What parameters really affect goals? Analysis of statistical triggers

What statistical parameters really affect goals in football

At the data level, a goal in football is the result of a combination of recurring patterns: series of shots, entries into the final third, turnovers, and set pieces. The key role is played not by overall ball control, but by the quality and frequency of actions near the penalty area. That is why shot metrics (total shots, shots on target, shots inside box), the indicator большие шансы and touches in the penalty area correlate with the number of goals scored significantly stronger than formal ball possession or the number of passes in the midfield.

Sports events API api-sport.ru allows working with these parameters in a structured form. In the object matchStatistics you get ready-made grouped indicators: overall match overview, blocks Shots, Attack, Passes, Duels, Goalkeeping. For predicting goals, the most important factors are: shots on goal and on target, shots from the penalty area, big goal moments, touches in the penalty area, passing accuracy in the final third, as well as the number of saves by the opposing goalkeeper.

Additionally, it is important to consider negative and hidden factors: the number of fouls and cards (especially red ones), offsides, interceptions, and clearances. They reflect one team’s pressure on another and the degree of «break» in the defense. The model can also use bookmaker coefficients, which come in our API in the field oddsBase and effectively condense market expectations of goals taking into account vast amounts of historical data.

• Strong positive goal triggers: a series of shots on target, an increase in touches in the penalty area, a series of corners, frequent incursions into the final third.
• Hidden triggers: a red card, a series of yellow cards, an increase in clearances and interceptions in one’s own penalty area.
• Contextual factors: match phase (end of the half), score, freshness of players after substitutions.

All these parameters can be extracted and analyzed automatically through the API, building your «goal probability» metrics and enriching applications, dashboards, or betting support systems with them.

// Пример базового запроса статистики матчей для анализа параметров голов
fetch('https://api.api-sport.ru/v2/football/matches?date=2025-09-03', {
  headers: {
    Authorization: 'YOUR_API_KEY' // получите ключ в личном кабинете api-sport.ru
  }
})
  .then(r => r.json())
  .then(data => {
    data.matches.forEach(match => {
      const stats = match.matchStatistics;
      // здесь вы можете извлекать показатели ударов, атак и обороны
      console.log(match.id, stats);
    });
  });

What match data needs to be collected via API for goal analysis

For a quality analysis of goals, it is important not to limit yourself to just the score. From the football API, you can gather multiple levels of data: overall match statistics, minute-by-minute events, and team structure. At the top level, this is an object матч, that includes домашнийСчет и выезднойСчет (current and by halves), tournament context, stadium, and match status. This layer gives you the fact of the goal and the phase of the game, but does not explain why it happened.

The deepest analysis of goal triggers works with the combination matchStatistics и liveEvents. In matchStatistics where aggregated statistics are located: shots (total, on target, from inside the box, and from outside it), big chances, touches in the box, entries into the final third, accurate passes and crosses, fouls, offsides, goalkeeper saves. Through liveEvents you get a chronological list of events: goals, cards, penalties, substitutions, match periods. This allows you to analyze not only the volume but also the sequence: what happened 5–10 minutes before the goal was scored.

Additionally, it is useful to retrieve lineups and formations through the blocks homeTeam.lineup и awayTeam.lineup. Player positions (G/D/M/F) and formations (e.g., 4-3-3 or 3-5-2) help assess how initially oriented a team is towards attack or defense. By combining this information with data on who scored and at what minute the player is on the field (from the events list), you can build more accurate models of the impact of substitutions and tactical adjustments on the likelihood of a goal.

// Получение списка матчей с включением счёта, статистики и базовых коэффициентов
async function loadMatchesWithStats(date) {
  const res = await fetch(`https://api.api-sport.ru/v2/football/matches?date=${date}`, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const json = await res.json();
  return json.matches.map(match => ({
    id: match.id,
    score: {
      home: match.homeScore?.current,
      away: match.awayScore?.current
    },
    stats: match.matchStatistics,
    events: match.liveEvents,
    odds: match.oddsBase // рынки ставок, включая тоталы голов
  }));
}

How to use sports event API for predicting goals based on xG and shots

The expected goals (xG) model is based on the idea that each shot has a probability of turning into a goal, which depends on distance, angle, type of attack, and situation. In our case, the API provides detailed statistics on shots and attacks, based on which you can build your own xG model or use simplified proxies. For example, a combination of metrics shotsOnGoal, totalShotsInsideBox, bigChanceCreated и bigChanceScored already provides a powerful set of predictors for assessing the quality of chances.

