During the first two and a half years of Kick's launch, it has established its presence in the livestreaming apps industry. The platform recorded an all-time peak viewership of over 1.7 million in October 2024. Colombian streamer Luis Fernando Villa Álvarez, known as WestCol on the platform, stood as the most followed streamer on Kick, with roughly two million followers. He had a record viewership of 1.4 million viewers. Among video games, Fortnite held the title of most watched game on Kick, with peak viewership of around 721 thousand in December 2023.

Between March and November, 2024, the number of active live streaming channels on Kick rose to over *** thousand. This represented an increase compared to the *** thousand channels recorded in March 2024. Kick is a social and live-streaming platform launched in October 2022 as an alternative to Amazon-owned live streaming platform Twitch. In June 2023, popular streamers xQc and Amouranth joined Kick, leaving behind the Twitch platform.

Financial overview and grant giving statistics of Teen Kick

As of April 2023, online live platform Kick reported that for 100 subscribers creators would earn *** U.S. dollars. Kick was launched in December 2022 as a direct competitor to Twitch. Kick reportedly charges **** percent of streamers revenues, in comparison to Twitch which is reported to retain between ** and ** percent of creator's earnings. Kick, which signed up streamers Amouranth and xQc in June 2023, reported that creators with ***** subscribers would be able to earn almost ****** U.S. dollars on the platform.

Experience the attraction 'Side Kick' anew! Perfectly plan with statistics from July 2024 on waiting times or queue times and weather data.

2020 NFL Stats Web Scrape

This dataset consists of basic statistics and career statistics provided by the NFL on their official website (http://www.nfl.com) for all players, active and retired.

Summary

All of the data was web scraped using Python code, which can be found and downloaded here: https://github.com/ytrevor81/NFL-Stats-Web-Scrape

Explanation of Data

Before we go into the specifics, it's important to note in the basic statistics and career statistics CSV files that all players are assigned a 'Player_Id'. This is the same ID used by the official NFL website to identify each player. This is useful in case of, for example, importing these CSV files in a SQL database for an app.

1. The first main group of stats is the basic stats provided for each player. This data is stored in the CSV file titled Active_Player_Basic_Stats.csv and Retired_Player_Basic_Stats.csv.

The data pulled for each player in Active_Player_Basic_Stats.csv is as follows: a. Player ID b. Full Name c. Position d. Number e. Current Team f. Height g. Height h. Weight i. Experience j. Age k. College

The data pulled for each player in Retired_Player_Basic_Stats.csv differs slightly from the previous data set. The data is as follows: a. Player ID b. Full Name c. Position f. Height g. Height h. Weight j. College k. Hall of Fame Status

1. The second main group of stats gathered for each player are their career statistics. Due to the NFL having a various amount of positions that players occupy, the career statistics are divided into statistics categories. The stats for active players and retired players are structured the same, but are stored in separate CSV files (ActivePlayer_(category)_Stats.csv and RetiredPlayer_(category)_Stats.csv). The following are the career statistics categories and accompanying CSV file names: a. Defensive Stats - ..._Defense_Stats.csv b. Fumbles Stats - ..._Fumbles_Stats.csv c. Kick Returns Stats - ..._KickReturns_Stats.csv d. Field Goal Kicking Stats - ..._Kicking_Stats.csv e. Passing Stats - ..._Passing_Stats.csv f. Punt Returns Stats - ..._PuntReturns_Stats.csv g. Punting Stats - ..._Punting_Stats.csv h. Receiving Stats - ..._Receiving_Stats.csv i. Rushing Stats - ..._Rushing_Stats.csv

Financial overview and grant giving statistics of Kick It Forward

In November 2024, Colombian streamer WestCOL was the most popular creator on Kick with over *** million hours of content watched by global audiences during the month. Drb7h ranked second with approximately *** million hours of live content watched. Xqc ranked third, with *** million hours of live content watched inNovember 2024.

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

In June 2025, the Just Chatting streaming category, which increased in popularity among all non-gaming live streamed content types, saw around ***million hours watched among global Kick viewers. Live streaming content involving slot and casino gaming was the second most watched category on live streaming platform Kick. This type of live streamed content - which is usually restricted on YouTube Gaming and Twitch - generated over ** million hours watched among global audiences for the month.

