What it is: Building on xG, we can dig deeper into the likelihood of a shot becoming a goal. When we do this, we can use xG to explore whether a certain player or team is under or over-performing in front of goal compared with their expectation. Of course, it is more reliable to draw insight from such information when you have a larger sample to work from, across a longer period of time. What we can do is add up a player or team’s xG throughout a game, period, or season to give a clearer idea of how many goals they should have scored, based on the quality of shots they had. How it can be used: The examples above use xG on a single shot to outline the metric itself, but it is often not too insightful to explore xG on a shot-by-shot basis. Therefore, the quality of a player’s shot execution might significantly improve that chance - which we will come onto next. Importantly, the value does not consider the quality of the player shooting, but instead provides a value based on how the “average” player would perform in a similar situation. The xG value is calculated using thousands of previous shots that were similar to that situation, and seeing how many of those were scored as a result. For example, this shot below taken by Bruno Fernandes vs Southampton had an xG value of 0.3, which means that the shot would be expected to be a goal 3 times out of 10 (or 30 times out of 100), given the situation he found himself in. Nevertheless, the xG value is always presented as a number between zero (no chance of a goal) and one (a certain goal). It is important to note that different data providers have slight differences in the factors they consider to go into their xG model. Whether there were multiple defenders in the way.Whether it was from a cross, through ball, short pass etc. Whether it was with the head, or with the weaker/stronger foot.Therefore, xG measures the quality of each shot before the player shoots, taking into account many factors, including: Not all shots are equal in their quality - one shot might be a speculative 40-yarder and another might be a two-yard tap-in. Put simply, xG is a way to measure the likelihood of a shot becoming a goal. What it is: A lot of the football world will have at least heard of xG by now. While he may not have accrued as many minutes as some of his peers, Bale’s rate of more than one goal per 90 minutes is an outstanding return for the time he was on the pitch. Considering only players who have played 900-plus minutes in the Premier League, the player with the highest goal rate was Tottenham Hotspur loanee Gareth Bale. When you look at this goal output per 90, we see Abraham actually scores at a higher rate (0.58 goals per 90) than Mane (0.47 goals per 90) - which makes more sense given their different roles across the forward line.Ī closer look at the top goalscorers from the 2020-21 season is interesting to see on a per 90 basis. This would initially point to Mane being the more clinical player in front of goal, but the added context is that Mane has played more than 2,000 minutes more than Abraham in this period. How it can be used: Since the start of the 2019-20 season, Liverpool’s Sadio Mane has scored 29 goals in the Premier League, while Tammy Abraham has only scored 21 for Chelsea.
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