Changes in Video Games Sales Since 1970, Game Genre Sales Comparison, Statistical Distribution of Game Rating, Relationship Between North American and Japanease Game Sales

Links to Contributors: James Bruce, Jack Garcia, Jose Romero.

For our project, we used a global video game sales data set. Each row in the data represents a single game and includes both descriptive and quantitative variables. Key fields include: title, console, genre, publisher, developer, critic score (on a 1–10 scale), total sales and regional sales (North America, Japan, PAL, and “other” regions), and release date. This structure allowed us to explore patterns in how the industry has evolved over time, how different genres perform in different markets, how critics rate games overall, and whether sales in one region are related to sales in another.

Using this dataset, we answered four questions with visualization-based analysis in Excel:

How has the number of video games released per year changed over time?

We were interested in this question because it shows the growth and possible saturation or decline of the video game industry over time. To answer it in Excel, we first extracted the release year from the full release date using the =YEAR() function and added this as a new column. Then we created either a pivot table (rows = Year, values = Count of games) or a summary table using COUNTIF/COUNTIFS to calculate how many games were released each year. From that table, we built a line chart with Year on the x-axis and Number of Games on the y-axis. The pattern shows that only a handful of games appear in the 1970s and early 1980s, followed by steady growth through the 1990s and an explosion in releases during the 2000s, peaking around 2009. After this peak, the number of releases in the dataset gradually declines in the 2010s and drops sharply in the early 2020s (likely partly due to data coverage). Overall, the visualization tells a clear story of rapid expansion leading up to the late 2000s, followed by a more modest and uneven release pace in recent years.

Which video game genre has the highest average PAL sales?

This question is interesting because different regions have different gaming preferences, and we wanted to see which genres perform best in the PAL region (primarily Europe and Australia). In Excel, we used the genre and PAL sales columns. We inserted a pivot table with Genre in the Rows area and the average of PAL sales as the Values field (changing the default from “Sum” to “Average”). From that pivot table, we built a bar chart with Genre on the y-axis and Average PAL Sales on the x-axis so that we could easily compare genres. The results show that sandbox games have the highest average PAL sales, driven by a relatively small group of very successful open-world titles. Among the more common genres with many games in the dataset, action-adventure and shooter games have the highest average PAL sales, indicating strong demand for cinematic, story-driven, and action-heavy experiences in PAL markets, while genres like puzzle and simulation tend to have lower average PAL sales.

#What is the distribution of critic scores across all games?

We wanted to look at critic scores because they summarize how reviewers perceive game quality and can hint at whether most games are considered good, average, or poor. In Excel, we worked only with the critic score column. We inserted a histogram chart (or used a pivot table with score bins created by helper columns) to group scores into intervals such as <5, 5–<6, 6–<7, 7–<8, 8–<9, and 9–10. The histogram shows that very low-scoring games (below 5) and extremely high-scoring games (9–10) are relatively rare. Most games cluster between 6 and 9, with the tallest bars in the 7–<8 and 8–<9 bins. The mean critic score is a little above 7, and the median is around 7.5, so the distribution is centered in the “good but not perfect” range. Overall, the visualization suggests that critics tend to rate most games as decent to good, with far fewer games being truly terrible or truly exceptional.

#Is there a relationship between JP sales and NA sales?

This question is interesting because it asks whether success in one region (North America) tends to go hand-in-hand with success in another (Japan), or whether regional tastes are largely independent. In Excel, we used the NA sales and JP sales columns and created a scatter plot with NA sales on the x-axis and JP sales on the y-axis. Each point in the chart represents one game. We also added a linear trendline and displayed its equation and R² value to quantify the relationship. Visually, most points cluster near the origin, with many games selling modestly in both regions; however, there are also many games that sell well in North America but not in Japan and a smaller number that perform strongly in Japan but only moderately in North America. The trendline is nearly flat and the correlation between JP and NA sales is very low, indicating only a weak positive relationship. Our conclusion is that there is no strong linear relationship between JP and NA sales in this dataset—regional preferences and market conditions appear to differ enough that high sales in one region do not guarantee high sales in the other.