Methods of data cleaning and normalization in sports statistics

What is data cleaning and normalization in sports statistics

Data cleaning and normalization in sports statistics is a fundamental step before any analytical tasks: from building forecasting models to calculating live odds. Data streams from sports APIs include information about matches, teams, players, events, and bets. Without bringing this data to a uniform and correct form, the risk of incorrect metrics, erroneous reports, and inaccurate models for betting and trading increases.

Cleaning refers to the removal or correction of incorrect, missing, and contradictory values: duplicate matches, incorrect timestamps, invalid team identifiers, incorrect statistical indicators. Normalization is the process of bringing the structure and format of data to a unified scheme: a single format for dates and times, standardized identifiers for leagues and seasons, a common field model for different sports. For example, the number of shots on goal in football and shots in hockey can be stored in one field shots_total, if the scheme is thought out in advance.

Using the Sport Events API from api-sport.ru, you initially receive a well-structured JSON for matches, tournaments, players, and bookmaker odds. However, even with this, an own preprocessing layer is important: selecting the necessary fields, setting up aggregation rules, checking the consistency of identifiers between sports statistics and your internal systems. A clean and normalized layer on top of the API facilitates the construction of data warehouses, analytical dashboards, mobile applications, and internal services.

• Cleaning eliminates noise and errors in the data, increasing trust in analytics.
• Normalization enables end-to-end analytics across different sports and leagues.
• A unified data model simplifies integration with internal systems and external partners.

Below is an example of a simple request to the Sport Events API that can be used as a source for further cleaning and normalization of match statistics:

curl -X GET "https://api.api-sport.ru/v2/football/matches?date=2025-09-03" \
  -H "Authorization: YOUR_API_KEY"

The resulting JSON can be transformed into an internal tabular structure, filter out technical matches, process missing values, and used as a reference data layer. You can obtain a personal access key in your personal account app.api-sport.ru.

Typical errors and duplicates in sports data: how to identify and correct them through the API

Sports data often contains typical errors that directly affect the quality of analytics. These include duplicate matches and events, incorrect game statuses, discrepancies in match start times, inconsistencies in team and tournament names, as well as discrepancies in match statistics. When working with multiple sources, such inconsistencies are amplified, so a centralized API with a unified identifier system, like that of api-sport.ru, becomes critically important.

The same event can enter the system multiple times. For example, a goal may be registered in the general list of match events and duplicated in the live feed. If uniqueness checks on key fields (event ID, type, player, time) are not set up, the totals of goals or cards will be distorted. Similarly, duplicate matches may occur when reloading the same day without proper checks on matchId. In the Sport Events API, each match has a unique identifier, which significantly simplifies deduplication on the client side.

It is convenient to start identifying errors with simple integrity checks: verifying the number of events, analyzing time intervals, matching the final score with the number of goals, checking the match status (finished, in progress, canceled, etc.). This is supported at the API level by structured fields. estado, puntajeLocal, puntajeVisitante, eventosEnVivo. Next, automated scripts can be built that find anomalies and duplicates and mark problematic records before loading them into storage.

Example of retrieving match events and basic duplicate checking by fields. tiempo, type and player:

import requests
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.api-sport.ru/v2/football"
match_id = 14570728
resp = requests.get(
    f"{BASE_URL}/matches/{match_id}/events",
    headers={"Authorization": API_KEY},
)
data = resp.json()
seen = set()
unique_events = []
for ev in data["events"]:
    key = (ev["time"], ev["type"], ev.get("player", {}).get("id"))
    if key in seen:
        # логируем потенциальный дубликат
        continue
    seen.add(key)
    unique_events.append(ev)
print(f"Изначально событий: {data['totalEvents']}, после фильтрации: {len(unique_events)}")

This approach can be expanded: checking the match score by events against the final score from the endpoint. /matches/{matchId}, controlling the sequence of match minutes and the correctness of statuses. All this allows for automatically finding and resolving issues even before the data reaches analytical dashboards.

Methods for cleaning sports event data when loading from the API (validation, filtering, deduplication)

Effective cleaning of sports data when loading from the API is built around three main steps: validation, filtering, and deduplication. Validation checks the compliance of incoming JSON with the expected schema: the presence of mandatory fields (match ID, teams, tournament, date, status), correctness of types (integers for minutes, numeric values for coefficients, strings for statuses), and validity of values (for example, the match minute cannot be negative). Such checks prevent broken records from entering the main storage.

Filtering allows you to discard technical or uninteresting matches for your scenarios: canceled meetings, friendly games, duplicate tournaments. In the Sport Events API, this is conveniently implemented through query parameters: estado, torneo_id, category_ids, equipo_id and others. For example, you can immediately retrieve only completed matches for historical analysis, and for live models — only matches with the status. en progreso. This filtering at the API level reduces the volume of processed data and speeds up subsequent cleaning steps.

