What is Odds & Betting Data?
Odds & betting data is information about winning probability, historical game results, and possible outcomes of sports games. It’s sourced from different betting websites. Odds predict how likely it is a team will win and dictate the amount you receive as your payout if you win the bet. Sport commonly used in betting are basketball, racing, football, baseball, and tennis. In addition, due to its popularity, esports games are now used for bets.
Best Odds & Betting Datasets & APIs
NFL Data (Historic Data Available) - Sports Data, National Football League Datasets. Free Trial Available
Football API | World Plan | SportMonks Sports data for 100 + leagues worldwide
Football API | Enterprise Plan | SportsMonks Sports data for 1,200 + leagues worldwide
API-SPORTS: Sports Data Product - Ultra Plan (Global Data Coverage)
TheSports - Soccer Basic & Real-time Data Plan from 1800 leagues & competitors
Football API Euro Plan | Sports Data for European Matches | SportMonks
API-SPORTS: Sports Data Product Pro Plan (Global Data Coverage)
API-SPORTS: Sports Data Product - Mega Plan (Global Data Coverage)
Monetize data on Datarade Marketplace
Top Odds & Betting Data Providers
Datarade considers factors such as data accuracy, coverage of sports events, real-time updates, historical data availability, data format compatibility, API integration options, pricing transparency, customer reviews, and data provider reputation when recommending odds betting data providers.
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SportMonksBased in NetherlandsSportMonks is a Sports Data provider for customers that want to build sports websites and applications by using our Sports API's. The Sports Data can by used... -
API-SPORTSBased in FranceRestful API for Football data +550 competitions covered with Livescore, standings, teams, odds, bookmakers, fixtures, events, line-ups, players, coachs, stat...
Odds & Betting Data Explained
Examples of odds & betting data include historical odds databases, odds data, and odds databases provided by sports betting data providers. This data is used for analyzing and predicting outcomes in sports betting, particularly in football. In this page, you’ll find the best data sources for odds and betting data.
How is Odds & Betting Data collected?
Odds & betting data is collected through various sources. Different leagues like the NBA, NFL, and NTL offers real-time scoring on their websites. This information can be gathered using web scraping tools. Sports channels can also be a source for this data. For esports games, game creators or organizations publish the results of matches. How the odds are calculated is based on the number of bet participants.
Main Attributes
Based on the number of data points in an odds & betting dataset, the common attributes for odds and betting data are as follows:
Winning probability: is a statistical prediction of the chances for a team to win. This is based on the previous performance of the team.
Number of Odds: this is calculated through which team has more bets placed on it. The payout is your principal amount times the number of odds. For example, for 3.00 odds, your $1 will be $3. There are many types of odds, like fractional odds, decimal odds, and American odds.
Win-lose record: this is the archived records of a team. This includes the number of times each team has played together and their win-loss record.
Use Cases
Investors and bettors use this data to gather information about the winning probability of a team. Odds & betting data is based on the statistical prediction of oddsmakers. This data helps new bettors decide which team to choose. A low odd means the chance of winning is likely. Odds & betting data also provide insights from expert analysts of each field.
Quality Assessment
To access high-quality odds & betting data, data providers must ensure that their data is clean, updated in real-time, and gathered from statisiticians and betting experts to ensure it’s accurate. Make sure that your odds & betting provider offers good customer service. One way of assessing a provider’s customer service is by checking the reviews of their datasets, and by requesting a data sample to counter check that their data is accurate.