Charting Liverpool's Expected Goal Surge Under Jurgen Klopp

Everyone with a passing interest in the developing football analytics movement will by now have heard of expected goals.

While far from  perfect, in common with most models, it does do an excellent job of examining the process behind the creation and attempted execution of goal scoring opportunities in a sport, such as football which has relatively few actual scoring events.

Much of the progress in recent years has revolved around improving both the descriptive and predictive qualities of the metric by incorporating firstly the shot type as well as location and also other pre-shot information, such as how the attack developed, often used as a proxy for defensive pressure.

Less attention has been paid to how the values of expected goals are presented for individual sides or players, with often a simple cumulative addition of the expected goals created and conceded being deemed sufficient for individual matches or seasons.

Simulations of each individual attempt using the expected goal value associated with that shot or header is an easy alternative, but this also converts the raw granular data into the different currency of win probability, when used on a single game or expected position or league points won if applied over a larger number of matches.

Retaining information about the distribution of the quality of the chances created, rather than simply taking a summation of the individual elements, is useful because of the way such distributions contribute towards the final range of possible outcomes.

Spreading your cumulative expected goals over a few shots compared to many has a different potential payoff.

In the former, you are foregoing the potential for an occasional bumper score line for the increased likelihood that you may be lucky and good enough to score at least one, which often yields some kind of return in a low score environment.

I first wrote about this here in 2014.

Here's an extreme example.

Would you rather have a penalty kick, with an ExpG value of 0.8 or eight shots, each with an ExpG value of 0.1.

The cumulative ExpG is 0.8 in both cases, but if the range of outcomes were combined in a match scenario, the lone penalty would win 35% of such games and the more frequent, but less likely attempts would win just 28% of the contests despite also summing to 0.8 ExpG.

Therefore, ExpG distribution matters.

Here's the distribution of the ExpG chances created by Brendan Rodgers' and Jurgen Klopp's Liverpool over their most recent 48 game span.

The opportunities have been grouped and counted by increasing ExpG per attempt and compared to the average league for quality and quantity, adjusted to a 48 game sequence.

The majority of chances created by a side has a relatively low expectations of scoring, falling between an expectation of near zero, rising to around a 15% chance.

Attempts with higher ExpG values are much less numerous, ranging up to so call big chances, where historically a team has been more likely to score than not.

Therefore, a secondary axis has been used to produce definition on these much rarer groups of bigger chances.

There's not much between the current Klopp managed Liverpool and the man he replaced, Rodgers in the lowest expectation region of chances created.

Klopp's side is above the average, volume-wise for attempts in the three initial groups that are quantified by the left hand axis, ranging from 0-0.15 expG.

Rodgers edges ahead in the volume of chances created with a grouped ExpG of between 0.2-0.25, the counts for which are shown on the right hand axis.

Once we encounter chances with a likely historical likelihood of 35% or greater, the present Liverpool set up dominates both the league standard and Rodgers' Reds.

No penalty kicks have been included.

Data from @Infogol