In July 2025, total video games sales in the United States amounted to **** billion U.S. dollars, representing a five percent year-over-year increase. Generally speaking, the video game industry has its most important months in November and December, as video game software and hardware make very popular Christmas gifts. In December 2024, total U.S. video game sales surpassed **** billion U.S. dollars. Birth of the video game industry Although the largest regional market in terms of sales, as well as number of gamers, is Asia Pacific, the United States is also an important player within the global video games industry. In fact, many consider the United States as the birthplace of gaming as we know it today, fueled by the arcade game fever in the ’60s and the introduction of the first personal computers and home gaming consoles in the ‘70s. Furthermore, the children of those eras are the game developers and game players of today, the ones who have driven the movement for better software solutions, better graphics, better sound and more advanced interaction not only for video games, but also for computers and communication technologies of today. An ever-changing market However, the video game industry in the United States is not only growing, it is also changing in many ways. Due to increased internet accessibility and development of technologies, more and more players are switching from single-player console or PC video games towards multiplayer games, as well as social networking games and last, but not least, mobile games, which are gaining tremendous popularity around the world. This can be evidenced in the fact that mobile games accounted for ** percent of the revenue of the games market worldwide, ahead of both console games and downloaded or boxed PC games.

GOG.com (formerly Good Old Games) is a digital distribution platform for video games and films, known for its DRM-free policy that allows users full ownership of their purchases. It offers a wide range of titles, from classic retro games to modern indie and AAA releases, often bundled with extras like soundtracks and manuals. Operated by CD Projekt, the company behind The Witcher series, GOG emphasizes user-friendly access and preservation of gaming history. Its Galaxy client also provides optional features like cloud saves and multiplayer integration without compromising its DRM-free philosophy.

This dataset includes data about 10k+ units (video games, DLC, additional materials) being available on GOG.com. Data was extracted by GOG.com API in June 2025.

Columns description:

• id - game store identifier (integer)
• developer - developer name (string)
• publisher - publisher name (string)
• gallery - list of images/screenshots (string -> list). Please note that screenshot links are placed without file extension - to access it, you need to add ".png" to the end.
• video - id of video (dict with two fields - "id" and "provider")
• supportedOperatingSystems - supported OS (string -> list)
• genres - list of genres (string -> list)
• globalReleaseDate - release date timestamp (integer)
• isTBA - is TBA status (system variable, boolean)
• isDiscounted_main - flag for discounted status (boolean)
• isInDevelopment - flag for development status (boolean)
• releaseDate - release date timestamp (similar to globalReleaseDate, integer)
• availability - availability status (dict with fields 'isAvailable', 'isAvailableInAccount')
• salesVisibility - sales visibility status (dict)
• buyable - flag for buyability (boolean)
• title - video game title (string)
• image - title primary image (string). Please note that image link is placed without file extension - to access it, you need to add ".png" to the end.
• url - title URL (sting)
• supportUrl - tittle support URL (string)
• forumUrl - title forum URL (string)
• worksOn - nested field for supported OS (similar to supportedOperationSystems, but as dictionary)
• category - primary genre (string)
• originalCategory - original genre (similar to category, but less up-to-date) (string)
• rating - system rating value, hard to interpret (integer)
• type - type of title, 1 for games, 2 for bundles, 3 for DLC (integer)
• isComingSoon - coming soon flag (boolean)
• isPriceVisible - flag for price visibility (boolean)
• isMovie - flag for movie status (boolean)
• isGame flag for game status (boolean)
• slug - title slug (part of URL defining game location) (string)
• isWishlistable - flag for ability to add game to wishlist (boolean)
• ageLimit - age restriction (integer)
• boxImage - game key image / box image (string). Please note that image link is placed without file extension - to access it, you need to add ".png" to the end.
• isMod - a flag for mod status (boolean)
• currency - currency type (string)
• amount - actual price (float)
• baseAmount - base amount (without discounts) (float)
• finalAmount - final amount (with discounts) (float)
• isDiscounted - a field similar to isDiscounted_main (boolean)
• discountPercentage - discount percentage (float)
• discountDifference - discount difference (float)
• discount - discount percentage (similar to discountPercentage) (float)
• isFree - is game is free-to-play (boolean)
• promoId - campaign name (string)
• filteredAvgRating - average rating for English Location/Language (float)
• overallAvgRating - average rating for all locations (float)
• reviewsCount - number of reviews (integer)
• isReviewable - flag for ability to add review (boolean)
• reviewPages - number of pages with reviews (10 reviews per page) (integer)
• dateGlobal - game release date (datetime)
• dateReleaseDate - game release time, a bit more accurate values (datetime)

