How to develop your own betting recommendation engine?

What is a betting recommendation engine and how does it work

A betting recommendation engine is a software module that analyzes large amounts of sports data and offers the user specific markets and events for betting. The system takes into account team and player statistics, match history, bookmaker odds, and the user’s own behavior. In the end, the client receives not just a list of games, but a ranked set of options with an assessment of the probability of outcomes and expected returns.

At the core of any engine is a quality data stream. For sports betting, this includes match schedules, meeting statuses, team lineups, live events, advanced statistics, and odds. All this data can be conveniently obtained through APIs because requests are automated and response formats are standardized. The recommendation service is left with tasks of storage, cleaning, feature calculation, and applying machine learning algorithms or predefined rules.

The working cycle of the betting engine looks like this. The service requests current and historical matches, along with odds and statistics, through the sports API. The data then enters the database, where features are formed: team strength, form, attacking and defensive metrics, odds movement, bookmaker margin. After that, the model assesses the probability of various outcomes and assigns a rating to each market. The user interface sees a filtered list of bets that adapts to their preferences and risk limits.

Which sports event APIs to use for developing a betting recommendation service

A reliable recommendation service is impossible without a stable and complete source of sports data. In this capacity, it is convenient to use by the sports events API api-sport.ru. The platform supports football, hockey, basketball, tennis, table tennis, esports, and other disciplines, with the list of sports constantly expanding. A unified set of endpoints is available for each sport, so the architecture of your engine remains the same when scaling to new leagues and disciplines.

The basic entry point is the method /v2/sport. It returns a list of available sports with slugs that are used later in the request paths. To obtain categories and tournaments, the methods /v2/{sportSlug}/categories и /v2/{sportSlug}/categories/{categoryId}. are used. With their help, you can compile a list of countries, leagues, and seasons that will be included in the coverage of your recommendation service, and, for example, limit the model to only top tournaments in the field defaultTournaments.

The key data source for calculating recommendations is the matches endpoint /v2/{sportSlug}/matches. Through it, you get the schedule, game status, score, advanced statistics, and basic bookmaker odds. Filters дата, tournament_id, team_id, status allow you to flexibly form datasets for training and online recommendations. Below is a simple example of a request for today’s football matches in JavaScript using the server’s REST API. https://api.api-sport.ru:

const API_KEY = 'ВАШ_API_КЛЮЧ';
async function loadTodayFootballMatches() {
  const url = 'https://api.api-sport.ru/v2/football/matches?date=2025-09-03';
  const response = await fetch(url, {
    headers: {
      'Authorization': API_KEY
    }
  });
  if (!response.ok) {
    throw new Error('Ошибка загрузки матчей: ' + response.status);
  }
  const data = await response.json();
  console.log('Всего матчей:', data.totalMatches);
  console.log('Пример матча для движка рекомендаций:', data.matches[0]);
}
loadTodayFootballMatches().catch(console.error);

How to obtain odds, results, and match statistics through APIs for betting analysis

For the recommendation engine, it is critically important to have full access to the odds and match results. In the endpoint responses /v2/{sportSlug}/matches the match object contains the field oddsBase. This is an array of markets with groups and outcome options. Each element stores the market name, type (for example, group 1X2), live flag, suspension status, and a list of outcomes with current and initial odds. The field изменить shows the direction of the odds movement, which is useful for market analysis and finding «dropped» lines.

Match results are present in the fields домашнийСчет и выезднойСчет broken down by periods. The event status is returned in the field status and helps to separate completed matches from those ongoing in live mode. Extended match statistics come in an array matchStatistics. There are hundreds of metrics: ball possession, shots, xG-like indicators, duels, passes, actions in the final third, and so on. These features significantly enhance the quality of the model because they reflect the real strength of teams, not just the score.

