Overview

Information of more than 110,000 games published on Steam. Maintained by Fronkon Games. This dataset has been created with this code (MIT) and use the API provided by Steam, the largest gaming platform on PC. Data is also collected from Steam Spy. Only published games, no DLCs, episodes, music, videos, etc. Here is a simple example of how to parse json information:

Simple parse of the 'games.json' file.

import os import json

dataset = {} if… See the full description on the dataset page: https://huggingface.co/datasets/FronkonGames/steam-games-dataset.

In April 2025, total video games sales in the United States amounted to **** billion U.S. dollars, representing a one 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.

A survey conducted in the third quarter of 2024 found that over 92 percent of female internet users aged 16 to 24 years worldwide played video games on any kind of device. During the survey period, 93 percent of male respondents in the same age group stated that they played video games. Worldwide, over 83 percent of internet users were gamers.

Games on Stadia: A Comprehensive List

The More You Play, the More You Achieve!

About this dataset

Do you like playing video games? Do you like achieving things? If you answered yes to both of those questions, then this dataset is for you!

The goal of this data is to provide a comprehensive list of all the games currently available on Stadia, as well as some basic information about each game. This dataset includes titles, genres, developers, publishers, Stadia release dates, original release dates, and more.

With this information at your fingertips, you can plan your gaming schedule around which games you want to achieve in and when they'll no longer be available for free on Stadia Pro. So what are you waiting for? Get achievement-hunting!

How to use the dataset

In order to use this dataset, simply download it and open it in your preferred spreadsheet application. From there, you can begin to explore the data and answer any questions you may have about the contents of each column.

Columns: 0: The name of the game. (String) 1: The type of product. (String) 2: The genre or genres that the game belongs to. (String) 3: The developer or developers of the game. (String) 4: The publisher or publishers of the game. (String) 5: The date that the game was released on Stadia. (Date) 6: The date that the game was originally released. (Date) 7:The date that the game was added to Stadia Pro. (Date) 8:The date that the game is no longer claimable on Stadia Pro. (Date)

Research Ideas

• Gamers could use this dataset to find new games to play on Stadia, based on their preferred genres, developers, publishers, etc.
• Game developers and publishers could use this dataset to track the success of their titles on Stadia, and compare release dates and player engagement across different platforms.
• Researchers could use this dataset to study player behavior on Stadia, and how different variables like genre or release date affect player engagement and satisfaction

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_16.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_20.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_18.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_11.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_1.csv | Column name | Description | |:----------------------------------------------|:---------------------------------------------------------------| | Title | The name of the game. (String) | | Genre(s) | The genre or genres of the game. (String) | | Developer(s) | The developer or developers of the game. (String) | | Publisher(s) | The publisher or publishers of the game. (String) | | Stadia release date | The date the game was released on Stadia. (Date) | | Original release date[a] | The date the game was originally released. (Date) | | Date added to Stadia Pro[b] | The date the game was added to Stadia Pro. (Date) | | Date no longer claimable on Stadia Pro[c] | The date the game is no longer claimable on Stadia Pro. (Date) | | Ref. | A reference to where the information was found. (String) |

File: df_4.csv | Column name | Description | |:--------------|:--------------| | 0 | | | 1 | |

File: df_21.csv

File: df_17.csv | Column name | Description | |:---------------|:------------------------------------------------| | Hardware | The hardware the game is available on. (String) | | Hardware.1 | The hardware the game is available on. (String) |

File: df_9.csv | Column name | Description | |:--------------|:-------------...

This is a collection of all competitive games played on League of Legends during 2020 (updated on 08/19). It includes all the data available from API Developer Riot.

I plan to use it in order to analyze which stats are most important in order to predict the outcome of the game.

Overview

The World of Warcraft Avatar History Dataset is a collection of records that detail information about player characters in the game over time. It includes information about their character level, race, class, location, and social guild. The Kaggle version of this dataset includes only the information from 2008 (and the dataset in general only includes information from the 'Horde' faction of players in the game from a single game server).

• Full Dataset Source and Information: http://mmnet.iis.sinica.edu.tw/dl/wowah/
• Code used to clean the data: https://github.com/myles-oneill/WoWAH-parser

Ideas for Using the Dataset

From the perspective of game system designers, players' behavior is one of the most important factors they must consider when designing game systems. To gain a fundamental understanding of the game play behavior of online gamers, exploring users' game play time provides a good starting point. This is because the concept of game play time is applicable to all genres of games and it enables us to model the system workload as well as the impact of system and network QoS on users' behavior. It can even help us predict players' loyalty to specific games.

