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Football Match Odds and Record Tracker

Data Analysis Updated 2026.08.30

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Please follow https://skillhub.cn/install/skillhub.md to install @user_5c739715/football-pools.

About this skill

Problem

Chinese football data is split across sports-lottery odds and score sites. Checking a match's win/draw/loss, goal total, or score odds requires the lottery calculator; checking a team's recent form and win rate requires Zhibo8 historical scores. Manual workflows are fragile because club names are inconsistent, dates are scattered, and a keyword like “Paris” can match multiple Paris clubs.

How It Works

The skill wraps three queries into a runnable Python tool:

  • Upcoming matches and odds: reads the JSON backend behind the lottery calculator, using poolCode to request currently selling matches and odds such as win/draw/loss, handicap, total goals, and score markets. It targets the JSON API rather than scraping the JS-rendered page.
  • Team fixtures: filters upcoming matches by team-name substring and displays odds.
  • Team records: reads Zhibo8 / qiumibao score JSON to summarize finished matches, opponent history, and win rate.

When the request includes both future fixtures/odds and recent form/win rate, the team query returns both halves in one pass. The script uses the Python standard library and calls live endpoints at runtime, making it suitable for feeding structured results into a model for explanation or lightweight analysis.

Boundaries

  • Historical score data covers roughly 45 days; longer seasons require segmented queries.
  • Odds are only for currently selling future matches; past odds are not provided.
  • Win rate is calculated from finished matches with final scores only.
  • Live endpoints may be rate-limited or temporarily unavailable, and the CLI catches HTTP or parse errors with friendly messages.

Use Cases

  • Compare upcoming football fixtures and odds across win/draw/loss, handicap, goal total, and score markets.
  • Filter future fixtures by a team keyword and confirm the exact club, dates, and odds before analysis.
  • Pull the last 45 days of finished scores to compute win rate and head-to-head form against a chosen opponent.
  • Use a ready Python CLI to read lottery and Zhibo8 JSON endpoints instead of parsing pages manually.

Best For

  • Sports betting analysts who need quick checks of upcoming matches and odds.
  • Football editors tracking team form, win rates, and head-to-head records.
  • Data analysts maintaining scripts that read lottery and Zhibo8 JSON endpoints.
  • Sports data journalists compiling fixtures, odds, and historical results.