**Expected Goals (xG)**

The ‘value’ of a shot or probability that a shot results in a goal depending on the distance and angle of the shot. For a better model and explanation check out the links in Michael Caley’s pinned tweet.

My method only takes into account the angle and distance of the shot, then a logistic regression is run giving the formula for any given angle and distance.

**Expected Assists (xA)**

I’m not too sure how this is generally calculated. For my values I took Key Passes and ran a logistic regression where the dependent variables are the start and end location of the key pass, whether or not it is a through ball and whether or not it is a long ball. Then did the same for crosses but using just the start and end location.

I’m *fairly* happy with the results but both this and expected goals but they also still need a lot of improving.

**Expected Passing (xP)**

This is taken from @FootballFactMan on Twitter explained in his post here. Like expected goals it just assigns a value to each pass based on the probability that is is completed. For this I split the pitch into 100 (10 x 10) zones and just did number of passes starting in one zone and ending in another divided by the number completed.

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**Expected Goals Added (xG Added)**

Taken from @NilsMackay and explained much better by him here xG Added takes the xG value at the start location and end location of a pass then subtracts the start from the end to give the xG that was added from the pass.

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**Possession Adjusted Stats**

These come from StatsBomb explained in a piece here. It’s a way to even out tackles and interceptions as these stats are much easier to rack-up for a team that spends the majority of the match without possession of the ball. I use the formula at the bottom of the article by @statlurker.

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