The voice blared through the car speakers and while the words were clear, the thoughts were jumbled— an incoherent mess.
The Browns had just hired Paul DePodesta— the “Moneyball” guy— to be the team’s Chief Strategy Officer, in an unprecedented move that seemingly sent the Browns into a full-blown, analytical approach to the game of football.
Of course, DePodesta worked in baseball for over 20 years, using data, statistics and mathematics to shape the most efficient rosters possible and was quite successful in said practice, becoming the first man to take five different teams to the playoffs as an executive.
The voice continued blaring through the speakers, trying to explain why DePodesta’s analytical approach could work in baseball, but not on the football field.
“In baseball,” the voice rambled, “you have the Minor Leagues, so you can develop a player through analytics. In football, you don’t have that.”
It went on.
“Baseball players can play multiple positions, where as in football, guys can only stick to one spot,” the voice said. “There are five pitchers that you can shuffle around and position players you can see in the lineup where they’ll fit best. In football, a guy plays one certain spot and that’s it. There’s no numbers to be shuffled, or analytics, or anything like that.”
Though this voice was the example used in this circumstance, there were many more like it, as the car sped along the highway.
It was immediately obvious that Browns fans— not all, but generally— didn’t really understand the concept of analytics and how they could be applied to football.
Said talk-show caller and others, I will try to explain analytics, in baseball and in football, as best as this Ohio-State educated brain can.
Analytics is defined, simply, as the systematic analysis of data and statistics.
In other words, statistics and numbers are used to measure the value or production of something, as opposed to the seeing eye.
The human eye— attached to the brain— has an inherent bias that can produce problems in objective judgement.
Analytics are free of a clouded, biased brain, proving to be an objective source of true judgement and value.
They, generally, have nothing to do with anything said caller outlined above.
Rather, the outcomes of analytical research could result in lineup shuffling or position switching, as players can prove— through the numbers— that they’re more valuable or more productive in a certain spot.
Analytics are not, however, the practice of developing players or the switching of positions.
Those are simply byproducts of the results of statistical analysis, not the practice of analytics.
In baseball, admittedly, statistics and analytics caught on more quickly because of the large sample size from which to work.
Every baseball game— of which there are 162 as opposed to 16— features hundreds of pitches and, therefore, tens of swings and at least 27 outcomes of every play.
In football, there are hundreds of plays in a given game, but again, there are only 16 games— 146 less than at Major League Baseball stadiums around the country.
A larger sample size means that data is less likely to be skewed and is more likely to be an accurate representation of the value or production of said individual.
DePodesta was one of the first— outside of long-time baseball sabemetrician, Bill James— to put into practice the concept of analytics, of reviewing a players performance through his statistics, rather than through in-person viewing.
Subsequently, the Cleveland Indians, the organization for which DePodesta got his start in 1996, was one of the first teams to put into practice the concept of analytics and became the first team to create their own database— “DiamondView”— to accurately measure the value of players through sabermetrics.
DePodesta, along with James and the Indians, saw that some of the statistics already used to measure the value of a baseball player— batting average, wins, earned run average— were not accurate in doing so.
Instead, he looked at the numbers extensively and found that the value of a baseball player, at the plate, should be measured by the amount of times he gets on base, the amount of runs he produces— not necessarily drives in— and the amount of pitches he forces the pitcher to throw, amongst others.
On the other side,hthe determined that a pitcher’s value shouldn’t be based on the amount of runs a pitcher allows.
Rather, a pitcher’s value should be measured on the amount of walks he permits, the amount of balls in play he allows and the number of times that batters swing and miss at pitches he throws, again, amongst many others.
This was all determined using extensive numbers, formulas and analysis and, in doing so, changed the game of baseball forever.
That’s what analytics in baseball, generally, is— using numbers and data from the outcomes of every pitch— to determine a player’s value, as opposed to determining that value using the naked eye.
Football, however, is a bit of a different story.
Where as baseball is a numbers-driven game to begin with, football is more of a strategic affair, which is why it’s taken until now for a team to take a full-on approach to the analytical aspect of the sport.
As of now, a football player’s value— statistically— is based almost entirely on the number of touchdowns he scores, yardage he makes or tackles he musters.
For instance, because Drew Brees threw for the most yards this season and because Tom Brady threw for the most touchdowns, many individuals would say that Brady and Brees were the two best quarterbacks this season.
While that may be true, other websites that rely less on those numbers and more on accurate throws, sound football decisions and and ball security, rank Ben Roethlisberger and Carson Palmer as the two best quarterbacks in the game.
As for wide receivers, DeAndre Hopkins is ranked third in the league based upon his number of receptions and yards, but Pro Football Focus— which takes into account other aspects, such as efficient route running and number of drops— ranks Hopkins as the eighth best receiver in the NFL.
In other words, analytics in football— just as in baseball— will take into account things that are not included in the traditional statistics seen over and over again.
It’s this approach— the deeper approach, the more intelligence-necessary approach— that the Browns are looking to harness as they go forward.
It’s about measuring value through numbers, rather than through the naked, imperfect human eye.
It’s also not about shuffling lineups or time for development. Sure, those are things that occur, but they have essentially nothing to do with analytics.
Analytics are tangible evidence of the value or success of a certain individual or practice, rather than an intangible, arbitrary thought or perception.
They are not, for the most part, what talk-show callers have seemed to have deemed that they are.
For all of your Indians news and updates from Cleveland, follow Hayden Grove on Twitter: @H_Grove.