i built a metric for my model called the playoff impact index. it measures how much a game is expected to move both teams' playoff odds. 100 would be a pure coin flip where the winner is in and the loser is out. the scale is fixed across weeks, so the numbers are comparable all season.

saturday's alabama at tennessee game scores 81.2, the highest on the board this week. here is why.

alabama (#7 in my ratings, 5-1) is a 61% favorite. win and their playoff odds jump to 79%. lose and they fall to 42%, with a trip to #9 lsu still waiting on nov 7.

tennessee (#13, 5-1) has everything on the table. they are at 26% playoff odds right now, right on the bubble. beat alabama and they jump to 47%. lose and they crash to 15%, and their sec title odds basically flatline (8% with a win, under 1% with a loss). their schedule after this is manageable, at south carolina and kentucky at home, which is exactly why this one game swings so much. it is the last big lever they have.

the rest of the week's top 5, for scale: 2. wisconsin at ucla (77.6) — ucla 66% to win, their playoff odds go 68% with a win and 27% with a loss 3. missouri at ole miss (72.9) 4. ohio state at indiana (71.9) — two huge teams, low stakes by comparison. both stay above 60% playoff odds win or lose 5. oklahoma state at houston (64.6)

full impact rankings for all 62 games this week, plus every team's playoff odds, are on the site if you want to dig in.

https://basedonactualmath.com/

I run a little college football model and post its picks every week. It's 76% on winners this season (265-84), but it also had a rough Week 6, so it's definitely beatable.

Big games this week

- Indiana over Ohio State (59%), the only big game where it goes against Vegas

- Texas over Florida (76%)

- Alabama at Tennessee: Bama (67%)

- Ole Miss over Missouri (57%)

- Notre Dame at BYU: Irish (70%)

- LSU over Mississippi State (75%)

There's also a free weekly pick'em where you pick 5 games against the model, and you can make a private league for your friends or group chat. Link's in my profile if you want in. fourth-data.com/pickem

Who are you taking in Indiana-Ohio State?

1. Miami
2. Alabama
3. Notre Dame
4. Texas
5. Indiana
6. Georgia
7. UCLA
8. BYU
9. Texas Tech
10. James Madison
11. Ohio State
12. Utah
13. Houston
14. Oregon
15. Mississippi State
16. Missouri
17. Nebraska
18. Kentucky
19. USC
20. North Dakota State
21. Boise State
22. Pittsburgh
23. Wake Forest
24. Wisconsin
25. LSU

Full ratings here

Rating methodology: calculated based on 3 components: win percentage, strength of schedule, and score ratio.

Win percentage (25%)

SOS (50%) - calculated in similar method to RPI, independent of team's record. Results vs non-FBS teams calculated into a placeholder value for formula.

Score Ratio - team's points scored vs total points scored in their games.

Greetings,

I'm sharing a new rankings model I've been working on for upwards of a decade on and off. I finally published it. Please forgive the corny backronym.

So I created a model that uses strength of schedule and other factors, but gives you the option to view the results with and without margin of victory factored in.

My main goals were this:

• Main inputs are simple: teams, scores, site, and at what point in the season the game is played
• Everyone starts the season fresh. No carry-overs.
• Opponents' and opponents' opponents' schedule strength is a factor
• Strength of Schedule is cumulative and normalized to the number of games a team plays in a season.
• Conference is not a factor (due to everyone not playing everyone in a conference)
• Margin of victory is capped (two standard deviations, all OT games are standardized to 1 point MOV)
• How many games you play matters in SOS (wear and tear is a thing)
• FCS losses get penalized severely (AS THEY SHOULD)
• Road wins get a boost, home losses get a ding

The results are below. Home page displays current rankings, refreshed every day.

I also have a page with results from previous seasons, going back to 2013. I'm adding to those as I get the opportunity. I'm also looking at comparing pre-bowl results to see how accurate the model is at predicting the playoff field (or showing where the committee got it wrong).