Deduplication is the final stage, during which duplicate records of matches, events, and odds are eliminated. In the Sport Events API, each object has a stable identifier (Match.id, Team.id, Player.id), which allows them to be used as keys for comparison. When working with time series of odds from the field oddsBase additionally, comparison by update time and change indicator is applied cambiar, to store only significant points. All deduplication logic is implemented in your ETL code and is easily scalable to meet the needs of a specific project.

Below is an example in Python: loading a list of matches with basic validation and filtering by status and date:

import requests
from datetime import date
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.api-sport.ru/v2/football/matches"
params = {
    "date": date.today().isoformat(),
    "status": "finished",  # сразу берём только завершённые матчи
}
resp = requests.get(BASE_URL, headers={"Authorization": API_KEY}, params=params)
resp.raise_for_status()
raw = resp.json()["matches"]
clean_matches = []
seen_ids = set()
for m in raw:
    # валидизация ключевых полей
    if not m.get("id") or not m.get("homeTeam") or not m.get("awayTeam"):
        continue
    if m["id"] in seen_ids:
        continue  # дедупликация по ID матча
    seen_ids.add(m["id"])
    if m.get("homeScore", {}).get("current") is None:
        continue  # пропускаем матчи без итогового счёта
    clean_matches.append(m)
print(f"Очистили {len(clean_matches)} матчей из {len(raw)} исходных записей")

Such a template is easily extensible: you can add checks for value ranges (for example, goals < 20), consistency of statistics, and even your own validation rules for specific leagues and betting markets.

Normalization of match and player statistics from different sports APIs into a unified structure

Normalization of sports statistics is especially important when you combine data from different sports, leagues, and tournaments. Even within one sport, different APIs may name fields differently, encode match statuses, and describe player statistics. By using a unified Sport Events API from api-sport.ru, you initially receive a unified structure: common identifiers for sports (sportSlug), tournaments, seasons, teams, and players, as well as consistent date and timestamp formats.

The task of applied normalization is to project nested JSON into tabular models that are convenient for reporting and machine learning. For example, several basic entities can be highlighted: a matches table with fields match_id, sport, fecha, home_team_id, away_team_id, home_score, away_score, torneo_id; a table of match statistics, where each row is a specific metric (shots, possession, red cards, etc.); a table of players with biographical fields and aggregated seasonal statistics. Within the API, this data is already logically connected, so on the client side, it only remains to choose the target structure and implement the transformation rules.

Special attention is required for extended fields, such as estadísticasDelPartido и eventosEnVivo. In estadísticasDelPartido the data is grouped by periods and logical groups of metrics (Shots, Attack, Passes, etc.). For normalization, it is convenient to «unfold» them into a wide table: one match — one row, where each metric becomes a separate column (for example, shots_on_target_home, shots_on_target_away). This facilitates the construction of models and reports that require quick access to specific metrics without complex JSON processing on the fly.

Below is a simplified example that builds a normalized record per match based on the response from the endpoint /v2/{sportSlug}/matches/{matchId}:

import requests
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.api-sport.ru/v2/football/matches/14570728"
resp = requests.get(BASE_URL, headers={"Authorization": API_KEY})
match = resp.json()
row = {
    "match_id": match["id"],
    "sport": "football",
    "date": match["dateEvent"],
    "home_team_id": match["homeTeam"]["id"],
    "away_team_id": match["awayTeam"]["id"],
    "home_score": match["homeScore"]["current"],
    "away_score": match["awayScore"]["current"],
    "tournament_id": match["tournament"]["id"],
    "category_id": match["category"]["id"],
}
# Пример нормализации одной статистики: владение мячом за весь матч
for stat_group in match.get("matchStatistics", []):
    if stat_group["period"] != "ALL":
        continue
    for group in stat_group["groups"]:
        for item in group["statisticsItems"]:
            if item["key"] == "ballPossession":
                row["ball_possession_home"] = item["homeValue"]
                row["ball_possession_away"] = item["awayValue"]
print(row)

The same approach can be used to normalize player data from the endpoint /v2/{sportSlug}/jugadores, combine it with match statistics, and build a unified multi-season showcase. This simplifies the development of recommendation systems, player valuation models, and personalized analytical services.

Tools and libraries for cleaning and normalizing sports data through the API

For applied cleaning and normalization of sports data loaded from the API, a combination of programming languages and specialized data processing libraries is most often used. In the Python ecosystem, the de facto standard is pandas и numpy, which allow for quick transformation of JSON responses from the Sport Events API into tabular format, performing aggregations, filtering, and deduplication. Additionally, pydantic or marshmallow are used for strict validation of schemas and data types, which is especially useful when building scalable ETL processes.

