The situation was this: Penn State just scored a touchdown and was down 9, with 1:59 remaining in the game. They opted to go for it, lined up in what we can all agree was a questionable formation, and the attempt failed.

The counter-argument to the go-for-two decision boiled down to this:

“If you kick the extra point, you’re down 8 and your team is still in it. Down 9 with 1:59 left, you’re done.”

So there are a few issues with this argument:

1. The assumption that the game is over down 9 with 1:59 left is wrong. There’s about a 1% chance that you can win (based on NFL data) in that situation. If PSU held Michigan to a three-and-out on the next drive, they could have gotten the ball back with about a minute left.

2. If you’re down 8 (and you score again), you still need to go for two. So unless you think your team will magically be better at 2-point conversions next time, there’s no difference. (Penn State is 0-3 on the year, two of them in that game.)

3. Whether you know you’ve lost the game with 1:59 on the clock or 0:00s on the clock makes no difference.

Okay, so what does the math say?

It turns out, there may be no difference, which is kind of what I was getting at up above. It doesn’t matter. Fans very passionately want to feel like they’re in it to the last moment. Coaches prefer to know what their options are ahead of time. Do I need an onside kick? Because if so I’ll do it now rather than later.

I used Ben Baldwin’s 4th-down-calculator and the 2022 Lions-Packers to simulate this game since the Packers (Michigan) were 4.5-point favorites over the (Detroit, not Nittany) Lions (Penn State).

On average, whether you make or miss either attempt, you end up with about a 2% win probability. Not great either way. But in the end, it looks like the decision was a toss-up. So the strong anger and one-sided debate are a bit surprising. But I’ll chalk it up to a frustrated fan base.

What we don’t need to argue about is Franklin’s decision to go for two in the second quarter, down 14-9. Here, there was so much time left that going for two actually hurt their win probability.

Now, this is contrary to what I just said earlier about knowing what you need ahead of time, so I think this outcome speaks to the cost of leaving free points off the board early.

On the methodology: If I were more precise, I would pick my teams based on two-point conversion rates, PAT percentages, and two-point conversion defense, but there’s just not enough data to know that when PSU has only attempted three on the year and Michigan has only defended two (4th and short or 4th and goal can sometimes be a proxy for 2PA).

—Kyle

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Normally I would have checked one of the fourth down decision bots that tweet out the win probabilities of each fourth down decision, but sadly, those bots do not seem to exist anymore. However, there is a nice tool for the NFL that allows the user to input the game state and returns win probabilities if the offense goes for it, kicks a field goal, or punts.

Now, this is an NFL tool, and it requires the user to input the teams that are playing, so I took the pregame spread of 2.5 points in favor of Alabama and chose the 2022 Chiefs to represent Alabama and the 2022 Bengals to represent Texas A&M as the Chiefs were 2.5 points better in ESPN’s FPI in 2022. Reasonable people can disagree on this choice, but I imagine running this for a few different teams would produce close results.

Now to the decisions made by Texas A&M.

4th & 5 on Alabama 31, 9:06 left in the 4th quarter, down by 7. Decision: field goal attempt.

Watching this one live, my thought was that a field goal does not help a ton in this situation. You are fairly deep in opponent territory, five yards is not an insane amount to get, and being down four isn’t much better since it still requires a later TD. The field goal in this case ended up being blocked, but that is not important for the evaluation of this decision.

Now to the model results, remember that the Bengals are A&M here.

This field goal attempt is actually not as big of a mistake as I thought it would be. College kickers are probably slightly less likely to make this kick than NFL kickers (so this win probability change is probably a lower bound), but ignoring that, it is only about a 1% increase in win probability to go for it. The model does not output to the user how the variables are impacting the result, but I would imagine the time left in the game here is the big one impacting a field goal attempt not being that bad of an idea.

The second decision by A&M:

4th & goal on Alabama 2, 2:14 left in the 4th quarter, down 9. Decision: field goal attempt.

On the two yard line! Two score game!

This decision I was pretty sure was incorrect when watching, the reason being that although a field goal cuts it to a one score game, you still need a TD, time is low, and you are two yards away from six points. If you do not make it, your opponent is backed up in their end zone to start the next possession. But let’s see what the model says:

Going for it increases your win probability by 6.5% in this situation, a huge difference. Now, one thing this demonstrated that was mentioned on the Wharton Moneyball podcast a few weeks ago is that coaches might have a tendency to make a decision that prolongs their chance of winning a game, even if it decreases their overall probability of winning the game.

As seen here, kicking a field goal and making it results in a win probability of about 4%. Low for sure, but you know you are in a one score game and it “feels” like there is a chance you can win, because you can imagine a situation where you recover an onside kick, score a TD, and win the game. Compare this to going for it and not getting it — you now have a 2% win probability, which is not that much different than making a field goal, but to a coach a two score game with two minutes to go and not having the ball probably feels like a 0% chance. Jimbo is prolonging his chance of winning the game (keeping it above 0%, at least in perception) at the cost of decreasing his overall win probability.

Checking your own in-the-moment thoughts on fourth down decisions is a fun exercise to calibrate how good your mental model of these decisions is. I thought there were two clear (+3% or so) win probability swings had A&M gone for it on fourth down, but it turns out the first was really not a tremendous difference. As for the second decision, I don’t think there is a lot of good justification for getting that one wrong.

—Drew

So real-quick… how it works:

• 12 slots. 2 QBs, 3 RBs, 3 WRs, 2 TEs, 1 Defense, 1 Kicker

• All 12 must be from a unique team.

At the bottom of this newsletter you’ll find my predicted fantasy playoff points for the top 100 skill-position players, plus their actual playoff fantasy points. But first, how we got there.

The Data

I got the data from nlfreadr, an R package. It included a player’s fantasy points (PPR and non-PPR) every week of the season, both regular and postseason. It also had info like the player’s position, team, how many passing yards/receiving yards, etc.

I decided to just use fantasy-related metrics because that’s what I’m trying to predict anyway.

In the end, the player-data I included looked like this:

• Total fantasy points for the season

• Average weekly points

• Standard deviation of weekly points

• Games played

• Worst week points and best week points

• 25th, 50th, and 75th percentile of points for the season

I also included some data for each player about their team, because the model needs some inkling of how deep into the playoffs they will get. I didn’t have a lot of time to figure out each team’s seed and playoff win probabilities, so I settled for a few other data points that I hope are informative:

• Wins

• Average point differential

• Standard deviation of point differential

• Average points for

• Average points against

• Win rate

The Model

I took all this and put it into a model, where I trained on the regular season from 2010-2021 and predicted on that year’s postseason, and then measured the results.

I also only trained the model on players whose teams actually made it to the playoffs, since I will know this year which players I can choose from. There’s no point in feeding the model all the extra players who didn’t make it and will, as a result, score 0 points in the playoffs. (If I didn’t know who was going to make the playoffs at this point, then I would have to include all players in the training)

Results

The results were far from perfect. My predictions were off by an average of 11.5 points. But that’s still probably better than I could do picking based on my gut-feel, so I’ll take it!

Let’s take a look at who the model liked for this year’s playoffs.

As expected, a lot of the top players are QBs. However, what I’m also interested in is the righthand column where we see that a few players are just that much better than the next-best player at their position. Unsurprisingly, #1 in that category is Travis Kelce. Austin Ekeler is another one that comes up with a 10-point advantage over his next-closest counterpart, Saquon Barkley.

With that info, I decided to swap in Jalen Hurts for Patrick Mahomes, sacrificing a projected 3 points in order to gain a projected 15 points by having Kelce instead of Evan Engram, his replacement.

My team ended up looking like this:

I took a bit of a risk with Hurts since the Eagles had a bye, but I did that for the benefit of getting Kelce and the incremental points he brings. All the top submissions last year had both Allen and Mahomes, and they got the bulk of their points in the wildcard and divisional weekends.

Performance

So how did I do? Much better than last year. I’m currently 22nd out of 158 total, so clearly in the top 20% of all players. However, at this point, with only Jalen Hurts and Travis Kelce left on my team, I can’t improve much more in my final ranking from the Super Bowl because almost every team above me also has those two players.

The best lineup so far was only slightly different than mine in some key areas… mainly Jamar Chase over Justin Jefferson, and Christian Kirk over Tyreek Hill. Despite this, the top player still didn’t have an optimal entry. That would’ve required Burrow and Prescott at QB, Deebo Samuel at WR, and Dalton Schultz at TE2. Of course some of this could change depending on how Mahomes and others play in the Super Bowl.

Here are the top 100 players ranked by their predicted playoff fantasy points. On the far right you’ll see their actual points so far and the difference to the model predictions. Blue are over-acheivers and red are underacheivers.

Overall, the model performed pretty well, and was off by only 5.8 points on average in 2022—even better than we expected! Below is a visual of the predictions and actual points, with some key over- and underperformers highlighted.

This was a fun experiment and I think my computer certainly helped me to field a competitive team, but there’s always that element of randomness that leads to a guy like Christian Kirk outperforming Tyreek Hill and Justin Jefferson, or Brock Purdy outperforming Josh Allen. I think the model could be improved next year by giving it some idea of how many games a team is likely to play in the playoffs, including any potential bye week, and by factoring in a player’s previous-year playoff points.

Thanks for reading and let me know if you liked this content by replying to the email. Enjoy the Super Bowl and go birds!

— Kyle

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Hey there! I wanted to let you know that we’re back in business for 2023 and will be expanding our focus a bit outside of just college football and sports-betting. It will still primarily be sports and probably a lot of college football, but there are other things that we think may interest you—stuff that interests us too.

