How to store sports statistics in a database: MySQL or PostgreSQL?

1. Which database to choose for sports statistics: MySQL or PostgreSQL

7. Storing sports statistics differs from regular business data. Here, the volume of historical information on seasons and tournaments grows simultaneously, while live event updates, odds, and lineups are constantly occurring. Any system that receives data through the sports events API must withstand intensive writing, fast analytical queries, and frequent changes in data structure. Therefore, the choice between MySQL and PostgreSQL is particularly relevant for developers of sports projects, betting platforms, and media services.

8. MySQL is traditionally chosen for its ease of administration, broad support in hosting, and high speed of typical OLTP operations: insertion, updating, simple index-based queries. This is a good option for web applications where the main load is related to retrieving fresh matches and live results from the API and promptly distributing this data to users. PostgreSQL offers more capabilities for complex analytics: powerful window functions, CTE, working with JSON, extensions for geodata, and advanced replication. If you are building a system where complex metrics, xG models, forecasts, and reports need to be calculated on top of the data, PostgreSQL provides more flexibility. by the sports events API api-sport.ru 9. In practice, both DBMSs successfully handle storing sports statistics with proper schema design. MySQL is more often chosen for high-load front-end services (match feeds, live lines, widgets for websites), while PostgreSQL is used for analytical modules, internal reports, and dynamic pricing systems for odds. The key to success lies not only in choosing the engine but also in how you integrate it with the external data source. By using a unified sports API that supports football, hockey, basketball, tennis, table tennis, esports, and other disciplines, you can build a unified data model and, if necessary, use both MySQL and PostgreSQL in parallel for different tasks.

In practice, both DBMSs successfully handle the storage of sports statistics with proper schema design.

2. Requirements for a database to store sports statistics and live results

The database for sports statistics must withstand the characteristic «pulse» of load: calm periods between rounds are replaced by sharp spikes on match days and especially during prime time. When integrating with endpoints such as /v2/{sportSlug}/matches и /v2/{sportSlug}/matches/{matchId}, the system receives a stream of updates on match statuses, scores, liveEvents, and matchStatistics. It is important not to lose data and not to block reading: users expect that the match page, betting coupon, or analytical dashboard will update in fractions of a second.

The second group of requirements is related to the rich data structure. In addition to basic information about matches and teams, it is necessary to store lineups, players, detailed statistics by periods, live events, as well as bookmaker data from the field oddsBase with odds and the dynamics of their changes. A good database schema should allow for quick responses to questions: which matches are currently ongoing, how the odds have changed, what actions a specific player took in the match. This requires well-thought-out indexes by date, status, tournament, team, and key attributes of the odds.

Finally, a modern sports data system must be ready for new ways of delivering and processing information. Today, you can primarily build the load on regular HTTP requests to api-sport.ru, and soon it will be possible to connect WebSocket channels for instant event delivery. At the same time, the direction of AI analysis is developing, where models of forecasts and personalized recommendations are built on top of raw data. This means that the database must not only quickly accept and provide information but also scale well, support replication, and, if possible, separate operational load from heavy analytics.

3. Database structure for sports events: teams, matches, players, statistics

To effectively store data obtained from endpoints /v2/sport, /v2/{sportSlug}/categories, /v2/{sportSlug}/matches, /v2/{sportSlug}/teams и /v2/{sportSlug}/players, it is important to design a clear and extensible schema. It is usually built around several key entities: sports, tournaments and seasons, teams, players, matches, live events, and aggregated statistics. Such a structure allows for storing both a long history (seasons from previous years) and detailed data for each match down to individual shots or cards.

The basic set of tables may look like this: sports (sports types), categories (countries/regions), турниры, seasons, команды, игроки, матчи, match_events, match_statistics, match_odds. At the same time, there is no need to break down absolutely all parameters into separate columns. Some complex structured data from the fields liveEvents, matchStatistics и oddsBase is conveniently stored in JSON fields, while the most in-demand attributes for filtering (tournament id, status, date, current score, key odds market) should be placed in separate indexes.

