Big Receiver vs "Little" Receiver - 2020

 

Back in 2019, we took a look at the performance of wide receivers based on size...and while bigger receivers received more targets on average, smaller receivers perform better on a yards per target basis (check it out here).  But how small is too small?

Let's look at the case of Devonta Smith.  Big time talent in a little body. But what does the historical trend tell us?

The Data

Data from every member of the NFL Combine wide receiver group since 2000 was gathered from Pro Football Reference.com.  From this information, the following numbers were aggregated:

• Body Mass Index 
• For the group, BMI Z score was derived (Z score represents the number of standard deviations from the mean each subject lies).
• Receiving Yards
• Snaps
• Receiving Yards per Snap (Y/S)
• Y/S Z score
• PFR historical snap counts are limited, so Receiving Yards for years where snap count was not available are eliminated from this calculation.
• Yards per Snap and not Yards per Target to get a better picture of how the team valued a player.  If we look at YPT a player could be a gadget guy who was called in on certain situations. What we are looking for is to measure a players worth through his utilization. 

The Big Picture

• There were 1089 wide receivers invited to the Combine over the 20 year period.
• Of those, 597 (or about 55%) generated less than 100 receiving yard during their time in the NFL.  
• Of those 413 had BMIs in the "average" range (with Z scores between -1.00 and 1.00).
• 94 below average  
• 90 above average
• So it would appear size is not a factor in terms of guys not having an impact in the NFL.

When we look at the performance of 263 WRs who had at least 404 snaps over the 20 year measurement period and break it down by size, here's what we get:

• Average Yards per Snap of the 263 qualifying receivers is 0.9058 y/s.
• Quartile 1 contains the larger receivers with average BMI of 6.21% greater than the sample average (26.90).
• The average player has a Z score (number of standard deviations from the mean) of 1.24.
• Regarding Yards per Snap, they have the highest rate of 1.10 yards per snap (the average Z Score is 0.23).
• Above Average Y/S: 37 vs Below Average: 29
•  Some receivers in this group with Yard/Snaps > 1.10x include Dez Bryant, A.J. Brown and Terry McLauren.
• Quartile 2 receivers are on average 1.75% higher BMI and produce at a rate of 1.04 Y/S which is 5.5% off the Quartile 1 average.
• Above Average Y/S: 37 vs Below Average: 29
• Some receivers with Y/S > the 1.04 average include Julio Jones, Allen Robinson and Julian Edelman.
• Quartile 3 receivers are on average 1.71% lower BMI and produce at a rate of 0.97 Y/S which is 11.2% off the Quartile 1 average.
• Above Average Y/S: 32 vs Below Average: 34
• Receivers with Y/S > the 0.97 Y/S average include TY Hilton, Antonio Brown and Mecole Hardman .
• Quartile 4 receivers are on average 6.39% higher BMI and produce at a rate of 0.95 Y/S which is 12.9% off the Quartile 1 average.
• Above Average Y/S: 27 vs Below Average: 38
• Receivers with Y/S > the 0.95 average include Hunter Renfrow, Robby Anderson and Calvin Ridley.

Conclusion

While this is just a small amount of data to consider, it looks like there is a material difference in Yards per Snap for a Wide Receiver based on BMI.  As BMI dropped so did the Y/S based quartile averages.  As BMI decreased, the number of receivers in each quartile who had below average Y/S production increased.   

This alone is not enough to indicate Smith will have any performance issues given his weight. The lightest guy on the list, JJ Nelson, the only guy with lower Combine BMI than Smith in the 20 year survey, currently has above average Y/S production of 1.12 which is better than Curtis Samuel (1.11),  Will Fuller (1.09) and Tyler Lockett (1.08).  But despite having been in the league as long as Lockett (entered in 2015) he has at least 900 fewer snaps registered than any of the three comp players.  

Smith put on a show in College Football during 2020 and we would love to see that carry over to the pro game.  Let's hope he can be an outlier and have a solid NFL career despite being so light.

