VERY interesting read IMO: http://www.sloansportsconference.com/wp-content/uploads/2012/02/Goldsberry_Sloan_Submission.pdf
Bits from the article:
3 Case Study: Who is the best shooter in the NBA?
As a means to evaluate the feasibility of spatial and visual analytics for the NBA, we conducted a case study
that attempted to answer one simple question: who is the best shooter in the NBA? Conventional metrics and evaluative approaches fail to provide a simple answer to this question.
We derived metrics that described spatial aspects of shooting performance throughout the scoring area.
The most basic metric is called “Spread,” which is simply a count of the unique shooting cells in which a player has attempted at least one field goal. The raw result is a number between 0 and 1,284 and summarizes the spatial diversity of a player’s shooting attempts. By dividing this count by 1,284 and multiplying by 100, we generated Spread%, which indicates the percentage of the scoring area in which a player has attempted at least one field goal.
[SPREAD FORMULA --> go to the article (link), couldn't copy it right.]
Spread = Total spatial spread of player across all scoring cells
FGAij = 1, if at least one field goal has been attempted in cell i, 0 if not
SA = Scoring area consisting of 1,284 scoring cells
Spread describes the overall size of a player’s shooting territory. League leaders in FG% generally have a
small Spread value since they tend to only shoot near the basket. For example, since centers generally thrive in limited areas near the hoop they tend to have lower Spread values than shooting guards. Kobe Bryant has the highest spread value in the NBA (table 1); Bryant’s value of 1,071 indicates he has attempted field goals in 1,071 of the 1,284 shooting cells or 83.4% of the scoring area. In contrast, Dwight Howard has attempted field goals in only 23.8% of the shooting cells. Although Spread% favors players who simply shoot frequently, it also reveals that some players like Dwight Howard who do shoot a lot, only do so in limited court spaces. For example, Al Jefferson attempted 400 more field goals than Ray Allen during the study period, yet his Spread value is only 595 (46.3%).
Shooting skill requires more than just attempts; the best shooters in the league are able to make baskets at effective rates from many court locations. To describe the spatial potency of players we created a metric called “Range,”which is a count of the number of unique shooting cells in which a player averages at least 1 point per attempt (PPA). PPA varies considerably around the court. As anyone who has ever shot a basketball knows, the probability of a shot attempt resulting in a made basket is spatially dependent; some shots are easier than others, and some players are unable to shot effectively from most court locations. Range accounts for spatial influences on shooting effectiveness. It is essentially a count of the number of shooting cells in which a player averages more than 1 PPA; we chose PPA over FG% because it inherently accounts for the differences between 2-point and 3-point field goal attempts, while Ray Allen’s is 952 (74.1%).
[RANGE FORMULA --> go to the article (link), couldn't copy it right.]
Range = Effective shooting range of player across all scoring cells
PPAij = 1, if points per attempt is > 1 in cell i, 0 if not
SA = Scoring area consisting of 1,284 scoring cells
By dividing this count by 1,284 and multiplying by 100, we generated Range%, which indicates the percentage of the scoring area in which a player averages more than 1 PPA. Steve Nash is ranked first. He has a Range value of 406, indicating that he averages over 1 PPA from 406 unique shooting cells, or 31.6% of the scoring area. Ray Allen was ranked second (30.1%), Kobe Bryant (29.8%) was third, and Dirk Nowitzki (29.0%) was fourth (table 2). Figure 3 visualizes the shooting range of these four players.
Steve Nash has the highest Range% in our case study, but does this mean he is the best shooter in the NBA? That obviously remains debatable; however it is certain that over the last few NBA seasons, Nash and Ray Allen are the most effective shooters from the most diverse court locations. The average shooter in the NBA has a Range% of 18.5, meaning they score efficiently from 18.5% of the scoring area. Nash and Allen are the only two players in the league whose Range% values exceed 30%; only a handful of players in the league average more than 1 PPA from at least 25% of the scoring zone (table 2), and unsurprisingly, despite being among the leaders in FG%, Dwight Howard (Range% = 6.5) and Nene Hilario (Range% = 3.7) are not on that list. Whether the Range% metric is the best way of quantifying shooting range or not, it seems to capture pure shooting ability better than FG% or eFG%.
Player Spread %
1. Kobe Bryant 1,071 83.4%
2. Lebron James 1,047 81.5%
3. Vince Carter 1,005 78.3%
4. Joe Johnson 992 77.3%
5. Rudy Gay 983 76.6%
6. Antawn Jamison 965 75.2%
7. Andre Igudola 962 74.9%
8. Ray Allen 952 74.1%
8. Kevin Durant 949 73.9%
10. Danny Granger 948 73.8%
Table 1: Top 10 players in Spread metric
Player Range %
1. Steve Nash 406 31.6%
2. Ray Allen 386 30.1%
3. Kobe Bryant 383 29.8%
4. Dirk Nowitzki 373 29.0%
5. Rashard Lewis 354 27.6%
6. Joe Johnson 352 27.4%
7. Vince Carter 343 26.7%
8. Paul Pierce 332 25.9%
8. Rudy Gay 332 25.9%
10. Danny Granger 331 25.8%
Table 2: Top 10 players in Range metric
^^ OPEN IN NEW TAB TO VIEW THE WHOLE THING
We argue that conventional analytics are unable to adequately reveal key spatial performance variables that
influence competitive outcomes in the NBA. In this paper we have evaluated the potential of spatial analysis and visual analytics as important new devices for NBA analysis. Via a case study that both quantified and visualized spatial aspects of shooting performance at high-resolutions, we have shown that new techniques can offer important new insights about basketball performance. We introduced new metrics that quantify the shooting range of NBA players in novel fashion; the results suggest that Steve Nash and Ray Allen are the most effective shooters from the most diverse locations. We provided many exciting examples of potential future applications of spatial and visual analytics for basketball expertise. In the end, we conclude that as the NBA becomes increasingly analytically driven, there is an exciting future for spatial and visual analytics in the league.