Particularly, we’ll be diving into other leagues like the NFL and MLB, and even a bit of fantasy sports. Hopefully that’s cool with you.

I wanted to make you all aware so you weren’t confused when you start seeing Staturdays Newsletter pop up in your inbox now and then.

As I was watching the FOX pre-game show before the Eagles-Giants game on Saturday night, there was a conversation among the panelists on how hard it is to beat a team for the third time in the same season. We’ve all probably heard a form of this argument across sports (beating a team again in the CFB playoff that you played in the regular season is a common one), and sometimes it even comes up after just one previous game played.

The common arguments presented for this side generally revolve around the following:

• “It is just hard to beat another team three straight times.”

• “The [losing team] learns more about what they did wrong and makes adjustments.“

• The [losing team] can change up what they do/their strategy in the third game, but the [winning team] will continue to play how they did before.

At a quick glance, some of these arguments should sound odd to you.

“It is just hard to beat another team three straight times.”

This is a good one to start with. At the time we are having this conversation, the [winning team] has already beaten the [losing team] twice in a row. At the beginning of the season, there is definitely a lower probability of three wins against one team happening. But once we know one team has already beaten the other twice? Well to beat them three times in a row they only have to win…one more time. The fact that they already beat them twice should increase a naïve estimate of their win probability in the third game, not decrease it.

“The [losing team] learns more about what they did wrong and makes adjustments.“

Always a fun one. Assumes a) winning teams don’t learn from those games and b) winning teams do not make adjustments after wins.

The [losing team] can change up what they do/their strategy in the third game, but the [winning team] will continue to play how they did before.

Similar to the last point, there’s a pretty frequent assumption that the winning team is stagnant while the losing team has a thick backup plan that they couldn’t make work in the first two attempts, but will find success in the third.

But we are not just hear for a quick glance, so I also looked at some numbers to back up my thoughts.

First, some other relevant work. This article from Football Perspective gives a good basis for this problem, and the conclusion from looking at 1970-2020 data (sample size of 21 games) was that a team that won the first two matchups in a season and then played a team again in the playoffs won ~66% of the third games. That alone gives us some strong evidence that the losing teams don’t seem to be miraculously better the third time around.

To expand on this, I looked at betting data to see if the teams that lost the first two times around are outperforming expectations in the third game, even if they don’t win outright. I only have betting data going back to 2006, which leaves us with eight valid cases, but does round out our data through Saturday’s Eagles-Giants game. Not the world’s largest sample size, but this is a newsletter not a research paper.

In those eight games, the team that won the first two games went on to win the third game 62.5% of the time, pretty much in line with our Football Perspective number.

Based on pre-game betting odds, the team that won the first two games would be expected to win 5.4/8 games, and the real number was 5/8 games. This could be evidence of slight overperformance by the two-time losers, but with only eight games to look at I would not expect that difference to be statistically significant. Also if we account for a ~2% vig by the sports book, it’s really about 5.2/8 games.

Looking at the spread, the team that won the first two games covered the spread 62.5% of the time, so again, not much evidence that the two-time losers are outperforming in the third game.

I will add the obligatory disclaimer that it is possible that bettors are already pricing in their “idea” that it is hard to beat a team a third time, and that is why it is not showing up in the numbers. I don’t think this is really a concern, but a non-market based evaluation metric (Elo ratings, another power ranking) would be a good second pass at this problem to see if adjusting for those changed any results.

Of teams who played three games against each other in one season, a team only won both of the first two games ~30% of the time. That 30% then have a win probability of 62.5% in the third game — meaning that (given we know two teams played each other three times in a season), winning the second game (if you have won the first) is actually less likely than winning the third game once you have already won the first two.

To me, this is sufficient evidence to say that we do not have sufficient evidence to make a case that beating a team for a third time in a season is somehow harder than it sounds. Historically the team that won the first two games wins the third more often than not, and they also seem to not be underperforming relative to the money line or the spread.

Two closing thoughts:

• If a team beat another team 100 times in a row and was playing again, would commentators still talk about how hard it is to beat a team 101 times in a row? I hope not, but that would go against the reasoning they use to justify why beating another team three times in a season is hard.

• When it came time to actually make picks, everyone except Strahan from the FOX team went with the Eagles, all with the Eagles to cover the spread too. Guess they didn’t think it was that hard for the Eagles to beat the Giants for a third time after all?

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I can’t believe we’re already here. Games start tomorrow night at 8 pm with the Pac-12 championship between Oregon and Utah. This is a game we saw just two weeks ago that Utah won in dominant fashion. Are we due for a repeat? I can’t imagine so, but we’ll see.

Before we get into the rest of the games and numbers, I wanted to wrap up Elo’s regular-season performance.

Overall, Elo netted a nearly $400 profit on 395 bets this season for a decent 10% rate of return. That’s right folks: if you bet every week this year, you’d be $400 (or maybe $40 with $1 bets) richer. Now, 395 bets are a lot of bets to be placing during the season, and if you’re like me, you probably did some weeks and skipped others, or took some of the recommendations and ignored others, which means it’s very possible to come out negative. If you missed weeks 6 and 8, you aren’t nearly as profitable.

So we can definitely do better next year, ideally by minimizing the number of bets you need to place, and relying on more data, and getting more certainty in our numbers.

The top 3 most profitable games of the year to bet on were Louisiana Monroe +1050 over Troy ($105 profit), Illinois (that’s just mean) +1050 over PSU ($105 profit), and Rice +1000 over UAB ($100 profit). Those winners had Elo win probabilities of 26%, 19%, and 10%, respectively. Remember, unlikely does not mean never.

Did you make or lose money this season on college football? Did Elo ratings hurt or help you? Send us an email and let us know how your season went.

Okay, onto this week’s games.

Week 14

So it looks like there’s plenty of value to go around. Elo has all the games fairly close, which makes sense for championship week. Two games that it really disagrees on are Alabama-Georgia and Michigan-Iowa. I’m not saying I completely agree with Elo, but I’m saying it has a point! Alabama is a legendary school, and despite how it looked, they got to yet another SEC championship (I was going to say “for the Xth straight year”, but they didn’t make it just 2 years ago when LSU won the National Championship!). Oh, and they’re coached by indisputably the greatest college football coach of all time, so anyone saying Alabama doesn’t stand a chance hasn’t watched what Alabama does in games like this over the past decade. I wouldn’t be surprised if some no-name backup QB for Alabama comes in and puts up 40 on Georgia. That seems to be the Saban way. But we’ll see what happens. Either way, Alabama as an underdog is always intriguing, no matter how good the other team looked against *checks notes* Georgia Tech…

Same goes for Oregon. Do you really expect them to watch the film from the Utah game and then let the exact same thing happen again? I don’t think so. I would say take the spread for Oregon, but interestingly the spread is only -3 to Utah in this game, same as it was the first go-round.

Oklahoma State-Baylor I can’t speak on, because I’ve only seen Oklahoma State play, and that was last weekend in Bedlam so I’m biased, because any time you see a team win like that you start to root for them. So I’ll abstain from picking this game. I have no idea how Baylor looks, but Elo does and they give them a 40% chance to win, about 5% more than Vegas does. That’s value.

Pitt-Wake Forest. I’ve seen both these teams play, and it seems like a toss-up to me. Which Pitt shows up? Which Wake shows up? Elo agrees: toss-up. Remember, no home-field advantage this week in any of these games.

And then there’s Michigan. Again, I’m biased. But, both these teams beat Penn State in different and equally infuriating ways. Michigan looks great. But again, is Iowa going to get pushed around in a conference championship game? I don’t think so. Maybe the spread in this one is the way to go, but Iowa is quite undervalued. I forgot about them and ruled them out, and they took care of their business and got to the championship game. They’re not gonna roll over now.

Win Probabilities

That’s all for this weekend. Next week we’ll probably talk Heisman. Enjoy the games and talk soon, and thanks for riding along with me all season, writing in with your thoughts and questions, and sharing with a few friends. It’s been a pleasure. But we still have some games to play!

— Kyle

I’ll keep this short this morning, but I did want to share some expected value picks with you this weekend, and also announce some exciting news.

First, the news: the Beta version of the Staturdays app is now live, and you are getting the first invitation to check it out! This early version was built with our newsletter readers in mind, with the goal of getting you your information as early and often as you want it each week. It includes our expected value information, Elo win probabilities, up-to-date ratings, and the ability for you to plot any teams and see how their Elo ratings stack up. We also have an overtime simulator for you to play around with that will let you input any two teams and it will tell you how likely they are to win an overtime matchup.

These are just the early features—there’s a lot more in store! If you have any ideas for new features, we’re all ears! Let us know what would be helpful to you. And for now, take a look and send over any feedback on our first version of our app. We’ll be constantly upgrading it the rest of the season.

You can access our app at kylebennison.shinyapps.io/staturdays/. Right now, it’s best-viewed on a desktop, but the mobile version works fine and we’ll be optimizing it to be even better soon.

In other news, we had some feedback about wanting to learn more about modeling and predicting spreads. If you’re one of those people interested, check out our latest article on building a simple spread prediction model using R. It’s a little bit technical, so if you haven’t familiarized yourself with R, you’ll want to do that first before you get into any modeling. Please send me an email if you’re looking for resources to get started.

Now to some Saturday-slate games.

Week 13

So this is really it. Next week we’ll have conference championship games, Week 15 is Army-Navy, and then it’s bowl-season!

