This was a familiar scene last year. New York Giants middle linebacker Chase Blackburn (93) during the game at Metlife Stadium. Andrew Mills/THE STAR-LEDGER via USA TODAY Sports

A 'Statistical Analysis' of the Green Bay Packers Needs

How do the Packers beat the mathematical odds?
Raymond T. Rivard photograph


Editor’s note: Caution – if you don’t have a sense of humor or aren’t a brilliant mathematician, this post will probably mean nothing to you. However, if you like to smile, laugh outloud or get a kick out of a guy who likes to have fun with his posts, then you will enjoy this. In other words, if you’re looking for a post that talks about bench press repetitions, 40-yard dash time splits, wingspans, and core strength, you might want to move on.

So I decided that in honor of George Washington’s birthday (thus, I will not tell a lie), the start of the Combine, and the fact that my editor (the wise and talented expert that he is) emailed me and asked me when I was coming back to my $20 million-a-year writing job, I figured it was time to do some deep statistical analysis of the Green Bay Packers and their needs going into the combine.

When we apply basic statistical analysis to the 2012 season, the first thing that jumped out to me was that the Packers were 11-0 in games where they outscored their opponents.  In the five games they lost, they did not score more points than their opponents (you can even check these facts as I found them on the Internet, and everyone knows that if it is on the Internet, it is true).

This means that on 11 different occasions, the offense performed at a level greater than (another math term) the other team’s defense.  In order to increase the percentage of wins the Packers must determine what needs to be supplemented to the offense to increase the total output and subsequently maximize their victory total next year.

In my analysis two things need to be done.  We need to significantly increase the production of the offensive line by adding a player who significantly produces an end product that will protect Aaron Rodgers from being sacked, and provide the ability to divide the defensive players which will then equate to the running lanes opening up, establishing the “X” quotient, which has been the missing running game.

Mathematically, if we assign the letter “O” to refer to the offensive line, the letter “A” for Aaron Rodgers and the letter “X” for unknown, our equation would look like this.  OA + X = ?

My pure genius of breaking this down to algebra is amazing and should revolutionize future draft boards and place me in the NFL Hall of Fame for revolutionizing the game of football.

This then brings me to the second need.  We need to have a running back who can be that guy who pounds the ball up the middle and out to the outside – that one horse who can pick the team up and carry it.

So then we need to adjust the equation (using “R” for the running back) to read, R(OA) + X = ?  By following this simple equation I would extrapolate that we should then augment the victory total from 11 to 16.  This would constitute a 69 percent increase in the total number of victories.

Now being the gifted mathematical genius I am (just ask my daughter), I will leave it up to those who subscribe to the theories of applied physics and Keynesian economics to determine which players would best fit the equation.  I do not like to pigeon hole myself by working outside of my normal wealth of pure awesomeness and comment on things I have no basis of knowledge to work from.

Defensively, the statistical analysis shows that on the five occasions that the other team’s offense scored more points than our defense, it was due to the fact that our defense allowed more points to be scored by the other team than we were able to score ourselves (again, you can check these facts on the Internet).

Remember when Clay did this to Brett? Well, not having him on the field for several games last year diminished the Packers chances of scoring more points than their opponents.
Raymond T. Rivard photograph

I would surmise that this was due to the fact that our two primary defensive players, Clay Matthews and Charles Woodson, were missing for most of the season.  Normally this subtraction would be a bad thing, but it allowed for the addition of numerous unknown quotients to be included in the overall equation.  Because these unknown factors had a high variable, they had a positive impact on the end result.

Whereas many teams faced with the same formula would normally see diminished returns, the Packers were able to use the theory of addition by subtraction to their advantage.  This will allow for a continuous increase in the overall production next year and the end result should be a decrease in the number of points scored more often by the other team than ours.

This would then create a direct relationship between the increase in the total number of victories.  It does not mea, however, that there are weaknesses and areas that need to be improved.  Since we have completely factored out Charles Woodson from the equation, we need to determine which unknown quantity can be implemented within the equation to offset the deduction.

In addition (pun intended), we need to multiply the number of prime numbers that can create a continuous increase in the number of times that the opposing quarterback  fails to score more points than our offense.

In this case the equation would look like (3.14D)+(X+Y)=V+5.  X and Y are are unknown factors and “D” represents the defense and “V” is for last year’s total number of victories.  Thus, the combination of the unknowns will correlate to the inverse proportionate numerical total of the sum of all factors divided by the quotient.

By applying the statistical analysis and mathematical formulas based on my factual research, I can honestly say (in honor of George Washington) that if Ted Thompson and Mike McCarthy apply these equations to their overall process of grading players, the end result will equal the sum of all work combined, resulting in the unknown being solved …

And that, my friends, is no lie.



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