Context: Valorant, developed by Riot Games, has quickly become one of the most popular tactical first-person shooter games since its release. The game emphasizes strategic team play, individual skills, and tactical execution, making it a fascinating subject for performance analysis. Understanding the various metrics that contribute to player success can offer insights into effective strategies and gameplay techniques. This dataset was created to help players, coaches, and analysts delve into the detailed aspects of player performance and identify key areas for improvement.

Sources: The data for this dataset was collected from various online sources, including:

In-Game Statistics: Aggregated from player profiles and match histories available within the game client. Third-Party Valorant Trackers: Websites and tools that track player statistics and match performance, such as Tracker.gg and Blitz.gg. Community Contributions: Insights and data shared by the Valorant community, including professional players, streamers, and analysts, who often provide detailed breakdowns of their gameplay. Inspiration: The inspiration for compiling this dataset stems from several key areas:

Performance Analysis: In competitive gaming, understanding the granular details of player performance is crucial for improvement. Metrics like win rate, damage per round, and headshot percentage provide actionable insights. Strategic Development: By analyzing this data, players and teams can develop better strategies, identify strengths and weaknesses, and tailor their training regimes accordingly. Predictive Modeling: The dataset serves as a foundation for building predictive models to forecast future performance, which can be useful for coaching, match preparation, and scouting new talent. Community Engagement: Providing this dataset to the wider Valorant community fosters engagement and encourages collaborative analysis. It allows enthusiasts to test hypotheses, share findings, and contribute to a deeper understanding of the game. Educational Purposes: For educators and students in data science, sports analytics, and game design, this dataset offers a real-world application of data analysis techniques and methodologies. Future Directions: The dataset can be expanded by including additional metrics such as agent pick rates, map-specific performance, and team composition analysis. Incorporating more granular data over longer periods can also enhance the depth of analysis and provide a more comprehensive view of player performance trends.

By sharing this dataset, we aim to empower the Valorant community with data-driven insights that can elevate gameplay, inform strategic decisions, and contribute to the overall growth of the esports ecosystem.

Introduction

Video Game Statistics: The video game industry has evolved from a niche hobby into a global entertainment giant, impacting technology, culture, and economics. With millions of players across the globe and a market worth billions, understanding video game statistics is vital for developers, investors, marketers, and enthusiasts.

These statistics offer valuable insights into player behavior, market trends, sales performance, and emerging technologies, enabling the identification of patterns, forecasting growth, and shaping strategies within the industry. This statistics will delve into the latest trends in video game sales, platform popularity, gamer engagement, and industry innovations, providing a data-driven overview of the gaming landscape today and in the future.

Video gaming is a popular way for gamers to connect with friends and family, and many gamers have formed relationships through video games. During a February 2025 survey, 78 percent of responding gamers in the United States agreed that playing games can introduce people to new friends.

Video gaming is no longer a hobby exclusively enjoyed by the young. As generations have grown up with video games a normal part of life, the age of the average gamer also increases. During a 2023 survey, 25 percent of video game players still come from the 27 to 42 years age demographic, and 19 percent are 59 years and older. Time spent gaming In 2023, Americans aged between 15 to 19 years spent 98.4 minutes on gaming or leisurely computer use during an average day. The age demographic which devoted the least amount of time to gaming was the 55 to 64 years category. Members of this age demographic spent an average of just 17.4 minutes playing on the computer during an average day.

Comprehensive gaming statistics including player counts, trends, and analytics

Statistics on a Blockbreaker-like Game

The author is in the process of creating a blockbreaker-like game, in which the jumping-off point is the "Block Breaker" section of the Udemy course, Complete C# Unity Developer 2D: Learn to Code Making Games

After making lots of levels, the author needed to sort them by difficulty. How does one measure the difficulty of a level? A first-cut solution is to make an auto-play bot that is not perfect, and see how well the bot does on each level, using thousands of trials.

Here is a video of the game in auto-play action.

