Special NFL 2018 Season Week Early Look

Looking ahead at week 1, several trends small trends stand out. The sample sizes are small, but the margins are notable. One of the things the makes NFL one of the hardest sports to handicap is the relatively smaller sample size of most trends that trend-setting sifts to the top. For example:

  • The Browns are 0-17 ATS since Jan 01, 2017 – 17 games but a massive -10.53 ppg average
  • The Packers are 16-0 OU (+12.12 ppg) as a favorite of more than six points vs a team with the same record.
  • The Steelers are 3-20-1 OU (-7.10 ppg) since Oct 13, 2013 as a road favorite
  • The Vikings are 28-8-2 ATS (3.59 ppg) since Nov 07, 2013 at home

Looks pretty good right? 4 solid bets…
Maybe…but then again maybe not. The Vikings are 28-8-2 ATS at home, and that is amazing, but look at what the line was historically: average -1.9 ppg, and we have the Vikings, with zero games under their belts in Week 1 lined now at -4.5

Capping the NFL is more about forgetting what you know, and making an attempt to ride on the coat-tails of the linesmakers. Or…if you think you’re just as savvy and want to overthink that basic strategy, you can more or less fade the public nearly on the blind in NFL. So much more the case than most of the other sports we look at. NFL = Low volume; big public week to week overreactions to nearly meaningless data. Typically, we like the thrill of being right on NFL games, but rare expect as much profit as we might see during say an NCAABB season.

Nonetheless, every once in a while, something notable comes along. We’ll usually have 3-5 of these strikingly simple, 150 games plus sample size, sub 0.05 p-value record, low number of condition situations that appear to have what we need to go hard on.

On the house, for week one, we have a trend that has 127 games in the books, three primary conditions, two minor filters and a record of 78-43-6 ATS (64.5%) — that is a solid standard score of 3.11

Here is the most key condition:

#1. We want a team that didn’t do well last season: 2 games or more below .500 record
#2. We are focusing only on early season: first half or just week 1 produces a slight lean 765-719-41 ATS for the entire first half, but thanks to a fairly linear trend upwards for our previous season poor-performers, Week 1 alone produces a 149-114-8 ATS record (56.7%)!

It gets better with another logical filter, but for now, the SDQL we’re using to query this is very simple:
SDQL= PRSW<=6 and week=1 ...and we have the following teams as active plays: Broncos, Browns, Colts, Dolphins, Giants, Bears, Texans, Buccs, 49ers, Jets, and the Raiders. Let's try and hone in on the best of that platter: #3. Let's just look at dogs: The record is now 111-79-6 ATS (59.0% ATS) Minor / optional filters: If we take home dogs +6 points or larger and road dogs no larger than 12 points, the record is 78-43-6 ATS (64.5%). Consider taking that to the bank in Week 1 perhaps with a little extra capping of your own or using some of our other active PCG Sports Betting NFL Systems available at the Trend Mart via discount link: http://killersports.com/trend_mart?store=PCG&discount_code=PCG_yH6zz2 Active on the Browns, Bears, Texans, Buccs, 49ers and Jets! Best of luck, PCG (ProComputerGambler.net)

Best Hidden Factor for NFL Futures


We are quickly approaching the start of the NFL season. What you will be getting mostly now, will be a lot of season wins forecasts. Most of those will be based on the obvious, draft picks, free agent signings, or free agents lost, etc. The numbers will be scrutinized publically to the point of exhaustion, with a lot of “stuffed suits” and so-called experts weighing in. The problem is, they all approach the predictions with similar, if not exacting methodology, as described above, and state their objective view based on how they see it playing out.

Today in the newsletter, we will look at an entirely different methodology, one that sits tucked firmly under the radar, as it sits on an island, awaiting discovery, and in my attempt to always be “stealthy” regarding my handicapping philosophy, I have looked at many, many approaches, out of the main stream, and it is what attributes to my success as a handicapper.

I discovered several years ago, a methodology, that is first and foremost logical, and then backed up the theory with a trial and error highly successful means to the end, with 20 NFL seasons of data, So here we go:


The NFL schedule is 16 games, and 25% of that schedule is comprised of inter-conference games. That is certainly a significant chunk. The beauty of it is, the NFL schedule is such, that these 4 games change every year, as inter-league play is rotated conference by conference through all 4 possibilities. here in lies the most predictive element I have found to forecast a team in the best spot to move up or down.


