Advice on making the most of basketball three-point shot data - Terhanian, G. (The Sport Journal, 2024)
This study`s primary goal is to help National Basketball Association (NBA) and other basketball teams worldwide increase their three-point shooting accuracy and decrease their opponents`, a key to winning more games. A related goal is to explain how a combination of good data, logistic regression analysis, likely effects reporting in probabilities or percentage points, and self-serve simulation can improve communication among data analysts, basketball coaches, and players, and enhance each group`s effectiveness. Logistic regression analysis of 32,511 NBA three-point shots shows six factors affect the three-point shooting percentage: closest defender`s distance to the shooter, time left on the 24-second shot clock, whether the player shot after dribbling or catching the ball, game period, shot distance, and venue. In the past, data analysts conveyed the results of such analyses to coaches and players using terms such as regression, logits, and odds. Some NBA executives say doing so again would be disastrous. An alternative is to emphasize probabilities and percentages in communication and create self-serve simulators coaches and players can use to predict how changes in critical factors affect three-point shooting percentages. NBA and other teams worldwide can apply this approach to new and existing datasets they maintain, enhance, and build.