtual no-shows if the opponent lost Su as a favorite in its last game, going 1-11 ATS.

4. FACT OR FICTION: ROAD WHO ARE 0-3 SU AND ATS THEIR LAST 3 GAMES CONTINUE TO STRUGGLE WHEN FACING AN OPPONENT THAT IS 3-0 SU AND ATS IN ITS LAST THREE GAMES.

Fiction. The truth of the matter is while struggling these teams bring ‘value’ to the contest as they are 64-51-3 ATS, including 20-10-1 ATS when taking more than 10 points.
Better yet, when taking more than 10 points in same conference games they are an eye-opening 12-1-1 ATS.

5. FACT OR FICTION: TEAMS OFF THREE STRAIGHT-UP WINS IN A ROW AS AN UNDERDOG CONTINUE THEIR WINNING WAYS.

Fact. Momentum goes a long way in the NBA. Teams off three consecutive upset wins in a row are 38-28-1 ATS. Put them up against division foes and they really turn things up, going 17-5 ATS, including 15-2 ATS versus sub .550 opposition.

There you have it. Five Super Systems to follow the 2nd half of this season. Happy hunting.

Article from Marc Lawrence at Playbook.com

Learning From the First Quarter in the NBA

I was participating in a panel discussion with some other handicappers last week on a local radio show here in Las Vegas. Having a couple of beers before the show, (just like in trying to meet women, I always thought having a few drinks beforehand always makes you sound much better) the discussion came around to what happens in the first quarter of an NBA game makes no difference at all. I said it might not in that game, but it definitely can in a team’s next game.

We found some good trends last week where a team in their previous game came back after being down at halftime to outright win the game. There was a definite effect on how the team performed in their next game. Let’s see if we can dig something up when a team is down at the end of the first quarter but comes back to win. The sample is games starting with the 2005 season.

Using the premise that our team was down only by one to four points at the end of the first quarter but ended up winning the game straight up, we looked at how they performed in their next game. To get any results of significance, our comeback team in the first game had to have been an underdog. In their next game, they only cover the spread 43.5% of the time, 123-160. The Under happens in 54.7% of these games. These are nice results for such a basic situation.

We are not specifying locations for either the previous or the next game. If we do add locations to the equation, we get a nice improvement on our Under to 66.7% if the come-from-behind game was on the road and we are now playing at home. But, if both the former game and the next one are both at home, our ATS rate drops to 28.6%. Our team must get a little too relaxed staying at home after their comeback win.

Let’s see how altering a couple of qualifiers can dramatically change the results of a trend. First, we are going to change the deficit at the end of the first quarter from being behind one to four points to five to nine points. Next, instead of the team that rallied and won being an underdog, we change them to have been the favorite. Comparing our previous situation where the first game was on the road and now playing at home and the Under happened at a 66.7% rate, just by changing from a dog to a favorite in the previous game and increasing the deficit, the next game now has completely opposite results with a 62.0% play on the Over!

If the team that rallied and won did it in an underdog role, it is probable to assume they are more focused in their next game and play better defense. However, if the team was a favorite and thus expected to win that game, even being down by a larger margin than the underdog had been after the first stanza, the favorite apparently doesn’t play with the same intensity in their next game. Perhaps an added emphasis is put on the offensive side after having to rally to win, but I do believe that it is easier to improve your defensive efficiency as compared to that on offense.

Now let’s increase the opponent’s lead at the end of Q1 to 10 to 15 points. We’ll keep the sites the same, previously away and now home. With the bigger margin to overcome for the win, the Over occurs 68.6% of the time in our next game. Interestingly enough, with the increase in the Q1 deficit to 10 to 15, there is very little difference between whether our team was a dog or a favorite in the first game. That significant enough of a comeback apparently affects all teams in a similar manner allowing the high Over rate in the next contest.

Since we looked at how teams do coming back to win a game after being down after only the first 12 minutes have been played, we need to turn the tables and discover if there are any trends for teams that blow a first quarter lead.

Our first situation will be a team that is ahead by one to four points after Q1 and ends up losing outright. There was nothing worth noting without adding some qualifiers. Let’s use the same sites that we did before: the first game on the road and the next at home. Let’s make our team favored in the first game.