Arguably, in the long haul, a team should finish 2-2 in the 4 games straight up. Maybe in a given season, when they face a weak conference they rise to 3-1, or 4-0, and visa versa if they play the strongest conference. This is the key element.

Since the start of the 1995-96 NFL season there have been 49 teams that ran the inter-conference table at 4-0 SU. The theory is, a team that went 4-0 in these games may have had an easy path, and with a more difficult schedule in front of them in these 4 games the following season, it may be quite difficult to reach the same win total.


The 49 teams that ran the table over the period, met this fate:

38 of them lost more games

9  of them won more games

2 of them won the same amount of games

34 of the 49 lost 2 more games or more 69.4%

The 38 teams that lost more games, did so to an average net wins over the previous season of -3.95!

So the fact is 78% of these teams lost an average of just about 4 more games the following season!

I’m not sure about you, but I have looked at 100s of season win forecasting tools, and none come remotely close to this!


Supporting evidence then should mean, that if a 4-0 team loses a significantly greater amount of games the next season under this hypothesis, then shouldn’t an 0-4 team win a lot more?

It certainly would make this hypothesis a lot truer, and acceptable if that were the case.


We now look at the 0-4 SU teams, and reverse what was said of the 4-0 SU teams, and here are the facts:

Since the start of the 1995-96 season, there have been 53 teams that went 0-4 SU in their previous inter-league schedule.

38 of the 53 won more games the next season

12 of the 53 teams lost more games

3 of the 53 won the same amount of games

34 of the 53 teams won 2 or more games the following season (64.1%)

The 34 teams that won more games, did so to an average additional wins of a whooping 4.1!

It certainly spells out the same outcome, and our theory is looking mighty strong!

COMBINING THE 4-0’s and the 0-4’s:

When we put this all together into one, since they are indeed the same thing we get:

Since the start of the 1995-96 season, there have been 103 teams that won all, or went winless in their 4 inter-conference games the previous season.


positive theory results in 76 of 102 case studies (74.5%)

negative results in 21 of 102 case studies

neutral results in 5 case studies

The average wins or losses above or below the previous season in the 76 positive outcomes was 4!

Think about that for a second. Out of all case studies, with a positive outcome(75%) the results from the previous year were positive by 25% of the schedule, 4 games up or down!

I can say with certainty, that no one can come close to this stealthy way to forecast a team’s season win totals, using just 1 predictive variable, tested on both sides, over a 20 year period, with stunning credibility.

4-0 teams to play under from last year:


0-4 teams to play over from last year:

NY Jets

Best of Luck,

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!


Making Sports Betting Lines Win For You

Bettors take all different kinds of approaches to gambling on sporting events. Some use tremendously thorough statistical data to ascertain probabilities, and then translate their probabilities into odds and pointspreads. Others use situational analysis. They study trends and angles. Many seek the advice of persons they assume to have more expertise and information than themselves. Then there are the palm readers and stargazers. To them, the outcome of a game is a matter of fate.

Whatever philosophy, or combination of philosophies, a handicapper uses to bet sports, he must understand he is fighting a battle on several fronts. He fights for accurate information, for meaningful data, for expanded opportunities. He fights for time, knowledge, luck, a few good calls from the blasted refs. He fights public opinion, probability and -110 odds. Most importantly, and often overlooked, he fights the point spread.

Now, we all know the basics about the point spread or sports betting line:
• It originates in Las Vegas on Sunday afternoons, and is disseminated to legal and illegal bookmakers throughout the country within a matter of minutes of being publicly released.
• It is based on probabilities, and is intended to divide betting between two teams involved in a game. By dividing the action evenly, and charging -110 odds, bookmakers assure themselves of a profit of around five percent.
• Bettors either play the favorite and lay points, or bet the underdog and get points.
• Bookmakers change the point spread according to the betting, and they do so to invite betting on the side that has taken less action.
• Some sports betting line movement is considered smart and should be closely followed. Other line movement is considered dumb and should be played against.

