Notes for Football Data from football-data.co.uk.

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Note that Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

Note also that English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = Stanleybet draw odds SYA = Stanleybet away win odds VCH = VC Bet home win odds VCD = VC Bet draw odds VCA = VC Bet away win odds WHH = William Hill home win odds WHD = William Hill draw odds WHA = William Hill away win odds

Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums BbMxH = Betbrain maximum home win odds BbAvH = Betbrain average home win odds BbMxD = Betbrain maximum draw odds BbAvD = Betbrain average draw win odds BbMxA = Betbrain maximum away win odds BbAvA = Betbrain average away win odds

MaxH = Market maximum home win odds MaxD = Market maximum draw win odds MaxA = Market maximum away win odds AvgH = Market average home win odds AvgD = Market average draw win odds AvgA = Market average away win odds

Key to total goals betting odds:

BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums BbMx>2.5 = Betbrain maximum over 2.5 goals BbAv>2.5 = Betbrain average over 2.5 goals BbMx<2.5 = Betbrain maximum under 2.5 goals BbAv<2.5 = Betbrain average under 2.5 goals

GB>2.5 = Gamebookers over 2.5 goals GB<2.5 = Gamebookers under 2.5 goals B365>2.5 = Bet365 over 2.5 goals B365<2.5 = Bet365 under 2.5 goals P>2.5 = Pinnacle over 2.5 goals P<2.5 = Pinnacle under 2.5 goals Max>2.5 = Market maximum over 2.5 goals Max<2.5 = Market maximum under 2.5 goals Avg>2.5 = Market average over 2.5 goals Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums BbAHh = Betbrain size of handicap (home team) AHh = Market size of handicap (home team) (since 2019/2020) BbMxAHH = Betbrain maximum Asian han...

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

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Comprehensive dataset of 2 Soccer practices in Free municipal consortium of Agrigento, Italy as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This statistic shows the value of the media rights to transmit soccer matches on Free-to-Air TV in Brazil in 2018, by distribution. That year, the value of the broadcast rights which were equally divided among teams reached *** million Brazilian reals, while the total amount paid in transmission rights by the broadcasting company Globo reached *** million Brazilian reals.

Brazilian Soccer Data

This repository consists of collecting the history and current data of all the most important competitions that Brazilian teams compete, the principal competitions are:

• Brasileirão(Brazilian soccer league)
• Libertatodes(Principal South america Competition)
• Sudamericana(South American secondary competition)
• Copa do Brasil(Brazilian Cup)

Next Steps: - structure the collection of the games of the sudamericana and copa do brasil - Gather data from the main state championships(SP, RJ, MG, RS) - Gather more data from these championships, such as match statistics

Any questions or suggestions are welcome, feel free to collaborate on the github repository

This dataset was originally introduced by [1] for soccer ball and player tracking from six synchronized videos. Since ball annotations provided by [1] are collapsed, new annotations of ball 2D coordinates are provided by [2] For sports ball detection and tracking evaluation, the first four video clips are used for training and the remaining two clips are for testing.

[1] T. D’Orazio et al., A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences, in AVSS, 2009. [2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, in BMVC, 2023.

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.

BackgroundThe assessment of body composition may assist in optimizing competitive efficiency and monitoring the success of training regimes for young soccer players. The purpose of this study was to determine the predictors for Fat-Free Mass (FFM) and Bone Mineral Density (BMD) of young soccer players. Also, the goal was to propose regression equations to estimate FFM and BDM through anthropometric variables.MethodsOne hundred and sixty-seven young soccer players ages 10.0 to 19.9 years old were studied. Weight, height, trunk-cephalic length, right arm circumference, diameter of the humerus, and length of the foot were assessed. FFM and BDM were determined by using dual X-ray absorptiometry (DXA). Maturity status using Peak Height Velocity (PHV) was calculated.ResultsMaturity status, weight, and circumference of the relaxed arm positively related to the FFM (R2 = 41–66%). Similarly, PHV, weight, diameter of the humerus, and length of the foot explained BDM in both groups of soccer players (goalkeepers and filed players) (R2 = 45–82%). Six equations to predict FFM (R2 = 62–69%) and six to predict BDM (R2 = 69–90%) were created. Chronological age had a limited use for predicting FFM and BDM.ConclusionResults suggested the use and application of the regression equations as a non-invasive alternative for everyday use in soccer clubs.

This dataset provides information on 196 in France as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Explore the historical Whois records related to free-soccer-betting-tips.co.uk (Domain). Get insights into ownership history and changes over time.

Context

Find insights that might help inform betting decisions.

