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Eleven
VLR.gg ScraperMap-level esports data, on tap.
Chapter concepts
- 1Role: Solo build.
- 2Status: Open source.
- 3Stack: Python, requests, BeautifulSoup, pandas, OpenPyXL.
- 4Key figure: CSV · XLSX clean exports.
The data engine behind the predictor: a polite scraper that turns VLR.gg matches into tidy, map-level datasets.
Why
A model is only as good as its data. Before the predictor could exist, I needed clean, map-level results, not just who won a series, but every map, score and team. So I built the scraper first.
CLI
$ python scrape.py? Event URL ▸ vlr.gg/event/2097/…→ parsing 18 matches→ 41 maps extracted✓ wrote results.csv (41 rows)✓ wrote results.xlsx (+ summary sheet)
Exhibit 11-3
Each row = one map
| Field | Example |
|---|---|
| Tournament | Champions Tour |
| Match type | Bo3, Playoffs |
| Teams | Team A vs Team B |
| Map | Ascent |
| Score | 13 to 9 |
| Winner | Team A |
Built to be robust
- 1Flexible input: event URL or match list
- 2Polite: 2s rate limiting
- 3Fallback parsing: survives layout changes
- 4Dual export: CSV + Excel
- 5Summary sheet: maps played / won / win%
Stackrequests · BeautifulSoup · pandas
OutputCSV + Excel
Manners2s delay · fallback parsing