Stat Keeping and the Science of Sports Analytics

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Stephen Curry about to pass while being guarded by John Wall and Nenê of the Washington Wizards. Keith Allison/FLICKR

Something for the sport’s fan in all of us! Recently, I was scouring ESPN (When I really should have doing lab work) and I stumbled upon “The Great Analytics Rankings.”  In the article, they listed all the professional sports teams in America by their belief in a system and field of study called sports analytics. Notably, since this fields has been introduced, most teams who are at the top of the list in their respective sports are enjoying a fair amount of success in comparison to others. (Concerning coming from a Lakers fan; those banners are prestigious, but the team has stunk it up the past few years).

This begs the question: What is sports analytics?

Let’s break down the term “analytics.” Analytics is the discovery and communication of meaningful patterns in data derived from statistics, computer programming, and operations research. This is basically the ability to predict the possibility of a certain event happening based on prior patterns and data. Now you ask, how could this type of science be applied to sports?

Well, The Oakland A’s spearheaded sports analytics by using stat lines on certain players to create a team that would give the best possible competition on a daily basis (and high win total). The first season they used this type of data analysis they almost went to the World Series (with a bunch of supposed “nobodies,” nonetheless)!

Ever since then, sports analytics has been taking the sporting world by storm. There is even the MIT Sloan Sports Analytics Conference, where all of the sports analytics nerds can go to refine how to better “scout” for players.

Take the NBA (the best sport in the world), for example. The biggest measure of a basketball player’s success out on the court is “real plus-minus” or RPM. This is the overall impact of a player on the court in comparison to his teammates, opponents, and other factors (injuries, time on floor, etc.) on net point differential per 100 possessions. Hometown favorites Joakim Noah, Jimmy Butler, and Derrick Rose all have  +.87  ,+4.66, +.7 RPM on the court during this season. This means that they are .87, 4.66, and .7 points better than their opponents on the court, a respectful total. (And, it helps they led a pretty strong team this year). 

In contrast, the MVP Stephen Curry had a RPM of +9.12 on the court, or his team is nine points better when he is on the court. Considering he is light years ahead of everyone else, this would be the season to have Stephen Curry on your team.

Another stat line used would be “wins above replacement” or WAR, which is defined as team wins based off of RPM. So in this case, Stephen Curry’s WAR is 19.69, or he contributes about 20 more wins to a teams total compared to Jimmy’s WAR, which is 10.87, or about 11 wins.

Stats like these are why some owners hire analysts to construct teams in order to be competitive and win championships. There is a general push back that this “science of stats” can’t replace intangible championship pedigree. But one cannot argue when you have teams in Houston and Oakland excelling in their sport the past few years, and past championship teams, such as Los Angeles and Boston, as current bottom dwellers.

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