1. SPB/BIS NFL Draft Outline Matt Bayer & Rocky Worley
Focus Area: WR
Justification:
WR- Mohamed Sanu and Marvin Jones are at the end of their respective contracts this year. If they both
do not return, it would leave a void in depth of receivers. Thus, affecting AJ Green’s output, due to the
defense being able to just focus on him.
As you will see below, there isn’t a great need for any position. As GM of the Bengals we are looking to
improve our team, based on the facts we know. Contacts are ending, as stated in the paragraph above.
Taking a standout WR would benefit the organization, no matter what Sanu and Jones decide to do. In
one aspect, drafting a substantial WR for late first round money could justify lower negotiating amounts
for Sanu and Jones, due to the fact that they don’t need really HAVE TO have them at that point. If they
do return, it will add yet another weapon to Andy Dalton’s arsenal, leading to increased performance
from Dalton, and less double coverage on Green.
Note: In the playoffs, Dalton has struggled due to injuries to the team’s key players and other team’s
standout CB’s covering his only target, A.J. Green, who also played through injuries.
If we provide Andy with another option, he will not have to lean so heavily on A.J. in key situations.
Particularly when a “Darelle Revis” type is hawking A.J.’s every move.
The Bengals are in the top half of every category, both offensively and defensively. Therefore, the
justification for them to choose a skill player is looked at even deeper, especially in a draft year that
contains so much talent in the skills positions. We ran a correlation analysis (see “Correlation Analysis”
below) of offensive categories to total points and found that passing/receiving yards have the highest
correlation to points scored. We believe due to the depth at wide receiver in this year’s draft, and our
contract situation at the WR position, we should draft a wide receiver, and we have the numbers to
prove it. Choosing a receiver doesn’t just help the quarterback; our correlation analysis shows that
increasing the team’s passing/receiving yards also increases the total scoring of the team.
Other than A.J. Green, the Bengals have not committed to any WR long term. This could be because
both players and the GM are comfortable with their contract, or because the Bengals are planning on
taking a look at the new WR coming out of college. We say the latter.
2. Correlation Analysis
Excel Analysis Summary:
We initially wanted to use the up-to-date Madden NFL 16 player attribute ratings and run a correlation
to season-leading statistics for WR’s to derive a formula which would weight each passing statistic
appropriately proportional to the rating. This would have allowed us to input the selected statistics of
collegiate receivers, determine by a regression analysis, to give the prospects a “rating” comparable to
NFL receivers with similar statistics. That would have allowed us to take an objective, non-biased
approach to evaluating a WR. Unfortunately, the regression analysis could not return p-values that are
within .05 tolerance, so we could not say with 95% confidence that the dependent variable, in this case
the Madden rating, was directly correlated to the independent variables we intended to use in the
evaluation of college WR’s.
Since we could not show strong correlation between the Madden ratings and receivers statistics, we had
to develop another way. As my teammate and I scanned over the top 40 receiving leaders in college,
obtained from ESPN.com, we decided to score each statistical category, determined by our correlation
analysis, on a scale of 1 to 10. As a baseline, the leader in each statistical category would receive a 10.
For example, Tajae Sharpe, WR at UMASS, lead the FBS (I-A) with 111 receptions. We decided to score
10 points for 100 receptions in that category, regressing incrementally down to zero. Then we sorted
our data in order of receiving yards leaders and decided to score 1500 yards as 10 points, as so on for
the other categories. The other 3 statistical categories that we used in our rating are TD’s, Average
yards/reception, and Longest reception. Our rating scales can be seen in the workbook title 2015, 2014,
2014 College Receiver Player Ratings and Grades, in the sheet names “Rating Score Sheet”.
Note: Some receivers have statistics that are above our Max values for points, but that limitation will be
address after scoring and grading the 2015 WR prospect.
Once the score sheet was developed, we set up the “2015 Receiver Prospects” sheet and used the
VLookup function in Excel to score each of the top 40 receiving prospect using the 5 receiving statistics
that we defined in our correlation analysis. We set up a column to calculate total points for each of the
receivers. The top score possible would be 50. In the “Player Rating” column in the 2015 Receiving
Player Ratings and Grades, we derived a formula, which is essentially a percentage based on point
totals divided by maximum points possible for each receiver. We then evaluated our finding to develop
a grading system. We averaged the scores and found that the average player rating for top 40 talent
was 67 for 2015. The lowest score from our prospects sheet was Alex Erickson, WR at WIS, who scored
YDS YDS/G PASS P YDS/G RUSH R YDS/G PTS PTS/GAME
YDS 1
YDS/G 0.999999505 1
PASS 0.808277889 0.808305335 1
P YDS/G 0.808117432 0.808144763 0.999999705 1
RUSH 0.028529161 0.028481873 -0.565502046 -0.5657263 1
R YDS/G 0.028242024 0.028194634 -0.565738438 -0.565962587 0.999999118 1
PTS 0.730635008 0.730469796 0.483241706 0.48319324 0.203029383 0.203043025 1
PTS/GAME 0.729912854 0.729747604 0.482820145 0.482770899 0.20273352 0.202745505 0.999973881 1
*Strong correlation between point and yards/yards per game
3. a 50, so we set that as our “C” to “C+” grade. The highest score was Corey Coleman, WR at Baylor, who
scored an 85, so we decided to score any rating over 80 as an “A”. The entire grading system can be
found on the “Rating Score Sheet”.
Our grading and evaluation spreadsheets are sufficient to evaluate WR’s, but we took it a step further
and developed an equation that more accurately predicts player ratings. Due to the limitations
mentioned in the Note above, WR with more than 100 receptions for instance, does not score points for
those “extra receptions”. By running a regression analysis of “Player Rating” and receiving statistics, we
were able to assign a coefficient value for each statistical category we evaluated. The regression
analysis can be found in our 2015, 2014, and 2013 Receiving Player Ratings and Grades workbook on the
“Regression 2015” sheet. The actual formula is:
Player Rating= 0.605 + 0.177(Rec) + 0.014(Yds) + 0.971(TD) + 0.887(Avg Yds per Rec) + 0.212(Long Rec)
To test the reliability of our model and evaluation techniques, we analyzed the top 40 receiving leaders
for 2014 and 2013 as well. The model remains the same, but the equations for each of these two years
are slightly different. Each year’s analysis, along with a regression analysis, are included in the 2015,
2014, and 2013 Receiving Player Ratings and Grades workbook.
After analyzing the top 40 college receiving leaders from 2015, using the methods above, we conclude
that the 27th
pick for the Bengals in the 2015 draft should be Corey Coleman from Baylor. He does not
lead FBS (I-A) in every receiving category, but our model and equation indicate that he is the best WR
prospect available.
Conclusion:
Our recommendations were to draft Corey Coleman, however, he was drafted 8th
in our mock draft by
the Cleveland Browns. We actually drafted Taywan Taylor, WR at WKU. Taylor was rated the 3rd
best
WR by our model and equation. It is important to note that 6 out of our top 10 WR’s that we evaluated
were drafted in the first round of our mock draft. In conclusion, we feel that we developed a very
reliable method of evaluating WR’s.