This dataset contains detailed data on all nba players from 2023/24 season.

• Player: The name of the player.
• Nation: The player's nationality.
• Pos: The player's position (e.g., guard, forward, center).
• Age: The player's age.
• MP (Minutes Played): The total number of minutes the player has played.
• Starts: The number of games the player started.
• Min (Minutes): The total number of minutes played by the player (similar to MP).
• 90s (90s Played): The equivalent number of 90-minute matches played by the player (e.g., 1.5 = 135 minutes).
• Gls (Goals): The total number of goals scored by the player.
• Ast (Assists): The total number of assists made by the player.
• G+A (Goals + Assists): The total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): The total number of goals scored excluding penalty kicks.
• PK (Penalty Kicks): The number of penalty goals scored by the player.
• PKatt (Penalty Kicks Attempted): The number of penalty kicks attempted by the player.
• CrdY (Yellow Cards): The number of yellow cards received by the player.
• CrdR (Red Cards): The number of red cards received by the player.
• xG (Expected Goals): The expected number of goals from the player's shots.
• npxG (Non-Penalty Expected Goals): The expected goals excluding penalties.
• xAG (Expected Assists): The expected number of assists from the player's passes.
• npxG+xAG (Non-Penalty xG + xAG): The total of non-penalty expected goals and expected assists.
• PrgC (Progressive Carries): The number of times the player carried the ball forward.
• PrgP (Progressive Passes): The number of passes made by the player that moved the ball forward.
• PrgR (Progressive Runs): The number of times the player made runs forward with the ball.
• Gls (Goals): (Repeated, already defined) The total number of goals scored.
• Ast (Assists): (Repeated, already defined) The total number of assists made.
• G+A (Goals + Assists): (Repeated, already defined) The total number of goals and assists combined.
• G-PK (Goals - Penalty Kicks): (Repeated, already defined) The total number of goals scored excluding penalty kicks.
• G+A-PK (Goals + Assists - Penalty Kicks): The total number of goals and assists minus penalty goals.
• xG (Expected Goals): (Repeated, already defined) The expected number of goals from the player's shots.
• xAG (Expected Assists): (Repeated, already defined) The expected number of assists from the player's passes.
• xG+xAG (Expected Goals + Expected Assists): The total expected goals and assists.
• npxG (Non-Penalty Expected Goals): (Repeated, already defined) The expected goals excluding penalties.
• npxG+xAG (Non-Penalty xG + Expected Assists): The total of non-penalty expected goals and expected assists.

Traffic analytics, rankings, and competitive metrics for kick.com as of June 2025

Financial overview and grant giving statistics of Kicks For Kids Inc

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Financial overview and grant giving statistics of Kick the Stereotypes

Context

This dataset was created because I wanted to analyze shooting tendencies in penalty shooting. It was built by watching every single penalty kick in a World Cup Shootout from Spain 1982 to Russia 2018.

Content

For each penalty I registered the following: - Kicking Team - Where the shot was taken. To do this, I divided the goal in 9 different zones. - Whether the shooter was left or right footed. - Where did the keeper dive. - Whether the shot was on target or not. - Whether the shot went in or not. - The penalty number in each series. - Whether or not the penalty was for elimination. This where the cases when, if the shot went in, the Game was over or the cases where a miss would end the game.

Notes

In the Zone column, I divided the goal into 9 different sections looking at it from the front: 1. Upper left corner 2. Upper center 3. Upper right corner 4. Middle left area 5. Middle center area 6. Middle right area 7. Bottom left corner 8. Bottom center zone 9. Bottom right corner

https://github.com/pablolopez2733/9plus6/blob/master/AnalisisPenaltis/Images/Goal.png?raw=true" alt="goal division">

For the Keeper column, looking at it from the shooter's perspective: I = the keeper dived to the left. C = the keeper kept in the middle of the goal. R = the keeper dived to the right.

https://github.com/pablolopez2733/9plus6/blob/master/AnalisisPenaltis/Images/Keeper.png?raw=true" alt="keeper dives">

These Kickstarter stats will show you what it takes to be successful on Kickstarter and what you need to know before launching your own campaign.