How to build a comparative statistics service for teams?

What is a sports statistics API and how does it work

A sports statistics API is a programming interface that provides structured data about matches, teams, players, tournaments, and betting odds in a machine-readable format. Using methods such as /v2/{sportSlug}/matches, /v2/{sportSlug}/teams, /v2/{sportSlug}/players, the developer obtains all the necessary figures for analytics: score, xG, possession, shots, cards, live events, detailed matchStatistics, odds oddsBase, as well as metadata on tournaments and seasons. The service API SPORT for sports events supports football, hockey, basketball, tennis, table tennis, esports, and other sports, allowing the construction of unified cross-disciplinary team comparison services.

Technically, the API operates over the HTTP(S) protocol in the REST paradigm. The client sends requests using the GET method to addresses of the form https://api.api-sport.ru/v2/football/matches, passes filtering parameters in the query string (date, tournament ID, teams, match status), and authenticates using the API key in the header Authorization. The response returns JSON with a clearly defined structure: match objects contain nested entities of the tournament, categories (countries), teams, lineups, fields currentMatchMinute, liveEvents, detailed statistics by periods and odds markets. This allows avoiding HTML parsing and focusing on business logic and visualizing comparative statistics.

An important feature of modern sports event APIs is working with current and historical data. You can request past matches for calculating rankings and team forms, current matches for live comparisons, upcoming games for predictions and content preparation. In the near future, persistent connections (WebSocket) will be increasingly used to receive real-time updates without polling the server, as well as AI tools for automatically calculating advanced metrics and personalized suggestions for the user. By using a stable infrastructure and regular API updates, you can build a team comparison service that will scale across sports and depth of statistics without reworking the core of the project.

Example of a basic request to the sports events API

Below is an example of obtaining a list of today’s football matches filtered by a specific team. Such a request can be used as a basis for the «Team Form» block in your comparison service.

curl "https://api.api-sport.ru/v2/football/matches?team_id=195801&status=finished" \
  -H "Authorization: YOUR_API_KEY" \
  -H "Accept: application/json"

How to choose a sports events API for a team comparison service

When choosing a sports data provider for a comparative statistics service, it is critical to assess not only the price but also the depth and stability of the data. First, it is important to ensure that the API covers the sports and tournaments you need: top leagues, international competitions, second divisions, youth tournaments. The next criterion is the detail of the statistics: the presence of advanced metrics in the field matchStatistics (shots by zones, possession, duels, passes), live events liveEvents, current odds oddsBase and links to match highlights. All of this directly affects the value of your comparison service for experienced fans and bettors.

No less important are the technical parameters: response speed and request limits, server stability, a transparent versioning system and changelog, the presence of SDK and code examples. Advanced providers, such as the platform API SPORT, offer a unified data model for different sports, which greatly simplifies the development of cross-sport comparison tables. Pay attention to the presence of development plans: support for WebSocket connections for streaming live updates, implementation of AI tools for calculating advanced metrics and recommendations, expansion of the list of supported disciplines and tournaments.

Special attention should be paid to documentation and the onboarding process. A good API provides detailed descriptions of all fields, ready-made examples of requests and responses, a clear authorization scheme, and a sandbox for testing. The less time a developer spends understanding the JSON structure, the faster your team comparison service will go into production. Ideally, the start should be possible in one day: registration, obtaining a key, a couple of test requests, deploying the first version of the comparison page for two teams with basic metrics.

Example of obtaining categories and popular tournaments.

Below is an example of a frontend request to the API to get a list of categories (countries) and recommended tournaments that can be used by default in the league selection widget for team comparison.

fetch('https://api.api-sport.ru/v2/football/categories', {
  headers: {
    'Authorization': 'YOUR_API_KEY',
    'Accept': 'application/json'
  }
})
  .then(response => response.json())
  .then(data => {
    const categories = data.categories;
    const defaultTournaments = data.defaultTournaments.ru || [];
    console.log('Всего категорий:', categories.length);
    console.log('Рекомендуемые турниры:', defaultTournaments);
  })
  .catch(console.error);

How to get team statistics through the API: key queries and parameters

For the team comparative statistics service, several key API methods become the foundation. First, this is obtaining a list of matches through the endpoint /v2/{sportSlug}/matches with filters by date, tournaments, and teams. Second, this is requesting detailed information about a specific game through /v2/{sportSlug}/matches/{matchId}, where extended fields are available matchStatistics, liveEvents, oddsBase, lineups, and stadium information. Third, this is working with teams through /v2/{sportSlug}/teams, which allows you to get general data about the club, country, manager, and player roster for building team cards and deeper analytics.

