These rankings are based on points per drive stats from   It is sorted by the Net Pts column, which is simply the points per drive scored minus the points per drive allowed.

Rank Team Pts Per Drive Net Pts
1 New Orleans Saints 3.32 1.16
2 Kansas City Chiefs 3.21 0.83
3 Los Angeles Chargers 2.6 0.76
4 Los Angeles Rams 2.82 0.62
5 Chicago Bears 2.17 0.55
6 Baltimore Ravens 2.07 0.5
7 New England Patriots 2.3 0.44
8 Pittsburgh Steelers 2.31 0.41
9 Indianapolis Colts 2.33 0.4
10 Seattle Seahawks 2.36 0.4
11 Houston Texans 1.97 0.28
12 Dallas Cowboys 1.94 0.22
13 Tennessee Titans 1.86 0.12
14 Green Bay Packers 2.06 0.08
15 Minnesota Vikings 1.79 0.05
16 Denver Broncos 1.91 0.02
17 Carolina Panthers 2.32 -0.02
18 Philadelphia Eagles 1.92 -0.1
19 Washington Redskins 1.77 -0.15
20 Tampa Bay Buccaneers 2.23 -0.17
21 Cleveland Browns 1.7 -0.19
22 Atlanta Falcons 2.39 -0.24
23 New York Giants 1.92 -0.3
24 Detroit Lions 1.92 -0.41
25 Jacksonville Jaguars 1.37 -0.44
26 San Francisco 49ers 1.85 -0.45
27 New York Jets 1.43 -0.47
28 Miami Dolphins 1.64 -0.48
29 Cincinnati Bengals 2.09 -0.57
30 Buffalo Bills 1.24 -0.67
31 Arizona Cardinals 1.16 -0.93
32 Oakland Raiders 1.61 -1.03

I tweeted these as separate tweets recently, but I thought I’d put them together in one post here.  Revised them a little bit, too.

The Saints are marching, their fans without fear.  The Panthers been pounded, the Falcons been grounded, and the Bucs stop here.

Cowboys have reloaded, the Redskins railroaded.  The Eagles hungover, season soon over.  Giants are midgets with little might, and though he is dearly, their Barkley is clearly, much bigger than their bite.

Rams have the high ground, taking their division by farce.  The Cards have succumbed, the desert is harsh.  The Hawks are circling, on a bit of a roll.  The 49ers are Mullen if the treasure they Nicked is really just Fools Gold.

The salmon run thick and the Bears are feasting, gorging and fattening, having their fun.  The Vikings, joined by their Cousins, have raided by the dozens, but mostly dry runs.  The Lions have been tamed, separated from their pride, and the Packers have packed it in, all kidding aside.

The Texans are steppin’, I tell you no lie.  Elementary dear Watson, the reason why.  Yet more of the same, the Colts pulled up lame, just not enough Luck to win enough games.  Titans are listing in Titanic fashion, Jaguar fans, lost all their passion.  There they sit, all yawning and BORED, ’cause under the hood it’s now understood Jaguars are all really just Fords.

The Patriots, never the squeamish, have planted their flags and fought back the English, to that king, their demands have been made.  Meanwhile the Jets have been grounded, the Dolphins have been drowneded, and the Bills have been paid.

Mahomie ain’t foolin’, the Chiefs they are rulin’, but the new NFL darlings, the Chargers are charging.  The Broncos have been broken, and over in Oakland, what they been smokin’?  Been raided by Rustlers, been raided by Bears, devoid of all talent,  and going nowhere.

The Stealers can steal it, such is their lore, but hark the Ravens, those clever mavens, had a secret weapon in store.  This man of action, this man named Jackson, Flacco their leader nevermore?  Bengals will bungle and Brownies will Brown, but with their new Baker if he’s not a faker, soon Mayfield a winner in that Ohio town.

Upsets happen in every sport, but is it more likely to happen in an NBA game or in an NFL game?  That’s the question I will attempt to answer in this blog entry.

I began by assuming the teams with the best records after a full (regular) season of games are the best teams and the ones with the worst records are the worst teams.  When one of the best teams loses I consider that an upset, and when one of the worst teams wins it’s also an upset.  (I completely ignore cases where a best team plays another best team and where a worst team plays another worst team.)

The data used consists of the records for the best 4 teams for each of the last 5 seasons and for the worst 4 teams each of the last 5 seasons in both leagues.  In other words, the average record of the best 4 teams from each of the last 5 seasons (20 records) and the same for the worst 4 teams (20 more records, 40 records in all for each league).