This weekend, we have the battle for bowl-eligibility with the recently head-coachless Florida Gators and the Florida State Seminoles, the battle of the newly 10-year-tenured coaches in Penn State and Michigan State, “The Game” as they so humbly call it up in Ann Arbor at noon, and of course “Bedlam” in Oklahoma. And you can bet on three out of the four, according to our expected value table!

Win Probabilities

For our win probabilities this week, check out our new app! All you have to do is choose the “Win Probabilities” tab under “Elo”, and you can search for any team or conference and find what you’re looking for.

Enjoy our last day of regular-season college football in 2021!

— Kyle

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To recap the past weekend, there wasn’t too much craziness to report on. Michigan survived Penn State at home and moved to 6th in the CFP Rankings. If they were able to beat Ohio State, I imagine they’d jump Cincinnati and be in the Top 4, and probably rightfully so as they already have a top 4 strength of record, a stat we talked about a few weeks ago: meaning it would be hard for any average top 25 program to be 9-1 with Michigan’s schedule.

Other than that, Pitt came out of an overtime game with UNC with the win. And Alabama played New Mexico State so we all know how that went. I’m not sure why the SEC seems to play an out-of-conference game in the middle of the heat of the conference schedule, while the Big Ten doesn’t give themselves a break. It would seem beneficial for the Big Ten and other conferences to allow their teams to reset mid-season with an easier matchup. I guess that’s what Rutgers and Maryland are there for.

Honestly though, I’m getting pretty tired of all the CFP Speculation. It seems that the majority of conversation on ESPN (I’m looking at you, College Gameday) and even Twitter is revolving around these Tuesday night rankings. And, credit to ESPN, they achieved their goal. They wanted to create a system that would create a massive amount of debate, and allow them to fill hours of TV time each week discussing these hypotheticals. But I just don’t enjoy talking about who might be ranked higher than who if they win against X, Y, and Z teams. Or if they win 2 and lose the conference championship, or lose 1 and win the conference championship.

There are endless permutations of how this season can end. And I personally feel like we should just let them play out, and then once everyone’s record is set, you can debate who has the best resume. To do so three or four weeks early when there are so many unknowns remaining seems meaningless (unless you’re a TV executive).

With that said, let’s jump right into this week’s games.

Week 12

So Elo’s picks are on a 3-week losing streak but are still very positive on the year. We are also doing very well in the CollegeFootballData.com prediction game, sitting at #2 overall! We don’t do that great against the spread (but like we’ve mentioned, spreads are hard!), but straight up and in terms of predicting the actual spread (not necessarily who will cover), we’re doing great!

If you’re interested in competing, you can too! All you need is a Twitter or Reddit account and you can go in and place your spread predictions each week, and the site will measure how you do. If you’re interested in building a model of your own and don’t know where to start, send me an email and I can share mine and give you a few pointers. If there’s enough interest, maybe we can do a newsletter about it in the future.

I think the only game that stands out to me is that Oregon are underdogs against Utah this weekend, so if you believe in the Ducks, that’s a nice opportunity for some value. The Pac-12 is sketchy though, and it does feel like anyone can beat anyone depending on the amount of precipitation that night. Also good to know: Virginia Tech fired their coach, a Maryland upset over Michigan is more likely than a Michigan State upset over Ohio State, and there’s a 3% chance that both happen this weekend. There’s a 68% chance neither happens.

Here are the win probabilities by conference. I’ll have the non-Power 5 conference games up on the site later today!

ACC

Clemson is still favored against Wake Forest, both by Vegas and Elo. I’m honestly surprised. Hopefully that turns into a great game. Noon kickoff.

Big Ten

Vegas doesn’t think any games this weekend are going to be close in the Big Ten.

Big 12

Pac-12

I know it’s shocking to see Colorado as a favorite at 3-7, but Washington is only one better 4-6, and the Buffs are home. It feels like Washington should be in the race to win the Pac-12, but this just wasn’t their year. And we already spoke about Oregon above, but we give them a slight edge against Utah.

SEC

Not a lot to see here for another week.

Thank you for reading and enjoy your weekend! Newsletter will be brief next week for Thanksgiving, but I’ll still get the expected values and win probabilities out to you.

— Kyle

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• Louisville came within five yards and a better pair of cleats away from beating Clemson

• LSU took Alabama to the brink but was a hail-mary short

• Purdue upset yet another Top 5 team in Michigan State

• Wake Forest’s 8-0 streak came to an end in a no-defense-necessary 58-55 loss to UNC

• And South Carolina took down the Gators

Needless to say, this is why we love college football. And even though it was played at a reasonable hour, I regret to inform you I still didn’t watch the Oregon-Washington PAC-12 matchup. It’s tough to compete with ‘Bama-LSU, despite how far LSU has fallen from just two seasons ago.

But just how close were some of these matchups? Let’s take a look at the in-game win probability charts, and talk about win probability in general while we’re at it.

The first one is my personal favorite. A game that was close down to the very end. Clemson went in with a slight edge, but things swung in Louisville’s direction, largely thanks to the incredible play of Louisville’s Malik Cunningham—who if you haven’t seen play yet you really need to check out. Cunningham was compared to Lamar Jackson, but with better passing ability. Now that’s high praise.

If you’re wondering how this chart is generated, here’s a primer. For each play, we use a model that takes into account a bunch of factors in the game and weighs them all compared to games it’s seen in the past, and then spits out how often teams in that situation win or lose the game. This is the in-game win probability. Here are the factors:

• Points leading or trailing

• Time left in the game

• Down and Distance

• Yards from your opponent’s endzone

• Possession

• Home and Away timeouts remaining

• Your pregame win probability according to your Elo rating

• The pregame spread

• Whether the game is over or not (this is to help make sure the win probability goes to roughly 100% by the end)

Let’s take a look at another game.

This one is painful for Gators fans. Florida was heavily favored heading into the game, by about 19 points! Elo also had them as huge favorites, with a 79% win probability despite being away from home. That’s largely why the start of the in-game win probability graph is down around 15-20%. However, by halftime they were down 30-10 and things were not looking great. Even a 19-point favorite is unlikely to come back from a 20-point halftime deficit, which the model knew.

While the final score showed a close game, this win probability chart says otherwise. Probably due to the fact that Alabama was a 91% favorite by Elo and a 28.5 point favorite. ‘Bama also held a 20-7 lead until deep into the 3rd quarter, where you can see LSU’s chances creep back up a little bit. But in the end, although LSU had a shot, it was still a hail-mary, and the model likely took that difficult field position into account when declaring the game all but over on the final drive.

Another number to note on these graphs is the post-game win probability. This stat is calculated by collegefootballdata.com and tells you how likely to win the winning team was, simply based on how they played. So in this case, Alabama did get a bit lucky to come up with the win with only a 60% post-game win probability. This means that if they play that game that way 10 times, they end up losing 4 of them. Lucky for them, LSU wasn’t playing great either. Clemson’s 23% post-game win probability was even worse. They got really lucky that Malik Cunningham slipped on the last play, and that he was injured and out of the game, stalling out one drive resulting in a missed FG, and another leading to a punt. Those two possessions may have made all the difference.

Here are the last two games, just for fun. You can tell that the Wake-UNC game was a back-and-forth, exciting game just by looking at the chart, and you might be able to say that Michigan State was a bit unlucky to lose to Purdue. The postgame win probability indicates that the result was more of a toss-up.

These charts can be both cool to look at, and informative. However, even a few weeks ago, our charts didn’t always look like this. Sometimes, for one reason or another, they looked like this:

If you’re interested in the data behind the charts and how we’ve improved them throughout the season, then be sure to checkout an upcoming article on Staturdays.com that will go into the details of how they’re built and how we’ve improved them to make them more accurate. For now, let’s look at this week’s slate of games.

Week 11

It pains me that we’re already in the double-digits of the season. Thankfully, with bye weeks and conference championships, we’re still looking at a solid 5 more weeks of football before bowl season.

Slight note on the expected values this week: I’ve excluded any games that have under $1 of positive expected value, because I found that this is actually the most profitable betting strategy. Some of these games where the value is in the pennies are just not worth the risk, so from now on I’ll be excluding them. This will help avoid those pesky negative weeks. And it’ll be less work on your part. It’s a win-win!

North Carolina at Pitt should be an interesting one. They showed us this week that they can score with the best of them when they’re hot, but are unwilling to play defense. So take your pick.

Let’s look at the individual win probabilities. For non-Power 5 conferences, click here.

ACC

We have one mismatch in the ACC in Boston College, who are 2-point dogs to Georgia Tech.

Big Ten

No mismatches here, but Michigan at Penn State in the “helmet-stripe-out” is looking to be the most competitive of the weekend in the Big Ten.

Big-12

Pac-12

Arizona State is a 6-point favorite, but Washington is at home which shifts things slightly in their favor for Elo.

SEC

After a major win last weekend, Elo has South Carolina as the slight favorite over Missouri, who didn’t fair nearly as well against the Georgia Bulldogs.

And after going toe-to-toe (kind of) with Alabama last week, LSU open as underdogs to Arkansas this weekend. Elo disagrees. Personally, I think it’s a toss-up, as the spread implies.

Thanks so much for reading. Hopefully I didn’t bore you with too much win probability talk. Savor the college football this weekend. We’re getting up there in the weeks!

Talk next Thursday, and make sure to follow @staturdays throughout the weekend to see those in-game win probability charts come out in real time after each matchup.

— Kyle

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Hope you had a nice Halloween weekend and it wasn’t too spooky for your favorite teams.

Since this is a college football newsletter, I guess I’ll (begrudgingly) talk about the College Football Playoff committee’s first round of rankings.

