SQL-PBI Portfolio - Game sales and critics DB

The game reviews db – from the dbpublic in
Projects Data

PBI Summary
Overall game analysis : It’s a booming
industry!
AGR:
nb_games = 185%
total_games_sold = 115%
The leader seems to be nintendo but
competition is growing fast

PBI Summary
Quantity vs Quality :
-critics have been more generous since
the 2010’s
-Is there a corelation between best
selling games and best critics scores?
I made up a comparing method using
ranks and the answer is : no!
I also asked chat GPT to evaluate :
-Pearson correlation = 0.072//0.062
-Spearman cor = 0.17 // 0.114

PBI Summary
And the winner is Nintendo!
The company started in 1985 with
Super Mario Bros 2nd best revenue
game of all time! The GOAT selling
game is the Wii sports in 2006 and the
best seller console is the Wii!
But they made a mistake when they
betrayed Sony in the early 90’s
because…

PBI Summary
…Trends are looking good for PS
More devs, more (exclusive) games,
more revenues!
And who knows maybe they will have
exclusive games that sale better than
Nintendo games ;)

The Data
Data available in datacamp for
students to practice their skills

How many games in game reviews:
SELECT COUNT(DISTINCT name) AS nb_games
FROM public.game_sales
Null values case + left join:
SELECT
SUM(CASE WHEN gs.name IS NULL THEN 1 ELSE 0 END) AS gs_name_null_value,
SUM(CASE WHEN r.critic_score IS NULL THEN 1 ELSE 0 END) AS critic_score_null_values,
SUM(CASE WHEN r.user_score IS NULL THEN 1 ELSE 0 END) AS user_score_null_values
FROM public.game_sales AS gs
LEFT JOIN reviews AS r ON gs.name = r.name;
Join to export raw data for an Excel analysis
SELECT gs.name, gs.platform, gs.publisher, gs.year, gs.games_sold, r.critic_score
LEFT JOIN public.reviews AS r
ON gs.name = r.name
WHERE r.critic_score IS NOT NULL
-nb_games_gs : 400 games
-nb_games_r : 400 games
-Null values in gs.name : 0
-Null values in cs : 1 / LJ : 31
-Null values in cs : 212 / LF : 222
-When Joined +30 Null values that we
can’t fill => only use cs!
-Many redundant values in name
(same company nut different names)
-Join to export csv or export both
tables to work on PBI (join col = name)
We can work with 369 rows!
Exploring
game_sales &
reviews

ANALYZING
game_sales &
reviews
game_sales basis stats:
SELECT COUNT(DISTINCT name) AS nb_games, SUM(games_sold) AS tot_sold,
MAX (games_sold) AS max_sold, MIN(games_sold) AS min_sold,
AVG(games_sold) AS avg_sold, STDDEV(games_sold) AS std_sold
FROM public.game_sales;
game_sales period:
SELECT MIN(year) AS first_year, MAX(year) AS last_year
FROM public.game_sales;
Reviews basic stats:
SELECT AVG(critic_score) AS avg_cs, MAX(critic_score) AS max_cs,
MIN(critic_score) AS min_cs, STDDEV(public.reviews.critic_score)
FROM public.reviews
WHERE critic_score IS NOT NULL
-gs overview =>
Between 1981 and 2020 400 games
generated 3 478.55M $, range 3,98-
82,90$, avg 8,70 (big gaps)
-r overview =>
AVG cs is 8,57! From 2 to 10 are critics
too generous?
AVG us is 7,72, from1,10 to 10, still
pretty high (but 212 null values!)

SALES Analysis in
game_sales
-In 2010, $219,30 total sales
-In 1981, $4,31 total sales
-Year with most games made ≠ Year
most selling
-Year most games 2011 with 26
-Year less games 1981 with 1
-AVG sales by year => 94,01$ (code in
notes)
-8/10 best selling games were made by
Nintendo !
Best and Worst selling games years:
WITH yearly_sales AS (
SELECT year, SUM(games_sold) AS total_sales
GROUP BY year),
max_min_sales AS (
SELECT year, total_sales, 'max' AS sales_type
FROM yearly_sales
WHERE total_sales = (SELECT MAX(total_sales) FROM yearly_sales)
UNION ALL
SELECT year, total_sales, 'min' AS sales_type
FROM yearly_sales
WHERE total_sales = (SELECT MIN(total_sales) FROM yearly_sales)
)
SELECT * FROM max_min_sales;
How many games per year? LIMIT 5:
SELECT year, COUNT(name) AS nb_games, SUM(games_sold) AS tot_sold
GROUP BY year
ORDER BY nb_games DESC
LIMIT 5;
Years Game industry sold x3 the avg_games_sold
SELECT year, name, publisher, SUM(games_sold) AS tot_sold
GROUP BY year,name, games_sold
HAVING games_sold > (SELECT AVG(games_sold)*3 FROM public.game_sales)
ORDER BY tot_sold DESC;

SALES Analysis in
game_sales by
year
-In 2011 made 26 games, 16 more
than the avg/year (10,81)
-Wii sports (from 2006) is the best
selling game ever, with 82.90M$ which
is 74M$ more than avg
-Worst selling game : Namco Museum
with 3.98M$ in 2005
Nb of published game by year vs the avg_year
-- Step 1: Count the number of games per year
WITH games_per_year AS (
SELECT year, COUNT(*) AS nb_games
FROM game_sales -- Using the correct table name
GROUP BY year
)
--Final query: Calculate the difference
SELECT gpy.year, gpy.nb_games,
gpy.nb_games - (SELECT COUNT(*) / COUNT(DISTINCT year) FROM
game_sales) AS diff_nb_games_avg
FROM games_per_year gpy
ORDER BY nb_games DESC;
Game sales compared to avg
SELECT year, name, SUM(games_sold) AS tot_sold,
SUM(games_sold) - AVG(games_sold) OVER () AS diff_tot_avg_sales
GROUP BY year, name, games_sold
ORDER BY diff_tot_avg_sales DESC;
Best and Worst selling games
SELECT name, games_sold, publisher, year
WHERE games_sold = (SELECT MAX(games_sold) FROM public.game_sales) OR
games_sold = (SELECT MIN(games_sold) FROM public.game_sales)