Looking forward to your feedback!

https://cosmosrankings.com/

week 6 update. the model re-ran 10,000 simulations of the rest of the season after saturday. ratings are through week 6, forecasts as of oct 11.

top 5 by predictive rating: 1. ohio state (0.981, 5-1) 2. notre dame (0.973, 6-0) 3. georgia (0.973, 6-0) 4. indiana (0.967, 6-0) 5. texas (0.964, 4-1)

playoff odds over 50%: notre dame 97%, miami 89%, georgia 87%, indiana 83%, ohio state 77%, texas tech 68%, alabama 64%, ucla 53%, texas 51%.

the ucla number is the one i keep staring at. a month ago they were not in anyone's playoff picture. south carolina (+9 to #24) and iowa (+5 to #16) were the biggest risers this week. florida fell 11 spots to #25, which somehow still feels kind.

biggest game this week by playoff impact: alabama at tennessee (impact 81.2 out of 100). the model has alabama 61% to win. alabama's playoff odds go to 79% with a win and 42% with a loss. tennessee's go to 47% with a win and 15% with a loss. the loser is not dead, but they are basically in win-out-or-else territory in october.

full ratings, every team's playoff and conference title odds, and the rest of the week's impact rankings are on the site if you want to dig in.

https://basedonactualmath.com/

I’ve been tracking Percera’s matchup results throughout the season, and I went back and graded the full Week 6 board exactly as it was frozen before the games were played.

Across 56 FBS vs. FBS games it finished 40–16 picking winners (71.4%).

The overall record was obviously nice to see, but what interested me more was how the results changed as the model got more confident.

• 60%+ favorites: 36–7
• 65%+: 32–4
• 70%+: 25–2
• 80%+: 13–1

The projected edge showed a similar pattern:

• 7–13.9 point edges: 19–2
• 14–20.9 point edges: 8–0

The weak spot was pretty obvious too.

Games where Percera saw less than a 3-point edge went just 4–8.

So at least for this slate, the model did a much better job when it had a strong opinion than it did trying to separate teams it viewed as basically even (which is somewhat expected).

I also thought the probability calibration was interesting. The average favorite probability across the board was 70.4%. If you add all of those probabilities together, the model would have expected about 39.42 wins and it got 40.

The margin projections were quite a bit noisier. Mean absolute margin error was 13.1 points and the median miss was 8.9, so that’s something I’m going to keep an eye on separately from simply picking winners.

The full week 6 rankings, CQI/CTSI methodology, week 7 matchup board, and everything else are live:

Site: perceraanalytics.com

For anyone else building models, I’d be interested in what you’d track as this sample grows, especially for calibration beyond just confidence buckets and margin error.

I run a college football prediction model (opponent-adjusted margin and efficiency ratings, plus early-season talent and returning production; backtested on 2017–2025). It's now listed in the Massey composite and on The Prediction Tracker. Every pick is locked and posted before kickoff.

**Where it picks a different winner than Vegas:**

- **#20 Iowa at Washington (Fri):** model takes Iowa, 56%. Vegas has Washington -3.
- **UCF at #18 Oklahoma State:** model takes UCF, 55%. Vegas has Oklahoma State -9.75. The biggest disagreement on the board.

**Same winner, very different margin:**

- North Carolina at #25 Pittsburgh: model Pitt by ~13, line Pitt -3.75
- #9 Ole Miss at Vanderbilt: model Ole Miss by ~2, line Ole Miss -9.75
- Maryland at #5 Ohio State: model OSU by ~29, line OSU -34.5

**The big one:** #2 Georgia at #6 Alabama. Model takes Alabama, 57% (Bama by ~3); Vegas has Alabama -1.5. It's also the game with the most playoff stakes this week: Georgia's playoff odds are about 90% with a win, 48% with a loss.

