How to automatically find value moments based on statistics?

What is the value of bets in sports and how to calculate them using statistics

Value bet (bet with an edge, «value») is a situation where the actual probability of an outcome is higher than that implied by the bookmaker’s odds. Formally, value can be described by the formula: value = P_model * k − 1, where P_model — your assessment of the probability of an event, and k — the coefficient. If the value is greater than zero, the bet has an expected profit in the long run, provided that the probability assessment is based on correct statistics and a large sample of matches.

The implied probability embedded in the coefficient is calculated simply: P_bookmaker = 1 / k (taking into account the margin at the line level). For example, a coefficient of 2.00 for a team’s victory means an approximate probability of 50%. If your model, based on sports statistics, shows that the team wins such a match in 60% cases, then value = 0.6 * 2.0 − 1 = 0.2, that is, the expected return of such a bet is about 20% in the long run.

The key point: a qualitative assessment of probabilities is impossible without systematic work with data. Modern sports event APIs allow you to obtain detailed statistics on matches, teams, and players in real time and for past seasons. It is on this data that models are built that reveal biases in the bookmaker’s line. Therefore, the correct approach to value betting is not intuition and «gut feeling,» but automated statistical analysis based on verifiable numbers.

Which sports event APIs to use for finding value moments

For systematic searching for value moments, a reliable source of sports data and coefficients is needed. The platform api-sport.ru provides a unified API for popular sports: football, hockey, basketball, tennis, table tennis, esports, and other disciplines. Through the endpoint /v2/{sportSlug}/matches, you get the match schedule, game status, score, live events, advanced statistics, and the oddsBase block with coefficients for various markets (1X2, totals, handicaps, etc.).

Thanks to the unified response format for different sports, you can build a single infrastructure for analyzing value bets: a single parser, a common database, and common probability calculation algorithms. For live analytics, the fields currentMatchMinute, liveEvents, and matchStatistics are important, while for pre-match — historical data on tournaments and seasons. On top of this, you can connect separate models for each sport, but the data itself is retrieved from a single API, which speeds up development and reduces integration costs.

In the near future, the API infrastructure plans to support WebSocket connections for even faster updates, as well as additional AI tools for working with data. Right now, you can automate requests to the Sport Events API and the odds API and use them as a basis for finding overvalued and undervalued events on the lines of different bookmakers.

Example request for a list of football matches with coefficients:

import requestsAPI_KEY = "ВАШ_API_КЛЮЧ"url = "https://api.api-sport.ru/v2/football/matches"params = {"date": "2025-09-03", "status": "notstarted"}  # матчи на выбранную датуheaders = {"Authorization": API_KEY}response = requests.get(url, params=params, headers=headers)data = response.json()for match in data.get("matches", []):    print(match["id"], match["tournament"]["name"], len(match.get("oddsBase", [])), "рынков коэффициентов")

What statistical data to obtain through APIs for analyzing value bets

To build probability models and find value moments, it is important to obtain not only the final score but also structured match statistics. In the Sport Events API, the field matchStatistics is provided in the response of the endpoints /v2/{sportSlug}/matches and /v2/{sportSlug}/matches/{matchId}. It contains breakdowns by periods (ALL, 1ST, 2ND, etc.) and groups of metrics: ball possession, shots, xG-like metrics, defensive actions, duels, passing accuracy, and much more. Based on these metrics, you can assess the quality of a team’s play, not just the result.

Through the parameters date, team_id, tournament_id, and season_id, it is convenient to collect historical data for training models: how teams played in different seasons, how their form changed, how they perform at home and away. Additionally, you can request match events through /v2/{sportSlug}/matches/{matchId}/events to analyze dynamics: who scores first, how often a team comes back, how a red card or penalty affects the final outcome. All this increases the accuracy of probability assessments and helps find value odds that the market has not yet reflected.

If you are building in-depth analytics, data about participants will also be useful: teams with rosters (/v2/{sportSlug}/teams) and players (/v2/{sportSlug}/players}). They allow you to take into account injuries, rotation, and coaching changes. Such factors are often not fully considered in the line, especially in smaller leagues and youth tournaments, where statistics are less transparent to the general audience. It is precisely there that automated analysis through the sports events API gives the greatest chance to find stable value moments.

