deanpatel2/webscraper-premdata

1. Repository shape

- This is a very small, single-purpose scraping project.

- The entire implementation appears to live in one Python script, FotMob_PremScraper.py, with one sample output file, PL_matchweek_1.csv, plus the README.

2. Primary purpose

- The script extracts Premier League match statistics from FotMob’s “by round” match list.

- It is a data acquisition pipeline, not a full application: input is a FotMob webpage, output is a CSV of match/team statistics.

3. Core runtime

- The scraper is driven by Selenium, so the browser is part of the architecture.

- A local ChromeDriver executable is required, and the script depends on a matching Chrome browser setup on the machine running it.

4. Main data flow

- The script starts at FotMob’s Premier League rounds page.

- It navigates matchweek by matchweek, opens each match, collects statistics from the match pages, and aggregates them into tabular records.

- Those records are then written out to a CSV file.

5. Input boundary

- The external dependency boundary is FotMob’s public web UI and its page structure.

- The scraper depends on the current layout/HTML behavior of FotMob pages, so the architecture is tightly coupled to that external site.

6. Output boundary

- The only persisted artifact described is a CSV file.

- The example CSV suggests a row-per-team or row-per-match-statistics representation containing metrics like xG, xGOT, possession, interceptions, duels, and opposition-half passes.

7. Configuration model

- The script is configured through global variables, specifically DRIVER_PATH and MATCHWEEK.

- That means the operational parameters are embedded in the script rather than managed through a config file, CLI, or environment-based settings.

8. Processing style

- This is a sequential scraper: discover matchweek matches, visit each match, extract stats, append results.

- There is no sign of distributed processing, queueing, database storage, or asynchronous pipeline stages.

9. Technology boundaries

- Python is the implementation language.

- Selenium handles browser automation and page interaction.

- pandas is used for tabular assembly and CSV generation.

10. What is not present

- There are no visible service layers, APIs, web servers, or UI components.

- There is also no evidence of modular packaging, tests, CI/CD, or a database-backed architecture.

11. Domain scope

- The current scope is limited to Premier League matchweek stats.

- The README notes future expansion toward shot maps and additional leagues, but those are not yet part of the implemented architecture.

12. How to diagram it

- The clean architecture view is: user runs script → Selenium-controlled Chrome navigates FotMob → scraper extracts match stats → pandas structures data → CSV file saved locally.

- The two important external boundaries are FotMob on one side and the local ChromeDriver/Chrome environment on the other.