There are emerging metrics in football such as Expected Goals, Key Passes, Progressive Passes and even now Expected Assists. These are all measuring single events in a football match and quantifying their utility or effectiveness with an indicator or a probability of happening. These are also all measurements of how effective a player is at executing actions whilst on the ball, particularly in offensive positions such as shooting or creating shots from passes. They give us a better idea for which players and teams are most (or least) effective at offensive events. The higher your Expected Goals and the more Key Passes you make, the more goals your team is likely to score. However, there aren’t similar metrics that measure defensive contribution. This may be due to the act of contributing to goal scoring being an objective decision, with each goal scored there is a single player who scored it and it’s easy to allocate contributions. With allocating defensive contribution, it is hard to quantify the presence of a non-event. It is hard to quantify how much of an effect a player or team has on the opposition not scoring. I think that if it’s possible to quantify a sensible defensive metric of any kind then it could be as useful as any of the offensive metrics above, I will use this series to brainstorm some ideas of such defensive metrics and how it would be possible to compute them.
Expected Shot Block:
Where better to start with defensive metrics than with the clearest act of denying a goal, the shot block. This concept is in direct competition to and is inspired by the concept of Expected Goals.
For Expected Goals, each shot is given a probability of being a goal based on historical shots of a similar type. For example, a shot that is taken with the head from a cross may be given 0.1 xG whereas a shot from a counter attack inside the 6-yard box may be given 0.5 xG. It suggests that the shot from a counter is 5 times more likely to go in than the headed effort. These may not be realistic numbers but the concept stands. Some shots are more likely to go in than others depending on a number of factors including where on the pitch the shot was taken, what play led to the shot, what the shot is taken with and game state of the match.
The concept of the Expected Shot Block would be to calculate the probability that the shot is blocked by a defender. This would require more information than just the event data of the shot, it would require the knowledge of the presence of a defender and how likely it is that a defender makes a block in a similar situation based on historically similar shots. The time this decision is made would be at point of contact of the shot. Based on shots from the past, you can categorise them into similar categories as that of Expected Goals but with the added factor of the presence of a defender or defenders between the ball location and the goal. The ball location and the two posts of the goal create a triangle and if there are any defenders in this area then the shot would be identified as having the potential to be blocked. The presence of the goalkeeper is expected and since we are looking at shots being blocked not saved then the goalkeeper’s location can be acknowledged but not required for calculation. The location of the goalkeeper may alter the shot direction of the attacker so may affect shot blocking numbers.
Furthering the concept of an Expected Shot Block would be to calculate the percentage of the goal that is open to the shot at the point of contact. When identifying if a shot has the potential to be blocked, you can calculate the percentage of the goal that is available to be shot at where the defenders wouldn’t make a block. This calculation could be done in either 2D or 3D. You can assume an average area for the defender’s body and block out the area of the goal that the defender is in front of. In 2D this would be less accurate than in 3D since it would be assumed that the defender can block a shot of any height. Whereas in 3D, you could create silhouettes of the defender’s limbs and create a more accurate percentage that way.
There are some problems with this concept but I think it has potential. You need more than just event information, you also need player location data which is harder to get. It also assumes that the shot is a direct hit straight at goal, whereas many players attempt to bend and curl the ball around defenders. Just because a defender is in the way of the goal, doesn’t guarantee a blocked shot in that location. There are many times where a shot goes through a defender’s legs or just past a limb, defenders and players aren’t perfect.
It’s hard to quantify defensive actions and shot blocking seemed to have the most relevant as it’s related to the current set of offensive metrics, it’s not perfect. If anyone has any thoughts or comments regarding other issues I may have missed, please do let me know!