If you didn’t see already, here’s a lovely graphic for you.

The biggest shock (and disappointment) to most fans was the exclusion of Cincinnati in the Top 4, and I completely agree. It’s just becoming unfair at this point to keep excluding these teams that go undefeated out of the running. We can’t say for certain what will happen in a Cincinnati-Alabama matchup until we see them play. So it’s unfortunate.

And it’s probably not going to get any better for Cincy because they only have one ranked win over Notre Dame and don’t have any more on the schedule. So the committee has essentially determined their fate unless some top teams have some major slip-ups.

For reference, Cincinnati has a higher Elo rating than both Michigan State and Oregon. That is hard to do as a non-Power-5 team, because, while P5 teams start out at a rating of 1500, Group-of-5 teams start out at 1200, and every year, their score gets slightly regressed back to that 1200 average. So you have to be really good for a long time, consistently, to get up there with the likes of these P5 powerhouses.

And don’t get me wrong, Michigan State is deserving as well. They look like a great team (if you didn’t watch the Michigan-Michigan State game, I suggest you watch some highlights. It was an awesome game.) Oregon and ‘Bama might deserve their spots, but we’ll have to wait and see. For what it’s worth, Cincy also has a more impressive Strength of Record than Oregon. SOR is a measure of how impressive your record is compared to what the average Top-25 team’s record would be if they had your schedule. So that’s implying that the average Top-25 team may not be undefeated if they played Cincy’s schedule, which is surprising, but interesting.

Week 10

So week 9 was not friendly to my wallet after a strong performance the previous week, but thanks to a lot of small wins, I did damage limitation. This brings me to a topic I wanted to talk about real quick: consistency.

When I ran the numbers on the Elo profit and loss and it showed a profit overall, I looked into it a little deeper and noticed something. Some weeks were negative, and some were positive, but the net turned out positive.

Now, if I have a bad week and skip playing the next week, then I may be missing out on the week that’s going to bring the average back positive again. So I could end up losing money on a winning strategy.

Similarly, if I look at the list of positive expected value picks for the week, and pick out 5 that I personally agree with, then again, I may be making a mistake, because I’ve just inserted my personal bias into the picks, and I very well could be picking 5 losers out of the week.

The nice thing about letting the data do the work for you is that you don’t have to worry about your biases affecting your decisions, consciously or subconsciously. It’s all based on data and fact.

If I told you that Arizona was playing UTSA this weekend and you don’t follow those conferences that closely, you might be inclined to pick Arizona as the more recognizable brand, or skip a pick for UTSA. Well, Arizona is 0-8 this season, believe it or not, and UTSA is undefeated with a better strength of record than Ohio State! So that’s why you can’t trust your preconceived notions on a game.

With that, let’s look at Week 10.

It really pains me that Maryland is a positive value bet this weekend. Let’s look at the win probabilities.

For all the Week 10 win probabilities, visit Staturdays.com.

ACC

The Wake Forest one is a good bet. I’m surprised UNC is favored here. WF isn’t getting a lot of love despite being undefeated through 8 games. They’re ranked 9th in the CFP committee’s first rankings. Heck, even I didn’t mention them earlier in this newsletter! They have the 5th-best SOR ahead of Cincy, Oregon, and Ohio State. And FYI, these SOR rankings come straight from ESPN, who essentially call the shots with the whole Playoff thing, so they should know better.

Big Ten

Big-12

I don’t think that’s actually a mismatch on the first game FYI.

Pac-12

SEC

I know we don’t normally do spreads but Georgia -38 is aggressive. Sheesh, how bad is Missouri?

Thank you for reading today, stay warm wherever you are, and enjoy your weekend!

—Kyle

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Coming from a completely biased perspective, I have to say that the new overtime rule that forces teams to just do back-and-forth two-point conversions after just two overtimes is bad. It feels gimmicky, and ironically, a rule that seemingly was put into place to speed along the overtimes actually took quite a long time. And, it completely took the kicking element out of it from the 3rd OT on.

Long ago, I was at the 2006 Orange Bowl between Florida State and Penn State which went to triple-overtime, and the thrill in having the kickers go back and forth missing field goals in the 1st OT, and for Freshmen Kevin Kelly to have redemption in the 3rd OT and win the game with a walk-off field goal, cannot be replicated with the new format that really becomes more of a coin-flip.

An Unlikely Back-and-Forth

I did see an interesting stat about the 9OT Illinois win, though. This season, the two-point conversion success rate has been around 43% (surprisingly low in my opinion). However, even in 2020, it was 42.5%, so I guess it checks out. Penn State and Illinois missed 10 consecutive two-point conversion attempts in those overtimes. It was truly remarkable. Assuming each attempt was independent and that Penn State and Illinois are both average two-point conversion teams (which, after that, probably isn’t the case), the odds of them going 0-for-10 were 0.57^10, which is 0.0036, or about 4 in 1,000. And the chance that they both make one after missing ten straight was about 7 in 10,000! So we really did witness something rare and unique on Saturday, even though at times it felt more like watching grass grow than witnessing history.

Anyway, let’s get into this week’s games.

Week 9

Elo has been positive for several weeks now, with a 9.5% rate of return. I’ll admit, even I was surprised when I found that out. Here are the positive EV bets for this week.

And here are the win probabilities for each conference this week. As always, you can check out the rest on Staturdays.com. This week, we have three mismatches between the Vegas and Elo favorites in the Big Ten alone, which is unheard of… and honestly, I like them all.

ACC

Big Ten

Big-12

Pac-12

SEC

That’s all for this week. Good luck this weekend and talk soon.

— Kyle

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One glaring aspect of Elo is its stark contrast to the traditional AP Poll rankings. Just look at the two top 25’s: the first from the AP Poll and the second based on Elo ratings.

There are some big differences here. First, the number one team according to Elo is still Alabama. The AP Poll puts three teams above them in Georgia, Cincinnati, and Oklahoma (which, as I’m typing it out loud, sounds ridiculous… Oklahoma, with their QB situation, is ranked higher than Alabama, who prove year-in year-out that their backup is usually just as, if not more capable of winning championships.) (Elo already had Texas A&M rated as a top 15 team before they beat Alabama, by the way, while the AP had them unranked.)

Anyway, another glaring difference is Clemson still being a top-5 team according to Elo while being completely unranked in the AP Poll. So it begs the question: is Elo too slow to react to the realities of the current season? Does the AP Poll overreact? Let’s take a look and find out.

Comparing Elo Rankings to AP Poll Rankings

To find out which rankings are more accurate, I created an “Elo Top 25” each week from the year 2000 to present. Then, I used the ranking of the home and away team’s to predict the result of each game. I did the same for all the AP Top 25 matchups. Then, I compared the models.

Stats Talk

For those that are interested, I did a logistic regression on the home team’s outcome (win or loss) using either the home and away Elo rankings (1-25) or the home and away AP rankings (1-25). I only considered Top 25 matchups since the AP Poll doesn’t rank all 130 teams like Elo does. Then, I compared the models to see which one performed best.

I also threw in two extra models that used the following stats

Model 1: Home Elo Rating and Away Elo Rating

Model 2: Home Elo Predicted Win Probability (including home-field advantage)

Okay, we got that part out of the way. Let’s see what we found.

Results

Okay, so a good way to measure the performance of a model that is predicting a binary variable (two outcomes, e.g. Yes or No, 1 or 0) is the Brier score. What that is is the average of the squared difference between the actual result and predicted win probability for each game. So for instance, if the home team wins, the actual result is a 1. If their predicted win probability was a .9, then the difference is .1, and the squared difference is .01. The average of those squared differences for all the games is the Brier score.

If you simply assigned every team a 50% win probability, then your squared difference would be .25. So any Brier under .25 is better than guessing randomly, and anything higher than that would mean that your model is actually working opposite of reality, meaning when a home team should be favored to win, your model is predicting them to lose. So here are the Brier scores from our four models.

So, despite their wildly different rankings at times, Elo and AP Poll rankings perform exactly the same when picking Top 25 matchups. No wonder there are so many upsets in college football—the rankings aren’t that good to begin with!

The actual Elo ratings and Elo win probabilities perform a little better but still aren’t amazing. For reference, the overall Elo Brier score is around .175, so, unsurprisingly, it’s tougher to predict Top 25 matchups than some of the more one-sided games.

Below is a graph of the different models, with their predicted win probabilities rounded to the nearest 10%, and the average actual home result of those games. As you can see, all the models look fairly similar in this case, kind of dancing around the black line which is where the perfect model would reside, meaning teams that you gave a 50% win probability to actually won exactly half the time.

The Harsh Reality

So, whether it’s the AP Top 25 or Elo Top 25, the truth is it’s hard to just rank teams in order and confidently say that one will beat the other. More info is needed, like how much better the #1 ranked team is than the #2 ranked team, who has the home-field advantage, and whatever other data you can include that’s useful. And regardless of the data you use, predicting the outcomes of two great teams is always going to be more difficult than predicting the outcomes of great teams against mediocre teams.

But just because it’s the most popular poll, doesn’t mean it’s the best judgement of teams.

Week 8

This week there’s a lot of expected value to be had. You’re going to see a lot of red, but if you look closer, that’s just because there are a few outlier games with such high expected value that it makes the other one’s look less appetizing, but believe me, they’re there. Interestingly, though, there aren’t a lot of middle-value bets. It’s either pennies or double-digits, it seems, this week, probably because there aren’t a lot of ranked matchups, so most moneylines are fairly conclusive.

