The video game industry is a huge growing sector of the economy and brings enjoyment to many people. For this project I wanted to take a closer look at video games and am asking the following question:

How can I find out how the industry has changed over years and what is being in demand?

The goal of the visualizations is to get a step closer to answering this question and find detailed changes in the video game industry over the years.

Inspiration for the Visualization

In preparation for this data visualization project I chose to look at the change in movie ratings and genres over time because it can be easily translated into genres and ratings of games, which are both part of the data set I am using to create my insights.

This first visualization shows the changes in the number of releases over the years. The issue I found with it was that it can be hard to see the amount of releases overall and for single genres. The overall view and flow throughout time is, however, helpful.

This second graph I found shows the change of movie ratings over the years. The fact that there is a year by year total rating and a average that connects all years is helpful to understand the changes. I would be interested in seeing how many movies were released in each specific year to see if the ratings are getting affected by the amount of movies.

Materials Used:

Tableau Public – Free Data Visualization Software

Kaggle – Online community of data scientists and machine learners who share public data sets and code snippets, owned by Google.

Methodology

I was fortunate to find a dataset that included data about genre releases and ratings (by IGN) from 1976 until 2016. I had to make sure the dataset is clean and can be used in Tableau. In order to do that had to take a closer look at the data.

I quickly discovered that early games (from 1976 until 1996) were very numbered in the dataset, so I decided to only use games that were released from 1996 until 2016. There were also games that had no genre assigned to them, so I had to clean those out.

The cleaned dataset was imported to Tableau and I started to recreate the visualizations I found about movie genres and ratings.

After creating the first version, I realized that it is hard to distinguish the single genre because of the amount of genres the dataset has. Like my inspirations, the change within the genres cannot be seen easily, so I decided to create two data visualizations. One focusing on the amount of releases and one focusing on the change of releases in genres over the years.

After creating this first graph and understanding its shortcomings I had the chance to review my work and adjust according to the improvements I saw that I could make. Tableau allowed for most of the changes to happen.

The creation of the first two visualizations resulting from the first attempt combined with the question that I wanted answer allowed me to create visualizations that helped me to understand the changes in the gaming industry over the years.

Results & Interpretation

Based on the data set, I created six data visualizations in Tableau.

You can see the whole workbook here.

Genre Releases Over Time

The first visualization shows the amount of games released (from the top 10 genres with the biggest amount of releases) with emphasis on the total amount. I also added the pointers that show major platform releases to show how these releases have increased the amount of games that are being released.

Genre Change from 1996 – 2016

The second visualizations focuses on the change of genre releases over the years. With a filter that allows for a specific time range to be selected it is possible to see what genre had the most releases in those years.

Platform Releases

I wanted to take a closer look at how platforms have influenced the release of games so I created a heat map that shows games created for different platforms and therefore show the cycles of platforms releases.

Releases During Each Month

Another interesting question I wanted to answer was when most games are being released during the year. I had a suspicion that most games are being released at the end of the year so games can be bought as presents for the holidays and that suspicion was confirmed with this visualization.

Highest Scored Genres

Lastly, I wanted to focus on scores given to games. How do different genres perform based on the ratings IGN gives them. First I created a visualization that shows the average score of each genre. I made sure to include the amount of games released in that genre to clarify that genres with only a few releases can have a different average score.

Average Scores Over the Years

The last visualization was inspired by the visualization on movie ratings. I included the number of game releases in bars and a line with the average score given in the specific year. The visualization shows that in the years where more games were released a slight dip in scores given can be seen. The amount of games created increased by a lot from 2006 to 2010 but their quality decreased. This might be caused by the high demand and the short amount of time given to develop the game.

Reflection

Overall, I was very interested in creating these data visualizations and finding out and communicating the results. I learned that the action genre has been consistently the most popular over the years and across all platforms and pressure on the developers can create many lower quality games.

When continuing this project I would like to look further into the data set and what connections can be found within. It would be great to add more variables and see, for example, what the demographic of players looks like.