The Use of Analytics in Professional Basketball (Paper 3) Watermark

The Use of Analytics in Professional Basketball 1

The Use of Analytics in Professional Basketball

James Crook

Brigham Young University-Idaho


Abstract

Research has recently proven the benefits of analytic statistics used by teams in the

National Basketball Association (NBA) to appraise their organizational strategies. Basketball

analytics is the use of data to understand how the game of basketball works. Analytics help NBA

teams by putting statistics such as points per game (ppg), assists per game (apg), rebounds per

game (rpg), steals per game (spg), and blocks per game (bpg) into perspective. Access to this

data allows teams to audit their offensive, defensive, and player acquisition strategies. The

evidence available proves that the use of analytics has positively improved the way that

decisions are made in the NBA.


The general manager of the Houston Rockets, Daryl Morey, is quoted as saying,

“Someone created the box score, and he should be shot” (as cited in Shea, 2014, p. 1). Morey’s

incendiary statement (although admittedly said jokingly) reflects an underlying movement

taking place in the National Basketball Association (NBA): A desire to move away from

traditional notions of how player performance should be graded. Over the last decade or so, a

new factor has entered the world of professional basketball: analytics. “Basketball analytics is

an umbrella term for the use of any form of quantitative information to gain insight into the

game of basketball. At the core of analytics is statistical analysis” (Shea, 2014, pp. 1-2). The use

of analytics has given NBA teams previously unknown and unavailable statistics to evaluate.

Teams now know how far their star athlete runs over the course of a season, how high a

player’s field goal percentage is when his defender is five feet away, and a player’s overall point

production when he is standing on a specific place on the court. Of course, with change comes

criticism, and some of the most outspoken critics of analytics have been coaches within the

NBA. Despite this criticism, research proves that NBA teams are able to improve the quality of

their offensive, defensive, and player acquisition strategies through the use of analytics.

As mentioned in the introduction, analytics are used to improve a team’s offensive

strategy. When the NBA was first formed, the only offensive statistics kept were points. While it

is good to know how many points a player has, without context total points do not help

evaluate the player who scored them. For example, a player who scores twenty points on ten

shots is obviously much more efficient than a player who scores twenty points on twenty-five

shots. Unfortunately, without further statistics a team cannot know how efficient the player

with twenty points actually is. In today’s NBA, statistics can now give context to raw data. For


example, analytics show the optimal time for a team to take a shot during a possession.

According to Brian Skinner (2012), the average points an NBA team scores per possession is .86

(p. 6). By using advanced mathematics, Skinner (2012) found that if a team were to take a shot

at the optimal time in the shot clock, its point probability would rise to .91 per possession (p. 6).

(Graph “b” helps illustrate Skinner’s findings. As the shot clock dwindles, so does the potential

points per possession.) Skinner (2012) said, “According to

the established “Pythagorean” model of a team’s winning

percentage in the NBA, such an improvement could be

expected to produce more than 10 additional wins for a

team during an 82-game season” (p. 6). To put 10 wins in

perspective, last season in the NBA, the Pheonix Suns took

9th place in the Western conference and just missed the

playoffs. Had the Suns won 10 more games, they would

have taken 3rd place and made the playoffs.

In addition to understanding when a shot should be taken, NBA teams now understand

where they should be shooting from. Mid-range jump shooting is perhaps the most often talked

about issue in NBA analytics because of its overall inefficiency. The shot’s inefficiency is

determined by using the “effective field goal percentage.” Effective field goal percentage is

found by adding the number of field goals made to the number of three-pointers made

multiplied by .5 all divided by the amount of field goal attempts ((FG + .5 * 3P) / FGA).

