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.

Data recording the circumstances and results of the full 'A' international football matches involving Brazil, from 9th of August 1993 to 31st of December 2010, as listed in the Rec.Sport.Soccer Statistics Foundation (RSSSF) database. (FIFA commenced releasing national team rankings in 1993.) Excludes matches from the 1996 and 2003 CONCACAF, in which Brazil fielded their under-23 Olympic squad, three friendly matches not recognised by the opponent's federation, and five matches where Brazil was the lower ranked team. Does not take account of goals outside regulation time.

FIFA updates its ranking of national soccer teams several times each year, using real-life results to determine where each country places worldwide. As of April 2025, Argentina led the men's rankings, having won the World Cup in 2022. Brazil was in fifth place, while France ranked third. Meanwhile, the highest-ranked country in women's soccer, the United States, ranked 16th. Which country has won the most World Cups? Following the 2022 FIFA World Cup, Brazil remained the country with the most men’s World Cup titles, with a total of five. However, the Seleção’s last success came in 2002, and the team has mostly failed to progress past the quarter-final stage in recent tournaments. Meanwhile, two countries have won four World Cups: Germany and Italy. While Germany last won the competition in 2014, Italy failed to even qualify in 2018 and 2022. However, the Azzurri have had success in other areas, winning a second UEFA European Championship title in 2021. Who are the best soccer players worldwide? While past debates have largely focused on Lionel Messi and Cristiano Ronaldo, a number of younger players have taken the sport by storm in recent years. Erling Haaland, one of the most valuable soccer players in the world, has impressed many since joining Manchester City. In his first season at the club, Haaland broke the record for the most goals scored in a single Premier League season, with 36. Meanwhile, Haaland’s former teammate Jude Bellingham made an immediate impact at Real Madrid, scoring 10 goals in his first 10 games.

Financial overview and grant giving statistics of International Soccer League

I didn't realise how many soccer games are played each year until I started collecting data. I've been collecting data for about two years now and have nearly 25,000 rows of data. Thats nearly 25,000 soccer games from all leagues all over the world

What makes this data set so detailed is that is contains 1) Statistics on the home and away teams 2) Home win, draw, away win odds and 3) Final result

The fields in the data set are: Columns A to E contains information about the league, home and away teams, date etc Columns F, G and H contain the odds for the home win, draw and away win Columns I to BQ contain the team statistics. Home team stats are prefixed with a "h" similarly, away team stats are prefixed with an "a". Examples include ladder position, games played, goals conceded, away games won etc
Columns BR to CA contain final result information. That is the result, the full time result and if available, the half time score aswell

The dataset ranges from January 2016 to October 2017 and the statistics have been sourced from a few different websites. Odds come from BET365 and the results have been manually entered from http://www.soccerstats.com

The motivations for publishing this data set is twofold: 1) Predictive Model - I am curious to know if a predictive model can be created from this dataset, or are results completely random! 2) Probability - Is it possible to calculate the probability of a home win, draw or away win based on this dataset.

As of 2024, the combined value of all Premier League players amounted to over 11.3 billion euros, significantly more than any other league in the world. England's second-tier, the EFL Championship, had a combined player value of over 1.5 billion euros - more than any other top-tier league outside of the Big Five.

Use our trusted SportMonks Football API to build your own sports application and be at the forefront of football data today.

Our Football API is designed for iGaming, media, developers and football enthusiasts alike, ensuring you can create a football application that meets your needs.

Over 20,000 sports fanatics make use of our data. We know what data works best for you, so we ensured that our Football API has all the necessary tools you need to create a successful football application.

• Livescores and schedules Our Football API features extremely fast livescores and up-to-date season schedules, meaning your app will be the first to notify its customers about a goal scored. This also works to further improve the look and feel of your website.

• Statistics and line-ups We offer various kinds of football statistics, ranging from (live) player statistics to team, match and season statistics. And that’s not all - we also provide pre-match lineups for all important leagues.

• Coverage and historical data Our Football API covers over 1,200 leagues, all managed by our in-house scouts and data platform. That means there’s up to 14 years of historical data available.

