Another Year


Well, fellow bloggers and readers everywhere, another year of American football has come and gone again.  Every season is like a beautifully crafted story of honor, courage, strength, and …well, let’s face it, cheating.  I can’t think of one nationally-recognized sport that doesn’t involve itself in some form of shystering.  Even Lance Armstrong needs drugs to ride his bike up and down the grassy hills of France.

Here’s the deal.  I just spent 4 hours straight trying to find a correlation between Super Bowl attendance and football game trends. Why?  I don’t know.  Perhaps I’m bitter my team didn’t win the Super Bowl… didn’t make it to the Super Bowl, heck, has never won a Super Bowl ring.   Perhaps I really like making graphs on Microsoft Office’s Excel (ooh, more on graphs and football in another post!).  Either way, I may or may not have some evidence for the NFL and/or other affiliates rigging Super Bowl games.

So read on.   There’s already no way to refute the the truth that football athletes take illegal ‘performance enhancing drugs,’ and there are many people and organizations who believe, but can’t prove, that games in the NFL are thrown.  This is just one more attempt to hack away at their facade of credibility.  I mean seriously, even if the NFL doesn’t orchestrate and plan their games ahead, it’s already such a corrupt organization in so many other ways that it doesn’t really matter.

People often forget that the NFL is an umbrella company, and the teams are franchises, just like we have a corporate McDonald’s corporation, with franchise restaurants that make money under its name and protocols.

The NFL is corporate headquarters Mickey D’s, the Broncos are the McDonald’s restaurant down the block, Peyton Manning is the Big Mac, and every 50 yard completion or hand-off to Moreno or McGahee for a touchdown is the beef.

And hey, please don’t misunderstand, I love McDonald’s.  I also just know it’s a company, and companies only have one goal: make money.  They don’t want to find a cure for cancer, or have empathy for suffering, or spend money on sheltering the homeless… well unless those activities happen to make them money, that is.  And I think that’s just fine, but let’s call a spade a spade and not kid ourselves…. They’re certainly not.

I really don’t care that the games are likely thrown, I will still love football until I die.  The athleticism and mathematics that run it are mind-blowing and will keep me occupied and diverted forever.

So here’s to truth and the eternal search for it:

After the Raven’s beat the 49ers to win the 2013 Super Bowl, I looked up the list of Super Bowl contenders and stats since 1967 on Wikipedia.  I played around with the numbers, trying to find a correlation between ‘exciting’ games and Super Bowl attendance.

Here’s some of the graphs I compiled with the data:

Capture

The Blue line is the attendance at the Super  Bowl over the years, and the Red line represents the total points scored per game.  The correlation on this is 0.027581.  For those of you who aren’t math geeks we’re looking for a .5 correlation at the very least to signal a strong enough correlation to consider using it as a predictive tool for future games.

I also drew up graphs for other correlations I thought might exist, but nothing with a correlation coefficient  higher than .003.   I also found out, just for fun that the average combined score for all Super Bowls is 46.98, with a standard deviation of 13.1545, a minimum total score of 21, and a max of 75.  But plugging those numbers into algorithms to try to find a pattern ended up nowhere.  Then I found this:

Capture1

The blue line is attendance again, but the red line represents how close the game was, i.e. how many points the winning team won by.  It looked visually like a good correlation, but when I did the math it was only a -0.014 (negative and positive coefficients can have the same strength, they’re just moving in opposite directions.  A positive correlation means that the more of x, the more of y.. and negative is the more of x, the less of y.  They’re just different correlations).

But then I thought about it, and it didn’t make sense that the attendance THAT YEAR would reflect or be reflected by the outcome of the game.  The fans wouldn’t know ahead of time how awesome the game would be.. but if there were to be an awesome game, they would sure as hell come next year.  And if there was a crappy game, that wouldn’t be reflected in attendance until the year after as well.  So, after I stopped assuming that fans are mind readers and psychics, I figured in a one year delay in correlation, and this is what I got:

Capture3

This is the same graph, only the attendance by the fans of the Super Bowl game is delayed by one year.

I found this extremely high correlation with a cheap old version of excel and 4 hours.  I’m pretty sure a huge corporation is going to notice pretty quick.. and not only this correlation, but dozens of others I haven’t found.  And what do you think an entity whose only existence is to make money will do once it finds something like this?  Not take advantage of this almost guaranteed way to raise their cash-flow?  No, I’m afraid not.  It’s impossible to think of them not exploiting this, and anything else.

Anyway, let me reiterate how much I love football, because I think despite my varied protestations to the contrary, some of you may take this in the wrong way.  I don’t care that football games are rigged, I just want people to wake up about it.  Don’t be like the pro-wrestling fans that still think it’s real.  🙂

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2 thoughts on “Another Year

  1. Interesting. What was the computed correlation coefficient for the last “extremely high correlation” example? One point upon which to quibble is that corporation, huge and tiny, seek to make money, yes, but that is a by-product of the goals of those corporations’ owners, the desire to live “satisfying” lives, however they each define that. In many of those cases, a more satisfying life is achieved by earning money to be traded for other goods. The production of money per se is not the ultimate goal. 🙂 Thanks for your diligence in cranking out those graphs.

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  2. The correlation coefficient was 0.7132, r^2 was .508, or 51%.
    I agree that the owner’s goals may or may not match the company’s, but in order for a company to be a ‘company,’ all of its actions must contribute in some way to the ultimate goal: make more money.

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