How to analyze defenders: what is the real effectiveness?

Performance metrics for defenders in football: which indicators are truly important

The evaluation of defenders has long gone beyond the number of tackles and duels. The classic «number of fouls and clearances» poorly reflects the player’s real impact on the result. Modern analytics relies on a set of indicators that allow measuring not only the volume of work but also its quality, the context of the episode, and the level of the opponent. At the data level, this means working with several groups of match and player statistics simultaneously.

In the sports events API, available through the service Sports events API for developers, key defensive work metrics are collected in match statistics blocks: tackles, interceptions, clearances, ball recoveries, percentage of duels won on the ground and in the air. At the team level, they are available in the matchStatistics section (Defending and Duels group), and at the individual player level — in the statistics object within the lineup (lineup.players). This allows viewing the defender both as part of the defensive line and as a separate combat unit.

• Intensity of defensive actions — the number of duels, tackles, interceptions, and recoveries per 90 minutes.
• Quality of actions — the share of won tackles and duels, successful attempts against dribblers, minimization of fouls.
• Aerial play — the percentage of won aerial duels (especially critical for central defenders).
• Penalty area management — clearances, blocked shots, the number of opponent’s incursions into the danger zone.

By combining these indicators, composite indices can be constructed: for example, the index of disrupting the opponent’s attacks or the reliability index in the penalty area. All the raw data for such metrics is available through a single API request: you do not need to collect it manually from different sources, just set up match statistics processing once.

// Пример: получаем ключевые защитные метрики команды по матчу
const MATCH_ID = 14570728;
fetch('https://api.api-sport.ru/v2/football/matches/' + MATCH_ID, {
  headers: { Authorization: 'YOUR_API_KEY' }
})
  .then(res => res.json())
  .then(match => {
    const stats = match.matchStatistics.find(s => s.period === 'ALL');
    const defendingGroup = stats.groups.find(g => g.groupName === 'Defending');
    const duelsGroup = stats.groups.find(g => g.groupName === 'Duels');
    console.log('Defending:', defendingGroup.statisticsItems);
    console.log('Duels:', duelsGroup.statisticsItems);
  });

How to analyze defenders’ play through statistics: a step-by-step breakdown

Practical analysis of defenders does not start with beautiful dashboards, but with the correct data processing workflow. It is important to establish a clear pipeline: from selecting match samples to interpreting the final metrics. Thanks to the structured API of sports events, this pipeline can be fully automated and integrated into the product — from scouting services to betting analytics.

The first step is to determine the context: league, season, defender role, team type (dominant or defensive). Next, through the endpoint /v2/football/matches, you collect the necessary sample of matches by tournament, season, team, or date. In the second step, from the match details (endpoint /v2/football/matches/{matchId}), you extract lineups and highlight players in the defender position. The third step is normalizing statistics (for example, per 90 minutes and considering the opponent’s status) and aggregating over segments of the season.

Having obtained an array of matches and a list of defenders, you can calculate stable metrics: average number of tackles and interceptions, percentage of duels won, foul frequency. An important point is not to limit yourself to a single match: the API allows you to go through dozens and hundreds of games, building a reliable statistical base for conclusions. This approach removes the noise of one or two unsuccessful games and shows the true level of the player.

// Шаг 1: получаем матчи команды за сезон
async function loadTeamMatches(teamId, seasonId) {
  const url = 'https://api.api-sport.ru/v2/football/matches'
    + '?team_id=' + teamId
    + '&season_id=' + seasonId
    + '&status=finished';
  const res = await fetch(url, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const data = await res.json();
  return data.matches;
}
// Шаг 2: по каждому матчу вытаскиваем состав и находим защитников
async function loadDefendersStats(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 players = [
    ...match.homeTeam.lineup.players,
    ...match.awayTeam.lineup.players
  ];
  const defenders = players.filter(p => p.position === 'D');
  return defenders.map(p => ({ id: p.id, name: p.name, statistics: p.statistics }));
}

What data about defenders’ play can be obtained through the sports events API

For systematic analysis of defenders, it is important to have access not only to the score and basic statistics but also to the details of defensive actions. Through the API of the service api-sport.ru you get the full context of the match: team lineups, the position of each player, detailed team statistics on defense, and event timelines. All this is available for both football and other sports where defense plays a key role (basketball, hockey, esports, and others).

