Here we are less than two weeks before the draft, and it is time to break down the most important players in the game.  I must say that this quarterback class looked better last year than it does this year coming up to the draft.   Top prospects like Bryce Young and CJ Stroud are being picked apart by the media for size, weight, speed, and system issues coming into draft day.  These pre-draft thoughts and opinions have the market all up in a tizzy.  The irony here is that last year’s quarterback class was ranked so poorly that there wasn’t even one selected in the first 20 picks, yet guys like Kenny Pickett, Brock Purdy, and Bailey Zappe performed quite well under the circumstances that they had during the season.   Sam Howell and Desmond Ridder are now expected to come in and start for their respective teams, and I think that last year’s class might end up much better than previously expected.   I think if teams just avoid the noise in the media, they can make good determinations on who is an NFL quarterback and who isn’t.   If you ask me, all of these guys in the top 5 are pretty close to each other, and I personally would not make a big reach for any one of them.  I give this class a B- right now coming into the season.

1.  CJ Stroud, Ohio State HT: 6-foot-3 | WT: 214 pounds

Stroud seems to have all of the measurables when looking for an NFL quarterback.   Stroud is tall enough at 6’3, he has good size hands at 10″ and he is a very accurate passer.  Being Justin Fields mentee probably helped him develop at Ohio State, but we do not know how fast this kid really is being that he has avoided the 40 yard dash.  That usually tells me that the candidate’s speed isn’t overly impressive, yet we did see him use his legs some in the college football playoffs which makes me think that he might have some more threats to a defense than we may even think.   The big knock against Stroud is that he has played in a system that has not been conducive to NFL success, and the verdict on his predecessor Justin Fields is still in deliberation.   I think that Stroud is the safest bet out of all the quarterbacks in this draft being that he has the size for the NFL, and has performed quite well on the big stage in college over the past two seasons.  Stroud has been rumored to go number one a few weeks ago, but that noise has now flipped over to Bryce Young.  I would not be surprised to see CJ fall to the Texans or even the Colts if something crazy happens.

2.  Hendon Hooker, Tennessee HT: 6-foot-3 | WT: 217 pounds

I honestly didn’t know that I was going to land on Hendon Hooker here before I started my research, but then I had to ask myself, why the heck not after looking at the total package?   Did Hooker play in an NFL system?  No.   Did Hooker finish the season healthy?  No, but why should that matter to a Houston Texans team drafting 2nd with an experienced quarterback to start the season in Davis Mills?   Hendon Hooker threw 58 touchdowns and only five interceptions over the last two years!   Who doesn’t love that ratio!?  Those numbers are absolutely amazing no matter how you slice it up.   Not only that, Hooker rushed for over 400 yards in both of his past two seasons which has gained popularity in the NFL.   Hendon was 69.6% accurate last year in his passes and he has stepped up to the competition against teams like Kentucky, LSU and Alabama.   Even though he played in a Josh Heuple college offense, Hooker’s measurables at 6’3, 217 lbs fits the part.  Having 10.5 inch hands also helps as well.  I do not see why he has fallen so low in the prospects for 2023 and Hendon is my second best quarterback on the board.

3.  Bryce Young, Alabama, HT: 5-foot-10 | WT: 204 pounds

Ok, 204 lbs?  I am guessing that he ate some of my mothers brownies before weigh-ins at the combine. So does this matter that much?   Bryce was the top performing quarterback all around over the past two years throwing 79 touchdowns and 12 interceptions.   He also played in more of a pro-style offense at Alabama.   Now Bryce has been the consensus number one pick for a long time now, but somehow that has changed when the media nerds got involved critiquing his height and weight.  The noise isn’t completely wrong here, and the fact that he is being compared to Kyler Murray doesn’t bode well.   I have to say that he certainly has a better emotional upside over Kyler, but that wasn’t known of Murray during the 2019 NFL draft.  What was known of Kyler Murray was his fast speed and unfortunately Bryce Young chose not to run the forty yard dash at the combine, or at his pro-day which raises some red flags.   Young however said he would run in some private workouts so maybe that changes some things.  I see the upside with Bryce here, but one must also have to take into account that he played on the most talented team in football.  The NFL game moves much faster than in college, and Young did not put up a lot of rushing yards.  Size, level of competition, and speed is why he drops down some on my list.  Bryce will probably be drafted before third to Houston or now the favorite Carolina, but he is the last person whom I give out a first round grade.

