The ultimate Football database for data analysis and machine learning

What you get:

+96,000 matches with detailed minute-by-minute history of the single game + players name (goals, yellow/red cards, penalty, var, penalty missed ect.) - factor INC Season 2021-2022 included

18 European Leagues from 10 Countries with their lead championship: - premier-league - 7600 matches (seasons 2002-2022) - laliga - 7220 matches (seasons 2003-2022) - serie-a - 7150 matches (seasons 2003-2022) - ligue-1 - 6757 matches (seasons 2004-2022) - championship - 6684 matches (seasons 2010-2022) - league-one - 6440 matches (seasons 2010-2022) - bundesliga - 5838 matches (seasons 2003-2022) - league-two - 6015 matches (seasons 2011-2022) - eredivisie - 5776 matches (seasons 2004-2022) - laliga2 - 5519 matches (seasons 2010-2022) - serie-b - 5286 matches (seasons 2010-2022) - ligue-2 - 4470 matches (seasons 2010-2022) - super-lig - 3504 matches (seasons 2010-2022) - jupiler-league - 3756 matches (seasons 2010-2022) - fortuna-1-liga - 3687 matches (seasons 2010-2022) - 2-bundesliga - 3503 matches (seasons 2010-2022) - liga-portugal - 3414 matches (seasons 2010-2022) - pko-bp-ekstraklasa - 3338 matches (seasons 2010-2022)

Betting odds +winning betting odds Statistics Detailed match events (goal types, possession, corner, cross, fouls, cards etc…) for +96,000 matches

Why this data?

You can easily find data about football matches but they are usually scattered across different websites and those data in my opinion are missing with good shaped game's events. Therefore the most usefull part of this DataSet is factor INC which is in fact the register of game events minute-by-minute (goals, cards, vars, missed penalties ect.) collected in python list. Example Swansea-Reading:

"INC": [
      "08' Yellow_Away - Griffin A.",
      "12' Yellow_Away - Khizanishvili Z.",
      "12' Yellow_Home - Borini F.",
      "21' Goal_Home - Penalty Sinclair S.(Penalty )",
      "22' Goal_Home - Sinclair S.(Dobbie S.)",
      "39' Yellow_Away - McAnuff J.",
      "40' Goal_Home - Dobbie S.",
      "46' Red_Card_Away - Tabb J.",
      "49' Own_Away - Allen J.()",
      "54' Yellow_Home - Allen J.",
      "57' Goal_Away - Mills M.(McAnuff J.)",
      "80' Goal_Home - Sinclair S. (Penalty)",
      "82' Yellow_Home - Gower M."
    ],

Those data are scraped form one of the livesscores web page provider. I own program written in python which can scrape data from any league all around the world (but anyway it takes time and the program itself needs constant updating as the providers changing source code).

Locally my Dataset is larger because it contains +100 factors, i.e. it contains infos about previous game with all infos about that games and more additional infos. I shortend the DataSet uploaded on kaggle to make it simpler and more understandable.

License

I must insist that you do not make any commercial use of the data. I give this DataSet to your none-commercial use.

Cooperation

sebastian.gebala@gmail.com

Dataset

This dataset was created by Emeka Okafor

Released under MIT

Contents

Football Game

## Overview

Football Game is a dataset for object detection tasks - it contains Object Detection HCxS annotations for 785 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Comprehensive database of Ohio high school football games including scores, schedules, statistics, and game results.

Comprehensive database of Texas high school football games including scores, schedules, statistics, and game results.

Comprehensive database of Washington high school football games including scores, schedules, statistics, and game results.

FPM outperforms the baseline prediction based on win-loss standings every season in our dataset. The overall accuracy of our system is 63.4%.

Have you ever found yourself with a football dataset that almost had it all, but left you short of happiness? Time after time, promising datasets failed to deliver the statistics that truly matter – match events, player performances, team results, and season standings.

That time is over!

This in-depth football dataset, curated straight from a RapidAPI endpoint, brings you the data points we've all been waiting for. From fixtures and injuries to goals, assists, and tactical breakdowns, this dataset unlocks the full picture of the beautiful game.

What You Get 🏆 - Fixture Stats & Events: Goals, assists, fouls, and match-defining moments across leagues up to 2024. - Player Performances: From tackles to dribbles, passes, and shots – every stat that makes a difference. - Season Stats & League Standings: Discover how teams dominate, stumble, or rise to glory each season. - Team Insights: Analyze home/away performance, goal-scoring patterns, and defensive strengths. - Match Highlights: Real-time events like own goals, red cards, and critical substitutions. - Injuries & Suspensions: Missing players and their impact on team dynamics. - Iconic Stadiums: Explore venues, capacities, and surfaces that set the stage for football's greatest moments.

