Although there is a common belief that more footballers are representing countries other than their native ones in recent World Cup editions, a historical overview on migrant footballers representing national teams is lacking. To fill this gap, a database consisting of 10,137 football players who participated in the FIFA World Cup (1930-2018) was created. In order to count the number of migrant footballers in national teams over time, we critically reflect on the term migrant and the commonly used foreign-born proxies in mainstream migration research. A foreign-born approach to migrants overlooks historical-geopolitical changes like the redrawing of international boundaries and colonial relationships, and tends to shy away from citizenship complexities, leading to an overestimation of the number of migrant footballers in a database. Therefore, we offer an alternative approach that through historical contextualization with an emphasis on citizenship, results in more accurate data on migrant footballers--contextual-nationality approach. By comparing outcomes, a foreign-born approach seems to indicate an increase in the volume of migrant footballers since the mid-1990s, while the contextual-nationality approach illustrates that the presence of migrant footballers is primarily a reflection of trends in international migration

Football Players Ontology and Dataset is a comprehensive resource built upon data retrieved from Transfermarkt, a leading platform for football player information. This ontology and accompanying dataset offer structured insights into football players' attributes, including position, personal information, current club, career history, to name a few. By leveraging Transfermarkt's rich repository of player data, this publication provides researchers and practitioners with a standardized framework for analyzing and categorizing football players, enabling advanced research in player profiling, talent identification, and performance analysis. Explore the ontology structure, dataset contents, and potential applications to unlock valuable insights into the world of football. The included files are:

1. players-transfermarkt.ttl, which is the dataset
2. players.ttl, which is the ontology
3. players.shexc, which is the modelling of the entities using the Shape Expressions language

During the 2022 FIFA Men's World Cup in Qatar, the Australian team, the 'Socceroos', played a total of four matches. During those matches, midfielder Aaron Mooy topped the team rank for the number of passes with 200 passes, while Craig Goodwin made the most crosses with 19.

Although there is a common belief that more footballers are representing another country than their native ones in recent World Cup editions, a historical overview on migrant footballers representing national teams is lacking. To fill this lacuna, we created a database consisting of 9.400 football players who participated in the FIFA World Cup (1930-2014). In order to count the number of migrant footballers in national teams over time, we critically reflect on the term migrant and the commonly used foreign-born proxy in mainstream migration research. We argue that such a foreign-born approach overlooks historical-geopolitical changes like the redrawing of international boundaries and colonial relationships, and tends to shy away from citizenship complexities, leading to an overestimation of the number of migrant footballers in the database. Therefore, we offer an alternative approach which, through historical contextualization with an emphasis on citizenship, results in more accurate and reliable data on migrant football players. We coin this the contextual-nationality approach. Although the reliability of the information on Wikipedia-pages can be questioned, we used this source because the data we needed was pretty straightforward and not readily accessible at other, perhaps more trustworthy, online football databases like Transfermarkt.co.uk or Footballdatabase.eu. In case a footballer was foreign-born or (possibly) a migrant, we verified the Wikipedia-data with information from (inter)national newspapers and football magazines. Reliable data on the genealogy of players was often harder to find, as the majority of (grand-) parents are, or were, not internationally famous themselves.The depositor provided the data file in XLSX format. DANS added the ODS format of this file.On April 16th 2018, a small correction was made in the rows related to football player Tony Cascarino.

While the presence of foreign-born footballers in national teams has a long history, it is often believed that the World Cup has become more migratory over time. The presumed increases in the volume and diversity of foreign-born footballers have, however, remained empirically untested. In this article, we empirically test whether the presence of foreign-born footballers at the World Cup has changed over time in respect to these two dimensions of migration. We conducted an analysis on 4.761 footballers, derived from the fifteen national teams that competed in at least ten editions of the World Cup between 1930 and 2018, which comprises of 301 foreign-born football players. We argue that countries’ different histories of migration, in combination with historically used citizenship regimes, largely influence the migratory dimensions of their representative football teams. Our outcomes show that the (absolute) volume of foreign-born footballers in World Cups is indeed increasing over time. Moreover, foreign-born footballers seem to come from an increasingly diverse range of countries. We, therefore, conclude that the World Cup has become more migratory in terms of volume and diversity from an immigration perspective.