At the match level for football, the matchStatistics object contains groups Defending, Duels, Match overview, and others. They include metrics such as total number of tackles and their success percentage, interceptions, ball recoveries, clearances from the penalty area, fouls, and yellow cards. Additionally, you can analyze possession, the number of opponent shots, entries into the final third, and crosses—these indicators help assess how much pressure the defensive team faced and how they coped with it.

It is worth highlighting the data on players separately. The endpoint /v2/football/players allows you to obtain a list of footballers indicating their position (including defenders with the position «D»), country, height, and other characteristics. In the match lineup, there is a statistics object for each player, where their individual game statistics are aggregated. Based on this, it is easy to build models that take into account both the defender’s style and their physical profile. In the near future, the platform plans to expand functionality through WebSocket connections and AI tools, making the collection and processing of defensive statistics even more flexible and responsive.

// Получаем список защитников команды и базовую информацию о них
async function loadTeamDefenders(teamId) {
  const url = 'https://api.api-sport.ru/v2/football/players?team_id=' + teamId;
  const res = await fetch(url, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const data = await res.json();
  return data.players.filter(p => p.position === 'D').map(p => ({
    id: p.id,
    name: p.name,
    country: p.country.name,
    height: p.height,
    preferredFoot: p.preferredFoot
  }));
}

How to use the sports statistics API to evaluate defenders’ performance

Properly organized access to data through the API opens up several scenarios for evaluating defenders’ performance: from simple match reports to complex scoring models for scouting and betting. Through a single request, you get lineups, detailed statistics, live events, and even bookmaker odds (in one oddsBase object), allowing you to link the defensive reliability of the team with the market reaction.

In practical application, it looks like this: your service or internal tool periodically requests matches from the necessary tournaments, extracts defensive metrics, and calculates its own indices. For example, the stability index of central defenders may consider the percentage of won aerial duels, the number of clearances, and the share of won tackles. For betting projects, these indicators can be supplemented with changes in odds in total goals or handicap markets to identify patterns between defensive statistics and line movement.

All this is implemented with minimal integration costs: it is enough to obtain an API key in tu cuenta personal en api-sport.ru and set up a few standard requests. Then you can move on to advanced scenarios—streaming live data through the future WebSocket, training AI models on match history, automatically generating reports for the coaching staff or the bookmaker’s risk management.

// Пример: сопоставляем защитную статистику и коэффициенты на матч
async function analyzeDefenceWithOdds(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 defendingGroup = statsAll.groups.find(g => g.groupName === 'Defending');
  const tacklesItem = defendingGroup.statisticsItems.find(i => i.key === 'totalTackle');
  const clearancesItem = defendingGroup.statisticsItems.find(i => i.key === 'totalClearance');
  // Коэффициенты 1X2 из oddsBase
  const fullTimeMarket = match.oddsBase.find(m => m.group === '1X2');
  return {
    tackles: tacklesItem,
    clearances: clearancesItem,
    odds: fullTimeMarket ? fullTimeMarket.choices : []
  };
}

Examples of calculating the effectiveness of central and wing defenders through the API

Central and flank defenders perform different tasks, so their effectiveness should be measured differently. Central defenders are more focused on play in the penalty area and in the air, while flank defenders combine defense with supporting attacks. By using data obtained through the sports events API, it is possible to build separate profiles for each role and compare players within the role, rather than across the entire pool of defenders.