In the NFL the average record for the best 4 teams was 79.09%, worst 4 teams was 17.84%.  For the NBA it was 73.12% and 25.24%.  The best teams in the NBA did not win as often as the best teams in the NFL (79.09% versus 73.12%).  Meanwhile, the worst teams in the NBA won more often (25.24% versus 17.84%) than their bad counterparts in the NFL.

Based on this, I conclude the better team wins more often in football than in basketball. One potential caveat to consider is the length of the seasons.  In football you only have 16 games whereas in basketball you have 82 games.  In football maybe we don’t really ever know who the best teams are because of the small sample sizes.  There is also the issue of tanking where teams sometimes try to lose in order to get better draft positioning, and in other cases where teams “rest” players late in the season in preparation for the playoffs, but I think both of these should cancel each other out to one degree or another.

Does this mean an underdog in basketball has a better shot at winning a championship than an underdog in football has?  Not all.  In basketball you have a best of 7 series to decide who advances in each round.  It’s much, much more difficult for an inferior team to win a best of 7 than it is to win a single game.

Seems like a crazy question, doesn’t it?  Why in the world would you ever want to lose a game  (aside from tanking for draft picks — and even then I’m personally opposed to it)?  The hypothesis is it’s really hard to win 18 in a row, which is what the Saints would need to do to win the Superbowl if they don’t drop at least one more game before the playoffs.

Of course, winning 18 in a row would be great, but that’s not the ultimate goal.  The ultimate goal is to win the Superbowl.  Problem I have with this line of reasoning is, yeah, it’s hard to win 18 in a row, but once you’ve won 15 in a row all you need at that point is to win 3 in a row.  You don’t have to win 18 more in a row, and even if you lose the last game of the regular season, you still have to win 3 in a row either way.  I’d rather be good enough (and/or lucky enough) to win 15 in a row and take my chances on the next 3.

Another line of reasoning goes, maybe the team gets over confident, but I don’t buy that.  It’s the playoffs.  Intensity will be sky high regardless.  If a player doesn’t get fired up for a playoff game, that player should not even be in competitive sports.  No player reaches the NFL level unless he’s got at least some competitive nature in his nature.

What does history tell us about this?

Since going to the 16-game schedule there have been only 6 teams that went 15-1, 2 of those won the Superbowl, 4 of them failed, so call it a 33.33% (2/6) probability of winning it all after going 15-1.  Would it be better to go 14-2 instead?  20 teams have gone 14-2, and 40% (8/20) of those teams won the Superbowl that year.  40% is better than 33.33%, so this would seem to confirm that notion of being better to lose another game.  Or does it?

What if you go 13-3?  45 teams have gone 13-3, and 20% (9/45) of those won the Superbowl, so losing 2 more games would probably be not the way to go.  And 12-4 isn’t any better with 14.1% (11/78) winning the big one.  Here’s a recap:

15-1 = 2/6 = 33.33%
14-2 = 8/20 = 40%
13-3 = 9/45 = 20%
12-4 = 11/78 = 14.1%

Big problem with this is 6 times is a very small sample size, compared to 20, 45, and 78.  Just 1 extra win or loss would swing those percentages by quite a lot, 3/6 would have been 50%, 1/6 would have been 16.67%.

My conclusion is I don’t find any of the logical reasoning in favor of wanting to lose another game or 2 to be persuasive, and even though the data would tend to confirm the notion that 14-2 is better than 15-1, it’s such a small sample size that I don’t buy it, either.  My guess is if we did this same analysis 15 or 20 years from now, the teams that went 15-1 will have a higher percentage of Superbowl wins.

How would you grade (statistically) an offensive line?  The O Line is the only position group without stats.  The skill players get all the stats, and on defense you can get sacks, tackles, interceptions, passes defensed, but the O Line gets no stats for blocks.  Services like Pro Football Focus attempt to grade offensive lines based on “film study”, but even this is problematic.

Last I heard (and maybe it has changed since then) PFF used broadcast footage to do their grading.  The broadcast camera follows the football, making this grading technique less effective than if you used the all-22 footage.  There is some guesswork involved, or at least I would think there is for many plays, or perhaps many players on many plays are simply left ungraded.

Another issue PFF has is they don’t always know the assignments of every player on every play, so there is some (perhaps educated, perhaps not) guesswork there, too.  For example, you don’t know if the center was supposed to help the left guard on that play, but for some reason didn’t, so now the left guard, expecting help inside and not getting it, gets beat badly inside.  The left guard gets a bad grade when in reality it was the center who didn’t do his assignment.