Content

Betting odds and results for European football/soccer leagues from 1993 - 2021. Key to leagues:

England E0 - Premier league E1,E2,E3 - Divisions 1, 2 & 3 respectively

Scotland SC0 - Premier league SC1,SC2,SC3 - Divisions 1, 2 & 3 respectively

Germany D1,D2 - Bundesliga 1 & 2 respectively

Spain SP1,SP2 - La Liga Premera & Segunda respectively

Italy I1,I2 - Serie A & B respectively

France F1,F2 - Le Championnat & Division 2

Netherlands N1 - KPN Eredivisie

Belgium B1 - Jupiler League

Portugal P1 - Liga I

Turkey T1 - Ligi 1

Greece G1 - Ethniki Katigoria

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = Stanleybet draw odds SYA = Stanleybet away win odds VCH = VC Bet home win odds VCD = VC Bet draw odds VCA = VC Bet away win odds WHH = William Hill home win odds WHD = William Hill draw odds WHA = William Hill away win odds

Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums BbMxH = Betbrain maximum home win odds BbAvH = Betbrain average home win odds BbMxD = Betbrain maximum draw odds BbAvD = Betbrain average draw win odds BbMxA = Betbrain maximum away win odds BbAvA = Betbrain average away win odds

MaxH = Market maximum home win odds MaxD = Market maximum draw win odds MaxA = Market maximum away win odds AvgH = Market average home win odds AvgD = Market average draw win odds AvgA = Market average away win odds

Key to total goals betting odds:

BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums BbMx>2.5 = Betbrain maximum over 2.5 goals BbAv>2.5 = Betbrain average over 2.5 goals BbMx<2.5 = Betbrain maximum under 2.5 goals BbAv<2.5 = Betbrain average under 2.5 goals

GB>2.5 = Gamebookers over 2.5 goals GB<2.5 = Gamebookers under 2.5 goals B365>2.5 = Bet365 over 2.5 goals B365<2.5 = Bet365 under 2.5 goals P>2.5 = Pinnacle over 2.5 goals P<2.5 = Pinnacle under 2.5 goals Max>2.5 = Market maximum over 2.5 goals Max<2.5 = Market maximum under 2.5 goals Avg>2.5 = Market average over 2.5 goals Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums BbAHh = Betbrain size of handicap (home team) AHh = Market size of handicap (home team) (since 2019/2020) BbMxAHH = Betbrain maximum Asian handicap home team odds BbAvAHH = Betbrain average Asian handicap home team odds BbMxAHA = Betbrain maximum Asian handicap away team odds BbAvAHA = Betbrain average Asian handicap away team odds

GBAHH = Gamebookers Asian handicap home team odds GBAHA = Gamebookers Asian handicap away team odds GBAH = Gamebookers size of handicap (home team) LBAHH = Ladbrokes Asian handicap home team odds LBAHA = Ladbrokes Asian handicap away team odds LBAH = Ladbrokes size of handicap (home team) B365AHH = Bet365 Asian handicap home team odds B365AHA = Bet365 Asian handicap away team odds B365AH = Bet365 size of handicap (home team) PAHH = Pinnacle Asian handicap home team odds PAHA = Pinnacle Asian handicap away team odds MaxAHH = Market maximum Asian handicap home team odds MaxAHA = Market maximum Asian handicap away team odds
AvgAHH = Market average Asian handicap home team odds AvgAHA = Market average Asian handicap away team odds

Acknowledgements

Data obtained from Football-Data Photo by Waldemar Brandt on Unsplash

Inspiration

1. What's the proportion of luck (if any) in positive bet results?

2. What's the proportion of misfortune (if any) in negative bet results?

3. Is there a relationship between game odds and the actual game outcome?

4. Comparing single-game (separate stakes) vs multiple game (single stake) bets. Which is more likely to win or lose given a fixed amount to stake?

5. How much influence does form (trend) have in deciding the team's outcome of their next game?

6. Are some leagues more difficult to predict than others?

Please help suggest any other insights I might have missed.

Remember to BeGambleAware

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The purpose of this study was to investigate the effects of playing position, pitch location, team ability and opposition ability on technical performance variables (pass, cross, corner, free kick accuracy) of English Premier League Soccer players in difference score line states. A validated automatic tracking system (Venatrack) was used to code player actions in real time for passing accuracy, cross accuracy, corner accuracy and free kick accuracy. In total 376 of the 380 games played during the 2011–12 English premier League season were recorded, resulting in activity profiles of 570 players and over 35’000 rows of data. These data were analysed using multi-level modelling. Multi-level regression revealed a “u” shaped association between passing accuracy and goal difference (GD) with greater accuracy occurring at extremes of GD e.g., when the score was either positive or negative. The same pattern was seen for corner accuracy away from home e.g., corner accuracy was lowest when the score was close with the lowest accuracy at extremes of GD. Although free kicks were not associated with GD, team ability, playing position and pitch location were found to predict accuracy. No temporal variables were found to predict cross accuracy. A number of score line effects were present across the temporal factors which should be considered by coaches and managers when preparing and selecting teams in order to maximise performance. The current study highlighted the need for more sensitive score line definitions in which to consider score line effects.

