Red Flags Part 3

There are still enough cancelations and overall 2020 haziness about the season to give me pause before declaring that the system is back to its winning ways again, but for those of you that haven’t been around for the fun and games of years gone by, I can tell you that this is what it feels like. The last 5 weeks for the system have gone, respectively, 3-10, 3-3, 4-4, 5-2, 9-5, seemingly a demonstration of a losing process gradually evening out and then turning the corner into a winner again. Every year college football gives me plenty of frustrations to stew on, such as this weekend did with Tulsa tying one game at the end of regulation, Hail Mary style, or Cincinnati literally deciding to take a seat on the one yard line in order to burn more time, as opposed to just taking the 2 score lead, while covering the spread, but unlike the first half of this season, the difference is seldom indicative of a potential winning week becoming a decisive loser, rather a 11-4 week turning into a 9-5 one. It’s still infuriating, but in the end anyone placing the bets could still count their winnings, and this is what I’m used to the system providing.

As for wrapping up this red flag series I have going, I’m mostly interested in discussing the tests I’ve run, am still running or might seek to in the future. Red flags, as I call them, have been very useful in upping a winning percentage, but keep in mind that their sole purpose is to eliminate dangerous games from the betting pool, and eliminating games not only reduces the sample size in a system that depends on it, but also gives potential to taking money off the table when a reduced winning percentage is still a winning one. There is a second scale, which I’ll get into next week, that not only flags some games that would otherwise be in play, but also adds in some more underdogs, basically a zero sum game with regards to sample size, but is starting to demonstrate that it just might be adding and substracting the right games. We’ll see, more testing is necessary here.

Initially I ran several tests to attempt assessing whether or not it was always better to either be taking the points or giving them up, exclusively, thus picking only one batch of games or the other. I’m not certain how the odds makers average out on this, but it’s not all that significant to me since I’m dealing only with a subset of their overall offering, and their numbers would include too many games my system has deemed unplayable anyway. Regardless, these attempts failed. Any differences I might have noticed would have fallen within even the most modest standard deviation calculations, which wouldn’t have been all that modest anyway because the sample size was reduced greatly, particularly with the favorites being taken off the board, which my system is consistently heavy on.

I’ve also given a lot more attention to home field advantage than the changes I’ve made indicate. I’ve always suspected that tossing out road picks altogether could be useful, but have found very little evidence that might confirm this, sans some of the anecdotal memories of good teams running into the occasional road hornet’s nest. I’m not finished exploring the home field thing, but obviously this year has offered little opportunity to explore different concepts on the subject. Last season demonstrated some interesting results when combined with the aforementioned second scale, and I’ll get into that as well next week. Additionally, when things return to normal I’ll be exploring the potential of non-conference road games being less troublesome for teams than conference road games are. I suspect I might find something here. Most often, what I find is that what I suspect turns out to be wrong.

Make no mistake about it, the decision to abandon brand new coaches, for or against, in their first year with a new program has been undeniably useful. The numbers are repeatedly decisive on the idea, even this year as well. The second year thing is not as clear. The numbers in the past have been erratic enough that for comfort’s sake, I’ve been satisfied with their omission from my slate, but if I were to find a more predictable red flag, it’s not out of the realm of possibility that I add these back into the pool, if for no other reason than to get the sample size back up. Part of the consistency issue here could have something to do with certain unique qualities one year’s class of second year coaches may maintain versus others. I’m not sure what to do with that, but the scrutiny on the subject is definitely more pencil than sharpie at this point.

The system and the ideas that gave birth to it are my own, but that doesn’t mean I haven’t had contributions from those close to me in the refining of it. My brothers and some of my friends are very knowledgeable sports fans and my ears are always open to ideas they might have on improving my numbers. The top influencer that comes up in conversation is a quarterback change and it’s inevitably my greatest cause for concern. While it’s not an apathetic stance I take on ignoring the phenomenon, I’m not exactly passionate on my reasoning for inaction either. It does present a problem though, and one of the most prominent is a lack of time. What if I did want to red flag every game that presented some recent change in QB for either team? How long would I give the new QB to establish himself? How many games would I have sapped from my sample size? And most significantly, how on earth would I go about tracking the starting QB for roughly 130 teams in FBS college football? After all, there have been times that I walked to Memorial Stadium in Lawrence, Kansas, having no idea who would be starting at QB for the Kansas game I was about to watch, and that’s the team I follow most closely. Plus, how much difference did it ever make? Well, it did for Todd Reesing, but pretty much never other than that. And the whole system is based on that premise anyway, that it’s really about the program and not the specific parts. In other words, go right ahead and let the subjective enter the odds making decisions, and we’ll account for the difference as a value play. So at least until I find a whole lot more time to test ideas on the subject, I’ll just let my system try to take advantage of the bold predictions of massive upward and downward mobility based on a quarterback that nobody has ever seen play at the college level, but still has the same offensive line and wide receivers as his predecessor did, not to mention the defense when he’s on the sideline.

The other mentionable topic is really an argument verging on nitpicking at times., what to do about gimmicky scenarios, primarily Hawaii and the armed forces style offenses. Traveling to the island state does seem to present a unique challenge, as do higher altitude sites, and yes, some teams seem more baffled by the triple option thing than others do. So far I haven’t noticed any decipherable statistical anomaly these games have had on the system, at least on the winning percentage, but I’m not completely dismissive of the idea either, because wow, these games just look different. This season hasn’t been great with regards to the armed forces teams, but then again, the system has had a problem with the pandemic in general, so maybe it’s not the time to make huge assumptions. Last season the system actually had a winning record on Army and Navy games, and Hawaii changed it’s coach, so while I will continue to monitor the outcomes, I doubt I’ll be drawing big conclusions on them anytime soon.

At the time of this post, one of the picks is already in process. When this happens, I do post the prediction prior to the game on the Facebook page, so please like and follow it there if you’d like to see the earlier picks in real time. This week gives us 13 games again with one fairly unsettling one, taking the points on Pittsburgh against Clemson. If you’re expecting Clemson to run it up in that one, well, so am I, but the system is right more often than I am so I have no intention of implementing some kind of veto power, now any more than I ever am. Happy Thanksgiving, albeit a bit belated, thank you for reading, and here are my picks:

Friday

Iowa -13.5 at home vs Nebraska

Oregon -13.5 on the road vs Oregon State

Saturday

Georgia State +1.5 at home vs Georgia Southern

Florida -23 at home vs Kentucky

Toledo -10 at home vs Ball State

Penn State +2.5 on the road vs Michigan

Kent State +7.5 on the road vs Buffalo

Rice -11.5 at home vs UTEP

Arkansas State -6.5 at home vs South Alabama

Louisiana Lafayette -28.5 on the road vs Louisiana Monroe

Pittsburgh +24 on the road vs Clemson

Texas A&M -14.5 at home vs LSU

UCLA -9 at home vs Arizona