One more issue with this grading system is how do you grade a pressure?  What is a pressure?  Different graders can have different definitions, and thus can grade the same play differently.

So how to do this statistically?  We have to look at stats for the skill players, for opposing defenses, and try to infer from those stats how well the offensive line is playing.  I will be looking at 4 stats: 1) sacks allowed, 2) quarterback passer rating, 3) rushing yards per game, and 4) rushing yards per attempt.

In the following table we have in the Total column the combined ranking for those 4 statistical rankings, unweighted.  By unweighted I mean each stat is treated as having equal importance (which might not be the best way to do it, but it’s how I’m doing it).  The Total column is simply the 4 rankings added together.  For example, if a team is ranked #1 in rush yards / attempt, #10 in rush yards / game, #20 in sacks allowed, and #30 in passer rating, then that team’s total would be 1 + 10 + 20 + 30 = 61, which would put it 14th best in this table.

Rank Total Team
1 14 Los Angeles Chargers
2 24 Carolina Panthers
3 24 Los Angeles Rams
4 27 Kansas City Chiefs
5 37 Indianapolis Colts
6 44 Chicago Bears
7 44 New Orleans Saints
8 49 Pittsburgh Steelers
9 49 Seattle Seahawks
10 53 Denver Broncos
11 54 New England Patriots
12 58 Washington Redskins
13 60 Green Bay Packers
14 61 Dallas Cowboys
15 63 San Francisco 49ers
16 65 Miami Dolphins
17 66 Cincinnati Bengals
18 68 Detroit Lions
19 69 Atlanta Falcons
20 75 Minnesota Vikings
21 76 Houston Texans
22 76 Philadelphia Eagles
23 80 Cleveland Browns
24 84 New York Jets
25 87 Jacksonville Jaguars
26 91 Oakland Raiders
27 93 Baltimore Ravens
28 97 Tampa Bay Buccaneers
29 97 Tennessee Titans
30 101 New York Giants
31 110 Arizona Cardinals
32 116 Buffalo Bills

Saints have the 6th best offensive line by this method of grading.  Looking at the teams at the top, looking at the teams at the bottom, this system would seem to have some merit.  The Bills, Cardinals, and Giants are among the worst teams.  (But maybe the Titans are out of place.)  The Saints rankings are #27 in rush yards / attempt (3.9), #15 in rush yards / game (112.10), #1 in passer rating (120.70), and #1 in sacks allowed (9).  27 + 15 + 1 + 1 = 44, good enough for 6th best (tied with Bears).

There are per-drive stats, courtesy of (@PFREF). In the table below we have average points scored per drive, average points allowed per drive, and the net points (points scored minus points allowed per drive).

For example, the Chiefs have scored an average of 3.31 points per drive, allowed 2.36 per drive, for a net of +0.95 points per drive.  The table is sorted by the Net Pts column.  League average is 2.02 points per drive.

Rk Team Pts For Pts Against Net Pts
1 Kansas City Chiefs 3.31 2.36 0.95
2 Los Angeles Rams 2.84 2.08 0.76
3 New Orleans Saints 3.28 2.55 0.73
4 Chicago Bears 2.25 1.54 0.71
5 Los Angeles Chargers 2.51 1.89 0.62
6 New England Patriots 2.47 1.89 0.58
7 Baltimore Ravens 2.1 1.59 0.51
8 Carolina Panthers 2.44 2.07 0.37
9 Seattle Seahawks 2.07 1.7 0.37
10 Indianapolis Colts 2.43 2.11 0.32
11 Pittsburgh Steelers 2.21 1.92 0.29
12 Philadelphia Eagles 1.98 1.7 0.28
13 Dallas Cowboys 1.81 1.54 0.27
14 Houston Texans 1.91 1.72 0.19
15 Minnesota Vikings 1.85 1.75 0.1
16 Atlanta Falcons 2.71 2.69 0.02
17 Green Bay Packers 2.01 2.04 -0.03
18 New York Jets 1.53 1.67 -0.14
19 Denver Broncos 1.84 2 -0.16
20 San Francisco 49ers 1.95 2.12 -0.17
21 Cincinnati Bengals 2.35 2.55 -0.2
22 Tennessee Titans 1.52 1.72 -0.2
23 Washington Redskins 1.81 2.03 -0.22
24 Detroit Lions 2.05 2.32 -0.27
25 Cleveland Browns 1.51 1.86 -0.35
26 Tampa Bay Buccaneers 2.31 2.7 -0.39
27 Miami Dolphins 1.63 2.05 -0.42
28 Jacksonville Jaguars 1.4 1.83 -0.43
29 New York Giants 1.69 2.18 -0.49
30 Arizona Cardinals 1.03 1.95 -0.92
31 Buffalo Bills 0.89 2.08 -1.19
32 Oakland Raiders 1.54 2.77 -1.23