Fields

• Date: date and time the game was auto-played
• Level: the name of the level (the 3-digit number is an estimate of the difficulty from a previous run, no longer valid after tweaking)
• NumBlocks: how many blocks have to be broken to win the level
• IsWin: True if autoplay broke all the blocks, False if the ball fell past the paddle.
• ElapsedTime: Seconds until either won or lost (game is played at 4x speed, so multiply by 4 to get an estimate of how long a human might play it)
• Score: total score when the game was won or lost
• Accuracy: the autoplay is tuned with a randomly-chosen accuracy. Higher numbers are more likely to win.

Introduction

Gaming Monetization Statistics: The gaming sector has undergone considerable transformation, moving away from its conventional model of selling games as independent units.

Instead, it has embraced a diverse ecosystem where various strategies are utilized to produce revenue. Gaming monetization pertains to the practices and tactics employed by game developers and publishers to derive earnings from their creations.

This multifaceted methodology holds immense importance in maintaining the industry's expansion, facilitating game development, and furnishing players with captivating interactions. The dynamic gaming monetization landscape is characterized by its ability to adapt to changing market dynamics, emerging player preferences, and technological advancements.

This adaptability will remain essential in sustaining the industry's growth while ensuring that players continue to receive captivating gaming experiences.

https://scoop.market.us/wp-content/uploads/2023/09/Gaming-Monetization-Statistics.png" alt="Gaming Monetization Statistics" class="wp-image-37223" style="width:624px;height:623px">

In 2024, total U.S. consumer spending on video game content amounted to **** billion U.S. dollars, a significant increase from ***** billion U.S. dollars in the preceding year. Video game spending surged in 2021 due to the COVID-19 pandemic.

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

An October 2023 survey of gamers in the United States found that arcade and puzzle games were among the most popular video gaming genres across all generations. About 64 percent of Gen Alpha gamers stated arcade games as one of their top three gaming categories.

I have created this dataset for people interested in League of Legends who want to approach the game from a more analytical side.

Most of the data was acquired from Games of Legends (https://gol.gg/tournament/tournament-stats/LEC%20Winter%202025/) and also from official account of the League of Legends EMEA Championship (https://www.youtube.com/c/LEC)

Dataset Contents:

• Player: Name of the player.
• Role: Role of the player (e.g., TOP, JUNGLE, MID, ADC, SUPPORT)
• Team: Name of the player's team
• Opponent_Team: Name of the opposing team
• Opponent_Player: Name of the opposing player
• Date: Date of the match
• Round: Round of the tournament
• Day: Specific day of the tournament
• Patch: Version of the game patch during the match
• Stage: Stage of the tournament
• No_Game: Game number in the series
• all_Games: Total number of games in the series
• Format: Format of the match (e.g., Best of 1, Best of 3)
• Game_of_day: Number of the game that day
• Side: Side of the map the team started on (Blue/Red)
• Time: Duration of the match

Team Performance Metrics:

• Kills_Team: Total kills by the team
• Turrets_Team: Total turrets destroyed by the team
• Dragon_Team: Total dragons killed by the team
• Baron_Team: Total barons killed by the team

Player Performance Metrics:

• Level: Final level of the player
• Kills: Number of kills by the player
• Deaths: Number of deaths of the player
• Assists: Number of assists by the player
• KDA: Kill/Death/Assist ratio
• CS: Creep Score (minions killed)
• CS in Team's Jungle: Creep Score in the team's jungle
• CS_in_Enemy_Jungle: Creep Score in the enemy's jungle
• CSM: Creep Score per Minute
• Golds: Total gold earned
• GPM: Gold Per Minute
• GOLD%: Percentage of team's total gold earned by the player

Vision and Warding:

• Vision_Score: Total vision score
• Wards_placed: Number of wards placed
• Wards_destroyed: Number of wards destroyed
• Control_Wards_Purchased: Number of control wards purchased
• Detector_Wards_Placed: Number of detector wards placed
• VSPM: Vision Score Per Minute
• WPM: Wards Placed per Minute
• VWPM: Vision Wards Placed per Minute
• WCPM: Wards Cleared per Minute
• VS%: Vision Score percentage

