https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F7d5374215511bb7cf264fab8a294bc3a%2Fheader.jpg?generation=1704969406449875&alt=media" alt="">

Data obtained using a program from the site backloggd.com.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F9bb6a4f0ee6d69ea160b12f4d1ca3e30%2Fdata_1.jpg?generation=1704968884700538&alt=media" alt="">

About backloggd.com

"Backloggd is a place to keep your personal video game collection. Every game from every platform is here for you to log into your journal. Follow friends along the way to share your reviews and compare ratings. Then use filters to sort through your collection and see what matters to you. Keep a backlog of what you are currently playing and what you want to play, see the numbers change as you continue to log your playthroughs. There's Goodreads for books, Letterboxd for movies, and now Backloggd for games." - from the site backloggd.com.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F4e12014a1f38e1167a5cf66202ebf9d7%2Fdata_2.jpg?generation=1704968935015630&alt=media" alt="">

"All game related metadata comes from the community driven database IGDB. This includes all game, company and platform data you see on the site." - from the site backloggd.com

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15126770%2F799831cb18c3b74f1c3f6e8a023af723%2Fdata_3.jpg?generation=1704968996471054&alt=media" alt="">

What can you do with the data set?

If you are new to data analytics, try answering the following questions: - in what year did the active growth in the number of video games produced begin? What year was the most successful from this point of view? - on what day and month were the largest number of video games released? What could be the reason for this pattern? - is there a dependence of the rating of a video game on the number of reviews left or the total number of players? - which game genres, platforms and developers are the most common (the most video games released of all time)? - which game genres, platforms and developers have the highest total number of players (have the highest total number of players ever)? - which game genres, platforms and developers have the highest average video game ratings?

If you have enough experience, try solving a multi-label classification problem. Train a model that can classify a video game description into one or more genres: - which models are best suited for this, and which should not be used? - what is the best way to convert text to features? How will lemmatization of text affect the predictive ability of the model? - which metric should be chosen to evaluate the model? - Is the model calibrated enough after training to trust its probabilistic forecasts? - can adding new data improve the predictive ability of the model?

Field descriptions:

The data contains the following fields: 1. games - basic data: - id - video game identifier (primary key); - name - name of the video game; - date - release date of the video game; - rating - average rating of the video game; - reviews - number of reviews; - plays - total number of players; - playing - number of players currently; - backlogs - the number of additions of a video game to the backlog; - wishlists - the number of times a video game has been added to “favorites”; - description - description of the video game. 2. developers - developers (publishers): - id - video game identifier (foreign key); - developer - developer (publisher) of a video game. 3. platforms - gaming platforms: - id - video game identifier (foreign key); - platform - gaming platform. 4. genres - game genres: - id - video game identifier (foreign key); - genre - video game genre. 5. scores - user ratings: - id - video game identifier (foreign key); - score - score (from 0.5 to 5 in increments of 0.5); - amount - number of users. 6. Video game posters.

Data update

The website backloggd.com contains detailed roadmap with changes that may be implemented over time on the website, among them: - additional information about the game: DLC status, all companies, alternative names and other extensive information about the game; - categorization of games: which games are DLC, demo versions, canceled, beta versions, etc.; - personalized game covers: IGDB now supports localized covers; - release dates: games with one date are too easy, in this case, multiple release dates will be shown for different stages/regions.

!...

In 2024, the gaming sector experienced a significant number of layoffs because of post-COVID industry contraction which has led to studio consolidation and ultimately, an estimated 14,800 video gaming employees losing their jobs. Additionally, 2023 had also not been kind to the industry, as already 10,500 game developers lost their jobs during industry layoffs during the year.

In this study, we investigated the extent to which adolescents who spend time playing violent video games exhibit higher levels of aggressive behaviour when compared with those who do not. A large sample of British adolescent participants (n = 1004) aged 14 and 15 years and an equal number of their carers were interviewed. Young people provided reports of their recent gaming experiences. Further, the violent contents of these games were coded using official E.U. and US ratings, and carers provided evaluations of their adolescents' aggressive behaviours in the past month. Following a preregistered analysis plan, multiple regression analyses tested the hypothesis that recent violent game play is linearly and positively related to carer assessments of aggressive behaviour. Results did not support this prediction, nor did they support the idea that the relationship between these factors follows a nonlinear parabolic function. There was no evidence for a critical tipping point relating violent game engagement to aggressive behaviour. Sensitivity and exploratory analyses indicated these null effects extended across multiple operationalizations of violent game engagement and when the focus was on another behavioural outcome, namely, prosocial behaviour. The discussion presents an interpretation of this pattern of effects in terms of both the ongoing scientific and policy debates around violent video games, and emerging standards for robust evidence-based policy concerning young people's technology use.

