Analysis of ‘FIFA - Football World Cup Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamsouravbanerjee/fifa-football-world-cup-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

The FIFA World Cup, often simply called the World Cup, is an international association football competition contested by the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every four years since the inaugural tournament in 1930, except in 1942 and 1946 when it was not held because of the Second World War. The current champion is France, which won its second title at the 2018 tournament in Russia.

The current format involves a qualification phase, which takes place over the preceding three years, to determine which teams qualify for the tournament phase. In the tournament phase, 32 teams, including the automatically qualifying host nation(s), compete for the title at venues within the host nation(s) over about a month.

The 21 World Cup tournaments have been won by eight national teams. Brazil have won five times, and they are the only team to have played in every tournament. The other World Cup winners are Germany and Italy, with four titles each; Argentina, France, and inaugural winner Uruguay, with two titles each; and England and Spain, with one title each.

The World Cup is the most prestigious association football tournament in the world, as well as the most widely viewed and followed single sporting event in the world. The cumulative viewership of all matches of the 2006 World Cup was estimated to be 26.29 billion with an estimated 715.1 million people watching the final match, a ninth of the entire population of the planet.

17 countries have hosted the World Cup. Brazil, France, Italy, Germany, and Mexico have each hosted twice, while Uruguay, Switzerland, Sweden, Chile, England, Argentina, Spain, the United States, Japan, and South Korea (jointly), South Africa, and Russia have each hosted once. Qatar will host the 2022 tournament, and 2026 will be jointly hosted by Canada, the United States, and Mexico, which will give Mexico the distinction of being the first country to host games in three World Cups.

Content

This Dataset consists of Records from all the previous Football World Cups (1930 to 2018)

Acknowledgements

For more, please visit - https://www.fifa.com/

--- Original source retains full ownership of the source dataset ---

Analysis of ‘FIFA FOOTBALL PLAYERS’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/marianaponce/fifa-football-players on 14 February 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Bennett Mhlanga

Contents

2022 FIFA World Cup Group Stage Data

Description

Here we have folders with country names on them- representing each country that participated in the 2018 FIFA WC Each folder contains:

1. TeamLineup: Lineup of the particular team under consideration 2. OppLineup: Opposition Lineup Lineup-attributes explained

3. MatchEvents:Home and away events happened in the game Match_Events-attributes explained

4. MatchesPlayed:A file containing information on all the different matches the country played in the tournament Matches_Played-attributes explained ~please do upvote if you download it or find it useful.

Dataset

This dataset was created by Fahad Azeem

Released under Apache 2.0

Contents

Here are a few use cases for this project:

1. Sports Broadcasting Enhancement: The "FIFA YOLO Project" could be employed to improve live broadcasting of football matches. It can provide real-time, on-screen metadata about players, ball location, gametime, and the score to enhance viewers' understanding and interaction with the game.

2. Gaming and Virtual Reality: Developers of football video games and virtual reality simulations could use this model to create more realistic and immersive experiences. Player movements, ball trajectories, and game dynamics could be modeled more accurately based on insights gathered from this computer vision model.

3. Player Performance Analysis: Sports analytics companies and football teams could use it to evaluate players' performances during games. Identifying where players are on the field, their interaction with the ball, and how they engage with other players could provide valuable insights into a player's skills, strengths, and weaknesses.

4. Smart Refereeing Systems: The model could be integrated into AI-based refereeing systems to assist in making correct decisions during a football game. It could track ball position, players' actions, goalie position relative to goalpost and determine if a goal was valid, a foul was committed or an offside rule was violated.

5. Sports Betting Applications: Sports betting platforms could evolve with the introduction of this model, providing real-time data analysis of ongoing matches. This can help bettors make more informed decisions based on the real-time performance of the teams and players.

This paper aimed to analyze the tactical behavior of Spain National Football Team during the FIFA(r) World Cup, in four different match statuses: Draw, Win+1, Win+2, and Loss-1. The sample comprised 894 offensive sequences performed over Spain's seven matches during the tournament. Data analysis was performed through observations of video footage recorded from a TV station. Following variables were analyzed of play: Achievement time of the attack, Number of contacts with the ball, Number of balls received/Number of passes, Ball transmission speed, Location of acquisition and recovery of ball possession and Form of acquisition and recovery of ball possession. Descriptive statistics were conducted, and so were Chi-Square and Kruskal-Wallis tests. The significance level was set to p < 0.05. Significant differences were found for the following variables: "ball transmission speed", "location and form of acquisition and recovery of ball possession". In conclusion, it was possible to infer that the tactical behavior of the Spanish team underwent few modifications during the tournament regarding match status.

Dataset

This dataset was created by AZAD SINGH S

Contents

Analyze the passing and receiving locations of the 2022 FIFA World Cup through spatial entropy .

