Context

In our Data Science lesson, we tried to predict the value of some soccer players, using their performance and their last market value. As we have not found a dataset on Kaggle that was convenient to us, we have tried to create our own dataset merging two ones finding on this platform. The 2 datasets are : ''Soccer players values and their statistics'' and ''Top Football Leagues Scorers''.

Content

The data are only from the season 2019-2020. We have 88 players remaining. Our work is not finish and can be significantly improved, particularly by increasing the number of player.

Acknowledgements

Thanks to Mohamed Hany and RSKriegs for their datasets.

🏆 Premier League Market Value Dataset (2025)

Welcome to my first real-world football dataset, scraped from Transfermarkt, containing detailed market value data for 499 Premier League players (2025).

📦 What This Dataset Provides

This dataset includes the following attributes for each player:

• 🧍‍♂️ Player Name
• 🧬 Age
• 🏟️ Club
• 🌍 Nationality
• 🧠 Position
• 💰 Current Market Value (in € millions)

Each field was carefully extracted and cleaned from public sources using custom Python scripts (available on GitHub below).

🔭 My Vision for This Dataset

This is just Phase 1. My goal is to:

• 📈 Add player form stats, contract data, and historical market values in future versions
• ⚽ Scrape other leagues (La Liga, Bundesliga, Serie A, etc.)
• 🤖 Build a machine learning model to predict future market values
• 📊 Enable research in sports analytics, scouting, and value forecasting

💡 Potential Use Cases

• Sports business & economics research
• Fantasy football value analysis
• ML model training (value prediction, clustering by position/value)
• Tableau / Power BI dashboards
• Scouting & recruitment simulations
• NLP + data fusion from other sources

📈 Update Frequency

• Dataset will be updated monthly
• Upcoming updates will include:
  • Player performance stats
  • Contract duration
  • Injury/transfer status
  • Form trend over time

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff0d45220cad473000b1e59942548dd45%2Fanimated_bubble_chart.gif?generation=1705615116968842&alt=media" alt="">This comprehensive football dataset, derived primarily from Transfermarkt, serves as a valuable resource for football enthusiasts, offering structured information on competitions, clubs, and players. With over 60,000 games across major global competitions, the dataset delves into the performance metrics of 400+ clubs and detailed statistics for more than 30,000 players.

Structured in CSV files, each with unique IDs, users can seamlessly join datasets to perform in-depth analyses. The dataset encompasses market values, historical valuations, and detailed player statistics, including physical attributes, contract statuses, and individual performances. A specialized Python-based web scraper ensures consistent updates, with data meticulously processed through Python scripts and SQL databases.

To use the dataset effectively, users are encouraged to understand the relevant files, join datasets using unique IDs, and leverage compatible software tools like Python's pandas or R's ggplot2 for analysis. The guide emphasizes the potential for fantasy football predictions, tracking player value over time, assessing market value versus performance, and exploring the impact of cards on match outcomes.

Research ideas include player performance analysis for fantasy football or recruitment purposes, studying market value trends for economic insights, evaluating club performance for strategic decision-making, developing predictive models for match outcomes, and conducting social network analysis to understand interactions among clubs and players.

Acknowledging the dataset's unknown license, users are encouraged to credit the original authors, particularly David Cereijo, if used in research. The dataset's dedication to accessibility is evident through active discussions on GitHub for improvements and bug fixes.

In conclusion, this football dataset offers a wealth of information, empowering users to explore diverse analyses and research ideas, bridging the gap between structured data and the dynamic world of football.

Economic Fitness (EF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis. The Universal Economic Fitness (UEF) extends this assessment to include services. Countries with the highest levels of EF or UEF have capabilities to produce a diverse portfolio of products and services, the ability to upgrade into ever-increasing complex industries, tend to have more predictable long-term growth, and attain a good competitive position relative to other countries. Countries with low EF or UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries. The starting data is the UN-COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and industries. A suitably designed mathematical algorithm applied to this network leads to the Fitness of all countries and the Complexity of all sectors. The comparison of Fitness to the GDP reveals hidden information about the capabilities, development, and growth of countries.

