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.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

Description:

This comprehensive dataset offers detailed information on approximately 17,000 FIFA football players, meticulously scraped from SoFIFA.com.

It encompasses a wide array of player-specific data points, including but not limited to player names, nationalities, clubs, player ratings, potential, positions, ages, and various skill attributes. This dataset is ideal for football enthusiasts, data analysts, and researchers seeking to conduct in-depth analysis, statistical studies, or machine learning projects related to football players' performance, characteristics, and career progressions.

Features:

• name: Name of the player.
• full_name: Full name of the player.
• birth_date: Date of birth of the player.
• age: Age of the player.
• height_cm: Player's height in centimeters.
• weight_kgs: Player's weight in kilograms.
• positions: Positions the player can play.
• nationality: Player's nationality.
• overall_rating: Overall rating of the player in FIFA.
• potential: Potential rating of the player in FIFA.
• value_euro: Market value of the player in euros.
• wage_euro: Weekly wage of the player in euros.
• preferred_foot: Player's preferred foot.
• international_reputation(1-5): International reputation rating from 1 to 5.
• weak_foot(1-5): Rating of the player's weaker foot from 1 to 5.
• skill_moves(1-5): Skill moves rating from 1 to 5.
• body_type: Player's body type.
• release_clause_euro: Release clause of the player in euros.
• national_team: National team of the player.
• national_rating: Rating in the national team.
• national_team_position: Position in the national team.
• national_jersey_number: Jersey number in the national team.
• crossing: Rating for crossing ability.
• finishing: Rating for finishing ability.
• heading_accuracy: Rating for heading accuracy.
• short_passing: Rating for short passing ability.
• volleys: Rating for volleys.
• dribbling: Rating for dribbling.
• curve: Rating for curve shots.
• freekick_accuracy: Rating for free kick accuracy.
• long_passing: Rating for long passing.
• ball_control: Rating for ball control.
• acceleration: Rating for acceleration.
• sprint_speed: Rating for sprint speed.
• agility: Rating for agility.
• reactions: Rating for reactions.
• balance: Rating for balance.
• shot_power: Rating for shot power.
• jumping: Rating for jumping.
• stamina: Rating for stamina.
• strength: Rating for strength.
• long_shots: Rating for long shots.
• aggression: Rating for aggression.
• interceptions: Rating for interceptions.
• positioning: Rating for positioning.
• vision: Rating for vision.
• penalties: Rating for penalties.
• composure: Rating for composure.
• marking: Rating for marking.
• standing_tackle: Rating for standing tackle.
• sliding_tackle: Rating for sliding tackle.

Use Case:

This dataset is ideal for data analysis, predictive modeling, and machine learning projects. It can be used for:

• Player performance analysis and comparison.
• Market value assessment and wage prediction.
• Team composition and strategy planning.
• Machine learning models to predict future player potential and career trajectories.

Note:

Please ensure to adhere to the terms of service of SoFIFA.com and relevant data protection laws when using this dataset. The dataset is intended for educational and research purposes only and should not be used for commercial gains without proper authorization.

This dataset contains data for last 10 seasons of Spanish La Liga including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

The FIFA Football Players Dataset is a comprehensive collection of information about football (soccer) players from around the world. This dataset offers a wealth of attributes related to each player, making it a valuable resource for various analyses and insights into the realm of football, both for gaming enthusiasts and real-world sports enthusiasts.

Attributes:

• Player: The name of the football player.
• Country: The nationality or home country of the player.
• Height: The height of the player in centimeters.
• Weight: The weight of the player in kilograms.
• Age: The age of the player.
• Club: The club to which the player is currently affiliated.
• Ball Control: Player's skill in controlling the ball.
• Dribbling: Player's dribbling ability.
• Marking: Player's marking skill.
• Slide Tackle: Player's ability to perform slide tackles.
• Stand Tackle: Player's ability to perform standing tackles.
• Aggression: Player's aggression level.
• Reactions: Player's reaction time.
• Attacking Position: Player's positioning for attacking plays.
• Interceptions: Player's skill in intercepting passes.
• Vision: Player's vision on the field.
• Composure: Player's composure under pressure.
• Crossing: Player's ability to deliver crosses.
• Short Pass: Player's short passing accuracy.
• Long Pass: Player's ability in long passing.
• Acceleration: Player's acceleration on the field.
• Stamina: Player's stamina level.
• Strength: Player's physical strength.
• Balance: Player's balance while playing.
• Sprint Speed: Player's speed in sprints.
• Agility: Player's agility in maneuvering.
• Jumping: Player's jumping ability.
• Heading: Player's heading skills.
• Shot Power: Player's power in shooting.
• Finishing: Player's finishing skills.
• Long Shots: Player's ability to make long-range shots.
• Curve: Player's ability to curve the ball.
• Free Kick Accuracy: Player's accuracy in free-kick situations.
• Penalties: Player's penalty-taking skills.
• Volleys: Player's volleying skills.
• Goalkeeper Positioning: Goalkeeper's positioning attribute (specific to goalkeepers).
• Goalkeeper Diving: Goalkeeper's diving ability (specific to goalkeepers).
• Goalkeeper Handling: Goalkeeper's ball-handling skill (specific to goalkeepers).
• Goalkeeper Kicking: Goalkeeper's kicking ability (specific to goalkeepers).
• Goalkeeper Reflexes: Goalkeeper's reflexes (specific to goalkeepers).
• Value: The estimated value of the player.