• Bookmakers and odds Build your football sportsbook, odds comparison or betting portal with our pre-match and in-play odds collated from all major bookmakers and markets.

• TV Stations and highlights Show your customers where the football games are broadcasted and provide video highlights of major match events.

• Standings and topscorers Enhance your football website with standings and live standings, and allow your customers to see the top scorers and what the season's standings are.

Context

This is the first live data stream on Kaggle providing a simple yet rich source of all soccer matches around the world 24/7 in real-time.

What makes it unique compared to other datasets?

• It is the first live data feed on Kaggle and it is totally free
• Unlike “Churn rate” datasets you do not have to wait months to evaluate your predictions; simply check the match’s outcome in a couple of hours
• you can use your predictions/analysis for your own benefit instead of spending your time and resources on helping a company maximizing its profit
• A Five year old laptop can do the calculations and you do not need high-end GPUs
• Couldn’t make it to the top 3 submissions? Nevermind, you still have the chance to get your prize on your own
• You can’t get accurate results on all samples? Do not worry, just filter out the hard ones (e.g. ignore international friendly) and simply choose the ones you are sure of.
• Need help from human experts for each sample? Every sample comes with at least two opinions from experts
• You wish you could add your complementary data? Just contact us and we will try to facilitate it.
• Couldn’t win “Warren Buffett's 2018 March Madness Bracket Contest”? Here is your chance to make your accumulative profit.

Simply train your algorithm on the first version of training dataset of approximately 11.5k matches and predict the data provided in the following data feed.

Fetch the data stream

The CSV file is updated every 30 minutes at minutes 20’ and 50’ of every hour. I kindly request not to download it more than twice per hour as it incurs additional cost.

You may download the csv data file from the following link from Amazon S3 server by changing the FOLDER_NAME as below,

https://s3.amazonaws.com/FOLDER_NAME/amasters.csv

*. Substitute the FOLDER_NAME with "**analyst-masters**"

Content

Our goal is to identify the outcome of a match as Home, Draw or Away. The variety of sources and nature of information provided in this data stream makes it a unique database. Currently, FIVE servers are collecting data from soccer matches around the world, communicating with each other and finally aggregating the data based on the dominant features learned from 400,000 matches over 7 years. I describe every column and the data collection below in two categories, Category I – Current situation and Category II – Head-to-Head History. Hence, we divide the type of data we have from each team to 4 modes,

• Mode 1: we have both Category I and Category II available
• Mode 2: we only have Category I available
• Mode 3: we only have Category II available
• Mode 4: none of Category I and II are available

Below you can find a full illustration of each category.

I. Current situation

Col 1 to 3:

Votes_for_Home Votes_for_Draw Votes_for_Away

The most distinctive parts of the database are these 3 columns. We are releasing opinions of over 100 professional soccer analysts predicting the outcome of a match. Their votes is the result of every piece of information they receive on players, team line-up, injuries and the urge of a team to win a match to stay in the league. They are spread around the world in various time zones and are experts on soccer teams from various regions. Our servers aggregate their opinions to update the CSV file until kickoff. Therefore, even if 40 users predict Real-Madrid wins against Real-Sociedad in Santiago Bernabeu on January 6th, 2019 but 5 users predict Real-Sociedad (the away team) will be the winner, you should doubt the home win. Here, the “majority of votes” works in conjunction with other features.

Col 4 to 9:

Weekday Day Month  Year  Hour  Minute

There are over 60,000 matches during a year, and approximately 400 ones are usually held per day on weekends. More critical and exciting matches, which are usually less predictable, are held toward the evening in Europe. We are currently providing time in Central Europe Time (CET) equivalent to GMT +01:00.

*. Please note that the 2nd row of the CSV file represents the time, data values are saved from all servers to the file.

Col 10 to 13:

Total_Bettors   Bet_Perc_on_Home    Bet_Perc_on_Draw   Bet_Perc_on_Away

This data is recorded a few hours before the match as people place bets emotionally when kickoff approaches. The percentage of the overall number of people denoted as “Total_Bettors” is indicated in each column for “Home,” “Draw” and “Away” outcomes.

