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

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.

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Comprehensive gaming statistics including player counts, trends, and analytics

Introduction

Board Game Statistics: ​Board games have experienced a notable resurgence, becoming a preferred pastime for diverse demographics. In the United States, 43% of board game enthusiasts engage in gameplay several times per week, while 25% play weekly. Demographically, 47% of board game players are aged between 18 and 34, with individuals aged 55 and above comprising 18% of the player base. Gender representation is balanced, with women accounting for 51% and men 49% of board game enthusiasts. Regarding game collections, 43% of gamers own more than 25 board and/or card games.

The average playing time for most board games ranges from one to two hours, though this can vary significantly depending on the game's complexity. These statistics underscore the enduring appeal and diverse engagement within the board gaming community.​

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

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.

A 2024 survey of global gaming studios found that over *********** responding video game developers thought that an increased adoption of ****************** was going to be the biggest industry trend in 2025. According to ** percent of respondents, an ************************ was going to be the biggest trend in the next year.

Cross‑platform gaming bridges device boundaries, letting players move seamlessly between smartphone, console, and PC. Today’s gamers expect flexibility, and the data reflects that demand. In retail and esports alike, developers report stronger retention and spending when titles support cross‑play, while platforms see growing engagement from portable access. Read on for...

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.

Introduction

Cloud Gaming Statistics: Cloud gaming, or gaming-as-a-service, enables users to play video games over the internet without needing a high-definition gaming console or a gaming personal computer. Here, the use of cloud servers means that processing power is provided offsite, allowing easy gaming on different devices like smartphones, tablets, and smart TVs.

In 2025, cloud gaming statistics will still dominate the gaming sector as a fundamental tool, owing to the state of the art and the availability of the Internet.

As of June 2024, 27 percent of adults in the United States spent one to five hours per week playing video games. A further 10 percent of respondents stated that they spent 11 to 15 hours on video gaming in an average week. Overall, 19 percent of respondents were non-gamers and did not spend any time on video games.

Introduction

Mobile Gaming Statistics: The mobile gaming sector has rapidly become the largest and most dynamic segment within the global gaming industry, fueled by the mass adoption of smartphones, low-cost internet, and an increasing demand for portable entertainment.

Enhanced by advancements such as 5G networks, augmented reality (AR), cloud-based platforms, and freemium revenue models, mobile games have transformed from simple diversions into billion-dollar ecosystems attracting vast user bases.

This growing momentum is evident in surging user engagement, expanding in-app purchases, and rising ad-based revenues, firmly establishing mobile gaming as a central pillar of the digital entertainment economy. This report presents up-to-date mobile gaming statistics, covering key areas including market size, consumer trends, revenue streams, genre-specific growth, and regional insights.

Video gaming is a hobby enjoyed by young and old across the globe. In 2025, there were over *** billion gamers across Asia Pacific, making it the largest market for video gaming worldwide, with Europe coming in second place with a gaming audience of *** million. In total, there were an estimated **** billion gamers across the globe.

This dataset provides granular results from major and minor esports tournaments, including team and player performance metrics, prize distribution, and event metadata. It enables deep analysis of competitive gaming trends, sponsorship impact, and broadcast reach, making it invaluable for game publishers, analysts, and sponsors seeking insights into the esports ecosystem.

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...

Dataset

This dataset was created by RushabhWadkar

Released under Data files © Original Authors

Contents

I don’t know if you do, but do you remember a time when PC gamers used to belittle mobile games? It used to be a scene where mobile games were not seen as good enough or impactful enough. And, boy, have those days passed! Today, the mobile gaming industry is...

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.