Function and Shape Of Sports Betting Lines

sports betting linesBettors must realize that sports betting lines function in different ways and that they can take advantage of these differences. First of all, the line is viewed differently by bookmakers and linemakers. Bookmakers, in general, merely want to divide the betting evenly between the two sides in a contest. They are not gambling and merely want to broker a deal between two bettors who have opposite opinions.

Bettors take a different view. They are looking for spots where the probability of a certain outcome occurring exceeds the offered point spread, which is generated at least in part by an expected probability. It’s a simplistic conclusion, but bookmakers and linemakers handicap public opinion and attempt to find the pointspread at which half the bettors will bet on one side of a game and half on the other. Bettors, at least good ones, are attempting to find the games in which pointspread and probability do not mesh.

Now, of course, no one is right all the time. Linemakers, bookmakers and bettors all make mistakes. Linemakers misjudge public opinion. Bookmakers change their lines at the wrong time. Bettors use the wrong data to predict probability. Chaos and luck play a part in everyone’s decisions and in the outcome of most contests. Thus, we can see the sports betting lines are essentially created by a variety of decisions made by fallible individuals, and that the shape of the line can take several forms.

Big Game and Super Bowl Betting

Thursday NFL today; I got several emails asking me who I’d pick for the sake of action or “beer money” as some put it. Drinking does a good job of removing the edge if you know what I mean. Anyways, these questions get me thinking about big marquee games like the Super Bowl in NFL and how to bet them. Let’s get back to basics and the tough reality of Super Bowl betting and other marquee games like today’s Broncos vs. Chiefs Thursday night game.


There is no question that the Patriots have dominated this game for the past 10 years or so under coach Bill Belichick and QB Tom Brady going an astounding 192-62-0 (+8.71 ppg, 75.6%) SU. In the 2014 Super Bowl, over 65% of the public was betting the Favorite: none other than the New England Patriots over the Seattle Seahawks. It is one thing for the Patriots to win in marquee playoff games; it is quite another for them to dominate it, as they have going 21-8-0 (+6.00 ppg, 72.4$%) SU since 2001 (Brady / Belichick era) . Last year, New England beat Seattle 28-24, and the game before that they crushed the Colts 45-7. Before that they were averaging 26.2 ppg in the playoffs and winning by roughly 6 points a game.

But, with this said, one must still approach this game as he would any other, handicapping the game step by step. Here is the same Super Bowl betting checklist most professional bettors will use as they look at the game:


1. Ask yourself which team the bookmakers need. The line will always be tilted in favor of this team. This means the Seahawks could be the play on this point alone — if that is as far as a handicapper wished to go. Over 65% of the action coming in on the Pats, hence the books need the Seahawks to win and they’ll do what it takes to increase the odds of the Seahawks covering while not passing up an opportunity to cash in with a Seahawk’s win.

2. What is the big-game experience of these two teams? There is something about a classic event in a game such as this that gives a big edge to the team with proven big-game experience. Experienced teams seem to handle the pressure better and are more apt to be able to play the kind of game that got them here.

3. What about injuries to key people? One must assume that everybody is banged up a bit at this point of the season. After all, each of these teams played at least four preseason games, 16 regular season games and two playoff contests. That’s a 22-game schedule and the human body simply is not made to do that. But pay attention to the skill positions, since that is where a player in pain can hurt you the most.

4. In analyzing the injury factor, remember that a team will lose its effectiveness in direct relationship to how many players are really hurt or questionable for the game: A team with one injured player probably can survive; a team with two key people hurt is a bit suspect; and one with three people out is in big trouble.

Super Bowl betting5. How has the coach performed in big games in the past? Top coaches figure out ways to win big games. The game plan Bill Belichick beat Seattle with in 2014 is a textbook lesson in destroying the strength of the other team. Belichick knew Seattle was so explosive defensively that he could never win in the trenches with the limited talent he had on that side of the ball. Thus he designed a game plan that simply kept the ball in Tom Brady’s hands — and it gave him a 28- 24 squeak by win which killed the books and sharps who were all over Seattle (including myself). Really though, the game came down to luck and it was a big ‘shit happens’ thing for the guys who made the right call: the Seahawks who led 24-14 going into the 4th quarter.