General video gaming use among the U.S. population increased significantly during the COVID-19 pandemic. Between May and December 2020, U.S. teens aged 15 to 19 years spent an average of 112.8 daily minutes on playing games and using computers for leisure, up from 73.8 minutes per day in the corresponding period of 2019. In 2024, the daily time spent on such activities among this age group decreased to 78.6 minutes per day.

Date - The date when the gaming session took place.

Session_Duration - The total time (typically measured in minutes or hours) that the player spent in the game during that session.

Game_Mode - The type of game mode played during the session (for example: Capture the Flag, Battle Royale, Team Deathmatch, or Search & Destroy).

Total_Matches - The total number of matches or rounds the player participated in during the session.

Wins - The number of matches that the player won during the session.

Losses - The number of matches that the player lost during the session.

Kill_Death_Ratio - The ratio of kills to deaths achieved by the player. It measures performance efficiency, where a higher ratio indicates better performance.

Average_Score - The average score the player achieved per match during the session.

Accuracy_% - The percentage of successful shots (hits) compared to the total shots fired by the player during the session.

Money_Spent - The total amount of real-world money spent during the session, often for purchasing in-game items, skins, or upgrades.

In_Game_Purchases - The number of in-game items purchased, whether using in-game currency or real money.

Subscription_Status - Indicates whether the player had an active subscription (such as a battle pass or premium membership) during the session. Typically noted as "Active" or "Inactive".

Friends_Playing - The number of the player’s friends who were online and playing at the same time during the session.

Average_FPS - The average frames per second (FPS) experienced by the player, reflecting the smoothness of game performance.

Latency_ms The average network latency (or ping) measured in milliseconds during the session. Lower latency generally means a smoother online gaming experience.

Device_Type - The type of device the player used during the session, such as PC or Mobile.

The Video game sales, Crime, Drop-out dataset is a comprehensive collection of information on various aspects of modern society. It includes data on video game sales, crime rates, and drop-out rates for a specified period of time and geographical area.

The video game sales portion of the dataset contains information on the sales of different video games, including their names, genres, publishers, and release dates. This data is useful for understanding trends in the gaming industry, as well as determining which games are most popular and profitable.

The crime portion of the dataset includes data on crime rate over the year. Finally, the drop-out rate portion of the dataset contains information about drop-out rate over the year.

Overall, the Video game sales, Crime, Drop-out dataset is a valuable resource for understanding different aspects of society and making data-driven decisions.

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.

