Technological advancements and breakthroughs in machine learning have led to a recent revolution in the way that certain sports are being analysed, coached, and enjoyed by fans. The most famous of these statistics is the expected goals (xG) metric used in football. This is a metric that gives the probability that a specific shot in football will result in a goal. This metric is expressed as a number between 0 and 1, with 0 being no chance of a goal and 1 being a certain goal. This statistic has taken the footballing world by storm and is being used by betting companies to generate more accurate odds, broadcasters, and many teams within the sport to analyse players and opposition teams to inform player development and acquisition as well as to inform offensive and defensive team tactics and strategies.

The main aim of this project is to create a similar metric for basketball, with a focus on the National Basketball Association (NBA) in particular. The first version of the metric will give the probability that a specific basketball shot made by the ‘average’ player will result in a score. This statistic will be named ‘expected shooting efficiency’ (xSE). It would be a basketball equivalent of the xG metric utilised widely in football and would represent the probability that a shot taken by the ‘average’ basketball player will result in a score. The project will also seek to derive a further metric to give the probability that a specific shot made by a specific basketball player will result in a score, this shall be called a ‘players expected shooting efficiency’ (xSEp). This would give an idea of how specific basketballer players compare to the ‘average’ basketball player in terms of their shot accuracy for various shot types.