Potential Uses:

Player Performance Analysis: Evaluate the performance of football players based on their attributes. Club Analysis: Investigate clubs, player distribution, and club statistics. Positional Insights: Explore the attributes specific to player positions. Player Valuation Trends: Analyze how player values change over time. Data Visualization:Create visualizations for better data representation. Machine Learning Models: Develop predictive models for various football-related forecasts.

Before using the dataset for analysis, it's advisable to preprocess the data, such as converting the "value" column into a numerical format, handling missing values, and ensuring consistency in column names. This dataset is a valuable resource for gaining insights into football, both in the context of the FIFA video game and real-world football.

All thanks and credit goes to FIFA Index

Version-info

This data includes statistics up to the final of the 2022/23 season.

Context

The UEFA Champions League (abbreviated as UCL) is an annual club football competition organized by the Union of European Football Associations (UEFA) and contested by top-division European clubs, deciding the competition winners through a group and knockout format. It is one of the most prestigious football tournaments in the world and the most prestigious club competition in European football, played by the national league champions (and, for some nations, one or more runners-up) of their national associations. (*** From wikipidea)

Note: This doesn't have any information about the European cup competition (1950-1992). It starts with the beginning of the Champions league (1992/93) season.

Content

So far this data has the following: 1- Each club's participation record in the competition 2- Each country's clubs participation records in the competition (summary of #1) 3- Top Player Appearances by club (i.e. number of times played for a club in the competition) 4- Top Player Appearances Total games (summary of #3) 5- Top Goal scorer by club (i.e. number of goals scored by a player for a club in the competition) 6- Top Goal scorer Totals (summary of #5) 7- Top Coach Appearances by club (i.e. number of times coached for a club in the competition) 8- Top Coach Appearances Total games (summary of #7) 9- Top Goal Scorer for each season in the competition with # of appearances 10- Number of goals scored per round per group in each season

Acknowledgements

All this data was provided by UEFA.com. All I did was download the PDF and then scrape the data and put it in csv format.

This dataset consists of the Premier League team stats for seasons 2022/2023, 2021/2022 and 2022/2021. The data was scraped from fbref.com and formatted into a csv file.

Columns:

date = Date of the match time = Kick-off time of the match comp = Competition of the match (i.e English Premier League) round = The match week the match took place on day = The day the match took place on (i.e Monday, Tuesday etc) venue = Whether team was Home, Away or Neutral venue result = Whether the team Won, Lost or Drew (W, L, D) gf = How many goals the team scored ga = How many goals the team conceded opponent = Who the team faced that day xg = Expected goals xa = Expected goals allowed poss = Possession attendance = How many people attended the match captain = Captain of the team for match formation = Formation the team used for match referee = The referee for the match match report = Please ignore notes = Please ignore sh = Shots total sot = Shots on target dist = average distance by shot fk = shots from free kicks pk = Penalty kicks made pkatt= Penalty kicks attempted season = The year the season took place (i.e for 2022/2023 season year would be 2023) team = The team the stats belong to (i.e Manchester City)