Open Questions

• Understand user gameplay behavior (game sessions, movement, leveling)
• Understand user interactions (guilds)
• Predict players unsubscribing from the game based on activity
• What are the most popular zones in WoW, what level players tend to inhabit each?

Wrath of the Lich King

An expansion to World of Warcraft, "Wrath of the Lich King" (Wotlk) was released on November 13, 2008. It introduced new zones for players to go to, a new character class (the death knight), and a new level cap of 80 (up from 70 previously). This event intersects nicely with the dataset and is probably interesting to investigate.

Map

This dataset doesn't include a shapefile (if you know of one that exists, let me know!) to show where the zones the dataset talks about are. Here is a list of zones an information from this version of the game, including their recommended levels: http://wowwiki.wikia.com/wiki/Zones_by_level_(original) .

Update (Version 3): dmi3kno has generously put together some supplementary zone information files which have now been included in this dataset. Some notes about the files:

Note that some zone names contain Chinese characters. Unicode names are preserved as a key to the original dataset. What this addition will allow is to understand properties of the zones a bit better - their relative location to each other, competititive properties, type of gameplay and, hopefully, their contribution to character leveling. Location coordinates contain some redundant (and possibly duplicate) records as they are collected from different sources. Working with uncleaned location coordinate data will allow users to demonstrate their data wrangling skills (both working with strings and spatial data).

Here are a few use cases for this project:

1. Sports Analysis Tool: The "sports ball" computer vision model could be used in a variety of sports analysis tools. These tools could automatically track the ball during a game, assessing player strategies, speed, and overall game dynamics.

2. Game Highights Creation: The model could be used to automate the creation of game highlights. By recognizing when and how a sports ball is used in action, it could automatically identify the key moments of a game.

3. Sports Equipment Inventory Management: The model can be utilized for inventory management in sports stores by automatically identifying different types of sports balls in storage.

4. Real-time Match Statistics: The model can be used in real-time applications, providing statistics on ball possession, passes, shots and goals during live sports broadcasts.

5. Sports-themed Video Games: The model could be used to design smarter, more realistic sports-themed video games. This could allow for dynamic play and more interactive gaming experiences.

This dataset is representative of the teamfight tactics set 2 champions, which is currently in beta. As a result, not all the champion data is included, but there is sufficient information to justify its usefulness as a dataset. Teamfight Tactics was created by Riot Games and was inspired by the AutoChess genre. It pits 8 players against each other in an all out brawl of wits and intuition, where decision making reigns as king. Please do consider checking it out. Teamfight Tactics is available through the League of Legends Client, which is free to download and free to play.

Inside this data set is all the currently available data and statistics for Teamfight Tactics set 2. As this set is currently in beta, not all the information is provided. For using this data set, it would be useful to know the background of the game Teamfight Tactics, as well as the items and abilities of each champion, which are not listed in this dataset, as it is hard to assign numerical quantities to them. If the application of this data set requires the items and champion abilities, I will be making another description based data set for items and champion abilities. As it would be description based, it may be more difficult to work with, so don’t feel like it is a requirement by any means.

The data for this dataset was collected with the help of tftactics.gg. This site is a very useful resource for teamfight tactics players that are looking to climb the competitive ladder. It includes helpful information on all champions, tier lists for origins, classes, champions, and items, as well as a team builder and overlay app. Please do consider paying them a visit to take in all that they have to offer. In addition, it is all free of charge.

Possible questions going into this dataset pertain to what is the most efficient and effective strategy for winning in teamfight tactics. To answer this question, the user would have to take into account items, abilities, rolling appearance rates, and RNG, to develop a model for the average flow of gameplay, and possibly a deep learning algorithm to predict the builds and styles that the population will go for. Alternatively, this data set could also be used to find the most powerful champion with the aid of champion abilities.

No license required for use or reproduction.

Good luck and happy coding :)

Version 4 of the dataset is available (Sep 19 2019)!

Note this version has significantly more data than Version 2.

Dataset description paper (full version) is available!

https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)

Tools for visualizing the data is available!

https://github.com/corgiTrax/Gaze-Data-Processor

=========================== Dataset Description ===========================

We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.

Q & A: Why frame-by-frame game mode?

Resolving state-action mismatch: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.