Below is an example of a simplified schema (for PostgreSQL) reflecting the relationship between tournaments, teams, and matches with live data:

CREATE TABLE sports (
  id          INTEGER PRIMARY KEY,
  slug        TEXT NOT NULL,
  name        TEXT NOT NULL
);
CREATE TABLE teams (
  id          BIGINT PRIMARY KEY,
  sport_id    INTEGER NOT NULL REFERENCES sports(id),
  name        TEXT NOT NULL,
  country     TEXT,
  image_url   TEXT
);
CREATE TABLE matches (
  id                 BIGINT PRIMARY KEY,
  sport_id           INTEGER NOT NULL REFERENCES sports(id),
  tournament_id      BIGINT,
  category_id        BIGINT,
  season_id          BIGINT,
  home_team_id       BIGINT REFERENCES teams(id),
  away_team_id       BIGINT REFERENCES teams(id),
  status             TEXT NOT NULL,
  date_event         DATE,
  start_timestamp    BIGINT,
  current_minute     INTEGER,
  home_score         INTEGER,
  away_score         INTEGER,
  live_events_json   JSON,
  statistics_json    JSON,
  odds_json          JSON
);

In MySQL, the structure will be similar, taking into account the differences in types and auto-increment. The main thing is that the data model directly reflects the structure of the API response: this makes it easier to write importers, maintain backward compatibility during API version updates, and add new sports without changing the architecture.

4. How to retrieve data from the sports events API and store it in MySQL

The integration of MySQL with the sports events API is built on a clear pipeline: obtaining fresh data, transforming it into the format of your schema, and saving it with updates in mind. First, you register and obtain an access key in your personal account to get the API key. Then your backend periodically (cron, queues, background tasks) calls the necessary endpoints, for example /v2/football/matches with parameters for date, status, or tournament. The API response contains an array of matches and related entities, which you distribute across MySQL tables.

Below is an example of a simple Python script that retrieves today’s matches and saves them to a table матчи using MySQL (simplified code, without processing all fields):

import requests
import mysql.connector
from datetime import date
API_KEY = 'ВАШ_API_КЛЮЧ'
SPORT = 'football'
cnx = mysql.connector.connect(
    host='localhost', user='user', password='pass', database='sportsdb'
)
cur = cnx.cursor()
url = f'https://api.api-sport.ru/v2/{SPORT}/matches'
params = {'date': date.today().isoformat()}
headers = {'Authorization': API_KEY}
resp = requests.get(url, params=params, headers=headers)
resp.raise_for_status()
data = resp.json()
for m in data['matches']:
    sql = '''
      INSERT INTO matches
        (id, sport_id, tournament_id, category_id, season_id,
         home_team_id, away_team_id, status, date_event, start_timestamp,
         home_score, away_score)
      VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
      ON DUPLICATE KEY UPDATE
        status = VALUES(status),
        home_score = VALUES(home_score),
        away_score = VALUES(away_score)
    '''
    cur.execute(sql, (
        m['id'], m['tournament']['sportId'], m['tournament']['id'],
        m['category']['id'], m['season']['id'],
        m['homeTeam']['id'], m['awayTeam']['id'], m['status'],
        m['dateEvent'], m['startTimestamp'],
        m['homeScore']['current'], m['awayScore']['current']
    ))
cnx.commit()
cur.close()
cnx.close()

In a production system, this approach is expanded: separate processes are allocated for loading reference data (sports, tournaments, teams, players), batch insertion is used, errors are logged, and liveEvents and oddsBase are processed in separate tables. MySQL is well-suited for such a pattern: it quickly processes frequent INSERT ... ON DUPLICATE KEY UPDATE, supports read replication, and easily scales horizontally through sharding by sports, tournaments, or time ranges.

5. How to retrieve data from the sports events API and store it in PostgreSQL

When working with PostgreSQL, you can use the same integration logic as for MySQL, but additionally leverage JSON capabilities and advanced SQL constructs. This is especially useful for storing structured fields matchStatistics, liveEvents и oddsBase, that come in the responses of endpoints. /v2/{sportSlug}/matches и /v2/{sportSlug}/matches/{matchId}. You can save them in JSON columns while building queries by individual keys without complex schema migrations when the API format changes.