**Every other ranked game:**

- Iowa State at #8 BYU (Fri): BYU 79%
- Texas A&M at #14 Missouri: Missouri 60%
- #7 Indiana at Nebraska: Indiana 70%
- South Carolina at #16 Florida: Florida 75%
- #21 UCLA at #13 Oregon: Oregon 82%
- Stanford at #3 Notre Dame: Notre Dame 99%
- #22 Houston at Kansas State: coin flip (K-State 50.1%)
- #1 Texas at Oklahoma: Texas 72%
- #15 Tennessee at Arkansas: Tennessee 79%
- #10 LSU at Kentucky: LSU 66%
- #19 USC at Penn State: Penn State 52%
- Kansas at #12 Utah: Utah 88%
- #23 Boise State at Fresno State: Boise State 63%

Honest caveat: in backtests it picks winners about 1.5 percentage points less often than the closing line (71.6% vs 73.1%) and has not beaten the spread, so treat the disagreements as "interesting," not locks. I'll post how it did on Sunday.

There's also a free weekly pick'em on the site where you can try to beat the model on the 5 biggest games.

Full predictions for every FBS game: fourth-data.com (free, no sign-up)

Which of these do you think it gets wrong?

I ran this on the NFL side first with broken tackles and yards after contact and got nothing against the spread, so I wanted to see if college was different since the talent gaps are bigger.

CFBD doesn't chart broken tackles so I used the closst thing it has from the advanced game stats: line yards per rush, second level yards (5 to 10 yards downfield), open field yards (10+ yards downfield, the closest thing to breaking tackles and getting loose), and stuff rate (runs stopped at or behind the line). For each team I used what the offense did and what the defense allowed, only from games already played that season, so it's info you'd have before kickoff.

FBS vs FBS, regular season, both teams with 3+ games played. 3,786 games from 2018-2024 and I held out 2025 (601 games) until the first results were in. Line is the CFBD consensus line, or the average of books when there's no consensus. Pass rule written down before running: 95% interval (resampling whole weeks) above or below zero in 2018-24 and same direction in 2025.

Did these stats actually predict winning? Yes, a lot more than the NFL stats did. Correlation with final margin: line yards 0.38, second level 0.34, stuff rate 0.28, open field 0.28. The spread on the same games was 0.65.

Do they beat the spread? Open field yards, the tackle breaking one, was dead flat. 0.003 in 2018-24 and wrong direction in 2025. Second level didn't pass either.

Line yards and stuff rate both technically passed, barely. Low end of the interval was 0.008 for line yards and 0.002 for stuff rate. Betting the bigger line yards edge in the top and bottom fifths went 787-709 (52.6%) in 2018-24 and 107-96 (52.7%) in 2025. Break even at -110 is 52.4%.

Here's why I don't think it's real:

Line yards and stuff rate correlate at 0.81 with each other, so that's one signal counted twice, not two.

2020 carried it. Line yards went 70-43 in 2020 alone. Take that season out and 2018-24 is 717-666, 51.8%, below break even. 2018 was 50.0% and 2022 was 47.7%.

The market moves toward it. When a team has the line yards edge, its line moves their way between the open and the late number (correlation -0.13 in 2018-24, -0.16 in 2025). The edge is a bit bigger against the opener than the late line, so by kickoff most of it is already priced in. CFBD only has an opener for about 41% of these games so take that part as rough.

So same answer as the NFL. These stats explain why a team wins but the line already has it, with one difference in college: the run blocking edge doesn't look fully priced at the open and gets there during the week.

Has anyone tested line yards against openers with a full set of opening lines? That's the only place I'd look for something left over.

Twelve days ago I posted the 5 weirdest things my model was saying through week 3. The Week 6 numbers just dropped, so here's the sequel. Same deal: I built a free college football prediction model (predictive ratings plus 10,000 season simulations, free site, no account).

1. Ohio State is the best team in the country by rating (0.981) and only fifth in playoff odds at 74%. Indiana (80%), Georgia (78%), and Miami (88%) are all ahead of them. The rating answers "how good are you," the odds answer "how good is your path," and right now those are different questions.

2. Notre Dame is at 97% to make the playoff. Same story as two weeks ago, except more so. The schedule is a pancake and the model just keeps simulating wins.

3. Miami is at 88%. The model has been the highest on the Canes all season. At some point I have to admit it's just right about them.

4. Missouri jumped 7 spots to #16 after hanging 45 on Florida, and Florida fell 4 to #14. The model had Florida at 63% to win that game. 45-17. The model is not above being wrong and neither am I.

5. Northwestern is up 10 spots to #24. Penn State is down 7 to #28 after losing 34-13 to Northwestern. The model moves on performance against expectation, and getting run off the field by Northwestern is about as against-expectation as it gets.

Full disclosure, I built the thing. Link at the bottom if you want to poke at the numbers. Free, no account.

https://basedonactualmath.com

Hi all! I am reposting a post that I just made because of changes I made from the original post. To recap what I said then, recently, I have been getting into making my own ranking system, with the goal of creating a ranking system that is as transparent and simple as something like the Colley Matrix while still taking things like margin of victory and power of a team into account. This is mostly out of boredom but also because I just really, really like sports analytics. However, I am by *no* means a mathematician (I had to talk to other, smarter friends of mine to get some of the terminology right here) and therefore need to keep it simple for me to understand a lot of this, or at least have something that can be explained in an easy to understand way. With all of that in mind, I settled on making what I call, for now, "Elo W25". In very simple terms, it works like this:

Method

1. Preseason: This includes two parts: finding a team's last season Simple Rating System (SRS) rating and converting that rating into an Elo score.
   • Last season's SRS = average margin + average opponent SRS.
   • Last season's SRS is then converted into an Elo score with Elo = 1500 + 18.5 × (Last season's SRS − FBS average SRS).
2. In-season Elo: Prior to a game, a calculation is done to determine the expected result from the game. After the game, another calculation is done which gives or takes points from a team depending on whether that team won or lost (and if the team was expected to win or lose) as well as the margin of victory.
   • Expected result = 1 / (1 + 10^(−((Team's pregame Elo − Opponent's pregame Elo) + home edge) / 400)),
     • Home edge equals 46 points (0 at neutral sites).
   • After each game, change = 75 × ln(margin + 1) × (result − expected).
     • If a team wins, result = 1. If a team loses, result = 0.
     • Margin is simply winner's score - loser's score.
3. W25 (wins above No. 25): At the end of the season, teams are ranked based on a benchmark equal to the 25th-best Elo at the end of the regular season.
   • To calculate the probability that the No. 25 team would win a game, run the calculation p = 1 / (1 + 10^((Opponent's final Elo − (benchmark + home edge)) / 400)) for every game.
   • A win earns 1 − p and a loss costs p. W25 = a team's actual wins − Σp, and teams are ranked by W25.

The results can be found here for FBS teams and here for lower division teams. Like I said before, the point of this is to 1. reward strength of schedule and strength of resume, 2. be relatively accurate compared to other computer polling, and 3. be incredibly simple and not beholden to information other than game data, which preserves flexibility. However, I am open to any criticism, even harsh criticism! I want to do anything I can to make this rating better while still sticking to those three main goals. Thanks for taking the time to read this!

I've been building Fourth & Data (fourth-data.com), a free college football analytics site, and just shipped a round of updates. Looking for feedback from people who care about the methodology.
What's new:
• Rebuilt rankings: a blend of résumé (Colley), margin (Massey, capped at 28), and SP+ power, with power weighted heavily early and fading as the season goes. I walk-forward backtested it on 2017–2025 (~5,900 games). Surprising finding: pure résumé was the weakest method, so it now carries much less weight than margin.
• Public pick ledger: every pick is logged and graded, misses included. Currently 229–62 straight up (78.7%). ATS is 30–26, which is too small a sample to mean much yet, and I'm tracking against the line at pick time.
• Filters: sort the ledger by team, conference, and season to see where the model is strong or blind.
• Ask Fourth & Data on Discord: a bot that answers questions about matchups, rankings, and teams using the site's data, and posts alerts. Join here: fourth-data.com/api/discord/invite
I'd genuinely like to hear where the model looks wrong to you, or what you'd want added. Happy to answer methodology questions in the comments.

A friend (Honsou) and I (YoyoYoshi) started a college-football recruiting spreadsheet years ago. I recently expanded it into a larger interactive database covering every current Power Four program plus Notre Dame.

It now includes 2001–2025 win-loss records, recruiting history from 2002–2026, one- or two-program comparisons, 25-year win baselines, conference-movement history, and a sandbox for building hypothetical conferences.

A few things it can answer:

How has one program performed against another over the last 25 years?

How closely have recruiting rankings tracked actual results?

Which programs have been promoted, demoted, stranded, or later rebounded through realignment?

How strong would a hypothetical conference be based on the historical performance and recruiting of its members?

For the win baseline, I used 25 years because it is long enough to smooth out individual coaches and short-term runs without letting very old eras dominate the result. Thought some people might find this useful!

Link: https://yoyoyoshihub.neocities.org/college-football-analytics

I posted here earlier last week about CQI (Contextual Quarterback Index), the QB model I’ve been building.

I got some useful feedback on that post, and the project has expanded quite a bit since then, so I wanted to share an update rather than just repost the QB rankings.

Percera now has two main models:

CQI — Contextual Quarterback Index
Measures observed QB performance using opponent-adjusted passing efficiency, sack avoidance and rushing value. The live rankings are now updated through Week 4.

CTSI — Contextual Team Strength Index
A separate team-level model designed to estimate team strength on a neutral-field point scale.

One thing I specifically did not want CTSI to become was another poll where #18 is treated as meaningfully better than #19 just because there has to be an ordering.

So I also added a CTSI Hierarchy.

Teams are grouped into tiers based on their modeled separation rather than arbitrary rank cutoffs. Under the current rule, the strongest team in a tier can be no more than roughly a 55% neutral-field favorite over the weakest team in that same tier.

That means teams within a tier should be viewed as broadly comparable even if one technically ranks several spots higher.

I also added a Matchups section. It takes the current CTSI ratings, applies venue context, and produces a projected edge and win probability for upcoming games. I’ve also been running historical reliability tests on the model rather than just judging it based on whether this week’s picks happen to look right.

The site now has:

• Week 4 CQI rankings
• Week 4 CTSI team ratings
• CTSI team-strength hierarchy
• Team and QB profile pages
• Upcoming matchup projections
• Methodology pages
• An optional weekly email signup for people who want the updates without checking the site manually

Site: https://perceraanalytics.com

I’m especially interested in feedback on the tier approach. Does grouping teams by modeled separation communicate team strength better than presenting it as a straight 1–138 ranking?

Also interested in anything about the matchup presentation that feels misleading or unclear. I’m trying to make the site useful without making the models look more certain than they actually are.

Built this as a side project for this sub specifically. Fourth & Data (fourth-data.com) ranks FBS teams from three blended components: Résumé (Colley matrix — who beat whom matters more than raw record), Margin (Massey rating, capped at 28 pts so blowouts don't get bonus credit), and Power (SP+, weighted heavy early when there's little data, fading to ~20% by November).

A few things beyond the rankings:

**•   Playoff odds** — thousands of simulated seasons under the 12-team format, broken out by losses each team can afford  
**•   "What if" mode** — pick next week's winners, watch the board and playoff odds update live  
**•   Build-your-own poll** — set your own résumé/margin/power weights, share the result  
**•   ATS tracking** graded against the line captured at pick time, not backfilled from today's odds

Still rough in places — mainly want this sub's take on the model itself, not trying to sell anything.

fourth-data.com

Longtime lurker, first time posting OC. I built a college football model over the offseason and just put up a free preview, figured this was the right crowd to stress-test it.

The short version of how it works: every team gets a rating from 0 to 1, which is the model's estimated chance of beating an average FBS team on a neutral field. It's explicitly predictive, not a resume ranking, so ratings move on performance against expectation. Then it simulates the rest of the season 10,000 times, including conference title games and a model of how the committee actually picks teams, and every probability is just how often something happened across the runs. Numbers below are ratings through week 3, forecasts as of Sep 20.

Some things it's saying that I found interesting:

1. The best team and the best playoff resume are very different things. Ohio State is #1 in the predictive rating (0.980) despite being 2-1, with the highest title odds at 16%. But Notre Dame (3-0) has the best playoff odds in the country at 94%, vs 66% for Ohio State. The model is basically saying: yeah they're the best team, they also might just miss the playoff.

2. North Dakota State is ranked 67th (0.552) and has 31% playoff odds, the best of any G5 team. Better than Oklahoma (11%), LSU (24%), Florida (23%). This is the auto-bid path doing all the work: 39% to win the MWC at 4-0, and the simulations love a conference favorite. I assume this is the one people will want to argue about, go ahead.

3. Texas Tech is the P4 version of the same phenomenon. Ranked 16th, 60% playoff odds, because it's a 37% favorite to win the Big 12. BYU and Utah (both 33% playoff odds from the low 20s) are the same story.

4. 3-0 doesn't impress it if you were supposed to be 3-0. Appalachian State is 3-0 and fell 19 spots to #109 this week. Narrow wins as a favorite actively hurt. Cincinnati is 3-0 and down 8 to #63. The model's view: tell me who you beat, not just that you won.

5. Week 4: Ole Miss at Florida is the week's biggest game by playoff leverage (53/47 toss-up), and Oregon is only a 58% favorite at USC. Also, Mississippi State is favored at home over Missouri despite being ranked three spots lower. Home field flips happen.

Known limitations, since the math is allowed to be wrong: it only updates weekly, early-season ratings are still noisy, and the committee model is my best guess at how humans vote, which is inherently a little silly. Happy to get into methodology details in the comments.

I built this as a side project, it's free, no account or anything: https://basedonactualmath.com/

I’ve spent the last few weeks building a college football analytics project, and I finally have the first model in a state where I’m comfortable putting it in front of people who actually care about this stuff.

It’s called CQI — Contextual Quarterback Index.

My goal is pretty simple: to measure observed quarterback performance while adding some of the context that raw box-score numbers miss.

CQI currently looks at:

• down-to-down success
• opponent-adjusted passing efficiency
• opponent-adjusted TD creation
• sack avoidance
• rushing value

The opponent-adjusted portions try to account for the quality of defenses each QB has faced, rather than treating production against every opponent equally.

A couple important things CQI isn’t trying to do:

It isn’t a projection, NFL scouting grade, recruiting grade, or an attempt to completely isolate the QB from his offensive line/receivers/scheme. It’s meant to describe how well a quarterback has actually performed in the sample being used have.

For the live rankings, I've added some limitations so QBs need 30+ season-to-date attempts and at least 2 usable context games. Small samples are flagged rather than removed or artificially penalized, skewing results.

I also published the methodology instead of making the rating a black box.

2026 rankings + methodology:
perceraanalytics.com

I’m genuinely interested in criticism here. If there’s something about the methodology, presentation, opponent adjustment, or rankings that looks wrong or incomplete, I’d rather hear it now while this is still starting out in v1.0.

I’ve been building a college football spread model called 6P Predictions and figured this would be a better place to share it than a general betting subreddit.