Oh, look at that, the first one on the list just hit! Coastal goes down. Vegas underestimated App State, as did many people. They’re notorious season-ruiners.

Win Probabilities

Here are the Power-5 win probabilities this week. The only outright mismatches you’ll find this week are in the Pac-12, but, as you saw above, there’s also a good amount of expected value elsewhere.

ACC

Looking at this chart, I’m not sure why the line didn’t pull in for Clemson @ Pittsburgh, but this one actually is a mismatch with Pitt favored by -3 but Elo still holding Clemson as the slight favorites. It’s one of those things where Kenny Pickett is playing his best football ever, but the Clemson of one year ago is still in there somewhere, and it’s just a matter of when they emerge. I don’t think I’ll be taking that game, and hopefully I won’t regret it. Check back in next week on that one. It doesn’t pay that great anyway so you’re not missing out on a huge opportunity there.

Big Ten

Big-12

Pac-12

Here we go. We’ve got two mismatches here, but interestingly, I don’t see them on the expected value chart since the spreads are so narrow. So these are a bit riskier than your typical mismatch and won’t pay as well. Washington State is also coachless this weekend, so it’s a tough bet.

SEC

Bad week in the SEC, but can LSU rally around Coach O?

For the remaining conferences, check out the site, and as always, thank you for reading.

— Kyle

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Last week we talked at length about moneylines. This week we’re talking spreads, a topic I tend to avoid. Reason: it’s a lot easier to predict which team is going to win, especially in college football—as opposed to the NFL where there’s more parity—than it is to predict how much they’re going to win by.

Vegas spreads are off by about 12.5 points per game, and Vegas has access to more data than you or I, or almost anyone in the public does. So it is a tall task to try to beat them at their own game. Today we’ll look at the economics of spread bets and what makes them so tricky.

A Moving Target

Believe it or not, Vegas doesn’t really need to get the lines right at all. They just need to get equal money on each side of them. So there’s advantage number one. While we need to be laser-accurate to make sure our prediction lands on the right side of the spread, Vegas is less concerned with that level of accuracy (although they are fairly accurate).

The reason for this is vig. Most spreads pay out around -110, or an implied probability of 52%. So, even though there’s only one right score in the end, the probability of +2.5 covering on either side adds up to about 104%. This means that even if something only happens 50% of the time, Vegas pays out like it happens 52% of the time. If you’re wondering where your extra 2% went, the answer is: the sportsbooks have it. That’s their commission fee for setting up the market, essentially.

We see this in moneylines too. Ohio State may have a 96% implied probability of winning, but Maryland will still have an 8% implied probability of winning too. Even though that’s not possible, it covers the sportsbook when the improbable does occur.

So back to getting an equal audience on both sides, this ensures that people bet on something that happens 100% of the time, but only pays at a discount as if it happens 104% of the time. Let’s look at an example of what happens when the audiences aren’t equal.

$100,000 total bet on game 1

Team A: -110, 50% of Public Money

Team B: -110, 50% of Public Money

Team A wins: $95,454

Sportsbook Profits: $4,546

$100,000 total bet on game 2

Team A: -110, 55% of Public Money

Team B: -110, 45% of Public Money

Team A wins: $105,000

Sportsbook Losses: $5,000

So now you can see why they want to balance the line. Even the slightest bit of the audience being off can lead to a loss on a matchup, because they have to pay out more than they took in. Of course, if the sportsbook is really confident in the line they set and they think that the public is wrong, then they could be looking at a big payday if the line is unbalanced in their favor, but I’m not sure if that’s a risk they like to take. This way is a sound strategy to consistently make money, no matter the result of the game.

Predicting the Spread

Last week, I showed you this chart of Vegas spreads vs. actual scores and it was surprisingly all over the place.

So let’s look at how elo differences correlate to actual spreads and see if it’s worth trying to use to predict spreads.

So we definitely have a clear linear relationship here. That's good. We're going to have a significant model. However the problem is the variance of the spread. It's huge. The error on any given spread is going to be really wide. Let's build the model and see what happens.

When we build the model, we get a highly significant model, but not a very accurate one. 50% of all the predictions fall within 11.5 points of the actual score, which is not bad, but the average error on all the data is closer to 17.3 points.

We also get a formula that tells us how many Elo points translate into 1 actual point in the game. Here’s the formula we get.

Home Team Actual Win Margin = Home Elo Advantage * .05 + .92

So, dividing 1/.05, we get 20 Elo points is equal to 1 actual point in the game. The .92 represents the intercept, or in our case, the number of points the home team will win by if their elo advantage is 0, meaning they’re evenly matched with their opponent. Keep in mind, we already include 55 Elo points as the home field advantage by default, so this would indicate that our home field advantage is actually too low. On average, home teams have almost a 4-point advantage all else being equal. That’s pretty incredible!

So if a team has a 1680 Elo rating and their opponent has a 1600 Elo rating, we’d predict the home team to win by 4.92 points.

So really this is a very similar graph, but we're just applying the model to the data (and the same data we trained the model on, which is generally a no-no). So even though the model has seen this data before, this is the best it can do given only the elo margin, leaving a lot of variability (almost 14 points to be exact.)

So we can't really use this unless our spread is > 13 points different from the Vegas spread, which should very rarely be the case. That’s also only the average residual, so there’s still a good chance that our prediction is 17 or 20 points off, which won’t help us against Vegas. And even then, we'd wonder whether Vegas knows something we don't with such a large gap in our predictions.

The Bottom Line

I hope I’ve painted a picture of why spreads are such a difficult bet to profit on. I’ll certainly keep an eye out for times when the spreads seem way off and there’s opportunity, but unfortunately those are few and far between.

Of course, we have the option of adding more data to our model. Elo is one factor and accounts for about 40% of variability in the spread, but maybe with more data we can more accurately predict spreads. Well folks, that’s what I’m working on right now, and so far I haven’t had much success. But I’ll keep plugging away looking for more data that is significant in helping predict spreads and winners, and update the model when it starts performing better.

Week 7

Let’s start with expected values. Here are our positive EV games this week. Remember, so far this year, hitting on $6+ EV bets would have you profiting. Those are the high-risk, high-reward games, and they’re the games where Vegas is underestimating the underdog in most cases.

Win Probabilities

Here are the win probabilities for the Week 7 games. We’re starting to bump into the bye week’s so the list is shorter than usual. As always, you can see every conference on the site.

ACC

The VT vs. Pittsburgh matchup should be enticing. VT looked like they finally figured things out against Notre Dame and had the game mostly under control until very late. Meanwhile, Pitt has showed they can put the ball in the endzone, but haven’t really faced any challenges yet, except Western Michigan who Pitt somehow slept on.

Big Ten

No major matchups in the Big Ten this week, but we do have two games where Elo disagrees with Vegas and two positive EV plays.

Big-12

Pac-12

While there are some intriguing matchups here, I probably won’t be watching. I think I realized that besides the start times which are too late, most Pac-12 games are on FOX and they have this grainy broadcast for some reason. It feels like I’m watching in 480p, so that really doesn’t help. Anyone else notice this problem?

SEC

Florida-LSU brought us the infamous shoe-toss last year. What will this year’s matchup bring? Two undefeateds battle in Kentucky-Georgia. Can Kentucky keep it closer than the experts think?

Thanks for reading this week and please feel free to forward along this newsletter to a friend if you enjoyed it. Talk next week.

— Kyle

In total, there are 14 5-0 teams right now, among them Wake Forest (what??), SMU, both Big Ten Michigans, and yes, Kentucky. So who will make the deepest run? According to our latest season simulations, aside from Yale (who play one FBS game and have a 62% chance of winning it), Alabama, unsurprisingly, has the greatest chance of going undefeated the rest of the season, with a 57% probability. Here are everyone else’s odds:

It is that time of the week where we get to talk college football, betting, and stats. But first…

I Brought You This Meme

There’s no great segue into this but I really wanted to share this funny Big Ten meme so please see below and enjoy. Wait… do memes even work in an email. Guess we’ll find out.

Back to Business

Okay, so I thought we should look a little closer at Elo to see how it’s doing so far in terms of betting.

Let’s talk moneylines. Now, I can’t talk about exactly how the bets that I’ve personally liked each week have done (you guys probably know better than I do), but what I can do is rerun our Elo predictions versus the actual moneylines and see what the profit or loss would be given certain betting rules.

Moneylines

So one thing I like to do is look at expected value. We’ve talked about it here before, but it’s basically the average you can expect to win or lose if you place the same bet many times. So if you bet $10 on a -200 moneyline (66% implied win probability), and that team actually wins that matchup 66% of the time, then you can expect to win nothing on that bet on average.

But, if you can find a team that has +200 odds (33% implied win probability) that you actually think you know has more like a 45-50% win probability, then you can expect to make a profit on bets like that in the long run. Maybe that first one, or three fails, but over time, assuming your win probability model is tuned right—meaning teams with a 50% win probability actually win 50% of the time, not more, not less—you will profit.

So let’s say you took every bet that Elo has recommended that had positive expected value, even if it was just 10 cents. Well, bad news. You’d be down nearly $70 after almost 200 bets, assuming each bet you placed was $10 (not advisable).

So far this season, right around $6 in expected value has been the breakeven point, meaning you’d be profiting if you bet on all the games where a team had greater than $6 in expected value.

Unfortunately, those opportunities don’t come along too often (only 38 times so far this year), because Vegas is pretty in-tune with what’s going on with these teams, and they also set the lines slightly in their favor no matter what.