This data set consists of engagement related multi-modal team behaviors & learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore/validate theoretical models of engagement. The dataset is inspired by our efforts of critically assessing engagement modelling in educational HRI contexts which eventually lead us to proposing the concept of 'Productive Engagement'. More on this can be found in [3,4]. The JUSThink platform consists of two screens and a QTrobot acting as a guide and a mediator. The platform aims to (1) improve the computational skills of children by imparting intuitive knowledge about minimum-spanning-tree problems and (2) promote collaboration among the team via its design. As an experimental setup for HRI studies, it also serves as a platform for designing and evaluating robot behaviors that are effective for the pedagogical goals or in general HRI problems such as trust, robot perception, engagement, collaboration. The minimum-spanning-tree problem is introduced through a gold mining scenario based on a map of Switzerland, where mountains represent gold mines labelled with Swiss cities names. The features in the dataset are grounded and motivated by the engagement literature in HRI and Intelligent Tutoring Systems. The dataset consists of team level data collected from 34 teams of two (68 children) where the children are aged between 9 and 12. More specifically, it contains: PE-HRI:behavioral.cvs: This file consists of team level multi-modal behavioral data namely log data that captures interaction with the setup, speech behavior, affective states, and gaze patterns. The definition for the each feature is given below: T_add: The number of times a team added an edge on the map. T_remove: The number of times a team removed an edge from the map. T_ratio_add_del: The ratio of addition of edges over deletion of edges by a team. T_action: The total number of actions taken by a team (add, delete, submit, presses on the screen). T_hist: The number of times a team opened the sub-window with history of their previous solutions. T_help: The number of times a team opened the instructions manual. Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game. T1_T1_rem: The number of times a team, either member, followed the pattern consecutively: I add an edge, I then delete it. T1_T1_add: The number of times a team, either member, followed the pattern consecutively: I delete an edge, I add it back. T1_T2_rem: The number of times a team, either member, followed the pattern consecutively: I add an edge, you then delete it. T1_T2_add: The number of times a team, either member, followed the pattern consecutively: I delete an edge, you add it back. redundant_exist: The number of times the team had redundant edges in their map. positive_valence: The average value of positive valence for the team. negative_valence: The average value of negative valence for the team. mean_pos_minus_neg_valence: The difference of the average value of positive and negative valence for the team. arousal: The average value of arousal for the team. smile_count: The average percentage of time of a team smiling. at_partner: The average percentage of time a team has a team member looking at their partner. at_robot: The average percentage of time a team is looking at the robot. other: The average percentage of time a team is looking in the direction opposite to the robot. screen_left: The average percentage of time a team is looking at the left side of the screen. screen_right: The average percentage of time a team is looking at the right side of the screen. screen_right_left_ratio: The ratio of looking at the right side of the screen over the left side. voice_activity: The average percentage of time a team is speaking over the entire duration of the task. silence: The average percentage of time a team is silent over the entire duration of the task. short_pauses: The average percentage of time a team pauses briefly (0.15 sec). long_pauses: The average percentage of time a team makes long pauses (1.5 sec). overlap: The average percentage of time the speech of the team members overlaps over the entire duration of the task. overlap_to_speech_ratio: The ratio of the speech overlap over the speech activity (voice_activity) of the team. PE-HRI:learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below: last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learn

Here are a few use cases for this project:

1. Sports Analysis: The "Shot Tracking" model could be used by basketball teams or analysts to track a player's made-basket percentage during actual games or during practice sessions. Data can be utilized to enhance player's shooting skills, determining their most efficient areas on the court, and tracking progress over time.

2. Game Highlights: Media companies or sports broadcasters could use the model to automatically generate game highlights, focusing on successful shots. This could streamline the video editing process and make it easier to deliver exciting content to audiences quickly.

3. Virtual Coaching: In a virtual training scenario, this model can be used to provide real-time feedback to players practicing their shots. This could help players understand their strong and weak shooting zones and improve accordingly.

4. Betting & Fantasy Leagues: The model could be utilized by sports betting companies and those involved in running basketball fantasy leagues to have access to real-time data on player shooting successes. It can also help users make informed decisions.

5. Sports Equipment Manufacturing: This model can be used in the development of interactive sports equipment (e.g., smart hoops that track shooting accuracy), helping users practice and improve their shooting skills.

Inspiration

As a personal fan of CSGO's competitive circuit, I decided to extract out data of 803 pro players from HLTV as an exercise for web scraping.

File Information

The whole data is compiled in a single file named as hltv_playerStats-complete.csv. The dataset has been extracted on 2nd May, 2022.