This paper aims to identify the patterns of offensive transition (defense-attack) of the Spain National Soccer Team during the 2010 FIFA® World Cup through the analysis of frequency of the forms of ball acquisition/recovery (FAR) and the areas of the field where these actions took place. The sample comprises 895 offensive sequences performed during the 7 matches of the Spain National Team in the tournament. For data collection, it was performed observations of televised matches. Data were registered and quantified by Excel spreadsheets. Descriptive analysis (frequency and percentage) was performed for the variables "area" and "FAR". Chi-squared test (χ2) was used to compare case distribution among the values of the analyzed variables with significance level of p < 0.05. It was calculated the standardized residuals (e) of "FAR" according to the area of the field and also of the areas of the field according to "FAR". For statistical procedures, IBM SPSS v.20 was used. The variables "interception" and "constant match fragments" showed significant higher frequency than the other FAR in all areas of the field, whereas "interception" presented higher frequency value in defensive (e = 2.73), pre-defensive (e = 5.51) and pre-offensive (e = 2.41) areas, and the variable "tackle" had significant higher frequency in the defensive area (e = 4.85). It is concluded that there were higher frequency of constant match fragments in the defensive area, interceptions in the pre-defensive and pre-offensive areas and, once more, of constant match fragments in the offensive area, what suggests that the Spain National Team shows predisposition to avoid 1v1 challenges in the defensive area.

The global women's soccer cup market is experiencing robust growth, fueled by increasing participation rates, rising media coverage, and enhanced commercialization. While precise figures for market size and CAGR are unavailable in the provided data, we can infer significant expansion based on observable trends. The substantial investment by governing bodies like FIFA, UEFA, and continental confederations (AFC, CAF, CONCACAF, CONMEBOL) in women's football indicates a commitment to developing the sport globally. This investment is manifesting in improved infrastructure, increased prize money, and broader media partnerships, all contributing to increased market value. The segmentation into club cups and national team cups highlights the diverse avenues for growth, with national team tournaments generating substantial viewership and sponsorship opportunities, while club competitions are fostering a more sustainable, long-term professional environment. The application segments (sports industry, television broadcasting, public welfare, others) show the broad appeal of women's soccer, extending beyond direct participation to encompass media rights, sponsorships, and social impact initiatives. Geographic distribution shows strong presence in North America and Europe, but significant untapped potential exists in Asia-Pacific, Middle East & Africa, and South America, as these regions experience rapid economic development and rising interest in women's sports. The presence of national football associations (e.g., CFA, REFA, FFF, DFB, FIGC) underscores the increasing participation and regulation at national levels, further propelling market expansion. Growth restraints might include the ongoing need to bridge the gender pay gap and continue improving infrastructure in developing nations, but the overall trajectory remains decidedly positive. The forecast period (2025-2033) promises continued expansion, with a likely CAGR in the high single digits to low double digits. This projection considers the increasing investments, the broadening global appeal, and the ongoing efforts to professionalize the sport. While precise figures require further market research, the available data strongly indicates a burgeoning market with promising future prospects. The inclusion of 'public welfare' as an application segment hints at the social impact of women's soccer, which is driving further investment and growth opportunities via partnerships with NGOs and government initiatives. Successful events like the Women's World Cup directly contribute to increasing the market value by boosting global viewership and sponsorships. The long-term success will depend on sustained investment, the continued development of the professional leagues, and increasing media exposure.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The men's football cup market exhibits robust growth, driven by increasing global viewership, lucrative sponsorship deals, and expanding media rights. The market's value, while not explicitly stated, can be reasonably estimated based on the presence of major leagues like the NFL, NCAA Football League, and international competitions such as the Olympics and FIFA World Cup. Considering these established players and their substantial revenues, a conservative estimate places the 2025 market size at approximately $15 billion. A compound annual growth rate (CAGR) of 5% is plausible, reflecting sustained interest and the potential for market expansion in emerging regions like Asia-Pacific and Africa. Key growth drivers include the increasing popularity of fantasy football, esports integration, and the rising influence of social media in fan engagement. The segmentation by application (sports industry, television broadcasting, public welfare) and type of cup (club cup, national team cup) highlights diverse revenue streams. The significant participation of major organizations and leagues indicates a consolidated yet dynamic market. Potential restraints could include economic downturns impacting sponsorship revenue and unforeseen global events that affect tournament scheduling and viewership. The North American market, particularly the United States, will continue to be a dominant player due to the NFL’s significant influence and established fanbase. However, substantial growth is expected from Asia-Pacific markets, driven by rising disposable incomes and increased interest in football, specifically in China and India. European leagues also maintain a strong presence, with consistent viewership and established broadcast deals. The market’s future trajectory is likely influenced by successful international tournaments, the ongoing development of new media platforms, and the engagement of younger demographics through digital and social media initiatives. Strategic partnerships between leagues, broadcasters, and technology providers will further propel market growth in the forecast period of 2025-2033. A comprehensive understanding of these factors is crucial for stakeholders to capitalize on emerging opportunities and navigate the competitive landscape.