Example of extracting key match statistics:

match = data["matches"][0]stats_periods = match.get("matchStatistics", [])all_period = next((p for p in stats_periods if p["period"] == "ALL"), None)if all_period:    overview = next((g for g in all_period["groups"] if g["groupName"] == "Match overview"), None)    if overview:        for item in overview["statisticsItems"]:            if item["key"] in ("ballPossession", "totalShotsOnGoal", "shotsOnGoal"):                print(item["name"], item["home"], "-", item["away"])

How to automatically find value moments based on odds and statistics

The automatic search for value bets is built around the comparison of two quantities: implied probability from the odds and the probability calculated by your model based on statistics. The algorithm typically looks like this: first, you get a list of matches and markets through /v2/{sportSlug}/matches with filled oddsBase, then for each market (e.g., 1X2, total over/under) you calculate P_bookmaker = 1 / k. After that, based on historical and/or live data, your model gives an estimate P_model, and you calculate value = P_model * k − 1. If the value is above the chosen threshold (e.g., 0.05), the event makes it to the list of candidates.

In pre-match mode, P_model is usually built on the basis of historical team statistics: average goals scored, conceded chances, strength of opponents, results in similar situations (home/away, tournament, stage of the season). In live mode, current metrics from matchStatistics and liveEvents are added: shots on goal, pressure in the opponent’s penalty area, yellow and red cards, current minute of the match. For example, if the favorite is shooting a lot and dominating possession but has not scored yet, and the odds on their victory have significantly increased, the model may identify a value moment for the win or for a goal by the favorite.

The entire selection logic can be fully automated and integrated into your backend or trading robot. The sports events API provides data, and your code makes the decision: to record the event in the database, send a notification, generate a signal in the interface or auto-betting module. At the same time, the thresholds for value, minimum market volume, allowable odds drop, and other parameters are flexibly adjusted to your risk management strategy.

An example of calculating implied probability and value:

def implied_prob(odds: float) -> float:    return 1.0 / odds if odds > 0 else 0.0def calc_value(p_model: float, odds: float) -> float:    return p_model * odds - 1.0# пример: модель оценила победу хозяев в 58%, коэффициент букмекера 2.1model_p = 0.58book_odds = 2.1print("Имплайд букмекера:", implied_prob(book_odds))print("Value:", calc_value(model_p, book_odds))

Algorithms and models for finding value bets based on API data

The choice of algorithm depends on the type of sport and the market where you are looking for value. For three-outcome markets (1X2), logistic regression or gradient boosting based on probabilities is often used: the model receives features collected through the sports events API (results of past matches, goals, allowable moments, possession, team form, opponent strength), and outputs the probabilities of outcomes «home win,» «draw,» «away win.» For total and handicap markets, scoring models based on the Poisson distribution are well-suited, where the expected number of goals for each team is evaluated separately.

For live betting, hybrid schemes are popular: the basic pre-match model provides starting probabilities, which are then dynamically adjusted during the game taking into account matchStatistics (shots, xG-like metrics, pressure), currentMatchMinute, and liveEvents (cards, red cards, penalties). Such updates can be implemented through Bayesian probability updates or through a machine learning model trained on sequences of game states. In both cases, the main source of signals is a unified sports events API that delivers data in a standardized format.

A separate direction is the use of ranking models (Elo, Glicko, and their modifications), which assess the «strength» of a team and form dynamics, as well as more complex ML approaches (neural networks, gradient boosting on trees, ensembles). In the future, planned AI tools on the infrastructure side will simplify the processing of large amounts of statistics, but even now you can implement your own model by connecting it to a data stream from API de Eventos Deportivos and automating the training and validation process.