4. Will Levis, Kentucky HT: 6-foot-4 | WT: 229 pounds

I totally am uncomfortable with this pick, and I feel that Will Levis could be the “Mitch Trubisky” of the 2023 draft.  At the same time, Levis has more of the NFL qualities than the rest of the candidates below, so he lands at number four.  At 6’4 229 LBS, this large man can see the field as well as take an NFL hit.  He also has a cannon for an arm.  One also has to appreciate in Will Levis that he has some experience in an offense that has some pro-style concepts at Kentucky.   Was his numbers all that good?   Not really, as he was only 65.4% accurate with 19 TDs and 10 Interceptions.  We also have to remember that the man had nobody to throw to last season as his whole receiving core was turned over from 2021 to 2022.   So why the Mitch comparison?   Quite simply that he has faltered in the big games.   I will give him some credit for beating Mississippi State, but his performances against Tennessee, South Carolina, and Georgia were quite abysmal.  I think that maybe the Colts or Raiders take a chance on Levis if they do not trade down, but I am somewhat concerned to waste a high pick on a potential bust.

5.  Stetson Bennett, Georgia HT: 5-foot-11 | WT: 192 pounds

If I lost all credibility with you ranking Hendon Hooker number two then, you will hate me even more for this pick.   I think that “fit” is the thing that we need to look at here.   For a team that is rebuilding, I don’t like it much, but If I’m the Miami Dolphins, the NY Jets (if for some reason they do not get Rogers) or the Baltimore Ravens, I have to take a look at this kid.  Look, at some point you have to acknowledge how these kids (meaning guy in his mid-twenties) performed during his tenure at his school.  Two college football championships in two years, the ability to read defenses, and big time clutch come-back performances.  This Stetson Bennett dude might be the size of Bryce Young, but he has accomplished more with less offensive weapons.   Bennett’s forty time of 4.67 is most likely a bit slower than Bryce Young’s forty time, but it isn’t as bad as some of the pocket passers in today’s NFL.  Stetson threw for over 4000 yards at Georgia with 27 touchdowns and 7 interceptions.  Had this team not been blowing out most of their opponents, those numbers might be even greater.   Bennett isn’t tall enough or large enough for a standard NFL quarterback by any means, but he might be as ready as they come when he gets to the next level being that he has palyed behind a tall offensive line in Georgia.

6.  Jake Haener, Fresno State HT: 6-foot | WT: 207 pounds

This guy reminds me a lot of Drew Breese.  Not a ton of arm talent, but extremely accurate, and very consistent.  Not a ton of measurables here, but the kid is an athlete who at least had a 35″ Vertical and 9’6″ broad jump at the combine.  Jake was at an amazing 72% completion percentage in a year where he was injured.   Haener aslo had an amazing 33 touchdowns and 9 interceptions and threw for over 4000 yards in 2021.   Obviously his size isn’t NFL, but this isn’t the year to get all caught up in that.   I could see teams like Green bay, NY Jets, or the LA Rams taking a shot with him.   Want to know how good Jake Haener really is?  Watch Fresno State games with him, and without him.

7.  Anthony Richardson, Florida HT: 6-foot-4 | WT: 244 pounds

Looks like Tarzan plays like….   Rick James?   We don’t know how well Anthony Richardson will play in the next level, but this specimen of a man had the best combine athletically as a quarterback.   Anthony is a large man compared to Cam Newton who has a cannon for an arm and ran a 4.43 forty!   His vertical jump tells me that he can hit his head on a basketball rim.  Now for the bad…  Anthony Richardson was at a dismal 54% completion percentage last year, and most of Florida’s wins came due to his legs.   This isn’t a sustainable system in the NFL, and even as big as Richardson is, he will get hurt unless he learns how to pass the ball with better accuracy, and go through his progressions.  A team drafting Richardson would be taking a risk here, and they will have to keep him on the shelf for a long time before he is ready.   Is the Colts ready for a rebuild?   Not sure they want to go that rout, but if not them, then maybe Washington, Seattle or Detroit makes at least some sense.   I don’t think Richardson is worth a first round pick unless you have a better plan A for the 2023 season.   Maybe if it doesn’t work out at quarterback for Richardson, he could transition to be a very good tight end.

