Analytics is defined as the systematic computational analysis of data or statistics. It is an advanced look into common stats that have been around for decades. Why has the NFL not evolved in the game of statistics? Major League Baseball is at a point of analytical takeover where the game is viewed, evaluated, and played on a computer. The National Basketball Association is not far behind, heavily featuring advanced statistics in roster construction and maximizing a player’s abilities while minimizing deficiencies. To put the NBAs state into perspective, James Harden has thrived in Houston’s analytical, on-ball system at a level once unimaginable. By contrast, the NFL is in a dark age while other sports are figuring out different, quantifiable ways to maximize a player and teams value.
The common perception is that football will likely never be baseball, where statistics provide answers to everything. The sample size in a season is not as large and there are too many variables in play. This stigma should not deter football minds, there is always room for more data and football is heavily lacking. The small revolution has taken shape in the form of tracking player movement, percentages, and probabilities. Scouting departments also utilize more advanced statistics in talent evaluation but too often players are missed due to common misconceptions provided by classic statistics, measurements and lackluster analytics. Front offices across the league must seek any competitive advantage available and it starts with looking past everything normal football has known for the past 99 years. Many teams are built through top-tier talent but lack depth to win consistently and play at a high level. The evaluation of coverage skills, fundamentals, big-play ability, arm talent, and overall game management can all be enhanced by possible new-age data. An example is, Man/Zone Coverage Efficiency Rating (MCE or ZCE) which measures a player’s ability to be in a favorable position in either man or zone coverage at the instant the ball leaves the quarterback’s hand. To quantify, a player will be rated in the green (good position), yellow (average position) or red (bad position) during each pass play. This can be transmitted numerically based upon the number of relevant snaps. Every play is crucial and being in a favorable position when the ball is thrown in such a pass dominant league is of the utmost importance. Talent evaluators lack the ability to calibrate and quantify a player’s true ability in different coverages. Progress is being made as Pro Football Focus ranks players based on how they perform after a complex breakdown of film. But, there is much more to be done.
Football coaches claim to know a fundamentally sound football player. It is true, this type of player can commonly be observed by the naked eye. Yet some of the most talented teams are plagued with the penalty bug and missed assignments. The result is consistent losing. Player Execution Grade (PEG) measures a player’s ability to avoid penalties and missed assignments in any given game. This data can be gathered by taking the amount of penalties and scheme miscues divided by the amount of plays accumulated. This stat leaves room for coach interpretation and this is a benefit to the analytical world. It forces people to think outside of the box. PEG is perfect for evaluating offensive linemen who often hinder drives with the ever-common false start or holding penalties. These types of stats are a strong lead into recognizing true value. The most valuable position on a team is the quarterback and pocketability has become an extreme point of emphasis for the NFL. Pocketability Variable is the average yardage (horizontal or vertical) behind the line of scrimmage the QB travels to avoid being sacked or be sacked.
The potential for more data in this space is ripe. Simple usage allows for a complex breakdown on what exactly a QB is physically and mentally offering a team in any given game. Arm speed, velocity, passer angles and a QBR system lay the foundation for deeper breakdown of overall quarterback play. Advanced stats need to be taken to the next level. In measuring pocketability, one usually observes mobility and athleticism to determine how an offense is built. In using my theorized pocketability variable, evaluators are measuring and quantifying a passers true ability to elude the rush while minimizing external factors such as a poor offensive line or a poor play call. Using yardage and not time elapsed as your independent variable for this is more a testament of both the line and quarterback alike.
A player like Deshaun Watson or even Patrick Mahomes (research the 9 players drafted before him) can often slip through the cracks due to conventional statistics. There are always stories about diamond’s in the rough and how much credit organizations deserve for finding them. What if a team could have the upper-hand on its opponent in talent evaluation and draft these types players around the time they should be drafted? A prime example of this is a scouting department that grades a defensive back prospect as an undrafted free agent due to his lackluster 40-yard time. Through tracking data, evaluators actually measure his game speed to determine that it should list the player as a mid-round pick. The effects of advanced statistics are not yet fully known in football and the league is much more secretive than other leagues in what they are doing to gain an advantage. It is time to take the leap into analytics and break down players like never before. He who hesitates is lost.