The National Women's Soccer League is the highest division of professional women's soccer in the United States. In the 2023 season, Angel City FC was the most valuable NWSL team, with a value of around 180 million U.S. dollars. The Los Angeles-based franchise was founded in July 2020 and is run by a majority female ownership group.

The National Women's Soccer League is the highest division of professional women's soccer in the United States. In the 2023 season, Angel City FC was the NWSL team that generated the most revenue, estimated at around 31 million U.S. dollars. The Los Angeles-based franchise was founded in July 2020 and is run by a majority female ownership group.

Lionel Messi was Inter Miami's highest-paid player in 2024, earning over 20.4 million U.S. dollars per year. Meanwhile, Messi's former Barcelona teammate Sergio Busquets earned around 8.8 million U.S. dollars per year.

Lionel Messi was the highest-paid MLS player in 2024, earning a guaranteed 20.45 million U.S. dollars. The iconic Argentinian forward moved to the U.S. in June of that year, having an immediate social media impact.

This is a collection of datasets used for research in the field of Temporal Networks

We have first used these datasets in the following publication:

O.Kostakis, N.Tatti, A.Gionis, "Discovering recurrent activity in temporal networks", Data Mining and Knowledge Discovery, Special Issue in Sports Analytics, 2016.

In summary, this collection contains three different datasets. The first is data about all matches in the 1996-'97 English Premier League. The second dataset contains a temporal network corresponding to team-passing activity of a handball team. Finally, the third dataset contains play-by-play information for 1101 basketball matches of the 2014-'15 NBA season. Within each folder, you will find a separate README file for each dataset.

Disclaimer:

We do not claim to have produced or own the data. We do not claim the correctness of the data.

We provide the data only for reasons related to Research, including but not limited to research reproducibility.

American College Football network of Girvan and Newman Mark Newman provides a football.gml file which contains the network of American football games between Division IA colleges during regular season Fall 2000. The file asks you to cite M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002). There are are two issues with the original GN file. First three teams met twice in one season so the graph is not simple. This is easily dealt with if required. Secondly, the assignments to conferences, the node values, seem to be for the 2001 season and not the 2000 season. The games do appear to be for the 2000 season as stated. For instance the Big West conference existed for football till 2000 while the Sun Belt conference was only started in 2001. Also there were 11 conferences and 5 independents in 2001 but 10 conferences and 8 independents in 2000. I have provided a set of files footballTSE* which define a simple graph with the correct conference assignments in the archive here. There is a read me file included with more details. Further information about the problems with this data and the solutions are given in T.S. Evans, “Clique Graphs and Overlapping Communities”, J. Stat. Mech. (2010) P12037 [arXiv:1009.0638] which would be the appropriate source to cite along with the original GN publication.Note that Gschwind et al, 2015, Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques, independently finds similar errors in this data.

The graph shows the results of an Ipsos survey on the level of interest in soccer worldwide as of May 2018. During the survey fielded in April and May 2018, ** percent of respondents worldwide stated that they were passionate soccer followers and would watch as many games as possible at any given time.

Source: https://www.kaggle.com/datasets/hugomathien/soccer by Hugo Mathien

  About Dataset





  The ultimate Soccer database for data analysis and machine learning

What you get:

+25,000 matches +10,000 players 11 European Countries with their lead championship Seasons 2008 to 2016 Players and Teams' attributes* sourced from EA Sports' FIFA video game series, including the weekly updates Team line up with squad formation (X, Y coordinates) Betting odds from up to 10 providers… See the full description on the dataset page: https://huggingface.co/datasets/julien-c/kaggle-hugomathien-soccer.

This activity (on page 2 of the PDF) is a full inquiry investigation into transfer of motion. Groups of learners will measure the length and circumference of their kicking legs and then takes turns spotting and measuring the distance of each other s one-step kick of a soccer ball. Learners calculate and graph the average distance as a function of leg length and as a function of leg circumference. Relates to linked video, DragonflyTV: Soccer Ball Kick.

