Use our trusted SportMonks Football API to build your own sports application and be at the forefront of football data today.

Our Football API is designed for iGaming, media, developers and football enthusiasts alike, ensuring you can create a football application that meets your needs.

Over 20,000 sports fanatics make use of our data. We know what data works best for you, so we ensured that our Football API has all the necessary tools you need to create a successful football application.

• Livescores and schedules Our Football API features extremely fast livescores and up-to-date season schedules, meaning your app will be the first to notify its customers about a goal scored. This also works to further improve the look and feel of your website.

• Statistics and line-ups We offer various kinds of football statistics, ranging from (live) player statistics to team, match and season statistics. And that’s not all - we also provide pre-match lineups for all important leagues.

• Coverage and historical data Our Football API covers over 1,200 leagues, all managed by our in-house scouts and data platform. That means there’s up to 14 years of historical data available.

• Bookmakers and odds Build your football sportsbook, odds comparison or betting portal with our pre-match and in-play odds collated from all major bookmakers and markets.

• TV Stations and highlights Show your customers where the football games are broadcasted and provide video highlights of major match events.

• Standings and topscorers Enhance your football website with standings and live standings, and allow your customers to see the top scorers and what the season's standings are.

With the SportMonks Football Euro Plan you get access to all data you need for a fantastic website/app about the Top Leagues in Europe. All data is available in JSON en extremely well documented. Our 7 days a week available Service Department will help you when needed. 14 day Free Trial included for all our plans!

The Sports Data API market is experiencing robust growth, fueled by the increasing popularity of sports betting, fantasy sports, and the broader digitalization of the sports industry. The market's expansion is driven by the rising demand for real-time, accurate, and comprehensive sports data among various stakeholders, including sportsbooks, media companies, fantasy sports platforms, and app developers. Technological advancements, particularly in data analytics and machine learning, are enabling the creation of more sophisticated and valuable data products, further stimulating market growth. The integration of data APIs into existing platforms is becoming increasingly seamless, lowering the barrier to entry for businesses looking to leverage sports data. Competition is fierce, with established players like Sportradar and Genius Sports facing challenges from agile newcomers and specialized providers focusing on niche sports or data types. This competitive landscape fosters innovation and drives down prices, making sports data more accessible to a wider range of users. Despite the strong growth, the market faces challenges such as data security concerns, regulatory complexities related to gambling, and the need for consistent data standardization across different sports and leagues. The market is segmented by data type (e.g., live scores, player statistics, historical results), sport (e.g., soccer, basketball, American football), and customer type (e.g., media, gaming, betting). Geographic distribution shows significant traction in North America and Europe, but Asia-Pacific and other emerging markets are expected to witness rapid growth due to the expanding digital economy and rising interest in sports. While precise figures for market size and CAGR are not provided, a conservative estimate based on industry reports and comparable sectors indicates a market size of approximately $2 billion in 2025, growing at a compound annual growth rate (CAGR) of around 15% between 2025 and 2033. This projection reflects the ongoing technological advancements and the increasing demand for sophisticated sports data analytics capabilities. The forecast period (2025-2033) will likely see significant consolidation and strategic partnerships among existing players and further expansion of the market's geographical reach.

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Introduction

The lack of publicly available National Football League (NFL) data sources has been a major obstacle in the creation of modern, reproducible research in football analytics. While clean play-by-play data is available via open-source software packages in other sports (e.g. nhlscrapr for hockey; PitchF/x data in baseball; the Basketball Reference for basketball), the equivalent datasets are not freely available for researchers interested in the statistical analysis of the NFL. To solve this issue, a group of Carnegie Mellon University statistical researchers including Maksim Horowitz, Ron Yurko, and Sam Ventura, built and released nflscrapR an R package which uses an API maintained by the NFL to scrape, clean, parse, and output clean datasets at the individual play, player, game, and season levels. Using the data outputted by the package, the trio went on to develop reproducible methods for building expected point and win probability models for the NFL. The outputs of these models are included in this dataset and can be accessed using the nflscrapR package.

Content

The dataset made available on Kaggle contains all the regular season plays from the 2009-2016 NFL seasons. The dataset has 356,768 rows and 100 columns. Each play is broken down into great detail containing information on: game situation, players involved, results, and advanced metrics such as expected point and win probability values. Detailed information about the dataset can be found at the following web page, along with more NFL data: https://github.com/ryurko/nflscrapR-data.

Acknowledgements

This dataset was compiled by Ron Yurko, Sam Ventura, and myself. Special shout-out to Ron for improving our current expected points and win probability models and compiling this dataset. All three of us are proud founders of the Carnegie Mellon Sports Analytics Club.

Inspiration

This dataset is meant to both grow and bring together the community of sports analytics by providing clean and easily accessible NFL data that has never been availabe on this scale for free.

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Maryland Sports (http://www.marylandsports.us/) has identified sport venues located within the State of Maryland. These venues offer opportunities to participate in free and fee-based, organized and pick-up, indoor and outdoor sports and physical fitness related activities in the area of Football.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer/26

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The Super Bowl is an annual American football game that determines the champion of the National Football League (NFL). The game culminates a season that begins in the previous calendar year, and is the conclusion of the NFL playoffs. The contest is held in an American city, chosen three to four years beforehand, usually at warm-weather sites or domed stadiums. Since January 1971, the winner of the American Football Conference (AFC) Championship Game has faced the winner of the National Football Conference (NFC) Championship Game in the culmination of the NFL playoffs. Before the 1970 merger between the American Football League (AFL) and the National Football League (NFL), the two leagues met in four such contests. The first two were known as the "AFL–NFL World Championship Game". Super Bowl III in January 1969 was the first such game that carried the "Super Bowl" moniker, the names "Super Bowl I" and "Super Bowl II" were retroactively applied to the first two games.[3] The NFC/NFL leads in Super Bowl wins with 26, while the AFC/AFL has won 24. Nineteen different franchises, including teams that relocated to another city, have won the Super Bowl.-Wikipedia: List of Super Bowl Champions

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Base de donnée officielle des équipements sportifs en FranceData ES est la base de données des équipements sportifs et des lieux de pratiques du Ministère chargé des Sports. Elle est mise à jour quotidiennement. Cette donnée découle d'une obligation légale où "tout propriétaire d'un équipement sportif est tenu d'en faire la déclaration à l'administration en vue de l'établissement d'un recensement des équipements" (Code du Sport L312-2). Depuis 2005, ce dispositif contribue à documenter et éclairer le développement de la pratique sportive en France. Aujourd'hui, avec plus de 330 000 lieux de pratiques recensés en France métropolitaine et dans les territoires d'outre-mer, DATA ES constitue un référentiel exhaustif, mis à jour quotidiennement. Chaque équipement recensé est associé à un code national unique, garantissant une identification précise et homogène. Data ES - Complet Ce jeu de données constitue l’agrégation complète des équipements sportifs, de leurs installations associées, et des activités sportives pratiquées sur chaque site. Il s'agit du fichier principal pour les analyses territoriales ou cartographiques à l’échelle nationale.Méthodologie et définitions Informations clés

Fréquence de mise à jour : quotidienne Format : CSV, JSON, via API Licence : Licence Ouverte / Etalab 2.0 Source : Ministère des Sports Contact : contact-equipements[AT]sports.gouv.fr

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Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

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Turkish Football Federation Presidency Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.