Player, match and tournament performance data

Table Tennis Statistics API for Player and Match Analytics

Build analytics dashboards, broadcaster tools, prediction models, scouting products and data-driven applications with structured access to wins, losses, current form, ranking history, set win percentage, match win percentage, tournament performance, streaks and supported head-to-head statistics.

Career Records Current Form Set Win % Tournament Stats
Player Stats 2026 Summary
{
  "player_id": "player_tt_101",
  "matches": 42,
  "wins": 31,
  "losses": 11,
  "match_win_pct": 73.8,
  "sets_won": 112,
  "sets_lost": 66,
  "set_win_pct": 62.9,
  "current_streak": 5
}
Analyse Player Form Recent wins, losses and streaks
Compare Long-Term Performance Rankings, sets and tournament history
Statistics notice: The field names, endpoint paths, sample values, historical depth and metric availability on this page are illustrative until the production Table Tennis API feed is confirmed. Publish only the metrics supported by the final data source.
Structured table tennis analytics

What Is a Table Tennis Statistics API?

A Table Tennis Statistics API gives applications structured access to player, match, tournament and historical performance data. Instead of calculating every metric from raw score pages, developers can request consistent records linked through stable player, match and tournament identifiers.

Statistics can support media graphics, player profile pages, ranking analysis, prediction models, scouting tools, betting products, research dashboards and internal business intelligence.

Every statistic should be displayed with its competition, date range, season and sample size so users understand what the number represents.

Table Tennis Statistics Available Through the API

Career Wins and Losses

Retrieve supported career match totals across available tournaments and seasons.

Current Form

Analyse recent match results and build form indicators over a selected number of matches.

Match Win Percentage

Compare wins with total completed matches across a selected period.

Set Win Percentage

Measure sets won versus total sets played where set-level history is available.

Ranking History

Connect performance statistics to current and historical ranking records where supported.

Ranking Movement

Track ranking gains and losses between supported ranking releases.

Tournament Performance

Analyse results, rounds reached, opponents and win rates by tournament.

Win Streaks

Track current and historical consecutive-win sequences.

Loss Streaks

Identify consecutive losses and shifts in recent form.

Average Match Duration

Calculate supported average match duration from available timing fields.

Average Sets per Match

Measure how many sets a player typically plays in completed matches.

Head-to-Head Statistics

Compare two players using previous meetings, wins, losses and set history.

Performance by Opponent Ranking

Group supported results by opponent ranking bands where historical ranking context exists.

Performance by Tournament

Filter player performance by event, stage, date range or season.

Historical Trends

Build time-series views of match success, set success, ranking and tournament performance.

Analyse Performance at Multiple Levels

Table tennis analytics can be grouped by match, tournament, season or career depending on the product.

Level Example statistics Best suited to
Product use
Match One completed match Final score, sets won, sets lost, duration and status Match pages and post-match analysis
Recent Form Selected previous matches Wins, losses, set differential and streaks Pre-match analysis and prediction inputs
Tournament One event Matches played, rounds reached, wins, losses and set record Event dashboards and broadcaster tools
Season One calendar or ranking period Match win %, set win %, ranking movement and tournament results Player profile and yearly analysis
Career All supported history Total wins, losses, ranking peaks and long-term trends Research, profiles and historical comparison
Career performance

Career Wins, Losses and Match Win Percentage

Career records should be based only on matches included in the supported historical dataset. Display the sample period so users do not assume unsupported matches are included.

Total Matches

Number of completed supported matches in the selected dataset.

Total Wins

Count of supported matches won by the player.

Total Losses

Count of supported completed matches lost by the player.

Match Win Percentage

Wins divided by supported completed matches in the selected sample.

match_win_percentage =
wins / completed_matches × 100
Set analytics

Analyse Sets Won, Sets Lost and Set Win Percentage

Set-level statistics add more detail than match results alone. A player can win a high percentage of matches while regularly dropping sets, or lose several close matches while maintaining a strong set record.

Sets Won

Total supported sets won within the selected filters.

Sets Lost

Total supported sets lost within the selected filters.

Set Differential

Sets won minus sets lost across the selected period.

Set Win Percentage

Sets won divided by all supported completed sets.

set_win_percentage =
sets_won / (sets_won + sets_lost) × 100
Recent form

Measure Current Table Tennis Form

Current form should use a clearly defined recent sample rather than an undefined label such as “hot” or “cold.”

