This is a review of the shot chart analysis research conducted by Stephen Chu.
Using statistics to analyze NBA game performance is becoming a higher priority with many teams hiring more statisticians, hoping that this will give them a competitive edge. Current NBA statistics focus mainly on the production of the individual player and do not look at how players interact.
One possible useful basketball analytical tool is the shot chart as it compiles much of the game’s important information in one place. It can also be used to determine the strengths and weaknesses of various offenses and defenses. Increasing the detail of information on shot charts would allow users to understand a team in even greater depth. One possible application would be understanding how the team’s field goal percentages change by substituting different players into the on-court lineup which would indicate how a player affects the shooting percentages of his teammates.
Current shot charts do not allow for the in-depth analysis of how well specific combinations of players work together or play against each other. Two of the most popular shot charts are provided by NBA.com and ESPN.com, which show how well an individual player or team shoots against another team as a whole but do not provide data on specific lineups.
NBA Hotspots splits the court up into various regions. This allows the user to see how well a player or team shoots from the different regions. In turn, it allows the user to understand how often a particular player or team shoot from a particular position.
ESPN Shot Chart marks the court as a map of made and missed shots. It shows much more detail about the specific shots such as extra information about the shooter, the distance from the basket, and the time of the attempt. However, shots are often closely clustered together, making it difficult to single out individual shots.
NBA statistics typically focus on individual stats but do not recognize that a player’s teammates will have either a positive or negative impact on his play. All 10 players on the court influence each other and this should be taken into account to create more accurate statistics. A new way of compiling stats needs to be implemented to overcome this problem.
For the shot chart to include player interactions it must include more information including who made the shot attempt, where they were on the court, and where the other nine players were when the shot was made. Tracking specific individual contributions as well as their interactions with the other 10 players creates a dataset that allow the user to look at team interactions in a new way. The analyst can use the statistics individually or combine them in order determine scoring averages, where players most frequently shoot from, and the position of the other players when they do make the shot. It makes it possible to look at how well a team performs when a certain player is on the floor.
This shot chart gives analysts a tool to evaluate NBA teams more in-depth, looking at the performance of specific lineups on the court. It is possible to see how team performance is affected by substituting players in and out of the lineup. Analysts can determine how players’ field goal percentages vary between locations and the preferred shot location for different players.
Teams will be able to evaluate strengths and weaknesses of offenses by determining if the team spreads across the floor with its shots and figuring out the areas of high percentage shots when a certain player combination is on the court. It can also be used to evaluate team defense by looking at their points distribution and the most common locations where the opponent scores. Finally, teams can use this shot chart to change their lineups based on the opponent’s players on the floor.
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