You’ve probably read a few threads full of stats telling you why this RB or that WR is going to be the next big thing. But were you convinced after reading? If so, the author may have laid out a compelling argument that painted a complete picture. If not you might have been asking for several factors to be weighed that were simply ignored. What gives?
Correlative reasoning means that the information we are discussing has a mutual relationship.
We typically use correlative evidence to try and predict the future. As the future is quite murky and full of possibilities, we attempt to grab the strongest threads and see where connections have been made in the past, which guides our reasoning for the future. While we often times make many correlations for a single event in an effort to strengthen our hypothesis, each of the correlations can usually be reduced to a single assumption that ties them together.
I.E. If you have a good QB you will have a strong passing offense.
This assumption is based on QB being valued as the highest position in football, therefore QB dictates success. While having a good QB is essential to a strong passing offense. This does not mean that having a good QB ensures a good passing offense. It is not equivalent. Meaning that there are a number of other factors at play that can influence the outcome of our given scenario. Play calling, offensive line talent, wide receiver talent, the overall health of your team, etc. can all play a factor in the outcome. All these factors are correlated to the whole picture but using any one of them as an indicator of success is incorrect. We need to include and weigh all the factors in order to paint an accurate picture.
Equative reasoning means that the information we are discussing has a direct relationship or cause and effect.
I.E. Aaron Rodgers chose not to attend minicamp, therefore Aaron Rodgers was not at minicamp.
Equative reasoning does not assume or over weigh factors based on a preconceived bias. Often times people will make convincing arguments full of correlated reasoning and insist that it is equivalent. While these arguments can be very compelling they are rarely free of assumptions or bias. Let’s take a look at one.
I.E. Laviska Shenault is an excellent player with the ball in his hands. Therefore he will be given the Percy Harvin role in Urban Meyers offense.
This is correlative reasoning.
While the above statement might turn out to be fairly correct in the future, it is currently based on a number of correlations that may or may not be correct. The tricky part of correlative reasoning is that it draws assumptions about the future based on events of the past. There is little accountability for new occurrences in this model. History is being read but it’s also being written.
The main correlation is that Urban Meyer will be bringing an offense that was molded by coaching against college athletes. This assumes that simply because Urban is the head coach he will be running the same offense he ran while playing against college athletes.
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The reality of the situation is that Urban Meyer is a rookie NFL coach and will likely be leaning on his staff of NFL Veteran coaches to help dictate what the team does. This does not mean that Darrell Bevel (offensive coordinator) won’t incorporate a number of screens and jet sweeps into the playbook to maximize what Shenault does well. However, there is a possibility that Urban and Bevel decide to use an offense that tries to challenge defenses vertically and the offense in Jacksonville resembles nothing like Urban’s time at OSU or UF. If that were the case our previous correlations would NOT have led us to the correct outcome, therefore they were not equative.
The truth is correlative reasoning is the best we can do when trying to predict the future. There is nothing inherently wrong with drawing conclusions this way. The trouble with correlative reasoning is EGO. When money, notoriety, and bragging rights are on the line it becomes difficult to tell what correlations are strong and which fulfill the expectations of the author. So the next time you see someone making a compelling argument, ask yourself where are the assumptions and if they are strong or weak.
After reading this, I hope that you will look more skeptically at long posts/videos full of stats and be able to see what assumptions are made and understand why the author came to their decision. Often the conclusion is formed first and the correlations are chosen secondarily. These correlations are typically weaker than normal. They are only being used to support one side of an argument, and key pieces of the puzzle are omitted as they contradict the chosen narrative. In an age of information overflow being able to discern what information is good and what is simply noise is key.
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