This is an automated archive made by the Lemmit Bot.
The original was posted on /r/cfb by /u/thesquareattack on 2025-07-08 17:19:32+00:00.
Hello r/CFB - I come bearing gifts today in the form of the sort of offseason turbo-nerdery that can only result from extended lack of exposure to actual, on-field college football.
Who am I? I write the Square Attack, a college football newsletter on Substack. I preview games weekly through a gambling perspective, but it’s specifically designed for CFB enjoyers of all varieties, degenerate or otherwise. Many of our readers aren’t gamblers, some barely follow CFB at all!
As we inch ever closer 9am PT on August 23rd, I’ve been chomping at the bit for some reason, any reason to think and write about what to expect this season, and lo, has our favorite human database of CFB intel, Phil Steele, provided an excellent one with the release of his 2025 College Football Preview.
INTRO
A brief aside for those unfamiliar: Phil Steele is a legend. He’s a college football super-savant: this year marks the 31st edition of the best preview magazine in the business. He’s a Heisman voter, he interviewed 125 of 136 FBS head coaches as part of the creation of this year’s almanac, and he’s one of the most respected members of the CFB media writ large. If you care deeply about (and/or care deeply about gambling on) college football, Phil Steele is a name you have to know, and it’s important for you to familiarize yourself with his work.
Over the last few weeks, I’ve been scouring this 350+ page tome for tidbits that might prove interesting, or perhaps more importantly, prove useful as I gear up for the 2025 season. The first 50 pages or so are dedicated to longitudinal coverage of the sport as a whole, and to me, that’s where the true value is to be found.
Phil and his team have put together a slew of objective measurements of what to expect based on a variety of observable factors, with the goal of giving the reader an idea as to the types of information you should be paying attention to as we close out last year and look ahead to this year.
OBJECTIVE
So, why am I writing this? To see which teams are MORE LIKELY to outperform their 2024 win/loss record in 2025, and which teams are LESS LIKELY to outperform their 2024 win/loss record in 2025, based on the objective metrics that Phil’s preview has compiled. We’re going to take Phil’s numbers, and fold them all onto each other into a couple of statistical indexes that should, in the aggregate, give us a sense of if a team is going to get better or worse in 2025.
Phil’s magazine provides us with some of the most impactful statistical and environmental trends that every college football team in the nation faces. My goal is not to replace the experience of reading through this invaluable resource for yourself, it’s to synthesize some macro findings and see if we can spot some cross-statistical trends that upon which we can capitalize.
Disclaimer: you really do need to buy Phil Steele’s 2025 College Football Preview if you really want to get the most out of this meta-analysis, but I’ll try and make it accessible as a standalone article. I’m not going to try to provide the rich context that the magazine does - we’re strictly talking numbers today.
METHODOLOGY
The first section of Phil’s magazine sets the stage for the season with a look at a variety of handy metrics. Here’s the full list, with the items I’m using for this meta-analysis called out in bold:
- returning production ranks
- top 25 draft picks for 2026
- points lost to 2025 draft ranks
- position ranks for 2026 draft eligible players
- preseason All-American team
- individual unit rankings
- bull/bear market teams
- net close wins/losses
- yards per point trends
- strength of schedule ranks and trends
- turnover margin ranks
- conference rankings
- plus/minus talent power rankings
- surprise teams
- most improved teams
- projected stats by unit
- projected stats by team
- power poll (H2H) rankings
- conf YPG vs conf record trends
The goal is to stick to the numbers - nothing subjective, only the information you can find in the game logs and box scores. As you can see, there’s a ton of subjective information in the magazine that is extremely valuable. This is the last time I’ll say it, buy the dang book for yourself!
The approach to this analysis is pretty simple: we’re going to look at which teams show up on each list, rank those teams accordingly such that the numbers associated with each team for each list can be compared, and then pile those rankings up across each of the lists and see what we find.