Apparently in their next game, they want to make up for blowing the early lead as they play good defense staying Under the lined total 66.7% of the time. Just out of curiosity, I wanted to see what affect there might be in their next game to a team that loses a first quarter lead quickly on the way to a loss.

I took a team with a 5 to 9 point advantage at the end of Q1. That good feeling of having a lead quickly vanished as I made them behind in the score at halftime before going on to lose the game. There is a hangover effect with our team going just 65-99 ATS, 39.6%, in their very next game without any additional qualifiers. The ATS rate is lowered to 33.0% if the second game’s type of location is not the same as the first. One is a road game, the other is a home game, in either order.

In this scenario, we get a big wagering improvement when the team that blew the Q1 lead in Q2 are playing their next game without any rest. A pitiful 28.0% spread coverage rate, 11-27, occurs without having any consideration for locations. The Over comes through at 76.3%, 29-9. The record of this trend this season is 6-1 ATS and OU. However, the real icing on the cake is if our team’s opponent is playing the game with at least a day off as then the Over improves to a fantastic 25-3, 89.3%.

Here is another reason to look for who led after every quarter. If a team goes wire to wire leading after the first quarter, halftime, and the third quarter on their way to a win, there is a very good trend on the Over in their next game, if it’s at home. The one other qualifier is the team had to have been an underdog in the first game.

An underdog getting a win where they never were behind at the end of a quarter creates a relaxed feeling about playing defense the next time on the hardwood. The Over is 162-110 since the tip-off of the 2005 campaign, a 59.6% success ratio. If you want to drill down just a little bit more to improve that Over rate, we hit 68.7% if their next game is a non-conference one. Most of the time there is not the same amount of focus when going against a non-conference foe. Our super sweet spot is if the previous game was also played at home, the Over skyrockets to an 86.7% mark, 26-4!

The extra factor of no travel plus the comforts of playing in your arena, and perhaps the chance to go out and celebrate an easy win, can have tremendous influence on a team’s next total. Again, let’s look at the opposite: a team that was behind after each of the first three quarters but ended up winning the game covers the point spread in their next game only 36.7% of the time if that game and the previous game were played at home. Sometimes it pays to go out on the road!

How about when a team starts out really clicking on offense and puts up over 30 points in the opening period. Their offense then goes cold in Q2 and they only tally 20 points or less and go on to lose the game. Apparently, our team loses some confidence if they are now playing their next game as an underdog because they only cover the linesmaker’s number 32.8% of the time. That poor line-covering rate falls to 22.2% if our team was favored in their previous game where they went cold in Q2 after a hot start.

Paying attention to what happens in the first quarter of a game can make you some cash in the following game. You just have to know what to look for.

Jim Kruger of Vegas Sports Authority did all this work, probably without beers.

Notre Dame Needs to have Fight at South Bend

Notre Dame was left for dead (playing in the NIT) after not even being competitive at UCLA, losing their seventh straight game, being blistered by the Bruins 89-63 on Feb. 7. After being ranked in the Top 10, the fall from the hierarchy of college basketball was complete. The Irish players had little fight on the west coast and their body language suggested the squad had lost complete confidence and was feeling sorry for itself. Without going into details, coach Mike Brey said he challenged his team to see if they could still be good enough to earn a NCAA tournament berth. While the world had written off Notre Dame, they found confidence and courage.

It started five days later at home against highly ranked Louisville in the Joyce Center, where the team that had looked so impressive in late November remerged. Notre Dame made the Cardinals look like DePaul, in steamrolling them 90-57. Suddenly, somebody besides Luke Harangody was making shots. Kyle McAlarney stopped forcing off-balance heaves, Ryan Ayers found the bottom of the net instead of clanging the rim with his attempts and Tory Jackson started attacking the basket again.

Notre Dame is 16-12 (8-15 ATS) having won four of last six and their loss at Connecticut, whom they essentially played even with for 38 minutes, probably earned respect among voters on the Huskies Senior Day. With two more wins, they are at the presumed magic number of .500 in the Big East to at least give themselves a chance for bid.

The Irish return home where they are 11-2 (3-5 ATS) to take on Villanova (23-6, 14-11 ATS). Both the Wildcats and Notre Dame are playing the dreaded Saturday-Monday Big East turnaround and the Irish are one of only two teams that have had this schedule four times in the league. Harangody and his teammates are 4-12 ATS versus teams outscoring their opponents by four or more points a game this season and are the only team in the Big East that has played at Connecticut, at Pittsburgh, at Louisville and at Syracuse. “You have to take it into consideration," Harangody said.