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.

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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.

The global single-player soccer mobile game market is projected to reach a value of USD XXX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The growth of the market is attributed to factors such as the increasing popularity of mobile gaming, the rising number of smartphones and tablets, and the growing demand for realistic and immersive soccer simulations. The free-to-play segment is expected to dominate the market due to its accessibility and low entry barrier, while Android is anticipated to be the dominant platform owing to its large user base and wide distribution of devices. Key players in the market include Final Kick, Dream League Soccer, KONAMI, eFootball, New Star Manager, Super Soccer Champs, Real Football, Score! Hero, Ultimate Soccer, and Soccer Manager. These companies are investing heavily in developing innovative and engaging games to cater to the growing demand. Additionally, the emergence of mobile esports is creating new opportunities for publishers and developers to capitalize on the competitive nature of the game and engage with fans. Furthermore, technological advancements such as 5G and cloud gaming are anticipated to enhance the gaming experience and further drive the market growth in the forecast period.

A huge share of consumers in the United States do sports in their free time. The popularity of soccer as a sport activity in the U.S. shows the following changes over time. Looking at the most recent data points there has been an increase from 2023 Q1 to 2024 Q1. The share of respondents grew from 13 percent to 15 percent during this time. These consumers playing soccer are choosing to stay active within this discipline. Like most sports, it requires not only motivation but also the matching equipment. If you want to know how consumers in the U.S. commonly stay active, you can check out the most popular sports activities in the U.S. next to find out how consumers playing soccer benchmark against other disciplines. The survey was conducted online among 5436 to 22626 respondents per quarter in the United States, between 2022 and 2024. Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than 2,000,000 interviews.

The single-player soccer mobile games market has witnessed significant growth in recent years, driven by the increasing popularity of smartphones and the growing demand for mobile gaming. The market is projected to reach a value of million by 2033, expanding at a CAGR of XX% over the forecast period of 2025-2033. Key drivers of this growth include the rising number of mobile gamers, the increasing accessibility of affordable smartphones, and the popularity of soccer as a global sport. The market is segmented by application (Android, iOS), type (free games, paid games), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). The Android segment holds the largest market share due to its wide availability and affordability. Free games dominate the market, as they offer a convenient and cost-effective way for users to experience soccer gameplay. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the increasing smartphone penetration and the growing popularity of soccer in the region. Leading companies in the market include Final Kick, Dream League Soccer, KONAMI, eFootball, and New Star Manager.

This dataset provides information on 18 in Free municipal consortium of Syracuse, Italy as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.govMaryland Sports (http://www.marylandsports.us/) has identified sport venues located within the State of Maryland. These venues offer opportunities to participate in free and fee-based - organized and pick-up - indoor and outdoor sports and physical fitness related activities in the area of Soccer. Last Updated: 08/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Survey of Sports Habits in Spain is a structural statistical operation developed by the Ministry as part of the National Statistical Plan. Aimed at people aged 15 and over, its main purpose is to obtain indicators relating to the sports habits of Spaniards. The sample design of the project has been carried out in collaboration with the National Institute of Statistics.

The global cage football field market is experiencing robust growth, driven by the increasing popularity of cage football, particularly among younger demographics and the rising demand for versatile sports infrastructure in both commercial and educational settings. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the relatively lower cost and ease of maintenance of cage football fields compared to traditional grass fields are making them increasingly attractive to schools, communities, and recreational facilities. Secondly, the adaptability of cage football fields—suitable for various sports and fitness activities—enhances their return on investment, further driving market expansion. Lastly, advancements in artificial turf technology, such as the development of fill-free options that offer superior performance and reduced environmental impact, are creating new opportunities for market players. The market is segmented by application (commercial sports facilities, teaching institutions, public places, others) and type (sand-filled artificial turf, fill-free artificial turf). The fill-free segment is expected to witness faster growth due to its environmental benefits and enhanced playing surface. Geographic growth will be predominantly driven by regions experiencing rapid urbanization and increased disposable incomes, with North America and Europe currently leading the market, followed by a strong rise in demand from Asia-Pacific regions. While the market exhibits significant growth potential, certain restraints exist. High initial investment costs for establishing cage football facilities could pose a barrier to entry for smaller organizations, particularly in developing economies. Furthermore, regulatory requirements and safety standards related to artificial turf installations may vary across different regions, influencing the market's expansion. Despite these challenges, the overall market outlook remains positive, with substantial opportunities for existing players and new entrants to capitalize on the growing demand for high-quality, versatile sports infrastructure. The continued innovation in artificial turf technologies and the growing emphasis on community sports development are expected to propel the market's expansion in the coming years.