- What metrics are the most important for predicting football matches
- What data can be obtained through the football statistics API for predictions
- Metrics xG, xGA, and xPoints: how to use advanced statistics in the API
- How to take into account the form of the team and players based on data from the sports events API
- How data on shots, ball possession, and chances affect the prediction of a football match
- How to use the sports events API for automatic prediction of match outcomes
What metrics are the most important for predicting football matches
An accurate prediction of a football match relies not on intuition, but on numbers. The final score reflects only the result, but tells almost nothing about why one team won. For building robust prediction models, the most important are basic and advanced metrics: the volume and quality of shots, the structure of attacks, the balance of power in midfield, the form of teams and players, as well as the context of the match. All this data can be automatically collected through the football API and turned into features for analytical models.
In practical tasks, analysts highlight several groups of key metrics. First, attacking indicators: total shots and shots on target, shots from the penalty area, big goal-scoring chances, actions in the final third. Second, defensive: conceded shots, expected goals against, interceptions, duels. Third, game control: ball possession, accurate passes, entries into the final third. Finally, context is important in the form of home and away matches, calendar density, and personnel losses. The combination of these metrics allows for assessing not only the current result but also the real strength of teams over time.
Through the sports events API, for example, through the service api-sport.ru with football statistics, The developer can obtain an extended block of matchStatistics for each match in a single request. It contains most of the metrics described above, suitable for machine learning and building custom team strength ratings. It is enough to periodically request data for the tournaments of interest and save it in your system to then train models that will operate not on dry scores, but on a deep statistical picture of the match.
curl -X GET https://api.api-sport.ru/v2/football/matches/14570728 \ -H 'Authorization: YOUR_API_KEY'
What data can be obtained through the football statistics API for predictions
The modern football statistics API provides much more than just the score and team names. Through the endpoint v2/football/matches, you can get a list of matches with detailed fields: lineups, stadium, match status, current minute, extended matchStatistics, live events, as well as the oddsBase block with bookmaker odds. This is the basis for any forecasting system: the same data is used by betting operators and professional analysts.
Flexibility of sampling is important for building forecasts. In requests, you can filter matches by date, tournament, season, team, or status. For example, you can export only completed matches of the desired league over a period and collect a training sample for the model. The same fields are then used for predicting upcoming rounds. To get started, it is enough to obtain a key in tu cuenta personal en api-sport.ru, add it to the Authorization header, and set up regular requests with the necessary filters.
Below is an example of obtaining a list of today’s football matches with basic statistics using the official REST API. In the response, you receive an array of matches, where each match has a matchStatistics field and, if available, an oddsBase with betting market odds.
fetch('https://api.api-sport.ru/v2/football/matches?date=2025-09-03', {
headers: {
Authorization: 'YOUR_API_KEY'
}
})
.then(function(res) { return res.json(); })
.then(function(data) {
console.log('Всего матчей:', data.totalMatches);
console.log('Первый матч:', data.matches[0]);
});
Metrics xG, xGA, and xPoints: how to use advanced statistics in the API
The metrics of expected goals xG, expected goals against xGA, and expected points xPoints have become standard in advanced football analytics. They describe not only the fact of scored goals but also the quality of created chances. xG shows how many goals a team should have scored based on positions and types of shots, xGA reflects the quality of the opponent’s chances, and xPoints estimates how many points a team usually earns with such a balance of chances. These metrics eliminate the influence of luck and help build more robust forecasts compared to goals and points in the table.
In the basic specification of the football API, aggregated xG fields may not be transmitted directly, but they can be calculated on top of the detailed match statistics. The matchStatistics block provides key indicators related to chance creation: totalShotsOnGoal, shotsOnGoal, totalShotsInsideBox, bigChanceCreated, bigChanceScored, touchesInOppBox, and others. With historical data available, you can train your own model that assesses the probability of a goal for each type of chance based on such features and calculates approximate xG and xGA for the team.
Below is a simplified example of how to calculate surrogate expected goals metrics from match statistics obtained via v2/football/matches. A simple linear formula based on shots in the box and big chances is used here. In real tasks on the platform por el API de eventos deportivos api-sport.ru such models are built on historical samples and machine learning methods.
function estimateXg(stats) {
var shotsInBox = stats.totalShotsInsideBox || 0;
var bigChances = stats.bigChanceCreated || 0;
var coefShotInBox = 0.10;
var coefBigChance = 0.35;
return shotsInBox * coefShotInBox + bigChances * coefBigChance;
}
How to take into account the form of the team and players based on data from the sports events API
The form of the team and individual players directly affects the probability of the match outcome, so forecasting models pay no less attention to it than to the total statistics for the season. Through the sports events API, it is convenient to collect the club’s performance history: it is enough to query the endpoint v2/football/matches with the team_id filter and the date of past rounds. Based on the last five or ten matches, you can calculate the dynamics of key indicators for the team and build a form index.