8.  Max Duggan, TCU HT: 6-foot-2 | WT: 207 pounds

This is a tricky one because no offense has more of a fast paced college spread/Air-raid style than Sonny Dykes.  Also, which Max Duggan are we gonna get in the NFL?   The 2021 Max Duggan, or the extremely clutch and extremely lucky Max Duggan that took the Horned Frogs to the college football national championship game?  I am gonna forget the way that TCU was embarrassed in that game being that they just couldn’t keep that crazy pace up, but the more important thing is if Max Duggan can translate into a pro-style offense.   Even in TCU’s miracle year last year, Duggan was only 63.7% completion percentage.   He did rush for over 400 yards which kept the Frogs in some big games.  The big shocker to me is that the man is faster than he even looks running a 4.52 forty at the combine.  That is the second fastest of all quarterbacks beating guys like Malik Cunningham and DTR!   Now if Max can balance his imperfections and stay healthy, he definitely has a shot succeeding in the NFL.  I think that the Raiders, Jets or even the Packers could benefit from his services.

9.  Dorian Thompson-Robinson, UCLA HT: 6-foot-2 | WT: 203 pounds

Let’s face it.  College offenses are better for college than pro!   Ask any of these coaches who rely on speed and spacing.  I think that there is a reason that some of the faster paced college quarterbacks like Joe Burrow, Kyler Murray, Patrick Mahomes, and Justin Herbert are now quickly having great success right from college into the next level.  One thing that we notice now with the pros is that they have taken some of the best concepts from college, like RPOs, and the read option, to help evolve and improve upon NFL offense.  Now we have Chip Kelly’s guy in here in Dorian Thompson Robinson who put up some nice numbers this year.  DTR is pretty fast running a 4.56 forty while having enough height at 6’2 that many scouts require.   DTR’s numbers are pretty good at 69.6% completion with 3,168 yards passing and a 27-10 TD to interception ratio.  This California kid stepped up big to win in some big games vs Utah this year and USC last year.  In saying all that, I do have a problem with Dorian’s weight at 203 lbs.  It seems a bit low for the NFL, and this kid will need to bulk up some.  I believe that DTR could be a late speculative pick by a team like the Eagles, Bears, Saints or even the Ravens as a project to try and shape the future of their organizations.

10.  Aiden O’Connell, Purdue HT: 6-foot-3 | WT: 213 pounds

If you want to look at shear volume, Aiden O’Connell threw 499 times last year at 64.1%. Here is another college system quarterback under Jeff Brohm who thrived in that offense, yet he at least has the size for the NFL.   Aiden O’Connell kept Purdue in many games last year and many thought that they should have beaten Penn State the very first game of the season.  So Aiden O’Connell seemed to fall short in most of his big games last year, but it would be interesting to see this kid with an actual defense, and a semblance of a running game in which Purdue ranked last in the country in 2022.   I could see a team like the Texans, or even the Colts taking a late flyer on this Aiden.

Honorable Mentions:

11.  Tanner McKee, Stanford HT: 6-foot-6 | WT: 231 pounds

His coaching wasn’t the greatest but he has some size.   Upside of Ryan Tannehill and downside of Mike Glennon.

12.  Clayton Tune, Houston HT: 6-foot-3 | WT: 220 pounds

When good competition set in, this quarterback changed his Tune.  Good size though.

13.  Malik Cunningham, Louisville HT: 6-foot | WT: 192 pounds

He looks like a college quarterback, plays like a college quarterback, and doesn’t look much good for the NFL.  His 4.53 forty might of actually hurt his draft stock.

14.  Jaren Hall, BYU HT: 6-foot | WT: 207 pounds

Not sure what screwed this kid more…  Playing for BYU?  Or the fact that Zach Wilson played for BYU.

