Why does the statistics differ on different sites: comparison of algorithms?

Why the statistics of sports events differ on different sites

Almost everyone who follows sports or develops sports services has encountered a situation where the numbers for the same match do not match on different resources. One place indicates 8 shots on target, another shows 6, and ball possession can differ by 5-10 percentage points. This is not an «internet» error, but a consequence of how exactly the statistics are collected and processed, what data sources are used, and what algorithms are behind the calculations.

Some metrics are automatically generated by tracking systems, others are recorded manually by scorers, and then go through several stages of verification and normalization. Each data provider defines its own rules: what to consider a dangerous moment, how to interpret rebounds, in which cases a shot is on target and in which it is not. Even the moment in time when an event appears in the feed can differ: one provider publishes «raw» data immediately, another provides it after validation. As a result, the final numbers that the user sees inevitably diverge.

If you are creating a website, mobile application, analytical system, or betting platform, it is critically important to rely on a single, consistent source. By using a specialized service such as API de eventos deportivos, you can obtain unified statistics for matches of different sports from one point. This eliminates the problem of discrepancies between pages within your project and helps explain to users why the values on your resource may not match the numbers on other sites.

How data sources and counting algorithms affect sports statistics

Sports statistics start with the source. Some providers work directly with the official protocols of leagues and federations, others use tracking systems and video streams, and others combine automatic event capture with manual tagging. Each approach provides data of varying accuracy and depth. For example, ball possession can be calculated based on the time the ball is under the control of the team or by the number and length of passes. Depending on the chosen method, you will get differing percentages, although the match itself remains the same.

The second level of differences is the aggregation and classification algorithms. The metric «shots on target» may not include blocked attempts or, conversely, take dangerous deflections into account. The xG indicator is built on different models, with different sets of input features and training samples, so a value of 1.8 xG from one system and 2.1 xG from another is a normal occurrence. In the API, this is reflected in the structure of the fields: somewhere you see only basic indicators, while elsewhere you see a detailed breakdown by periods, areas of the field, and types of attacks.

В Sports data API These features are taken into account at the schema level. The matchStatistics field returns match statistics in the form of structured groups: possession, shots, duels, passes, defense, goalkeeper line, and so on. Each metric is described and standardized, allowing developers to understand exactly how it is calculated and which values can be compared between matches and tournaments. This allows you to build your own analytics algorithms without worrying that the counting logic will quietly change from one request to another.

What is a sports statistics API and what data can be obtained through it

The sports statistics API is a programming interface that provides access to data about matches, teams, tournaments, players, and betting odds in a machine-readable format. Instead of parsing HTML and manually cleaning data, you send HTTP requests to REST endpoints and receive a neat JSON response. In the case of the Sport Events API, this is the basic path of the sport, for example, /v2/football/ or /v2/basketball/, and a set of methods for working with categories, tournaments, matches, and events.

Through the API, you can get a list of supported sports via the /v2/sport endpoint, learn about categories and tournaments, download seasons, schedules, and results. For matches, not only the score and status are available, but also the fields currentMatchMinute, liveEvents, as well as detailed matchStatistics by periods and groups of indicators. Additionally, you can request oddsBase with current bookmaker odds and highlights with video highlights. All of this is built around one principle: you authenticate with a key in the Authorization header and then scale the integration as you see fit.

Below is a simplified example of a request for a list of football matches using fetch in JavaScript. The API key can be obtained at la cuenta personal. after registration:

const apiKey = 'YOUR_API_KEY';
async function loadTodayMatches() {
  const url = 'https://api.api-sport.ru/v2/football/matches?date=2025-09-03';
  const response = await fetch(url, {
    headers: {
      Authorization: apiKey
    }
  });
  if (!response.ok) {
    throw new Error('Ошибка запроса: ' + response.status);
  }
  const data = await response.json();
  console.log('Всего матчей:', data.totalMatches);
  console.log('Первый матч:', data.matches[0]);
}
loadTodayMatches().catch(console.error);

The same approach works for basketball, tennis, table tennis, esports, hockey, and other disciplines: just change the sportSlug in the path, and the entire response structure will remain predictable.

How to collect and compare match statistics from different sites using an API

If you want to understand why data for one match differs across different resources, it is most convenient to use the API as a reference layer. First, you collect statistics for the match of interest through the Sport Events API (endpoint /v2/{sportSlug}/matches/{matchId}), then you compare the obtained indicators with what third-party sites display. This way, you can see which specific metrics diverge: the number of shots, possession, duels, offsides, or cards.