In the Node.js/TypeScript environment, popular choices are axios or node-fetch for API calls, as well as libraries for data handling and validation: ajv for checking JSON schemas, class-validator for type-safe control of incoming structures, various wrappers over SQL/NoSQL databases for convenient loading of cleaned information. For storing normalized data, analytical DBMS (PostgreSQL, ClickHouse) or cloud storage (BigQuery, Snowflake) are often chosen, where pre-processed data from the API is already sent.

A separate class of tools is orchestrators and data pipelines: Apache Airflow, Prefect, Luigi, etc. They allow building complex task graphs: regular extraction of matches and odds from betting markets (oddsBase), their cleaning, normalization, and loading into data marts for BI systems. Combined with the capabilities of the API, which on the side api-sport.ru are constantly expanding (WebSocket connections for streaming live data and AI tools for anomaly detection are planned), you get a flexible infrastructure for analytics of any scale.

An example of a simple pipeline in Python using requests и pandas for the initial cleaning of matches:

import requests
import pandas as pd
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.api-sport.ru/v2/basketball/matches"
resp = requests.get(
    BASE_URL,
    headers={"Authorization": API_KEY},
    params={"status": "finished"},
)
raw_matches = resp.json()["matches"]
df = pd.json_normalize(raw_matches)
# Фильтрация только основных турниров
main_tournaments = {7, 17}  # пример ID
mask = df["tournament.id"].isin(main_tournaments)
# Базовая очистка: убираем матчи без счёта
mask &= df["homeScore.current"].notna() & df["awayScore.current"].notna()
clean_df = df.loc[mask].drop_duplicates(subset=["id"])
print(clean_df[["id", "dateEvent", "homeTeam.name", "awayTeam.name"]].head())

Such code can be developed by adding schema validation, handling missing values, normalizing statistics, and integrating with the chosen data storage. Using standard libraries reduces development time and simplifies the maintenance of complex analytical solutions.

Building an ETL pipeline for automatic processing of sports statistics from the API

A full-fledged ETL pipeline (Extract–Transform–Load) for sports statistics allows for the complete automation of the data workflow: from regular data collection from the Sport Events API to loading cleaned and normalized datasets into the analytical storage. At the Extract stage, periodic requests to the endpoints are implemented /v2/{sportSlug}/partidos, /v2/{sportSlug}/jugadores, /v2/{sportSlug}/torneo/{tournamentId}, as well as loading the odds of betting markets from the field oddsBase. At this stage, API filters by date, status, tournament, and team can already be used to reduce the volume of processed data.

The Transform stage includes everything mentioned above: schema validation, duplicate removal, filling in missing values, transforming nested structures into tabular form, enriching with additional information (for example, the geography of leagues or internal user segmentations). It is important to build a modular architecture: separate functions for cleaning matches, events, players, and betting odds. This facilitates maintenance and testing. In the future, with the emergence of WebSocket connections in the API api-sport.ru, transformations can be applied in streaming mode, processing live data with virtually no delays.

At the Load stage, cleaned and normalized data is loaded into target storages: analytical databases, data marts for BI systems, caches for frontend applications, internal services for calculating odds and risks. It is important to maintain versioning and historical data: for example, to store the complete time series of changes in odds across betting markets and all relevant match metrics, in order to subsequently retrain models and conduct retrospective analysis. The ETL pipeline can be scheduled (every N minutes/hours) or triggered by events, integrating with orchestration and monitoring systems.

Below is a simplified example of the structure of an ETL script in Python for football matches:

import requests
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.api-sport.ru/v2/football/matches"

def extract(params):
    resp = requests.get(BASE_URL, headers={"Authorization": API_KEY}, params=params)
    resp.raise_for_status()
    return resp.json()["matches"]

def transform(matches):
    clean = []
    seen_ids = set()
    for m in matches:
        if m["id"] in seen_ids:
            continue
        seen_ids.add(m["id"])
        if m["status"] not in ("finished", "inprogress"):
            continue
        if m.get("homeScore", {}).get("current") is None:
            continue
        clean.append({
            "match_id": m["id"],
            "date": m["dateEvent"],
            "home_team": m["homeTeam"]["name"],
            "away_team": m["awayTeam"]["name"],
            "home_score": m["homeScore"]["current"],
            "away_score": m["awayScore"]["current"],
        })
    return clean

def load(rows):
    # здесь может быть запись в БД или отправка в очередь сообщений
    print(f"Готово к загрузке записей: {len(rows)}")

if __name__ == "__main__":
    raw = extract({"status": "finished"})
    normalized = transform(raw)
    load(normalized)

Such a framework can be easily scaled to other sports (through sportSlug), tournaments and betting markets, as well as supplemented with AI modules for automatic detection of anomalies and errors in data. The foundation remains unchanged: a reliable Sport Events API, a layer of cleansing and normalization, and a robust ETL pipeline that ensures high-quality sports statistics for any tasks.