Why football statistics have become more important than expert opinions

Why football statistics are more accurate than the opinions of football experts and eye-based predictions

Over the past ten years, football has become one of the most «digitized» sports: top league clubs use tracking systems, collect dozens of metrics for each episode, and make decisions not based on the coach’s intuition, but on data. Human opinion is still important, but it is inevitably limited by subjectivity, the last match effect, and team biases. Statistics, on the other hand, describe the game through specific numbers: shots, possession, key passes, off-ball pressure, pressing intensity. Where an expert «feels» that a team is good, metrics show how much it actually dominates and how often it creates chances.

Practice shows that long-term predictions based on statistics are more accurate than the averaged opinions of analysts. Research on expected goals (xG) in European leagues demonstrates a high correlation between the quality of chances created and season results. A single match can end with a random score, but the distance of dozens of games levels out luck, and it is the numerical picture that reflects the real strength of clubs. The same applies to formulas that take into account a series of matches, workload, squad depth, and opponents’ playing style. Such models are simply impossible to keep in mind; they need to be calculated programmatically, based on large data sets.

To achieve this level of accuracy, it is not enough to «watch football» — operational and structured data that can be processed by algorithms is needed. Here, the football statistics API comes to the forefront: it automatically delivers detailed match statistics in real time to your servers, without manual collection and without the risk of errors. By connecting such an API, an analyst, tipster, application developer, or media project gains access to the same level of information that professional clubs and major betting companies rely on.

What is a football statistics API and how does real-time data access work

The football statistics API is a programming interface through which you can request data about matches, teams, tournaments, and players in a machine-readable format (usually JSON). Instead of parsing websites or collecting information manually, you access uniform endpoints of the type /v2/football/matches or /v2/football/matches/{matchId} and receive a structured response: game status, score, detailed statistics, live events, bookmaker odds, and other parameters. Authorization occurs via an API key, which is passed in the request header.

On the service api-sport.ru football data is updated in real time. When an event occurs in a match — a goal, card, substitution — the provider records it, processes it, and updates the information in the API within seconds. The client application can periodically poll the endpoints (pull model) or, as the service evolves, connect to a WebSocket channel to receive push updates without unnecessary requests. This approach allows building live centers, betting trackers, dashboards, and bots that instantly react to changes on the field.

Technically, access looks simple: you make an HTTP request to the desired endpoint, pass filtering parameters (date, tournament, match status), and in response, you receive a JSON object. Below is an example of a basic request to the list of current football matches using the Sport Events API:

curl -X GET "https://api.api-sport.ru/v2/football/matches?status=inprogress" \
  -H "Authorization: ВАШ_API_КЛЮЧ" \
  -H "Accept: application/json"

What data can be obtained through the football match API: goals, xG, shots, cards, line-ups

Through the football statistics API, you receive a multilayered description of each match. The basic level is information about the tournament, season, round, and stadium: fields tournament, season, место. Next are the key parameters of the meeting: status (status), start time (startTimestamp), the current minute (currentMatchMinute), score by time (домашнийСчет, выезднойСчет). This allows building live feeds and result tables without manual updates.

The next level is events and advanced statistics. The endpoint /v2/football/matches/{matchId} returns a block liveEvents, where for each event the type (goal, card, substitution), minute, team, players, and current score after the event are specified. The block matchStatistics contains grouped metrics for the game: total shots and shots on target, ball possession, fouls, corners, tackles, interceptions, passing accuracy, duels, and much more. Inside each group of statistics, there is an array statisticsItems with numerical values for the home and away teams. Depending on the data provider, these groups may also include advanced metrics, such as indicators functionally close to xG, share of attacks through the flanks, number of touches in the penalty area, etc.

It is worth highlighting the data on team lineups separately. In the match response, objects are returned homeTeam и awayTeam with a block lineup, where for each player the position, number, status (starter or substitute), as well as personal statistics are specified. This allows building graphical formation schemes (4-4-2, 4-3-3), tracking substitutions, and assessing the contribution of each player. Below is an example of a request for the details of a specific match:

curl -X GET "https://api.api-sport.ru/v2/football/matches/14570728" \
  -H "Authorization: ВАШ_API_КЛЮЧ" \
  -H "Accept: application/json"

In the response, you will receive a single object матч, including a score, extended statistics (matchStatistics), live events (liveEvents), team compositions and, when connecting the bookmaker module, an array oddsBase with market odds.

