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Expected goals
In association football, expected goals (xG) is a performance metric used to evaluate team and player performances. It can be used to represent the probability of a scoring opportunity that may result in a goal. It is also used in ice hockey.
The expected goals metric is generally calculated by determining the likelihood of a shot being scored based on various factors, taken from the moment before the player shoots. These factors may vary depending on the statistical model, but include the distance to the goal, angle, shot type, and other contextual factors. Each shot is then given a probabilistic value, representing how many times that shot is likely to be scored based on similar shots. For example, a shot with a value of 0.3 goals is likely to be scored about 3 out of every 10 times. The expected goals metric has become more common with the increase of data analytics in sports, as analysts based the metric on accumulated years of sports data.
There is some debate about the origin of the term expected goals. Vic Barnett and his colleague Sarah Hilditch referred to "expected goals" in their 1993 paper that investigated the effects of artificial pitch (AP) surfaces on home team performance in association football in England. Their paper included this observation:
Quantitatively we find for the AP group about 0.15 more goals per home match than expected and, allowing for the lower than expected goals against in home matches, an excess goal difference (for home matches) of about 0.31 goals per home match. Over a season this yields about 3 more goals for, an improved goal difference of about 6 goals.
Jake Ensum, Richard Pollard and Samuel Taylor (2004) reported their study of data from 37 matches in the 2002 World Cup in which 930 shots and 93 goals were recorded. Their research sought "to investigate and quantify 12 factors that might affect the success of a shot". Their logistic regression identified five factors that had a significant effect on determining the success of a kicked shot: distance from the goal; angle from the goal; whether or not the player taking the shot was at least 1 m away from the nearest defender; whether or not the shot was immediately preceded by a cross; and the number of outfield players between the shot-taker and goal. They concluded "the calculation of shot probabilities allows a greater depth of analysis of shooting opportunities in comparison to recording only the number of shots". In a subsequent paper (2004), Ensum, Pollard and Taylor combined data from the 1986 and 2002 World Cup competitions to identify three significant factors that determined the success of a kicked shot: distance from the goal; angle from the goal; and whether or not the player taking the shot was at least 1 m away from the nearest defender. More recent studies have identified similar factors as relevant for xG metrics.
Howard Hamilton (2009) proposed "a useful statistic in soccer" that "will ultimately contribute to what I call an 'expected goal value' — for any action on the field in the course of a game, the probability that said action will create a goal".
Sander Itjsma (2011) discussed "a method to assign different value to different chances created during a football match" and in doing so concluded:
we now have a system in place in order to estimate the overall value of the chances created by either team during the match. Knowing how many goals a team is expected to score from its chances is of much more value than just knowing how many attempts to score a goal were made. Other applications of this method of evaluation would be to distinguish a lack of quality attempts created from a finishing problem or to evaluate defensive and goalkeeping performances. And a third option would be to plot the balance of play during the match in terms of the quality of chances created in order to graphically represent how the balance of play evolved during the match.
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Expected goals
In association football, expected goals (xG) is a performance metric used to evaluate team and player performances. It can be used to represent the probability of a scoring opportunity that may result in a goal. It is also used in ice hockey.
The expected goals metric is generally calculated by determining the likelihood of a shot being scored based on various factors, taken from the moment before the player shoots. These factors may vary depending on the statistical model, but include the distance to the goal, angle, shot type, and other contextual factors. Each shot is then given a probabilistic value, representing how many times that shot is likely to be scored based on similar shots. For example, a shot with a value of 0.3 goals is likely to be scored about 3 out of every 10 times. The expected goals metric has become more common with the increase of data analytics in sports, as analysts based the metric on accumulated years of sports data.
There is some debate about the origin of the term expected goals. Vic Barnett and his colleague Sarah Hilditch referred to "expected goals" in their 1993 paper that investigated the effects of artificial pitch (AP) surfaces on home team performance in association football in England. Their paper included this observation:
Quantitatively we find for the AP group about 0.15 more goals per home match than expected and, allowing for the lower than expected goals against in home matches, an excess goal difference (for home matches) of about 0.31 goals per home match. Over a season this yields about 3 more goals for, an improved goal difference of about 6 goals.
Jake Ensum, Richard Pollard and Samuel Taylor (2004) reported their study of data from 37 matches in the 2002 World Cup in which 930 shots and 93 goals were recorded. Their research sought "to investigate and quantify 12 factors that might affect the success of a shot". Their logistic regression identified five factors that had a significant effect on determining the success of a kicked shot: distance from the goal; angle from the goal; whether or not the player taking the shot was at least 1 m away from the nearest defender; whether or not the shot was immediately preceded by a cross; and the number of outfield players between the shot-taker and goal. They concluded "the calculation of shot probabilities allows a greater depth of analysis of shooting opportunities in comparison to recording only the number of shots". In a subsequent paper (2004), Ensum, Pollard and Taylor combined data from the 1986 and 2002 World Cup competitions to identify three significant factors that determined the success of a kicked shot: distance from the goal; angle from the goal; and whether or not the player taking the shot was at least 1 m away from the nearest defender. More recent studies have identified similar factors as relevant for xG metrics.
Howard Hamilton (2009) proposed "a useful statistic in soccer" that "will ultimately contribute to what I call an 'expected goal value' — for any action on the field in the course of a game, the probability that said action will create a goal".
Sander Itjsma (2011) discussed "a method to assign different value to different chances created during a football match" and in doing so concluded:
we now have a system in place in order to estimate the overall value of the chances created by either team during the match. Knowing how many goals a team is expected to score from its chances is of much more value than just knowing how many attempts to score a goal were made. Other applications of this method of evaluation would be to distinguish a lack of quality attempts created from a finishing problem or to evaluate defensive and goalkeeping performances. And a third option would be to plot the balance of play during the match in terms of the quality of chances created in order to graphically represent how the balance of play evolved during the match.