NFL Draft QB Prospect - Unforced Errors

Own Worst Enemy

Recently, I looked at how some of the prospects in the upcoming NFL draft responded to pressure (see the post right here).  With defenders in their faces, we looked at who was able to overcome the pressure and put up numbers.  Performing well under pressure is a desirable characteristic but let's not forget how critical it is to limit one's own mistakes in a clean pocket.  But which players were most prone to making unforced errors?  Let's take a look.

The Data

Data for this analysis is pulled from PFF.com.  For this exercise, Unforced Errors are:

• Interceptions
• Sacks
• Batted Passes
• Turnover Worthy Plays which are a PFF metric defined as "a pass that has a high percentage chance to be intercepted or a poor job of taking care of the ball and fumbling".

It is acknowledged that all of the above can be debated as potentially being "forced errors". However,  given the QB is ultimately responsible for making the decision of where to go with ball we consider the above errors "unforced". 

The Unforced Error numbers for Total Plays and Pressure Plays were compiled to calculate the Unforced Errors percentage ([Total Plays Unforced Errors minus Pressure Plays Unforced Errors] divided by Total Plays Unforced Errors).  The Z-score (representing the number of standard deviations from the mean) is also calculated. 

Here are the results:

Comments

• Table is sorted by 2020 performance and includes 2019 numbers.
• Trey Lance is offset from the rest of the table because he had just one start in 2020 which reflect horrible numbers; his 2019 numbers looked good.
• Sam Ehlinger had the lowest Unforced Error % and showed improvement of 9.7% over 2019.
• Mac Jones, who had the best numbers under pressure from my prior analysis, is pretty abysmal when it comes to avoiding unforced errors with 2/3 of his tracked errors coming with no pressure. 
• Justin Fields increased the most on a percentage basis (by 13.0% year over year) however, he still remained above average in terms of Z score. 
• Ian Book improved the most reducing his forced errors by 23.3%.
• Presumptive QB1 Trevor Lawrence and QB2 Zach Wilson are at the wrong end of the the unforced errors table with both below average based on the criteria.   
• Lawrence also had below average performance numbers for my QB pressure article, so perhaps these areas could be a point of emphasis for his coaching staff during his rookie season in Jacksonville. 
• Kyle Trask, who could be QB6 coming off the board, was very consistent with performance well above average both here and in the pressure performance article. 

Final Thoughts

Of course, the table above is not a ranking nor is it a prognostication for the order the QBs who will be picked this April. It is not as an attempt to "win' an argument with some random numbers, but this exercise as a whole should be used a tool to help when watching game footage to see what is causing these unforced errors to accumulate or if the errors are even of a magnitude to be included in any scouting evaluation.  You can be the judge of that.  

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Don't Stop Now, You Quitter!  Other Posts To Read

Comparing Free Agency Spending with Performance over 10 Years - Looking for Free Agency trends that to see how they impact winning.  

https://boombearfootball.blogspot.com/2021/03/comparing-nfl-free-agency-dollars-spent.html

QB Prospect Response to Pressure Analysis - 2020 - Taking a look at the NFL Draft Prospects for  2021 when the heat is on .  https://boombearfootball.blogspot.com/2021/02/qb-prospect-response-to-pressure.html

Jets Reset Part II - The Elephant in the Room - Part II of my look at my NY Jets and this time we have to attack head on the issue of the quarterback.  https://boombearfootball.blogspot.com/2021/02/jets-reset-part-ii-elephant-in-room.html

Comparing NFL Free Agency Dollars Spent to Win Totals 2011-2020

A Mystery as Old as Time Itself

In the NFL, there are just two ways to bring new players onto your team - draft them into the league or offer free agent contracts.  The former is fairly simple - all new prospects register for the player draft and teams engage in an orderly selection process.  The latter is a bit more tricky as teams must make competitive contract offers in hopes of the targeted players agreeing to their terms.  While the draft salaries are fixed based on the union contract, free agent contracts have only a minimum.  So, a team's management must really be thoughtful in its approach to spending because the long term impact of free agent contracts is evident even after the player has moved on.