Below is an example of a request for odds and statistics for a specific football match. The code selects market groups 1X2 and calculates the simple bookmaker margin for the base outcome. The data can be conveniently used as input features for the model or for manual analysis:

const API_KEY = 'ВАШ_API_КЛЮЧ';
async function loadMatchData(matchId) {
  const url = `https://api.api-sport.ru/v2/football/matches/${matchId}`;
  const response = await fetch(url, {
    headers: {
      'Authorization': API_KEY
    }
  });
  const match = await response.json();
  // Поиск рынка 1X2
  const fullTimeMarket = match.oddsBase.find(m => m.group === '1X2');
  if (fullTimeMarket) {
    const choices = fullTimeMarket.choices;
    const invSum = choices.reduce((sum, c) => sum + 1 / c.decimal, 0);
    const margin = (invSum - 1) * 100;
    console.log('Маржа рынка 1X2, %:', margin.toFixed(2));
  }
  console.log('Статистика матча для признаков модели:', match.matchStatistics);
}
loadMatchData(14570728).catch(console.error);

Algorithms and machine learning models for a sports betting recommendation engine

After you have set up data collection from the sports API, the next task is to choose algorithms. Most betting recommendation engines are based on predicting outcome probabilities and ranking events by expected profitability. For binary tasks (win/not lose, total over/under), logistic regression, gradient boosting trees, random forests, or modern boosting libraries are usually applied. These models work well on tabular features that can be conveniently formed from match fields and odds.

For more advanced scenarios, time series and recurrent or transformer models that take into account the dynamics of team form and bookmaker lines are suitable. A separate layer of logic can be built on collaborative filtering. In this case, the engine tracks user preferences by sports, markets, and odds ranges and adjusts the output to personal interests. An important part is the correct logging of which recommendations the client saw and which bets they accepted, allowing the models to be fine-tuned on real behavioral data.

Below is a simple example of a pipeline in Python that prepares a training dataset for the model: combining outcome probabilities, bookmaker margins, and several statistical features. In a real project, such code is moved to a separate service or ETL process, and in the future, you will be able to supplement it with ready-made AI modules planned for the api-sport.ru platform:

import pandas as pd
# matches_df содержит выгрузку из /v2/{sportSlug}/matches
# с полями oddsBase, homeScore, awayScore, matchStatistics
def build_features(matches_df: pd.DataFrame) -> pd.DataFrame:
    rows = []
    for _, row in matches_df.iterrows():
        match = row['raw_match']  # исходный JSON матча
        market = next((m for m in match['oddsBase'] if m['group'] == '1X2'), None)
        if not market:
            continue
        c_home, c_draw, c_away = [c['decimal'] for c in market['choices'][:3]]
        inv_sum = 1 / c_home + 1 / c_draw + 1 / c_away
        margin = inv_sum - 1
        # Пример извлечения одной метрики владения мячом за матч
        possession = None
        for period in match.get('matchStatistics', []):
            if period['period'] == 'ALL':
                for group in period['groups']:
                    for stat in group['statisticsItems']:
                        if stat['key'] == 'ballPossession':
                            possession = float(stat['homeValue'])
                            break
        label = int(match['homeScore']['current'] > match['awayScore']['current'])
        rows.append({
            'odd_home': c_home,
            'odd_draw': c_draw,
            'odd_away': c_away,
            'margin': margin,
            'home_possession': possession,
            'label_home_win': label,
        })
    return pd.DataFrame(rows)

Architecture and technology stack selection for your own betting recommendation engine

The architecture of the betting recommendation engine should ensure reliable data collection from the API, scalable processing, and quick result delivery to the user. At the lowest level is the integration module with the sports API. It is responsible for regular requests to REST endpoints /v2/{sportSlug}/matches, /v2/{sportSlug}/tournament, updating live data, and accounting for rate limits. Right now, you can build this layer on HTTP requests to the server https://api.api-sport.ru, and soon expand it with WebSocket subscriptions that are planned to be launched on the platform. api-sport.ru.