Recent form field Example interpretation
Last 5 match record Wins and losses across the five most recent supported matches
Last 10 match win % Win rate over the ten most recent supported matches
Recent set differential Sets won minus sets lost across the selected recent sample
Current win streak Consecutive supported wins immediately before the selected date
Current loss streak Consecutive supported losses immediately before the selected date
Always calculate pre-match form using matches completed before the target match when statistics are used for prediction.
Ranking analytics

Connect Statistics With Ranking History

Ranking context helps explain how a player’s competitive position changes alongside results.

Current Ranking

Latest supported ranking position.

Ranking Points

Ranking points where included by the ranking source.

Previous Ranking

Earlier supported ranking position for comparison.

Ranking Movement

Positions gained or lost between supported releases.

Highest Supported Ranking

Best ranking position found within the available historical data.

Ranking Timeline

Time-series history for charts and player profile pages.

Tournament performance

Analyse Results by Tournament

Tournament-level statistics help developers compare how a player performs across different events and stages.

Statistic What it represents
Matches played Completed supported matches in the tournament
Wins and losses Player match record within the tournament
Sets won and lost Set-level record for the event
Best round reached Deepest documented stage reached by the player
Average match duration Average duration where timing data is available
Opponent ranking profile Ranking context of opponents where historical ranking data exists
Head-to-head analytics

Combine Statistics With Player Head-to-Head Records

Head-to-head records are particularly useful when comparing two players before a tournament match.

Previous Meetings

Number of supported matches played between the selected players.

H2H Wins

Direct wins for each player within the selected sample.

H2H Set Record

Sets won and lost across direct meetings.

Recent H2H

Most recent direct matchups with tournament and ranking context.

Form Comparison

Compare recent overall form beside direct matchup history.

Ranking Comparison

Compare current or historical rankings for both players.

API request

Retrieve Table Tennis Player Statistics

Illustrative Endpoint

GET /v1/table-tennis/players/{player_id}/statistics

Example Query

GET /v1/table-tennis/players/player_tt_101/statistics
    ?season=2026
    &tournament_id=tournament_211
    &recent_matches=10

Possible Filters

Parameter Example Purpose
season 2026 Limit statistics to one season
tournament_id tournament_211 Filter by one tournament
date_from 2026-01-01 Start of a custom historical period
date_to 2026-08-07 End of a custom historical period
recent_matches 10 Calculate recent form from a selected sample
opponent_id player_tt_204 Limit results to direct matches against one opponent
Example JSON

Example Table Tennis Statistics Response

{
  "data": {
    "player": {
      "id": "player_tt_101",
      "name": "Player A",
      "nationality": "EX"
    },
    "filters": {
      "season": "2026",
      "tournament_id": null
    },
    "ranking": {
      "current": 8,
      "previous": 10,
      "movement": 2
    },
    "matches": {
      "played": 42,
      "won": 31,
      "lost": 11,
      "win_percentage": 73.8
    },
    "sets": {
      "won": 112,
      "lost": 66,
      "win_percentage": 62.9
    },
    "form": {
      "last_10": [
        "W", "W", "L", "W", "W",
        "W", "L", "W", "W", "W"
      ],
      "current_win_streak": 5
    },
    "tournaments": {
      "played": 9,
      "best_finish": "semifinal"
    },
    "average_match_duration_seconds": 2280,
    "updated_at": "2026-08-07T05:30:00Z"
  }
}

All values are illustrative. Final statistics and field names must match the production data model.

JavaScript

Retrieve Player Statistics With JavaScript

const playerId = 'player_tt_101';

const params = new URLSearchParams({
  season: '2026',
  recent_matches: '10'
});

const response = await fetch(
  `/api/table-tennis/players/${encodeURIComponent(playerId)}/statistics?${params}`
);

if (!response.ok) {
  throw new Error(
    `Statistics request failed: ${response.status}`
  );
}

const payload = await response.json();

console.log(payload.data.matches.win_percentage);
console.log(payload.data.sets.win_percentage);
Python

Retrieve Player Statistics With Python

import requests

player_id = "player_tt_101"

response = requests.get(
    (
        "https://api.example.com/v1/table-tennis/"
        f"players/{player_id}/statistics"
    ),
    params={
        "season": "2026",
        "recent_matches": 10,
    },
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Accept": "application/json",
    },
    timeout=15,
)

response.raise_for_status()
stats = response.json()
PHP

Retrieve Player Statistics With PHP

<?php

$playerId = 'player_tt_101';

$query = http_build_query([
    'season' => '2026',
    'recent_matches' => 10,
]);

$url = sprintf(
    'https://api.example.com/v1/table-tennis/players/%s/statistics?%s',
    rawurlencode($playerId),
    $query
);

$ch = curl_init($url);

curl_setopt_array($ch, [
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_HTTPHEADER => [
        'Authorization: Bearer YOUR_API_KEY',
        'Accept: application/json',
    ],
    CURLOPT_TIMEOUT => 15,
]);

$body = curl_exec($ch);
$status = curl_getinfo($ch, CURLINFO_HTTP_CODE);

curl_close($ch);
Historical analytics

Build Historical Table Tennis Performance Trends

Historical statistics can power ranking charts, season comparisons, long-term player profiles and model features.

