Expected goals (xG), love ‘em, or hate ‘em, are increasingly being accepted across the soccer world, with misguided notable exceptions. While there are multiple xG models in the soccer analytics world, the concept basically boils down to quantifying the likelihood of a shot being scored based upon where and how the shot was taken. xG quantifies what you may understand intuitively, a shot taken close to goal is more likely to be scored than a shot taken 30 yards away. There are many ways to misinterpret expected goals, one of the most common is that xG tells you exactly how many goals a team will score in a game. Obviously, this cannot be the case, as the sum of xG values of shots in a game is rarely a round number. A team cannot score 1.62 goals in a game, but it can score 1 or 2. xG gives the most likely outcome for goals scored in a game. But since goals come in discrete units of 1, and no more than 1 goal can be scored per shot, calculating the probability of goals scored in a game gets a bit complex. The number and quantity of shots that go into a team’s overall xG for a game matter, it’s not just the sum of xG.