A collection of 2,010 forward players from the 2024-2025 soccer season, extracted from Transfermarkt.com. This dataset provides detailed performance metrics and market valuations for attacking players across the world's top 15 leagues and competitions.

Variables

Dataset

This dataset was created by Abdulmajeed33

Contents

Dataset

This dataset was created by sean ngeo

Contents

This dataset is undertaken to create a predictive model for the transfer values of football players. We will utilize data from football players and construct a model to predict transfer fees based on that data. Player data includes basic information such as age, height, playing position, as well as professional statistics like goal scoring, assists (in 2 season 2021-2022 and 2022-2023), injuries, along with total individual and team awards in their career.

We had gathered information on players competing in several top-tier global football leagues:

11 European leagues, including the Premier League and Championship in England, Bundesliga in Germany, La Liga in Spain, Serie A in Italy, Ligue 1 in France, Eredivisie in the Netherlands, Liga NOS in Portugal, Premier Liga in Russia, Super Lig in Turkey, and Bundesliga in Austria.

4 American leagues, including Brasileiro in Brazil, Major League Soccer in the United States, Primera División in Argentina, and Liga MX in Mexico.

1 African league, namely the DStv Premiership in South Africa.

4 Asian leagues, comprising J-League in Japan, Saudi Pro League in Saudi Arabia, K-League 1 in South Korea, and A-League in Australia.

Among the industries presented in the data set, health and fitness had the highest cost-per-action (CPA) for Facebook ads as of February 2025, with ***U.S. dollars. The lowest value belonged to the real estate industry, with ** U.S. dollars.

Context

This dataset was built as a supplementary to "[European Soccer Database][1]". It includes data dictionary, extraction of detailed match information previously contains in XML columns.

Content

• PositionReference.csv: A reference of position x, y and map them to actual position in a play court.
• DataDictionary.xlsx: Data dictionary for all XML columns in "Match" data table.
• card_detail.csv: Detailed XML information extracted form "card" column in "Match" data table.
• corner_detail.csv: Detailed XML information extracted form "corner" column in "Match" data table.
• cross_detail.csv: Detailed XML information extracted form "cross" column in "Match" data table.
• foulcommit_detail.csv: Detailed XML information extracted form "foulcommit" column in "Match" data table.
• goal_detail.csv: Detailed XML information extracted form "goal" column in "Match" data table.
• possession_detail.csv: Detailed XML information extracted form "possession" column in "Match" data table.
• shotoff_detail.csv: Detailed XML information extracted form "shotoffl" column in "Match" data table.
• shoton_detail.csv: Detailed XML information extracted form "shoton" column in "Match" data table.

Acknowledgements

Original data comes from [European Soccer Database][1] by Hugo Mathien. I personally thank him for all his efforts.

Inspiration

Since this is a open dataset with no specific goals / objectives, I would like to explore the following aspects by data analytics / data mining:

1. Team statistics Including overall team ranking, team points, winning possibility, team lineup, etc. Mostly descriptive analysis.
2. Team Transferring Track and study team players transferring in the market. Study team's strength and weakness, construct models to suggest best fit players to the team.
3. Player Statistics Summarize player's performance (goal, assist, cross, corner, pass, block, etc). Identify key factors of players by position. Based on these factors, evaluate player's characteristics.
4. Player Evolution Construct model to predict player's rating of future.
5. New Player's Template Identify template and model player for young players cater to their positions and characteristics.
6. Market Value Prediction Predict player's market value based on player's capacity and performance.
7. The Winning Eleven Given a season / league / other criteria, propose the best 11 players as a team based on their capacity and performance.

This dataset provides comprehensive information on the top 500 most valuable footballers (football players) in the world. The dataset includes essential details for each player, such as their position, age, nationality, club affiliation, and market value in million euros. The information is up-to-date till the end of the 2022/2023 season. By analyzing this dataset, researchers can uncover trends, identify players with exceptional market value, track the value fluctuations of individual players over time, and perform in-depth statistical analyses related to player worth.

Whether you are interested in understanding the market dynamics of football or conducting advanced analytics in the domain of sports, this dataset provides a solid foundation for exploration and research. Join the league of data-driven football enthusiasts and dive into the exciting world of player valuations with the Top 500 Most Valuable Footballers in the World dataset.