Given the two ways to integrate new players, the age old argument comes up every off season...should my team focus on the draft or free agency?  And if free agency is the focus, how much money should they spend?   While that will always be up for debate, I wanted to look back at the numbers to see how teams that spent the most free agency money performed in terms of wins compared to the prior season. 

Let's take a look:

The Data - Using Spotrac.com, I compiled the annual Free Agency spending data from 2011-2020 and determined each team's the average free agency expense per free agent for each year.  In the table below you will find the following:

• Div - NFL Division
• Avg $/FA Z Score - The 10 year average Z Score for each teams' annual free agent contract values/the number of free agency players acquired.
• Wins over the Last 10 Year - Total Number and Z score of the total wins between 2011 and 2020.
• 10 Yr Division Place - The average and Z score for the last to divisional finishes. 
• Total GMs - Number of different GMs over the ten year period observed. 

 

If we go with the hypothesis that the lower a team's average FA contract expense,  the higher their win total over a ten year period, here is a breakout of the data:

Observations

• The table above is sorted first by Division and second by Avg $/FA Z Score.  
• From here, we can see how Wins and Divisional Place relate to Average Value of Free Agency contracts written.
• Looking at the AFC East (AE), New England has historically spent the least in terms of average Free Agency contract which is well below the mean (Z Score of -0.66 standard deviations).
• They have accumulated the most regular season wins in the league over the measurement period.
• They have an average final divisional position of 1.20 over the last 10 years. 
• Conversely, the NY Jet spent more than the mean in terms of FA contracts (Z Score of 0.30) and have the fewest wins in the division as well as the worst average placing at 3.2
• For the entire league you will see that generally, as the teams spent more money in free agency on a per contract basis their average win total and division placing worsened. 
•   Big Spenders:  
• Jacksonville has been heavy handed with FA spending at 2.00 standard deviations over the mean over the last 10 years!  The return has been a league low 44 wins. 
• Cleveland comes in second with spending 1.10 standard deviations above the mean.  The Browns have the distinct honor of having the worst annual divisional placement of 3.7 on average over the last 10 years.

What Can We Infer?

Lower per player average Free Agent contracts tends to drive win total in the NFL.  From the above table, the team with the most wins in each division is the team that has spent the LEAST in free agency in terms of average FA player contracts with the exception of the NFL North where the Packers have the most wins but they trail the Bear's division leading $/FA Z Score is just 2 basis points.   There is a -75% correlation between Avg $/FA Z Score and Divisional Wins over the 10 year period. 

 

General Managers -  While there is much work to figure out what is going on here, if you look at the number of GMs each team has employed over the 10 year period, it's easy to see that having one GM is a goal but two of the three teams who have had 4 GMs are 31st and 32nd in the league in terms of wins over the measurement period.   Consistency seems to be the key.  

• Teams with one GM spend the least per contract in free agency and have more wins and better Division Placing than teams with more than one GM over the last 10 years. 
• Teams with four GMs spend the most in F/A and had the worst performance. 

 

Final Thoughts

While the above is interesting,  it's really nothing new.  Spend wisely, not like a drunken sailor.  Nothing earth shattering about that.  What is interesting is I have a workbook with lots of details that can lead to better insight into what is really driving the decisions leading to the numbers.  Looking forward to digging into the free agency trends and tendencies of each team over the past 10 years to get a better handle on the industry as a whole.  Hope you will check back for updates.

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Don't Stop Now, You Quitter!  Other Posts To Read

QB Prospect Response to Pressure Analysis - 2020 - Taking a look at the NFL Draft Prospects for  2021 when the heat is on .  https://boombearfootball.blogspot.com/2021/02/qb-prospect-response-to-pressure.html

Jets Reset Part II - The Elephant in the Room - Part II of my look at my NY Jets and this time we have to attack head on the issue of the quarterback.  https://boombearfootball.blogspot.com/2021/02/jets-reset-part-ii-elephant-in-room.html

The Nut Doesn't Fall Far from the Coaching Tree - Looking at the lineages of some of the NFL head coaching prospects for 2021.  Boombear's College Football Analysis: Strong Roots: NFL Head Coaching Vacancies and Coaching Trees (boombearfootball.blogspot.com)