Analyzing data trends in the video Gaming Industry

Posted on Feb 21, 2018
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Motivation:

Within the last decade, data shows there have been major shifts in the way games are dispensed to consumers. While companies like Game Stop are on the decline, [1] online gaming sites like steam have dominated the mainstream gaming culture. This change in the way games are sold has had an impact on what kind of games are sold. Retailers are taking a sizable risk by selling hard copies.

If the game title doesn’t sell then that impacts the retailers who invested space, and money into that game title. Selling digital copies on a website like steam however, takes virtually no risk. This means steam doesn’t need to be discriminatory as to what titles it takes. This has lead to explosion of Indie titles as the bar for entry has been lowered.

This project is targeted at providing useful insights for small indie teams that don’t necessarily have the same marketing resources that a large production company might provide. This project looks through two websites to see determine what kind of aspects of a game correlate with popularity in this new gaming market.

Method:

The first website is http://store.steampowered.com/. Here we attempt to web scrape tags and game prices that appear on the “top sellers” section as well as the “new releases” section, to get a sense of the most popular and least popular tags in this online gaming store currently. Unfortunately steam limits the number of pages that can be scrapped at once by redirecting URL request. This wasn’t a problem that could be solved by my code. This meant that I had to request each URL that I wanted to scrap individually.

The next site to be web scraped was https://www.gamespot.com/reviews/. Here thousands of game reviews were scraped along side there rating. The goal was to see what kind of words were most associated with commonly played games. We also accumulated meaningful words that were least associated.

Data Results:

Analyzing data trends in the video Gaming Industry

Here in this plot we see some words that match our expectation of most common tags on steam. Tags such as indie, multiplayer, action, and adventure.

Looking at the least occurring tags we get the graph below. It should be noted that there are a multitude tags that appeared 0 times. These are tags that may have been used in the past but have not been used in recent games.

Analyzing data trends in the video Gaming Industry

Moving on to the next website we determine the most meaningful word in the reviews. Using the Natural Language Processing Tool Kit we removed stop words (words such as the, as, should…) and proceeded to isolate meaningful words. We get the following graph below.

Analyzing data trends in the video Gaming Industry

Below is a sample of some of the least commonly mentioned words in the reviews that were still meaningful in terms of Gaming language.

Comparing the results of both methods we notice some similarities. Adventure, Role Playing Gaming and Action Games represent the most widely sold content. Puzzles, Mystery, Detective and Racing games appears to be on the lower end.

Limitations of this study

There are a couple thing that must be noted in this study. First this data collected is biased towards more recent trends simply because as time passes games that were in the new release section would have been removed. Items that were on the top sellers categories may lose there position to new releases as well. Secondly this words that were isolated relied picked partially based on human interpretation. The last thing to note relates to the implication that may be drawn from these trends. Just because a genre is larger doesn’t necessarily mean it’s a good idea to enter that market space This could mean that the market space is saturated in

              Articles

[1]:https://www.theverge.com/2017/3/25/15059380/gamestop-store-closings-2017-digital-sales-collectibles-business

[2]: https://venturebeat.com/2017/09/07/why-triple-a-devs-are-going-indie-and-why-indie-arent-going-triple-a/

[3] steam.powered.com

[4] gamespot.com

About Author

chima okwuoha

Chima has a B.S. in physics and is currently studying Electrical Engineering at New York University. In undergrad he spent 2 years researching Fourier Optics in and Image processing. During this time he cultivated computer science skills to...
View all posts by chima okwuoha >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI