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/

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

Hello everyone,

I’m a recent Data Science grad student and just released my first app, CFB Predict on the app store.

CFB Predict uses a machine learning model I developed to forecast the outcomes (wins & losses) of college football games. Trained on data from the past 10 seasons, the model achieved an 86.6% accuracy rate, with additional holdout testing confirming its reliability on unseen matchups.

If you’re a college football fan, I’d love for you to check it out and reply with any feedback.

If people are interested I’ll drop the link. Also, feel free to pm for free access to the premium version of the app.

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 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/

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/

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

First time sharing this with the world, so here it goes. Last year I built a model for week 1 of the season. It takes returning production, transfer numbers, returning starters, and other data points. Once that info is formatted an additional "human" analysis is sprinkled in. Spread & over/under results were right under 57% accuracy (it picked up USF vs Boise -5.5; I have a Reddit post from last year on that). Moneyline results were at 80%. I emailed BetMGM customer service to give me all my results for the 2025-2026 season and parsed out that weekend in a CSV format for evaluation. I noticed I got greedy with parlays (would hit 3 of 4 etc.) out of a total of 123 events (some games were bet more than once in a parlay or solo that weekend). 

So I am doing the same thing for this year. Sharing what I am seeing for this upcoming weekend. Let me know your thoughts on some of these picks.

* Odds are from earlier this week (may be stale).

Confidence = High
Stanford -5.5.  
NM State +31.5
Jacksonville +7
Sacramento +9.5

Confidence = Moderate
Virginia -5.5
NC +7.5

Confidence = Low (iffy)
San Jose State +38.5
Memphis +5.5

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

“We’re an elite program - NO YOU’RE NOT”’

We’re a top 10 program. X program is better than Y. We’re as good a program as anyone. CFB fans argue about this stuff all the time. What does it mean? How do you quantify it? The rest of the post attempts to do both of those things.

Where we perceive a program is currently “at” - I call this the “recency ranking” - is different from what it did last season. It’s also not the same as all-time program history. What is it? It’s somewhere in between those 2 concepts - last season and all-time history. I believe it relies on the view that the more recent a season the more it counts in our collective minds. For instance, Minnesota has one of the best histories out there. But the Gophers aren’t a top program currently; their history is old enough that it barely factors into their recency ranking. However, consistently solid play for a decade has improved the program’s perception amongst CFB fans to a degree. Another example: I think most USC fans would say they currently have a top 10 program. Fans of other schools might say “This isn’t the early 2000s anymore. USC has been good but not elite for 2 decades”. Who’s right? It’s an inherently subjective question, but we can attempt to answer it by applying a reasonable and consistent quantitative methodology to all programs.

How do you quantify this concept?

1st you have to come up with a methodology to rank every team every season. I did this and posted about it here. Thanks to the r/CFBanalysis community for helping me improve my methodology. My algorithm looks at record, strength of performance via SP+, and other things that matter in terms of how fans perceive a team's greatness - final ranking, natties, CFP results, bowls and conference titles. My whole updated methodology is at the bottom of this post.

Next you have to figure out how to progressively minimize the impact of older seasons. I did this using a half-life model (think carbon decay). The most recent season counts 100%. Older seasons are minimized using a 10 year half-life. So 10 years ago counts as 50% of its base value, 20 years ago counts 25% and so forth. This is inherently subjective - a decade half-life is clean and feels right to me, but if you think older seasons should decay faster or slower, you can adjust it in my app (more below).

One thing I love about the half-life model is you can change the max year to see the program pecking order for any point in history. For instance, if you only include data from 1869 - 1940, then 1940 is weighted 100%, and you can generate a list of programs sorted by our recency ranking for the year 1940 (my Gophers were on top, yea I’m a homer) with 981 pts, almost 300 pts above 2nd place Pitt. An interesting modern example: Indiana climbed from #85 in 2023 to #73 in 2024 to #35 in 2025.

Current Top 25 Recency Rankings

Analysis

• Bama lost 30 pts last year despite a CFP quarterfinal run. This is because their starting score is so high that they're draining like 90 pts each year due to the half-life. Their sustained excellence has forced them to maintain an incredibly high level of play to not drop their standing as a program.
• Contrarily, Iowa and Utah were able to boost their scores and ranks in '25 despite having worse seasons than Bama. This is because they had lower scores to start with.
• USC isn't in the top 10 (I'm going back to our example from above).
• Nebby's great 90s run is holding them inside the top 25 still, but just barely. They'll drop out in 1-2 years without a major turnaround.