STEP 1: Scrape Data
First, I pulled the teams and data values associated with each of the fifteen total lists across each of the seven data categories identified above, and entered them into Google Sheets. I assure you, this was not the fun part. For example:
POINTS LOST TO DRAFT
Ohio State: 59
Georgia: 47
Texas: 46
Oregon: 41
Ole Miss: 34
Each data category has its own full explanation in the magazine. This is an example of the Points Lost To Draft metric and the associated list.
STEP 2: Categorize and Rank
Having these lists in Sheets format instead of on a printed page was a good start, but the data values were all so varied that they were effectively useless on their own. To make these more useful, I came up with the following three metadata categories so that I could put these data points up against each other:
- Binary: A simple yes/no. Is your team on the list? If it’s a positive trending list (e.g., a team with a strength of schedule that is getting easier compared to last year on the SOS Going Up trend list), you get a “1”. If it’s a negative trending list (e.g., SOS Going Down trend list), you get a “-1”. If you’re not on either list, you get a 0. This helps me ID which teams are on multiple lists - more on this in a second. A quick note on Going Up vs Going Down - in this case, SOS Going Up shows which teams are trending up (more likely to improve in 2025) as a result of a decrease in their strength of schedule. I could have titled these more clearly, but hey, it’s my spreadsheet.
- Unweighted: This is an unweighted ranking of where a particular team’s data value sits in a list. Let’s use Net Close Wins as an example - the list is 14 teams long, with Arkansas State at the top at seven Net Close Wins in 2024. Since this is a negative trending list, Arkansas State gets -14 points in the Unweighted column, Sam Houston gets -13 at six Net Close Wins, and the three teams all tied at five Net Close Wins all get -10.The reason this category isn’t exactly what we need is that the metric lists are all different lengths. It seems unfair that a team gets rewarded or penalized more just for being at the top or bottom of a longer list, which brings us to the next category.
- Normalized: This is a normalized ranking of the unweighted data values on a scale of 1-10, which allows us to normalize the scores so that the lengths of the lists don’t disproportionately affect the score for a team in a given category. For example, the Turnover Margin Going Down trend list has 17 entries, so we’re normalizing the unweighted values to the nearest rank on a scale of 1-10 within the list.
Step 3: Synthesize
Great, we have our data, and more importantly, we have normalized values that we can use to meaningfully compare data across disparate categories. Now what? Well, let’s write out every FBS team name and work up some conclusions!
First, we’ll use the Binary data to see which teams appear on the most positive trending lists and which teams appear on the most negative trending lists. I totaled up the Binary scores across all fifteen lists for each of the seven categories highlighted above, and…
…voila. Here are the top ten teams (plus ties) with the highest Binary scores:
Purdue: 5
Cincinnati: 4
Wyoming: 3
Air Force: 2
Akron: 2
Houston: 2
Kansas: 2
Nevada: 2
Temple: 2
Troy: 2
UAB: 2
UCF: 2
UMass: 2
Utah: 2
UTSA: 2
Washington: 2
A quick pit stop here: are these going to be the best ten teams in college football next season? Absolutely not. (If they were, that ESPN College Football Playoff reveal show would be an all-timer.) All this means is that these are the ten teams that have the highest net Binary score in an aggregate of the most positively trending metrics lists, i.e., they’re mentioned more frequently on lists of metrics that would indicate they’re likely to win more games in 2025 than in 2024 than on lists of metrics that would indicate they’d win fewer games.
It stands to reason that there are some pretty poor teams on here - it’s easier for bad teams to get better year over year. But, flipping the causal relationship, one might say that these teams are due to improve because a variety of statistical factors (difficult schedule, bad turnover luck, losing a couple close coin-toss type games, etc) deflated their record in 2024, and regression is poised to turn in their favor this season.
Here are the bottom ten teams (plus ties) with the lowest Binary scores:
Marshall: -5
Colorado: -4
Indiana: -4
Navy: -4
Army: -3
Duke: -3
Notre Dame: -3
Oregon: -3
UNLV: -3
Boston College: -2
Buffalo: -2
BYU: -2
Illinois: -2
Louisiana: -2 …
Content cut off. Read original on old.reddit.com/…/is_your_team_going_to_be_better_…