- What is the API for sports events for corner betting
- What data on corners can be obtained through the API in real time
- How to choose a corner statistics API provider for betting
- Step-by-step setup of automated corner monitoring via API
- Integration of corner API with scripts and bots for betting
- Main risks and limitations when automating corner betting through API
What is the API for sports events for corner betting
The sports events API for corner kicks in football is a software interface that allows you to automatically receive match statistics, including the number of corners for the entire match and by halves. Instead of manually monitoring broadcasts or statistics websites, you connect the API to your script, bot, or analytical system and receive fresh data in a standardized JSON format.
La plataforma api-sport.ru provides a multifunctional API for football and other sports, as well as data on bookmaker odds. For corner betting, real-time parameters are especially important: the current number of corners, the minute of the match, the pressure of one of the teams through shot and possession statistics. All of this can be retrieved through a single match endpoint, without complex website parsing and unstable sources.
Technically, the API works on the principle of simple HTTP requests. You specify the sport, filters by tournaments, teams, or match status, and in response, you receive a structure with matches, scores, live events, and a detailed statistics block. For football, in the matchStatistics section within the match, there is a corner indicator with the key cornerKicks, which can be used for triggers of your corner betting bot.
Example of a basic request to the football matches API
Below is an example of a simple request for current live football matches using curl:
curl -H 'Authorization: YOUR_API_KEY' \ 'https://api.api-sport.ru/v2/football/matches?status=inprogress'
In the response, you will receive a list of matches, each of which has fields currentMatchMinute, liveEvents, matchStatistics, oddsBase, and others available. You can then programmatically extract the necessary corner kick metrics and build your betting strategy based on them.
What data on corners can be obtained through the API in real time
Through the football events API, you can obtain both the total number of corner kicks and the distribution by match periods. In the matchStatistics object for each match, there are groups of statistics, among which is the corner kick metric with the human-readable name «Corner kicks» and the key cornerKicks. For the ALL period, you see the total corners for the entire match, and for the 1ST and 2ND periods — separately by halves.
In addition to the corners themselves, the API returns many related metrics that help assess the dynamics and likelihood of new corners: ball possession, total shots, shots on target, crosses, number of attacks, and entries into the final third. Such context allows for building more nuanced monitoring algorithms than just tracking the dry corner kick counter.
It is important that the data can be updated as the match progresses. You can request a list of matches with the status inprogress or take a specific match by its identifier. Based on the current minute and the value of cornerKicks, it is not difficult to implement triggers like «if by the 60th minute there are less than 6 corners in the match, and both teams are attacking a lot, send a signal to bet on total corners over.».
Example of extracting corners from matchStatistics
[pb]Below is an example of Python code that retrieves a match by ID and gets the number of corners for the entire match and by halves.[/pb]
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}',
headers={'Authorization': API_KEY}
)
data = resp.json()
# В ответе матч лежит на верхнем уровне как объект Match
match_stats = data.get('matchStatistics', [])
corners_all = None
corners_1st = None
corners_2nd = None
for period_block in match_stats:
period = period_block.get('period')
for group in period_block.get('groups', []):
for item in group.get('statisticsItems', []):
if item.get('key') == 'cornerKicks':
if period == 'ALL':
corners_all = (item.get('homeValue'), item.get('awayValue'))
elif period == '1ST':
corners_1st = (item.get('homeValue'), item.get('awayValue'))
elif period == '2ND':
corners_2nd = (item.get('homeValue'), item.get('awayValue'))
print('Все угловые:', corners_all)
print('1 тайм:', corners_1st)
print('2 тайм:', corners_2nd)
Similarly, you can analyze other metrics from matchStatistics to enhance your corner betting model with deeper statistics.
How to choose a corner statistics API provider for betting
When choosing an API provider for automating corner monitoring, it is important to look not only at the availability of basic statistics but also at the depth of data, stability of operation, and scalability options. For corner betting, accuracy and speed of statistical updates are critical, as most decisions are made live, within a few minutes.