Analysis of ‘Women's International Football Results’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/martj42/womens-international-football-results on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a work-in-progress sister data set to the men's international football results dataset. If you're interested in helping out, submit a pull request here.

Content

Currently, the dataset includes 4,169 women's international football results. All major tournament results should be complete. Some international friendlies, particularly tournaments, are included. A LOT of results are not yet in the dataset.

results.csv includes the following columns:

• date - date of the match
• home_team - the name of the home team
• away_team - the name of the away team
• home_score - full-time home team score including extra time, not including penalty-shootouts
• away_score - full-time away team score including extra time, not including penalty-shootouts
• tournament - the name of the tournament
• city - the name of the city/town/administrative unit where the match was played
• country - the name of the country where the match was played
• neutral - TRUE/FALSE column indicating whether the match was played at a neutral venue

Acknowledgements

The data is gathered from several sources including but not limited to Wikipedia, fifa.com, rsssf.com and individual football associations' websites.

Inspiration

Some directions to take when exploring the data:

• Who is the best team of all time
• Which teams dominated different eras of football
• What trends have there been in international football throughout the ages - home advantage, total goals scored, distribution of teams' strength etc
• Can we say anything about geopolitics from football fixtures - how has the number of countries changed, which teams like to play each other
• Which countries host the most matches where they themselves are not participating in
• How much, if at all, does hosting a major tournament help a country's chances in the tournament
• Which teams are the most active in playing friendlies and friendly tournaments - does it help or hurt them

The world's your oyster, my friend.

Contribute

If you notice a mistake or the results are being updated fast enough for your liking, you can fix that by submitting a pull request on github.

✌🏼✌🏼✌🏼

--- Original source retains full ownership of the source dataset ---

Context

The dataset provided include players data for the ultimate team mode in fifa 21 .

Columns Description

here is a description of some columns in the dataset : - RAT : Player Rating - POS : Player Position - VER : Card Version - PS : Price on Playstation , if 0 then it is not available in market - SKI : Skills rating of player ( from 0 to 5 ) - WF : Weak Foot Skills ( from 0 to 5 ) - WR : Work rate of player on the field , and given in the formula ( Attack Work rate / Defence Work rate ) , each value can be ( low , medium , high ) - PAC : Player Pace (Speed) - SHO : Player Shooting power
- PAS : Player Pass - DRI : Player Dribble - DEF : Player Defence - PHY : Player Physicality - Body_info : player height given in cm and feet followed by type of body of player ( for some players the game have custom body for them ) - Popularity : popularity of using the player - BS : Base stats - IGS : In game stats

The Video Assistant Referee (VAR) market is experiencing significant growth, driven by the increasing adoption of technology to enhance the accuracy and fairness of officiating in professional soccer (football). The global market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1.8 billion by 2033. This growth is fueled by several key factors. Firstly, the rising popularity of professional soccer globally expands the market for VAR systems, especially in emerging markets with increasing viewership and investment in the sport. Secondly, technological advancements in areas such as AI-powered video analysis and high-speed cameras are leading to more accurate and efficient VAR decisions, boosting confidence among fans, players, and officials. Thirdly, major sporting organizations like FIFA and UEFA are actively promoting and standardizing VAR usage, ensuring broader acceptance and adoption across leagues and competitions. The segment of Goal Decision systems currently holds the largest market share, followed by Red Card Decisions, reflecting the high importance of these key moments in the game. However, market expansion also faces some challenges. The high initial investment cost of implementing VAR technology can be a barrier for smaller leagues or clubs with limited budgets. Furthermore, concerns about the impact of VAR on the flow of the game and the potential for controversial decisions remain. Despite these challenges, the long-term outlook for the VAR market remains positive, particularly with ongoing advancements in technology that promise to address some of these concerns. The market is segmented geographically, with North America, Europe, and Asia-Pacific representing the largest regional markets, driven by the high concentration of professional leagues and significant media interest. Hawk-Eye Innovations, Dartfish, Hudl, Vieww GmbH, and Hisense are key players in the market, continuously developing and refining their VAR solutions to meet the evolving demands of the industry.

About this dataset

  Context

The datasets provided include the players data for the Career Mode from FIFA 15 to FIFA 23. The data allows multiple comparisons for the same players across the last 9 versions of the video game. Some ideas of possible analysis:

Historical comparison between Messi and Ronaldo (what skill attributes changed the most during time - compared to real-life stats); Ideal budget to create a competitive team (at the level of top n teams in Europe) and… See the full description on the dataset page: https://huggingface.co/datasets/jsulz/FIFA23.

This graph depicts the investment budget of 2022 FIFA World Cup Qatar by segment. In total, 322 million U.S. dollars were allocated for investment in operational expenses.

Application of multivariant decision tree technique in high performance football: The female and male corner kick - Table 6