The popularity of martial arts has increased steadily in recent years in the United States. In 2024, there were almost ***** million martial arts participants. This marked an increase over the previous year's figure of *** million.

Between ************* and *************, approximately ******* people participated in martial arts in England. This represented a decrease over the previous period, during which ******* individuals participated.

The statistic depicts the wholesale sales of martial arts equipment in the United States from 2007 to 2024. In 2024, U.S. wholesale sales of martial arts equipment amounted to about *** million U.S. dollars, an increase of about **** percent compared to 2019's figures.

In 2022, the number of participants in mixed martial arts for fitness in the United States amounted to approximately **** million. This was an increase of 7.9 percent on the previous year and was the highest number of participants since 2015.

Financial overview and grant giving statistics of Martial Arts in the Public Interest

The UFC Fighters dataset is a comprehensive collection of information about professional mixed martial artists who compete in the Ultimate Fighting Championship (UFC), one of the most popular and prestigious mixed martial arts (MMA) organizations in the world. This dataset aims to provide a wealth of data and statistics on the fighters, allowing for in-depth analysis and exploration of their careers, performances, and characteristics.

Market Size statistics on the Martial Arts Studios industry in the US

Financial overview and grant giving statistics of Martial Arts for Everyone

Number of Businesses statistics on the Martial Arts Studios industry in the US

Context

This is a refactored version of https://www.kaggle.com/calmdownkarm/ufcdataset

The original dataset was separated only by fights, not by fights and fighters, replicating the metric columns and making it hard to work with.

Content

Additionally the statistics you would see would be the sum of all the fights BEFORE the fight that is referenced in the row itself. Every time a new fighter appeared their metrics would be NA for their first fight. If you're interested in predicting fights or knowing what metrics are the most important in a fight itself I think it's best separated. So I've undone this so every fight what you see is the statistics for the fight itself. If you want the sum that's easy enough to compute in R or Python.

Acknowledgements

All credit goes to https://www.kaggle.com/calmdownkarm for making this dataset

Inspiration

MMA is a highly unpredictable sport. It's the epitome of competition in my opinion where fighters pour their hearts out on the line for us.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. 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 Martial Arts. Last Updated: 08/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The dataset constructs a high-quality martial arts knowledge dataset for large model applications centered on the theme of martial arts, covering multiple core dimensions such as schools, figures, and encyclopedic knowledge. The dataset mainly uses information from three authoritative platforms—the China Wushu Association, the General Administration of Sport of China, and Baidu Baike—as well as the civil platform Chinese Wushu Heritage Network as primary data sources. Through manual screening, standardized processing, and quality monitoring, approximately 1,000 original data entries were generated, including information on martial arts encyclopedic knowledge, martial arts schools, renowned martial arts masters and their lineage relationships, martial arts rules and regulations, and martial arts exchanges. Using large language models, more than 25,000 triple (subject–predicate–object) relationships were extracted from the constructed martial arts data. Among them, the inheritor files contain introductions of inheritors from various schools and their lineage relationships; the competition rules, regulations, and policy documents include competition rules within the martial arts domain; the school files contain introductions to various martial arts schools; the Martial Arts Encyclopedia and Chinese Martial Arts Encyclopedia files include knowledge about the meaning, historical development, and significance of martial arts; the martial arts masters and martial arts institutions files include well-known masters, famous institutions, and their introductions; and the martial arts exchange and information release files contain detailed information related to communication and information sharing in the martial arts field. This dataset provides a solid data foundation for the application of question-answering models in intelligent knowledge-based QA tasks and also promotes the digital dissemination of martial arts culture and the deeper application of intelligent systems in the field of traditional culture.

In 2024, the number of participants in mixed martial arts (MMA) for competition in the United States amounted to approximately *** million. This was a decrease over the previous year's figure of roughly *** million.

Financial overview and grant giving statistics of American Martial Arts Foundation

The statistics shows the share of Americans who participated in martial arts (e.g., boxing, judo) in 2022. The results were sorted by income tier. In 2022, * percent of respondents who stated their income was high said they participated in martial arts.

The domestic shipment value of martial arts equipment in Japan was forecast to amount to around **** billion Japanese yen in 2025. Commonly practiced martial arts in Japan include sumo, judo, kendo, and kyudo, among others.

Financial overview and grant giving statistics of Totality Martial Arts

Wilkes Martial Arts recorded an import turnover of USD 9,821.1 million and an export turnover of USD 0 between February 2024 and January 2025. Explore detailed trade value insights, supply chain analytics, HS code-wise data, shipment history, partner countries, customs trade values, top import and export commodities with pricing, buyers, suppliers, ports, and key competitors in null.

The statistic shows the results of a survey carried out in Brazil in September 2015 to find out about Brazilians' sports habits. As of September 2015, **** percent of respondents who said fight sports and/or martial arts had been their main sports activity in the previous year stated that they had practiced these disciplines ***** times per week.

Financial overview and grant giving statistics of Martial Arts for Life