6. Which team seems to be better right now? Look at both teams closely and try to make some objective judgment on which is playing the better football. It will mean something in this game.

7. If the line makes it difficult to make a decision as to which side to play, then look for other ways to bet the game. Maybe an over or under is the play?

8. If you like the underdog to win outright, bet the money line on the game. Instead of getting even-money for your bet, you can get the odds, which should be somewhere be-tween 3-1 and 4-1 on the ‘dog. In these tight spread games, the ATS results correlate very closely with the SU results so it is more about picking the winner in most NFL games close to the smaller handles (3, 7).

9. When you look at the talent of each team, determine whether the underdog can win the game if it plays its best football. Forget about the jinx and just match the underdog up with the favorite and ask yourself if this team could win it outright, if it plays its game.

10. Take a look at all other betting gimmicks offered on the game. You will see lines made on which team will have the most rushing yards, the most passing yards, the most interceptions, etc.,. Many times one can make an intelligent and objective judgment on these little things. Other gimmicks, such as who will score first, or who will kick the first field goal, are Sucker bets unless you’re getting significant plus odds because it literally comes down to a coin flip that vast majority of the time.

We know it is difficult to bet against the Favorited mega elite team in the Super Bowl, but if you think the Seahawks/’Dog plus the points is the play, take a stand. The dog seems jinxed, but that will die, and it just might be this year. One thing is for certain: if you take the dog you have two chances to win, i.e. outright or by covering. If you bet the favorite, you only one chance to win, and that is by covering.

Final note: These are just some thoughts on how you might make a small “beer money” wager; however, in the long haul, marquee games are best avoided completely. That includes, the playoffs in any sport, Monday, Thursday, Friday night Football, Weekend games in B2b sports (ie. NBA, MLB), big name school rivalry matchups, etc. etc.. The best bets are usually obscure games that no one is interested in and being right in marquee games is more of an ego thing that doesn’t really get you anywhere in the long run.

Bet sharp!

Computer Generated Sports Picks

Injury Traps? | Beat the Bookmakers

Bookies’ Injury Traps?

There are some bettors who think that it is bookmakers themselves who put out phony injury reports, hoping to confuse bettors, especially on first inning MLB betting, where the players were beating bookmakers big-time in the not-too-distant past. In fact, some Las Vegas bookmakers would and still will only take a $25 or $50 bet on a prop.

Public Sentiment

The feeling among the betting public is that bookmakers spread lies on any game involving marquee NBA teams for example. Let’s say that, in NBA, an injury report comes out that the Spur’s Tony Parker is injured. Some big bettors were making a killing on the Spurs at one point while you could balance the team on the edge of a knife. However, by saying he was hurt and might not play, the bookies accomplished two things:

1. They could legitimately tell their big bettors who beat the bookmakers and regular customers that they were taking only limited action on an individual game, since the status of Parker was uncertain.

2. They could scare away many bettors who, when faced with uncertainty, merely pass on such games.

Beat The BookmakersRumors

When rumors fly, bookmakers can take a game off the board or put it in a circle and limit the action on it. And, as we said, this permits them to maintain their credibility while protecting themselves from getting hit again by a hot team.

It is highly unlikely that the NBA is going to establish an injury policy; this means bettors themselves are going to have to work harder to take an edge against the lies.

First of all, every serious basketball bettor must have more than one outlet. Maybe if one bookmaker is not taking action on a team, another one will be. There is also the issue of betting lines, especially NBA totals, varying from one betting shop to another. Smart bettors who beat the bookmakers shop for numbers in order to “get” games that some bookmakers are not handling. They also get to see different lines and then place their wagers with the bookie who offers them the best number.

Beat the Bookmakers

By having a number of betting outlets, you will get a consensus opinion on the accuracy of injury reports. You will also be surprised to find that while one bookmaker carried Parker as questionable and would take no action on the game, another one, just a few doors away, was taking San Antonio action anybody wanted to place.

It also is interesting to note that for the first time in many years, more NBA basketball teams are playing to a consistent profile. This means that if a bettor can cut through all the lies and get to the truth each night, he knows that he is wagering on a team which is playing a consistent game.

-Tom Herbert

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.





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)


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)


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.

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