NBA Teams Need Free Throws to Go

SportsBook Breakers NBA STUDY: Teams Need Free Throws to Go

In basketball, there is one way to score that is significantly more effective than any other means – getting to the foul line.  And for even poor shooting free throw teams, on the whole going the foul line is more efficient one a per play basis than taking a two or three-point attempt.

There is also a common misperception about going to the free throw line and the fouls that get a team there.  Many NBA fans, and even bettors, just consider drawing fouls to be a mostly random occurrence.  That is not the case at all.  There are some teams, and particularly players, that are very skilled at drawing fouls.  So if drawing fouls and going the line are particularly critical parts of basketball, it would stand to reason that the inability to get to the foul line would have equally negative impact.

No one is disputing that in-game, the ability or inability to get to the free throw line is critical as it relates to the betting result.  Teams that attempt less than 15 free throws in a game win just 37.8% of games.  What is important to bettors is if an inability to get free points has a carryover affect that can span multiple games.  This gets back to the luck vs. skill debate on free throws.

With the powerful SDQL, we can easily determine the impact of going to the free throw line the previous game has on a team in their next game.  To do this we use the p: prefix to signify a team’s game and the shortcut FTA to designate free throw attempts.  By leaving this query open ended, we can look at all results based on the previous free throw attempts.  We list here all performances coming off a game with 12 free throw attempts or fewer.

Previous Free Throws ATS # of Games
1 0-1-0 (-11.00, 0.0%) 1
2 1-0-0 (3.50, 100.0%) 1
3 1-4-0 (-3.30, 20.0%) 5
4 7-7-0 (0.39, 50.0%) 14
5 16-24-0 (-2.98, 40.0%) 40
6 22-37-0 (-0.92, 37.3%) 59
7 45-56-1 (-1.51, 44.6%) 102
8 69-95-3 (-1.12, 42.1%) 167
9 132-130-3 (-0.91, 50.4%) 265
10 212-167-9 (1.60, 55.9%) 388
11 257-232-9 (0.26, 52.6%) 498
12 329-323-5 (0.10, 50.5%) 657

Looking at these results, it is quite clear that there is a strong correlation in the previous free throw attempts and results based on expectations in the next game.  The tipping point is when teams attempted eight or fewer free throws in their last game, these teams are just 161-224-4 ATS.  That is just a 41.8% percentage and these teams have won just 42.2% of the games outright.

Now the proof really comes when looking at these teams free throw attempts next game.  Teams attempts 24.8 free throws in the average game.  In these games, teams attempted just 22.4.  The difference in the free throw scoring of 1.6 free throws made below average makes of the entire margin of the 1.37 points per game these teams have failed to cover by on average.

This is just one of over 100 systems SportsBook Breakers looks at as part of its daily handicapping.  And now, with the new Killersports.com Trend Mart, you can receive daily access to active, must-have systems such as this.  Visit Killersports.com/trend_mart to learn more!

 

NCAAF Football Teams | SBB Revenge Betting System

SportsBook Breakers’ NCAA Study: Revenge on the Mind

There are a number of factors that make college football a different animal than the pro game.  A major one of those factors is the way that emotion and motivation factor into preparation and performance in each game.  And when looking at betting lines there are several ways to take advantage of these key differences.

sports-bettingIn pro football, the idea of revenge from year-to-year is a bit of a dubious one.  Besides divisional opponents, NFL teams rarely play the same opponents for several consecutive seasons.  And with those three divisional opponents, the opportunity to play a team for a second time during the same season lends itself to a revenge opportunity far better than during the next season when much of the team has turned over.

In the college game, rivalries are a far bigger deal.  Beyond traditional major rivalries, almost every opponent is a rival to some degree, as teams play the same conference opponents, and often the same non-conference opponents year-after-year.

For these 18-22 year olds playing at the same school for 3-4 seasons, what they did against a team the last time they faced them is a huge deal and will affect the importance they place on a game to a far greater extent than a seasoned pro.

What we want to look at in this study is how football teams perform when they were blown out by this squad in their last meeting.  For the first time in 2015, we can run a NCAA football query to answer this question right on Killersports.com.

The SDQL to generate this chart is P:margin.  The uppercase P in SDQL signifies the last meeting versus the opponent.  So where p:margin would look at the margin in a team’s last game, P:margin looks at the result of the last meeting against a particular opponent.

While this SDQL generates results for all margins, we are going to focus on losses by 40 points or more.