Below is an example of Python code using the psycopg2, library, which retrieves live matches and saves them in PostgreSQL using the upsert construct ON CONFLICT.:

import requests
import psycopg2
import json
API_KEY = 'ВАШ_API_КЛЮЧ'
SPORT = 'football'
conn = psycopg2.connect('dbname=sportsdb user=user password=pass host=localhost')
cur = conn.cursor()
url = f'https://api.api-sport.ru/v2/{SPORT}/matches'
params = {'status': 'inprogress'}
headers = {'Authorization': API_KEY}
resp = requests.get(url, params=params, headers=headers)
resp.raise_for_status()
obj = resp.json()
for m in obj['matches']:
    sql = '''
      INSERT INTO matches (
        id, sport_id, tournament_id, category_id, season_id,
        home_team_id, away_team_id, status, date_event, start_timestamp,
        current_minute, home_score, away_score,
        live_events_json, statistics_json, odds_json
      ) VALUES (
        %(id)s, %(sport_id)s, %(tournament_id)s, %(category_id)s, %(season_id)s,
        %(home_team_id)s, %(away_team_id)s, %(status)s, %(date_event)s,
        %(start_timestamp)s, %(current_minute)s,
        %(home_score)s, %(away_score)s,
        %(live_events)s, %(statistics)s, %(odds)s
      )
      ON CONFLICT (id) DO UPDATE SET
        status = EXCLUDED.status,
        current_minute = EXCLUDED.current_minute,
        home_score = EXCLUDED.home_score,
        away_score = EXCLUDED.away_score,
        live_events_json = EXCLUDED.live_events_json,
        statistics_json = EXCLUDED.statistics_json,
        odds_json = EXCLUDED.odds_json
    '''
    params_sql = {
        'id': m['id'],
        'sport_id': m['tournament']['sportId'],
        'tournament_id': m['tournament']['id'],
        'category_id': m['category']['id'],
        'season_id': m['season']['id'],
        'home_team_id': m['homeTeam']['id'],
        'away_team_id': m['awayTeam']['id'],
        'status': m['status'],
        'date_event': m['dateEvent'],
        'start_timestamp': m['startTimestamp'],
        'current_minute': m.get('currentMatchMinute'),
        'home_score': m['homeScore']['current'],
        'away_score': m['awayScore']['current'],
        'live_events': json.dumps(m.get('liveEvents', [])),
        'statistics': json.dumps(m.get('matchStatistics', [])),
        'odds': json.dumps(m.get('oddsBase', [])),
    }
    cur.execute(sql, params_sql)
conn.commit()
cur.close()
conn.close()

This approach allows for quickly launching new projects based on data, api-sport.ru, and then gradually scaling up analytics. You can add materialized views for complex reports on odds, use window functions to calculate team streaks, and in the future, connect a WebSocket API channel for instant event recording without unnecessary polling of HTTP endpoints. PostgreSQL is well-suited for the role of a single «source of truth» for sports data, on top of which both user interfaces and AI forecasting subsystems are built.

6. Query and index optimization for sports statistics analytics in MySQL and PostgreSQL

Once the basic integration with the sports events API is implemented, query optimization comes to the forefront. Typical scenarios in sports and betting projects include querying matches by date and status, retrieving a team’s game history, analyzing odds dynamics across markets, and building comparative statistics by seasons. For all this, it is important to have the right indexes and a well-thought-out query structure in both MySQL and PostgreSQL.

The minimum set of indexes for the table матчи usually includes composite indexes by sport type and date, by match status, by tournament, as well as by teams. This allows for quickly building live match feeds and tournament pages. In MySQL, you can use covering indexes for the most frequent frontend queries, and for PostgreSQL, additionally utilize GIN indexes on JSON fields if you actively filter by keys within statistics_json or odds_json. Below is an example of simple indexes applicable in both DBMS:

-- быстрый поиск матчей по виду спорта и дате
CREATE INDEX idx_matches_sport_date
  ON matches (sport_id, date_event);
-- лента live‑игр
CREATE INDEX idx_matches_status_start
  ON matches (status, start_timestamp);
-- поиск матчей конкретной команды
CREATE INDEX idx_matches_team
  ON matches (home_team_id, away_team_id);

For analytics of odds and statistics, it is useful to extract aggregates into separate tables or materialized views. For example, you can calculate the minimum, maximum, and average odds for the market from the field every few minutes oddsBase, as well as key metrics from matchStatistics, and then store the result in an aggregated table with separate indexes. This will unload the main match table and speed up reports. In PostgreSQL, such views can be conveniently updated on a schedule, while in MySQL, prepared pivot tables can be used. In any case, optimization begins with analyzing real queries (EXPLAIN, pg_stat_statements, slow query log) and closely aligning the schema with how you actually use the data obtained through the API of sports events and betting odds.