Humans have a tendency to sort data in their brains into opposing categories; this or that, up or down, right or left, etc. This proclivity to do so shows in the most basic ways how binary thinking can restrict creativity by assuming a pseudo-scientific method is somehow more objective than a different approach or paradigm. Huh?
In simpler terms, if we allow ourselves to ‘believe’ that data is only correct if it fits some mathematical equation, then we are guilty of ignoring the possibility of a ‘statistical outlier’. Terms like “statistical outlier” are frequently used incorrectly in contemporary discussions regarding almost any topic.
Many people use these terms without considering what they really mean. Statistical outliers are unappreciated variables that often get tossed aside in research studies as meaningless data points that obscure the “real truth” of a mathematical concept. This is a dangerous practice for several reasons.
There is a bias in our culture to “cut through the junk” and make conclusions based only on the data that seems to fit the original thesis. If a variable doesn’t “fit” the norm, it is tossed aside as “insignificant”, when arguably nothing could be further from the truth.
Instead of looking at “aberrant” results with chagrin, we should not only be including these data points in our analyses, but studying them further for clues as to why they exist in the first place. I would posit that studying the full spectrum of data respects the original binary thesis that all data points in any study fit a predictable mathematical pattern.
What the heck does this have to do with the Buffalo Bills or NFL football? Sadly, it has far too much to do with our beloved team and the game we love.
It has to do with the widespread use of “analytics” like it is some sort of New Age religion when it comes to the game of football. It’s as if a nation of people suddenly studied an entry-level statistics class and now consider themselves experts in statistical analyses.
This is a dangerous assumption because the bias of the observer often leaves out huge dots that need to be connected that may refute naïve inferences made from a limited paradigm. Let me explain.
Let’s use Nate Hackett’s play calling as an example to illustrate my point. Mr. Hackett has a brilliant mind, and is among one of the many “new age” offense coordinators that uses analytics to construct a game plan. Fans complain constantly about the predictability in play calling as one of the main reasons the Bills offense has suffered a significant failure to thrive so far this season.
When you listen to Hackett talk about offensive game strategy, you frequently hear him refer to analytics as a way to explain his course of action. This swing of the pendulum toward data analyses being the driving factor in generating game plans is in stark contrast to a guy who is toting enough jewelry to open his own Tiffany store in the AFC East, Bill Belichick.
Belichick’s success is a perfect example of the theory of “alpha”. It is the one data point that stands out from all the rest in terms of performance. He is a living, breathing statistical outlier. He’s made his living by turning any opponent’s “understanding” of his scheme into a joke. He is a master of ‘alpha’.
The ease at which guys like Belichick operate makes me wonder why the majority of people can’t see how his consistent use of statistical outliers to separate himself from the majority of his competitors works time and time again. If you understand the principle of ‘alpha’, he will always strive to find the one piece of data that will allow him to craft a game plan that will make a fool of his opponent.
What people with a limited understanding of statistics often fail to realize is that just because something may be described as “statistically significant”, it doesn’t mean the connection of the two points of data is 100% accurate. Unfortunately, the average football fan often fails to consider this important point when they look at data points puked out by websites and other media outlets.
They often fail to realize the significance of statistical outliers that are casually thrown out when one keeps “drilling down” on the data. By throwing out “extraneous” variables, you are automatically misrepresenting the context of the data as a whole.
So while Hackett may craft a game plan by looking at tendencies, guys like Belichick thrive by creating statistical outliers that will twist these ‘analytics’ guys in knots. It has worked like a charm for him for years. Only when people realize that any assumption or inference can be turned on its ear by creating a statistical outlier will they have a real understanding about how one person can be so exceptionally successful for so long as Bill Belichick in creating innovative game plans.
There is one guy who does “get it” and has had arguably the most success against Belichick’s schemes, Rex Ryan. He is another ‘alpha’ game planner. This is the exact reason why the Bills have had more trouble against Rex Ryan’s Jets than when they had different head coaches.
The Bills have put together the best team on paper in the AFC East, and still cannot translate it to the best winning record in their division. Many people are quick to blame this on the lack of a franchise QB for the nearly decade and a half playoff drought. While that may be true in part, the other reason this has happened is because they do not put creative game plans together effectively against opponents.
For example, human brains have a tendency to sort for “sameness” or “difference”. Most of us sort for difference, but not all people do. What this means is that if you see a line of numbers and one number is different, do you notice that number immediately? If so, you brain sorts for “differences”. If you see tend to see the big picture without noticing the little “insignificant” details, you are more likely to be a person that sorts data for “sameness”.
The reason this is important is that if you understand this tendency, you can then become a more accurate predictor of how someone will construct a theory, or in this case a game plan. These principles of central tendency are important to understand because they form a basis for “thinking outside the box”. Sadly, those who rely too much on analytics are easy prey for the ‘alpha’ coaches that make a living with their creativity.
The nature of creativity and innovation in game planning requires the ability to think like a statistical outlier. If you want results that are outliers in terms of success, you must constantly strive to be different and unpredictable. By it’s very nature, ‘analytics’ try to sort data for “sameness”, so they can be neatly packaged and promoted as “scientific” when in fact they are merely a bunch of data points that often leave out the meaningful “outliers” that don’t fit their “formula” for success.
This is not to suggest in any way that these data points are meaningless, far from it. However, they need to be taken in with the understanding of their limitations in terms of what they can reasonably contribute to developing a creative game plan. Nate Hackett would do well to set aside some of the data points and focus on innovating game plans using less data driven “scientific” strategies and focus more on the art of the game.