IntroductionThis study aimed to assess the development of speed, endurance and power in young football players and to create percentile charts and tables for standardized assessment.MethodsCross-sectional data were collected from 495 male players aged 12–16 years at RKS Raków Częstochowa Academy in 2018–2022. Players participated in a systematic training in which running time 5 m, 10 m, 30 m, lower limb power (standing long jump), and Maximum Aerobic Speed (MAS) were measured using the 30–15 Intermittent Fitness Test. All tests were performed under constant environmental conditions by qualified personnel. Statistical analysis included ANOVA and percentile distribution for P3, P10, P25, P50, P75, P90, P97.ResultsResults indicated that the most significant improvements occurred between the ages of 13 and 14, with increased speed over all distances and a significant increase in power. Percentile tables were developed, highlighting improvements in speed 5 m: 0.087–0.126 s; 10 m 0.162–0.215 s; 30 m: 0.438–0.719 s and power in the long jump test: 31–48 cm. Improvements in MAS ranged from 0.3 to 0.6 m/s across the percentiles.DiscussionThe results highlight the need for individual training programs tailored to the biological maturity of players. The developed percentile charts and tables offer a valuable tool for coaches and sports scientists to monitor progress, optimize training loads, and minimize the risk of injury, providing a frame of reference for assessing the physical development of young soccer players. Future research should focus on extending these charts and tables to other age groups and genders to refine training methodologies further.

Key parameters for the normalized tau distance curves for the respective random leagues, together with coefficient and r-square values for the power law curve fits.

Key parameters for the Spearman correlation curves for the respective random leagues, together with coefficient and r-square values for the mean ensemble power law curve fits.

UNITY is a next-generation Odds Feed and Betting API built for the dynamic needs of modern sportsbooks, betting websites, mobile apps, and professional trading teams/individuals. With comprehensive sports coverage—including top leagues in Football (Soccer), Basketball, and Tennis—UNITY provides real-time odds updates and supports seamless in-play betting across a wide range of markets like Asian Handicap, Over/Under Totals, Corners, and Cards.

The platform combines a robust odds feed with a fully functional Betting API, enabling direct bet placements and full automation of trading strategies. UNITY integrates effortlessly with major Asian bookmakers and betting exchanges, supporting both back and lay positions through a single, central wallet.

Designed with developers in mind, UNITY includes detailed documentation, code samples, and a staging environment for integration and testing. Its customizable data feed allows users to filter by sport, league, event, or market and choose between raw or formatted content, making it a flexible solution for platforms of any size.

Backed by technical support and continuous system updates, UNITY ensures your betting operation stays ahead of the curve. Whether you're building a new platform or enhancing an existing one, UNITY delivers the tools and reliability to take your betting experience to the next level.

The graph shows the distribution of live soccer programming time in the United States in 2015, by network. According to the source, 29 percent of the live soccer programming in 2015 was broadcast on FOX Networks.

This graph shows selected sports types by the number of participating members in Germany in 2024. That year, around 11.3 million people were registered in fitness studios, while almost eight million were members of soccer clubs.

This graph depicts the top 10 soccer leagues based on World Cup players in 2010. 75 soccer players from the Italian league Seria A participated in the World Cup in 2010.

This graph depicts the number of German Football Association (DFB) members from 1950 to 2021. In 2021, the DFB had roughly 7.06 million members. The DFB is the umbrella association for 26 football associations in the Federal Republic of Germany, which in turn include almost 25,000 football clubs. The non-profit association has its headquarters in Frankfurt.

Unlock Soccer Balls trends 2025: Track sales velocity, growth patterns & top-performing tags through interactive analytics. Discover data-proven opportunities with our dual-axis charts comparing product sales vs. keyword demand acceleration - your ultimate toolkit for winning eCommerce assortment strategies.

Unlock Soccer Shoes trends 2025: Track sales velocity, growth patterns & top-performing tags through interactive analytics. Discover data-proven opportunities with our dual-axis charts comparing product sales vs. keyword demand acceleration - your ultimate toolkit for winning eCommerce assortment strategies.

Juventus Football Club - 当前值，历史数据，预测，统计，图表和经济日历 - Jun 2025.Data for Juventus Football Club including historical, tables and charts were last updated by Trading Economics this last June in 2025.