Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take into account shot attempts, which are better predictors of a team and player’s performance than goal totals alone.
A notable example is Brian Macdonald’s expected goals model dating back to 2012, which used shot differentials (Corsi, Fenwick) and other variables like faceoffs, zone starts and hits. Important developments have been made since then in regards to the predictive value of those variables, particularly those pertaining to shot quality.
Shot quality has been the subject of spirited debate despite evidence suggesting that it plays an important role in predicting goals. The evidence shows that shot characteristics like distance and angle can significantly influence the probability of a certain shot resulting in a goal. Previous attempts to account for shot quality in an expected goals model format have been conducted by Alan Ryder, see here and here.
In Part I, an updated expected goals (xG) model will be presented that accounts for shot quality and a number of other variables. Part II will deal with testing the performance of xG against previous models like score-adjusted Corsi and goals percentage.