15. Tyson Bagent, Shepherd HT: 6-foot-3 | WT: 213 pounds

Is this the Tony Romo of this year’s draft?   He had some great numbers in division II football, but then again, so did Trey Lance.  My advice to him is to start yelling at people immediately so that eventually, people will start to think that he is in charge.

As discussed from previous years, football is a game of inches, and by being off just one, could be the difference between a win and a loss in a league with limited games.   The shape of the football itself creates it’s own sort of randomness on circumstances such as situation like field position during punts and how the ball spins during field goals.   There is also a certain amount of randomness of “when” the points are scored that pertains to a team’s final win record.  Let’s face it, there is a good amount of luck that happens within the football season the best teams do not always win against the worst teams.  We can just go back to January 9th of this year to find an easy example of that for when the Jaguars beat the Colts.

Many of us NFL football analysts and sports bettors want to have good methods for prediction for next year’s football season in order to be more accurate.  Wins and losses do not tell the whole story and they are also rarely a good indicator by itself for a team’s future success.  If we find some scientific methods that will correlate well using a team’s past performances to their success in the future, it will help us become not only more knowledgeable, but it could also help us become more profitable.  The method that we will be discussing today is the 2022 Pythagorean win total calculation as a method to help predict the 2023 football season results.

Is it possible for an NFL team to score more points than they give up, and have a losing record?  How about score less points than they allow, and have a winning record?  The answer to that is that it is very possible.  This happens every year.  In 2019 the Houston Texas scored 378 total points, yet gave up 399 to win 10 games.  In that same year, the Chargers scored 337 points while only giving up 345 (almost 50/50) and only won only 5 games.

Sometimes teams score many of their points during blowouts, and as luck will have it, those same teams might lose their close games.   Due to these discrepancies, we need to formulate the data to find out what some of these teams were expected to do based on points scored compared to their actual win/loss results.   Using a function that takes a look at the total points scored as a data point with the total points allowed is, at many times, a better indicator of a team’s future success compared to their actual record.   This is why we can use a Pythagorean win total compilation to compare what was expected to happen based on points scored for all of the NFL teams, to what actually did happen in how these teams finished out their year.  This way we can have a better idea on what to expect for next season. The 2011 edition of Football Outsiders Almanac states, “From 1988 through 2004, 11 of 16 Super Bowls were won by the team that led the NFL in Pythagorean wins, while only seven were won by the team with the most actual victories.  Since then, the Pythagorean wins theorem has continued to be statistically significant.

The Pythagorean theorem is a^2+b^2=c^2.  It basically figures out the distance between two points of a right triangle (c), or for what we are interested in, the expected value between the relationship of sides.   Please see the figure.  Think of points scored as (a), and points allowed as (b) where (c) is a function of the (a and b) linear equation and we just want to know what the relationship is between a^2/c^2 (c^2 is a^2 + b^2) to get a percentage of wins multiplied by the total games.  Going by the diagram, if it is a tall and thin triangle, the team scored a lot more points compared to what they have given up, and if it is a short and long triangle, the team has given up many more points compared to what they actually have scored.   

I know what you are thinking.  Why can’t we just use the points scored over total points as a basis for predicting the expected outcomes?  The answer to that simply is that the data would be wrong.   For example, if a team scores 75% of the total points and only allowed 25% of the total points throughout the year, would we only expect that team to win just 75% of their games?   Heck no.  That is like averaging 30 points per game on offense and only allowing 10 points per game on defense.  Even the 16-0 2007 patriots who scored 68.3% of the points averaging 36.8 on offense and giving up 17.1 on defense won many more than 12 games.   The 2007 Patriots’ Pythagorean win total certainly didn’t equate to winning every game, but was at 13.76, which is much higher than the 12 and a much more relatable number to their fantastic season.