Overview:

Football (soccer) player and football (soccer) ball detection dataset from Augmented Startups. * Project Type: Object Detection * Labeled/Annotated with: Bounding boxes

Classes:

• football, player

How to Use:

This is a great starter-dataset for those wanting to test player and/or ball-tracking for football (soccer) games with the Deploy Tab, or the Deployment device and method of their choice.

Images can also be Cloned to another project to continue iterating on the project and model. World Cup, Premier League, La Liga, Major League Soccer (MLS) and/or Champions League computer vision projects, anyone?

Roboflow offers AutoML model training - Roboflow Train, and the ability to import and export up to 30 different annotation formats. Leaving you flexibility to deploy directly with a Roboflow Train model, or use Roboflow to prepare and manage datasets, and train and deploy with the custom model architecture of your choice + https://github.com/roboflow-ai/notebooks.

Tips for Model and Dataset Improvement:

• https://blog.roboflow.com/pip-install-roboflow
• https://help.roboflow.com/implementing-active-learning

Although the reliability of the information on Wikipedia-pages can be questioned, we used this source because the data we needed was pretty straightforward and not readily accessible at other, perhaps more trustworthy, online football databases like Transfermarkt.co.uk or Footballdatabase.eu. In case a footballer was foreign-born or (possibly) a migrant, we verified the Wikipedia-data with information from (inter)national newspapers and football magazines. Reliable data on the genealogy of players was often harder to find, as the majority of (grand-) parents are, or were, not internationally famous themselves.The depositor provided the data file in XLSX format. DANS added the ODS format of this file.On April 16th 2018, a small correction was made in the rows related to football player Tony Cascarino.

The data and do file are used in the paper: EMOTIONAL EXPRESSIONS BY SPORTS TEAMS: AN ANALYSIS OF WORLD CUP SOCCER PLAYER PORTRAITS

Сlinical symptoms in soccer players with COVID-19 infection.

Context

Football is one the most exciting sports worldwide. LaLiga (Spain football legue) has been known for having the best players in the last years and every match gives us the opportunity to enjoy amazing plays and goals. I am planning to upload data from EPL, Bundesliga, Serie A and Ligue 1, but I wanted to upload this one first to check if changes are needed to be made in the other datasets.

Content

This dataset contains information and stats of each match from LaLiga seasons from 2014/2015 to 2018/2019. This information has been taken from different sources. (Goals, assistances, cards, corners, fouls, shots, offsides and ball possesion.)

Inspiration

This is a rich dataset that offers lots of rooms for exploration. These are some of the question that I would like to explore: - Most frequent score in LaLiga in the last 5 years? - How often a team with a lower odd wins the match? - How often there is a missed penalty in a match? - How much money would I earn/lose if I bet $10 on draw in all 380 matches per season?

This dataset is about books and is filtered where the book is The all-time World Cup : the quest for football's greatest team, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Additional file 3. Match: Dataset of GPS monitoring during the matches.

Additional file 2. Dataset of heart rate and locomotor responses during the 5v5 format

Complete list of all-time European national teams international football matches, euro football results

Due to the increasing level of professionalism, the high frequency of competitions, and the alarming injury rate observed in elite female soccer players, multidisciplinary strategies, including nutritional monitoring, need to be implemented. This study aimed to quantify energy, macronutrient and micronutrient intakes during the competitive period and to analyze the effects of two different nutritional interventions on nutritional knowledge, anthropometric data, biochemical values and physical performance. 19 elite female soccer players were randomly divided into two groups: the controlled-diet group (CG, n = 10), that followed a diet based on pre-established menus, and the exchange-diet group (EG, n = 9), that designed their own menus with an exchanged list. A cross-sectional study was designed to evaluate the dietary intake, while an experimental randomized controlled trial was designed to compare the effects of both 12-week nutritional interventions. Total energy, CHO, PROT, fibre and micronutrients intakes were below the general recommendations for athletes while, total and saturated fat intakes were above these. Moreover, there were no differences in diet during weekdays, pre-competition and competition days. The study also revealed a low nutritional knowledge and exchanged diet has demonstrated to be a better strategy to improve this. Biochemical monitoring showed that participants presented decreased concentration of haemoglobin and controlled diet may lead to greater effects on haemoglobin concentration and in anemia prevention. Both EG and CG showed significant reduction on skinfolds sum after intervention, but no significant differences were observed in thigh and calf indices. However, no significant changes were observed in soccer-related skills for any group.

Kendall’s Tau between network properties and club functionalities in different league categories.