How Expected Goals (xG) Will Change The Way We Bet On Football

Whilst all goals in football are worth an equal amount, the likelihood of a shot being scored varies. That’s where Expected Goals (xG) comes in. It’s a metric used in football analysis to assign a “quality” value to every attempt based on what we’ve learnt about similar past cases.

Expected Goals stats could have a huge impact on how we approach sports betting.

 

Expected Goals (xG) Explained


On average, 9.7% of shots in the Premier League were converted into goals over the last five seasons.

Some were long-range, some were headers, and others were straight forward ‘tap-ins’. Importantly, not all shots have an equal chance of hitting the back of the net. Expected Goals accounts for this, and determines how many goals a team ought to have scored in a match. The accuracy of the model largely depends on what factors are used to calculate the xG rating for individual chances.

You might have heard of “expected goals” or seen it used on Match Of The Day. Here’s an example.

 

Man United v Stoke Expected Goals Stats (Opta)

How Goal Expectation (xG) Will Change The Way We Bet On Football

 

Man United’s chances are shown in red/pink, and Stoke’s in dark grey. The bigger the square, the better the chance was. It shows that Man United created more clear-cut chances than Stoke — some of which were inside the six yard box. In this case, the final score of 3-0 isn’t too far away from the xG estimation of 2.2-0.4.

 

► The Logic Behind Expected Goals. How does it work?

Expected Goals (xG) is calculated by assessing the aspect which influences the scoreline of any football match: goal scoring chances.

The higher the Expected Goals (xG) — up to a value of 1.0 — the better the chance was. If a chance is rated at 0.7 xG, then you expect it to be scored 70% of the time. Goal scoring opportunities are individually assessed, and totalled out for both teams over a match to create an xG final score, as shown in the example above.

For example, if a team created four chances in a game, each of exactly 0.25 xG (25%), then their xG would be 4×0.25 = 1 goal.

The logic is simple — and it makes sense. If a team fails to create any real chances, they won’t be expected to score. More clear-cut chances means more expected goals.

 

► So how is the xG metric useful for football Match analysis?

Firstly it gives fans and pundits a great way of justifying who was superior in a game — regardless of the actual score.

Previously we might have relied on basic stats, like shots on goal. But with Expected Goals we have the ability to delve deeper, and account for the quality of those shots. Now we can say “that game should have been 3-0″, with enough detail to justify the statement we’re making.

I’ve spoken before on this site about the limitations of using basic statistics for football analysis. It’s important to recognise that some results simply don’t tell the full story. The scoreline isn’t the only stat that matters for your betting analysis. It’s important to have some sort of context to a past fixture, and even better to be able to answer questions, such as:

  • What was the most likely final score?
  • Were [Team X] fortunate to have won?
  • In total, how many “clear-cut” chances did [Team X] have in the match?
  • Should [Player A] have scored from his opportunities?
  • This season, how much below or above average has [Player A] or [Team X] performed?

Now, thanks to Expected Goals, we can mathematically, without bias, answer these questions and make better judgements on football matches going forward. From a betting perspective, it has the potential to vastly improve on the basic football prediction models I detailed in my post: The Basics Of Creating a Football Prediction Betting Model.

 

► Using Expected Goals for Analysing Runs of Form

Runs of form often deceive us throughout the season. It’s is something i’ve touched on before in my post on Best Tips For Premier League Football Betting. I said this:

Mid-table clubs like Watford, West Brom or Southampton often have their moments throughout a season. It’s easy to get swept away by the positive press, but when it really comes down to it they don’t have the class or squad depth to match the top Premier League teams over a season. This is something that may be temporarily overlooked.

Little did I realise, there was Expected Goals there all along. It helps to prevent bettors from being “swept away” in the hype. It encourages us to remain realistic.

So if your team is performing well above or below expectations at the start of the new season, a look at the expected goals difference could tell you whether that run is likely to last.

I’ll show you how “expected goal difference” works in the next section…

 

► Expected Goals For Individual Players

The Expected Goals (xG) figure has a lot more meaning for individual strikers than it does for other players who aren’t necessarily expected to score (e.g. defenders).

I was intrigued by something:

Are the top scoring strikers in the Premier League flattered by the fact their team creates more chances for them?

I looked into it and found that during the 2016/17 season, the player who outperformed the number of goals he was expected to score by the biggest margin was in fact… top goalscorer Harry Kane.

Harry Kane is deservedly regarded as the best, most prolific striker in the Premier League last season (not that i’m biased or anything). He was expected to have scored 18.59 goals, but managed to score an extra 10.41.

 

I’ve taken these stats from the bbc. They owe me one anyway.
Player Goals Expected Goals Expected Goals Difference
Only includes players with 50+ shots
Kane (Tottenham) 29 18.59 10.41
Lukaku (Everton) 25 15.32 9.68
Llorente (Swansea) 15 7.09 7.91
Son (Tottenham) 14 6.73 7.27
King (Bournemouth) 16 9.56 6.44

 

This table also sheds light on why the top Premier League clubs were interested in Swansea’s Llorente (#3) at the end of last season. He performed well despite playing for a struggling team.

Provided the level of detail is high enough, Expected Goals could be used to compare and even value strikers, based solely on their ability to convert chances.

 

► Precisely How Detailed is Expected Goals?

xG can include as many or as few factors as your like in order to calculate the average likelihood of each shot being scored. The more your model knows about past cases, the more accurate it is. Granted, this isn’t an easy system for most of us to replicate!

Football data experts Opta create their own Goal Expectation figures. They’ve analysed over 300,000 shots (a large sample) to calculate the likelihood of an attempt being scored, given a specific position on the pitch, during a particular phase of play.

Opta’s model accounts for factors such as:

  • Distance from goal
  • Angle of the shot
  • Header or at feet
  • 1v1 situations
  • Quality of the assist (the type of ball put in)
  • Passage of play (e.g. open play, direct free-kick, corner kick)
  • Player’s energy (has he just beaten an opponent to create the chance)
  • Rebounds

To obtain an accurate Goal Expectation figure, advanced data gathering and statistical analysis skills are required. However, it is still possible to gain insight using a far simpler expected goals system.

Understat is a great site for monitoring the expected goals from past fixtures. I haven’t yet found a website that enables you to download xG figures for all historical fixtures.

 

Expected Goals — The Future Of Football Betting?


To summarise xG, it’s worth reiterating that the scoreline isn’t the only stat that matters.

OK — in terms of points, it is. But the Expected Goals value reveals a number of things about a game, series of games, and individual players. It enables us to analyse:

  • Whether a chance really is as good as we think.
  • What the scorelines should have been, given the chances.
  • A team’s goal-scoring record in comparison the chances they’ve created.
  • How many goals individual players should have scored from the chances they’ve had.
  • Whether ‘runs of form’ are likely to continue (using the xG difference).

Expected goals enables punters to better judge teams and players, reduces bias, and helps to more accurately predict future results. The applications to betting are certainly there — and it doesn’t need to stop at football. In fact, the logic behind xG can be applied to any sport.

For now it’s being used for football — a complex game that’s very difficult to predict, and requires equally complex stats to derive something meaningful. Expected Goals isn’t an all-knowing metric that’ll instantly turn your betting model into a money-making machine, mind you. In fact, it could just make the odds more accurate. But for now it is a step in the right direction for football prediction and value hunting.