Don’t expect Villanova to be pleased coming into South Bend. The Wildcats still have visions of earning a first round bye in next week’s conference tournament, but those aspirations took a monster hit after shooting a season a low 33.3 percent, in shocking 56-54 home loss to Georgetown, who had lost nine of previous contests.

"I thought we played hard but we didn't execute and didn't adjust to their defense, “ coach Jay Wright said. "They played better. They knew our personnel and did a great job of playing our personnel and executing their game plan." Villanova could still finish fourth and earn the double bye into the quarterfinals; however two wins and a numbers of events would have to go perfectly for them. After converting just 3 of 16 three-point shots, ‘Nova is 21-8 ATS after a game where they made 20 percent or less beyond the arc.

Bookmaker.com has Notre Dame as 3.5-point favorites with the total at 159. Villanova would seem to have no problem playing in a high scoring affair and are 6-0 ATS versus offensive teams scoring 77 or more points per game in the second half of this season. The Irish are not nearly on as solid a footing with 4-8 ATS record as a favorite (8-4 SU) and 1-5 against the spread off a cover.

This is the last Big Monday of the year on ESPN and Scottie Reynolds is key for Villanova. He was 2 for 10 against the Hoyas, for a dozen points. When he scores 18 or more points, the ‘Cats are perfect 9-0.

The action starts at 7 Eastern and both teams need a win if they hope to accomplish their goals.

The Effect of the Quality of NBA Opponents

When handicapping an NBA game, a common thing to look at is who does the team play next. Is it a good team, a divisional foe, or one that a team can take very lightly as they aren’t very good?

Also, most cappers like to look at a team’s previous schedule. Have they played tough teams or have then been facing cupcakes recently? Is the team off of a game versus one of the elite squads in the league or were they playing a cellar dweller?

Are there any advantages in looking at different situations based upon the quality of the teams as measured by their season winning percentage? Do teams perform better after directly playing a high quality team? Is there a pattern to the results if a squad that has just played a bad team? (Editor's Note- Picture is cheap ploy to have you read)

We are calling a good team as one who is winning 60% or more of their games. A poor team is one who is winning less than 40% of their contests and an average one is winning straight-up below 60% down to 40% of their games.

Currently, there are an equal number of teams playing 60% or better ball as well as playing below 40%, nine in each category. There are 12 “average” teams with a winning percentage below 60% but no lower than 40%.

Let’s start with something very basic and see how two bad teams playing each other do against the spread and against the lined total. I have friends who swear to taking the Over in these situations. The general belief is that bad teams don’t play very good defense and that pairing two such teams means the Over is a money cow. Let’s see if history backs that premise up.

Basic Two Bad Teams Playing

Over the past three seasons, when two bad teams meet, the visiting team covers 54.6% of the time with the game going Over the total at a 53.5% rate. That’s a start, but let’s drill down a bit.
We quickly find a better betting angle to pay attention to by making the home team an underdog. The road team now covers 59.0% of the time with a surprise in the result of the totals as 63.4% of these games go Under the total. If the home team lost their previous game, the Under jumps to 69.4%.

The Over is predominant when the home team is favored to win the game, a 56.7% OU rate. That jumps to 61.9% if both teams are off of losses. However, if the home fave’s opponent is off of a win, the Under raises its head and is a 60% play.

So, while there are some nice situations to play the Over when two bad teams meet each other, you need to know a few more details to avoid making a wager with a big edge against you.

Basic Two Good Teams Playing

Looking at the other end of the spectrum, how do two good teams do matching up against each other? With no qualifiers, the home team covers 56.2% of these games with the Under winning at a 56.4% rate. If they are a home dog, that ATS rate moves up to 59.3% and the Under ticks up to 62.0%. The Under even goes up to a 66.7% mark if the home dog lost their previous game. The best basic angle I found was if you’re a home dog and both of you won your previous game, the home team getting points covered the point spread 63.4% of the time.

In looking at what quality of opponent our team played in their previous game, there was nothing worth noting on the results in our team’s next game. It didn’t matter if they played a bad or good or average team, the ATS and Over/Under results of the next games were all very close to 50%. In order to find some trends that will make us money, we need to add some qualifiers.