Full Rankings/Make Your Own

I built a free/no ads web-based app that allows users to customize their own rankings + see all 136 teams. So if your team isn’t in the top 25 I pasted above, check that out. It defaults to “History Rankings”, which are very cool but answer a different question - every season is weighted the same. You can change the “Ranking Type” to “Recency Rankings” to see the full list with the 10 year half-life on. You can also change the max year to see what the recency rankings looked like at any point in history. And you can customize the methodology, including tweaking the half-life value, on the “Settings” tab.

Methodology

Core Scoring

• Base Score: Each team starts with 10 points each year. This rewards longevity and reduces the number of teams with negative scores. Without it, way too many G5 teams have negative history and recency scores.
• Wins and Losses: 1 point for a win, -1 for a loss.
• Ranked Finishes: 1-25 point bonus for finishing ranked. I use the AP poll most years from 1936+. I use the coaches poll from 1961-1967 because the AP only ranked 10 teams. I give top teams from before the AP Poll was founded in 1936 bonuses based on Billingsley ratings.
• Strength of Schedule (SP+): I use Bill Connelly’s SP+ ratings to account for strength of schedule/strength of performance. I use SRS when that's unavailable and adjusted Billingsley ratings when that’s also unavailable. By default, positive values are counted at 100% and negative values are counted at 60%. This reduces the number of teams with negative all-time scores and makes bad seasons less punishing.

National Titles / CFP

• National Titles: 100 points for a recognized national title (split titles are shared).
• CFP 1st round loss: 9
• CFP Quarterfinal loss: 16
• CFP Semifinal loss: 25
• CFP/BCS Ntl Championship Game Loss: 40

Conference Titles, Bowls, and The Heisman

• *Conference Titles: ~*1.5-25. Conference champions are awarded bonuses based on conference strength. Bonuses range from about 1.5 for a conference title in a modern weak conference, up to 20+ points for winning a very strong conference in the pre-BCS era.
• Bowl wins: ~0.5-20. Teams are awarded bonuses based on bowl strength, from 0.5 for a low-end modern bowl to about 20 for a very high end pre-BCS bowl.
• Conference championship and bowl losses: Teams that lose in a bowl game get 25% of the winner bonus. For low-end conferences and bowls, this isn’t enough to offset the -1 point from losing a game. For high-end games, it’s a small bonus.
• Era Fading: I diminish the value of modern conference titles and bowl games. Pre-BCS results get 100% of the base value. BCS era results get 90%. 70% for the 4-team CFP era and 50% for the 12 team era.
• Heisman: 5 point bonus for having the Heisman winner on your team.

Sources

• sports-reference.com — Historical CFB team records, bowl results, etc.
• collegefootballdata.com — Recent team records, AP finishes, bowl results, SP+ ratings, and more.
• Bill Connelly SP+ historical sheet — Simple points-based SP+ model used for older seasons.
• ncaa.com natty history — National title winners.
• cfrc.com — Seasons missing from other sources for certain schools; Billingsley ratings for pre-AP polling eras.
• Wikipedia — Conference records and bowl appearance data for seasons imported from Billingsley.

Feedback Appreciated

I hope this concept makes sense. Whether you think it’s great or you think I’m totally off base, I’d love to hear about it.

Since yall have been on this journey with me I figured I would share the results of my Monte carlo simulator! With 1 game left to play Id say it has gone really well hitting ATS over 70% which is fantastic!!

Cant swear that itll last forever but for now the heater feels really good!