Maximizing human performance: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.

Highlighting critical states that require multiple eye movements: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.

============================ Readme ============================

1. meta_data.csv: meta data for the dataset., including:

GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.

trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.

subject_id: Char. Human subject identifiers.

load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.

highest_score: Integer. The highest game score obtained from this trial.

total_frame: Number of image frames in the .tar.bz2 repository.

total_game_play_time: Integer. game time in ms.

total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.

avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.

max_error: Float. Max eye-tracking validation error.

low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.

frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.

fps: Integer. Frame per second when an action key is held down.

1. [game_name].zip files: these include data for each game, including:

*.tar.bz2 files: contains game image frames. The filename indicates its trial number.

*.txt files: label file for each trial, including:

frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.

episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.

score: Integer (not available for some trials). Current game score for that frame.

duration(ms): Integer. Time elapsed until the human player made a decision.

unclipped_reward: Integer. Immediate reward returned by the game engine.

action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.

gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.

1. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment.

============================ Citation ============================

If you use the Atari-HEAD in your research, we ask that you please cite the following:

@misc{zhang2019atarihead,

title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},


author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},


year={2019},


eprint={1903.06754},


archivePrefix={arXiv},


primaryClass={cs.LG}

}

Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.

@inproceedings{zhang2018agil,

title={AGIL: Learning attention from human for visuomotor tasks},

author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},

booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},

pages={663--679},

year={2018}

}

Context

The Sims series has been around for over 20 years. Sims 4 is the latest iteration of the series and has been around since 2014. Since Sims 4 hasn't been met with uniform high praise, many fans have compared it to previous versions of the Sims especially Sims 2 and Sims 3. Many have discussed what was available and gameplay interactions in Sims 2 and Sims 3. This is why there is information about Sims 3 included in the dataset.

Sims 4 has had quite a bit of controversy from its dedicated fans about paid-content add-ons that range from 5 USD to 40 USD. Much of the downloadable content (DLC) has been reviewed or discussed in the Sims community across the internet.

Let's take a look at what is available in the DLC's available.

Updates

Planning monthly updates and to update when there are additional announcements about the Sims 4. Currently, patch data is not available in this dataset, nor is there much information about Sims 3 and Sims 4 Bundles. These will be updated.

Content

Collected information about the Sims 4 with its iterations and add-ons. Sims 4 Individual Packs follows the available Sims 4 pieces available on Origin - the official EA place to play the Sims 4. So far there are numerous add-ons and they continue to be released despite the Sims 4 coming out in 2014. (Official US PC release date is September 2, 2014)

Release time is available for some DLC because of current anticipated releases or DLC already released.

This data was collected from many sources including Origin, EA, Steam, SimsCommunity.info, SimsVIP, Carl's Sims 4 Guide, Sims.fandom.com, MetaCritic, Amazon, and more. This initial upload has some information about each available purchase of the Sims 4 and DLC including Release Date, Price, and some ratings.

** V1 Update: ** Included Sims 3 Data under Individual Packs for Title, Release Date, Steam Rating, and Price. Information will need to be moved around, but data is still useable. Information collected from Origin, Steam, and Sims.fandom.com .

Acknowledgements

Shoutout to all the dedicated Simmers who paid over $900 USD to play the Sims 4 with every add-on and DLC. Also to those who just play with custom content (like myself).

Carl's Sims 4 Guides are a great place to get an understanding of the Sims 4 and they had a great collection of information about the Sims 4 on their website and YouTube channel.

Inspiration

I'd love to see someone decide which type of DLC is worth the money like a front-runner for kits, or stuff packs, or expansion packs.

Context

I have generated this set of auxilary tables to complement the dataset of Kickstarter projects with the focus on videogames.

Content

Currently the set contains three tables:

SteamSpy table contains aggregate information on released games tracked by SteamSpy

KSreleased table links the Steam appid's with Kickstarter project IDs for those KS games, that after a successful campaign were finished and released on Steam

Currencies table shows historical currency exchange rates to USD($) for each week since the earliest campaign deadline among those in KSreleased

Acknowledgements

SteamSpy table was created using the site's API and I would like to take this opportunity to praise the site's creator Sergey Galyonkin

KSreleased table was generated by crawling Kickstarter "Play now" pages

Currencies table was generated using Fixer.io API

If you would like to know the details/see the code that I wrote to generate the data, I uploaded it as the "DEMO: generate data" kernel. It won't work online (otherwise I wouldn't have the need to create the dataset in the first place), but you can download the notebook and run it locally or just check my poor coding style :)