Two Good Teams playing and one just played a Good Team

The first qualifier I am going to add is the quality of the previous opponent. Let’s check results depending upon whether the opponents are good, average, or bad teams and add some other qualifiers as we go along.

A trend that happens a number of times the remainder of this season is when you have a good team, 60%+, who just played another good team and their current game is also against a good team. If this game is away, our team is only covering the spread 41% of the time. If the game is at home, you win against the number 55.5% of the time and the game goes Under at a 57.3% rate. That Under improves to a 69.4% winning situation at home if the team is an underdog. If our good team has just played an average or bad team in their game before facing a good team, there are no fairly basic trends that give us an edge.

Two Bad Teams Playing after playing a Good Team

If we switch our team to a bad team who has just played a good team and their current foe is a fellow bad team, we have a couple of advantageous edges to talk about. If the current game is away, our team off of playing a good team has an ATS record over the past three years of 65-41, 61.3%. The sweet spot in this situation is if this team happens to be a favorite in this road game, you will see an ATS improvement to 71.4%.

As for Over/Under winning trends, the Over is happening between 59 to 59.5% of the time if our team is an away dog or if it is listed as a home favorite in their game against their equally bad team. However, if, after playing a good team, our team is an away favorite, the totals results are the opposite with the Under cashing tickets at a 68.0% clip.

Average Team after playing a Bad Team vs. another Bad Team

For average teams, 40-60%, how about playing a bad team and then playing another bad team? It’s not a great day for you if you are on the road and lined as an underdog. In the past, you have covered the number only 32% of the time versus that bad team after previously playing a bad squad.

Opposite type of previous opponents

What would the results be if one team played a bad team previously and their opponent recently faced a good team? Would the team be more rested after playing a lower quality squad, perhaps not having to put out quite the same level of effort or intensity that they would have against a better unit. Would they have “more in the bank” than their opponent who is off of a contest against a good team?

Would the other team after playing a good team not have played as hard knowing they will lose and they are saving it up for the next squad they face. Or, does playing a good team prepare you better when you face an equally bad team in your next game?

Taking a game between two bad teams, our team’s previous game was against a bad team and their opponent has just played a good team. This is very interesting in that the team off of playing the bad team only covered the spread 28.6% of the time if this game was at home.
After playing a poor squad, combined with playing this game at home, our team was not prepared very well for this game very well. The lack of intensity for our team on the defensive side in this game also shows with the Over happening 64.5% of the time. With the other team off of a match against a good team, they apparently are the sharper team, at least for this game.

Playing a bad team can put another team into exhibiting bad habits, especially if they aren’t a good team to begin with. And just the opposite is true when a bad team plays a good one. They have to be at the top of their game to have a shot at winning. Playing at that higher level can carry over to the next game.

SUMMARY

Obviously there is an effect on a team depending upon what type of team they just played even in just the few cases we looked at. Here is a short cheat sheet to help in handicapping the NBA.

Two bad teams playing each other:
Play on the Visitor

Play Over if the home team is favored
Play Under if the home team is a dog

Two good teams playing each other:
Play on the home team, especially if a dog
Play the Under

Good vs. Good after playing a Good:
If home, look at playing Under, especially if dog
If away, play on home team

Bad vs. Bad after playing a Good:
Play on road team, especially if favorite

Avg Team after playing a Bad vs. Bad:
Play against Avg. Team if road dog

Jim Kruger of Vegas Sports Authority wrote this NBA article.

Methods to Improve your College Hoops Handicapping

College basketball is a sport that has more variance by teams from season to season than any other. You are dealing only with five players competing at one time against another team. One player can make a huge difference in a team’s results on a straight-up basis and also in covering the point spread. If you were a NCAA hoops fan in 1988, you will remember Danny and the Miracles winning it all for the University of Kansas. Can anyone name any of Danny Manning’s teammates?

It is never too early to start looking at a team’s characteristics and tendencies in college hoops. After all, you want to get on a team or find squads to bet against as early as possible when the point spreads and totals might not be entirely in line. Many of the teams you find to bet against will not be in the same form they were last year or not living up to expectations. And, obviously, just the opposite is true when looking for teams to put on your “play on list”. You can find line value on the surprise teams that everybody else hasn’t already spotted and you can find get extra points going against the disappointing teams.

