What Is The Story On Oakland Raiders RB Latavius Murray?

June 20, 2013 by

Have you ever thought what it would be like to understand an additional dimension to the one you are viewing?  The easiest concept for that picture is imagining a 3D object in 2D world.  With Oakland Raiders RB Latavius Murray, this concept is valid.

Despite having good on the surface numbers, 5.5 yards per carry or greater over the past 3 seasons, a deeper look shows a different picture.  His rushing average dropped to 3.4 yards per carry in his challenge games, which raises a red flag.  It concerned me heavily until I took an analytical look encompassing a much higher dimensional space.  He compared to a group of running backs that had all been taken earlier than him with the closest comparison being RB Andre Brown of the New York Giants.  Without the analytical view using additional dimensional space, I would have wrote him off for poor performance in challenge games.  Using a view that encompasses additional dimensional space, he compares to Andre Brown and a little more loosely to a running back with multiple pro bowls.  As a 6th round selection at pick 181, the Raiders made a selection that has all the signs of arbitrage.

Filed Under: Football Analytics, New York Giants, NFL Draft, Oakland Raiders, Running Backs (RB), Scouting Department

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)

San Francisco 49ers: Pro Football’s Gold Standard

March 16, 2013 by

NFL Free Agency is a firestorm for creating football discussion.  It garners the majority of attention, while often the true mechanisms of winning go unnoticed.  One such organization that has taken steps necessary to be an elite organization is the San Francisco 49ers.

On March 1, the San Francisco 49ers announced a partnership with SAP, becoming the first NFL organization to partner with big data to improve inefficiencies in their system.  More importantly, it provides a measuring stick, and this system provides it on the fly.  Every other organization that has not embraced this system or something very similar has no chance  for success in a league built on parity and difficult financial decisions.  The longer organizations wait to embrace analytical based systems, the wider the gap will grow between have and have-nots.

The Baltimore Ravens punctuated this further by bringing home a Super Bowl trophy.   They are one of the few NFL organizations with an analytical department.  The parity environment that the NFL has setup is one that manifests a snowball effect.  For every personnel decision that is above the league average success rate, it will create winning momentum.  Success percentage over the league average provides the ability to play with house money.  Every organization that is below the league average in success percentage is handing over wins to those willing to take them.

To highlight a recent example, lets compare two personnel decisions.  One of these decisions is a great decision because it has a high probability of returning success.  The other decision has a low probability of success.

Transaction 1: The San Francisco 49ers acquire Aquan Boldin from the Baltimore Ravens for a 6th round pick and pay him $6 million.

Transaction 2: The Miami Dolphins sign Brian Hartline to a 5 year deal valued at $30.775 million, including $12.5 million guaranteed.  It is an amazing figure for a player that was the #1 WR a year ago yet only scored 1 TD.  There are four years of data to also demonstrate that the 1 was not an aberration.  It was his season total in three of four seasons.

Boldin equaled or bettered the number in 10 of his 10 seasons in the league and has scored 7 or more TDs in half of his 10  seasons.  Boldin is an investment in winning while Hartline is just an investment in a player that plays wide receiver.  As long as Miami makes personnel decisions with no fruit in winning, they will be handing over wins to organizations willing to take them, like the San Francisco 49ers.

Filed Under: Featured Articles, Football Analytics, Miami Dolphins, San Francisco 49ers, Scouting Department, Wide Receivers (WR)