By The Numbers: Analytics and Sports

By The Numbers: Analytics and Sports

By:
03/28/2014

Today, it is impossible to read an article about sports and avoid having a bunch of numbers thrown in your face. In the last ten years, the way that sports is analyzed and reported has completely transformed. In a business that used to be centered on great stories, it is interesting that data analysis has made such an emergence. The emergence of websites like Grantland and Fivethirtyeight, show that there is a demand for this type of data analysis. The modern fan cares about the what the QBR rating for a quarterback is in every quarter of every game of every season and what Lebron’s shot chart is when he is playing on the second half of a back-to-back.

While this information has existed in the past, it has not been as front and center as it is now. The emergence of data analysis has not only changed the way that fans view the game and how analysts work, but also the way games are played. More and more teams are hiring analysts and statisticians to help them to strategize and build the strongest teams possible. In the sports world, where getting a draft pick right or getting the right free agent is the difference in millions of dollars of revenue, people are looking for any advantage that they can get, and the newest manifestation of that is sports data analysis.

Data analytics has affected some sports more than others as it is easier to glean meaning from stats in certain sports. The classic example is that baseball is a sport where data analytics has been very helpful because, while it is a team sport, it is largely an individual game. A large number of teams, in addition to taking into account the opinion of scouts, take into account Sabermetrics–a term that has been coined for the use of data analysis specifically in baseball.

This shift has often been referred to by those outside the industry  as the “Moneyball effect.” In 2003 Michael Lewis published the book, “Moneyball: The Art of Winning an Unfair Game.” The book is about the 2002 Oakland A’s, who made the playoffs and won more than 20 games in a row with the third lowest payroll in the league by using Sabermetrics. Billy Beane, the General Manager of the A’s at the time decided to throw out old, imperfect standards of evaluating a player such as scouting reports and instead focused purely on production. The two main numbers he looked at were on-base percentage and slugging percentage. The idea was the the more that people got on base, regardless of whether if it was through a hit or walk didn’t matter. The mathematical chances of scoring run go up if someone is on base. By just focusing on these few factors, Beane was able to get undervalued talent for very low prices.

Beane’s top assistant at the time, Paul DePodesta has been quoted as saying “[Baseball executives are] constantly trying to predict the future performance of human beings. We’re trying to get out our arms around that uncertainty…we looked at it and said, ‘How can we further decrease that uncertainty?’ And being able to use data was one of the ways we could do that.” The strategy changed baseball and a whole slew of teams changed their strategy when it came to scouting and recruiting talent as a result of “Moneyball.”

The Moneyball effect in baseball has caused people to take this kind of data analysis and apply it to other sports, such as basketball and football. The difference between baseball and other sports is that there are more individual statistics that exist. Hitting, pitching and fielding all have individual statistics associated with them and multiple different ways to measure success. While looking at ERA and BB/SO ratio doesn’t necessarily tell the entire picture of whether a pitcher is winning or losing, it does a decent job. A lower ERA and a higher strikeout to walk ratio will usually mean more wins. On the other hand, looking at a point guards assists per game or even at a scorer’s points per game doesn’t necessarily tell you anything about whether his team is successful. It is almost impossible to blame the success or failure of an entire basketball team on individual players. Statisticians sometimes forget that a big part of whether a player is able to succeed in basketball is based on the environment in which they are placed. Some players thrive in situations where they are the core focus of the offense because they always need to the ball in their hands, while others are better playing as complementary pieces.

Dallas Mavericks owner Mark Cuban said about data analysis in basketball, “It’s not like Moneyball in baseball, where analytics are a good way to determine who to sign or who not to sign, unless where they were is analogous to where you’re trying to bring them. [A basketball player] might have X number of win shares on a team that likes to push the ball, and a team that slows it down is a different beast. A guy might be a great rebounder if a team keeps him close the basket, but if we show on pick-and-rolls or play zone, those numbers are going to be very different.”

Despite the factors that Cuban points out, there is a huge of influx of basketball analytics, such as player efficiency rating and true shooting percentage, into the game of basketball. According to ex-players, general manager and executives, there is a growing divide between statisticians and ex-players, who feel as though they have a great understanding of the game. The problem is that many teams are hiring data analysts rather than ex-basketball players.

It is possible that the same statistics that help baseball players know when they should swing or lay off a pitch will help basketball players decide where to shoot from or defenses predict what plays an offense will run in certain situations. While statistics aren’t exact, they are able to provide probabilities. A defensive coordinator may not call the right formation on every third down, but if he knows that the opposing team has, in the past, tended to throw the ball on 3rd and five, then he is able make a call that has a higher probability of succeeding.

The pursuit for perfection is one of the main goals in sports, so it is only fitting that every data analyst strives for the same perfection. The ability to perfectly predict a team’s record or the ability to perfectly predict a March Madness bracket is the goal. Yet the reason that analysts are driven to find perfection and the reason that fans are drawn to sport is exactly because that goal is impossible to reach. The unpredictability, the luck and the chance is what makes sports the ultimate form of entertainment.

Photo: Timothy Vollmer/Flickr

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Arjun Gupta


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