Key selection criteria include:
- Support for a full set of match statistics, including corners broken down by periods and additional indicators of attacking activity.
- Low data update latency and high API availability throughout the match.
- Wide coverage of tournaments and countries, presence of top leagues and niche championships where the corner market may be less efficient.
- Availability of bookmaker odds data to immediately tie statistics to lines and track odds movements.
- Good documentation, clear JSON structure, request examples, SDK, and ready-made solutions.
- Product development: support for WebSocket for data streaming and AI tools for advanced analytics.
La plataforma api-sport.ru Combines detailed match statistics, including the matchStatistics block with the cornerKicks indicator, and the oddsBase array with markets and odds. Football, basketball, tennis, table tennis, esports, hockey, and other sports are supported, and functionality is constantly expanding. It is already possible to build stable corner monitoring through HTTP requests, and soon WebSocket channels and AI services for even faster and more accurate analysis will be available.
Example of checking available football tournaments
Before building a corner strategy, it is convenient to get a list of countries and recommended tournaments to limit monitoring to only the necessary leagues.
curl -H 'Authorization: YOUR_API_KEY' \ 'https://api.api-sport.ru/v2/football/categories'
In the response, you will receive a list of categories with the defaultTournaments object, where for the Russian language there is a list of popular tournaments. This allows you to quickly gather a pool of leagues where the corner market is most interesting for your model.
Step-by-step setup of automated corner monitoring via API
To fully deploy automated corner monitoring for bets, you just need to follow a few sequential steps. All logic can be easily implemented in any programming language that can send HTTP requests and process JSON.
Step 1. Obtain an API key
First, register and get a personal access key to the API in your personal account at app.api-sport.ru. This key is passed in the Authorization header for all API requests and allows you to identify your project as well as control limits.
Step 2. Define the pool of matches and polling frequency
Next, you need to determine which tournaments and matches you want to track. Usually, filtering by status inprogress, date, and tournament is used. The polling frequency is chosen based on limits and the required sensitivity of the strategy: for most corner tasks, updating data every 10–30 seconds is sufficient.
Step 3. Implement the polling loop and trigger logic
In the application, a constant loop is set up: we retrieve the list of live matches, extract cornerKicks metrics and related parameters from matchStatistics, check them against the conditions of the strategy, and, if necessary, generate a betting signal.
Example of simple corner monitoring in Python
import time
import requests
API_KEY = 'YOUR_API_KEY'
BASE_URL = 'https://api.api-sport.ru/v2/football/matches'
HEADERS = {'Authorization': API_KEY}
# Условие: к 60 минуте суммарно меньше 6 угловых
MINUTE_THRESHOLD = 60
CORNERS_THRESHOLD = 6
POLL_INTERVAL = 20 # секунд
while True:
resp = requests.get(BASE_URL, params={'status': 'inprogress'}, headers=HEADERS)
data = resp.json()
for match in data.get('matches', []):
minute = match.get('currentMatchMinute')
stats = match.get('matchStatistics', [])
corners_home = corners_away = 0
for period_block in stats:
if period_block.get('period') == 'ALL':
for group in period_block.get('groups', []):
for item in group.get('statisticsItems', []):
if item.get('key') == 'cornerKicks':
corners_home = item.get('homeValue', 0)
corners_away = item.get('awayValue', 0)
total_corners = (corners_home or 0) + (corners_away or 0)
if minute and minute >= MINUTE_THRESHOLD and total_corners < CORNERS_THRESHOLD:
print('Сигнал: рассмотреть ставку на ТБ угловых в матче', match.get('id'))
time.sleep(POLL_INTERVAL)
This framework can be easily expanded: add filtering by leagues, more complex conditions, logging to a database, and sending signals to a messenger or trading bot.
