NBA Stituational Bet, SDQL

Originally Posted by Wojo

Some good conversation. Some people in this thread have misinterpreted what I have said in earlier posts, so perhaps I could be doing the same thing here.

I do believe that ATS records and even ATS game results are helpful to picking the point-spread winner in games. You want to find value in a line or total. Oddsmakers can over or under-value a team due to how they are performing against the spread, and obviously straight-up.

Evaluating a team's overall ATS trend over the past 10 years is fruitless when there have been major personnel changes and different head coaches, IMO.

As stated above, SDQL isn't perfect but it is a very valuable tool. It gives you an advantage over most other cappers, but only if you know how to use it. Some services provide systems and trends for a fee. There are some touts that follow those "systems" blindly, you see them quoted in tout's write-ups all the time. Unfortunately, those systems aren't always correct or they follow very flawed logic.

Of course, those purely ATS performance based trends are not completely useless and without any value whatsoever. However, I believe that one must look on them as an indication that bookies frequently over-estimate or under-estimate a particular teams or situations, and from there on one must search for factors, what made bookies set the line which was way off.

Please, everyone, do not take this as a personal insult, it's just that there are so many so called "touts" who base their picks on wild trends which contain little logic or none of it.

And, guys, I'm really trying hard to put any input of my own in finding some great value and contribute to this thread.

2-1, and thanks to J's late quiz entry today, 3-1.

This is is why I enjoy SDQL research, because of those 4 games if I were left to my own capping abilities I would have picked 3 of them wrong.

Originally Posted by JMon

We want to fade a home dog on a same season road revenge game getting blown out by 30 or more; off a road loss. Good, but with taking away rest of more than two and high dogs of more than 8.5...we have something extraordinary since 2002.

I love it when school is in session!
Thanks, JMon!

Originally Posted by b1slickguy

I love it when school is in session!
Thanks, JMon!

Finally a class worth actually attending...

Anything today fellas? Thank you again for this thread, best of SBR.

Originally Posted by JMon

-9.5<=line<=-3.5 and tA(points)>=102 and 102>=oA(o: points)>=98 and wins+losses>=42 and p: points+po: points>=205 and pp: points+ppo: points>=205 and ppp: points+pppo: points>=205 and pppp: points+ppppo: points>=205

Need to eliminate spaces for emoticon..(freaken annoying as hell)

Let me know if anyone needs a translation.

You're playing Minnesota tonight?

I would love to hear you guy's thoughts on this sdql for the over play:

tA(points)>=102 and oA(o: points)>=100 and op:dps<=-10 and 215>total>200 and rest=0

and got this at a different angle but still pointing over

p: points+po: points>205 and pp: points+ppo: points>205 and 103>oA(o: points)>=100 and rest=0 and 215>total>200 and op: points<100

Originally Posted by azn1234

You're playing Minnesota tonight?

average play

Originally Posted by cofaga

I would love to hear you guy's thoughts on this sdql for the over play:

tA(points)>=102 and oA(o: points)>=100 and op:dps<=-10 and 215>total>200 and rest=0

and got this at a different angle but still pointing over

p: points+po: points>205 and pp: points+ppo: points>205 and 103>oA(o: points)>=100 and rest=0 and 215>total>200 and op: points<100

I like the second one better but wouldn't play either. These are examples of queries that are frustratingly close but ultimately fail on a few fronts. In these two case the low sample sizes combined with a little inconsistency from season to season would make me move on. But they're not bad, I doubt anyone would lose money on them over time...but I doubt they'd make money either.

Good attempts cofaga, keep it up.

Originally Posted by JMon

average play

That's the only one I had a game come up for today, nothing else was worth it in terms of value.

New one, no play with it today but it passes the tests for consistency over the years, Z-value (win % + volume), good ATS margin, and decent logic:

HF and WP>=72 and game number>=25 and p: LF and line<=-9.5 and total>=187.5

I'll explain a bit more about how it came about for those still learning "why" we choose to pair up certain filters but not others when tinkering with queries.

The logic is that you're playing the elite team (72+ win % on the season) who is a heavy home fav (laying 9.5 points minimum) after the team just lost their last game also as the fav (home or away). There are tons of 'bounce back' queries and scenarios in the NBA, some work, some don't, and when you chase them in SDQL you need to be patient because it can take time to put the puzzle together properly while filtering out the noise.

For this particular one it's later in the season to make sure we're not betting in November on some fluke pretender that had a lucky hot start (doesn't begin until 25 games in or more), and since we need a shit-ton of scoring from the fav to cover that massive line we're eliminating any game where the total is below 187.5 (gets rid of some mediocre heavy dog opponents that actually show up defensively for 'big' games and lock down the fav's scoring versus the norm).

It works inside or outside of division or conference games (a lot of systems break down if a game is, or is not, a well-known division opponent, always screen for that when you feel you've found a winning query), and it allows the comfort of being a true square (you're on the same elite marquee home that the rest of the clueless public is).

That's it, pretty simple. It's not perfect but it's an example of what you're chasing with SDQL, something that only has excess filters if each of the filters fit (mostly) within the logic of the play, and something that can be applied in most circumstances within said logic.

Pacers 8 points in the 1st

Originally Posted by Mako-SBR

That's the only one I had a game come up for today, nothing else was worth it in terms of value.

Me too, in average at that. No feel.

Originally Posted by Mako-SBR

New one, no play with it today but it passes the tests for consistency over the years, Z-value (win % + volume), good ATS margin, and decent logic:

HF and WP>=72 and game number>=25 and p: LF and line<=-9.5 and total>=187.5

I'll explain a bit more about how it came about for those still learning "why" we choose to pair up certain filters but not others when tinkering with queries.

The logic is that you're playing the elite team (72+ win % on the season) who is a heavy home fav (laying 9.5 points minimum) after the team just lost their last game also as the fav (home or away). There are tons of 'bounce back' queries and scenarios in the NBA, some work, some don't, and when you chase them in SDQL you need to be patient because it can take time to put the puzzle together properly while filtering out the noise.

For this particular one it's later in the season to make sure we're not betting in November on some fluke pretender that had a lucky hot start (doesn't begin until 25 games in or more), and since we need a shit-ton of scoring from the fav to cover that massive line we're eliminating any game where the total is below 187.5 (gets rid of some mediocre heavy dog opponents that actually show up defensively for 'big' games and lock down the fav's scoring versus the norm).

It works inside or outside of division or conference games (a lot of systems break down if a game is, or is not, a well-known division opponent, always screen for that when you feel you've found a winning query), and it allows the comfort of being a true square (you're on the same elite marquee home that the rest of the clueless public is).

That's it, pretty simple. It's not perfect but it's an example of what you're chasing with SDQL, something that only has excess filters if each of the filters fit (mostly) within the logic of the play, and something that can be applied in most circumstances within said logic.

This is really good. I saved it in my personal database. Not big on total filters in NBA, but it makes sense with HF. I saved it as is. But in my description... noted o:rest<3 and line>=-10.5

Very nice Mako

Originally Posted by figue

guys what is the sdql for this :

This matchup fits into a system to play on road teams like the Clippers when they are coming off a home win by 10 points or more and have won 60 to 75 percent of their games on the season, and they are facing a team that has won 51 to 60 percent of their games on the year. This system has a 47-23 (67%) ATS record over the last five seasons.

Fig..you are going to have to learn this eventually. What can you come up with by yourself? Then we will help you out.

Originally Posted by JMon

Fig..you are going to have to learn this eventually. What can you come up with by yourself? Then we will help you out.

a and p:hw<10 points and wp>=60 and 75>= and op:wp>=51 and 60>=