Inspiration

I intend to finalize my analysis on KS games that were released on Steam and publish it here, but of course I would like you to find more uses for this data beyond what I would have thought of. And again, I don't think this dataset is useful on its own, so please don't forget to connect to the KS projects dataset by Kemical

Content

This data was simulated using a realistic simulator according to the most common set of rules in Las Vegas casinos:

• 8 deck shoe (6.5 deck penetration)
• 1st card of shoe is burned
• Blackjack pays 3:2
• Double down allowed on any first 2 cards
• Double down after split allowed
• Split any same first 2 cards up to 4 hands
• Re-splitting Aces is not allowed
• Splitting Aces receives 1 extra card only, no Blackjack
• Late surrender allowed
• No surrender after split
• Dealer hits soft 17

Things to note:

• All hands are played "heads up" (single player vs the dealer)
• Starting bet for each hand is always 1
• 10's, J's, Q's, K's are all considered the same in Blackjack and are all recorded as 10's in this dataset
• A's are all recorded as 11's in this dataset, regardless of whether they are valued as 1 or 11 in the particular hand
• Suits are irrelevant and not recorded
• Run count and True count values (truncated to Integer precision) are recorded according to the Hi-Lo system at the start of each round before any cards are dealt
• The number of cards remaining in the shoe is recorded at the start of each round before any cards are dealt. This number includes cards which will not be played due to less than 100% deck penetration
• The actions taken by the player are recorded and are as follows:

Acknowledgements

The player in this dataset played according to Basic Strategy which can be found here: https://wizardofodds.com/games/blackjack/strategy/4-decks/

Atari-style video game learning fMRI

This dataset contains behavioral and functional MRI (BOLD) data from 32 human subjects learning to play different Atari-style games. Additionally, it includes code for analyzing the data using two different models:

• Explore, Model, Plan Agent (EMPA): a theory-based reinforcement learning agent (see Tsividis et al. (2021)),
• Double Deep Reinforcement Learning network (DDQN): a deep reinforcement learning agent (see van Hasselt et al. (2015))

This dataset was collected for the following paper:

• Tomov, M.S., Tsividis, P.A., Pouncy, T., Tenenbaum, J.B., & Gershman, S.J. (2022). The neural architecture of theory-based reinforcement learning.

For more information, see the Methods section of the paper.

Here are a few use cases for this project:

1. Golf Sports Broadcasting: This model could be used in live broadcasts or replay analysis to automatically identify and highlight key elements such as the player's swing technique, golf club position, and ball trajectory. This can enhance viewer experience by providing insightful, real-time details of the game.

2. Golf Training and Instruction: Golf instructors could use this model as a teaching tool to analyze and improve their students' golf playing techniques. They can better understand students' swing mechanics, club usage, and game-play strategies on the green.

3. Golf Game Software Development: Game developers can use it to create more realistic and interactive golf video games. The model can help generate a virtual environment that mirrors real-life scenarios, which can enhance the gaming experience for users.

4. Sports Equipment Marketing: Entities selling golf-related equipment or clothing can use the model to create interactive advertisements. It can detect the use of golfing objects in common videos and interlay their product ads or information.

5. Maintenance of Golf Courses: Golf course management could use this model to monitor the use and condition of their courses. By analyzing the visual data, they can identify the heavily used areas and potentially enhance the maintenance strategy accordingly.

Analysis of ‘LEC Regular Season 2021 / LOL’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jordipompas/lec-regular-season-2021 on 28 January 2022.

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

Context

League of Legends is one of the world's most famous video games. It's played by over 100 million active users every month. League of Legends is perhaps the most prominent Esport game. Nowadays, their competitions are becoming more and more professional and little by little new information about the players is appearing.

Content

This dataset includes the statistics of all LEC (League of Legends European Championship) players for the 2021 regular season.

The same variables are found in all 3 datasets:

• Role: The position of the player
• Name: Name of the player
• Games: Number of games played
• Wins: Games won
• Loses: Matches lost
• WR: Win rate (Wins/Games)
• K: Average kills per game
• D: Average death per game
• A: Average assists per game
• KDA: Kills+Assists/Deaths
• CS: Average minions killed per game
• CS/M: Average minions killed per minute
• G: Average gold per game (per thousand)
• G/M: Gold per minute
• KPAR: Kill Participation (Kills+Assist / Team Kills)
• KS: Kills Share(Kills / Team Kills)
• GS: Team Gold %
• CP: Champs Played

If you need any further information, you can contact me in the Discussion section or on Twitter (https://twitter.com/jordipg05).