Context

For all Football/Soccer Lovers, Find over 50+ stats for over 2 seasons to determine a players market value, asses and visualize various parameters and show your results!

Content

Football and Love for Football!

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Context

For most football fans, May - July represents a lull period due to the lack of club football. What makes up for it, is the intense transfer speculation that surrounds all major player transfers today. Their market valuations also lead to a few raised eyebrows, lately more than ever. I was curious to see how good a proxy popularity could be for ability, and the predictive power it would have in a model estimating a player's market value.

Content

name: Name of the player

club: Club of the player

age : Age of the player

position : The usual position on the pitch

position_cat :

• 1 for attackers

• 2 for midfielders

• 3 for defenders

• 4 for goalkeepers

market_value : As on transfermrkt.com on July 20th, 2017

page_views : Average daily Wikipedia page views from September 1, 2016 to May 1, 2017

fpl_value : Value in Fantasy Premier League as on July 20th, 2017

fpl_sel : % of FPL players who have selected that player in their team

fpl_points : FPL points accumulated over the previous season

region:

• 1 for England

• 2 for EU

• 3 for Americas

• 4 for Rest of World

nationality

new_foreign : Whether a new signing from a different league, for 2017/18 (till 20th July)

age_cat

club_id

big_club: Whether one of the Top 6 clubs

new_signing: Whether a new signing for 2017/18 (till 20th July)

Inspiration

To statistically analyse the beautiful game.

Contains web scrapped (rvest) Market Value information, FIFA variables, and other related data on Players from the English Premier League. This includes but is not limited to, Market Value, Accumulated Market Sums, Highest Ever Market Value, Player Team, Player Name, Position, Agents, Player Sponsors, Birth Places etc....

Context This dataset includes information on the most 100 valuable teams in the world. The information was scraped from www.transfermarkt.com

Content** **Club: The team name Competition: In which league does the club compete? Age: Average age of players Squad_size: The number of players in the team Market Value: ةarket value of the club market value of players: Average market value of team players MV Top-18 players: The total market value of the 18 most valuable players on the team Share of MV: The ratio of the total market value of the 18 most valuable players in the team to the total market value of all the team's players

Acknowledgements The data was scraped from www.transfermarkt.com

The dataset contains information about 644 Brazilian football players, with the following columns:

1. Player: Name of the football player.
2. Team: The team the player is associated with.
3. Age: Age of the player.
4. Position: The player's position on the field (e.g., goalkeeper, center back).
5. Market Value: The estimated market value of the player (in Euros, according to Transfermarkt).
6. SofaScore: The player's performance rating according to SofaScore, though some values are missing.

The data can be useful for analysis of player performance and market value trends in Brazilian football.

The Dairy Goods Sales Dataset provides a detailed and comprehensive collection of data related to dairy farms, dairy products, sales, and inventory management. This dataset encompasses a wide range of information, including farm location, land area, cow population, farm size, production dates, product details, brand information, quantities, pricing, shelf life, storage conditions, expiration dates, sales information, customer locations, sales channels, stock quantities, stock thresholds, and reorder quantities.

Features:

1. Location: The geographical location of the dairy farm.
2. Total Land Area (acres): The total land area occupied by the dairy farm.
3. Number of Cows: The number of cows present in the dairy farm.
4. Farm Size: The size of the dairy farm(in sq.km).
5. Date: The date of data recording.
6. Product ID: The unique identifier for each dairy product.
7. Product Name: The name of the dairy product.
8. Brand: The brand associated with the dairy product.
9. Quantity (liters/kg): The quantity of the dairy product available.
10. Price per Unit: The price per unit of the dairy product.
11. Total Value: The total value of the available quantity of the dairy product.
12. Shelf Life (days): The shelf life of the dairy product in days.
13. Storage Condition: The recommended storage condition for the dairy product.
14. Production Date: The date of production for the dairy product.
15. Expiration Date: The date of expiration for the dairy product.
16. Quantity Sold (liters/kg): The quantity of the dairy product sold.
17. Price per Unit (sold): The price per unit at which the dairy product was sold.
18. Approx. Total Revenue (INR): The approximate total revenue generated from the sale of the dairy product.
19. Customer Location: The location of the customer who purchased the dairy product.
20. Sales Channel: The channel through which the dairy product was sold (Retail, Wholesale, Online).
21. Quantity in Stock (liters/kg): The quantity of the dairy product remaining in stock.
22. Minimum Stock Threshold (liters/kg): The minimum stock threshold for the dairy product.
23. Reorder Quantity (liters/kg): The recommended quantity to reorder for the dairy product.

