Analytics careers
A missing season should stop a recruitment ranking
An analyst’s judgement becomes visible when they explain what their dataset cannot support.
The dashboard works. The filters respond. A Ghanaian midfielder sits near the top of the shortlist. Then the analyst discovers that some fixtures are missing and cannot say which ones. In this hypothetical portfolio project, the professional decision is to pause the ranking. Explaining that pause can reveal more about the analyst’s judgement than another polished chart.
Missing fixtures change the comparison
Hypothetical fixture coverage
- Fixture 1✓Observed
- Fixture 2✓Observed
- Fixture 3?Missing
- Fixture 4✓Observed
- Fixture 5?Missing
- Fixture 6✓Observed
Identify the missing fixtures before comparing players. A blank record is not evidence of an uneventful match.
The symbols indicate record availability only. They are not scores, results, or player ratings.
Make the gap part of the work
Beggs argues that analytical purpose should determine the method and the information collected. For this hypothetical recruitment task, that means asking whether the available records can support a comparison of the role the club needs. A complete-looking table is insufficient if its coverage cannot be established.
A strong portfolio can show the discovery rather than hide it. Include the coverage check, explain which conclusions it prevents, and identify what information would allow the comparison to proceed. This turns an unfinished ranking into a finished piece of reasoning. It also gives a reviewer a way to distinguish your judgement from your software skills.
Separate the analyst and engineering contributions
An analyst might narrow the question to observable actions in the available matches and accompany the result with video review. A data engineer might build the fixture check that detects missing matches, preserve stable player identifiers, and document how a failed refresh is reported. Both contributions matter, but they solve different parts of the problem.
Do not replace the missing fixtures with assumptions that conveniently preserve the shortlist. An estimate can be useful when its purpose, uncertainty, and validation are explained. Here, however, the first issue is that the analyst does not know which football situations the sample contains. A more elaborate model would not establish that coverage.
Give the reader something they can challenge
Publish a compact case: the club’s question, the data available, the problem found, and the decision changed. Include a reproducible check and one visual that advances that argument. If the data licence prevents redistribution, explain how the check works without publishing restricted records.
For an African player being assessed abroad, this discipline matters because an uncertain comparison can become a confident career judgement. Your portfolio should show how you keep that judgement proportionate to the evidence. The attractive output is the last part of the work; the defensible decision is the reason for doing it.
Build a project that exposes the limitation
- Choose a football decision with a clearly stated user.
- Show one data limitation and how it changed your conclusion.
- Include a reproducible check alongside the visual.