Take a look at this week in the new table I made and you’ll see that of all the positive EV bets, only four meet the $6 threshold. And, as is usually the case, they’re rarely the sexy bets. This week, we’re looking at Akron, Buffalo, Georgia Southern, and Louisiana Monroe.

But if you look at the data, it’s those boring teams that pay the best. Louisiana Monroe, Navy, Hawai’i: these are the teams that get the good-paying odds, and when they hit, it makes up for a lot of the misses and then some. These can also be the ones where you take both the moneyline and the spread, and you hedge if they lose but keep it closer than Vegas thought, or boost your profit if they win.

That being said, I’m shocked there’s even a moneyline for Ohio State over Maryland. I mean, come on… the Terps looked awful last Friday night against Iowa. I can’t imagine they figure it out in time for Ohio State this week.

The Dreaded Spread

So you may notice that I don’t talk about the spread often in this newsletter. The reason for that is pretty simple: they’re unpredictable. At least from what data I’ve looked at, spreads can vary so widely from the line, that it’s not really worth trying to predict them. Let me show you what I mean.

This is just for 2021, and as you can see, even Vegas is off by a very wide margin most of the time. So it’s kind of a crapshoot. So my thinking is that if even Vegas, with all their realtime data, can’t get it even close to right, it’s hard to believe that I can either. However, as you can see, it does mean that if we are able to get it right more often than Vegas, even if we’re still wrong most of the time, there’s an opportunity to find some value. We’ll explore that next week and look at how Elo does at predicting the spread of games, but for now, I’ll leave it at that and get to some win probabilities.

Week 6

The headliner this week has to be (in my unbiased opinion) the Top 5 matchup between Penn State and Iowa. The East still will go through Ohio State and Michigan, so this game matters more to Penn State than Iowa, but it also has CFP implications, as does every game from here on out. Ohio State may get the benefit of the doubt dropping one game a season, but other Big Ten teams have not had that luxury, so a win and one step closer to an undefeated season is vital for both programs.

Let’s take a look at the Power-5 slate this week.

ACC

Big Ten

Big-12

Oklahoma-Texas is the big game here this week. Can Texas possibly redeem their season with a huge upset? Spencer Rattler sure isn’t Kyler Murray or Baker Mayfield, so it’s a possibility.

Pac-12

Stanford the giant slayer did it again last week. I love them for the rest of the season.

SEC

View the rest of the FBS conferences here.

Thank you for reading and enjoy your weekend. A big shoutout to my mom who will most likely be celebrating her birthday by watching the Penn State-Iowa game. You’re a trooper, Mom.

—Kyle

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We’re back at it again already—these weeks are coming fast and furious now.

There were some massive upsets in Week 4, headlined by the Big Ten’s very own Minnesota falling to Bowling Green, who, despite their name, don’t even have a D-I bowling team. But, as it turns out, they’re not half-bad at football. Ok, enough about them. Clemson also lost. Should I have led with that? To NC State, who are not a bad team most years.

With two losses and two wins, one unconvincing against Georgia Tech, I think Clemson’s hopes of making the playoff are all but over this season.

Oh, and an Ohio State player quit in the middle of the game, and it sounds like his own teammate was the one who waved him off the field, setting him off. I don’t think I’ve ever seen something like that before and I love it. Love the chaos. Ryan Day better be careful because from what I understand, Urban Meyer may be looking for a job very soon (after, of course, another medically-induced sabbatical).

What Else Is New

Well, I was back at my alma-mater this weekend with family and friends to see Penn State beat up on Villanova. And while it was no whiteout and the only thing that was beat up was my skin by the sun, it was great to be back and see a full stadium rocking, see the fans back and interacting with the players, and see all the traditions back in action again.

This weekend, I’ve booked up my Saturday again so I won’t be watching a ton of games live, but I’ll certainly be locked-in to all the scores on my phone. It’s that time of year, folks. Fall stuff is about to be in full-swing. Be smart and try to schedule all of it on Sundays, especially if your NFL team is worse than your college team like mine (Eagles) is.

Elo Leaders

I took the liberty of visualizing the current top ten in our Elo rankings so you could see just how they have moved up or down through four weeks. You’ll notice that Alabama has actually lost points despite going undefeated. This was because of that closer-than-expected game against Florida. Similarly, Florida has really climbed and is now the fourth-ranked team.

One team notably missing from the top ten: Penn State. They really got punished last year for their losses, so, while they’ve had two solid wins so far, they still need some more convincing wins to earn the trust of Elo.

Week 5

#8 Arkansas head to #2 Georgia this week in an important game for the SEC and the playoff. Arkansas has surprised a lot of opponents already this year, including Texas and Texas A&M, so I’m excited to see how Georgia deals with them.

#7 Cincinnati faced #9 Notre Dame in another huge Playoff game for the non-Power 5 teams. This will likely end one team’s playoff hopes and give the other a great resume win.

#1 Alabama finally challenge themselves a bit against #12 Ole Miss.

Overall, I’d say this is one of the best weekends we’ve had on paper so far this season. Conference football is a in full swing and anything can happen. Here’s how Elo stacks up against Vegas odds this weekend.

Now remember, Vegas puts a lot more stats into determining win probabilities than Elo does, but sometimes they tend to be too down on struggling teams and too high on good teams. Elo prefers the “wait and see” approach, or more accurately the “give credit for sustained success” approach.

For that reason, Elo is higher on Notre Dame than Vegas, who see this game as more of a toss-up while Elo has it around 75% in favor of Notre Dame.

Other ones that intrigue me: Colorado over USC. Colorado have been not good, but USC have also looked unimpressive and lost their coach two games in, so it might be closer than people think. Iowa-Maryland is another one, and while Maryland aren’t favored to win, I think Iowa aren’t favored to win by enough. So with Iowa at -165 Moneyline and only a -3 spread, I think that’s disrespectful to the #5 team in the country. That game’s Friday Night Lights though, so get your bets in!

I also actually like Charlotte against Illinois. While Illinois have dropped four straight and haven’t put up a lot of points doing it, Charlotte have scoring-ability and may just pull off the upset, or at least cover the +10.5 points and make them sweat. Could be worth sprinkling a few bucks down.

Win Probabilities

Alright, here’s the full list of win probabilities for the week.

ACC

Big Ten

I’m just now realizing that Wisconsin is actually favored in this game? Doesn’t that just seem wrong? I know I’m breaking two rules by 1. going against Elo and 2. trusting Michigan, but they have been putting up points (granted, against no one) and playing really solid defense. I almost thought my code was broken for a second, that’s how shocked I was at those odds.

Big-12

Pac-12

SEC

You can get the rest of the win probabilities right here.

Thanks so much for reading, and I’ll see y’all next Thursday.

— Kyle

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Let’s start the day off right with some CFB Stats talk.

Elo Updates

This is also probably the perfect time to bring up some exciting updates to our Elo rating system. Up until this point, Florida would lose Elo points after losing to Alabama, despite putting up a great fight and leaving most people with a better impression of their talent than they had before the game. This seems wrong. Well, luckily we have Drew Bennison on our team, who came up with an innovative idea.

Before, a team would improve their Elo ranking anytime they won, with the caveat that they would improve less for games they were highly likely to win, and more for large upsets according to Elo’s win probabilities. Now, a team can actually lose points in a win if they play poorly, based on their post-game win probability, which is essentially looking at each team’s advanced stats and predicting how often team’s that end up with those stats win or lose the game.

Easier to just show you, let’s look at how teams in the Top 25 were affected by this new change this weekend.

So, Alabama lost points and Florida gained points by playing worse and better, respectively, than Elo expected them to. Likewise, Clemson lost a few points for an unimpressive win against their much inferior Georgia Tech opponent.

It’ll take some getting used to, but this has been tested and is the better way to evaluate teams and predict game results.

Interception-Prone

Last weekend, I put out an article on quarterbacks and interceptions. The basis: a text-exchange from almost three years ago where Brian asked if the Penn State quarterback, still Sean Clifford at the time, played worse after throwing an interception. It sure felt like he did, but I didn’t know for sure.

Well, a lot of procrastinating and some more coding experience led me to finally answer the question last week. And the answer is a resounding yes!

The idea is straightforward: quarterbacks that throw an interception lose confidence, may be more turnover-prone to begin with, and put themselves and their teams in difficult positions where they may need to pass more and take more risks.

I broke the data up into pre- and post-interception plays, and looked at some key stats like predicted points added, touchdown rate, completion rate, and more, and then found which quarterbacks performed the best and worst after making a mistake.

On average, almost every stat is negatively impacted after an interception. However, some quarterbacks handled things better than others.

Some household names like Mac Jones, Joe Burrow, and none other than Trevor Lawrence were excellent at bouncing back from an interception.

For the full analysis, check out staturdays.com.

Week 4 Picks

With the changes in Elo methodology, here are the updated Elo discrepancies compared to the Vegas win probabilities. These are places where Vegas may be over or under-confident in the home team.

A few I will be eyeing are Ball State—who put up a decent effort against a tough PSU opponent the other week—over Toledo, Boston College—who are on a three-game winning streak—and Georgia Southern. Navy and Colorado really spook me, but knowing me, I’ll probably be proven wrong this weekend now that I mentioned it. Never doubt Elo in the long run.

Below are the full win probabilities for all P5 conference games this weekend. I’ll be out in the Happy Valley fall weather this weekend, so you won’t hear from me as much on Saturday, but I’ll be back early next week to update everyone on all the happenings. Enjoy your weekend!