Thursday Boise State -6 ❌️ o63❌️ Ohio +14 ✅️ o47.5✅️ Wyoming -7 ✅️ o50.5❌️ ECU +11.5 ✅️ u60.5✅️ Jax St +18.5 ✅️ o55.5 ❌️ Buffalo +18 ✅️ u44.5 ✅️ Cincy +7.5 ✅️ u53.5 ✅️ Miami OH +17.5 ✅️ u39✅️

Friday App State -4.5 ✅️ and u51.5✅️ Wake -17.5❌️ and u51.5✅️ WMU +21 ✅️and o49.5❌️ Auburn -2.5 ✅️and u57.5❌️ GT -4 ✅️ and o54.5 ❌️ CMU +14.5 ✅️u51.5✅️ UNLV -10.5 ✅️and u60.5✅️

Saturday noon Ball State +17.5 ❌️ o48.5❌️ Tennessee -13.5 ✅️o52✅️ FAU/Maryland is a legit push on 14 will not be taking it but o61❌️ Ohio State PK ✅️u47✅️ Tulane -4.5✅️ u46✅️ MSST -14✅️ o60.5❌️ Toledo +10✅️ u48✅️ ODU +24 ✅️ o48.5✅️

Saturday afternoon Marshall +38.5 ✅️u52⚪️ Bama -13.5 ❌️ UVA -13.5✅️ o56.5❌️ UTSA +23.5 ✅️ u58.5❌️ Michigan-34.5 ❌️ o48✅️ UTEP +6 ❌️ u60.5✅️

Saturday night LSU +4.5✅️ o56.5❌️ Ole Miss -32.5 ✅️o60✅️ Eastern Michigan +14❌️ u56.5❌️ Louisiana-10❌️ o48❌️ Georgia Southern -1❌️ o48✅️ Zona -15.5✅️ u54.5✅️ Cal PK✅️ u48.5❌️ CSU +21.5✅️ u52.5❌️ Utah -5✅️ u49❌️

Sunday SC -7 ✅️o51.5❌️ Miami +3✅️ o50.5✅️ Added bonus play of Miami ML ✅️ if you're feeling brave

This formula could be tweaked a little with other variables but, I think it points in a better direction. It rewards teams that win a conference championship and doesn't punish teams for playing in them. (Something that seems to not matter in some cases right now).

The initial top 25 is based on records and a team gets this equation applied when inside the top 25.

[100-(season losses + points lost by)] + (conference championship margin of victory + 10 for a W and 0 for a loss)

Based on this formula being applied to the topic 25. These are the 12 teams I ended up with.

1. Georgia 127 points (dominating Alabama moved them up)

2. Indiana 113

3.Ohio State 109

1. Texas Tech 105

2. James Madison 95

3. Notre Dame 94

4. Ole Miss 91

5. Oregon 89

6. Texas A&M 89

7. Miami 89

8. Alabama 82

9. Iowa 81 (their worst losses were by 5 points to USC and Indiana. They can surely compete.)

One tweek that could be made would be a to factor in losses to teams with less than 4 losses all season where that loss is only half a point as long as the loss wasn't by more than 14 or something like that. This really helps analyze a teams quality and serves justice in the big picture of college football.

Hi r/cfbanalysis, I'm working on a larger write-up on this, but wanted to share the below project I was working on and check my process and rationale:

For whatever reason, I've always wondered about what kind of season it takes for a kicker to win the Lou Groza award.

To establish performance thresholds and build a predictive scoring model, I collected 28 data points apiece on 70 elite kickers from 2001-2025 (22 Groza winners and 46 runners-up/consensus All-Americans).

Full dataset

To establish a statistical floor, I looked at 17 key categories and found that winners outperformed runners-up in 15 of those areas on average. Looking at the average gap between winners and non-winners and filtering out some noise, five key categories emerged. For these, I established Minimum (historical floors that winners have hit, but as outliers) and Ideal (what 90% of winners have exceeded) thresholds:  

To see if this held water retroactively, I converted these thresholds into a 10-point scale:

• 1 point per Minimum threshold met
• 2 points per Ideal threshold met

This makes the max score 10, which has never been achieved (though a few have hit nine). Backtesting this from 2004-2025 we see:

• Winners earned 7.63 pts. vs. 6.52 for the runners-up on average
• Since 2015, the Groza winner has tied or outscored all runners-up every season
• Since 2006, no non-Groza winner has beaten the actual winner by more than one point
• Lowest winning score was 5 pts (2x, and both times the winner was outscored by the runner-up)
• When a 9-point kicker clearly outscores the field, they've won 100% of the time (5 of 5 instances). The only times 9-point kickers have lost were 2022 and 2012, when they tied with another 9-point kicker.
• Scoring 8 points puts a kicker in the mix, but it's often crowded and puts you at roughly a 50% chance even if you're the clear leader.
• Below 8 points, you're relying on weak competition or tiebreakers.