According to effective field goal percentage, three-point shots from the corner were the most

valuable shot during the 2013-2014 NBA season (not including shots from a four-foot radius


around the rim), while mid-range shots were the least valuable. By using effective field goal

percentage to analyze efficiency, several NBA teams have found success by shooting more

corner three-pointers. Tom Haberstroh (2013) of

ESPN reported, “The strongest correlation with

offensive efficiency over the past 17 seasons is

the corner 3-pointer.” For example, the two

teams that matched up in the 2013 NBA

championship series, the Miami Heat and the

San Antonio Spurs, took the first and third most corner threes respectively out of anyone in the

NBA during the first fourth of the 2012-2013 season. In fact, using statistics collected from

basketball-reference.com, Grant Hughes (2014) of bleacherreport.com found that the Spurs

have consistently taken the most corner threes out of any team in each season since 2001 to

the present. Over that span, the Spur’s average rank among NBA teams in corner three ointers

taken is 1.6. The graph to the right compares San

Antonio’s win percentage in relation to their corner

three point attempts taken. Since 2001, the Spurs

have on average taken third place in the overall NBA

standings and have won five NBA championships. Greg

Popovich, the head coach of the San Antonio Spurs,

uses analytics to make strategic adjustments. Shooting

guard for the Spurs, Danny Green, commented about Popovich’s use of corner threes and

analytics: “Pop's a pretty smart guy. Even though he hates it, he knows it's a thing you need to


be successful in this league. He looks at statistics and he knows what successful and not

successful teams do well” (Flannery, 2014). The San Antonio Spurs’ success over the last

fourteen years proves the validity of using analytics to adjust offensive strategy.

In addition to teams using analytics to make changes to their offensive strategy, teams

now use analytics on the defensive side of the ball as well. Defense is just as important to

winning as offense. According to Bartholomew and Collier (2012), “The goal of defense in

basketball is to frustrate and antagonize the offense in a way that encourages a change of

possession and lowers field goal percentage and total points scorded” (p. 21). Analytics provide

a number of ways to accomplish the goals set forth by Bartholomew and Collier. As mentioned

earlier, effective field goal percentage is a very important factor to measuring success. During

the 2013-2014 NBA season, the Miami Heat and the San Antonio Spurs (the two teams that

matched up in the NBA Finals) had the highest and second highest effective field goal

percentage respectively in the NBA. Because of analytics, NBA teams now have the knowledge

needed to lower their opponents’ effective field goal percentage. In order to lower effective

field goal percentage, teams should target the two areas on the court (see graph 2) where

effective field goal percentage is the highest: the corner three and the restriced area (radius of

four feet around the hoop).

Stopping the corner three can only be consistently done by teams that understand

analytics. The picture to the right illustrates what has to occur during a possession to set up a

three-point attempt from the corner. Lamarcus Aldridge

(12) has been given the ball off of a “pick and roll” play

which has resulted in Aldridge being double-teamed. The


logical next step for Aldridge is to pass the ball to the open player, Wesley Mathews (2),

immediately to his right. When Mathews gets the ball, if the Rocket’s player underneath the

rim, Chandler Parsons (25), does not understand the analytic value of the corner three point

shot, he will run out to Mathews to stop him from taking the wide open three. If Mathews

becomes covered, his logical decision will be to pass the ball to Nicolas Batum (88) in the

corner. When Batum gets the ball he will have a wide

open corner three-point attempt with no one to guard

him. Assuming that Mathews and Batum are equally efficient three-point shooters, Parsons has

actually increased the chances of the Trail Blazers scoring on that possesion by electing to guard

Mathews.

However, Parsons does know about analytics. In fact, the Houston Rockets are heavily

involved in the basketball analytics movement as explained by Beckley Mason (2013) of

ESPN.com: “They play a fast-paced … style, but it's not some happy accident. These aren't kids

on a playground. This is big business. This is the numbers telling you unexpected things your

eyes might miss. This is analytics in action.” Revisiting the example, because of Parsons’s

knowledge of analytics, he pretends to go out to guard Mathews but instead runs to Batum in

the corner. Besides stopping Batum from getting the ball, Mathews becomes confused and

stands still long enough to allow a defender to get to him and eliminate the chance for

Mathews to find an open teammate. The Parsons scenario is an excellent example of an NBA

team using analytics on defense. By using analytical methods during the 2013-2014 season, the

Houston Rockets kept their opponents to an effective field goal percentage of 48.9 (eighth in

the league) and achieved an overall win percentage of 65.9 (tied for fifth in the league).