The next level is storage and processing layer. For historical data, it is convenient to use relational databases (PostgreSQL, MySQL) or analytical systems like ClickHouse. They contain matches, events, statistics, and odds. For live updates, message queues and in-memory storage are suitable, allowing for quick data transfer to the feature calculation service. The feature-engineering module transforms raw JSON from the API into compact tables with numerical and categorical features suitable for machine learning.

Above the data layer are the model services and the recommendation engine API. They are usually implemented in Python (ML library ecosystem), Go, or Node.js, depending on the team. The model service receives prepared features, returns probabilities and quality metrics, while the frontend API generates human-readable recommendations: a list of events, market type, odds, value assessment, and risk level. External clients — website, mobile application, Telegram bot — do not directly access the sports API, but rather your backend, which combines responses from its own engine and data loaded via api-sport.ru.

Step-by-step development of a betting recommendation engine based on API data

Developing your own recommendation engine is most conveniently started by registering and obtaining an access key to the sports API. On the api-sport.ru platform, this is done through the personal account api-sport.ru. After obtaining the key, you define the coverage: a list of sports, countries, and tournaments. For this, you use the methods /v2/sport и /v2/{sportSlug}/categories. At this stage, it is useful to create a map of the leagues on which you plan to build recommendations and immediately exclude less interesting tournaments.

The next step is to collect historical data. Using /v2/{sportSlug}/matches by dates and tournaments, you load an array of completed matches along with odds and statistics. This data goes into storage, where you label outcomes (win/loss of a specific market) and prepare the training sample. Next, you build a feature pipeline: extracting features from the fields oddsBase, matchStatistics, scores, and tournament metadata. Once you have a tabular dataset, you train the first model, evaluate quality, and adjust basic metrics (ROI, hit-rate, maximum drawdown).

After testing hypotheses, you move on to the online part. Your service updates the list of upcoming matches on a schedule or by event, recalculates features, and stores pre-calculated recommendations in a separate cache. The external API of the engine remains simple: it accepts filters by sport type and leagues, a parameter for acceptable odds, and returns a sorted list of bets. An example of a basic HTTP wrapper for the sports API in Node.js is provided below. Such a service can be integrated into your ETL or called from a separate module with models:

const API_KEY = 'ВАШ_API_КЛЮЧ';
const BASE_URL = 'https://api.api-sport.ru/v2/football/matches';
async function fetchMatchesByDate(date) {
  const url = `${BASE_URL}?date=${date}&status=notstarted`;
  const res = await fetch(url, {
    headers: {
      'Authorization': API_KEY
    }
  });
  if (!res.ok) {
    throw new Error('Ошибка запроса к спортивному API');
  }
  const data = await res.json();
  return data.matches;
}
module.exports = { fetchMatchesByDate };

Integration of the betting recommendation engine with a website, mobile application, and Telegram bot

When the core of the recommendation engine is ready, it needs to be integrated into user interfaces. Most often, the first channel is a website that already has a line and basic statistics. In this case, your frontend accesses the internal API of the engine, which returns a list of recommended bets. The response is conveniently formatted in a unified format: match identifier from the sports API, market type, outcome, odds, probability, and comment for the user. By these identifiers, the frontend already loads match details from its cache or directly from the sports API.

Mobile applications require special attention to latency and data volume. It is useful to store pre-calculated recommendations in a fast database and update them on a schedule or through push events. In the future, when api-sport.ru WebSocket becomes available, you will be able to build real live feeds with instant updates of the betting list with each match event or odds change. This is especially important for esports, football, and hockey live markets, where the decision-making window is very narrow.

For the Telegram bot, the architecture is similar: the bot receives the user’s command, passes the filtering parameters (type of sport, odds range, market type) to the internal API of the recommendation engine, and returns a brief list of events. Each item in the list may contain a link to the detailed match page on your site, where the client can see extended statistics and the history of line movements. This scenario makes the betting engine a universal core that serves multiple channels at once, while the sports API remains the sole source of reliable data for all interfaces.