MediumScouting Uncertainty: adding value to data scouting results | by Marc Lamberts | Jul, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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Scouting Uncertainty: adding value to data scouting results

Marc Lamberts

9 min read·<br>Jul 8, 2026

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I have been looking at spreadsheets for football scouting for over a decade, and every now and then, my perception completely changes. The biggest change I have experienced is when I started to realise that we use data mostly to shape profiles and how they fit in the profile. Pure quality can be measured, but it often is so conflicted with different variables, that it is difficult to make a definitive contribution.<br>Lately, I’ve been creating more and more on a meta level, mostly in data engineering. In that light, I wanted to share something I have introduced in my day-to-day data scouting when working with aggregated data: scouting uncertainty.<br>The idea is to add an uncertainty score to the scouting score to add a level of trust towards the data. If the data is trustworthy and uncertainty is low, the quality of data scouting for that specific player will be higher.<br>Data<br>For this little research, I have had a look at Wyscout’s aggregated data of the J2-J3 league of 2026. Normally these are split into two different leagues, but for this anniversary year, things are a little bit different.<br>This data focuses only on strikers, so I’ve only looked at players with CF as a position and/or players with multiple positions, but CF as a dominant position in the games they have played.<br>The data has been collected in XLSX files from the Wyscout platform on June 15, 2026. Things will not have changed since the World Cup has been going on and the new season has to start, but for clarity's sake, here it is.<br>The geometry of control: short passes in Eredivisie 2025–2026<br>If you ever wonder how I get to my ideas and think there’s a ridiculously academic process to it, let me kill that…

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Profiles<br>Not every striker is created equal. What I mean by this is that a striker is the position, but the profile is different. In other words, what kind of striker is the player?<br>I have categorised the strikers into five distinct profiles:<br>Goalscoring Striker<br>Target Striker<br>Dynamic Striker<br>Second Striker<br>Complete forward<br>All these profiles are created from all the data we have available, but are created from weighted composite scores in z-scores, which are normalised from 0–100. This doesn’t say anything about the quality of the striker, but much more about the tendencies of said striker. A score of 67 means that this player scores 67% on the perfect striker profile scale.<br>Press enter or click to view image in full size

We have 70 players that fit the filters, and if we look at the image above, we can state the following. The Target Striker leads the profiles with 31%, followed by the Goalscoring Striker with 23%, and the Second Striker with 23%. The Dynamic Striker has 20%, and finally, we end with a complete forward with only 3%. Note for the complete forward, they are only complete forwards if they score 65% or higher in each of the categories, which is a strict cutoff.<br>Press enter or click to view image in full size

In the table above, you can see all 70 players and how they rank in the particular categories of striker profiles. This is more or less just information about each player and nothing definitive in terms of quality. What are the biggest relations are within two profiles, that is, between the Goalscoring Striker and the Target Striker?<br>Press enter or click to view image in full size

In the visual above, you can see the five player per category who score the highest in their respective categories. Sagawa is the best fit for Target Striker, Mori for Dynamic Striker, Nishino for Goalscoring Striker and Anderson for Second Striker.<br>Calculating the uncertainty score<br>Every Scouting Score in this model comes paired with an Uncertainty Score, a 0 to 100 rating of how much to trust the number, where higher means less reliable. The idea is simple: a striker who has racked up 90 in a full, injury-free season means something very different from a striker who has hit the same number in half as many minutes off a hot streak of shooting luck. Rather than quietly averaging over that risk, the model surfaces it as its own figure, so a big Scouting Score can be read alongside a clear-eyed sense of how much of it is signal versus noise.<br>Press enter or click to view image in full size

The Uncertainty Score is built from five factors, each scored 0 to 100 and blended into a weighted total:<br>how much game time the player has actually logged relative to a settled 20-match sample (35% of the total, the single biggest driver);<br>how many shots they have taken, since shooting and conversion numbers are notoriously unstable on small volumes (20%);<br>how far outside a 24 to 29...