A typical scenario for the «Team Form for the Last N Matches» block looks like this: first, you request a list of past games for a specific club using the filter team_id and status завершено. Then, for the selected matches, if necessary, you obtain detailed data for each ID, analyzing the fields домашнийСчет, выезднойСчет, groupings in matchStatistics (strikes, possession, martial arts, defense), as well as the dynamics of coefficients oddsBase. Based on this, averaged indicators are formed, which are then conveniently compared with similar metrics of the opponent.

The main parameters used in API requests for sports events to compare teams: team_id (team ID), tournament_id (one or more tournament IDs separated by commas), season_id (specific season), дата (filtering by date), status (match status: notstarted, inprogress, завершено etc.), as well as lists of match IDs ids. After receiving the key in the API SPORT personal account, you can combine these parameters and build finely tuned selections on which the logic of your service will be based: from simple result comparisons to advanced rankings taking into account the strength of opponents and form by tournaments.

Example of obtaining the latest matches of a team for comparison

Below is an example of a request that retrieves the 10 most recent completed matches of a specific team and then prepares aggregated statistics for the comparison block.

async function getTeamLastMatches(teamId) {
  const url = `https://api.api-sport.ru/v2/football/matches?team_id=${teamId}&status=finished`;
  const response = await fetch(url, {
    headers: {
      'Authorization': 'YOUR_API_KEY',
      'Accept': 'application/json'
    }
  });
  const data = await response.json();
  const matches = data.matches.slice(-10); // последние 10 игр
  return matches.map(match => ({
    id: match.id,
    date: match.dateEvent,
    homeTeam: match.homeTeam.name,
    awayTeam: match.awayTeam.name,
    score: `${match.homeScore.current}:${match.awayScore.current}`
  }));
}

How to design a database for comparative statistics of sports teams

A reliable team comparison service relies on a well-designed database. At the center of the model are usually the entities «sport», «tournament», «season», «team», «match», and «match statistics». The data obtained from the API is normalized: each team is stored once and linked to matches through foreign keys, tournaments and seasons are placed in separate tables, and detailed metrics (shots, possession, xG, odds, live events) can be placed in separate tables or in JSON fields if the DBMS allows it. This approach simplifies the construction of complex comparative queries, reduces duplication, and organizes the caching of API responses.

Often, to speed up analytical selections, separate aggregating tables are created, where pre-calculated metrics are updated on a schedule or by event: average goals per match, win percentage, average possession, number of shots on goal, expected goals metrics, home and away game efficiency. This data is then used for instant team comparisons, without heavy queries on raw events. It is also important to consider indexing on key fields: team ID, tournament, season, match date, and status — to quickly find the required set of matches when switching filters on the user side.

The optimal database structure usually combines a relational approach (for relationships between entities) and storage of complex statistics in JSON fields or a separate repository for analytics. This allows for gradually deepening the model as new data appears in the API (for example, additional metrics in matchStatistics or new markets in oddsBase) without a global redesign of the scheme. Thoughtful architecture will facilitate the integration of new sports, as well as the transition to streaming data reception (WebSocket) and AI modules that can generate separate tables with team strength ratings and predictive metrics.

Example of a simplified table structure

Below is an example of a minimalist table schema for storing teams, matches, and basic statistics, on the basis of which a comparative statistics service can be built.

CREATE TABLE sports (
  id SERIAL PRIMARY KEY,
  slug VARCHAR(50) UNIQUE NOT NULL,
  name VARCHAR(100) NOT NULL
);
CREATE TABLE teams (
  id BIGINT PRIMARY KEY,
  sport_id INT NOT NULL REFERENCES sports(id),
  name VARCHAR(255) NOT NULL,
  country VARCHAR(100)
);
CREATE TABLE matches (
  id BIGINT PRIMARY KEY,
  sport_id INT NOT NULL REFERENCES sports(id),
  tournament_id BIGINT,
  season_id BIGINT,
  date_event DATE NOT NULL,
  status VARCHAR(32) NOT NULL,
  home_team_id BIGINT NOT NULL REFERENCES teams(id),
  away_team_id BIGINT NOT NULL REFERENCES teams(id),
  home_score INT,
  away_score INT
);
CREATE TABLE match_statistics (
  match_id BIGINT PRIMARY KEY REFERENCES matches(id),
  raw_json JSONB NOT NULL
);

How to build a team comparison service based on sports APIs: step-by-step instructions

Creating a comparative statistics service for teams can logically be divided into several stages. First, you formulate the product task: what sports and tournaments are needed, what metrics will be key (results, shots, possession, expected goals, bookmaker odds), who your main audience is — fans, media, or bettors. Next, you choose and configure the API provider: register, obtain a key in your personal account, check the limits, test the main requests for matches and teams, and ensure the completeness and relevance of the statistics.