Col 14 to 15:

Team_1 Team_2   

The team playing “Home” is “Team_1” and the opponent playing “Away” is “Team_2”.

Col 16 to 36:

League_Rank_1  League_Rank_2  Total_teams     Points_1  Points_2  Max_points Min_points Won_1  Draw_1 Lost_1 Won_2  Draw_2 Lost_2 Goals_Scored_1 Goals_Scored_2 Goals_Rec_1 Goal_Rec_2 Goals_Diff_1  Goals_Diff_2

If the match is betw...

Financial overview and grant giving statistics of Spanish International Soccer League Association

Soccer is one of the biggest sports in the world and is enjoyed by fans of all ages across the globe. In 2022, an estimated ** percent of soccer fans were male, compared to only ** percent of fans that identified as female.

During the FIFA Women's Soccer World Cup, hosted in Australia and New Zealand from **** June to **** August, 2023, the number of attendances at games played by the Japanese national team in their quarter final game against Sweden amounted to about **** thousand. The game against Sweden ended in a *** defeat for Nadeshiko Japan.

The UNITY Odds Feed API – Historical Data Access offers a rich dataset of sports betting odds, covering a global array of leagues and events. This API enables users to retrieve detailed historical odds for both pre-match and live/in-play markets. It includes specific betting metrics such as Asian Handicap, Totals (Over/Under), Corners, and Cards, with data sourced from numerous major Asian sportsbooks and exchanges.

This historical feed is particularly well-suited for:

Data scientists and analysts building predictive models

Sportsbooks improving odds-making strategies

Media platforms generating betting insights

Researchers analyzing market efficiency and odds movement

Key Features: Pre-match and In-play Odds: Track how betting lines moved before and during events.

Multi-Sport Coverage: Includes football (soccer), basketball, and tennis—spanning top leagues like the Premier League, NBA, and Grand Slam tournaments.

Market Breadth: Extensive odds data for niche markets such as corners and cards.

Bookmaker Diversity: Historical odds from a wide range of Asian bookmakers and betting exchanges with low spreads and back/lay functionality.

Structured & Filterable: Access raw or formatted data by sport, league, event, or market.

This API delivers the tools needed to extract meaningful insights from betting markets—whether you're building advanced algorithms, enhancing app features, or deep-diving into betting behavior trends.

Introduction This dataset presents a comprehensive list of men's footballers who have achieved the remarkable milestone of scoring 50 or more goals in international matches. The dataset is curated with the aim of providing detailed statistics and insights into the most prolific scorers in the history of international football.

The dataset includes the following columns:

Rank: The player's ranking based on the number of international goals scored. Player: The name of the player. Nation: The country the player represents. Confederation: The continental confederation the nation belongs to (e.g., UEFA, AFC, CONMEBOL). Goals: The total number of international goals scored by the player. Caps: The total number of international appearances made by the player. Goals per match: The average number of goals scored per match. Career span: The years during which the player played international football. Date of 50th goal: The date on which the player scored their 50th international goal.

This dataset is a re-upload of the original dataset created by Kamran Ali and licensed under CC BY-SA 3.0. The original dataset can be found at Original Dataset:

This dataset describes all the matches made available. Each match is a document consisting of the following fields:- competitionId: the identifier of the competition to which the match belongs to. It is a integer and refers to the field "wyId" of the competition document;- date and dateutc: the former specifies date and time when the match starts in explicit format (e.g., May 20, 2018 at 8:45:00 PM GMT+2), the latter contains the same information but in the compact format YYYY-MM-DD hh:mm:ss; - duration: the duration of the match. It can be "Regular" (matches of regular duration of 90 minutes + stoppage time), "ExtraTime" (matches with supplementary times, as it may happen for matches in continental or international competitions), or "Penalities" (matches which end at penalty kicks, as it may happen for continental or international competitions);- gameweek: the week of the league, starting from the beginning of the league;- label: contains the name of the two clubs and the result of the match (e.g., "Lazio - Internazionale, 2 - 3");- roundID: indicates the match-day of the competition to which the match belongs to. During a competition for soccer clubs, each of the participating clubs plays against each of the other clubs twice, once at home and once away. The matches are organized in match-days: all the matches in match-day i are played before the matches in match-day i + 1, even tough some matches can be anticipated or postponed to facilitate players and clubs participating in Continental or Intercontinental competitions. During a competition for national teams, the "roundID" indicates the stage of the competition (eliminatory round, round of 16, quarter finals, semifinals, final);- seasonId: indicates the season of the match;- status: it can be "Played" (the match has officially finished), "Cancelled" (the match has been canceled for some reason), "Postponed" (the match has been postponed and no new date and time is available yet) or "Suspended" (the match has been suspended and no new date and time is available yet);- venue: the stadium where the match was held (e.g., "Stadio Olimpico");- winner: the identifier of the team which won the game, or 0 if the match ended with a draw;- wyId: the identifier of the match, assigned by Wyscout;- teamsData: it contains several subfields describing information about each team that is playing that match: such as lineup, bench composition, list of substitutions, coach and scores: - hasFormation: it has value 0 if no formation (lineups and benches) is present, and 1 otherwise; - score: the number of goals scored by the team during the match (not counting penalties); - scoreET: the number of goals scored by the team during the match, including the extra time (not counting penalties); - scoreHT: the number of goals scored by the team during the first half of the match; - scoreP: the total number of goals scored by the team after the penalties; - side: the team side in the match (it can be "home" or "away"); - teamId: the identifier of the team; - coachId: the identifier of the team's coach; - bench: the list of the team's players that started the match in the bench and some basic statistics about their performance during the match (goals, own goals, cards); - lineup: the list of the team's players in the starting lineup and some basic statistics about their performance during the match (goals, own goals, cards); - substitutions: the list of team's substitutions during the match, describing the players involved and the minute of the substitution.

The UNITY Soccer API is a powerful solution for delivering highly accurate, real-time football (soccer) odds to sportsbooks, betting apps, affiliate platforms, and data-driven systems. As part of the broader UNITY Odds Feed API, the Soccer API is engineered for speed, scalability, and flexibility—allowing seamless integration of betting markets across the world’s most popular sport.

The UNITY Soccer API is a robust, enterprise-grade solution that powers football betting platforms with real-time, historical, and highly accurate data. With extensive market coverage, flexible customization, and deep global reach, it supports any betting-related use case—whether you're building a full-scale sportsbook, launching a mobile app, or analyzing data for predictive modeling.

Combined with a powerful support infrastructure, seamless integration tools, and competitive bookmaker data, the UNITY Soccer API is the ideal foundation for your next-generation football betting solution.

We analyze the effects of top tax rates on international migration of football players in 14 European countries since 1985. Both country case studies and multinomial regressions show evidence of strong mobility responses to tax rates, with an elasticity of the number of foreign (domestic) players to the net-of-tax rate around one (around 0.15). We also find evidence of sorting effects (low taxes attract highability players who displace low-ability players) and displacement effects (low taxes on foreigners displace domestic players). Those results can be rationalized in a simple model of migration and taxation with rigid labor demand.

La Liga Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the La Liga capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

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 authors investigate whether the home advantage in soccer differs by various dimensions of distance between the (regions of the) home and away teams: geographical distance, climatic differences, cultural distance, and disparities in economic prosperity. To this end, the authors analyse 2,012 recent matches played in the UEFA Champions League and UEFA Europa League by means of several regression models. They find that when the home team plays at a higher altitude, they benefit substantially more from their home advantage. Every 100 meters of altitude difference is associated with an increase in expected probability to win the match, as the home team, by 1.1 percentage points. The other dimensions of distance are not significantly associated with a higher or lower home advantage. By contrast, the authors find that the home advantage in soccer is more outspoken when the number of spectators is higher and when the home team is substantially stronger than the away team.

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 ---