Upward Mobility in College Football
OK results first, and they weren’t great. On Saturday, I went 2-3, winning with Louisiana Tech (barely) and Tulsa, which was easy enough, but I lost badly with my big win the previous week, Louisiana Lafayette, also Wake Forest in an infuriatingly close game and then Tulane, who was piling on the previously established Navy sorrow train, but somehow managed to blow one of those leads teams don’t ordinarily blow. There is actually a rather lengthy discussion to cover on the armed forces teams, but this is more of a hot spot topic between those that already know my system well, and given how much I still have to cover on bringing you up to speed on how the system works in the first place, well, we’ll get there.
This brings my record to 4-6 on the season. That’s troubling, but maybe not for the reasons you may think. The obvious one is that this is Covid year and I’m already concerned about the viability of a system like mine in a season that has the potential for so many unpredictable factors, so you know, some winning would go a long way to ease the mind of a guy that saw fit to release his concepts in possibly the worst year imaginable. For everything. And then there’s the sample size issue, which anyone that knows anything at all about analytics is fully aware of this consideration. Truthfully, it’s not at all unusual for me to have a 4-6 stretch and similarly not outrageous for me to have a week where I finish 2 games below .500, but for there to be 3 weeks combined where my system only selects 10 games worthy of playing, that’s unheard of and a very real reason for concern. Just so you know what I’m working with here, there are very few weeks in an average season where I’m dealing with 10 or fewer games on the docket. Um, yeah…gulp.
To get to the greatest predicament of all, however, we now need to enter the first testable premise of my system, not the big epiphany of sorts, but one I’ve been operating on for as long as I can remember being a college football fan, well before actual data analysis was my bag. And it’s this: programs, year in, year out, generally pick up where they left off. Now, I know where your head is going. You’re thinking of all the worst to first, first to worst stories, right? But also consider the obvious fallacy here, these stories are your anecdotal evidence. How often is Alabama bad? Oklahoma struggling to compete for a Big 12 title? Kansas good? Rutgers a contender? Additionally, my contention is that the lack of mobility reaches all levels, 6 or 7 win teams are likely 5-8 win teams the following season. 4-5 teams, similar results. 8-9 win teams, same. There might be some decline or advancement, but for the most part it’s fairly insignificant, besides the fact that the difference between a 5 win team and a 6 win team is a bowl game.
Now your exceptions still matter, but test any that come to mind by this thesis: the greatest opportunity for upward and downward mobility in college football is a coaching change. I include downward intently, because as a Kansas football fan, I can assure you that bad teams can in fact get worse, and a coaching change does not necessarily predict upward mobility, it is only the greatest opportunity for it. Do exceptions still exist? Of course they do. Sometimes one player, particularly the QB, can escalate a team to greatness, but just as often, if not more, good quarterbacks are replaced by other good quarterbacks, bad ones replaced by younger bad ones and so on; the program is what the program is and the singularly most significant occurrences of upward and downward mobility result from coaching changes.
This is of particular significance at the beginning of each season. Players graduate and recruiting experts do their thing. They are very good at what they do too, undoubtedly providing us with far superior information on incoming players than we’ve ever had at our fingertips, at any point in the history of our college football fandom. This I don’t question. But what we do with that information is pretty important early in every season and by sitting on it, and just betting the consistency of the program, year in year out, the system finds great value here and repeatedly does very well in the early weeks of every season. Just make sure to tip-toe around those coaching changes.
And then along comes 2020. Entire conferences aren’t playing football. Some are, but are starting at different points in the season, a few skipping right to conference play, others only participating in one non-conference game, thus alleviating a good portion of the season the system thrives in, not to mention the game cancellations we’re seeing when teams turn up riddled with coronavirus. Down, down, down goes the sample size and with it the opportunity to find value in these early games. It’s not over and this week I have eight games to play, more than any other week, so by Sunday we could be looking at the numbers and seeing the overall value grab as a success. But then again, I would have had nine games had Notre Dame not cancelled, and would have had many more to play had this been a normal season. Is it possible for someone to do well at shooting skeet on a windy day? Absolutely. Just be sure not to bet your kid’s education on someone trying to shoot skeet through a tornado.
Here are this week’s picks:
Thursday
South Alabama +7 at home vs UAB
Saturday
TCU +2.5 at home vs Iowa State
Southern Mississippi +3.5 at home vs Tulane
Louisiana Monroe -10 at home vs UTEP
Arkansas State -2.5 at home vs Tulsa (CANCELED)
Army +13.5 on the road vs Cincinnati
Virginia -5.5 at home vs Duke
Virginia -6.5 at home vs North Carolina State
I am tracking a 9th team, Auburn, but my system won’t take them unless the line drops to -4.5 against Kentucky. I doubt it happens, but in the event that it does, check back on this post, as I will have added it to the list when I get the line I’m looking for. Thank you for reading this, and please forward it along to any friends that may have interest!