American College Football network of Girvan and Newman Mark Newman provides a football.gml file which contains the network of American football games between Division IA colleges during regular season Fall 2000. The file asks you to cite M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002). There are are two issues with the original GN file. First three teams met twice in one season so the graph is not simple. This is easily dealt with if required. Secondly, the assignments to conferences, the node values, seem to be for the 2001 season and not the 2000 season. The games do appear to be for the 2000 season as stated. For instance the Big West conference existed for football till 2000 while the Sun Belt conference was only started in 2001. Also there were 11 conferences and 5 independents in 2001 but 10 conferences and 8 independents in 2000. I have provided a set of files footballTSE* which define a simple graph with the correct conference assignments in the archive here. There is a read me file included with more details. Further information about the problems with this data and the solutions are given in T.S. Evans, “Clique Graphs and Overlapping Communities”, J. Stat. Mech. (2010) P12037 [arXiv:1009.0638] which would be the appropriate source to cite along with the original GN publication.Note that Gschwind et al, 2015, Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques, independently finds similar errors in this data.

This dataset contains data for last 10 seasons of Italian Serie A including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

In 2023, the Dallas Cowboys were the only NFL franchise to report revenue of over one billion U.S. dollars. In that year, America's Team generated revenue of *** billion U.S. dollars. Meanwhile, the Detroit Lions generated less than **** that amount. The Dallas Cowboys As well as being the NFL franchise with the highest revenue, the Dallas Cowboys was also the most valuable NFL franchise. As of August 2024, the franchise was valued at **** billion U.S. dollars. This success off the pitch, however, has not translated to on-field success in recent years. Despite winning an impressive * Super Bowl titles, the last of these was back in 1995. While the Cowboys made it to the playoffs in the 2022 season, they lost out to the San Francisco 49ers in the divisional round. NFL revenue streams Sponsorships, media, partnerships, ticket and concession sales are some of the most important revenue streams for the NFL. In 2023, the revenue of all 32 NFL teams totaled ***** billion U.S. dollars, the highest figure to-date. Meanwhile, NFL league and team sponsorship generated **** billion U.S. dollars that same year. Some of the main sponsors for the league include Verizon, Pepsi, and Nike.

Football statistics on first half goals, including goal timings and betting insights. Updated daily.

As one of the most popular sports leagues in the world, the NFL attracts huge television audiences and thousands of fans flock to the stadiums every week to see their favorite teams in action. However, fans of the Las Vegas Raiders, a franchise which moved from Oakland ahead of the 2020 season, had to fork out a league-high average of over 168 U.S. dollars to see their team play live at the RingCentral Coliseum. In contrast, the Arizona Cardinals charged a comparatively low 98.54 U.S. dollars for an average home game.

La Liga 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset provides an in-depth look at the 2023/24 La Liga season, covering various aspects of team and player performances across all matchdays. With over 50 individual CSV files, the dataset includes statistics on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings, among others. Whether you're interested in analyzing top scorers, understanding team strengths, or delving into player-specific contributions, this dataset offers a rich foundation for football analytics enthusiasts and professionals.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights for La Liga 2023/24.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly viewable data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - Proper Attribution: Please attribu...

While the Dallas Cowboys may have last tasted Super Bowl success back in 1996, the Texas outfit is still by far the most valuable franchise in the National Football League. Latest estimates value the franchise at 10.1 billion U.S. dollars, some two billion U.S. dollars more than its nearest rival, the Los Angeles Rams. Cowboys lead the way off the pitch... In 2024, the Cowboys’ franchise was valued at nine billion U.S. dollars, whilst the average franchise value in the NFL stood at around 5.6 billion U.S. dollars. In 2023, the Cowboys generated 1.2 billion U.S. dollars in revenue, the most of all NFL teams. The franchise also ranked top of the list of most valuable sports team brands worldwide, just ahead of other household names such as the New York Yankees and Real Madrid. Along with the San Francisco 49ers, the Dallas Cowboys have been crowned Super Bowl champions a total of five times. The Cowboys' Golden Era came in the 1990s, when the team claimed three Super Bowl Rings between 1993 and 1996. ...while the Patriots dominated on the pitch The Boston-based New England Patriots were the third most valuable franchise in the league, valued at 7.4 billion U.S. dollars in 2024. The team, who plays its home games at Gillette Stadium in Foxborough, Massachusetts, dominated the Super Bowl this century, with all six of their titles coming under head coach, Bill Belichick, since 2002. During this period of success, the team was led by legendary quarterback, Tom Brady, who held the record for most career playoff wins - his 35 career playoff wins are more than double that of his closest rival on the list, Joe Montana. It is unsurprising, then, that the now-retired quarterback also featured top of the list of players with the most passing yards in NFL history. Indeed, he was only one of four players in NFL history with more than 70 thousand passing yards, the other three being Brett Favre, Peyton Manning, and Drew Brees.