To summarize all of that--to seriously contend for the Groza, a kicker must:

1. Clear all 5 minimum thresholds above
2. Hit the Ideal thresholds in at least 3-4 categories
3. Score at least 8 Groza points

To make this a little easier to understand, I built an interactive calculator where you can input any kicker's stats and see their Groza Points score along with their historical win probability.

Curious to hear people's thoughts--look forward to holding this rubric up against the 2026 season and seeing how it aligns with the semi-finalist and finalist lists and correctly predicts the winner come December.

I’ve added a lot to this. It’s fully operational, and I can keep it operational with regular updates. With the cloudflare issues, I’ve been delayed in adding the CFP Rankings.

Fbs-penalty-analytics-dashboard.streamlit.app

Some of yall may have been following along with the CFB Monte Carlo simulator that Ive been running this season, but even if you haven't, I have something new Id like to share!

I used the simulator to simulate every possible game for each team in the 12 team field and turned it into an interactive bracket simulator. Basically you can go through an select winners for each game and the bracket with automatically display new national championship odds for every team based on the selected result and display the simulated result for the next game in the bracket!

Would love to have some of yall play with it and give me your thoughts!

https://www.volumepigs.com/p/visualizing-what-you-should-already

I looked over the 2025 schedule of the power 4 because of uneven scheduling we could possibly see as many as 30 p4 teams finishing 10-2 (before conference championship games including Notre dame) the teams are

Notre Dame

Big10- Ohio state, Oregon, Penn state, Indiana, Washington, Michigan Illinois, Nebraska and USC

SEC- Georgia, Texas, Alabama, LSU, South Carolina, Florida and Ole miss

ACC- Clemson, Miami, Georgia tech, SMU, Florida state and Cal

Big12- Arizona State, Kansas State, Utah, Texas Tech, Iowa state, Cincinnati and West Virginia Not saying it will happen just that this is possible

I posted this to r/CFB and someone recommend I come here to post it and this is the first I'm hearing of this subreddit so now I'm excited for other football number nerds.

I'm looking for some opinions on a new computer poll that I created. It's similar to the BCS poll but I'm using Quadrants just like with the basketball Net Rankings. I'm not going to post the results currently because you're not going to like them which is why I am asking on your opinions for how much to weight the following items:

Item 1: This is what I'm using as the different Quadrants for 1-4 and for Home, Neutral, and Away. **I'm using 135 teams because any FCS school is being considered #135 and a Q4 win or loss**

Item 2: This is what I'm currently using as the weighted averages and how much of a factor it plays. This is what I'd like everyones opinions on. If there's a metric I don't have listed, please let me know what it is and why you think that should play a vital roll in the rankings.

The formula that I'm currently using is below. Will be curious if I add metrics or change weights to see how things play out:

NET = (WP*55%)+(SoS*20%)+(Eff.*15%)+(SoR*10%)+(Q1W*40%)+(Q2W*30%)+(Q3W*20%)+(Q4W*10%)+(Q1L*10%)+(Q2L*20%)+(Q3L*30%)+(Q4L*40%)

Any and all helpful opinions are welcomed.

Thanks!

https://www.sevenyardsback.com/post/the-point-s-after-2024-s-added-value-college-kicker-ranking

I have built a machine learning program that predicts the AP poll in real time. Along with that, I've thought of building a college rank em contest where you can use the predictive tool to see how the AP poll will likely vote, and then you can make your own changes. I have built out all of the infrastructure, now curious on who would want to participate.

Here is how it works:

1. The web page shows all of the projected scores from all games (Vegas sports books).
2. The user would update the scores they believe are wrong or want adjusted
3. The user runs the simulation and the model spits out the results of how the AP / College Football Selection committee poll would vote in that circumstance
4. The user can then move around the predicted outputs to fit the result they think is going to be the real outcome
5. The user could then submit their results. All submissions have to happen before noon kickoff on Saturday, and results will then get posted after the new rankings have been released.

I think it would be a lot of fun and a new twist on Pick Em. Would anyone else be interested in participating in this?

I'm breaking down the top 5 teams in the big 10 that have lost gamed in which they are favored in the last 10 years.

A favored game is designated when a team has a greater than 50% pregame win probability.

**Maryland**

Coming in at #5 are the Maryland Terrapins with a 56-15 record losing 21% of their favored games.