Columns

• nick: The in-game name of the pro player.
• country: Country of the pro player.
• stats_link: Link to the statistics page of the player on hltv.org
• teams: Current and previous teams of the player.
• maps_played: Total maps played by the player.
• round_played: Total rounds played by the player.
• kd_difference: Kill-Death difference.
• kd_ratio: Kill-Death ratio of the player.
• rating: Average rating of the player.
• total_kills: Total kills of the player in competitive play.
• headshot_percentage: Headshot percentage of the player.
• total_deaths: Total deaths of the player in competitive play.
• grenade_damage_per_round: Average utility damage (using Molotov, HE grenade etc.) done by the player per round.
• kills_per_round: Kills per round of the player.
• assists_per_round: Assists per round of the player. Assist only counts when the player does 41 damage or more to the opponent and the other player kills the opponent.
• teammate_saved_per_round: Average number of teammates saved per round by the player.
• saved_by_teammate_per_round: Average number of times the player was saved by his/her teammate.
• kast: According to HLTV: "Percentage of rounds in which the player either had a Kill, Assist, Survived or was Traded."
• impact: Average impact of the player in games. The parameter is calculated based on number of factors like multi-kills, 1vX clutches won, damage done, economic decisions, flash assists and many other factors.

European Recorded Media, Films and Video Games Exports Share by Country (Thousand Euros), 2023 Discover more data with ReportLinker!

A global consumer survey conducted in March 2024 found that 18 percent of respondents were more likely to buy a video game if it was advertised as a collector or limited edition. However, 45 percent of respondents stated that they were not interested in limited edition releases.

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/LPL%20Regional%20Finals%202024/)and also from official site of the League of Legends LPL (https://lpl.qq.com/)

Dataset Contents:

(If you want to compare this dataset to my other ones (LEC or LCK), please note that some columns are missing in this dataset!)

• 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

Early Game Metrics

• GD@15: Gold Difference at 15 minutes

Survival and Economy:

• Total damage taken: Total damage taken

https://cdn2.unrealengine.com/Fortnite%2Fblog%2Fseason-8%2FBR08_News_Featured_Launch_ScreenKeyArt_Announce-1920x1080-f831323339109ab3c6a8d9e4c670f1973b8796d0.jpg" alt="Fortnite Video Game">

Context

This Dataset contains the data of 80 endgame fortnite statistics corresponding to the mental state of the player. The data helps in providing possible conclusions when it comes to sobriety and specific statistics in the game.

Content

The Dataset contains various fortnite statistics such as position of the player after the battle is over, number of eliminations, number of assists, number of revives, accuracy percentage, number of hits and number of headshots. All these stats are calculated keeping in mind the mental state of the player at a particular time and date.

Acknowledgements

The data was obtained from the data world website. The precise data was collected by Kristian Reynolds who was the player. This data can be found from the following link https://data.world/kreynol3/fortnite-statistics80-games with detailed analysis.

Inspiration

Fortnite has been one of the most successful video games in the history of the gaming industry and still continues to be one. The early seasons of fortnite are generally considered its peak. According to PlayerCounter, Fortnite brings in three to eight million concurrent players. Being a huge gaming fanatic inspired me to share this interesting dataset which can be used to study the relation between the accuracy and performance of the player with respect to their mental state.

As of 2025, puzzle games had the overall shortest time between installation and first purchase across mobile operating systems. On Android, the time to first purchase was 1.6 days after installation, compared to 1.7 days on iOS. The largest discrepancy occurred with match games, with only 1.7 days between installation and first purchase on iOS compared to three days on Android.

League of Legends Professional Game season 12 statistics from Gol.gg. The website data would have semi-colon [;] breaks so I recommend using some find-and-replace tool to replace everything with commas [,].

There aren't any percentages so if you would like to convert picks and bans into percentages you would need to calculate the total games from: (Picks + Bans)/Presence Then you can divide the picks and bans by the total games to get the values in percentages for easier analysis.

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

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/LCK%20Summer%20Playoffs%202024/) and also from official account of the League of Legends LCK Youtube Channel (https://www.youtube.com/@LCKglobal)

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

Context

I've been recently exploring Microsoft Azure and have been playing this game for the past 4 or so years. I am also a software developer by profession. I did a simple pipeline that gets data from the official Clash Royale API using (Python) Jupyter Notebooks and Azure VMs. I tried searching for public Clash Royale datasets, but the ones I saw don't quite have that much data from my perspective, so I decided to create one for the whole community.