How to use the football statistics API for analytics and predicting match results

The main advantage of the football statistics API for analytics is the ability to systematically collect large volumes of homogeneous data and turn them into metrics on which forecasting models are built. You can retrieve all matches of the desired league for the season, aggregate indicators by teams (shots, possession, conceded chances, conversion), take into account home/away games and calendar density. Based on this, strength ratings, expected score models, tournament simulations, and other tools are built, which are significantly more accurate than intuitive assessments even by very experienced experts.

Technically, the process looks like this: through the endpoint /v2/football/matches you get a list of matches by date, tournament, or team, then for each match you request details through /v2/football/matches/{matchId}, extract blocks matchStatistics и liveEvents, normalize the data and store it in a repository (DB or data lake). Then, on top of this data, you can run your algorithms: from simple regressions to complex ML models. In the near future, the platform api-sport.ru plans to introduce ready-made AI modules that will calculate the probabilities of outcomes and totals based on already pre-processed statistics, which will reduce the time to production for models.

Below is a simplified example in Python demonstrating how to extract basic metrics on shots and ball possession for an analytical report:

import requests
API_KEY = "ВАШ_API_КЛЮЧ"
BASE_URL = "https://api.api-sport.ru/v2/football"
headers = {"Authorization": API_KEY}
# Получаем сегодняшние матчи нужного турнира
matches = requests.get(
    f"{BASE_URL}/matches",
    headers=headers,
    params={"tournament_id": "7", "date": "2025-09-03"}
).json()["matches"]
for m in matches:
    match_id = m["id"]
    details = requests.get(f"{BASE_URL}/matches/{match_id}", headers=headers).json()
    stats = details.get("matchStatistics", [])
    overview = next((g for g in stats if g["period"] == "ALL"), None)
    if not overview:
        continue
    # Ищем общие удары и владение мячом
    shots = None
    possession = None
    for group in overview["groups"]:
        for item in group["statisticsItems"]:
            if item["key"] == "totalShotsOnGoal":
                shots = (item["homeValue"], item["awayValue"])
            if item["key"] == "ballPossession":
                possession = (item["homeValue"], item["awayValue"])
    print(match_id, shots, possession)

In real projects, you will supplement this data with opponent rankings, head-to-head history, weather, injuries, and other factors. But the foundation of any quality model remains clean and complete data, which is most conveniently obtained through specialized APIs.

How to apply the sports events API for betting on football and developing betting strategies

Betting is one of the areas where quality football statistics and sports event APIs provide a maximum advantage. Most players rely on superficial indicators and subjective feelings of a team’s «form,» while professional strategies take into account dozens of metrics and the dynamics of their changes. By connecting to sports event APIs, you can track not only the score in real-time but also pressure, number of shots, possession, dangerous attacks, as well as bookmaker line movements to find overvalued and undervalued outcomes.

In the Sport Events API for football, the match object includes an array oddsBase — a set of betting markets with current odds: 1X2, totals, handicaps, etc. Each entry contains the market name, period (e.g., Full-time), a live mode flag, and outcome options with current and initial odds, as well as the direction of change. This allows building dashboards for line movements, searching for situations with sharp odds changes, and comparing them with the picture on the field obtained from the block matchStatistics. It is important to remember that no data guarantees a win, but proper work with statistics helps reduce variance and avoid chaotic betting.

An example of a response fragment for the 1X2 market in oddsBase might look like this:

{
  "name": "Full time",
  "group": "1X2",
  "period": "Full-time",
  "isLive": true,
  "choices": [
    { "name": "1", "decimal": 1.85, "initialDecimal": 2.10, "change": -1 },
    { "name": "X", "decimal": 3.60, "initialDecimal": 3.40, "change": 1 },
    { "name": "2", "decimal": 4.20, "initialDecimal": 3.90, "change": 1 }
  ]
}

By combining such information with live statistics, a bettor or analytical system can, for example, capture moments when the favorite creates many chances but has not yet scored, while the odds on their victory have risen due to a prolonged scoreless tie. With the addition of a WebSocket connection (which is planned to be implemented on the api-sport.ru platform), such monitoring can be conducted almost instantly, without periodic API polling, which is especially important for live betting.