Integration of corner API with scripts and bots for betting
After the basic monitoring of corners through the API is set up, the next step is to integrate it with betting bots and analytical dashboards. In the simplest version, your script only generates signals, while the decision to place a bet is made by a person. A more advanced option is to connect with an automatic betting bot and bookmaker APIs to place bets according to predefined rules.
La plataforma api-sport.ru provides not only sports statistics but also bookmaker APIs: lines, odds, market statuses. This allows matching the current statistics on corners from matchStatistics with the relevant markets in oddsBase and lines from bookmaker APIs. Based on this, for example, you can automatically find matches where the statistics suggest an increase in corners, and the total corners odds are above your target threshold.
Integration is usually built according to the scheme: a data collection module from sports event APIs, a signal analysis and generation module, a module for interacting with bookmaker APIs, and a results logging module. Each of them is isolated, making the system flexible and scalable.
Example of signal transmission from the statistics module to the bot
def check_corner_signal(match):
minute = match.get('currentMatchMinute')
stats = match.get('matchStatistics', [])
corners_home = corners_away = 0
for period_block in stats:
if period_block.get('period') == 'ALL':
for group in period_block.get('groups', []):
for item in group.get('statisticsItems', []):
if item.get('key') == 'cornerKicks':
corners_home = item.get('homeValue', 0)
corners_away = item.get('awayValue', 0)
total_corners = corners_home + corners_away
if minute and 50 <= minute <= 70 and total_corners <= 5:
return {
'match_id': match.get('id'),
'type': 'corners_total_over',
'comment': 'Низкий тотал угловых к середине второго тайма'
}
return None
def process_matches(matches, bot):
for m in matches:
signal = check_corner_signal(m)
if signal:
# Здесь может быть вызов методов бота, работающего с API букмекеров
bot.handle_signal(signal)
Next, a module is connected that, using the bookmaker API, checks the availability of the corners market, current odds, and, when all parameters match, forms and sends a bet.
Main risks and limitations when automating corner betting through API
Automating corner monitoring through the API provides a significant advantage in speed and breadth of match coverage, but does not eliminate the risks associated with betting itself. Before launching the bot into production, it is important to understand the technical and financial limitations and to correctly account for them in the solution architecture.
The main risks include:
- Data update delays. Even with a very fast API, there can be a lag of several seconds between the actual event on the field and its appearance in the statistics. During this time, the bookmaker can adjust the line or close the market.
- Limitations on request frequency and data volume. Exceeding the limits may result in errors, slower responses, or temporary key blocking.
- Uncertainty of outcomes. Even the best statistical model does not guarantee profit, especially over short distances. Corners are a fairly volatile market with a high degree of randomness.
- Regulatory and contractual restrictions. Different bookmakers have different rules regarding automated betting and the use of bots.
From a technical standpoint, it is necessary to handle network and server errors, respond correctly to response codes, and flexibly manage the frequency of requests. It is advisable to implement data source backups and degradation logic: if statistics are temporarily unavailable, the bot should not make blind decisions.
Example of error handling and simple request throttling
import time
import requests
API_KEY = 'YOUR_API_KEY'
BASE_URL = 'https://api.api-sport.ru/v2/football/matches'
HEADERS = {'Authorization': API_KEY}
while True:
try:
resp = requests.get(BASE_URL, params={'status': 'inprogress'}, headers=HEADERS, timeout=5)
if resp.status_code == 429:
print('Достигнут лимит запросов, делаем паузу')
time.sleep(60)
continue
resp.raise_for_status()
data = resp.json()
# Обработка данных по угловым здесь
except requests.exceptions.Timeout:
print('Таймаут запроса к API, повтор через 10 секунд')
time.sleep(10)
except requests.exceptions.RequestException as e:
print('Сетевая ошибка:', e)
time.sleep(30)
time.sleep(15)
Automation through API, including future support for WebSocket and AI tools, makes working with corner bets more structured and faster. However, it should be viewed as a tool in the hands of a responsible bettor or company, not as a guarantee of profitability. Always test strategies on historical and demo data, adhere to bankroll management, and consider risks.