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

Here are a few use cases for this project:

1. Video Game Development: Developers can use the "orange_bots" model to create more immersive and interactive games, especially for games that include 'bots' as part of their character roster. The model could help developers more easily create non-player characters (NPCs) and categorize them properly.

2. eSports Analysis: This model could be used to study and analyze gameplay in eSports, particularly in recognizing bot strategies and player interactions with bots. This data could then be used to improve game design, player training, or competitive strategies.

3. Content Moderation: For platforms hosting user-generated gaming content, the model can help identify the portions of the game that include bots. This can assist in moderating content, ensuring the fair play principles are adhered to, and identifying any bot-related cheating.

4. User-Generated Content Curation: The model can be used as a tool for curating user-generated content, like videos or streamed content featuring gameplay. By recognizing bots, videos could be correctly labeled and categorized for easier discovery.

5. Interactive Entertainment: This model could be employed in theme parks or virtual reality experiences for user interaction with bots. As users engage with virtual bots, their behaviors and responses can be analyzed to enhance the user experience.

Introduction

Video games are a rich area for data extraction due to its digital nature. Notable examples such as the complex EVE Online economy, World of Warcraft corrupted blood incident and even Grand Theft Auto self-driving cars tells us that fiction is closer to reality than we really think. Data scientists can gain insight on the logic and decision-making that the players face when put in hypothetical and virtual scenarios.

In this Kaggle Dataset, I provide just over 1000 competitive matchmaking rounds from Valve's game Counter-strike: Global Offensive (CS:GO). The data was extracted from competitive matchmaking replays submitted to [csgo-stats][1]. I intend for this data-set to be purely exploratory, however users are free to create their own predictive models they see fit.

Counter-Strike: Global Offensive

Counter-Strike: Global Offensive is a first-person shooter game pitting two teams of 5 players against each other. Within a maximum of 30 rounds, the two teams find themselves on either side as a Counter Terrorist or Terrorist. Both sides are tasked with eliminating the opposition or, as the terrorist team, planting the C4 bomb at a bomb site and allowing it to explode. Rounds are played out until either of those two objectives or if the maximum time is reached (in which the counter terrorists then win by default). At the end of the 15th round, the two teams switch sides and continue until one team reaches 16 round wins first. CS:GO is widely known for its competitive aspect of technical skill, teamwork and in-game strategies. Players are constantly rewarded with the efforts they put it in training and learning through advancing in rank.

![enter image dhere][2]

[Read more about the competitive mechanics of CSGO][3]

The Dataset

This dataset within the 1000 matches provides every successful entry of duels (or battle) that took place for a player. That is, each row documents an event when a player is hurt by another player (or World e.g fall damage). There are over 750,000 entries within approximately 25600 rounds.

I will describe each field below within mm_master_demos.csv:

• file: The file name that the demo was scraped from. This is a unique field for each match.
• map: The Valve official map the match was played on.
• date: Date the match was played (unverified if they are correct).
• round: The round that the duel took place.
• tick: The current tick in the demo the entry took place. A tick is represented as a state in the game, Valve's competitive matchmaking sets every match at 64 ticks which represents that there are 64 states within each second of the game.
• seconds: The converted tick to seconds within the game since match start.
• att_team: The team that the attacking player is on that dealt damage to the victim. Usually Team 1 and 2 but in some recorded pro matches, can have custom team name e.g Games Academy.
• vic_team: The team that the victim player is on that received damage from the attacker.
• att_side: The side that the attacker was on. Can be Terrorist or CounterTerrorist.
• vic_side: The side that the victim was on. Can be Terrorist or CounterTerrorist.
• hp_dmg: The total damage dealt in that duel to the victim. Each player starts the round with 100 max hp.
• arm_dmg: The total damage dealt to kevlar. Three things to note: 1. Kevlar is an optional item that players choose to buy 2. Kevlar only protects the chest area and 3. Damage to kevlar is already accounted for in hp_dmg, that is if hp_dmg = 50 and arm_dmg = 50, the player has only lost 50 hp and is still alive.
• is_bomb_planted: Has the bomb been planted as of this entry.
• bomb_site: The site the bomb is planted at (only A or B) and empty if is_bomb_planted is false.
• hitbox: The body area the victim was struck in.
• wp: The weapon that the attacker used to deal damage.
• wp_type: The type of weapon that the attacker used
• award: The kill reward (in $) that the player get should they kill that person. The kill reward changes purely based on the weapon they are using.
• winner_team: The team that won at the end of that round.
• winner_side: The side that the team that winner_team was on.
• att_id: The steam id of the attacker. This is a unique identifier for each player.
• vic_id: The steam id of the victim. This is a unique identifier for each player.
• att_pos_x: The X position of the attacker when they started the engagement. Note that this is an in-game coordinate and need to be converted to positive X,Y coordinates when plotting on a map.
• att_pos_y: The Y position of the attacker when they started the engagement. Note that this is an in-game coordinate and need to be converted to positive X,Y coordinates when plotting on a map.
• vic_pos_x: The ...