The next step is to implement the backend layer: the module for interacting with the API, the caching system, and the data update scheduler. Usually, cron or a task queue is used, which regularly requests new matches, updates live events and statistics for ongoing games, and aggregates historical data for analytics. At the same time, a database is designed and deployed, where normalized entities and pre-calculated metrics for comparison are stored. After that, the internal API of your service is built, which quickly returns a prepared dataset for visualization upon client request (two teams, tournament, date range).

In the final stage, a user interface is created: pages or widgets for comparing teams, filters by tournaments and periods, blocks for «Form,» «Head-to-head,» «Match Statistics,» as well as integration of odds and predictive ratings. It is important that the logic on the frontend is as simple as possible: all heavy calculations are done in advance in the database or background tasks. With this approach, your service easily scales in terms of traffic and the number of sports, and expanding functionality (for example, adding AI ratings of team strength or live updates via WebSocket) comes down to connecting new modules and minor interface changes, without a complete redesign of the architecture.

Example of a backend endpoint for comparing two teams

Below is a conditional example of a Node.js handler that accesses an external sports events API and prepares a basic object for the team comparison page.

import express from 'express';
import fetch from 'node-fetch';
const app = express();
const API_BASE = 'https://api.api-sport.ru/v2/football';
const API_KEY = process.env.SPORT_API_KEY;
app.get('/compare', async (req, res) => {
  const { team1, team2 } = req.query;
  try {
    const url1 = `${API_BASE}/matches?team_id=${team1}&status=finished`;
    const url2 = `${API_BASE}/matches?team_id=${team2}&status=finished`;
    const [r1, r2] = await Promise.all([
      fetch(url1, { headers: { Authorization: API_KEY } }),
      fetch(url2, { headers: { Authorization: API_KEY } })
    ]);
    const [d1, d2] = await Promise.all([r1.json(), r2.json()]);
    res.json({
      team1: { id: team1, matches: d1.matches },
      team2: { id: team2, matches: d2.matches }
    });
  } catch (e) {
    res.status(500).json({ error: 'Comparison failed', details: e.message });
  }
});

Such an endpoint can be used as a basis for your own internal API service, supplemented with database queries and pre-calculated metrics. You still obtain current data from a reliable external sports statistics provider, such as API SPORT.

Visualization and display of team statistics on the website: tables, charts, rankings

The user value of the comparative statistics service for teams is largely determined by how exactly you present the data. At the interface level, it is important to combine clear tables with compact graphs and visual indicators: comparison bars (bar charts), sparkline forms, heat maps of shot zones, and combat maps. The foundation consists of tables: a general summary of goals and results, a block with indicators of attacking and defensive play, a «Form» section for recent matches, as well as a separate block of live statistics for current games, which is updated based on API data or WebSocket stream.

For more advanced users and bettors, it is useful to introduce ratings and indices based on aggregated data: attack and defense strength ratings, current form, calendar difficulty, home and away game efficiency, comparison of expected and actual metrics. On the client side, it is convenient to use JSON structures prepared in the backend, where percentages, averages, deltas, and indices have already been calculated. The frontend code in this case is only responsible for neat display and interactivity: tooltips on hover, period switches, tournament tabs.

Pay special attention to responsiveness and loading speed. The comparison service should work equally well on desktop and mobile devices, and key metrics should be visible without scrolling the screen. Optimizing JSON responses, caching, and lazy loading of charts will help maintain high interface speed even with a large volume of statistics. If you plan to connect live updates and AI modules in the future (for example, predictive xG charts or expected goal difference), make sure to allocate space in the design for dynamic blocks and tooltips with metric explanations.

Example of data preparation for a comparison table on the frontend

Below is an example of a simple transformation of two sets of team statistics into a structure for an HTML table or any JS chart.

function buildComparisonTable(team1Stats, team2Stats) {
  return [
    {
      metric: 'Средние голы за матч',
      team1: team1Stats.avgGoals,
      team2: team2Stats.avgGoals
    },
    {
      metric: 'Удары по воротам',
      team1: team1Stats.shotsOnTarget,
      team2: team2Stats.shotsOnTarget
    },
    {
      metric: 'Владение мячом (%)',
      team1: team1Stats.possession,
      team2: team2Stats.possession
    },
    {
      metric: 'Жёлтые карточки',
      team1: team1Stats.yellowCards,
      team2: team2Stats.yellowCards
    }
  ];
}

The resulting array can be easily rendered as a table or passed to a visualization library, leaving all the heavy lifting of collecting and calculating metrics to the API of sports events and your backend layer.