In the last 3 weeks the Saints have wins over teams ranked #2, #7, and #15 in this metric, with 2 of the 3 wins coming on the road.  Saints only have 2 remaining games against top 10 teams, the 2 games against Carolina at the end of the season.

Next game is @ #21 Cincinnati, then hosting #12 Philly and #16 Atlanta.  Following that game 3 on the road @ #13 Dallas, @ #26 Tampa Bay @ #8 Carolina.  Then Saints host the final 2 games at home #11 Pittsburgh and #8 Carolina.  If you average all those rankings up it comes to 14.375.  In other words, average opponent ranking is #14.375.

The good news for Saints fans is some of the toughest games are at home: #12, #16, #11, #8, and some of the easiest games are on the road: #21, #13, #26, #8.  I don’t like having the 3-game road trip, finishing up @ #8 Carolina, but it sets up the possibility of closing out the season with 2 straight home games and, if things go very well, a bye week in round 1, then more at home in the playoffs, probably against Carolina and Los Angeles, then to Atlanta for the Superbowl against likely the Patriots in what would really be a matchup of GOATs, not that fiasco inflicted upon the football world on Sunday night.

The following table is based on stats from  The Pts per drive column shows the average number of points scored per possession by that team’s offense.  The Net pts column (which the table is sorted by) shows the average points per drive scored minus the average points per drive allowed.

Rk Team Pts per drive Net pts (scored – allowed)
1 Los Angeles Rams 2.93 1.18
2 Baltimore Ravens 2.13 0.9
3 Kansas City Chiefs 3.27 0.88
4 New Orleans Saints 3.19 0.66
5 Los Angeles Chargers 2.56 0.59
6 Chicago Bears 2.23 0.43
7 New England Patriots 2.52 0.41
8 Seattle Seahawks 2.07 0.39
9 Dallas Cowboys 1.81 0.27
10 Philadelphia Eagles 1.92 0.24
11 Indianapolis Colts 2.21 0.21
12 Carolina Panthers 2.13 0.11
13 Green Bay Packers 2.01 0.09
14 Pittsburgh Steelers 2.09 0.09
15 Minnesota Vikings 1.82 0.08
16 Detroit Lions 2.32 0.02
17 New York Jets 1.75 -0.01
18 Washington Redskins 1.9 -0.01
19 Houston Texans 1.69 -0.02
20 Denver Broncos 1.85 -0.07
21 Cleveland Browns 1.41 -0.17
22 Tennessee Titans 1.52 -0.2
23 Cincinnati Bengals 2.31 -0.22
24 Miami Dolphins 1.78 -0.31
25 Atlanta Falcons 2.53 -0.33
26 Tampa Bay Buccaneers 2.25 -0.37
27 Jacksonville Jaguars 1.33 -0.43
28 New York Giants 1.79 -0.47
29 San Francisco 49ers 1.9 -0.52
30 Oakland Raiders 1.51 -0.94
31 Arizona Cardinals 0.96 -1.11
32 Buffalo Bills 0.98 -1.12

It’s surprising to me the Ravens were at #2.  Next week’s opponent, Minnesota, is very average by this metric at #15.  But of the 10 remaining games on the Saints schedule, all but 3 (TB, CIN, ATL) are in the top half.

Drew Brees now holds the record for the most regular season passing yards (72,103 and counting…) in NFL history.   He has more passing yards than Tampa Bay’s top 5 quarterbacks in their history, combined, but Bucs aren’t the only one to fit that category.  Brees has more passing yards than the top 5 passers combined of 5 different franchises, including one that has been playing football since 1920:

Total Passing Yards for these Franchises’ top 5 QB’s in their history

Texans 46269
Ravens 60201
Buccaneers 65752
Panthers 70011
Bears 71481

(Take note: these stats only include the yardage of those top 5 quarterbacks while they were with that team.  For example, Steve Young’s numbers while in San Francisco don’t count towards Tampa Bay’s numbers.)