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.

The purpose of this project is to determine how college students distribute their activities in time (with a particular focus on academic and athletic activities) and to examine the factors that influence such distributions.

Each R reported once about each of the seven days of the week and an additional time about either Saturday or Sunday. Rs were told the week before they were to report which day was assigned and were given a report form to complete during that day. They entered the i nformation from that form when they returned the next week.

The activity codes included were: 0: Sleeping. 1: Attending classes. 2: Studying or preparing classroom assignments. 3: Working at a jog (including CAPS). 4: Cooking, home chores, laundry, grocery shopping. 5: Errands, non-grocery shopping, gardening, animal care. 6: Eating. 7: Bathing, getting dressed, etc. 8: Sports, exercising, other physical activities. 9: Playing competitive games (cards, darts, videogames, frisbee, chess, Tr ivial Pursuit, etc.). 10: Participating in UNC-sponsored organizations (student government, band, sorority, etc.). 11: Listening to the radio. 12: Watching TV. 13: Reading for pleasure (not studying or reading for class). 14: Going to a movie. 15: Attending a cultural event (such as a play, concert, or museum). 16: Attending a sports event as a spectator. 17: Partying. 18: Religious activities. 19: Conversation. 20: Travel. 21: Resting. 22: Doing other things

DIARY1-8: These datasets contain a matrix of activities by times for a particular day. Included is time period, activity code (see above), # of friends present, # of others present. (Rs were allowed to report doing two activities at once. In these cases they were also asked to report the % of time during the time period affected which was allocated to the first of the two activities listed.)

THE DIARY DATASETS ARE STORED IN RAW FORM. SUMMARY FILES, CALLED TIMEREP, CONTAIN MOST SUMMA RY INFORMATION WHICH MIGHT BE USED IN ANALYSES. THE DIARY DATASETS CAN BE LISTED TO ALLOW UNIQUE CODING OF THE ORIGINAL DATA.

Each R reported once about each of the seven days of the week and an additional time about either Saturday or Sunday.

TIMEREP: The TIMEREP dataset is a summary file which gives the amount of time spent on each activity during each of the eight reporting periods and also includes more detailed information about many of the activities from follow-up questions which were asked if the respondent reported having engaged in certain activities. Data from additional questions asked of every respondent after each diary entry are also included: contact with family members, number of alcoholic drinks consumed during the 24 hour period reported on, number of friends and others present while drinking, number of cigarettes smoked on day reported about, and number of classes skipped on day reported about.

Follow-up questions include detail about kind of physical activity or sports participation, kind of university organization, kind of radio program listened to and place of listening, kind of TV program watched and place of watching, kind of reading material read and topic, alcohol consumed while partying and place of partying, conversation topics, kind of travel, activities included in 'other' category.

Special processing is required to put the dataset into SAS format. See spec for details.

The NetHack Learning Environment (NLE) is a Reinforcement Learning environment based on NetHack 3.6.6. It is designed to provide a standard reinforcement learning interface to the game, and comes with tasks that function as a first step to evaluate agents on this new environment. NetHack is one of the oldest and arguably most impactful videogames in history, as well as being one of the hardest roguelikes currently being played by humans. It is procedurally generated, rich in entities and dynamics, and overall an extremely challenging environment for current state-of-the-art RL agents, while being much cheaper to run compared to other challenging testbeds. Through NLE, the authors wish to establish NetHack as one of the next challenges for research in decision making and machine learning.