Analysis of ‘Premier League’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zaeemnalla/premier-league on 12 November 2021.

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

Context

Official football data organised and formatted in csv files ready for download is quite hard to come by. Stats providers are hesitant to release their data to anyone and everyone, even if it's for academic purposes. That was my exact dilemma which prompted me to scrape and extract it myself. Now that it's at your disposal, have fun with it.

Content

The data was acquired from the Premier League website and is representative of seasons 2006/2007 to 2017/2018. Visit both sets to get a detailed description of what each entails.

Inspiration

Use it to the best of your ability to predict match outcomes or for a thorough data analysis to uncover some intriguing insights. Be safe and only use this dataset for personal projects. If you'd like to use this type of data for a commercial project, contact Opta to access it through their API instead.

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

During the 2023/24 season, the Baltimore Ravens had the highest player payroll among the 32 teams competing in the National Football League (NFL). The team had a payroll amounting to approximately *** million U.S. dollars. By comparison, the two NFL teams with the lowest player payroll during the 2023/24 season were the Los Angeles Rams and the Indianapolis Colts at *** million U.S. dollars. What is the average ticket price for a Los Angeles Rams game? The average ticket price for a Los Angeles Rams home game was around *** U.S. dollars in the 2023 season. During that period, the Los Angeles Rams recorded an average home attendance of ****** spectators. The Los Angeles Rams average regular season home attendance peaked in 2016 at ******. What NFL team has the highest franchise value? Even though the Dallas Cowboys have neither won nor competed in a Super Bowl since 1996, the team is still by far the most valuable franchise in the NFL. In 2024, the Cowboys has a franchise value of **** billion U.S. dollars; this was over *** billion U.S. dollars more than its closest rival, the Los Angeles Rams. Meanwhile, the New England Patriots had the third-highest franchise value that year at *** billion.

NFL passing statistics since 2001. Contains record of every player who attempted a pass within the time period. Tracked metrics include passing yards, passing touchdowns, pass attempts, completions, interceptions, and touchdown/interception/completion percentages. More advanced metrics like yards per attempt, adjusted net yards per attempt, and other similar metrics are also included. I used this dataset, accompanied with the NFL Rushing Statistics dataset to predict the NFL MVP winner in 2024.

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Fantasy Sports Analytics – Statistics and Analysis 2022-2026

The fantasy sports market analytics statistics provide and accurate prediction of the whole market. This data enables vendors to make informed decisions. The fantasy sports market share is expected to increase by USD 6.11 billion from 2021 to 2026, and the market's growth momentum will accelerate at a CAGR of 6.51%.

The fantasy sports market statistics also provide information on several market vendors, including Blitz Studios Inc., Dream Sports, Fantasy Power 11, Fantrax, Flutter Entertainment Plc, Fox Corp., GamesKraft Technologies Pvt. Ltd., Head Digital Works Pvt. Ltd., Josh Clemm, LivePools Pvt. Ltd., MyTeam11 Fantasy Sports Pvt. Ltd., Paramount, Playerzpot Media Pvt Ltd, Roto Sports Inc., RotoBash apps Pvt Ltd, Sachar Gaming Private Limited, The Football Association Premier League Ltd., The Walt Disney Co., and Yahoo Inc among others.

Only a specific statistics will certainly address your current needs but if you wish to get a glimpse about the full report, here is the sample for fantasy sports market analytics report 2022-2026, it will help you strengthen your plans and strategies for better growth.

This detailed analytics helps the new and established market players to access their current strategies and substitute them according to the data. this report extensively covers fantasy sports market segmentation by type (fantasy soccer, fantasy baseball, fantasy basketball, fantasy football, and others) and geography (North America, Europe, APAC, South America, and Middle East and Africa).

One of the key factors driving the global fantasy sports market growth is the launch of various apps for fantasy sports. The launch of various apps for fantasy sports is notably driving the fantasy sports market growth, although factors such as increasing traction of mobile video games and traditional e-sports may impede market growth.

This detailed analytics helps the new and established market players to access their current strategies and substitute them according to the data. You can get more information on the key market drivers, fantasy sports market trends, and challenges as well.

In 2022/23, the Premier League had an aggregate attendance of around 15.3 million - nearly five million more than the EFL Championship. In terms of average attendance, England's top-tier ranked behind only the Bundesliga in the Big Five leagues.