OAN helps you reach gamers across the world. Our gaming audience data offers categorized audience segments into gamer behavior and gaming trends. This powerful dataset provides a deep understanding of the gaming industry by delivering unique categories such as: demography, interest, hardware, spenders, genres and titles, e-sports fans and players.

By understanding this data, businesses can make data-driven decisions to optimize their marketing strategies, game development, and monetization efforts.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through the last 30 days. There are also gamer audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Furthermore, we offer segments for: - Virtual Reality - Interest in Gaming Subscriptions - Payments - Micropayments - Devices and Platforms.

We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Themes (e.g. Historical), which are definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Gaming audience data is just a part of all audience data we provide. We deliver millions of users’ profiles gathered globally and grouped into IAB-compliant segments. You can choose which target groups you want to reach. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: - Marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. - Ad tech companies Enriching 1st party data or using our raw data by your own data science team.

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 | |:--------------|:-------------...

Get Your Game On: Metacritic, Recommendations, and Price

A Comprehensive Look at What Makes a Game Worth Playing

About this dataset

1. Metacritic score: The Metacritic score of a video game is a number that represents the critical reception of the game.
2. Price: The price of a video game on the Steam store.
3. Recommendations: The number of recommendations that a video game has on the Steam store

How to use the dataset

To use this dataset, you will need to have a basic understanding of video games and the Steam gaming platform. The data includes information on the game's Metacritic score, price, and number of recommendations. Using this data, you can analyze which games are the most popular on Steam and compare their prices and Metacritic scores. You can also use the data to find games that are recommended by other Steam users

Research Ideas

1. Examining the relationship between a game's Metacritic score and its price
2. Finding out which games are most recommended by users
3. Investigating the relationship between a game's Metacritic score and number of recommendations

Acknowledgements

These data were collected by the Steam API

This dataset is released under the Steam API Terms of Use

License

See the dataset description for more information.

Columns

File: games-features-edit.csv

Game Boy Color Games

Games Released for Gameboy Color

About this dataset

The Game Boy Color Games Dataset is a comprehensive list of officially licensed Game Boy Color game released. The dataset includes information on each game's title, developer, publisher, release date, and intended region. Additionally, it notes which games are compatible with the Game Boy Advance, as well as any special features each game's cartridge may have.

How to use the dataset

This dataset is a great resource for anyone looking to compile a comprehensive list of every Game Boy Color game released.

Research Ideas

• Create a comprehensive list of every Game Boy Color game ever released.
• List the best Game Boy Color games of all time.
• Study the most popular Game Boy Color games by region

Acknowledgements

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_1.csv | Column name | Description | |:-----------------------|:----------------------------------------------------------------------| | Regions released | The regions in which the game was released. (String) | | NA+EU+JP | The game was released in North America, Europe, and Japan. (Boolean) | | NA+JP | The game was released in North America and Japan. (Boolean) | | Unique | The game was released in a unique region. (Boolean) | | Total | The total number of regions in which the game was released. (Integer) | | Region description | A description of the regions in which the game was released. (String) |

File: df_4.csv | Column name | Description | |:--------------------|:--------------------------------------------------------------| | Title | The title of the Game Boy Color game. (String) | | Developer | The developer of the Game Boy Color game. (String) | | Publisher | The publisher of the Game Boy Color game. (String) | | Intended Region | The region the Game Boy Color game was intended for. (String) | | Status | The status of the Game Boy Color game. (String) |

File: df_3.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Title | The title of the Game Boy Color game. (String) | | Developer | The developer of the Game Boy Color game. (String) | | Publisher | The publisher of the Game Boy Color game. (String) |

File: df_2.csv | Column name | Description | |:-------------------|:---------------------------------------------------------------| | Developer | The developer of the Game Boy Color game. (String) | | Publisher | The publisher of the Game Boy Color game. (String) | | JP | The release date of the game in Japan. (Date) | | NA | The release date of the game in North America. (Date) | | EU/PAL | The release date of the game in Europe and PAL regions. (Date) | | Nintendo eShop | The release date of the game on the Nintendo eShop. (Date) |

File: df_6.csv

File: df_7.csv

File: df_5.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------| | Title | The title of the Game Boy Color game. (String) | | Developer | The developer of the Game Boy Color game. (String) | | Backward compatible | Whether the game is compatible with the Game Boy Advance. (Boolean) |

Context

This public data includes pitch-by-pitch data for Major League Baseball (MLB) games in 2016. With this data you can effectively replay a game and rebuild basic statistics for players and teams.