Overview of popular football statistics APIs in 2025: paid and free services

By 2025, the market for football statistics APIs has formed and is conditionally divided into several groups. Firstly, there are large international providers focused on global products and betting companies. They cover hundreds of leagues worldwide but often offer complex pricing and English-language documentation, which is not always convenient for local projects. Secondly, there are official APIs of individual leagues and federations that sometimes provide part of the data for free, but vary greatly in format, stability, and volume of information. Finally, there are local and niche services tailored to specific markets and tasks.

A separate niche is occupied by Russian and Russian-speaking solutions, such as api-sport.ru, which offer clear documentation in Russian, support for popular sports (football, hockey, basketball, tennis, table tennis, esports, etc.), and flexible pricing for projects of any scale—from small websites to professional analytical platforms. Unlike disparate open sources, specialized APIs provide a unified data format, versioning, changelog, and predictable SLA, which is critical for commercial services and products that handle user money.

Free solutions are usually limited by traffic volume, update latency, or the set of available leagues. They are suitable for prototypes and educational projects, but for production systems, live centers, and forecasting services, it is more practical to use paid plans with priority support, documented limits, and a transparent development roadmap. In particular, the platform api-sport.ru plans to expand the range of sports disciplines, implement WebSocket subscriptions, and AI forecasting modules, making it a promising foundation for long-term projects in sports analytics and betting.

How to integrate the football statistics API into a website, application, or analytical system

Integrating the football statistics API into a website, mobile application, or internal analytics system usually occurs in several stages. First, you register on the platform and receive an API key in the personal account of API-Sport. Then you determine what specific data you need: only the score and status of matches, advanced statistics, live events, bookmaker odds, or everything together. Based on this, you create a list of used endpoints (for example, /v2/football/matches, /v2/football/matches/{matchId}, /v2/football/teams) and think through the data storage and caching scheme to avoid exceeding limits and ensure quick responses to users.

Next, the integration layer is implemented: usually, this is a backend service that periodically calls the API, saves data in the database, and delivers it to the frontend in a convenient format. You can use cron jobs to update statistics on a schedule and separate processes for live matches. In the near future, with the emergence of WebSocket subscriptions on api-sport.ru, it will be easier to maintain a constant connection and receive updates on selected matches without polling, which is especially relevant for live centers and streaming applications. It is important to provide for error handling, logging, and monitoring: for example, automatically switching to cache during temporary network issues or when exceeding request limits.

An example of the simplest integration in JavaScript (Node.js), which outputs a list of today’s football matches to the console, might look like this:

const fetch = require('node-fetch');
const API_KEY = 'ВАШ_API_КЛЮЧ';
const BASE_URL = 'https://api.api-sport.ru/v2/football';
async function loadTodayMatches() {
  const today = new Date().toISOString().slice(0, 10); // YYYY-MM-DD
  const res = await fetch(`${BASE_URL}/matches?date=${today}`, {
    headers: { Authorization: API_KEY }
  });
  if (!res.ok) {
    console.error('Ошибка запроса', res.status);
    return;
  }
  const data = await res.json();
  console.log('Всего матчей:', data.totalMatches);
  data.matches.forEach(m => {
    console.log(`#${m.id}`, m.homeTeam.name, '-', m.awayTeam.name, '=>',
      m.homeScore.current, ':', m.awayScore.current, '| статус:', m.status);
  });
}
loadTodayMatches().catch(console.error);

After debugging the football integration, you will be able to connect other sports with virtually no changes to the architecture, simply by changing the parameter sportSlug in the request paths (for example, basketball, ice-hockey, tennis). This makes the sports events API a powerful and scalable foundation for any digital products in the sports field.