Skip to content
Project: When Was the Golden Era of Video Games?

Video games are big business: the global gaming market is projected to be worth more than $300 billion by 2027 according to Mordor Intelligence. With so much money at stake, the major game publishers are hugely incentivized to create the next big hit. But are games getting better, or has the golden age of video games already passed?

In this project, you'll analyze video game critic and user scores as well as sales data for the top 400 video games released since 1977. You'll search for a golden age of video games by identifying release years that users and critics liked best, and you'll explore the business side of gaming by looking at game sales data.

Your search will involve joining datasets and comparing results with set theory. You'll also filter, group, and order data. Make sure you brush up on these skills before trying this project! The database contains two tables. Each table has been limited to 400 rows for this project, but you can find the complete dataset with over 13,000 games on Kaggle.

game_sales table

ColumnDefinitionData Type
nameName of the video gamevarchar
platformGaming platformvarchar
publisherGame publishervarchar
developerGame developervarchar
games_soldNumber of copies sold (millions)float
yearRelease yearint

reviews table

ColumnDefinitionData Type
nameName of the video gamevarchar
critic_scoreCritic score according to Metacriticfloat
user_scoreUser score according to Metacriticfloat

users_avg_year_rating table

ColumnDefinitionData Type
yearRelease year of the games reviewedint
num_gamesNumber of games released that yearint
avg_user_scoreAverage score of all the games ratings for the yearfloat

critics_avg_year_rating table

ColumnDefinitionData Type
yearRelease year of the games reviewedint
num_gamesNumber of games released that yearint
avg_critic_scoreAverage score of all the games ratings for the yearfloat
Spinner
DataFrameavailable as
best_selling_games
variable
-- best_selling_games

SELECT *
FROM game_sales
ORDER BY games_sold DESC
LIMIT 10;
Spinner
DataFrameavailable as
critics_top_ten_years
variable
-- critics_top_ten_years
SELECT year, COUNT(DISTINCT name) as num_games, ROUND(AVG(critic_score),2) as avg_critic_score
FROM game_sales
INNER JOIN reviews
USING(name)
GROUP BY year
HAVING COUNT(DISTINCT name) >= 4
ORDER BY avg_critic_score DESC
LIMIT 10;
Spinner
DataFrameavailable as
golden_years
variable
-- golden_years

SELECT c.year, c.num_games, avg_critic_score, avg_user_score, (avg_critic_score - avg_user_score) AS diff
FROM critics_avg_year_rating AS c
INNER JOIN users_avg_year_rating AS u
ON c.year = u.year AND c.num_games = u.num_games
WHERE avg_critic_score > 9 OR avg_user_score > 9
ORDER BY year ASC;