 

Margin

Last

Meeting

ATS # of Games
-40 34-35-1 (1.59, 49.3%) 73
-41 61-62-2 (-0.82, 49.6%) 131
-42 79-86-3 (-0.88, 47.9%) 178
-43 23-25-0 (0.74, 47.9%) 50
-44 27-28-0 (0.14, 49.1%) 59
-45 62-63-2 (-0.27, 49.6%) 135
-46 29-29-0 (-3.47, 50.0%) 64
-47 16-16-1 (2.24, 50.0%) 34
-48 47-33-1 (0.54, 58.8%) 90
-49 49-36-2 (2.29, 57.6%) 91
-50 16-14-1 (-0.05, 53.3%) 32
-51 11-19-1 (-6.19, 36.7%) 33
-52 31-25-3 (0.94, 55.4%) 62
-53 20-9-2 (8.35, 69.0%) 33
-54 9-10-0 (0.18, 47.4%) 21
-55 13-16-0 (-1.07, 44.8%) 33
-56+ 93-90-1 (0.66, 50.8%) 184

 

Looking at this chart, we see that there is a bit of an uptick in performance when a team was blowout last meeting, starting with losses of -48 or worse.  But is that advantage enough to be significant for bettors?  Running the SDQL P:margin<=-48 produces a result of 289-252-11 ATS, a significant factor but not enough of an edge to bet on with the 53.4% winning percentage.

The query that has been run so far fails to consider one very big factor — when that last meeting between these NCAAF teams took place.  While using the “p” prefix looks back at the last game which took place during this same season, this “P” prefix back at the previous matchup between these teams, no matter how long ago it took place up to the beginning of the college football database in 1980.  When this last meeting took place would seem to be very important for the basis of motivation.

There are a few ways to generate query with SDQL, and our favorite in this case is to use a parameter of season-P:season. This will determine how many seasons ago that last matchup took place, by using a simple subtraction function when looking at the year which each game took place.

Seasons Ago ATS # of Games
1 214-170-8 (1.01, 55.7%) 409
2 11-16-2 (-2.79, 40.7%) 39
3 26-13-0 (7.53, 66.7%) 40
4 7-13-1 (-4.38, 35.0%) 23
5 6-7-0 (-3.04, 46.2%) 14
6 2-6-0 (-4.81, 25.0%) 8
7 1-2-0 (1.33, 33.3%) 5
8 0-5-0 (-6.30, 0.0%) 7
9 4-2-0 (2.25, 66.7%) 6
10+ 18-18 (0.99, 50.0%) 36

The first thing you notice about this chart that indeed college teams generally do play in consecutive seasons, even football teams where there was a blowout in the last meeting.  As far as the system goes, this chart shows exactly what we were hoping, that this system performance is only relevant if the matchup happened in the last three seasons — when there are players on the team that were there for that previous beatdown.  When isolating those previous three seasons, using the SDQL P:margin<=-48 and season-P:season<=3, the results are 251-199-10 ATS, a worthy play on with a 55.8% success rate.

Other factors to consider:

Considering this system is all about value, with an average line of +20.6 points, teams have done better in this situation with bigger lines.  When teams are underdogs of more than 30-point dogs they are 66-37-5 ATS (64.1%). (SDQL: P:margin<=-48 and season-P:season<=3 and line>30)

Teams have done better in this spot when they did not fail expectations in that last game as miserably as it is possible considering the final margin.  When the ATS margin was not -25 or worse in the last meeting, teams are 97-61-4 ATS (61.4%). (SDQL: P:margin<=-48 and season-P:season<=3 and P:ats margin>-25)

Football teams that are having a bad season particularly get up for these revenge games.  NCAAF Teams that have won less than 30% of their games on the season are 111-61-5 ATS (64.5%) (P:margin<=-48 and season-P:season<=3 and WP<=30)

Summary:

The best part about previous matchup systems is we know exactly when they will be active during the upcoming season well before week one.  Run this system yourself and mark the calendars for the 10 times during 2015 when this winning system will be active.

Check out more from Kyle (Sport’s Book Breakers) at Killercappers.com

SportsBook Breakers’ NFL Study

Beware of Low Winning Percentage Teams

It is easy to get sucked into betting on a bad team, but there are also many, many times when that makes sense.  In fact, when looking at all betting situations, in general you are better off betting on teams having losing seasons, as they cover in 50.8% games.  However, the key to betting on all teams, particularly those who are not of a playoff caliber, is value.  What we’ve uncovered is a situation where the value has been sucked completely dry.

We are curious about when non-elite, non-playoff caliber teams are given expectations they don’t usually face.  What happens when these teams are favored, and potentially as a significant favorite?