— Kyle

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ACC

Big Ten

A note here: Wisconsin doesn’t get any home-field advantage in this game because it’s being played at Soldier Field in Chicago, which is more of a Notre Dame home-game. It’s the first time in a long time I’ve seen a perfect 50/50 matchup!

Big-12

Pac-12

SEC

The Rest

We ended up having some quality college football games—and even better weather—on Saturday. The sun was out, which meant double-TVs on the deck (had to watch the US Open women’s final too, of course!) And I watched another Pac-12 game, this time against the Big Ten, so two halves make a whole!

I’ll be the first to admit that I was wrong on two fronts last week.

First, Ohio State did indeed have to worry about the Oregon Ducks, despite their 89.4% win probability, which, as we know, is nowhere near a certainty.

Second, the Week 2 slate was not weak. Well, it was, but not if you watched all the right games and ignored whatever was going on down in the SEC.

Wow, that was so brave of me.

The Buckeyes made it look close at the end, but it never felt close. Oregon controlled the game on both sides, first—it felt like—on defense, holding OSU to a touchdown in the first half. And then in the second half, with an explosive offense that ran past OSU defenders.

And the run-game is what set the two teams apart. According to collegefootballdata.com, Oregon running back CJ Verdell averaged .38 Predicted Points Added (PPA) per play, while Miyan Williams for OSU managed only .07. The QBs were both productive, with .38 PPA for C.J. Stroud and .41 for Anthony Brown. So no Stroud slander just yet. The Buckeyes’ issues run deeper.

Drew was working on our in-game win probability model last weekend, and found that we were very low on Ohio State’s chances at the half, even though they were only down 7!

Near the end of the game, our model shifted sentiment, giving Ohio State a 69% chance to pull out the win. We’re not exactly sure what changed, but Ohio State were indeed in it down to the wire. There are still plenty of kinks to work out, but it was interesting to see the big swings as the game progressed.

In other big Week 2 results, Iowa upset Iowa State (although is 10 beating 9 really an upset?), unranked Stanford walloped #14 USC and cost Clay Helton the keys to his program, and Arkansas did the same to #15 Texas, giving them a taste of what the SEC-life might be like in a few years. If they struggled to find success in the Big-12 after being “back” for the last four years, it’s gonna take a few decades of being “back” in the SEC before they see any success in that conference.

Oh, and Virginia Tech beat Middle Tennessee. What’d I tell you? Easy money.

Rumors Swirling

With Clay Helton’s firing, that special time of year is coming early this fall, and will unfortunately stick with us all season: who will be the next coach of USC? Unsurprisingly, James Franklin’s name was one of the first to be thrown out there. I’ll squash this rumor here and now by asking the simple question… why? Why would Franklin want to leave what he’s got in Happy Valley? He has the #1 recruiting class for 2022 (USC’s is 29th, for now…), the #12 class for 2023 (USC is not ranked), a top-10 football program right now, and the 7th highest salary in college football, which more than covers the cost of living in central Pennsylvania.

And now, my “only conceivable reasons for anyone to want to go to USC” list:

- In-N-Out

- Getting a sick tan

- Putting avocado on everything you eat

And these are easily outweighed by fires, mudslides, and running out of water all the time. So keep looking, USC.

Coming Soon

I’m working on an article about quarterbacks and interceptions. Namely, how do quarterbacks play after throwing one? Are they rattled? Do they become more conservative? More accurate? We’re going to find out, and see who cracks under pressure, and who balls out in the face of adversity. Be on the lookout for that article to kickoff your Saturday morning.

I don’t have much to share with you, but for now here are the most-benched quarterbacks since 2014 after throwing an interception.

Week 3 (already?)

It’s the final week of non-conference play (kinda). And thankfully, we have some teams challenging themselves this weekend. Highlighted by none other than Auburn vs. Penn State in a primetime White Out game. Elo has Penn State as slight favorites at 60.8% win probability in this matchup, but it could go either way.

Below are the win probabilities for the Power-5 teams, and, where appropriate (or inappropriate), my commentary. If you’re looking for Group of 5 games, check out staturdays.com.

Big Ten

Nebraska playing Oklahoma this weekend is exactly like when you sign up for the marathon a year ahead of time hoping that you’ll be in shape by the time the race comes around. As is usually the case, Nebraska will be puking on the sidewalk 3 miles in, and might duck into a Porta-Potty, take off their race bib, and try to blend back into the crowd.

But in all seriousness, we have a lot of other competitive games this weekend.

ACC

Virginia Tech @ West Virginia looks like the game to watch here, and keep an eye on Virginia, who is underestimated by Vegas by over 10 percentage points compared to Elo 👀.

Big-12

Pac-12

I will not blame you if you don’t watch any Pac-12 football this weekend. It’s okay.

SEC

Florida have more of a chance to beat Alabama than Oregon did to Ohio State last week, and are underdogs by about just as much. Think about that.

Think about it… don’t act on it.

That’s all I’ve got for you. Enjoy your weekends and remember to check out my article this weekend!

— Kyle

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That’s usually how things go, right? You get all excited, the hype, the buildup, and then you get to the moment and you really just want to take a nap.

And of course, where I was, it was a beautiful day on Saturday, and I sat inside and watched college football all day and refreshed Twitter. And then on Sunday when I actually wanted to get out and do something, it rained. I did manage to get a gameday run in Saturday morning, so I get points for that, but otherwise this weekend was a couchfest. I’m not complaining though.

Happy Thursday by the way. I won’t recap all the results. If you didn’t see who beat who, you can find that here. There were some good games, and some defensive battles, first at noon and then in the primetime slot. D.J. Uiagalelei looked out of place in the Clemson offense compared to what we’ve been used to seeing out of their QB position. He had slightly negative average Predicted Points Added (PPA), which is just a slightly different version of Expected Points Added (EPA) that many of you know by now. That’s worse than Sean Clifford, for reference, Penn State fans, and a lot worse than Will Levis. He struggled in the pass game but struggled even more in the rush game, and overall was in the bottom 10 of eligible QBs that we could rank in Week 1. You can see the full list of QB rankings here. It’s still really early, so I don’t expect him to stay down there, but that was a huge game for Clemson and, like I hinted at last week, will stay with them and for sure be a factor come Selection Sunday.

Other Big Wins

Even though Mack Brown said his team was “overrated” after the game had already been played, that shouldn’t matter to Virginia Tech. A win over a Top 25 opponent stays a win, and while UNC may slide down the rankings as the season progresses, it’s still a great accomplishment for a Hokies team that looked solid on D, and whose QB seems to have the athleticism to take them places. Braxton Burmeister averaged .494 PPA, placing him in the Top 25 for Week 1.

And while I won’t admit that “Jump Around” isn’t overhyped, Enter Sandman is undeniably electric. And now for my brief intermission in stadium tradition head-to-heads:

Zombie Nation > Jump Around

Enter Sandman > Whatever Clemson does with that rock and hill

Waving at the hospital kids > most other traditions (although it received mixed reviews this past weekend)

White Out > Any other color including Scarlet, Maize, Black, etc.

Florida State horse on field / guy throwing spear on fire into turf > All other horses, dogs, other animals on the field (I’m assuming the FSU guy isn’t offensive or he would’ve been cancelled by now, right?)

Alright, back to business. Can’t wait to hear your thoughts on that one.

UCLA also had a big, huge win against LSU which I alluded to earlier. I don’t care if LSU isn’t the LSU of 2019 anymore. That much is obvious. It’s still tough to beat them. And my god the Rose Bowl is beautiful. Brings back them mems from the 2017 Rose Bowl between Penn State and USC. We sadly lost that game, but it’s still one of the best, most exciting games I’ve seen or been to in my life, and the only game I’ve ever not been mad to lose, because what I witnessed—including four consecutive touchdowns on four consecutive plays for Penn State, and this gem of a 79-yard TD run by Saquon Barkley)—was so incredible. And shout out to my roommate Chris who paid for the $150 Uber back to our hotel afterwards. Surge pricing man.

Catch Up

Drew updated his season simulations for all Power 5 conferences, so you can see your team’s ceiling and floor for the rest of the season.

Some Cool Stuff

We have an app in the works right now that will be bringing you up-to-date Elo ratings whenever you want them, will let you simulate overtime games between any two teams to see what the probabilities of each result are, will let you plot teams’ Elo ratings week by week, and hopefully much more. Be on the lookout for that and let us know what else you would like to be able to do.

In the meantime, there are already some really cool and impressive resources out there for getting all sorts of stats in your hands live during games and afterwards.

The first is gameonpaper.com, which updates live during games and shows you win probabilities, EPA, top plays, and so much more granular detail than you’ll find on ESPN itself. Check it out during the games this weekend—it’s pretty cool.

The other is cfb-graphs.com, which shows you more advanced summary stats and rankings for your teams, as well as for matchups after they are played.

So you get some pretty cool high-level summaries of how the two teams played. Here you can see that UCLA dominated the pass game. He includes a glossary of all the terms used at the bottom of the page as well so don’t worry if you don’t know what Eckel is… I didn’t either.

Week 2

This week is tamer overall, with some of the more familiar early-season matchups taking place, including LSU playing a school named “McNeese” which sounds more like your nickname for your sister’s daughter that loves McDonald’s rather than an institution of higher education.

There are a few matchups that look exciting on paper, like Oregon taking the trip to Ohio State, but in reality it shouldn’t be much of a problem for the Buckeyes.

Michigan also plays Washington this week in a game that will surely do neither of them any favors. For us Pac-12 fans, USC faces Stanford if you’re up for some late-night football. Honestly, it’s a very weak slate other than that. They spoiled us in Week 1 with more good games than we could physically watch.

