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Home Data Analysis

Data Analysis: Identifying young under-valued players in the Swiss top-flight

Lee Scott by Lee Scott
April 28, 2022
in Data Analysis, Player Analysis, Recruitment Analysis
Reading Time: 5 mins read
0
Data Analysis: Identifying young under-valued players in the Swiss top-flight

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

This time we will be turning our attention to the top-flight of Swiss football as we look to find players who are not necessarily playing for one of the biggest sides in the country. But first, we will give some context around the methods that are used for this piece. The data that we are using is all from Wyscout although as you will see as we work through the recommended players there are some custom metrics that have been built using the base data provided. We also use Python, in order to find the percentile data and code the pizza charts, and Tableau in order to create the visualisation showing the rankings of the players. By using techniques like percentile rankings we are better able to standardise and understand the data that we are working with.

The whole point of these articles is to shine a light on some of the players in these leagues who do not necessarily play for the biggest and strongest sides in their respective countries.  If we just take the data as it comes then there will always be a tendency for that data to be skewed towards the players who are playing for clubs at the top of the table. Instead, we can use calculated fields within tableau to calculate the % that each player has contributed to their team. This starts to highlight the players who may be undervalued as a result of playing for teams who are not performing as well.

For the purposes of this article, we will look to identify three players who may make interesting transfer targets for clubs this coming summer.

The analysis

switzerland3

First of all, lets look at the % that each player in our data set 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 within the data with Goal Contributions per 90. We can then calculate the % that each player contributes to their team.

As you can see from the ranking above there is a clear outlier in the Swiss league in the 23-year-old Luzern midfielder Filip Ugrinic who has contributed to a staggering 22.07% of his teams goals so far this season. There is then a drop off to the 20-year-old Senegalese forward Kaly Sene of Grasshoppers.

Using th

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    Lee Scott

    Lee Scott

    Lee Scott is an analyst for Total Football Analysis and a professional scout For Southampton FC. You can follow Lee on X @FMAnalysis.

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