The workflow scenario is as follows: you collect historical matches through the endpoint /v2/football/matches with enabled matchStatistics, label the number of goals and the metrics you need (shots, big chances, touches in the box, etc.) for each match, and then train a statistical or ML model. For live predictions in real-time, you request current matches with the status inprogress, calculate accumulated metrics during the match, and feed them into the trained model, obtaining an estimate of expected goals and total probabilities.

At the same time, data on odds in oddsBase allows you to compare model xG estimates with market expectations. If, for example, your model shows a high probability of another goal, and the total in the odds is undervalued, this may be a signal for algorithmic betting strategies or alerting analysts. Furthermore, the emergence of a WebSocket connection in api-sport.ru will further simplify the streaming update of xG models without constant polling of HTTP endpoints.

// Упрощённый пример расчёта «псевдо-xG» по статистике ударов
async function getLiveExpectedGoals(matchId) {
  const res = await fetch(`https://api.api-sport.ru/v2/football/matches/${matchId}`, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const match = await res.json();
  const statsAll = match.matchStatistics.find(s => s.period === 'ALL');
  const shotsGroup = statsAll.groups.find(g => g.groupName === 'Shots');
  const attackGroup = statsAll.groups.find(g => g.groupName === 'Attack');
  const shotsInsideBox = shotsGroup.statisticsItems.find(i => i.key === 'totalShotsInsideBox');
  const shotsOnGoal    = shotsGroup.statisticsItems.find(i => i.key === 'shotsOnGoal');
  const bigChances     = attackGroup.statisticsItems.find(i => i.key === 'bigChanceCreated');
  // Очень грубая иллюстрация — реальную модель нужно обучать на истории
  const pseudoXGHome =
    0.1 * (shotsInsideBox.homeValue || 0) +
    0.07 * (shotsOnGoal.homeValue || 0) +
    0.3 * (bigChances.homeValue || 0);
  const pseudoXGAway =
    0.1 * (shotsInsideBox.awayValue || 0) +
    0.07 * (shotsOnGoal.awayValue || 0) +
    0.3 * (bigChances.awayValue || 0);
  return { pseudoXGHome, pseudoXGAway };
}

How to analyze goal triggers by timing of attacks and player positions through API

The goal trigger is not a single action, but a short segment of the game in which dangerous events accumulate: a series of shots, set pieces, turnovers, and cards. Through the endpoint /v2/football/matches/{matchId}/events you receive a minute-by-minute event feed liveEvents and can analyze sequences: how many minutes before the goal the pressure started, how many corners and shots preceded the goal, whether there were substitutions or red cards.

Although the API does not provide real-time player coordinates, you can use tactical information from homeTeam.lineup и awayTeam.lineup (position and formation), as well as group statistics on entries into the final third and touches in the penalty area (fields finalThirdEntries, touchesInOppBox in the statistics blocks). Comparing these indicators by halves and periods allows you to highlight segments when the team shifts the game closer to the opponent’s goal and plays with wider or higher lines — this is a typical precursor to a goal.

Practical approach: for each goal, you build a «window» of events from the previous 5-10 minutes. You count in it the shots, corners, dangerous fouls, yellow/red cards, the number of entries into the final third, and then compare these same indicators with the «baseline» background of the match. Where the contrast is greatest, you find your key goal triggers for different leagues and teams. All of this can be automated using the API as a reliable source of time-synchronized events.

// Анализ событий за 10 минут до каждого гола
async function analyzeGoalTriggers(matchId) {
  const res = await fetch(`https://api.api-sport.ru/v2/football/matches/${matchId}/events`, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const json = await res.json();
  const goals = json.events.filter(e => e.type === 'goal');
  return goals.map(goal => {
    const windowStart = goal.time - 10;
    const windowEvents = json.events.filter(e => e.time >= windowStart && e.time < goal.time);
    const shots = windowEvents.filter(e => e.class === 'shot').length; // класс событий зависит от конкретной реализации
    const cards = windowEvents.filter(e => e.type === 'card').length;
    const subs  = windowEvents.filter(e => e.type === 'substitution').length;
    return {
      goalMinute: goal.time,
      team: goal.team,
      shotsBeforeGoal: shots,
      cardsBeforeGoal: cards,
      subsBeforeGoal: subs
    };
  });
}

How to set up and integrate football statistics API for automatic calculation of goal metrics

Integration begins with obtaining an access key. Register in the personal account app.api-sport.ru, create a project and generate an API key. Next, select the sport football and specify which endpoints you need: the list of matches (/v2/football/matches), match details (/v2/football/matches/{matchId}), match events (/v2/football/matches/{matchId}/events). At the level of your system, it makes sense to allocate a separate service or module that will periodically poll the API and convert the data to an internal format.