Why It’s Exciting 🌟

This isn’t just another football dataset – it’s the ultimate resource for fans, analysts, and strategists who want to dig deeper. Whether you're predicting outcomes, analyzing player form, or crafting the next big football insights project, you now have all the tools you need.

Get ready to unlock stories, trends, and insights like never before – because this time, the stats you actually care about are all here. Let’s kick it off! ⚽✨

In terms of fixture stats for players, the endpoint provides data from 2015 up through the 2024 season and I plan to make one more update at the end of all league/cup seasons in June of 2025.

Disclaimer: This dataset is intended for non-commercial, academic purposes and does not infringe upon any intellectual property rights of the original data providers, including RapidAPI or associated sources. For full details, please refer to the respective terms of use provided by the data sources.

If you have questions about the data or simply want to connect, reach out on LinkedIn and if you plan on using this data for any type of analysis, can you please share that with me!

PS: I am a Ronaldo fan... Suiiiii !!!

Leagues/Cups in datasets: - La Liga - Ligue 1 - Serie A - World Cup - Bundesliga - NWSL Women - Pro League - Championship League - Copa America - Premier League - CONCACAF Gold Cup - Euro Championship - UEFA Europa League - MLS - Africa Cup Of Nations - CONCACAF Champions League

Other Datasets: - Spotify - Zillow

The stand-alone football game market is a dynamic and rapidly growing sector within the broader gaming industry. While precise market size figures for 2025 are not provided, considering the substantial popularity of football globally and the consistent growth of the gaming market, a reasonable estimate for the 2025 market size would be $2.5 billion. This is based on the understanding that football games represent a significant portion of the overall sports gaming market. Assuming a Compound Annual Growth Rate (CAGR) of 10%—a conservative estimate given technological advancements and increasing mobile gaming penetration—the market is projected to experience substantial growth over the forecast period (2025-2033). Key drivers for this growth include the rising popularity of esports, technological advancements leading to enhanced game realism and immersive experiences (e.g., improved graphics and AI), and the increasing accessibility of gaming through mobile platforms. Furthermore, the continued expansion of global internet penetration and rising disposable incomes in emerging markets contribute to the market’s expansion. However, challenges remain. The market faces constraints such as the high development costs associated with creating high-quality, realistic games, intense competition from established players like EA Sports and Konami, and the cyclical nature of gaming trends. Maintaining player engagement and innovating to keep pace with evolving consumer preferences will be crucial for long-term success within this competitive landscape. Segment analysis shows a relatively even distribution across PC, mobile, and console platforms, with Steam, Origin, and other digital distribution platforms playing significant roles in revenue generation. The geographical distribution mirrors the global popularity of football, with North America, Europe, and Asia-Pacific representing the largest market segments. Successful companies leverage strategic partnerships, effective marketing, and consistent updates to maintain their market share and capture new player bases.

Football Games Analysis

## Overview

Football Games Analysis is a dataset for object detection tasks - it contains Player annotations for 1,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Discover the booming standalone football game market! Explore market size projections to 2033, key growth drivers, leading companies like EA Sports & Konami, and regional trends impacting this $2.5 billion industry. Learn how mobile gaming, esports, and technological advancements are shaping the future of football gaming.

NCAA college football games always attract millions of television viewers in the United States and the games of the 2024 regular season were no different. The game between Georgia and Texas, broadcast on ABC and ESPN on December 7, 2024, was watched by an average of 16.6 million viewers.

Comprehensive database of Connecticut high school football games including scores, schedules, statistics, and game results.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

Football Game Film Angle

## Overview

Football Game Film Angle is a dataset for classification tasks - it contains Film Angles annotations for 595 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

According to a survey on the consumer behavior of football fans in the Middle East and North Africa (MENA) region in 2022, ** percent of respondents accessed online sports websites to view content after football games. TV ranked second as a source for post-football game related content at a share of ** percent of respondents in that year.

Discover the explosive growth of the online football games market, projected to reach $7.88 billion by 2033 with a 15% CAGR. Explore market trends, key players (EA, Konami, Tencent), and regional insights in this comprehensive analysis. Learn how free-to-play models and mobile gaming are driving this exciting sector.

A total of **** million viewers tuned in on ABC to watch the college football matchup between FSU and LSU on September 3, 2023. This made it the most-viewed college football week one game of the 2023 season.

The difference represents . Significance codes: ***: p < .001, **: p < .01, *: p < .05. The home team advantage is also presented.