Cincinnati Bengals Roaring With DE Margus Hunt

June 19, 2013 by

When the Cincinnati Bengals selected Defensive End Margus Hunt in the 2013 NFL draft out of Southern Methodist University, they were doing based upon his athletic potential.  At 277 pounds, he ran a 4.62 and benched 38 reps at the combine.  As fantastic as those were, it demonstrates a very limited approach to an analytical profile.  It does however suggest that he has a safety net for impact in the league on special teams.

In the measures that correlate with impact at DE, he met the barometer for future success.  Despite the label of being a raw prospect, he was challenged more than most DE prospects and had more experience doing it than you would believe from scouting reports alone.  He also triggered another major indicator that his best ball is not behind him.  That said, one of his indicators suggest he may have a slower start to his career, but he will be around for the long term due to his projected peak.  Fortunately, for him, his knack for blocking kicks and impacting special teams are critical for his opportunity to develop as a defensive end.

Filed Under: 4-3 Defensive Ends (DE), Cincinnati Bengals, Football Analytics, NFL Draft

Seattle Seahawks Prey On Talent With TE Luke Willson

June 18, 2013 by

The Seattle Seahawks made one of the best arbitrage selections in the 2013 NFL draft when they selected TE Luke Willson out of Rice in the fifth round with pick 158 overall.  What stood out to NFL Data Consultants was the comparative analysis, which brought up the name Jimmy Graham.  Jimmy Graham was taken by the New Orleans Saints in the 2010 NFL draft with pick 95 overall in the third round.  Jimmy Graham was a two sport athlete, playing basketball for four years before transferring his skills over the football field for a single season while at Miami.

Just like Jimmy Graham, Luke Willson is a two sport athlete.  He was signed by the Toronto Blue Jay organization in 2011.  Prior to his final collegiate season, he spent the offseason playing baseball.  He carries himself as a professional and was noted for his blocking skills in scouting reports, an area of his game in which he has an advantage to where Jimmy Graham was entering the league.  After our filter process, the analysis noted that Graham played against tougher competition than Luke Willson, but Willson had a better challenge metric score.  Additionally, his reliance metric was a strong score, demonstrating that unlike some Tight Ends that have demonstrated athletic ability in physical tests alone, his analytical profile suggests he is not fool’s gold.  Reliance is a metric that provides a window of what a player’s collegiate staff really thought of a player.  Based on my analysis, it is my prediction that he will be the starting tight end for the Seattle Seahawks within three years.

Filed Under: Football Analytics, New Orleans Saints, NFL Draft, Seattle Seahawks, Tight Ends (TE)

Denver Broncos Having A Montee Ball

June 17, 2013 by

There are a lot of varying viewpoints on the Montee Ball selection by the Denver Broncos, but NFL Data Consultants is a fan after much analysis. One of the data points of contention with RB Montee Ball was his short shuttle. A number of sources reported 4.11 and a number of them reported 4.31 as his short shuttle time. This is one of the reasons that NFL Data Consultants believe in a holistic approach and multiple points of analysis.

I take a specific approach when looking at a player’s production. In that analysis, Ball was graded at 5.01 yards per carry despite a non-adjusted figure of 5.6 yards per carry. From a production standpoint, this is a very good measure, especially considering his competition, challenge, and reliance metrics were all very good. Thus, it meant further investigation into some of the physical measures, including the 4.66/4.51 split in his 40 times between the combine and pro day. The culprit? Medication and sinus infection. One of the interesting things we had to do was go to our scientific approach with comparative analysis to get a much better read on his physical profile. The human bias would have been to say he was too slow, but the analytical approach is key to removing human bias. At the end of the process, Ball compared as a plus on LeSean McCoy, a runner that has started 44 games and averaged 4.6 yards per carry.