For central defenders, the basic set of metrics usually includes the percentage of aerial duels won, the number of clearances from the penalty area, interceptions, and ball recoveries near their own goal, as well as the share of successful tackles. At the team statistics level (matchStatistics, groups Defending and Duels), you get the overall workload of the defensive line, and with the help of data from the lineup (lineup.players.statistics), you distribute the contribution among specific players. Flank defenders should be evaluated based on the intensity of tackles and interceptions in their half of the field, the number of fouls against dribblers, as well as the stability of their performance throughout the match and the season.

In practice, the formula may look like a weighted combination of indicators: for example, central defender index = 0.4 × (aerial play index) + 0.3 × (clearances and blocks index) + 0.3 × (interceptions and recoveries index). You adjust the weighting coefficients according to the task: for scouting, stability and minimizing errors are more important, while for betting, the ability of a team with such defenders to «hold» a low total is crucial. Thanks to the API, you have full control over the raw numbers and can recalculate indices for any scenarios.

// Упрощённый пример: рассчитываем командный индекс обороны по матчу
async function defenceIndex(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 defending = statsAll.groups.find(g => g.groupName === 'Defending');
  const duels = statsAll.groups.find(g => g.groupName === 'Duels');
  const tacklesWon = defending.statisticsItems.find(i => i.key === 'wonTacklePercent');
  const clearances = defending.statisticsItems.find(i => i.key === 'totalClearance');
  const aerialDuels = duels.statisticsItems.find(i => i.key === 'aerialDuelsPercentage');
  // Простейший комбинированный индекс (пример, вы можете задать свои веса)
  const score = (
    (tacklesWon.homeValue || 0) * 0.4 +
    (aerialDuels.homeValue || 0) * 0.3 +
    (clearances.homeValue || 0) * 0.3
  );
  return score;
}

How to apply data on defenders from the API for scouting and tactical analysis

Data on defenders obtained through la API deportiva api-sport.ru, allows for the establishment of comprehensive scouting and match preparation processes. Scouts can filter players by position and basic parameters, and then compare them based on defensive indices calculated over several seasons. Coaching staffs use statistics to analyze the opponent: how often their defenders lose aerial duels, how many fouls they commit in dangerous areas, and how deep the defensive line drops under pressure.

Tactical analysis benefits from the combination of several sources within a single API: match statistics, timed events (goals, cards, substitutions), player data, and bookmaker market odds. This allows, for example, to assess how changes in the defensive lineup affect the likelihood of conceded goals and the reaction of bookmaker lines. For betting analysts, this is a basis for calculating their own total and handicap models, and for clubs, it is a way to quantitatively confirm or refute coaching hypotheses.

In the future, the integration of WebSocket and AI tools in the api-sport.ru ecosystem will open up new scenarios: from real-time alerts about defensive failures to automatic classification of types of errors made by defenders during a match. Even today, you can lay the foundation for such solutions by integrating the API into your systems and building a unified data layer for all departments—from scouting to the betting analytics department.

// Пример: формируем дашборд соперника по защите для скаутинга
async function buildOpponentDefenceProfile(teamId, date) {
  const url = 'https://api.api-sport.ru/v2/football/matches'
    + '?team_id=' + teamId
    + '&date=' + date
    + '&status=finished';
  const res = await fetch(url, {
    headers: { Authorization: 'YOUR_API_KEY' }
  });
  const data = await res.json();
  const profiles = [];
  for (const match of data.matches) {
    const statsAll = match.matchStatistics.find(s => s.period === 'ALL');
    const defending = statsAll.groups.find(g => g.groupName === 'Defending');
    const duels = statsAll.groups.find(g => g.groupName === 'Duels');
    profiles.push({
      matchId: match.id,
      tackles: defending.statisticsItems.find(i => i.key === 'totalTackle'),
      clearances: defending.statisticsItems.find(i => i.key === 'totalClearance'),
      duelsPercent: duels.statisticsItems.find(i => i.key === 'duelWonPercent')
    });
  }
  return profiles;
}