The next step is to automate the calculation of metrics for goals. After receiving the «raw» data, you can compute proxy-xG, shot intensity, pressure indices (series of shots and corners), «dangerous segments» metrics, and any custom indices in the background. Typically, background tasks (cron-job, task queue, or cloud scheduler) are set up to update statistics for active matches on a schedule. In the future, switching to a WebSocket connection (planned in the roadmap api-sport.ru) will allow for instant updates without polling.

It is recommended to implement caching and logging. Caching (for example, in Redis or in a database) reduces the number of requests to the API and speeds up access to frequently requested metrics, while logging requests and responses helps track anomalies and debug models. As a result, you get a stable technical outline: the API delivers data, your service automatically calculates metrics for goals, and the frontend or analytics dashboard consumes already aggregated indicators.

// Пример простого планировщика обновления live-метрик по голам (Node.js-подход)
const MATCH_IDS = [14570728, 14586240]; // подставьте актуальные ID матчей
async function updateLiveGoalMetrics() {
  for (const id of MATCH_IDS) {
    const res = await fetch(`https://api.api-sport.ru/v2/football/matches/${id}`, {
      headers: { Authorization: 'YOUR_API_KEY' }
    });
    const match = await res.json();
    // здесь можно пересчитать ваши метрики xG, давления, вероятности следующего гола
    // и сохранить их в базу данных или кэш
    console.log('Updating metrics for match', id, match.homeScore, match.awayScore);
  }
}
// вызывать функцию по расписанию (cron, Cloud Scheduler и т.п.)

Examples of API requests for obtaining goals, shots, xG, and dangerous moments

Below are several practical examples of how to obtain data necessary for analyzing goals, shots, and dangerous moments through the API. To start, you can request a list of matches for a specific date with already calculated scores, basic statistics, and total odds: endpoint /v2/football/matches?date=YYYY-MM-DD. Fields домашнийСчет и выезднойСчет contain current and halftime scores, matchStatistics — blocks for shots and attacks, and oddsBase — betting markets, including total goals (over/under).

For a detailed analysis of a specific match, use /v2/football/matches/{matchId}. Here you get the full set of statistical groups: shots, attacks, passes, duels, goalkeeper play. Dangerous moments can be roughly assessed through a combination of bigChanceCreated, bigChanceScored, touchesInOppBox, totalShotsInsideBox. The ready xG value is not provided in the scheme, but you can calculate it on your side based on these parameters and historical data.

To analyze goal triggers by timing, use the endpoint /v2/football/matches/{matchId}/events. In the response, you will receive an array события, where each event has a field время (minute), type (goal, card, substitution, etc.) and the score after the event. By filtering by type goal and building time windows before the goal, you get an array of «dangerous segments» that can be visualized or used in models. All these requests are easily integrated and scalable in a production environment.

// 1. Матчи за дату с головами, ударами и коэффициентами тоталов голов
fetch('https://api.api-sport.ru/v2/football/matches?date=2025-09-03', {
  headers: { Authorization: 'YOUR_API_KEY' }
})
  .then(r => r.json())
  .then(data => {
    data.matches.forEach(m => {
      console.log('Match', m.id,
        'Score', m.homeScore?.current, ':', m.awayScore?.current,
        'Markets', m.oddsBase?.map(o => o.group));
    });
  });
// 2. Детальный матч: статистика ударов и опасных моментов
async function loadMatchDetails(matchId) {
  const res = await fetch(`https://api.api-sport.ru/v2/football/matches/${matchId}`, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const match = await res.json();
  const allStats = match.matchStatistics.find(s => s.period === 'ALL');
  const shotsGroup = allStats.groups.find(g => g.groupName === 'Shots');
  const attackGroup = allStats.groups.find(g => g.groupName === 'Attack');
  const shotsOnTarget = shotsGroup.statisticsItems.find(i => i.key === 'shotsOnGoal');
  const shotsInsideBox = shotsGroup.statisticsItems.find(i => i.key === 'totalShotsInsideBox');
  const bigChances = attackGroup.statisticsItems.find(i => i.key === 'bigChanceCreated');
  return { shotsOnTarget, shotsInsideBox, bigChances };
}
// 3. Лента событий матча для поиска триггеров голов
async function loadMatchEvents(matchId) {
  const res = await fetch(`https://api.api-sport.ru/v2/football/matches/${matchId}/events`, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const json = await res.json();
  const goals = json.events.filter(e => e.type === 'goal');
  console.log('Goals timeline:', goals.map(g => ({ minute: g.time, score: `${g.homeScore}:${g.awayScore}` })));
}