This is an easy subject to investigate with the power of the Sports Data Query Language (SDQL).  To explore the subject, we need to use just two parameters, “line” and “WP,” an easy shortcut for winning percentage.  For an easy and quick way to explore the subject, we will look at how teams perform in the SDQL using the grouping feature.  We defined the winning percentage to investigate as teams winning less than 62.5% of their games at the current time, the equivalent as a 10-win team, the number it generally takes to make the playoffs.  The SDQL text “WP<62.5 and line<0, -2, -3, -4, -6,-7, -9” produces the following result, grouped together by lines larger than the given number.  NOTE: Results date back to the beginning of the NFL database in 1989.

Line ATS SU # of Games
line < 0 1452-1578-88 (-0.12, 47.9%) 2023-1091-4 (4.77, 65.0%) 3118
line < -2 1212-1350-83 (-0.05, 47.3%) 1767-874-4 (5.45, 66.9%) 2645
line < -3 856-968-29 (0.06, 46.9%) 1317-533-3 (6.69, 71.2%) 1853
line < -4 644-755-26 (-0.04, 46.0%) 1037-385-3 (7.47, 72.9%) 1425
line < -6 392-522-21 (-0.38, 42.9%) 695-239-1 (8.30, 74.4%) 935
line < -7 240-343-11 (-0.69, 41.2%) 462-131-1 (9.07, 77.9%) 594
line < -9 131-174-6 (-0.80, 43.0%) 244-67-0 (10.44, 78.5%) 311

 

These results above are exactly what we like to see to back up such a hypothesis.  From the top, when looking at all favorites in this situation, they cover only 47.9% of time.  While that is not a beatable number in itself, considering it accounts for over 3,000 active instances, it is statistically significant. As the lines get larger, the results get steadily worse until reaching a play against point.  To find that exact point we use an open-ended parameter with the SDQL text “WP<62.5 and line

Line ATS SU # of Games
-5.0 51-54-3 (-0.69, 48.6%) 70-37-1 (4.31, 65.4%) 108
-5.5 65-58-0 (1.52, 52.8%) 89-33-1 (7.02, 73.0%) 123
-6.0 80-61-2 (1.58, 56.7%) 108-35-0 (7.58, 75.5%) 143
-6.5 65-87-0 (-1.28, 42.8%) 98-54-0 (5.22, 64.5%) 152
-7.0 87-92-10 (1.33, 48.6%) 135-54-0 (8.33, 71.4%) 189
-7.5 28-67-0 (-2.35, 29.5%) 67-28-0 (5.15, 70.5%) 95
-8.0 31-38-3 (1.22, 44.9%) 60-12-0 (9.22, 83.3%) 72
-8.5 19-31-0 (-0.60, 38.0%) 41-9-0 (7.90, 82.0%) 50
-9.0 31-33-2 (0.02, 48.4%) 50-15-1 (9.02, 76.9%) 66
-9.5 25-32-0 (-0.18, 43.9%) 41-16-0 (9.32, 71.9%) 57
-10.0 27-36-3 (-1.15, 42.9%) 51-15-0 (8.85, 77.3%) 66
 -10.5 17-29-0 (-1.28, 37.0%) 36-10-0 (9.22, 78.3%) 46

 

This data shows that the sweet spot for where this becomes a play against system is between 6.5 and 7.5 points.  We’ll play it conservatively here, taking the more than TD spreads.  Since 1989, teams that have won less than 62.5% of their games are more than TD-favorites are an underwhelming 240-343-11 ATS (SDQL: WP<62.5 and line<-7)

Other factors to consider:

Since winning percentage is a far more accurate measure later in the season than in the early weeks, it would seem this should make a different on the results.  When adding the SDQL parameter week, we find that this is not a major factor.  From weeks 2-4, when winning percentage is the least accurate, teams are 48-66-3 ATS (42.1%) in this spot.  From weeks 15-17, when winning percentage is best representation of a team’s ability, the system has gone 52-77-2 ATS (40.2%)

Looking at various winning percentages along this range, there is no significant different in the ATS result based on winning percentages below the 62.5% standard.

It is obviously quite rare for these teams to be road favorites of more than a TD, but when they are, the result is a brutal 21-43-2 ATS (32.2%) (SDQL: A and WP<62.5 and line<-7)

This system has not performed particularly well when facing a winning team.  While you might that that would be an even greater advantage to play against, these large favorites are actually 50-48-2 ATS against teams that have won at least half their games on the season.

This system is active most often when these average or worse teams are facing terrible teams and the results are quite juicy.  When facing a team that is winless, or has won no more than 10% of their games, these teams have gone 46-93-5 ATS (33.1%) (SDQL: line<-7 and WP<62.5 and o:WP<=10)

Summary:

When you are evaluating a team that does not normally play at an elite level, there is just too much that can go wrong to expect an elite performance, even when the matchup sets up well on paper.  Do not trust non-playoff caliber teams with big lines.