College football systems that predict improvement or decline
Following up last week’s piece on College Football Stability, I like to take the stability data and combine it with key stats and other tidbits from the prior season to highlight systems that have predicted potential improvement or decline in the past. If you recall, I did something similar for pro football a couple of months back.
College sports present some different challenges from the pros in that there is typically much greater turnover in rosters and coaching staffs from one season to the next. Evaluating these changes properly is one of the most crucial aspects to season-long success for bettors. There are a lot of factors that can affect a team’s strength and/or how much different they might wind up being from the season prior. You have to consider personnel losses, coaching changes, momentum lost or gained and many other factors. Sometimes the change is immense. Adding to the difficulty is the thought that many programs are taking players right out of their most recent recruiting class or transfers and plugging them into starting spots.
The last three seasons have presented some new variables, due to the COVID-19 pandemic and the super senior eligibility rules that are still working their way through the system. But what I have sought to do in recent years was to try to quantify the signs of potential improvement or decline out of teams when considering some year-to-year transitional situations. All of the things that come into play in this piece were factors in my College Football Stability Scores. After identifying some key criteria for each of the factors, I went back and looked at teams from recent seasons fitting certain criteria to determine their average improvement or decline.
Admittedly, the fact that the number of teams qualifying for these various systems has wavered tremendously over the last few seasons has skewed the statistics. However, I view the methodology as most important and don’t believe the fundamental logic has changed based on the unusual circumstances of the last few years.
As you will see from my findings, there are a number of obvious reasons why teams have gotten significantly better or worse from one season to another. Taking this a step further, I am using my findings to spotlight some teams to watch in 2023. Hopefully this will give you a foundation on which to start a successful handicapping run this season, both in terms of game-by-game handicapping and in attacking season win total props.
The data I used dates to the 2013 season, or the last 10 seasons.
Next week, in part 2 of this miniseries, I will address some STATISTICAL transition systems that the 2023 teams fit into.
Analyzing Number of Returning Starters
Just looking at the sheer number of returning starters on a team can provide great clues. Here are some of the systems I have discovered and have continued to track annually:
· Over the last 10 years, 115 teams have had at least six fewer returning starters than the prior season, and only 28 of them have improved that season. The average drop-off was a winning percentage decline of 12.5% and ATS drop of about 4.0%.
Potential decline teams for 2023: Bowling Green, Kent State, NC State, Old Dominion, South Florida, Stanford, TCU, Troy, UAB