Potential Use-Case:

This dataset can be used by researchers, analysts, and businesses in the dairy industry for various purposes, such as:

1. Analyzing the performance of dairy farms based on location, land area, and cow population.
2. Understanding the sales and distribution patterns of different dairy products across various brands and regions.
3. Studying the impact of storage conditions and shelf life on the quality and availability of dairy products.
4. Analyzing customer preferences and buying behavior based on location and sales channels.
5. Optimizing inventory management by tracking stock quantities, minimum thresholds, and reorder quantities.
6. Conducting market research and trend analysis in the dairy industry.
7. Developing predictive models for demand forecasting and pricing strategies.

Note: This dataset includes data from the period between 2019 and 2022, and it specifically focuses on selected dairy brands operating in specific states and union territories of India. There is an intentional drift highlighted in the dataset's figures due to its opensource and creative license, currently !

Overview :

This dataset contains detailed information on a wide variety of vegetables from different regions across the world. Each entry includes data on the vegetable's category, color, seasonality, origin, nutritional value, pricing, availability, shelf life, storage requirements, growing conditions, health benefits, and common varieties. The dataset is structured to facilitate research and data analysis, offering insights into agricultural trends, nutritional science, and market dynamics. Ideal for use in academic research, market analysis, and agricultural studies.

Vegetable dataset Columns Details :

1. Vegetable ID: Unique identifier for each vegetable entry.
2. Name: Common name of the vegetable (e.g., Carrot, Broccoli).
3. Scientific Name: Scientific or botanical name of the vegetable.
4. Category: Type of vegetable (e.g., Root, Leafy, Fruit, Tubers).
5. Color: Color of the vegetable (e.g., Orange, Green).
6. Season: Season(s) when the vegetable is typically harvested (e.g., Spring, Summer).
7. Origin: Geographic origin or region where the vegetable is commonly grown.
8. Nutritional Value: Key nutritional information (e.g., calories, vitamins, minerals per 100g).
9. Price: Average market price per unit or weight.
10. Availability: Availability status (e.g., Year-round, Seasonal).
11. Shelf Life: Average shelf life in days.
12. Storage Requirements: Specific storage conditions (e.g., Refrigeration, Dry, Cool place).
13. Growing Conditions: Ideal growing conditions (e.g., Soil type, Water requirements, Sunlight).
14. Health Benefits: Notable health benefits or uses.
15. Common Varieties: Different varieties or types of vegetables.

Context: This dataset has been created to assist gamers, football enthusiasts, and data analysts in discovering valuable insights about the FIFA 23 Ultimate Team (FUT) mode. Ultimate Team is a popular game mode within the FIFA series, where users can build their dream squads by collecting and trading virtual cards representing real-life football players. Player performance in FUT is determined by various statistics and attributes, which are crucial factors when building a competitive team.

Sources: The dataset is sourced from FUTBIN, a widely recognized website that provides comprehensive information on player cards, including statistics, attributes, market values, and more. FUTBIN constantly updates its data to reflect the latest player ratings and market trends, ensuring the dataset remains relevant and accurate.

Inspiration: The inspiration behind this dataset is to provide users with a comprehensive and structured collection of player data, enabling them to make informed decisions when constructing their Ultimate Team. By analyzing this dataset, users can:

• Identify top-performing players based on various attributes, such as pace, shooting, passing, dribbling, defending, and physicality.
• Discover hidden gems or underrated players with high potential or desirable stats.
• Evaluate the market value of different players to make cost-effective decisions when buying or selling cards.
• Analyze team chemistry, player positions, and preferred formations to optimize overall team performance.
• Create custom analytics tools, visualizations, or predictive models to gain a competitive edge in FIFA 23 Ultimate Team.