(Take further note: Drew Brees has more passing yards for his career than Steve Young and Joe Montana combined, more than Troy Aikman and Tony Romo combined, more than Matt Ryan and Cam Newton combined, and yes, more than Bobby Hebert, Aaron Brooks, and Archie Manning combined.)

If we limit this to the top 2 passers for each franchise, Brees has more yards by himself than 25 franchises’ top 2 passers combined.

Combined numbers for franchises’ top 2 passers in their history:

tam 28354
htx 36612
rai 36663
chi 38129
jax 42151
rav 45176
car 45215
ram 46572
cle 47297
was 47791
kan 49966
nyj 51443
det 51844
sea 55566
crd 57410
min 57873
phi 59836
cin 59987
oti 60826
buf 63057
sfo 66672
atl 66867
dal 67125
den 68587
pit 80718
nor 81489
mia 86453
nyg 86525
clt 94596
sdg 94883
nwe 97075
gnb 101729

Records, of course, are made to be broken.  How long will this one stand?  How much more will Brees add to it before he hangs up his six-guns?  Time will tell.  The closest active player to catching him is Tom Brady with 67,418.  In 2017, the average team had a total of 3,589 passing yards for the season, so if we use that average and think of it in terms of years, then these active QB’s would be these number of years behind:

1.3 Brady, 5.3 Manning, 5.4 Big Ben, 5.6 Rivers, 8 Ryan, 8.9 Rodgers, 9.7 Flacco, 10 Stafford, and everybody else 10+ years to go.  So, if Brees retired today only Brady would be able to catch him in less than 5 years.  Interestingly, if Brady hadn’t missed that year with the knee injury and if he hadn’t been suspended those 4 games for that InflateGate rubbish, these 2 would likely be knocking heads in a Sammy Sosa / Mark McGwire (only without the juice) race for the record.  That would have been something to see.

Do the Saints avoid drafting LSU players or is it just coincidence?  During Sean Payton’s tenure the Saints have only drafted 1 LSU player (All Woods, 2010).  Is it because they purposely avoid LSU players or is it just the way the boards have fallen over the years?  Is there a statistical way to answer that question, and if so, what do the numbers say?

Since 2006, (not counting the currently still on-going, at the time of this writing, 2018 draft) there have been 77 LSU players drafted.  The Saints have only drafted 1 of those 77.  By the numbers, with 32 teams, the average team drafted 77 / 32 = 2.4 per team.  That stat doesn’t really tell us much, so let’s take a look from another angle.

If we took the same group of players drafted each year into the NFL since 2011, and then randomly distributed them among the NFL teams, what is the probability the Saints would have 0 LSU players?  I calculate that number at about 19.6%.  In other words, there would be a better-than-80% chance (80.4%) the Saints would have drafted at least one LSU player since 2011 if the drafted players were randomly distributed.

year number of LSU players drafted that year number of LSU players drafted by the saints probability of getting an LSU player that year probability of not getting an LSU player that year cumulative probability of not getting an LSU player over the years
2011 6 0 18.75% 81.25% 81.25%
2012 5 0 15.63% 84.38% 68.55%
2013 9 0 28.13% 71.88% 49.27%
2014 9 0 28.13% 71.88% 35.42%
2015 4 0 12.50% 87.50% 30.99%
2016 5 0 15.63% 84.38% 26.15%
2017 8 0 25.00% 75.00% 19.61%

The above table (via research done at PFREF.COM) shows the year, the number of LSU players drafted that year, the number the Saints took that year, and then 3 probabilities: probability of getting at least one LSU player that year in a blind randomly distributed draft, probability of *not* getting at least one LSU player that year, and in the final column a cumulative probability of not getting at least one LSU player in any of those years.

In 2011, there were 6 LSU players drafted.  With 32 teams, if these 6 players are randomly distributed among the teams, the percentage probability of the Saints getting at least one of them would be 6 / 32 = 18.75%.  That’s obviously not a very high probability, and so shouldn’t raise any eyebrows when it happens exactly that way.  Saying there is an 18.75% chance of getting at least one LSU player in the draft is the same thing as saying there is a 100% – 18.75% = 81.25% chance of *not* getting an LSU player in a blind, randomly distributed draft.  In other words, I’m equating blind, randomly distributed with meaning there is no bias either in favor of LSU players or against LSU players by the Saints.