To account for player forms, a combination of team and player endpoint is used. First, through v2/football/teams with the parameter ids, the club’s lineup is obtained, then through v2/football/players with the filter team_id, detailed information on each football player is obtained. This allows tracking the participation of key players, their position, role changes, and in conjunction with liveEvents and matchStatistics, assessing contributions based on specific actions such as shots, passes into the final third, tackles, and interceptions.
Below is an example of a simple request for the last matches of a team by its identifier. In a real system, you go through the necessary date range, save match statistics, and then calculate moving averages for the metrics of interest, including goals, shots, big moments, and conceded goals.
curl -X GET \ https://api.api-sport.ru/v2/football/matches?team_id=195801&date=2025-09-03 \ -H 'Authorization: YOUR_API_KEY'
How data on shots, ball possession, and chances affect the prediction of a football match
The number of shots and ball possession are often cited as the main arguments when evaluating a match, but for accurate forecasting, it is important to understand which specific metrics carry predictive power. In the football API, this data is contained in the matchStatistics block. There are metrics such as ballPossession, totalShotsOnGoal, shotsOnGoal, totalShotsInsideBox, shotsOffGoal, bigChanceCreated, finalThirdEntries, and many others. Based on these, one can assess whether the team truly dominated, created high-danger moments, or simply had a lot of ball possession in safe areas.
Research shows that the difference in shots on target, shots from the penalty area, and big moments correlates with the goal difference more strongly than possession itself. Therefore, when building models to predict future matches, developers more often use composite indicators: expected goals, the number of shots with a high probability of scoring, and the share of dangerous attacks. Through the API, it is sufficient to regularly obtain statistics of all played matches in the league of interest and calculate average and median values for these metrics for each team.
The example below shows how to quickly extract shot and possession statistics for a single match from the response of v2/football/matches for further processing. In your code, such operations turn into a feature engineering stage, after which the data is fed into a machine learning model or a scoring system that assesses the chances of teams before the game starts.
function extractShotStats(match) {
var allPeriod = match.matchStatistics.find(function(p) { return p.period === 'ALL'; });
var overview = allPeriod.groups.find(function(g) { return g.groupName === 'Match overview'; });
var shots = allPeriod.groups.find(function(g) { return g.groupName === 'Shots'; });
return {
possession: overview.statisticsItems.find(function(i) { return i.key === 'ballPossession'; }),
totalShots: shots.statisticsItems.find(function(i) { return i.key === 'totalShotsOnGoal'; }),
shotsOnTarget: shots.statisticsItems.find(function(i) { return i.key === 'shotsOnGoal'; })
};
}
How to use the sports events API for automatic prediction of match outcomes
Automatic forecasting of football match outcomes is built around three main blocks: data collection, feature calculation, and model application. The sports events API takes on the first and most labor-intensive stage. With its help, you can schedule the extraction of past matches with detailed matchStatistics and bookmaker odds from oddsBase, as well as upcoming matches for which forecasts need to be built. The data then goes into your analytical system, where a machine learning model or another AI system is trained based on historical samples.
When predicting upcoming matches, you call the API again, obtain a list of games for the required date, and form a set of features for each team: form over the last rounds, attacking and defensive metrics, the difference in expected goals, home field advantage, and bookmaker lines. These features are fed into the model, which returns probabilities of outcomes, totals, or other markets. On the service side, api-sport.ru development is planned towards WebSocket and built-in AI capabilities, which will allow obtaining data and forecasts in real-time in the future.
Below is a simplified example of a pipeline in JavaScript. The script retrieves matches for a date, extracts key features, and passes them to an abstract function predictOutcome. In a real project, such calls are wrapped in background tasks, and the API key is managed through a secure config or issued in a separate workspace through la cuenta personal del desarrollador..
function loadMatches(date) {
return fetch('https://api.api-sport.ru/v2/football/matches?date=' + date, {
headers: { Authorization: 'YOUR_API_KEY' }
}).then(function(res) { return res.json(); });
}
function buildFeatures(match) {
// Здесь вычисляются признаки из matchStatistics, oddsBase и формы команды
return {
homeTeamId: match.homeTeam.id,
awayTeamId: match.awayTeam.id,
odds: match.oddsBase,
stats: match.matchStatistics
};
}
loadMatches('2025-09-03').then(function(data) {
data.matches.forEach(function(match) {
var features = buildFeatures(match);
var prediction = predictOutcome(features);
console.log(match.id, prediction);
});
});