I started pulling in the data at the beginning of the month of December until season 18 ended. This covers the season reset last December 07, and the latest balance changes last December 09. This dataset also contains ladder data for the new Legendary card Mother Witch.

The amount of data that I have, with the latest dataset, has ballooned to around 37.9 M distinct/ unique ladder matches that were (pseudo) randomly being pulled from a pool of 300k+ clans. If you think that this is A LOT, this could only be a percent of a percent (even lower) of the real amount of ladder battle data. It still may not reflect the whole population, also, the majority of my data are matches between players of 4000 trophies or more.

I don't see any reason for me not to share this to the public as the data is now considerably large that working on it and producing insights will take more than just a few hours of "hobby" time to do.

Feel free to use it on your own research and analysis, but don't forget to credit me.

Also, please don't monetize this dataset.

Stay safe. Stay healthy.

Happy holidays!

Content

Card Ids Master List is in the discussion, I also created a simple notebook to load the data and made a sample n=20 rows, so you can get an idea on what the fields are.

Inspiration

With this data, the following can possibly be answered 1. Which cards are the strongest? The weakest? 2. Which win-con is the most winning? 3. Which cards are always with a specific win-con? 4. When 2 opposing players are using maxed decks, which win-con is the most winning? 5. Most widely used cards? Win-Cons? 6. What are the different metas in different arenas and trophy ranges? 7. Is ladder matchmaking algorithm rigged? (MOST CONTROVERSIAL)

(and many more)

Implementation

I have 2 VMs running a total of 14 processes, and for each of these processes, I've divided a pool of 300k+ clans into the same number of groups. This went on 24/7, non-stop for the whole season. Each process will then randomize the list of clans it is assigned to and will iterate through each clan, and get that clan's members' ladder data. It is important to note that I also have a pool of 470 hand-picked clans that I always get data from, as these clans were the starting point that eventually enabled me to get the 300k+ clans. There are clans who have minimal ladder data, there are some clans who have A LOT.

To prevent out of memory exceptions, as my VMs are not really that powerful (I'm using Azure free credits), I've put on a time and limit of battles extracted per member.

My Clan and Handle

My account: https://royaleapi.com/player/89L2CLRP My clan: https://royaleapi.com/clan/J898GQ

Acknowledgements

Thank you to SUPERCELL for creating this FREEMIUM game that has tested countless people's patience, as well as the durability of countless mobile devices after being smashed against a wall, and thrown on the floor.

Thank you to Microsoft for Azure and free monthly credits

Thank you to Python and Jupyter notebooks.

Thank you Kaggle for hosting this dataset.

About the game

osu! is a music rhythm game that has 4 modes (check this link for more info). In this dataset, you can examine the rankings of the standard mode, taken on 05/28/2021 around 9 PM EST. The ranking is based on the performance points (abbreviated as PP) that are awarded after every play, which are influenced by play accuracy, score, and the number of misses; PPs are then summed with weights: your top play will award you the whole PPs of the map, then the percentage is decreased (this can maintain a balance between strong players and players who play too much).

Content

The dataset contains some columns (see below) reporting statistics for every player in the top 250 of the game in the standard mode. The ranking is ordered by Performance Points. This set contains the following information of each player: * Rank; * Username; * Accuracy (%); * Play count; * Performance (PPs); * Number of SS plays; * Number of S plays; * Number of A plays;

Acknowledgement

The inspiration came from seeing this data set: Osu! Standard Rankings, which is a little bit outdated (the last update was made 4 years ago). I created this dataset on my own, so if you find something wrong please report it. The data is public and accessible on this link. Unfortunately, I am new to data scraping and I didn't know how to actually extract the country rank, country of the player, or their hours.

The share prices of four companies producing some of the world's best video games from January 2000 to February 2024.

