Historically the primary method of judging how well a player or team was performing was the eye test. This term refers to the intuitive feeling that people with long experience in a sport acquired from watching games and practices. Today, professional sports teams are utilizing analytics to provide data-driven judgments.

The analytics revolution in professional sports began in baseball, as highlighted in the book and the movie Moneyball. Baseball is a relatively simple sport to analyze statistically because it centers on a sequence of one-on-one confrontations between a batter and a pitcher. Further, each play has an obvious start point and end point. Statisticians call each of those plays a state.

In contrast the discrete, state-to-state action in baseball, basketball is a constant flow. Players switch instantly from offense to defense. Moreover, regardless of a player's position, he or she can be anywhere on the court at any time. The game has no states, so analysts could not statistically determine the odds of a given outcome (e.g., a player making a shot).

Consequently, modern analytics focuses on the locations and movement of the players and the ball. Essentially, analytics in the National Basketball Association is a mapping and data visualization problem. The challenge is to visually depict data about movement through space and time; that is, to make numbers visible.

The Solution

The First System. Kirk Goldsberry, a longtime basketball fan with a Ph.D. in geography, undertook the task of developing analytics software for professional basketball. First, he divided the 1,284 square feet of the court where players actually shootthe area that stretches from just outside the three-point line (roughly 25 feet) to the basketinto cells. Then he searched for data.

Obtaining the relevant data for accurate analysis was difficult. Tracking 10 players in constant motion is not a simple process. Goldsberry found statistics for every shot taken in the NBA, including who took the shot, from where, and whether it went in the basket. He then developed a database with the spatial coordinates for all 700,000 shots taken in every NBA game from 2006 to 2011.

Goldsberry next analyzed his data to generate maps that showed where a given player shot, how often, and whether or not the shot was good. Goldsberry called his system CourtVision. This system revealed differences in players that had not been previously quantified. For example, Ray Allen, one of the NBA's best shooters, had several areas where he made a high percentage of his shots from three-point range. However, he rarely attempted any mid-range shots.

For the first time, fans could see the types of shots their favorite players took as well as the relative value of those shots. However, CourtVision did not take into account variables such as who the defender was or what else was happening on the court. Nevertheless, Goldsberry's system provided team management with an initial tool to evaluate players.

Today's System. The next opportunity to collect data came when a company called Stats developed a six-camera setup for basketball. The camera system, which is now employed in all 29 NBA arenas, tracks each player on the court throughout every game. It therefore provides a complete view of the entire game, including tracking individual players and ball possession.

Stats offered its data to Goldsberry. Once Goldsberry had the data, he could analyze them to answer any number of questions.

Players who draw the defense can be quantified as ones who pass the ball effectively when two or more players are guarding them. Getting good spacing visualizes which players control which parts of the court. On-ball defense assesses how effectively a player defending the ball decreases his opponent's chance of scoring. In addition, analyzing the camera data enabled Goldsberry to understand one of the most difficult aspects of basketball: defense. Historically, teams had relied on counting statisticsfor example, how many steals, how many blocksto capture a player's defensive value. The new system provided a much more sophisticated picture of the game.

Goldsberry began by observing that the area right around the basket is the most important real estate on the court to defend because this area is where offensive players shoot the highest percentage. Therefore, he analyzed how effectively defenders within five feet of the basket were able to prevent opponents from scoring. He found that the average NBA defender allowed a shooting percentage of 49.7 in that area.

Utilizing his new data, Goldsberry identified two classes of defense. In the first type, defenders blocked or altered their opponents' shots that is, they reduced shooting efficiency. In the 2014 NBA season, for example, Indiana Pacers center Roy Hibbert and Milwaukee Bucks center Larry Sanders led the NBA, holding opponents to 38 percent.

The second approach to defense was more subtle. Goldsberry found that some players reduced the frequency of their opponents' shots, not just their efficiency. By comparing the average rate of shots to the rate when specific defenders were guarding the area, he could calculate when the number of shots decreased. Again in the 2014 NBA season, the best player at this type of defense was Houston Rockets center Dwight Howard, who caused opposing teams to shoot 9 percent less frequently around the basket. As opponents shot less often around the basket, they took more mid-range shots, which are the least productive shots in the NBA.

The Results

Goldsberry essentially divided basketball games into slices of time and then employed the same kinds of analyses that analysts had been applying to the states in baseball. He could then quantify the valuein terms of pointsof every move on the court, from an entry pass into the post (the area close to the basket) to a drive to the basket. These analyses created a new method to evaluate everything a player and team does.

Let's consider one example, the Houston Rockets. Utilizing the results of its analytics software, the team rarely shoots long-range two-point jump shots, because the Rockets believe this type of shot is among the worst strategies in basketball. The shots are too far away from the basket to be a high-probability scoring opportunity, yet not far enough (behind the three-point line) to be awarded an extra point for the risk in taking an even longer shot.

Analytics are impacting not only the Rockets, but the entire NBA as well. For instance, in January 2015 NBA data analysis revealed that players attempted more three-point shot attempts than free throws. In fact, three three-point shot defines the shift to analytics in the NBA. It is not a coincidence that the Golden State Warriors, the 2015 NBA champions, were the league's best three-point shooting team during the regular season.

1.Provide an example of the use of descriptive analytics for an NBA team using Goldberry's system. 2.Provide an example of the use of predictive analytics for an NBA team using Goldberry's system. 3.Provide an example of the use of prescriptive analytics for an NBA team using Goldberry's system. 4.What are the advantages and disadvantages of Goldsberry's system to NBA players? Provide specific examples to support your answer