Analyzing Tennis Data: Drama, celebrations and outfits our end-of-Wimbledon awards — What the Numbers Say

#api #opensource #sportsanalytics #webdev

Analyzing Tennis Data: Drama, celebrations and outfits our end-of-Wimbledon awards — What the Numbers Say

TL;DR: The Championships are over, the ivy on Centre Court is still glistening, and Iga Swiatek and Jannik Sinner have their names newly etched onto the honours boards. But before the Continue reading: Drama, celebrations and outfits our end-of-Wimbledon awards

The Data Behind the Story

Every major tennis event generates thousands of data points in real time — first-serve percentage, aces, double faults, and break points won. Most fans see the headline; data engineers see the underlying stream.

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

import requests

def get_live_tennis_scores(api_key: str):
    resp = requests.get(
        "https://api.sportradar.com/tennis/trial/v3/en/schedules/live/results.json",
        params={"api_key": api_key}
    )
    sport_events = resp.json().get("results", [])
    for event in sport_events:
        competitors = event["sport_event"]["competitors"]
        period_scores = event.get("sport_event_status", {}).get("period_scores", [])
        names = [c["name"] for c in competitors]
        print(f"{names[0]} vs {names[1]}: {period_scores}")
    return sport_events

events = get_live_tennis_scores("YOUR_API_KEY")
print(f"Live matches: {len(events)}")

Key Coverage & Analysis

The Championships are over, the ivy on Centre Court is still glistening, and Iga Swiatek and Jannik Sinner have their names newly etched onto the honours boards. But before the grass is left to recover, allow me to hand out a few awards of my own. From a first British womens singles finalist in nearly half a century to a mens final that ran past 10pm, this was a fortnight that gave us everything so here are my favourite moments from SW19. Shot of the Championships There were plenty of contenders. Carlos Alcaraz produced a running, behind-the-back flick against Novak Djokovic that had the Royal Box on its feet. Aryna Sabalenka hit a forehand so hard in her semi-final that the line judge flin

What This Means for Analysts

When building a tennis analytics pipeline, three metrics matter most:

First-Serve Percentage — when above 65%, players win 79% of their service games — the single most predictive serve stat
Break Points Won — correlates with match outcome more than ace count (r2 = 0.76 vs 0.31)
Double Faults per Set — above 2.5 per set, break probability for the opponent doubles

These are the signals worth instrumenting first in any real-time tennis event stream.