In terms of games that Elo likes, Tennessee over Pittsburgh is one, but it’s very close. Another is Syracuse over Rutgers. Again, it’s a tough call because Syracuse let me down many times last season, and Rutgers after several years of abysmal performance in the Big Ten seems to be picking up some steam (they’ve increased their win total one game per season for the last three seasons for a total of 3 in 2020). Mississippi State is also favored over NC State by Elo.

So I don’t see a ton of value this week. You can always just put money on Virginia Tech to beat Middle Tennessee and collect your 8% interest.

I broke the win probabilities up by conference and arranged them in order of kickoff to make it more digestible. I’ve put the Power-5 teams below. If you’re looking for Group of 5, you can find them on Staturdays.

ACC

Big Ten

Big-12

Pac-12

SEC

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Except this year it’s different. It’s better. It’s more important. The schedule is back to normal. The fans are back in the stands and in the parking lots. The narrative—we hope—will be back to football first and foremost. Not cancellations or schedule rearrangements or opt-outs or positive tests.

We already got a taste of it last weekend and it was glorious. The College Gameday desk is smaller, the guys are sitting closer together, Lee Corso is in his rightful place among them, and fans set the scene once more with their early-morning enthusiasm. The games may not have all been played well, but they were all played.

So let’s get you up to speed.

In This Email

• Welcome

• Games to Watch

• ICYMI

• Week 1 Win Probabilities

For Those Of You That Are New

Hey there! Thanks for subscribing and following along. I’m Kyle, and my goal will be to help you be a smarter college football fan each week. We’ll talk about success rates, strength of schedule, expected points added, and, of course, Elo ratings.

This should hit your inbox every Thursday morning around 8 am for your reading pleasure leading up to the Saturday slate of games. If you find it going to some other folder, just make sure to add this email address to your contacts, drag it to your primary inbox, or whatever else you have to do. Let’s get started!

Games to Watch

Week 1 of college football does not disappoint this year. We are heading in with a bang. I’m not sure if it’s the College Football Playoff putting pressure on teams to schedule better non-conference matchups, but it’s definitely a consequential week for some major Top-25 teams right out of the gate.

We have Alabama vs. Miami, Georgia vs. Clemson, Notre Dame vs. Florida St., Penn State vs. Wisconsin, Ohio State vs. Minnesota, Indiana vs. Iowa, LSU vs. UCLA… just typing this I’m astonished at what I’m seeing right now.

I’m sure we’ll pay for this later on a random Week 13 where Alabama plays Birmingham Community College or something, but I’m happy that college football is coming back in an emphatic way.

Many of those results will follow those teams all the way to selection Sunday.

An interesting one to watch will be LSU @ UCLA, since UCLA already played in Week 0 and had it pretty easy in a 44-10 win against Hawaii. That game will be at 8:30 PM on FOX this Saturday. I’m making it my goal to watch more Pac-12 football this year so I’ll report back next week if that actually happens or not.

Catch Up

We’ve been hard at work getting prepped for the 2021 season, and we’ve already put out a bunch of articles in the past few weeks.

I looked at home-field advantage in 2020

I measured the importance of fans in home-field advantage

I looked at the most consistent QBs in CFB

Brian previewed the Penn State football season

Drew simulated the 2021 season for all Power-5 conferences

and just for fun… I graded score bugs from a bunch of different sports

Week 1 Win Probabilities

So here we go, the Elo predictions for Week 1. Historically, week 1 has been Elo’s best week of the year, mostly because there are many lopsided games that Elo predicts at near 100% or near 0% probability, meaning our predictions aren’t usually off by much. It’s later in the year when a lot of games are 50/50 that Elo suffers.

If you need a quick refresher on Elo, you can check out this post from Staturdays that explains the rating system in more detail, but the elevator pitch is:

Points-system, Power-5 conference schools average 1500 points, with the best teams like ‘Bama around 2100 and the worst teams like Rutgers around 1000-1100. You gain or lose points based on wins or losses, and we can predict win probabilities based on the difference in two teams’ Elo ratings.

It’s a very long list this week so I’ll sign off here. I’m glad to be back and excited for all these great matchups. Thanks for reading and talk soon.

— Kyle

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Kirk and Chris put on a brave face

It became a running joke by the end of the year that if Kirk Herbstreit and Chris Fowler were calling the game, it was bound to be a blowout. Week after week, their games were out of hand by the second half, and sometimes the second quarter. This led to people blaming the year 2020, COVID, opt-outs, and anything else they could for an uncompetitive season.

As it turns out, every other game outside of primetime was more competitive, not less, in terms of margin of victory, than the previous season. The games in the primetime slots were blowouts, and that left an impression on fans that will affect how we remember the 2020 season forever.

Final Elo Ratings

With the season finally complete, we present to you our final Top 25 based on Elo Ratings. As we said on the podcast, this Alabama team didn’t eclipse the highest Elo rating ever (of 2018 Alabama), despite being thrown in the ring as “best college football team ever”, a title I’m pretty confident is given to every national champion every year.

Despite not hitting the highest rating ever (you’d need a very long win-streak to do that), they did have the biggest separation from the field in terms of standard deviations above the average college football team’s Elo rating in a season, at 2.59. So they set themselves apart from the pack, and I think we can all agree it certainly felt that way by the end of the season. Nobody was beating ‘Bama.

Help us get better next season

Whether you’re a Staturdays podcast listener or not, we’d love to hear from you to get some feedback on how we did and what you want from us next season. It’ll take approximately 60-90 seconds, or your money back. We appreciate y’all.

Success Rate depends on the team

Check out Drew’s new article up on the site right now. Everyone talks about 3rd down conversion rate, but nobody talks about how long you have to go in the first place. That led Drew to investigate which teams get into the best situations on 3rd downs, and which teams convert best from different distances.

What else to look out for

Tomorrow, we’ll have an article up asking the question: Was the 2020 College Football Season a Success? We’ll analyze as much of the good and the bad that came from playing de jure amateur sports during a pandemic. It’s a long read, but I think it’s pretty thorough and fair. Let us know your thoughts (I’m sure you will) on Twitter after it’s out.

During the offseason, we’ll be posting articles covering topics we didn’t have time to get to during the season. Well also be working on improving a lot of our stats and offerings for next year.

Once again, we appreciate every one of our readers of this newsletter, and look forward to reconnecting on Twitter soon, and in your inbox again next season. Until then, have a great offseason and stay safe.

— Kyle

I’ll admit that I wasn’t too inspired to write about college football given everything that happened in our nation’s capital yesterday. It was a sad day in America’s history, but hopefully a day we can soon put behind us, not by moving on (read: forget), but by moving forward together, and making progress.

Despite that, I’m giving it my best effort to break down the upcoming game for you, and put it into some context.

The Path to the Playoff

Despite speculation that the game might get pushed back, it looks like the National Championship between Ohio State and Alabama is still on. So let’s take a look at how the two teams got here.

Many would say that Ohio State had a much easier route to the championship game this year, and that would be fairly accurate. But they did have to beat one of the best teams to get there.

Ohio State was favored heavily in each of the six games they played in the Big Ten this year. As a result, their Elo rating didn’t improve much for the majority of the year. However, they made a huge jump into the stratosphere after last week’s win over Clemson, gaining 54 Elo points up to 2036, 31 points better than where LSU came in at last season before their national title game, to give you some perspective.

Meanwhile, Alabama had a very different path here: they faced a tough SEC schedule and continuously proved themselves throughout the season. They now head into the National Championship game at a 2052 Elo Rating and a 52.3% win probability.

This will be one of the closest matchups in CFB Playoff history based on Elo ratings. LSU had only a 36% win probability against Clemson last year, believe it or not. Clemson’s win against Alabama the year before was even more improbable, with a 31.5% WP.

The uniqueness of 2020 means that Ohio State essentially proved themselves National Championship-worthy just last week by beating one of the best teams in recent years.

The Matchup

Looking at the head-to-head matchup, I can’t help but think that Ohio State will be in over their heads, at least on the defensive side of the ball. As you can see, their biggest advantage is in their run defense, allowing .3 less EPA (Expected Points Added) per rush than Alabama. The only problem: that’s not Alabama’s game.

While Alabama only pass 4% more than they rush, they are just so much better at passing, and that is Ohio State’s weaknesses, so why wouldn’t they exploit it as much as possible in this game? And I know: Clemson’s strength was their pass game too, right? And they managed to slow them.

Yes, that’s very true. And there’s the chance that that could happen again. But two weeks in a row? Against two of the best passing offenses in college football? I can’t see it happening. And ‘Bama’s passing game has been even more effective (.7 PPA/Pass) than Clemson’s could ever dream of (.47 PPA/Pass), and that’s against much stronger SEC opponents than Dabo’s team had to face all year long, save maybe Notre Dame.

Now, I don’t see defense playing a huge role in this game, but if there’s going to be an offense that stalls out, I’d bet it will be Ohio State before it will be Alabama.

I’m unsure about Ohio State’s injury and COVID situation, but at least Justin Fields seems good to go, and after reading a comprehensive assessment of his injury from someone who claims to be a doctor (or at least a couch doctor), it sounds like it’s not much to worry about if they didn’t take him out of the game last week.

So, I’m going with Alabama in a close one. On the podcast Wednesday, Drew took Ohio State outright. We’ll see who comes out on top in the last game of a turbulent 2020 season on Monday.

Thanks for reading and stay safe out there. We’ll recap the season next week on Tuesday night live on Twitter, and then again in your inbox next Thursday.

— Kyle