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Expected Goals: A Pioneering Method of Analysing Football


The Football Code has a look at expected goals, a new metric set to revolutionize statistics-based analysis of football


Football is an incredibly hard sport to analyse. Managers dither over which players to select for their starting eleven; fans argue over which teams are the best; pundits debate about which tactical systems clubs should adopt. Matches are dictated by the whims of chance and randomness, making it difficult to draw accurate conclusions about the sport. Good teams often lose, good players often flop, and good managers often boast poor records. And, of course, the reverse is also true.

The data which is currently used to assess teams and players is inadequate at allowing us to separate what is true and what is noise. Possession, passing accuracy, shots on target, corners and other stats which are conventionally deployed are more descriptive than predictive. They depict the play of a match, but do not act as indicators as to which teams are the best. For example, Leicester City won the 2015/16 Premier League with some of the worst possession and passing stats that the division had ever seen. A new form of analysis is needed; a more analytical approach to the beautiful game that can tell us which teams and players are actually good, and which are just lucky.

In the coming years, the traditional stats that are used to assess football will come to be replaced by a new metric. This form of data is known as “Expected Goals”. This metric offers by far the most profound insight into the ability of football teams that football has ever seen. The analytics community has become obsessed with the idea of Expected Goals. This form of stats, otherwise known as “goal probability”, is commonly referred to as “xG”. The metric has achieved unparalleled predictive success. You heard it here first: Expected Goals is the future of football.

Essentially, xG indicates how many goals a team could have expected to score based on the quantity and quality of chances that they create. Fans often come away from football matches thinking, “We created much better chances then the opposition, we definitely should have won”. The Expected Goals metric is a way of quantifying these scoring opportunities, allowing a better insight into the ability of teams than the actual scoreline does.

Teams are often judged by the quantity of shots that they have in match, or indeed in a season. The central premise of xG is that the quality of those shots is of equal importance to the quantity. Analysts can work out the number of goals that a team would expect to score from a certain amount of shots of a certain quality, thus creating a clearer image of which teams are performing at the highest level. The teams that create scoring opportunities of a greater quantity and quality than other teams, whilst at the same time conceding fewer shots and at a lower quality, are clearly the best.

Collecting xG data is very simple. Each shot is assigned a probability that it will end up in a goal. How do you determine the likelihood of a shot’s success? The location of a shot has a large bearing on how likely it is to result in a goal. A shot which is taken from a wide position, thirty yards out from goal, will only have a small chance of going in. On the other hand, a close-range shot from a central position will have a high probability of scoring.

An analyst could look at a large sample of shots taken from an exact position and find how many beat the goalkeeper. Say an analyst looked at 1,000 shots taken from the right-hand corner of the penalty area. Suppose only 50 of these shots ended up in a goal. He could conclude that future shots from this location have a 5% chance of beating the goalkeeper. Thus, the xG shot probability from this position is 0.05. Other variables are also considered. Shots when the ball is struck on the ground are much more likely to end up in a goal. Headers are more difficult to convert than shots with the football. Additionally, it is obviously harder to score if there are several defenders in the way. These factors are all considered.

Football is generally averse to change, especially when it comes to the advancement of mathematics within the sport. However, the beautiful game is slowly but surely embracing the Expected Goals method as a means of analysis. You may have noticed that the BBC have even started displaying xG data on Match of the Day. Sky Sports have also got on board this season.

Nevertheless, even the increased exposure which the metric has seen over the last couple of weeks is simply a drop in the ocean of what is to come. The reason why is that every footballing judgement, opinion or prediction ever made is based on an assessment of the ability of a team or players. The Expected Goals method offers by far the most advanced, profound and accurate gauge of footballing performance. It allows us to quantify exactly how well teams are playing. It strips randomness from the beautiful game, enabling us to see a clear image of what is actually happening on the field of play.

Soon, those who do not understand or pay attention to Expected Goals data will be left behind. xG allows you to speak a completely different, much more intelligent language with which to analyse football.

“Isn’t X an incredible manager?!”

No, xG shows that his team have incredibly lucky to get the results that they have.

“How could Y’s defence play so badly?!”

Actually, the Expected Goals data shows that they played very well.

“Why does Z keep missing absolute sitters?!”

Sorry to correct you, but xG suggests that this player is actually scoring more goals than would expected of him.

Too often in football, the result dictates the narrative. A team who plays badly and wins has “a great mentality” and is able to “grind out results even when not playing well”. However, a team who plays badly and loses will be deemed to have obvious flaws. Both these teams have performed at the same level (i.e. badly), but notice how our analysis has been changed dependent on their result. In order to avoid being fooled by randomness, we should ignore the actual scoreline of football matches and focus solely on the Expected Goals totals amassed from each game. This will allow us to assess performances, rather than results.

The predictive nature of the Expected Goals method has been validated by one club in particular. In South-West London you will find a team called Brentford FC. The club have spent the last several decades playing in the third and fourth tiers of English football, in front of crowds rarely exceeding 5,000. They were on the verge of administration as recently as 2005. The small size of their fanbase and the limited funds available to the side regularly see the Bees tagged as ‘tin-pot’ or ‘Little Old Brentford’.

In 2012, things suddenly changed for the club. In a matter of seasons, Brentford were transformed from an innocuous third tier side into a team gunning for their first ever promotion to the Premier League. They have achieved three successive top ten finishes in the Championship, despite possessing the fourth lowest playing budget in the division. Brentford’s miraculous upturn in fortunes is down to Matthew Benham, a professional gambler who took over the West London club in 2012.

Benham does not possess vast amounts of wealth, but he does own a betting consultancy called Smartodds. The company, which he founded in 2005, collect innovative match data based on Expected Goals. They use these statistics to win money through betting on football matches. When Benham took over Brentford, he began to use the system that had made him millions in the gambling markets to bring incredible success to the Bees.

Benham’s ground-breaking, top secret system is entirely founded on the Expected Goals method. The metric has won him millions of pounds in the gambling markets, and has allowed him to bring hidden gems to Brentford FC. Benham has been able to identify highly talented players who are undervalued by the rest of the footballing world. He has signed high quality players such as Andre Gray, Scott Hogan, James Tarkowski and Jota Peleteiro to Brentford for minimal money. In fact, the four players listed above were bought for a combined £3.15m, and sold for a total of £36.2m, leaving the Bees with a profit of £33.05m. Brentford have managed three consecutive top ten Championship finishes under Benham’s guidance, despite having the fourth lowest playing budget in the division.

The Expected Goals method looks set to revolutionise the sport. The media are slowly beginning to adapt it, but the world of football must strive to fully embrace the new method of analysis. Only then can pundits begin to more accurately comment on football. Only then can managers give more reasonable post-match interviews. Only then can the fans select the best players for their fantasy teams. Only when Expected Goals is fully embraced by every major platform can we haul football out of the dark ages, and into a more intelligent era.


The Football Code: The Science of Predicting the Beautiful Game is available to buy on Amazon now

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