Damage Metrics:

• Total_damage_to_Champion: Total damage dealt to champions
• Physical_Damage: Total physical damage dealt
• Magic_Damage: Total magic damage dealt
• True_Damage: Total true damage dealt
• DPM: Damage Per Minute
• DMG%: Percentage of team’s total damage dealt by the player

Combat Metrics

• K+A_Per_Minute: Kills and Assists per Minute
• KP%: Kill Participation percentage
• Solo_kills: Number of solo kills
• Double_kills: Number of double kills
• Triple_kills: Number of triple kills
• Quadra_kills: Number of quadra kills
• Penta_kills: Number of pentakills

Early Game Metrics

• GD@15: Gold Difference at 15 minutes
• CSD@15: Creep Score Difference at 15 minutes
• XPD@15: Experience Difference at 15 minutes
• LVLD@15: Level Difference at 15 minutes

Objective Control:

• Objectives_Stolen: Number of objectives stolen
• Damage_dealt_to_turrets: Total damage dealt to turrets
• Damage_dealt_to_buildings: Total damage dealt to buildings

Healing and Mitigation:

• Total_heal: Total healing done
• Total_Heals_On_Teammates: Total healing done on teammates
• Damage_self_mitigated: Total damage self-mitigated
• Total_Damage_Shielded_On_Teammates: Total damage shielded on teammates

Crowd Control Metrics:

• Time_ccing_others: Time spent crowd controlling others
• Total_Time_CC_Dealt: Total crowd control time dealt

Survival and Economy:

• Total_damage_taken: Total damage taken
• Total_Time_Spent_Dead: Total time spent dead
• Consumables_purchased: Number of consumables purchased
• Items_Purchased: Number of items purchased
• Shutdown_bounty_collected: Total shutdown bounty collected
• Shutdown_bounty_lost: Total shutdown bounty lost

Gen Z is reshaping the gaming landscape with bold preferences and habits. They flock to mobile for convenience, lean into PCs and consoles for depth, and thrive in cross-platform worlds that offer choice and social sync. Real-world use cases span everything from mobile esports viewership rising 340% since 2022 to platforms...

Employment statistics on the Video Games industry in the US

Video gaming is no longer just a hobby for teenage boys - it is enjoyed by people of all genders, ethnicities, and ages, and this is beginning to be reflected in the representation of society within video games. In 2016, just two percent of video games had female protagonists, but this figure jumped to 18 percent in 2020.

In the land of Terra Mystica dwell 14 different peoples in seven landscapes, and each group is bound to its own home environment, so to develop and grow, they must terraform neighboring landscapes into their home environments in competition with the other groups. (https://boardgamegeek.com/boardgame/120677/terra-mystica)

The Dataset

People started playing games on the board game geek forums. To help manage the complexity of tracking the game state, an online implementation of the game was created on https://terra.snellman.net/.

This resulted in a dataset of all the games played in that website which generated a lot of interesting statistics about the game. Here, I took the json dataset available on https://terra.snellman.net/data/ and transformed them into csv files for easy processing with tools like pandas or R.

The hope is we can uncover even more insights and strategies about the game. Enjoy!

The files included are .txt and .png files which can be opened using any text editing and image processing software.

The Game Art Outsourcing Service market has emerged as a pivotal segment within the global gaming industry, catering to the growing demand for high-quality visual content in video games. With the rapid advancements in gaming technology and the increasing complexity of game development, studios are turning to outsour

Statistics illustrates consumption, production, prices, and trade of Video Game Consoles (Not Operated by Means of Payments) in Latin America and the Caribbean from 2007 to 2024.

games-stats.com is ranked #365711 in US with 75.45K Traffic. Categories: Computer and Video Games. Learn more about website traffic, market share, and more!

The virtual reality (VR) video game market has emerged as a transformative force within the gaming industry, captivating players with immersive experiences that transcend the boundaries of traditional gameplay. Currently valued at approximately $15 billion, this dynamic market has seen significant growth over the pa