GWB College Football Betting Guide: Week 10

You know how there are times in your life you look back and say, “Man, I didn’t realize it at the time, but those were the good times?” Well, these next 4 months are those “good times” of the sports betting world. Appreciate it. Soak it up. In college football, the pretenders will fall by the wayside and we will march towards the CFB Playoff. We will be knee deep in bowl games. NFL hits the meat of the schedule. NBA is rocking. College basketball, the ultimate grind time, will run through its entire season and right into March Madness. Hockey will hang out on the side and cost you some units. International soccer will have almost 100 games every weekend. You high volume grinders, it’s your time to shine. It’s your time to sink or swim. I hope you swim.

I say it all the time. Be funded. You will go broke even if you are a winning bettor. Think about that and let that sink in.

If you are a high level bettor and aren’t funded enough, you will go broke even if you are a winning bettor.

You need to be liquid hundreds of units if you are blasting over 100 bets in one day. I know I repeat this often, but it is vital. Money management and having bullets to fire are the two most important parts of sports betting. You need to be able to safely and responsibly absorb the action. That means having the money to tackle all of your edges and having the discipline to not chase, stake correctly, and stay on your plan.

My college basketball Saturdays are staggering amounts of action for some. It’s what I’ve built towards and it’s absolutely not for everyone. But it’s apart of my plan and it can be managed properly with my bankroll. Hell, just hearing from the followers on the app, I know that my style of blasting must look like a shit show to some of you. It’s my style that I have built towards and thankfully have found success with. What works for you and the approach you take to high volume betting won’t necessarily look like my approach, and honestly it shouldn’t. It should look like what makes sense to you.

Last week’s GWB Betting Guide shook things up and went 1-0-1 for the win column! GWB’s favorite father figure, Rocky Long, absolutely delivered for us and shut the game down almost exactly how we projected it. Another no sweat Rocky Long Conference Favorite UNDER spot cashes. For what it’s worth, that Rocky Long 2H angle cashed as well. Good news for our other play - South Alabama +27 pushed, and most of you probably got +27.5 as the line crept to that number a few hours after we went live with the play. For our record keeping purposes, it will be a PUSH for the GWB, but I hope some of you cashed a W on South Alabama. They are truly a horrible football team.

Let’s keep the wins rolling, who are we on this week?

Coastal Carolina +1 vs Troy, consensus line

This week I want to explore some systems that are a touch “experimental” I guess. Angles that are outside of our simple, clean wheelhouse that I like to stay in. Peter and I talked about this a few weeks ago- it’s important to experiment in labs. It’s beneficial to break the rules and build without censoring yourself. Now, it’s important to then be critical and examine what you’ve built, but it’s helpful for your mind and capping to push the boundaries of what you find acceptable when building historical models.

Every year you have those teams that screw you when you back them, and screw you when you go against them. That’s Coastal Carolina for me this year. I’m gun shy backing them this week, but I can’t deny a really interesting angle that I have lit on them this week. Let’s take a look:

FadeBad

This is a Fade system. Also notice, this is a ML system that I fire on. It’s disgustingly good and this year it’s 4-0. The ML cost was -105, so the GWB is rolling with the +1 for Coastal Carolina. Let’s discuss the line items we are dealing with:

Teams that are +Pythag are teams that have underperformed against their expected Win %. Grabbing teams with a win % of 0-42% AND with +Pythag helps us identify teams that are having a bad year and were maybe expected to be closer to an “average or good” team performance wise.

The game number is meaningful to me here because there are enough games played to help us determine that what we are seeing is real in regards to a team’s performance. When Troy is 0-1 it’s hard to say the data we are looking at is meaningful. When they are 3-4 we are more confident in the results.

With the last two line items - Spread and Previous game margin, I’m trying to cap the mindset of these underperforming teams. They are off a loss - not an obscene blowout where we might get some bounce back and not a close gritty loss either. Just a standard 17 point drubbing late in an already disappointing season.

Now they are tasked with being favored or right around a pick’em! Fading teams in this spot is 87-78 for +85.2 Units!

The logic is there to me. It is a touch too built out for me, but the continued success makes it hard to shake. Fading late season underperformers who are taking the field as favorites. Coastal Carolina, Cash dat!

KState vs Kansas UNDER 54.5, consensus line

Forget everything I’ve ever discussed. Line items, cherry picking, moving parts. There are no rules. I’m going to discuss an angle that I can’t fully explain. The logic is there to me. I’ve done my best to piece it together within BetLabs.

Epic sample size. Epic performance. Another year cashing bigly. So what’s the problem? Well, what the hell have I actually built?

OverTeamDataUNDER

What the hell is this shit? 1,100 game sample size hitting at 58%! +153 Units betting the UNDER. What are we looking at here...

The original motivation for this system was to find home teams that went over the total at a high rate with the public also backing the OVER. Then mix in high wind speeds with a drop in the total. My brain sees that and thinks, “what a bunch of random shit put together.” In the line item sense, sure. But in the capping sense - We have the public backing an over with a team that has gone over a fair amount. We throw in a total drop and some wind and it feels like a sneaky sharp angle to back UNDERS.

Yes, it feels dirty. Yes, it feels like connecting somewhat random data points. Yes, this seems like arbitrary ranges layered over each other. I think it’s probable that these line items have a relationship that my brain isn’t smart enough to identify. Reread that last sentence and think about how relevant that is to capping sports.

I built this system 3 years ago and have cashed it every year. It has an awesome sample. The chart is absolutely beautiful, take a look:

UnderChart

This isn’t just theory BS. I’ve been firing in real life.

This weekend we get a nice little rivalry game between the Cats and the Jayhawks. Kansas has gone over 62% of the time, 55% of bets are on the over. Winds will be 10 mph and the total has dropped 3.5 points.

Getting creative in labs and trying to build ambitious ideas is helpful for us as cappers. Although we can’t recognize the correlation of all the line items together, a perfect chord is strummed with an 1,100 game sample.

Kansas UNDER 54.5, Cash Dat!

GWB Betting Guide YTD: 10-8-1 +1.3 U

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