The average spread in those games was set to –7 with an average of a 68% pregame win probability. 67% of those losses came at home with both home and away games set at a 68% chance to win. The most upsets came against Rutgers with Temple and Purdue tied for second with 2 each. The largest upset came in 2018 to Temple with a 86.5% pregame win probability and -16 spread favoring the terrapins.

In the last 10 years, they are averaging 1.5 upsets per season, with 5 of those seasons finishing with 2 upsets.

**UCLA**

At number 4 is one of the new Big 10 members, the UCLA Bruins. The Bruins are 72-20, losing 22% of their favored games with an average spread of -6. Their total win probability was 67%, 69% at home and 61% away. 

70% of the Bruins upsets occurred at home with an average spread of -7.4. The teams that have upset UCLA the most are Arizona State at 4 and California at 3, with the largest upset occurring last year against Aruzona State with a 83.3% odds to win.

They are averaging just under 2 upset losses at 1.8 upsets per season. Are the Bruins going to go over 2 losses after their first season in the Big 10?

**Northwestern**

Northwestern Wildcats are third at a 55-17 record in games they are favored, losing 24% of the time to the underdog. The average spread in these losses is -7 with a 68% pregame win probability. Northwestern lost 82% of their upset losses at home, the highest percentage of home losses of anyone on this list.

Duke is the team that has upset Northwestern the most in the last 10 seasons at 4 games, with Michigan and Michigan State being the second most at 2 games each. Their biggest upset came to Akron back in 2018 with the likelihood of winning that game set to 92.6%.The Wildcats also have losses to two FCS opponents: against Southern Illinois and Illinois State. No other team in this list has an upset loss to an FCS team. 

After averaging 1.5 losses per season since 2013 and no upset losses last season, can Northwestern turn the tide and drop their per season upset total below 1?

**Nebraska**

Nebraska comes in at number 2 with losing 26% of their favored games and a record of 74-26. The cornhuskers have the most upset losses in the big 10. 65% of their losses occurred in Lincoln, Nebraska at a 67% pregame win probability while 9 games happened on the road at 68%. Total, they were favored in these games at 67% and an average spread of -7. 

Minnesota lead the pack with most upset wins over the Cornhuskers at 4, but Nebraska has also lost 3 games each to Iowa, Illinois, Northwestern and Purdue.  Nebraska’s biggest upset loss came to Georgia Southern back in 2022 at home with a pregame win probability of 94.7%, the largest upset on this list. They are averaging 2.4 upset losses per season, **also the most on this list**.

In Matt Rhule’s first season, he suffered two upset losses. Can he right the ship, or are they headed for another 2+ upset loss season?

**Purdue**

The team that has the worst winning percentage as the favorite in the last 10 seasons is the Purdue Boilermakers, losing 31% of their favored games to underdogs with a 45-20 record. 65% of their upset losses came at home with an average win probability of 62% with their away probability set to 65%. The total spread was -5. 

Their biggest upset loss was to Eastern Michigan in 2018 with their chance to win at 85.7%. Purdue is averaging just shy of 2 upset losses per season at 1.8, losing as much as 5 back in 2018.

With the Big 10 Expansion, there is bound to be more unpredictability within conference play. However, whenever these teams are given the benefit of the doubt, I wouldn’t place any confidence in them.

Who’s going to make you upset this season?

My article is at https://www.sevenyardsback.com/post/the-point-s-after-2021-s-value-added-fbs-kicker-ranking

Best kicker was Missouri's Harrison Mevis. It's not very sophisticated so I will happily take any advice or feedback anyone can offer!

Beating the spread > 55% is pretty much a common a goal to most sports bettors. I recently analyzed > 3500-matchups from 2012-2018, with each team having 463-features. My logistical-regression based Classifier hit > 60% when pegged to the opening line. It's basically noise when pegged to game-time line.

1. I would strongly suggest NOT excluding the opening line from your analyses.

2. The idea that the opening line signal would deteriorate as the bookmakers tweak the odds during the week has some interesting ramifications.

3. The opening line seems elusive to bet on. There's the added difficulty of most off-shore sites don't stick to exclusively (-110) when betting against the spread. They dick around with -120, -115, -105 which renders all my analysis moot. I think I need to actually be in Vegas to make money! Which is fine except I suck at Blackjack and strip clubs ;)