Content

games_wide - Every pitch, steal, or lineup event for each at bat in the 2016 regular season.

games_post_wide - Every pitch, steal, or lineup event for each at-bat in the 2016 post season.

schedules - The schedule for every team in the regular season.

*The schemas for the games_wide and games_post_wide tables are identical.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

Dataset Source: Sportradar LLC

Use: Copyright Sportradar LLC. Access to data is intended solely for internal research and testing purposes, and is not to be used for any business or commercial purpose. Data are not to be exploited in any manner without express approval from Sportradar. Display of data must include the phrase, “Data provided by Sportradar LLC,” and be hyperlinked to www.sportradar.com.

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.

Here are a few use cases for this project:

1. Game Assistance and Tutorials: The "DSTFULL" model could be used to create an intelligent game assistant which identifies different gaming elements and provides real-time tips, strategies and tutorials to players, thus improving the overall gaming experience.

2. Automated Game Testing: Game developers could use this model for automated game testing; by identifying different in-game objects and classes, it would enable thorough and efficient identification of any bugs or glitches related to these elements.

3. Game Accessibility for Visual Impairments: This model could be utilized to make video games more accessible for visually impaired individuals. By recognizing game objects, it could generate descriptions or auditory feedbacks to communicate the on-screen situations to them.

4. Gaming Content Creation: The model can aid in generating automatic highlight reels or summary clips based on the identified objects. This could be used by content creators or streamers on platforms like Twitch or YouTube to provide summarized content quickly.

5. Improve AI Game Bots: The model's ability to identify a wide range of in-game classes can be used to train better AI bots for video games. These AI bots can interact with more elements, understand the gaming environment better and provide a more human-like competition.

Atari 7800: A Comprehensive Video Game Console Library

The Most Thorough Collection of Video Game Console Games for the Atari 7800

About this dataset

This dataset contains a list of video game console games released for the Atari 7800, including their genres, developers, publishers, and release dates

How to use the dataset

This dataset contains a list of all the video game console games released for the Atari 7800. The games are listed in chronological order, with the most recent releases appearing first. Each game is labeled with its release date, region(s) released, developer(s), publisher(s), genre, and a brief description

Research Ideas

• This dataset can be used to create a list of all the video game console games released for the Atari 7800.

• This dataset can be used to create a list of all the video game console games released for the Atari 7800, including their genres, developers, and release dates.

• This dataset can be used to create a list of all the video game console games released for the Atari 7800, including their genres, developers, publishers, and notes/reasons

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_1.csv | Column name | Description | |:-----------------------|:--------------------------------------------------| | Regions released | The regions where the game was released. (String) | | Region description | A description of the region. (String) | | Released | The release date of the game. (Date) |

File: df_4.csv | Column name | Description | |:-------------------------|:--------------------------------------------------------------------------------| | Genre | The genre of the video game. (Categorical) | | Notes/Reasons | Any notes or reasons given for the inclusion of the game in the dataset. (Text) | | Developer(s)[13][14] | The developer(s) of the video game. (Categorical) | | Publisher(s)[13][14] | The publisher(s) of the video game. (Categorical) |

File: df_3.csv | Column name | Description | |:--------------------|:----------------------------------------------| | Genre(s) | The genre(s) of the video game. (Categorical) | | Release date(s) | The release date(s) of the video game. (Date) |

File: df_2.csv | Column name | Description | |:-------------------------|:--------------------------------------------------| | Genre(s) | The genre(s) of the video game. (Categorical) | | Publisher(s)[13][14] | The publisher(s) of the video game. (Categorical) | | Release date(s) | The release date(s) of the video game. (Date) |

File: df_5.csv

The dataset consists of interview transcripts with people who spend a lot of time playing video games. The interviewees include people who play video games competitively for at least 30 hours a week and people who have sought help for compulsive gaming. The interviews are follow-up interviews, and the same individuals were interviewed for the first time a year earlier. For the dataset containing the first round of interviews, see dataset FSD3678 archived at FSD. In the first part of the follow-up interviews, the interviewees were asked whether there had been any changes in their digital gaming habits compared to a year ago. The interviewees were also asked about any changes in their career, family and friends. Next, they were asked to give a day-by-day description of what a normal week of digital gaming was like for them and to describe in as much detail as possible one digital gaming experience from the previous month. Additionally, the interviews included questions about the interviewees' other hobbies and their satisfaction with their current job. In relation to gaming, the interviewees were asked whether they felt that they spent too much time playing digital games. Background information included, among others, the interviewee's gender, information on which interviewee group the interviewee was part of, and the date of the interview. The interview identifier makes it possible to compare data between each interviewee's first interview and follow-up interview. The data were organised into an easy to use HTML version at FSD.

