Madden NFL 17 Statistical Analysis
Correlation Between Combine Scores and In-Game Ratings
Version 2.0
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Idea Origin, Fundamental Questions, and Methodology
Correlation Between Combine Scores and In-Game Ratings
Version 2.0
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Idea Origin, Fundamental Questions, and Methodology
Spoiler
While creating the incoming rookie class on Madden NFL 16, I came into quite the dillemma when trying to rate players on a consistent basis. My philosophy was that if Player A and Player B ran the 40 Yard Dash in 4.44 seconds, their speed rating should reflect that, and be exactly the same. So, realizing how deep the operationsports.com community was when it came to roster creation, I figured I'd provide them and myself a tool to rate players fairly based on combine numbers. This would be done with a linear regression analysis, with the following attributes being listed with their real-life combine counterparts:
Speed: 40 Yard Dash (time in seconds)
Strength: Bench Press Drill (quantity of repetitions of 225 lbs.)
Jumping: Vertical Jump (height in inches)
Agility: 3-Cone Drill (time in Seconds)
Acceleration: 20 Yard Shuttle (time in seconds)
In order to calculate the correlation between these ratings, I needed a spreadsheet with the 2016 Rookie Class's NFL Scouting Combine scores on the individual drills, which can be found here. After using the Madden NFL 17 Player Ratings Database graciously provided to us by EA to input the correlating in-game attributes, I would use THIS online graphing calculator to prepare and present the mathematical correlations in graphic form after the insertion of the five bivariate sets of data gathered. In other words, da graphs, yo! Sections on all five ratings can be found below.
Speed: 40 Yard Dash (time in seconds)
Strength: Bench Press Drill (quantity of repetitions of 225 lbs.)
Jumping: Vertical Jump (height in inches)
Agility: 3-Cone Drill (time in Seconds)
Acceleration: 20 Yard Shuttle (time in seconds)
In order to calculate the correlation between these ratings, I needed a spreadsheet with the 2016 Rookie Class's NFL Scouting Combine scores on the individual drills, which can be found here. After using the Madden NFL 17 Player Ratings Database graciously provided to us by EA to input the correlating in-game attributes, I would use THIS online graphing calculator to prepare and present the mathematical correlations in graphic form after the insertion of the five bivariate sets of data gathered. In other words, da graphs, yo! Sections on all five ratings can be found below.
Speed
Spoiler
In Madden, a player's top speed is determined by their speed rating. The players with the fastest and slowest speed ratings in Madden NFL 17's base roster will be presented below in a graphic that also includes their 40 Yard Dash time.


As quite evident, the fastest player's all had the same speed rating, but ran different 40 Yard Dash times. What is similar about these group of players, is that their 40 Yard Dash times are considered outstanding compared to a typical 40 Yard Dash for their position, HB/WR. In Madden, speed tends to be valued quite a lot, since the ability to stretch the field is quite an advantage in a game that is somewhat far from simulation.
On the topic of mathematical correlations, here is the graph that represents thre correlation between the 2016 NFL Rookie's 40 Yard Dash scores and their Speed Ratings in Madden NFL 17.

Quite basic analysis of the graph will reveal a quite strong correlation between Speed and the 40 Yard Dash time. This is evident in the regression line's r value, which is approx. -.96. It seems that a majority of the data points are above the regression line in the left half of the graph, with the opposite being true for the right half. The image has some handy information in the top left corner, which will be broken down here:
-228 Rookies in Madden NFL 17 ran the 40 Yard Dash at the NFL Combine
-The average 40 Yard Dash for a rookie in Madden 1 time was a 4.81
-The average speed for a rookie in Madden 17 is 80
-The equation of the regression line is: f(x)=-32.16x+234.33
For those looking for charted data, here you are:







As quite evident, the fastest player's all had the same speed rating, but ran different 40 Yard Dash times. What is similar about these group of players, is that their 40 Yard Dash times are considered outstanding compared to a typical 40 Yard Dash for their position, HB/WR. In Madden, speed tends to be valued quite a lot, since the ability to stretch the field is quite an advantage in a game that is somewhat far from simulation.
On the topic of mathematical correlations, here is the graph that represents thre correlation between the 2016 NFL Rookie's 40 Yard Dash scores and their Speed Ratings in Madden NFL 17.

Quite basic analysis of the graph will reveal a quite strong correlation between Speed and the 40 Yard Dash time. This is evident in the regression line's r value, which is approx. -.96. It seems that a majority of the data points are above the regression line in the left half of the graph, with the opposite being true for the right half. The image has some handy information in the top left corner, which will be broken down here:
-228 Rookies in Madden NFL 17 ran the 40 Yard Dash at the NFL Combine
-The average 40 Yard Dash for a rookie in Madden 1 time was a 4.81
-The average speed for a rookie in Madden 17 is 80
-The equation of the regression line is: f(x)=-32.16x+234.33
For those looking for charted data, here you are:





Strength
Spoiler
Strength, in Madden, is determined mainly off of the Bench Press drill, repetitions of 225 lbs. Strength, as a raw attribute, should theoretically factor into Pancake Blocks, run blocking, driving players forward, stiff arms, trucking, etc. As well as what scheme a player fits into and what type of player they are. For example, Madden may classify a Tight End with high strength as a "run blocking" Tight End. Here are the strongest and weakest players in Madden:


After analyzing the images, it appears that some more attributing factors to EA's rating of strength are things that usually require raw power, such as pocket disruption, stuffs, pushing back/off OLman, etc.
Let's look at the statistics:

After analyzing that graph, it appears that the data has a quite passable correlation. There is an obvious outlier, which is Daryl Worley, but other than that, the spread seems satisfactory. This is also indicated in the r value, which is .86. The equation for the regression line is quite simple, f(x) = x + 55. Very intuitive system for rating rookies on Strength, EA.
Charted data:



After analyzing the images, it appears that some more attributing factors to EA's rating of strength are things that usually require raw power, such as pocket disruption, stuffs, pushing back/off OLman, etc.
Let's look at the statistics:

After analyzing that graph, it appears that the data has a quite passable correlation. There is an obvious outlier, which is Daryl Worley, but other than that, the spread seems satisfactory. This is also indicated in the r value, which is .86. The equation for the regression line is quite simple, f(x) = x + 55. Very intuitive system for rating rookies on Strength, EA.
Charted data:

Jumping
Spoiler
Jumping, seemingly only inportant for how high a player can hurdle/jump, is determined by the NFL Scouting Combine's Vertical Jumping drill. Here are the jumping graphics:


Byron Jones was an absolute freak of nature in the 2015 NFL Scouting Combine.
Here is the raw data:

The data for Jumping here seems quite spread. In the left 1/2 of the graph, it seems that most data points are below the regression line. However, the right half displays just the opposite. The two x's at the bottom left of the graph displays some of the inconsistencies on EA's part with rating Rookies based on the vertical jump. The equation for the regression line goes as follows: y = 1.74x + 23.61. The correlation coefficient here is .824, which is close to the previous value, that of strength.
Charted Data:



Byron Jones was an absolute freak of nature in the 2015 NFL Scouting Combine.
Here is the raw data:

The data for Jumping here seems quite spread. In the left 1/2 of the graph, it seems that most data points are below the regression line. However, the right half displays just the opposite. The two x's at the bottom left of the graph displays some of the inconsistencies on EA's part with rating Rookies based on the vertical jump. The equation for the regression line goes as follows: y = 1.74x + 23.61. The correlation coefficient here is .824, which is close to the previous value, that of strength.
Charted Data:

Agility
Spoiler
Agility is defined by the quickness in which the body changes direction. The 3-Cone drill is a measurable of agility and is used as well in Madden when scouting players. The correlation is simple, the quicker the 3-cone, the higher the agility. Players with higher agility will be able to change direction quicker, wheher they are a ballcarrier or a defender. Understanding the difference between speed, agility, and acceleration is key in understand player ratings. Here are the graphics:


Here is the raw data:

Yuck. There appears to be A correlation, but that's it. In the left half of the graph, there is an abundance of data points above the regression line, the opposite it true for the other side. However, there may be a rationale behind this. EA could add/decrease from the Agility rating based on other change of direction and speed skills, such as Juking and Spinning. The correlation coefficient is -.69, which is quite. underwhelming
Charted data:









Here is the raw data:

Yuck. There appears to be A correlation, but that's it. In the left half of the graph, there is an abundance of data points above the regression line, the opposite it true for the other side. However, there may be a rationale behind this. EA could add/decrease from the Agility rating based on other change of direction and speed skills, such as Juking and Spinning. The correlation coefficient is -.69, which is quite. underwhelming
Charted data:







Acceleration
Spoiler
Acceleration is defined by the rate of change of velocity. Negative acceleration would indicate slowing down, positive acceleration indeicates speeding up. Madden players obviously accelerate, however, usually the objective is not to decelerate. Acceleration therefore in Madden terms is how fast it takes a player to reach their "top speed". Acceleration, as a measurable, is determined by the "Short Shuttle" or the 20 yard shuttle. Here are the graphics:


It is noteworthy that Marquise Goodwin is an Olympian long jumper.
Here is the raw data:

It appears that the data here is quite spread, but is definitely more consistent than the regression line was for Agility. Correlation coefficient here is -0.75, and it shows. The equation goes as follows:
(approx) y = -13.8x + 146.18 In which x = 20 yard shuttle score and
y= Madden rating for Acceleration
Not much else to say here. If you'd like tabled ratings for acceleration, they are provided below:










It is noteworthy that Marquise Goodwin is an Olympian long jumper.
Here is the raw data:

It appears that the data here is quite spread, but is definitely more consistent than the regression line was for Agility. Correlation coefficient here is -0.75, and it shows. The equation goes as follows:
(approx) y = -13.8x + 146.18 In which x = 20 yard shuttle score and
y= Madden rating for Acceleration
Not much else to say here. If you'd like tabled ratings for acceleration, they are provided below:








Conclusion
Spoiler
So, after finishing this statistical analysis, it was quite evident that when EA rates players, specifically rookies, they take into account more than just combine numbers. However, my goal was to provide a consistent ratings scale so that roster creators could rate players more fairly and consistently based on actual combine numbers. So, salute.
Possible Future Works
Spoiler
-Find Correlation between broad jump and jumping/strength, find a system to attribute this in rating players
-Find Correlation between 60 yard shuttle and acceleration, factor this into rating players on acceleration
-Correlate Wonderlic scores to awareness
-Correlate 10 yard split into a percentage of the Speed rating
-Test to see if stride length affects straightline speed and agility
-Find Correlation between 60 yard shuttle and acceleration, factor this into rating players on acceleration
-Correlate Wonderlic scores to awareness
-Correlate 10 yard split into a percentage of the Speed rating
-Test to see if stride length affects straightline speed and agility
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