The great thing about using science in comparative analytics is that you can get a much stronger indicator of what a player is capable of doing at the pro levels. The other benefit is the ability to do a qualitative analysis to see what the scouting reports were of the comparable player. A common theme on physical elements of McCoy was that he is plenty quick enough to get to the corner, but lacked the elite speed. Interestingly enough, in his first 3 years he posted longs of 60 or more yards in each. According to Sports Science, Montee Ball had the 2nd fastest burst through the line and a stiff arm of 21.7% more force than Vikings RB Adrian Peterson. So what does the analytical package say? It appears the Denver Broncos have a good running back of the future regardless of any mixed reports that you see out there.

Filed Under: Denver Broncos, Featured Articles, Football Analytics, Minnesota Vikings, NFL Draft, Running Backs (RB), Scouting Department

The Green Bay Packers and the Alex Green Effect

June 16, 2013 by

Alex Green continues to be an intriguing running back for NFL Data Consultants due to his analytical profile.  Remember, one of our key objectives is to measure and predict the careers of players as they enter the NFL.  Unfortunately, Alex Green has been battling a knee injury that he suffered in his rookie year.  It is one that typically takes 2 years to fully heal.

In 2012, a picture of how a potentially healthy Alex Green would project became available since he was the comparable for RB Bryce Brown of the Philadelphia Eagles.  Bryce Brown averaged a very good 4.9 yards per carry on 115 rush attempts, and Alex Green is on the plus side of the comparison between the two.  Even the qualitative measures for Alex Green are more favorable to that of Bryce Brown.

At Hawaii, Alex Green played in a very pass happy offense and still possessed a very strong analytical profile as a runner.  If healthy, Green is the best fit for an Aaron Rodgers offense predicated on the passing game and spreading out the defense.  They did draft Eddie Lacy and Johnathan Franklin, but Lacy brings a different element and a healthy Green is a superior prospect to Franklin based on both the analytical profile and the comparisons.

At NFL Data Consultants, we take a holistic approach with comparative analysis because all elements are combined into a single framework.  NFL Data Consultants will be releasing a daily series on June 17 that will highlight the best decision each organization made with the 2013 draft and undrafted rookie signings.  The key is to feature one player from each organization that had a better analytical profile for success than the player’s market value (draft position) indicated.  The objective is to finish all 32 NFL Teams before the start of training camp when the pads go on.

Filed Under: Football Analytics, Green Bay Packers, NFL Draft, Philadelphia Eagles, Running Backs (RB), Scouting Department

Post NFL Draft Analysis: The New England Patriots WRs

May 2, 2013 by

The 2013 NFL Draft has come and gone and the New England Patriots decided to attempt to break their trend of drafting ineffective Wide Receivers.  The original article can be found here:  The Patriot Way and Wide Receiver Evaluation

Typically a player is challenged with tougher competition, but not always.  And the utilization of a player in easier versus more challenging games provides another measurable view on a Wide Receiver.   These things bring into light three proprietary metrics, known as Competition, Reliance, and Challenge, isolated towards the player to provide a clearer picture of their on the field production and what their college coaches really thought of them.  It also helps identify system players versus players that would likely succeed outside of their system.

Using these measures along with other analytics and science, it is a good bet that the Patriots broke their string of bad WR selections when they selected WR Josh Boyce with the 102nd pick in the 2013 NFL Draft.  Boyce was an arbitrage selection, one which will pay off in the long term.  In the games measured, his competition score was just shy of the top 10% of all WRs historically in the system, and his challenge score indicates he was challenged heavily on his way to a high Wide Receiver production score.  His reliance score was neutral, showing that there were no major red flags in the production.  This combined with his physical measures profile a WR with a high likelihood for success.

The New England Patriots did take a WR earlier in the draft with the 60th pick in WR Aaron Dobson, and while he has a solid production score, it was about 10% lower than Boyce’s score and Dobson faced weaker competition and it was just as much of a challenge for Dobson.  Thus, it is a red flag for when his challenge level is raised.   Physically, he has a good skill set, but so did a few other Wide Receivers the Patriots have busted on in the past.

Filed Under: Featured Articles, Football Analytics, New England Patriots, NFL Draft, Scouting Department, Wide Receivers (WR)