But wait, there is more!  This Pythagorean equation does have it’s faults if adjustments are not made to it.   This equation tends to bunch  all of the teams more towards the middle when actual outcomes may deviate further away from the mean.  Statistician Daryl Morey found this in football among other sports and was able to develop a more statically significant exponent of 2.37 (rather than 2) as a constant for better accuracy while utilizing this equation.  Without getting too far into the weeds, we must make adjustments correlated to the actual variances that happened over the years pertaining to each sport, and for this column, football itself.   Aaron Schatz of Football Outsiders takes this concept even farther by stating that each team’s exponent should be different as a function of their points scored.  According to Schatz, the formula for each team’s exponent that works best in the NFL is 1.5 * log ((PF+PA)/G).  What is new for 2023 is that I have decided to use the Logarithmic function as a better basis for compiling the data being that the league is scoring more points than ever before, and I do not think that the exponent should be linear.   Much of this randomness comes from inconsistent officiating, injuries, and pure luck itself.  The average exponent comes to 2.46.

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One thing that I found that we can somewhat account for is turnovers.   Every year there are teams that have something called turnover luck.  Whether the luck is good or bad, turnovers affect the scores and the results of each game.  For example, some teams could have had a 3 point field goal and instead they fumble the ball away while some defenses are put on the back of their heels when teams turn the ball over in their side of the field.  The 49ers had the best turnover ratio of +13  while the Colts had the worst at -13.  Big shocker right?  Now it is well known that turnovers are worth close to about 4 points to each team respectively and the old school way of thinking is that turnovers are mostly random, but I tend to disagree with that notion.  If you look at the recent histories of each team, turnovers happen to be somewhat predictable.  For example, before Tom Brady became a Tampa Bay Buccaneer, Jameis Winston used to turnover the ball quite often and it would show up significantly negative year over year in Tampa Bay’s turnover margin.  Indianapolis is another team that you have been able to pencil in the negatives for a while now.  On the flip side of the coin, there are teams that take care of the ball more often on a consistence basis while them ore other teams might have great ball-hocking defenses that tend to force more turnovers than the others.  The New England Patriots fit that bill and the Bengals have been trending positive with Joe Burrow over the last few years.  Due to this, I have adjusted turnovers to be about half of what they are worth to the number from 4 to 2 points per turnover and I award or punish each team only 1 point on offense and 1 point on defense (rather than 2 and 2) with respect to their 2022 turnover ratio.  Normalizing turnovers give you a better idea of what each team’s expected wins should have been based on a cleaner season.  In essence, a negative turnover ratio helps those sloppier teams with better expected wins as they punish the cleaner teams that had a high positive turnover margin with lower expected wins due to the luck factor that I have applied.  Even though some teams tend to take care of the ball more doesn’t mean that there isn’t some luck to the outliers and visa versa.  One thing that I will add to this is that fumble luck is much less predictable than interceptions and the 2022 Cowboys were the big outlier here of +12 fumbles which is something that you should account for separately when predicting your 2023 numbers.  Please see our chart below for our actual wins vs our Pythagorean expected win totals.

Going by this diagram we can make informed judgements and educated decisions on the actual strength of these teams with respect to not only how well they can score against their opponents but also how well that they can defend.  As you can see from the difference column between the actual results from last year’s 17 game schedule with the Pythagorean (or expected) results that we can actually see how teams have either over-achieved, or under-achieved in the 2022 season.  Here are the five outliers on each side:

Over-achievers:

1. 1. Minnesota Vikings: The Vikings had only 8.32 expected wins compared to their 13 actual wins last year. A -4.68 difference.  Minnesota went 11-1 in one score games last year with their only one score loss in the playoffs.   They were certainly the luckiest team in the NFL.  Now we must factor in some that Kirk Cousins performed pretty well in the clutch, but is that sustainable?  I think not.  Let’s see how that 1st place schedule treats them in 2023.
   2. Philadelphia Eagles: The Super Bowl runner up Eagles had 11.45 expected wins compared to their league leading 14 actual wins last year.  A -2.55 difference.  Let’s be honest though, the Eagles were a great team last year, but a +8 turnover ratio, and a very easy schedule certainly helped.  Even with the difference, this Eagles team was great.
   3. Kansas City Chiefs:  The Chiefs had expected wins of 11.71 compared to their 14 actual wins last year.  A -2.29 difference.  The Super Bowl champion Chiefs over-achieved some last year, but they certainly were not a great ATS team as a favorite going 6-10-1.  Being 10-3 in one score games certainly helps.   In saying that, Patty Mahomes definitely has the “clutch” gene which is so coveted in professional sports.
   4. Pittsburgh Steelers:  The Steelers had expected wins of 7.09 compared to their 9 actual wins last year.  A -1.91 difference.  I think that one has to give a ton of credit to Steelers coach Mike Tomlin for always getting this team to at least .500.   Add the fact that he had to do it last year with a rookie quarterback as well as the beginning with Mitch Trubisky at the helm, makes this even more impressive.  Taking a Steelers under 8.5 is something that I do not recommend doing blind.
   5. LA Chargers:  The LA Chargers had expected wins of 8.42 compared to their 10 actual wins last year.  A -1.58 difference.   The Bolts giving up a 27 point lead in the playoffs to lose the game vs the Jaguars pretty much sums it up for this team.   A ton of talent with poor coaching really doesn’t mix well.