Useful Historical Series

  • Ranking position by date
  • Ranking points by release
  • Match win percentage by season
  • Set win percentage by season
  • Win streaks over time
  • Tournament finishes by year
  • Head-to-head record by date
  • Average match duration by period
Historical depth may vary by tournament and metric. Do not imply a complete career record when older data is not available.
Prediction and modelling

Use Statistics as Inputs for Table Tennis Prediction Models

Table tennis statistics can become model features when they are built only from information available before the predicted match.

Current Ranking

Use ranking difference as one player-strength feature.

Recent Form

Calculate recent wins, losses and set differential before kickoff.

Head-to-Head

Use recent direct meetings where the sample is relevant.

Set Win Percentage

Measure match depth beyond simple wins and losses.

Tournament Performance

Include event-specific performance where historically available.

Opponent Strength

Adjust results using opponent ranking or another validated strength measure.

Historical performance does not guarantee future outcomes. Prediction systems should output probabilities rather than guaranteed winners.
Data quality

Handle Missing and Partial Statistics Correctly

Not every tournament will contain the same historical or event-level detail.

Situation Recommended handling
Statistic unavailable Return null or documented unavailable state
True zero value Return numeric zero
Partial historical period Return the covered date range and sample size
Player identity changed Preserve stable internal or provider identifiers
Walkover Exclude from set-based calculations if no sets were played
Retirement Use a documented inclusion policy for match and set statistics
Caching

Cache Statistics According to How Often They Change

Data type Suggested strategy
Historical career statistics Longer cache with refresh after completed matches
Ranking history Refresh after a new supported ranking release
Current form Refresh after each completed match
Tournament statistics Refresh during active tournaments
Head-to-head summary Refresh after the players meet again
Commercial use cases

What Can You Build With a Table Tennis Statistics API?

Broadcaster Graphics

Display ranking, recent form, career record and H2H statistics during coverage.

Player Profile Pages

Combine identity, ranking history, results and long-term statistics.

Prediction Models

Build leakage-safe features from rankings, form, sets and H2H data.

Betting Analytics

Add historical performance context beside separately licensed odds and markets.

Scouting Tools

Compare player trends, tournament performance and matchup records.

Research Dashboards

Analyse historical ranking and performance data across players and tournaments.

Production checklist

Table Tennis Statistics Integration Checklist

  • Use stable player, match and tournament identifiers
  • Display the selected season or date range
  • Show sample size with percentages
  • Distinguish zero from unavailable data
  • Document walkover and retirement treatment
  • Use only pre-match information for prediction features
  • Confirm historical depth before publishing career claims
  • Refresh recent form after completed matches
  • Cache slower-changing historical statistics
  • Confirm storage and commercial-use rights
Frequently asked questions

Table Tennis Statistics API FAQs

What statistics can the Table Tennis API provide?

Depending on coverage, it can provide wins, losses, match win rate, set win rate, form, streaks, ranking history, tournament performance and H2H statistics.

Can I retrieve career wins and losses?

Yes, where sufficient historical match coverage exists. Display the supported date range with the totals.

Does the API provide set win percentage?

It can be calculated or supplied where set-level match history is available.

Can I retrieve current form?

Yes. Recent form can be calculated from a selected number of completed supported matches.

Does the API provide ranking history?

Ranking history may be available through the dedicated rankings data where supported.

Can I filter statistics by tournament?

Tournament filtering can be supported through stable tournament identifiers.

Can I use statistics for predictions?

Yes, provided features are created only from information available before the target match and sufficient historical data exists.

How are retirements handled?

Use the documented data policy for whether retired matches count in match and set-level statistics.

How far back do the statistics go?

Historical depth varies by tournament and metric. Confirm the required period through the coverage reference.

Build deeper table tennis analytics

Integrate Table Tennis Statistics Into Your Product

Combine player records, form, ranking history, set performance, tournament results and head-to-head context through structured API endpoints.

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