Here are a few use cases for this project:

1. Game Development and Testing: Game developers can use the ObjectDetection model to identify and track game pieces during the testing phase. They can monitor the game's mechanics and how these pieces interact with the game environment. It would also be useful for online or AR/VR gaming applications where a physical component interacts with a digital interface.

2. Educational Tools Design: The model can be applied in the design of educative or therapeutic games for children, specially those who are learning shapes and colors. With the ability to recognize distinct game pieces, it can provide real-time feedback which helps enhancing a child's cognitive and motor skills.

3. Automated Sorting / Packaging: In toy manufacturing industry, this model can be used to automate the sorting or packaging process. It can identify the game pieces and ensure that they are correctly packaged based on shape and color.

4. Robotics and Automation: Robots can use this model to interact with specific objects during a task. For instance, in robot competitions or obstacle courses, where robots are programmed to manoeuvre around or interact with specific objects like cones and cubes.

5. Surveillance and Safety: In a setting like a construction site or a crowded event where safety cones are used, the model could detect whether cones are in the appropriate position and if they have been moved or knocked over. This can help improve safety protocols and accident response times.

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.

Aineisto koostuu paljon videopelejä pelaavien henkilöiden haastattelulitteraatioista. Osa haastateltavista on videopelejä kilpailullisesti vähintään 30 tuntia viikossa pelaavia henkilöitä, ja osa pelaamisen takia apua hakeneita henkilöitä. Haastattelut ovat seurantahaastatteluja. Samoja haastateltavia on haastateltu ensimmäisen kerran vuotta aikaisemmin. Ensimmäisen haastattelukierroksen haastattelut on arkistoitu numerolla FSD3678. Seurantahaastatteluissa vastaajaa pyydettiin aluksi kertomaan siitä, onko pelaamisessa tapahtunut muutoksia vuoden takaiseen tilanteeseen verrattuna. Haastateltavaa pyydettiin myös kertomaan mahdollisista muutoksista työssään ja perheessään tai ystäväpiirissään. Seuraavaksi pyydettiin kuvailemaan tavallinen peliviikko päivä kerrallaan. Lisäksi pyydettiin kertomaan mahdollisimman tarkkaan yhdestä mieleen jääneestä pelikokemuksesta viimeisen kuukauden aikana. Haastatteluissa kysyttiin myös tutkittavan muista harrastuksista. Pelaamiseen liittyen kysyttiin myös esimerkiksi sitä, pelaako haastateltava omasta mielestään liian paljon. Taustatietona mainitaan mm. sukupuoli, haastatteluajankohta sekä tieto siitä, kumpaan haastateltavien ryhmään tutkittava kuuluu. Haastattelutunnisteen avulla seurantahaastattelu on yhdistettävissä saman henkilön aikaisempaan haastatteluun. Aineistosta on tehty html-versio, jonka hakemiston avulla haastatteluja on helppo selata. The dataset consists of interview transcripts with people who spend a lot of time playing video games. The interviewees include people who play video games competitively for at least 30 hours a week and people who have sought help for compulsive gaming. The interviews are follow-up interviews, and the same individuals were interviewed for the first time a year earlier. For the dataset containing the first round of interviews, see dataset FSD3678 archived at FSD. In the first part of the follow-up interviews, the interviewees were asked whether there had been any changes in their digital gaming habits compared to a year ago. The interviewees were also asked about any changes in their career, family and friends. Next, they were asked to give a day-by-day description of what a normal week of digital gaming was like for them and to describe in as much detail as possible one digital gaming experience from the previous month. Additionally, the interviews included questions about the interviewees' other hobbies and their satisfaction with their current job. In relation to gaming, the interviewees were asked whether they felt that they spent too much time playing digital games. Background information included, among others, the interviewee's gender, information on which interviewee group the interviewee was part of, and the date of the interview. The interview identifier makes it possible to compare data between each interviewee's first interview and follow-up interview. The data were organised into an easy to use HTML version at FSD.