Many times when a coach leaves a program, especially after a few good years, it seems the program takes a downturn, as if the outgoing coach knew the incoming and returning talent wasn’t going to be able to keep up with the success of prior campaigns. This appears to be the situation at Wichita State with Mark Turgeon leaving the wheat fields of Kansas for the Aggies of Texas A&M last year. WSU struggled with all types of bad luck last season even though they were able to hire a fine coach, Greg Marshall, with a very good track record. As head coach of Big South Winthrop, Marshall led his team to seven NCAA Tournament appearances in nine years. That is even more impressive when you realize this is a league that gets one invitation to the Big Dance.

Marshall has taken a diverse team of newbies and covered the spread three games in a row against quality competition including Georgetown and Michigan State. This is a team that was picked in the bottom three of the Missouri Valley Conference. Marshall is an excellent teacher and I expect WSU to outperform preseason predictions. They are currently sporting a 4-0 against the spread record and a team worth watching.

UNLV is a team that was picked to win the Mountain West and finish in the Top 25. A strong recruiting class was supposed to help the three returning starters, especially in the middle where the Rebels started 6-7 Joe Darger at center last year. Five-star recruit 7-0 Beas Hamga has seen virtually zero minutes as he is the epitome of a project. UNLV is counting on 3-point shots to fall as their lack of an inside presence has hurt them. The offense revolves around star guard Wink Adams. If he is not playing up to par, UNLV is an average team at best. The Rebels are 1-4-1 ATS even though they were picked to win the Mountain West Conference. UNLV dropped two games over the weekend with Adams going 5 for 25 from the field averaging 7.5 ppg.

In determining which teams to wager on, a statistic I like to look at is the difference in offensive field goal shooting and defensive shooting percentage. I have long maintained that good shooting teams are ones you want to look at to back against the point spread. Playing good defense only makes a team tougher to beat. Wake Forest is among leaders the nation in this category along with Arizona and Utah. Stew Morrill’s teams are always tough Utah State and they lead the country in shooting percentage at 56.6 percent. These are the types of teams I will look to play on as the season progresses.

Teams that are at the other end of the spectrum are Wright State, Louisiana-Monroe, Drexel, and UC-Irvine. It doesn’t take a rocket scientist to realize that a team that doesn’t shoot well and doesn’t defend well is not a good team to bet on. These are teams I put in my “play against” file.

Another statistical area I like to examine is a teams’ turnover differential. When you have many teams only taking 57 to 63 shots per game, a discrepancy in the number of net turnovers each team has can make a difference in the outcome of the game. Teams that have a very good differential include Louisville, Houston, Davidson, West Virginia, Missouri, and Nebraska. These teams also have a 14-6 ATS mark at the time of writing this article. Protecting the rock while being able to steal it are two qualities I want in teams I back.

I am always wary of putting my money on teams that shoot an extraordinary number of three-point goals relative to their two-point field goal attempts. If hoisting shots up from downtown is a team’s main method of offense, it can be a long day if the bombs are not going down. A bad shooting night can obviously happen, especially on the road away from the comforts and familiarity of your home gym. Teams rarely get to the free throw often when they are camping out behind the three-point arc which increases the reliance of making those 3’s.

Some good examples of teams shooting a relatively high number of 3’s and a low number of free throws are Iowa State, 14th out of 344 teams on three-point attempts, 326th on free-throw attempts. Troy is 22nd in TPA’s and 340th in FTA’s, Tennessee-Martin, 42nd and 324th, and Akron, 47th and 320th. Combined, these teams have a 3-11 ATS record. These will be teams I will avoid playing on and will be on my play against list when they are on the road.

On a side note, it is still too early to determine if moving the three-point line back a foot to 20’9” will make much of a difference. Currently there is a 1.2% reduction in the percentage of 3’s being made out of the 344 Division-1 teams, 33.2% this year compared to 34.4% last year. Teams overall are cutting back just a shade on the percentage of shots from behind the arc, 33.3% of all field goal attempts this year versus 34.4% last season.

These are some basic methods to start making a play on/against directory of teams. With so many lined teams, it is wise to have some methodologies to par your respective lists down.

Jim Kruger of Vegas Sports Authority contributed this article.