Top MLB Sports Betting System

I haven’t done this in a while; today, I am going over over a year of performance of sports betting system and trends I put in my relatively new Trend Mart product that you guys get from my partners and me for a member discounted amount on the side of your PCG subscription.


sports betting system

The ‘PCG Trend Mart’ Results are as follows:
Some trends did well and other trends did not; Overall, many more trends performed positively than negatively, and overall, there has been a net gain of profit INCLUDING loss of vig where two bets cancel each other out; that is exactly what I expected to see, but not with so much success. SDQL is truly an amazing thing.
In particular, we see trends that strongly follow my points outlined in the what-to-look-for-in-a-system write up here: PCG TREND MART SAMPLE SPORTSBETTING SYSTEM did much better than things that strayed from the rules I set from the start.
In particular, I found that sports betting systems that followed rules #1, #2, and #4 were the real winners. (That’s #1. Logical system #2. Simple (not-backfitted) #4. Large sample size)
For MLB, here is an sdql system of ours that started off 788-324 210.46 units, 11.1% roi SU This trend went 77-31 19.72 units since it was found / developed and inserted into the database you subscribe to for me over the Killersports.
The Sports Betting System Parameters:
1. Play on team averaging less than 0.260 hits / at bats
2. Large -150 to -300 favorite
3. Opponent starter has sub 4.50 ERA
4. Late regular season (from July to September)
5. Team was not just shut out and opponent did not just score over 6 runs last game. No momentum either way.
Full trend description: In database history (since 2004), -150 to -300 favorites averaging less than 0.260 hits / at bats are 790-326 ( 1.6 rpg, 70.8% SU, 208.91 units) in the second half of regular season as long as they weren’t shut out last game and/or opponent doesn’t have strong forward momentum.
Logic: This system pinwheels around the ‘Nose-Pincher’ ‘Gut-Twister’ premise: That is, the more a bet stinks in the average Joe’s eyes, the better it probably is. Think about it on this one: who wants to bet huge chalk against a good starting pitcher for a team with a struggling offense. Seems too easy to take the dog here; when something looks too easy and feel very comfortable to bet, it probably is.
Simple: While this isn’t the most simple system I have in my arsenal (compare to my NFL system where we’re just looking for a team that hasn’t won a single game); it is still pretty much to the point. You’re making a contrarian chalked up wager banking on the idea that the linemakers did the work for us on this one; they probably know something we don’t and we’ll let them sweat out the hard work.
Large sample size: it started with a sample size of over 1000 games and a massive z-score. The z-score is now over 4.4 for JUST the forward results (77-31 SU).
tA(hits/at bats) <= .26 and -150>line>=-300 and o:STDSERA <= 4.5 and 10>month>6 and p:runs>0 and op:runs<7
Please read and act:
Note: In the plain English descriptions via the PCG Trend Mart, I time stamp the trends putting the initial record in the text. I’ve been pushing for a better way, and apparently so has my friend Ed Meyer (brother of Joe Meyer who is the owner and brains behind SDQL).
*My thought is to show two records on active trends: overall record for the sdql trend PLUS record since the date you saved the trend in the database. If you’re for this progressive feature as well, please send a product feature request to Mr. Joe Meyer at
Also, do let me know at if you liked this writeup and would like to see more emails like this!

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