Research on user churn prediction has been conducted across various domains for a long time. Among these, the gaming domain is characterized by its potential for diverse types of interactions between users. Due to this characteristic, many studies on churn prediction have considered the relationships between users and have primarily applied social network analysis. Recently, the use of Graph Neural Networks (GNNs) has been actively applied. However, existing studies utilizing GNNs have limitations as they use static graphs that do not effectively capture the dynamic nature of interactions that change over time. This study addresses these limitations by proposing a dynamic graph model for predicting user churn in games based on user interactions. Data are sourced from 10,000 users of ’Blade & Soul’ by NCSOFT. The proposed model effectively captures changes in user behavior over time and predicts user churn with a focus on interactions among users. Experimental results reveal that the proposed model achieves a higher F1 score compared with conventional algorithms and static graph models. Dynamic graphs more accurately reflect changes in user behavior compared with static graphs, particularly in domains with active interactions such as massively multiplayer online role-playing games. This work highlights the significance of user churn prediction in the gaming industry and demonstrates the effectiveness of the predictive models that use dynamic graphs.

Gambling Email List offers complete information about online casinos. This database is a fantastic way for marketers to do good business. Users can maximize their outreach by having the most up-to-date information. This email database provides the names of people who have used a specific casino. Furthermore, it details the results of every transaction they’ve made. Accordingly, you can target people interested in gambling, online betting, and gaming. This list also includes real estate, payments, and promotions. For this reason, if you want to market to this specific audience, you should use an email database.

Gambling Email List provided is a correct and constantly updated list. Especially in the consumer market for gaming, betting, and gambling, this list is highly effective. On the other hand, it also works for anyone who wants to promote various products or services. Moreover, List to Data is a reliable provider of email databases globally. All of the information they In addition, their experts verify all data to ensure full records. In conclusion, you can keep the gambling email list and receive a new, updated list regularly. Essentially, this database is perfect for everyone who wants to go ahead in the casino. Gambling Email Database is one of the best marketing tools available. You can use it to reach people who might buy your products or use your services. For instance, sending emails to casual gamblers is a proven way to engage them. If you’re trying to help a friend win money at the casino, our service can be helpful. We promise you will be happy with the results of using this data. Therefore, you can get our email list without any hassle.

Gambling Email Database can be extremely effective if you use it correctly. For this reason, casinos often use our long list for advertising sports gambling. This helps them attract many people to their websites. Above all, these email addresses will bring in a lot of new users and a lot of money. If you own a business where people gamble, this information is perfect for you.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, Game Genre, Title and Studio segments. However, we provide also plenty of specific User Types, which contain e.g. Hardcore Gamers, Big Spenders or Parents of Gamers. There are also audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Moreover, we offer segments for Virtual Reality, interest in Gaming Subscriptions, Payments, Micropayments, Devices and Platforms. We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Theme (e.g. Historical), which is definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Our data base include millions of profiles divided into popular categories. You can choose which target groups you want to reach. Segments based on users' interests, purchase intentions or demography. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

We are ready for a cookieless era. We already gather and provide non-cookie ID - for example Universal IDs, CTV IDs or Mobile IDs.

Gambling Data Greece gives you access to valuable contacts across the gambling industry. This database includes player demographics, betting trends, revenue figures, and competitive landscape. Most importantly, we collect these contacts from a dependable source through verification. The gambling market in Greece has grown a lot in recent years. Thus, the reason is people’s lifestyles are changing and they have more money to spend. This is becoming more popular here. Moreover, it includes online casinos, sports betting platforms, and poker rooms. Moreover, Gambling Data Greece can help you connect with people in the gambling industry. Therefore, it gives important information about gambling activities in the country. Thus, this database shows who gambles, the types of gambling they do, and the money involved. Anyone can find gamblers’ contact numbers, names, ages, and more in our database. However, businesses can use this data to plan better ways to sell products to gamblers. Greece Gambling data is a helpful resource for businesses looking to reach the right audience. Besides, this country has a population of over 10 million and has many people who enjoy gambling. This database includes accurate contact details such as names, phone numbers, and emails of active gamblers. This makes it ideal for targeting potential customers. Whether your business focuses on casinos, online gaming, or sports betting, this data helps target the right audience. Consequently, you can efficiently promote services and grow sales. Moreover, with Greece Gambling data, you can connect with more customers, improve your marketing strategies, and boost your profits. You can use it to send special promotions, share new game updates, or offer exclusive deals directly to interested customers. This saves time and helps you achieve better results. Lastly, it is an easy way to grow your business and succeed in the gambling market.

We will create a customized NBA dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our NBA datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the basketball industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.