How I Built a Real‑Time Tennis Stats Tracker: Zverev storms into semi‑finals despite late bedtime

Description and use

Built a real‑time tennis stats tracker that pulls live data from Sportradar, computes key metrics, and delivers real‑time updates for fans and analysts.

Problem

Need to ingest and analyze live tennis match data in real time to provide actionable insights for fans and analysts.

Outcome

The pipeline successfully streamed live match data, enabling real‑time display of scores and key statistics, and highlighted Zverev’s performance metrics during his semi‑final run.

Workflow

• 1. Register for Sportradar API and obtain API key.
• 2. Write a Python function to call the live scores endpoint and parse JSON.
• 3. Extract competitors, period scores, and other relevant fields.
• 4. Compute key metrics (first‑serve %, break points won, double faults per set).
• 5. Output formatted live match summaries and store data for downstream analysis.
• 6. Deploy the script to run continuously during tournaments.

Evidence

Here is a minimal Python snippet to pull live tennis data:

Source

When building a tennis analytics pipeline, three metrics matter most: First‑Serve Percentage, Break Points Won, Double Faults per Set.

Source