This is the second part of a mini-series in which we will use data analysis techniques to identify young players who are undervalued in their current leagues. If you want to give the first part, where we focused on the Serbian top flight, then you can find it here.

This time we’ll turn our attention to the elite of Swiss football as we look to find players who don’t necessarily play for one of the biggest teams in the country. But first, we’ll give some background around the methods that are used for this piece. the Data that we use comes entirely from Wyscout, although as you will see as we work through the recommended readers some custom metrics have been built using the source data provided. We also use Python, to find the percentile data and code the pizza charts, and Tableau to create the visualization showing player rankings. By using techniques such as percentile rankings, we are better able to normalize and understand the data we are working with.

The purpose of these articles is to highlight some of the players in these leagues who do not necessarily play for the biggest and strongest teams in their respective countries. If we just take the data as it comes, there will always be a tendency for that data to be skewed in favor of players who play for clubs at the top of the table. Instead, we can use calculated fields in the table to calculate the % each player has contributed to their team. It starts to highlight players who may be undervalued due to playing for teams that aren’t performing as well.

For the purposes of this article, we’ll be looking to identify three players who could be interesting transfer targets for clubs this summer.


First, let’s look at the percentage each player in our dataset has contributed to their team in terms of goals and assists. To do this, we use goals per 90 and assists per 90 to create a new metric in the data with goal contributions per 90. We can then calculate the % that each player contributes to his team.

As you can see from the rankings above, there is a clear outlier in the Swiss league with 23-year-old Lucerne midfielder Filip Ugrinic contributing 22.07% of his team’s goals so far. now this season. Then there is a return to 20-year-old Senegal striker Kaly Sene from the Grasshoppers.

Using this data point and ranking the players as we did above gives us a snapshot of each player into which we can feed more data that has context for us. We always seek, for example, to include age, minutes played and contract expiration.

Next, we’ll do something similar by looking to calculate the percentage that each player in our dataset has contributed to their team in terms of expected goals. To do this, we’ll do a similar calculation to find the value each player has for expected goal contributions (expected goals + expected assists) and then we can use that to calculate each team’s xG contribution % so far this season. .

This time, the best player in our ranking is 21-year-old Swiss international Kastrior Imeri from Servette with a 28.19% contribution alongside him xG. Just below him, however, Filip Ugrinic is seen making another appearance.

Finally, in this section, we’ll look at how effective each player in our dataset is at advancing the ball for their team. To find this information, we again use a new metric in Progressive Actions. It is a relatively simple combination of progressive runs and progressive passing by a player. We then do the same with the team data by combining each team’s progressive runs and progressive passes before calculating the % each player has contributed towards that.

As you can see at a glance, the positional composition of the leaderboard has changed. With goal contributions and expected goal contribution, we tend to see attacking players dominating at the top of the leaderboard. When it comes to progressive actions, we are seeing a shift towards players who are defenders or full-backs. This is purely a result of the actions we tend to see in different parts of the pitch. Defenders are more likely to advance the ball as they get the ball deep with slightly more time while forwards are more likely to contribute goals.

The top performer this time around is 22-year-old St. Gallen midfielder Isaac Schmidt, although we’ve actually picked another player to take a closer look at. 22-year-old Hungarian right-back Bendeguz Bolla is on loan at Grasshoppers from Wolves and he’s been a player we’ve enjoyed for some time now.

#1 Filip Ugrinic, 23, midfielder, Lucerne and Switzerland

First, we’ll take a look at Luzern midfielder Filip Ugrinic, 23. The pizza chart format allows us to compare key metrics for the player across the defensive, offensive, and possession phases of the game. To do this, we use percentile data so that each field’s range is always between 0 and 100.

As you can see, we continue to use custom metrics in our pizza slices to get as much information from each player as possible. It’s clear that Ugrinic is a creative threat on the ball with strong outings for box entries, progressive moves and dangerous passing but he also carries a goal threat.

#2 Kastriot Imeri, 21, Midfielder, Servette and Switzerland

Next we have Kastriot Imeri from Servette. The 21-year-old has already been capped by Switzerland at international level and it’s easy to see why. He is somewhat similar to Ugrinic in that his possession metrics are very strong with good results for area integers, progressive plays and dangerous passes.

However, he is also strong in the attacking phase of the game with very good results for goal contributions, expected goal contributions and shots on goal.

#3 Kaly Sene, 20, Forward, Grasshoppers (on loan from Basel) and Senegal

Then we have the first striker on our list with 20-year-old Senegal striker Kaly Sene. He is currently on loan at Grasshoppers Basel.

As you would expect from a more out and out striker, his strengths lie in goal contributions and expected goal contributions, as well as shots on goal and touches in the box.


It’s clear that when working in football recruitment, the ability to create custom metrics and approach player identification from different angles is key to better understanding the players in our markets. Using % rankings, percentile rankings and custom metrics are a few ways to screen players and use widely available metrics to create bespoke templates and profiles unique to our clubs.