While stopping the corner three-point shot is important to defenses in the NBA, the

most important task lies with stopping teams from scoring in the restricted area. Goldsberry

and Weiss (2013) explain:

Over 70% of shots near the rim either result in points, a shooting foul or an offensive

rebound. Good shots near the rim are clearly advantageous. For this reason, the league

shoots over 1/3rd of its shots from the tiny portion of the court close to the basket. (p.

2)

Fortunately for NBA defenses, analytics provide solutions to reducing the efficiency of shots in

the restricted area. Blocks per game is one way for a team to analytically predict how well they

disrupt their opponents’ field goal percentages. Graph 4 to the right shows the downward

positive correlation between opponents’ field goal percentage at the rim and blocks per game.

By possessing this data, NBA teams have the ability

to change their strategies to go after more blocks.

Despite the fact that Stephen Shea (2014) initially

pointed out the relationship between field goal

percentage and blocks per game, he argues that

“blocked shots is at best a reflection (as opposed to a

direct measure) of a player’s ability to defend the rim” (p. 149). While it is true that blocks may

only be a reflection of other factors, as evidenced by this graph it still remains that blocks can

be used to reliably predict field goal percentage.

Teams that desire to decrease their opponent’s field goal percentage at the rim need to

acquire players who can block shots, which leads to the discussion of using analytics in player

Graph 4: (Shea, 2014 p. 151): 2013-2014 Team Opponent Field Goal Percentage at the Rim to Blocks per Game.


acquisition. In order to use analytic strategy on the court, teams have to acquire the right

personnel at the right price to execute the coach’s plan. In the NBA, a salary cap exists to keep

teams with more money from dominating the teams with less money. The salary cap makes it

difficult for teams to get every player they want, which makes it crucial for teams to find players

that can achieve the team’s needs at prices equal to their worth. Lionel Hollins, the former head

coach of the Memphis Grizzlies, disparaged the idea of using analytics to make personnel

decisions by using a baseball team, the Oakland Athletics, as an example, “I'm still trying to

figure out when the Oakland Athletics won a championship with all the analytics they have. It

takes talent” (van Horn, 2013). Although Hollins is correct that the Oakland Athletics have not

won a championship, it should be noted that the Athletics have one of the smallest payrolls in

professional baseball where a salary cap does not exist, and yet have managed to make the

playoffs in 6 of their last 13 seasons by religiously using analytical methods. In comparison, the

Boston Red Sox, with one of the highest payrolls in professional baseball, have gone to the

playoffs seven times over the same time span. In the NBA, teams with bigger markets (such as

Los Angeles, Miami, New York, Chicago, etc.) attract the most skilled free agents known as

“super stars.” Teams in smaller markets have to sign less-talented players and use the NBA

draft to acquire talent. However, because of analytics, smaller market NBA teams are now able

to build their teams more effectively than in the past.

The Memphis Grizzlies epitomize a small market team using analytics to make personnel

decisions. (As mentioned earlier, head coach Hollins was not a fan of the Grizzlies emphasis on

analytics. This fact eventually contributed to his dismissal from the team after the 2012-2013

season.) During the 2012-2013 season, the Grizzlies traded away their highest paid player, Rudy


Gay, in order to improve their offensive efficiency and to save cap space. No one in the league