Site is 6ppredictions.com if anyone wants to poke around. Sign up with a google account, and when it asks for a free access pin, it's 1797.

The basic idea is to create a model number for each game, compare it against the market, and then track whether the model actually identified value rather than just grading wins and losses after the fact.

A few pieces of the model currently include:

- opponent-adjusted team ratings

- dynamic home-field advantage rather than a fixed HFA number

- travel distance and attendance effects

- market spreads and bookmaker movement

- confidence tiers based on the size/quality of the model-market disagreement

- frozen predictions so historical results can’t move after the fact

I’ve also been experimenting with a system I call SmartFreeze, which tries to determine when a market number is worth preserving instead of simply taking whatever line exists at a predetermined time. It looks at things like key numbers, bookmaker consensus/scarcity, price pressure, and line movement. That part is still very much in prospective testing, so I’m collecting data before making any conclusions about whether it actually adds value.

For 2026 so far the published spread predictions are 30-18-1 ATS (62.5%), but I’m very aware that 49 plays is nowhere near enough data to claim I’ve discovered some huge edge. I’m more interested right now in whether the underlying process holds up over a much larger sample.

I’ve tried to make the site unusually transparent: predictions are timestamped/frozen, results stay visible, methodology is documented, and I’m starting to measure performance versus the closing market in addition to ATS results.

I’d especially appreciate criticism from people who build their own models. Things I’m interested in hearing about:

- where you think overfitting could creep in

- better ways to evaluate performance beyond ATS record

- how you handle market information without just turning the model into a copy of the market

- thoughts on CLV as a validation metric

- features you think are missing

- anything on the site/model that looks statistically questionable

I’m trying to build something that can survive scrutiny rather than just post picks when it’s running hot, so feel free to tear it apart.

In theory vegas(or any betting site) should have a power ranking based off of who they think would win on a neutral field. Although statistically it doesn't have to be linear. I.E Oregon might have a closer spread vs #1 Ohio State, than say Texas, but Texas might be favored over Oregon. But is this data publicly accessible anywhere? I believe that would be extraordinarily helpful

I.E

1. Ohio State -2.5 Oregon

2. Oregon -0.5 Texas

3. Texas -1 Notre Dame

4. Notre Dame -2 Georgia

Etc

Notre Dame - 20.5
Washington - 23.5
Louisville - 6.5
SMU - 2.5

Friday 2/8

Saturday Slate

Coastal Carolina + 20.5
Liberty + 7
North Texas + 40.5
Alabama -27.5
Nebraska - 23.5
Houston - 20.5
New Hampshire +55.5
Lafayette + 19.5
Ball State +50.5
Texas State + 30.5
Oregon -24.5
Auburn -7.5
Oklahoma St - 14.5
FIU + 14
Missouri State +40.5
Memphis - 11.5
Kansas State - 40.5
Michigan - 28.5
Clemson +10
Florida -26.5
Utah Tech +51.5

Eastern Michigan -3
NC AT + 28.5
Purdue - 38.5 Low
Michigan State -9.5
OU -41.5
Fresno +22.5
Stanford +24.5

Here we go again. Week zero results were 6/8 at 75% success. Details as posted in Reddit link here

Thursday games below. Odds as of last night (some stale). Didn’t evaluate each game. Posting each slate the day before or morning of. Let’s see how it goes!

Confidence = High 
Eastern Illi + 42.5
AK Pine Bluff + 54.5

Confidence = Moderate 
GA Tech -7
UAB + 27.5
E. Michigan - 3
Utah -34.5

Confidence = Low

UMass + 28.5
Akron +24.5
UCF - 42.5

Hey all, I’ve built a ML models on team-level data along with game outcomes of the past season. I’m getting OOS errors of 17.0 RMSE and 13.8 Absolute Error; how does this compare to models looking at this level of data in your experience? (Models are ENET, Neural Network, and XGBoost optimized for RNSE).