Every club needs a player who is good at scoring goals. However, the general need for prolific goal-scorers doesn’t make it easier to ultimately sign one. Instead, the competition is huge, and the fees rise accordingly.
That makes it even more important for financially disadvantaged clubs to become creative and scout in lower leagues to find talent at a reasonable price. One of these lower leagues can be Germany’s so-called 3. Liga. While Bayern, Dortmund & Co. are supposed to return next weekend, the 3. Liga still fears for a continuation of its season. Most clubs in this league are operating at a deficit anyway and will be forced to sell players in order to pay the bills.
In this data analysis, we will examine the most prolific attackers of Germany’s third division based on their statistics. In addition to that, this analysis will briefly profile some of the most interesting players that come up in the data.
Shots and goals
When analysing the output of a striker, the first step is usually to take a look at the underlying numbers in terms of shots and expected goals. This gives us an overview of attackers that are able to get into dangerous positions and the number of shots they take. To get a reasonably meaningful sample size, we only include players with more than 900 minutes this season. Since penalties have an xG-value around 0.75, they are excluded in this analysis because they dont give us many indications of a players quality and might bias the results. Philipp Hosiner is a good example for that: He already scored 16 goals in the league, which sounds remarkable at first. However, as it turns out, seven of them came from penalties. Furthermore, as we want to find the most prolific attackers, it might be useful to not only look at central strikers but also at attacking wingers and attacking midfielders.
Additionally, we included a third variable in our graph: touches in the box. It would be logical to assume that players who often manage to get into dangerous positions also have a correspondingly higher number of touches in the box.
Evaluating the graph, we can see that there are some obvious outliers with – on first glance – pretty decent numbers while others may go unnoticed. Especially Deniz Undav in the top right corner stands out with an enormous shot volume. As expected, touches in the box seem to have a positive influence on the xG-value. The graph also gives us some conclusions for the xG/Shot-value, although it’s not explicitly stated. Vincent Vermeij, for example, averages a sky-high xG/Shot of 0.23, illustrated by a high xG per 90 combined with a relatively low amount of shots.
Based on this graph, we will now take a look at some players that come up with impressive numbers in more depth to gain a better understanding of their underlying style and player role.