(except statisticians) expected the Memphis Grizzlies to improve after trading their highest paid

player and leading scorer, but improve they

did. Rudy Gay was traded on January 31st of

2013 to the Toronto Raptors. Before Gay was

traded, the Memphis Grizzlies had a win

percentage of 64.4 and effective field goal

percentage of 46.3, and after he was traded the

Grizzlies had a win percentage of 70.3 and an

effective field goal percentage of 48. How can a team improve after trading their highest paid

player and leading scorer? The answer lies in using analytic statistics to study efficiency. While

on the Grizzlies, Rudy Gay had an effective field goal percentage of just 43.1 percent during the

2012-2013 season. To put Gay’s effective field goal percentage into context, the league average

that season was 49.6. In addition to Rudy Gay’s poor effective field goal percentage, he had a

total offensive win share of -.2. An offensive win share uses a player’s entire offensive

performance and produces an approximate number of wins added by that player to his team.

So according to analytic statistics, Rudy Gay was actually an impediment to the Grizzlies’

success. The fact that analytics can be used to evaluate a player’s contributions to his team

supports the claim that analytics can be used to effectively evaluate personnel decisions in the

NBA.

Analytics have revolutionized the way NBA teams are able to evaluate themselves.

Because of analytics, NBA teams can now structure their teams using analytical models to

Graph 5: Memphis Grizzlies effective field goal percentage with and without Rudy Gay during 2012-2013 season. From data by basketball-reference.com and NBA.com.


optimize their overall efficiency. The evidence that proves analytics can be used to accurately

evaluate offense, defense, and player acquisition strategies is overwhelming and should not be

ignored. Teams that are struggling in the NBA should seriously consider restructuring

themselves from top to bottom using analytics.

References


Bartholomew, J., & Collier, D. (2012). The benefits of forcing offensive basketball players to

their weak side. Journal of Multidisciplinary Research, 4, 21.

Basketball-reference.com (2014). Memphis Grizzlies and San Antonio Spurs statistics [Raw

Data]. Retrieved from www.basketball-reference.com/leagues/NBA_2015_totals.html

ESPN (2014). San Antonio Win Percentages [Raw Data]. Retrieved from

http://espn.go.com/nba/standings

Flannery, P. (2014, June 8). Gregg Popovich hates three-pointers and other notes from a too

long NBA Finals break. Retrieved from

http://www.sbnation.com/nba/2014/6/8/5790178/gregg-popovich-threes-nba-finals-

notebook-heat-vs-spurs

Goldsberry, K., & Weiss, E. (2013). The Dwight effect: A new ensemble of interior defense

analytics for the NBA. MIT Sloan Sports Analytics Conference.

Hughes, G. (2014, July 30). Who’s responsible for the NBA’s corner three revolution? Retrieved

from http://bleacherreport.com/articles/2146753-whos-responsible-for-the-nbas-

corner-three-revolution

Lowe, Z. (2013, December 17). Life beyond the arc. Retrieved from

http://grantland.com/features/the-reliance-3-pointer-whether-not-hurting-nba/

Mason, B. (2013, April 5). Moreyball: The rockets are ready for liftoff. Retrieved from

http://espn.go.com/nba/story/_/id/9024190/moreyball-how-houston-rockets-became-

nba-most-exciting-team

National Basketball Association (2014). Rudy Gay and Memphis Grizzlies statistics [Raw Data].

Retrieved from stats.nba.com


Shea, S. (2014). Basketball analytics: Spatial tracking. Seatle, WA: CreateSpace Independent

Publishing Platform.

Shea, S. (2014, August 22). 2013-14 League Average Shooting Percentages by Region [Graphs].

Retrieved from http://www.basketballanalyticsbook.com/2014/08/22/the-decline-of-

mid-range-jumpers-and-related-topics/

Skinner, B. (2012). Problem of shot selection in basketball. PLOS One, 7, 6.

doi:10.1371/journal.pone.0030776

van Horn, S. (2013, January 11). Lionel Hollins criticizes analytics in radio interview. Retrieved

from http://www.sbnation.com/nba/2013/1/11/3866592/lionel-hollins-memphis-

grizzlies-interview-2012

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