At the implementation level, it looks like a series of requests and simple comparison logic. In the API, you get a detailed field matchStatistics broken down by periods (ALL, 1ST, 2ND) and groups (Shots, Attack, Passes, etc.). It is enough to bring external data to the same structure or, at least, to the same metric keys, after which differences can be highlighted in the interface or exported to a report. This is especially useful for media, analytical platforms, and betting services, where it is important to explain to users where the numbers come from.

Below is an example of a function in JavaScript that takes match statistics from the Sport Events API and compares the selected metric with an external value:

const apiKey = 'YOUR_API_KEY';
async function getMatchStats(matchId) {
  const url = `https://api.api-sport.ru/v2/football/matches/${matchId}`;
  const response = await fetch(url, {
    headers: { Authorization: apiKey }
  });
  const match = await response.json();
  return match.matchStatistics.find(block => block.period === 'ALL');
}
async function compareShotsOnTarget(matchId, externalHome, externalAway) {
  const allStats = await getMatchStats(matchId);
  const shotsGroup = allStats.groups.find(g => g.groupName === 'Shots');
  const metric = shotsGroup.statisticsItems.find(i => i.key === 'shotsOnGoal');
  return {
    apiHome: metric.homeValue,
    apiAway: metric.awayValue,
    externalHome,
    externalAway,
    diffHome: externalHome - metric.homeValue,
    diffAway: externalAway - metric.awayValue
  };
}

This scheme allows not only to identify discrepancies but also to manage them systematically: you can choose which source to consider as a priority, where to show averaged values, and how to explain the nature of the differences to the user.

How to choose a reliable sports data API for a website or application

When choosing a sports statistics API, it is important to look not only at the price and the number of sports but also at the depth, stability, and predictability of the data. A reliable provider offers clear documentation, understandable response schemas, stable endpoints, and a transparent change history. In the case of the Sport Events API, you can immediately see which fields are supported (currentMatchMinute, liveEvents, matchStatistics, oddsBase, highlights), what filters are available for requests, and how they will evolve in new versions.

Pay attention to several key criteria. First, coverage: football, basketball, tennis, table tennis, esports, hockey, and other disciplines should be supported on a single API core to avoid having to combine multiple providers. Second, the depth of statistics: it is important that, in addition to the score and simple events, there are advanced metrics, lineups, player and tournament data. Third, timeliness and delivery modes: the presence of live data, planned support for WebSocket for streaming updates, and the development of AI analytics tools.

By choosing API de datos deportivos api-sport.ru, you get a unified standard of structured responses for all sports, access to betting markets through oddsBase, and flexible filters by tournaments, seasons, teams, and match statuses. This simplifies integration into websites, mobile applications, internal dashboards, and betting platforms. The availability of a personal account, test rates, and detailed documentation lowers the entry threshold for developers and speeds up the launch of new products.

Delays, errors, and discrepancies in sports APIs: causes and how to account for them

Even when using a reliable API, it is impossible to completely avoid delays and discrepancies in online data. Match information travels from the stadium to the end user: it is recorded by scorers, enters the central system, is processed, and then distributed through channels. At each stage, delays of several seconds or minutes are possible. In live betting and streaming applications, this is especially noticeable: the score may update in the API a couple of seconds after the actual goal, while the television broadcast itself is delayed.

In addition to temporal lags, there can also be logical discrepancies. Controversial episodes (a shot or own goal, a foul in attack, VAR decision) are initially recorded one way, and after official confirmation, they are corrected. If your interface caches data or builds complex aggregates, changes may arrive later than you expect. Therefore, it is important to design the system with possible corrections in mind: regularly update live data, consider the match status field, and analyze events from liveEvents.

Below is an example of a request for only current matches in the inprogress status with handling typical network errors:

const apiKey = 'YOUR_API_KEY';
async function loadLiveMatches() {
  const url = 'https://api.api-sport.ru/v2/football/matches?status=inprogress';
  try {
    const response = await fetch(url, {
      headers: { Authorization: apiKey }
    });
    if (!response.ok) {
      console.error('API вернул ошибку', response.status);
      return;
    }
    const data = await response.json();
    data.matches.forEach(match => {
      console.log(`#${match.id}`, match.currentMatchMinute, match.homeScore.current, '-', match.awayScore.current);
    });
  } catch (e) {
    console.error('Сетевая ошибка при обращении к API', e);
  }
}
loadLiveMatches();

By approaching integration with inevitable delays and possible adjustments in mind, you reduce the risk of critical errors in betting logic, notifications, and analytics, while users receive a stable and understandable service, even when «live» sports change every minute.