For those unaware, the release of the new movie “Moneyball” is more than just another sports movie. It marks the final chapter of the emergence of intense, innovative statistical analysis into the mainstream of sports culture. It champions the work pioneered by such minds as Bill James and Craig Wright, and explores the application of this work by the likes of Billy Beane and Peter Brand into a winning formula on the baseball field.
The film, based on the book, “Moneyball: The Art of Winning an Unfair Game,” by Michael Lewis, centers on the Oakland Atheltics teams of the early 2000s that defied the odds and won huge amounts of games with a relatively minuscule team payroll. Once again, for those of you who do not know about this story (or even those who might), this story is just the tip of the numbers crunching iceberg that now resides in the sea of sports.
The 2011 NBA Finals saw the Dallas Mavericks defeat the Miami Heat. The Mavericks, owned by the indescribable Mark Cuban (in a few words, that is), won behind the play of Dirk Nowitzki, a bunch of aged stars, and some misfit players. Cuban’s secret? He paid a large sum of money for statistical research that he used to sign those pieces that he placed around his German-born star. This is just one example of the increased role of statistical analysis in the NBA.
“Soccernomics: Why England Loses, Why Germany and Brazil Win, and Why the U.S., Japan, Australia, Turkey—and Even Iraq—Are Destined to Become the Kings of the World’s Most Popular Sport,” a book by Simon Kuper and Stefan Szymanski, includes a section about the innovations made by the French team, Olympique Lyonnais (OL,) in the analysis of soccer. OL, a team that won Ligue 1 (the top league in France) a record 7 consecutive times beginning in 2001, has done so with a relatively small payroll. They have also had triumphs in UEFA Champions League (the top club tournament in Europe) over teams such as Real Madrid, a team that spends vast amounts of money on some of the biggest names in the game.
There are a myriad of other publications, blogs, and groups dedicated to the construction of creative methods of thinking about all sports. These ways are primarily aimed at finding methods of evaluating players and teams in an objective manner. One example of this is the new Total Quarterback Rating (QBR) that was developed by the Production Analytics team at ESPN to update the Passer Rating system used in the NFL. This new system takes into account all aspects of a quarterback’s game, and their level of play in given game scenarios. It weighs plays in clutch scenarios more than plays in meaningless situations; it also takes a look at sacks, fumbles, and rushing ability. This is a departure from the old passer rating that takes a one-dimensional look at a quarterback’s passing numbers.
This new rating system, along with watching the first three weeks of the NFL season, made me think of a new rating system that would be beneficial to both NFL teams and fans, alike.
In my short, juvenile football career, I played wide receiver. I prided myself on good route running and good hands. Speed, strength, general athletic ability, and height escaped me, however. These are just some of the things that are mentioned as the tools of a great receiver. Great receivers get noticed by scouts and find their way to NFL rosters via the draft, or do they?
Wes Welker, Miles Austin, Josh Cribbs, Lance Moore, Davone Bess, Danny Amendola, Malcolm Floyd, Victor Cruz, and Nate Washington; these are just some of the names on the extensive list of receivers who went undrafted. They have all found some amount of success in the NFL, and some quite a bit. Wes Welker, for example, has twice led the league in receptions, and this season, leads the league in receptions and receiving yards. That being said, it would appear that on some level, there exists a disconnect between the perceived talents of receivers by NFL scouts, and their actual prowess in the league.
There is no doubt, that a receiver such as Welker is the benefactor of a strong system around him that fits his strengths. But what are those strengths that allow him to be a good receiver? And, more importantly, can they be quantified? As I stated before there are a set of tools that would appear to be the tools of great receivers. These tools would be included in a “Total Wide Receiver Rating” (WRR). This rating would undoubtedly need to be adjusted for the quality of a receiver’s quarterback (a receivers strong hands or good route running would be meaningless if their quarterback cannot make accurate throws on time), but strong numbers in some areas may point teams to receivers that would flourish in their respective offensive schemes.
The quality of a receiver’s quarterback is just one piece of the complication of the quantification of a receiver’s potential in the NFL. Quantifying a receiver’s route running ability may include separation from the covering defensive back, or spacing from other defenders on the field, or even the ability to pull other defensive backs off of other routes. All of these ratings would be dependant on the quality of defensive back and the defensive scheme that they play based on coaching.
All of that being said, the quantification of a receiver’s ability would be immensely nuanced and would take algorithms and equations of vast complexity. If I were a coach, however, and I could have a receiving corps of Welker, Austin, Moore, and Cribbs (or some of their undrafted predecessors such as Wayne Chrebet and Rod Smith) without having to spend more than a 7th round pick, I would jump at it faster than Rex Ryan at food.
Who are some other players you may know who “weren’t good enough” for the 7 rounds of the draft. Warren Moon, Kurt Warner, or Tony Romo could be throwing footballs to the afore mentioned receivers and Antonio Gates, or handing the ball off to Priest Holmes, Willie Parker, or Ryan Grant. Other teams could be running against a linebacking corps of Bart Scott, James Harrison, London Fletcher, and Antonio Pierce behind a defensive line featuring John Randle, Pat Williams, and Adewale Ogunleye. If I were a coach, I would want some number crunching scout to tell me to draft those players. There is clearly more statistical innovation left in sports, and potentially more movies to follow.
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