Board Game Ratings by Country

Global User Ratings of Board Games

By Michael Petrey [source]

About this dataset

> This dataset, originating from the beloved board game community site BoardGameGeek and subsequently expanded by Jesse van Elteren to create a more detailed canvas of data, is now further enriched here with additional geographic location information. Broadening the original framework beyond gaming metrics alone enables researchers and enthusiasts to explore international trends, regional preferences, and cultural influences that may permeate the rich tapestry of games. > > 1. userID: This indicates a unique identifier assigned to each user within the BoardGameGeek online community. It helps track individual users' behavior as they rate various games. > > 2. gameID: This specifies a unique identifier aligned with each board game listed on their platform. It serves as an index that allows us to distinguish between different games receiving ratings. > > 3. rating: Reflects the score out of 10 given by a user for a specific game in their review posted on BoardGameGeek's platform allowing us to understand just how well-received or popular any particular game is among its audience. > > 4. country: A newly added field denoting which country the respective reviewer resides in - be it USA, UK, Australia or elsewhere - enriches this dataset with crucial geographic detail initially absent can now enable examinations of demographic patterns and trends based around location. > > By adding this layer of geolocational context for users who contribute reviews and rates games on Boardgamegeek.com (BGG), this dataset opens up new avenues exploring not only which games are rated high but also where these ratings coming from globally; creating opportunities for deeper study into localised impacts within global gaming communities. > > This versatile compendium forms an essential database for those interested in analyzing trends in board gaming as it provides both comprehensive detail-oriented insights about individual games based on user approval ratings while simultaneously enabling larger-scale contemplation regarding how localized norms potentially influence review scores across diverse geographical regions worldwide relating back directly towards central theme - an appreciation of board games

How to use the dataset

> > - Understand the Dataset: > The first step is to understand what data is there and what it represents. This dataset includes board game ratings from users along with their country information. Each row represents a unique rating by a user for a particular game from a specific country. > > - Load the Data: > Using Python libraries like pandas, you can conveniently load this dataset for computational analysis. You would use pd.read_csv('file_path') function. > > - Data Exploration: > Start digging into this data by checking its distribution, outliers and missing values etc using plots like histograms or boxplots as well as statistical methods . These all tools are present in seaborn, matplotlib and pandas libraries in python. > > - Statistical Analysis: > You could then compute average ratings per country or rank countries according to their mean rating, thus comparing how different countries score games on an average level. > > - Identify Top Rated Games: > Identify the board games with highest overall user ratings regardless of geography providing insights about global preferences about certain boardgames that would serve valuable for manufacturers and retailers globally alike. > > - Countrywise Phenomenon: > Analyze game popularity within specific countries - are some games more popular in certain places? Does popularity correlate strongly with high ratings? > > 7a.Machine Learning Modelling: Based on user reviews make machine learning models for predicting which type of games will be liked/disliked by people belonging to different geographical locations > > 7b.Or ML models can predict future trends based on historical data or provide interesting pattern recognition capabilities that could result in potential business strategies . > > 8b.Make recommendations based on users previous reviews also termed as collaborative filtering > > 8a.Or use popular recommendation algorithms such as cosine similarity measures to recommend new games they might enjoy. > > - While using any form of modelling don't forget to split the dataset into training and testing set before developing and validating your model. > > - cuda, tensorflow or pytorch libraries can be used for applying deep learning techniques. > > In sum, this data set offers a comprehensive view of user rating trends by country which can give some valuable insights about the taste and preference of users belonging to specific countries. Remember not just statistical toolkits but libraries as networkx can help you visualise relationships among games in much more efficient way! > > Additionally always remember while manipulating data always create checkpoints at important steps because if manipulation goes wrong it would be convenient to go back. > It is also crucial that we only

Research Ideas

> - Analyzing the popularity and user ratings of different board games across various countries. This can provide insights into cultural preferences and trends in board gaming across the globe. > > - Identifying geographical areas that have high demand (based on positive ratings) for certain types of board games, which could be utilized by manufacturers, retailers or event organizers for targeted marketing efforts or localization strategies. > - Studying correlations between geographical location and preference for certain genres or styles of board games, potentially opening up avenues for new product development in line with regional tastes and preferences

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > See the dataset description for more information.

Columns

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Michael Petrey.