Conditional pseudocode for the model pipeline:

1. Сбор данных через /v2/{sportSlug}/matches за 2–3 последних сезона.2. Обогащение статистикой команд и игроков (teams, players).3. Формирование признаков: голы, xG-прокси, удары, владение, форма и т.д.4. Деление выборки на train/validation/test.5. Обучение модели вероятностей исходов.6. Ежедневное обновление данных и дообучение при необходимости.7. В бою: запрос матчей на сегодня → расчёт P_модель → сравнение с коэффициентами из oddsBase → отбор value.

Example of implementing value moment search through a sports API in Python

Below is a simplified example of a Python script that retrieves a list of upcoming football matches through the Sport Events API, extracts the 1X2 market from oddsBase, and applies a simple heuristic to estimate the probability of a home win. In practice, instead of such a heuristic, a trained model should be used, but the example shows how to technically link the API and the logic for finding value. To run it, you will need an API key, which can be obtained at tu cuenta personal en api-sport.ru.

import requestsAPI_KEY = "ВАШ_API_КЛЮЧ"BASE_URL = "https://api.api-sport.ru"def get_upcoming_matches(date_str: str):    url = f"{BASE_URL}/v2/football/matches"    params = {"date": date_str, "status": "notstarted"}    headers = {"Authorization": API_KEY}    resp = requests.get(url, params=params, headers=headers, timeout=10)    resp.raise_for_status()    return resp.json().get("matches", [])def simple_home_prob(match: dict) -> float:    # игрушечная оценка: учитываем турнир и домашний фактор    t_name = match.get("tournament", {}).get("name", "").lower()    base = 0.45    if "league" in t_name or "лига" in t_name:        base += 0.03    return max(0.35, min(0.65, base))def find_value_matches(matches):    results = []    for m in matches:        markets = [mk for mk in m.get("oddsBase", []) if mk.get("group") == "1X2" and mk.get("period") == "Full-time"]        if not markets:            continue        market = markets[0]        home_choice = next((c for c in market.get("choices", []) if c.get("name") == "1"), None)        if not home_choice or not home_choice.get("decimal"):            continue        odds = float(home_choice["decimal"])        p_model = simple_home_prob(m)        value = p_model * odds - 1.0        if value >= 0.05:  # фильтр по порогу value 5%            results.append({                "matchId": m["id"],                "homeTeam": m["homeTeam"]["name"],                "awayTeam": m["awayTeam"]["name"],                "odds": odds,                "p_model": round(p_model, 3),                "value": round(value, 3),            })    return resultsif __name__ == "__main__":    matches = get_upcoming_matches("2025-09-03")    candidates = find_value_matches(matches)    for c in candidates:        print(c)

In a real project, instead of the simple_home_prob function, you connect a trained model that uses historical statistics from matchStatistics, data about teams and players, form, and other factors. The logic for finding value can be moved to a separate service that polls the API at certain intervals, records signals in the database, or sends them to the trader’s interface. Due to the stable format of responses from the Sport Events API, you can easily scale this logic to other sports and markets.

Risks and limitations of automatic value bet search based on statistics

Automating the search for value bets based on sports API data does not eliminate betting risks. Even the most accurate model works with probabilities, not guaranteed outcomes. Over a short distance, variance can be very high, and even value bets can lose in streaks. It is important to have a well-thought-out bankroll management strategy, limit the bet size, and understand in advance the maximum allowable capital drawdown.

The quality of the results directly depends on the quality of the data and the correctness of the models. Errors in data collection, incorrect handling of missing values, overfitting on historical samples, or ignoring changes in team compositions and motivations can lead to systematic miscalculations. Moreover, the bookmaker’s line is dynamic: odds change under the influence of the market, and the identified value moment can quickly disappear. Therefore, it is critical to minimize delays between receiving data from the API, calculating probabilities, and making decisions.

Finally, any work with bets should be conducted in accordance with the legislation of your jurisdiction and age restrictions. The use of sports event APIs and automated strategies is not a financial recommendation or guarantee of profit. The platform provides reliable sports data and odds, but the responsibility for using this data, developing models, and making decisions lies with the user. Approach the construction of value strategies professionally: test models on historical data, conduct backtests and stress tests, track actual returns, and update algorithms in a timely manner.