Under-achievers:

1. 1. Las Vegas Raiders:  The Raiders had expected wins of 2.32 compared to their 6 actual wins last year.  A +2.32 difference.  My have the tables have turned on the Raiders.  Last year they were the number two over-achieving team, and this year they are the number one under-achievers.  The theme of this Raiders team was choke central going 4-9 in one score games.  Now this team has a new quarterback this year which I look at as more of a horizontal move in Jimmy G.
   2. Chicago Bears:  The Bears had expected wins of 5.09 compared to their 3 actual wins last year. A +2.09 difference.  It is hard for me to say that this team really under-achieved last year when they traded away some of their best defenders during the season.  One thing that I will say is that they have a lot of cap space to make a real splash in free agency.
   3. New Orleans Saints:  The fighting Who-Dats had expected wins of 8.71 compared to their 7 actual wins last year.  A +1.71 difference.  New coach and no good quarterbacks were the culprit for the New Orleans Saints.   That choke job vs the Buccaneers cost them the division.  I also have to say that the officiating in their games in general was garbage to say the least.   Now with Derek Carr at the helm, it will be interesting to see which direction this team goes for 2023.
   4. Houston Texans:  The Texans had expected wins of 4.93 compared to their 3.5 adjusted actual wins last year.  A +1.43 difference. Had the Texans lost their last game to retain the top draft pick, then this number would be that much larger.   The Texans were a very weird team last year that wasn’t favored in any of their games, and had a rare tie with the Colts in the first game of the season.   The interesting part of this team is that they were only blown out just once by more than 15 points.
   5. Denver Broncos:  The Broncos had expected wins of 6.36 compared to 5 adjusted wins with the tie.  A +1.36 difference.  This team certainly wasn’t about offense last year as they ranked last in points per game at 16.9.  The Bronco’s defense is what kept this team in many of their games.  I think with a proven coach like Sean Payton, this team could be 2nd best in their division as long as Russell Wilson stops being a pocket passer.

In conclusion, it should now be easier to see the randomness in football were some teams will have better records than they actually deserve, while some teams will have worse records than they actually should have achieved.  Remember to take this information for what it’s worth.  When predicting season wins, we also must factor in some of the less quantifiable information such as the clutch performances from quarterbacks like Jalen Hurts and Patrick Mahomes along with the anti-clutch performances of Matt Ryan and Kirk Cousins.  Strength of schedule is another data point that is less quantifiable when it comes to actual points or season wins, but equally important when determining what these teams went through, not only in the previous season, but also in what they will be facing in the near future.

Pythagorean win totals are an important indicator of the future success of NFL teams going into their 2023 seasons and it should take some precedence over last year’s actual win results.  The Pythagorean win total will remain a great method for your predictive analysis for next season and beyond.  Should you have any questions or want a list that can be copied and pasted, please tweet me @OBKiev.  Have a great NFL season and best of luck in your research.

Ref 1:  Football Outsiders: https://www.footballoutsiders.com/dvoa-ratings/2011/week-13-dvoa-ratings

Ref 2: Wikipedia: https://en.wikipedia.org/wiki/Pythagorean_expectation#:~:text=The%20formula%20is%20used%20with,referred%20to%20as%20Pythagorean%20wins.