Based on the initial content read from the uploaded files, we can identify the following characteristics:

- The first three files (`file-wepCOk8EA8Lgf393jfalAZ3I`, `file-c4gk2wrH5WeZxQEyLRm1MqUx`, `file-sDHaWq9SOZzFHLhUmGmMRWjo`) appear to contain comma-separated values (CSV) with player data. The headers indicate these files to be datasets of player attributes from FIFA games.
- The fourth file (`file-cOQyXsmHnK6UCSP1qRODfrkF`) is a markdown file (`readme.md`) describing the dataset about FIFA 21 player data, mentioning the context, potential analysis ideas, and hinting that the dataset includes data from FIFA 15 to FIFA 21.

The hint provided for the review focuses on the presence of a missing column in one of the datasets. To address this hint systematically, the next steps will involve:

1. Reading the header of each CSV file to compare their structures and identify any missing column relative to what is expected based on the hint and the description in the `readme.md` file.
2. Reviewing the `readme.md` file content more thoroughly to understand what columns are expected to be in these datasets.

Let's start with step 1 by reading the headers of each CSV file, and then we'll proceed to review the `readme.md` appropriately.The truncated output limits a complete comparison of columns across all files directly through this method. However, a specific issue we can note without full visibility is the potential presence or absence of certain columns that may lead to inconsistencies among the datasets. Given the nature of the truncated output, to precisely identify a missing column in any of the datasets, I will compare the columns programmatically and highlight any differences found.

Let's proceed by comparing the column lists of the datasets against each other to identify any missing columns in accordance with the hint provided.The comparison of the headers from the CSV files did not reveal a direct indication of missing columns between them based on the programmatic comparison conducted. This result suggests that, at least within the limits of the first columns checked (and given the challenges of comparing truncated output directly), there does not seem to be an issue with missing columns among the datasets titled "players_19.csv", "players_20.csv", and "players_21.csv".

Given this outcome, my next step is to review the content of `readme.md` more thoroughly. This will help ensure there are no discrepancies mentioned within the description that could point towards expected columns or any other details that infer an expected structure which was not observed in the datasets.

Let's proceed to read the complete content of the `readme.md` file to search for any details related to expected dataset columns or structures that might have been overlooked in our initial analysis.The `readme.md` file provides a general description of the dataset, including historical player data from FIFA 15 through FIFA 21, with over 100+ attributes covering various aspects, such as player positions, attributes, personal data, and more. It does not specify column names directly but does imply a broad and consistent structure across datasets to allow for comparative analysis over different versions of the game. 

Given the absence of a specified list of expected columns in the readme and the programmatic comparison not indicating missing columns, the analysis does not directly identify an issue related to a "missing column" based on provided instructions and the content of the `readme.md`.

However, with the information available and the broad descriptions provided, if there were a missing column, it would likely relate to either player attributes, personal data, or other referenced comparative statistics not directly observable through the provided methods here without explicit column expectations.

Without specific evidence of an omission from the `readme.md`, one might conclude that any further analysis to identify missing columns would require a deeper, more detailed comparison against a standard dataset structure expected for FIFA player data, which isn't explicitly outlined in the provided hint or readme content provided.

If a detailed list of expected columns were provided or if the `readme.md` included a definitive schema for the datasets, further analysis could more precisely identify discrepancies. As of now, the review suggests that the datasets' structures are consistent with each other to the best observable extent within the scope of provided documents and analysis capabilities.