In fact, many of today's 30 National Basketball Association teams have analytical expertise either on staff or on call. Basketball research site NBAstuffer.com is keeping tabs: It lists 21 teams as having analytics departments. Some of these are one-man shops, but others are counting on numbers work from multiple basketball analytics professionals.
These quant jocks use a bunch of different metrics to assess player performance and evaluate team play. NBAstuffer.com gives a rundown of the metrics in use, including:
Adjusted plus-minus ratings, which indicate how many additional points are contributed to a teamís scoring margin by a given player in comparison to the league-average player whose adjusted +/- value is zero over the span of a typical game.
- Diamond rating, determined from "a simple formula that works with any per-minute statistics." The formula...
subtracts the player's rating per game from his rating per 40 minutes to figure out how much his per game stats undervalue his potential contributions. He then subtracts league average from the player's per-40 minute rating and adds this amount to ensure the player is actually playing well in the minutes he does get.
- Player-efficiency rating, a metric that "boils down all of a player's contributions into one number." Using a statistical point value system, the formula "adds positive stats and subtracts negative ones."
Beyond these sorts of basics lies the potential for game-changing insight. Using topological data analysis and visualization, for example, one combo basketball-and-analytics aficionado crunched last season's stats for 452 NBA players and came up with a whole bunch of new ways to group players based on performance, as described in a Wired report. Specifically, he came up with a baker's dozen of new positions:
The 13 positions are based on how players compare to the league average in seven statistical categories: Points, rebounds, assists, steals, blocked shots, turnovers and fouls. The stats were normalized on a per-minute basis to adjust for playing time, so starters got the same consideration as backups.
The positions stem from the typical roles of guard, forward, and center but are more refined, Wired noted. The offensive ball-handler, for example, "handles the ball and specializes in points, free throws and shots attempted, but is below average in steals and blocks." This compares to the defensive ball-handler, naturally a "defense-minded player who handles the ball and specializes in assists and steals, but is only so-so when it comes to points, free throws and shots." And that's not to overlook the combo ball-handlers, who are "adept at both offense and defense but donít stand out in either category."
And then there's SportVU, a stats tracking system that ESPN Playbook said has the "potential to change everything we know about analyzing NBA basketball."
The system includes cameras in stadium rafters that log typical player stats like assists, possession time, points, and missed shots. Then the fun stuff begins:
These cameras are synched with complex algorithms extracting x, y and z positioning data for all objects on the court, capturing 25 pictures per second.
Each picture is time-stamped and automatically processed by a computer, which connects the data to the play-by-play feed and delivers a report within 90 seconds of a play...
Almost instantly, coaches and stat guys have this information at their disposal on their computer or iPad.
SportVU dumps so much data on basketball teams the analysts don't quite know what to do with it all, ESPN Playbook reported. And while this is about showing who's fastest, the best shooter at what range, and other feats of athleticism, ultimately teams could use it to help guide fitness for overall player health (not to mention optimization of the NBA franchise in general).
As much as some might wish it away, advanced analytics has a place in basketball today. It won't be infallible, as AllAnalytics.com blogger Matthew Brodsky points out in his Counterpoint post. But it'll be there, guiding the decision making on the court.
Of course, advanced analytics will never take away the thrill of those extraordinary players like Bird, Jordan, and James. They are and always will be what makes the game what it is -- which brings me back to that advanced data visualization exercise and the resulting 13 new positions. The 13th position is for one-of-a-kind guys who are "so good they are off the charts -- literally. The software could not connect them to any other player."
Derrick Rose is one such player. So perhaps that explains why my hometown team, the Chicago Bulls, doesn't yet have an analytics professional listed on its front-office roster. Fighting the numbers when an oversized statue of basketball's greatest ever graces your stadium entryway can't be too hard to do.
Using advanced analytics in the sporting world is one of those things you love or hate. What's your opinion?