The bookies are confident in Manchester City: what data show us that they are right?
Written by: Yiftach Omesi
Most European football leagues are in the midst of another grueling season of extreme overloads, tight schedules, ongoing pressure to meet goals, and continuous adaptation of professional sport while a global pandemic is still raging. However, some things are stable. Manchester City are again fighting for the Premier-League crown, and alongside, the familiar names of Liverpool and Chelsea are in the race.
13 rounds into the season, the Citizens are in second place with more losses than their two rivals, fewer goals scored and an inferior goal difference. Having said that, let's look at the odds of winning the Premier-League title this season as of November 30, according to Oddschecker.
As you can see, Pep Guardiola's team are clear favorites, despite the numbers we’ve just mentioned and a general notion many journalists and fans share (or at least shared) that Chelsea should be the top candidates.
We don’t know what the bookies’ considerations are. Betting operators can sometimes play around with odds, but if we want to try to estimate why the Sky Blues are not only ranked first but with quite a significant margin, we can try to do it using data.
We will rely on official Premier League data, and first, examine the difference between Man City’s attack efficiency last season and in the 13 games we’ve seen so far this season. The champions are scoring fewer goals per match this season (2.18-2.08), and a lower percentage of their shots find the target.
On the other hand, City creates more shot attempts this season (17.7 compared to 15.7), meaning the Citizens do shoot more on target per game. In addition, the champs average significantly more crosses this season compared to last year (23.5 to 16.6), a stat that, when connected to the number of shots on target, shows how the team’s number of big chances created grew. Even though it is scoring less, Man City isn’t showing lower attack scores. It has yet to reach its limit, and the bookies understand this.
Attacking key metrics analysis
2021/22 (13 rounds)
Goals per match
Shots per match
Shots on target %
Pass accuracy %
Crosses per match
Cross accuracy %
Big chances created per match
It’s not only offense; If you want to see the true progress Pep’s team has made this season, look at its defense. A glance at the chart below tells it all. City concedes 0.54 goals per match so far this season, a significantly lower number than the 0.84 it recorded last season.
The Citizens also keep a clean sheet in 61.5% of their matches this season compared to 50% in 2020/21 and hold a higher rate of successful tackles. When this clear improvement is associated with the traditionally more vulnerable part in Guardiola’s machine, the Sky Blues have good reason to be optimistic this season as well.
Defensive key metrics analysis
2021/22 (13 rounds)
Goals conceded per match
Tackle success %
Clean sheets %
Another testament to City’s form in comparison to last year’s title season can be found if we look at the xG data. In the chart below, we can see that City has been performing better than last season, both offensively and defensively. A low number of GA (7) compared to a higher xGA value (8.13) indicates high defensive quality and the ability of the team to outperform itself.
In terms of its attack, City scores fewer goals than xG predicts it would (or should). This is a welcome state to be in at this stage of the season. A coach’s objective is to bring its players into their top form in March-April and the fact that City’s ceiling seems to still be high leaves much room for optimism.
The xG data predicts City will improve
2021/22 (13 rounds)
xG (Expected goals) reflects the probability that a shot will result in a goal based on the characteristics of that shot and the events leading up to it
xGA (xG allowed) indicates a team’s ability to prevent scoring chances by limiting its opponents’ ability to take high probability shots.
xA (Expected assists) is a model that determines how likely it is for a pass to result in an assist to goal. The calculation is based on the type of pass, pattern of play, where the pass was received, and where it was made from.