Improving shots

The amount of shots is often used alongside possession and pass success to describe, which team played better in a match in addition to the result. However, a team can shoot the ball 5 times from 30 yards out or 5 times from 6 yards away from the goal, so the shot statistic unfortunately gives us no indication, of how dangerous the shots of a team were and how likely they were to turn into a goal. Nevertheless, it would be definitely an improvement for shots to be weighted thus spectators could see, which team had better chances in the game and therefore was more likely to win.

Expected goals
Certainly, this approach came up as soon as additional data to shots was tracked. It is usually called “expected goals (XG)” as it shows you, how many goals you can expect on average according to the shots taken. There are quite a few different models out there, where football analysts weighted shots according to different criteria. Often-used criteria for weighting the shots are the shot location, the angle to the goal, the situation of play and whether the shot was a header or not. The more criteria are used to weight the shot, the more precise a probability becomes to determine, to which degree a shot turns to a goal in average.

Where can you find XG?
Articles on different XG-models can be found on the websites of various football analysts, but they usually do not publish player-based expected-goals data on a regular basis. For matches however, you can find data on expected goals on 11tegen11’s twitter account. He tweets match-based XG-data for a ton of matches on a regular basis. Fortunately, expected goals have found their way into mainstream football coverage from this season on with BBC’s football show “match of the day” displaying the expected goals value for certain matches and players. The expected goals value used is directly provided by Opta.

XG on Footballelixir
I am running my own expected goals model, which values shots by looking at 4 factors: The shot location, whether it was a clear-cut chance or not, the situation of play and whether it was header or not. Blocked shots are left out of the equation, except if the blocked shot was a clear-cut chance. The analysis is based on a total of 500000 shots and should therefore be precise. It is less detailed than the models by Opta or 11tegen11 however, because I simply do not have as much information on shots. The biggest weakness is, that I do not have exact coordinates on the location of a shot, and can only distinguish by whether a shot was taken from the 6-yard box, the 18-yard box or from outside the box. Assists on shots are not contained in the analysis as well. Nevertheless, the model certainly has its value, since it is a better predictor for a player’s offensive capability than the amount of his shots and goals. It will be used to describe the offensive prowess of players, the performance of a team in a match and it serves as a key factor in my predictions.