Activision Blizzard Activision Blizzard, Inc. is an American video game holding company based in Santa Monica, California. It was founded in July 2008 through the merger of Activision, Inc. (the publicly traded parent company of Activision Publishing) and Vivendi Games. It is traded on the Nasdaq stock exchange under the ticker symbol ATVI, and since 2015 has been a member of the S&P 500 Index. Activision Blizzard currently includes five business units: Activision Publishing, Blizzard Entertainment, King, Major League Gaming, and Activision Blizzard Studios. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F44131e11275e32094a02f2c2c7ed437c%2Factivision-office.png?generation=1682852644217105&alt=media" alt=""> The company owns and operates additional subsidiary studios, as part of Activision Publishing, including Treyarch, Infinity Ward, High Moon Studios, and Toys for Bob. Among major intellectual properties produced by Activision Blizzard are Call of Duty, Crash Bandicoot, Guitar Hero, Tony Hawk's, Spyro, Skylanders, World of Warcraft, StarCraft, Diablo, Hearthstone, Heroes of the Storm, Overwatch, and Candy Crush Saga. Under Blizzard Entertainment, it invested in esports initiatives around several of its games, most notably Overwatch and Call of Duty. Activision Blizzard's titles have broken a number of release records. As of March 2018, it was the largest game company in the Americas and Europe in terms of revenue and market capitalization. Nintendo Nintendo Co., Ltd. is a Japanese multinational video game company headquartered in Kyoto. It develops, publishes and releases both video games and video game consoles. Nintendo was founded in 1889 as Nintendo Karuta by craftsman Fusajiro Yamauchi and originally produced handmade hanafuda playing cards. After venturing into various lines of business during the 1960s and acquiring a legal status as a public company, Nintendo distributed its first console, the Color TV-Game, in 1977. It gained international recognition with the release of Donkey Kong in 1981 and the Nintendo Entertainment System and Super Mario Bros. in 1985. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F78fc67916f57f5b2f336fa3144d14c48%2Fnintendo.900x.jpg?generation=1682852735183488&alt=media" alt=""> Since then, Nintendo has produced some of the most successful consoles in the video game industry, such as the Game Boy, the Super Nintendo Entertainment System, the Nintendo DS, the Wii, and the Switch. It has created numerous major franchises, including Mario, Donkey Kong, The Legend of Zelda, Pokémon, Kirby, Metroid, Fire Emblem, Animal Crossing, Splatoon, Star Fox, Xenoblade Chronicles, and Super Smash Bros. Nintendo's mascot, Mario, is internationally recognized. The company has sold more than 5.4 billion video games and over 800 million hardware units globally as of 2022. Ubisoft Ubisoft Entertainment SA is a French video game publisher headquartered in Saint-Mandé with development studios across the world. Its video game franchises include Assassin's Creed, Far Cry, For Honor, Just Dance, Prince of Persia, Rabbids, Rayman, Tom Clancy's, and Watch Dogs. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F0c41ffee9463b847774c9b6bacaf3123%2Fistockphoto-584479884-170667a.jpg?generation=1682852814452144&alt=media" alt=""> Electronic Arts Electronic Arts Inc. (EA) is an American video game company headquartered in Redwood City, California. Founded in May 1982 by Apple employee Trip Hawkins, the company was a pioneer of the early home computer game industry and promoted the designers and programmers responsible for its games as "software artists". EA published numerous games and some productivity software for personal computers, all of which were developed by external individuals or groups until 1987's Skate or Die!. The company shifted toward internal game studios, often through acquisitions, such as Distinctive Software becoming EA Canada in 1991. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F67f54151bb4c494165cd2e1ff72900df%2F1200px-EA_Building_RedwoodShores.jpg?generation=1682852940548941&alt=media" alt=""> Currently, EA develops and publishes games of established franchises, including Battlefield, Need for Speed, The Sims, Medal of Honor, Command & Conquer, Dead Space, Mass Effect, Dragon Age, Army of Two, Apex Legends, and Star Wars, as well as the EA Sports titles FIFA, Madden NFL, NBA Live, NHL, PGA and UFC. Their desktop titles appear on self-developed Origin, an online gaming digital distribution platform for PCs and a direct competitor to Valve's Steam and Epic Games' Store. EA also ...