Okay, so with an 81.25% chance of *not* getting an LSU player, we can’t draw any conclusion from the fact the Saints did not get an LSU player in 2011.  But let’s continue on to 2012.  5 LSU players are drafted that year, and doing the same math we get the number at 84.38% that none of the 5 would land with the Saints.  So we have 2 probabilities to work with: 81.25% and 84.38%.  If we multiply those together we get 81.25% * 84.38% = 68.55%.  68.55% is the probability percentage that the Saints would not get *any* LSU players in *either* year.  It’s still over 50%, so it shouldn’t be a shocker that the Saints didn’t end up with any LSU players in 2011 through 2012.  Let’s continue on for the following years, where the numbers continue dropping to 49.27% in 2013, 35.42% in 2014, 30.99% in 2015, 26.15% in 2016, and now 19.61% as of 2017.

Already in the first 2 rounds there have been 4 LSU players drafted in the 2018 draft.  A reasonable expectation is there will be 4 or 5 more LSU players drafted, so call it 8 LSU players in total that get drafted in 2018.  If the Saints don’t get any of them, the number would be the same as it was in 2017 when also 8 LSU players were drafted.  This means we would need to multiply 19.61% by 75%, which gives us a 14.71% that the Saints would have skipped out on all of those LSU players just by random coincidence.  Personally, I’m not buying it.

I’m not a conspiracy theorist, not by a long shot, but something is fishy here.  You can’t tell me something with a 14.71% chance of happening has happened all just by random coincidence.

On the other hand, the Saints *did* draft Al Woods back in 2010.  Also, we’re looking at overall numbers and assigning equal probability to all 32 teams when, in fact, the Saints have very happily traded away 42 picks (in exchange for 30 picks) over this time frame, not to mention *never* getting a single compensatory pick due to losing free agents and not replacing them in free agency.  In short, the Saints have had the fewest draft picks of any team, and by a considerable margin, over the years.  Having fewer picks (about 2 fewer per year than the average team) means the Saints are the least likely team to have ended up with an LSU player, assuming blind random distributions.

Final conclusion?  I think there’s a bias at work here against drafting LSU players.  Not sure why, could be the Saints just don’t think LSU does a good job coaching those players up (contrary evidently to how the rest of the league views the situation).  Maybe it’s just a location bias, in that the Saints want to try to cast a wide net rather than just focusing on the local players, and hence are bending over backwards too far.

Saints just drafted a player, Defensive End Marcus Davenport, who weighs in at 264 pounds.  Is weight an indicator of how successful a pass rusher might be in the NFL?  If so, how does Davenport’s weight figure into the numbers?

Using the play index at PFREF.COM (and doing the addition in my head) I broke it down in ranges of 10 pounds, e.g. 240-249, 250-259, etc.

Stats compiled from 2000-2017
Weight 10+ Sack Seasons
200-229 0 (0 players)
230-239 23 (8 players)
240-249 19 (7 players)
250-259 67 (29 players)
260-269 79 (30 players)
270-279 50 (25 players)
280-289 46 (21 players)
290-299 17 (8 players)
300 or more 10 (7 players)

Bear in mind we’re using the same weight value for each player for his entire career, and obviously players (like the rest of us) tend to put on weight as the years go by, and weights will also tend to fluctuate throughout the year and even within the same game.  So, take these numbers with a grain of salt and with the realization they’re not necessarily telling the complete story.  It’s what we have to work with.

The thing in these stats that really stands out to me is the sweet spot is fairly broad (from 250 to 290), with 260-269 being the peak of the bell curve.  Bottom line, at least according to these numbers, if you want a 10+ sack player, try to draft one in that 250-290 range, preferable between 260-269.

Another interesting thing about Davenport is his speed in the 40 at his weight.  According to my research using the Combine search tool at the same site as mentioned above, Davenport is the only player in the last 3 Combines (not since Bud Dupree did it in 2015) to run a 4.58 or faster while weighing in at 264 pounds or heavier.  In other words, this guy is freakishly fast considering his weight.

Does that mean this guy is a lock to have his bust in Canton some day?  No, of course not, statistically speaking he is probably more likely to be a bust of a different sort.  But one thing is very clear, the Saints believe he will be a big time player because there’s no way they would have spent 2 1st rounders and a 5th rounder to get him otherwise.  Personally, I would not have made that deal just based on what *I* know about the player (which is next to nothing aside from what I’ve already detailed herein).  The Saints made the deal based on what *they* know (or think they know) about the player, and presumably, they’ve done the necessary homework in the scouting department.

By the way, kudos goes out to Larry Holder of the TP for calling this one about 2 hours before the draft.

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