SALES Analysis in
game_sales by
platform &
console
-4/5 best selling platforms (consoles) is
Wii and the other one is NES
(Nintendo)
-In 2006 with 6 games, NINTENDO sold
151,78M$
-In 2014 PlayStation (PS) managed to
reach top 5 selling 84,51M$, with 12
games
-PS tends to make more games 7/10 in
the top 10, and 151 games made tot
since 1996 => avg of 6,3 per year (vs
nintendo (4,02)
Nb of games, and sales by platform over the years
SELECT year, platform, COUNT(name) AS nb_games, SUM(games_sold) AS tot_sold
GROUP by public.game_sales.platform, year
ORDER BY tot_sold DESC, year DESC
LIMIT 5;
Console sales and nb of published games
SELECT CASE
WHEN platform LIKE 'PS%' THEN 'Playstation’
WHEN platform LIKE 'X%' THEN 'Xbox’
WHEN platform = 'PC' THEN 'PC’
WHEN platform IN ('GEN','2600') THEN 'Other consoles’
ELSE 'NINTENDO’
END AS p_category, COUNT(name) AS nb_games, SUM(games_sold) AS tot_sold
GROUP BY p_category
ORDER BY tot_sold DESC, nb_games DESC;

SALES Analysis in
game_sales by
decade
-AVG growth rate :
nb_games = 185%
total_games_sold = 115%
Nb of games, sales and critic score by decade
SELECT CASE
WHEN gs.year BETWEEN 1980 AND 1989 THEN '1980s’
ELSE NULL
END AS decade,
COUNT(gs.name) AS nb_games, SUM(gs.games_sold) AS total_games_sold, AVG(r.critic_score)
AS avg_critic_score
LEFT JOIN public.reviews AS r ON gs.name = r.name
GROUP BY
CASE
ELSE NULL
END
HAVING
CASE
ELSE NULL
END IS NOT NULL
ORDER BY decade;

Quality Analysis
in reviews
-10/10 games for the critics : Super
Mario, Minecraft, GTA V , IV, Mario
Kart SNES, ZELDA-Link to the Past (3
NINTENDO!)
-10/10 games for users : Zelda - BOTW,
God of War, FF X, Zelda – Ocarina, RDR
(2 NIN & 2 PS)
Comparing critics vs users max and mins
WITH max_cte AS (
SELECT name,critic_score, user_score
FROM public.reviews
WHERE critic_score = (SELECT MAX(critic_score) FROM reviews) OR user_score =
(SELECT MAX(user_score) FROM reviews)
),
min_cte AS (
SELECT name,critic_score, user_score
FROM public.reviews
WHERE critic_score = (SELECT MIN(critic_score) FROM reviews) OR user_score =
(SELECT MIN(user_score) FROM reviews )
)
SELECT name, critic_score, user_score
FROM max_cte
UNION ALL
SELECT name, critic_score, user_score
FROM min_cte

Quality Analysis
in reviews
-XBOX has the best critic score avg !
But only 48 games
-Mojang is the best developer
according to cs 1 game a10/10.
-Rockstar Games is the best publisher
with 16 games and 9.5 critic score
Critic score by console
SELECT CASE
WHEN gs.platform LIKE 'PS%' THEN 'Playstation’
WHEN gs.platform LIKE 'X%' THEN 'Xbox’
WHEN gs.platform = 'PC' THEN 'PC’
WHEN gs.platform IN ('GEN','2600') THEN 'Other consoles’
ELSE 'NINTENDO’
END AS p_category, COUNT(r.critic_score), AVG(r.critic_score) AS avg_cs
GROUP BY p_category
ORDER BY avg_cs DESC
Critic score avg and nb_games by publisher
SELECT gs.publisher, AVG(r.critic_score) AS avg_c_s, COUNT(gs.publisher) AS
nb_games, SUM (gs.games_sold) AS tot_sold
LEFT JOIN public.reviews AS r
ON gs.name=r.name
GROUP BY gs.publisher
HAVING AVG(r.critic_score) IS NOT NULL
ORDER BY avg_c_s DESC, nb_games DESC;

Quality Analysis
in reviews –
overall_score
Overall score = cs + us
-> remember many null values in us
-Ocarina/BOTW/GoW/RDR/ Mario
Galawy
-Nintendo, Sony Rockstar and
Activision have the best os
Best games overall_score
SELECT gs.name, (r.critic_score + r.user_score) AS overall_score, gs.publisher
FROM public.reviews AS r
LEFT JOIN public.game_sales AS gs
ON r.name = gs.name
WHERE (r.critic_score + r.user_score) IS NOT NULL
ORDER BY overall_score DESC
LIMIT 5;
Publishers by overall_score
SELECT gs.publisher, (r.critic_score + r.user_score) AS overall_score, RANK()
OVER(ORDER BY (r.critic_score + r.user_score) DESC)
LEFT JOIN public.reviews As r
ON gs.name = r.name
GROUP BY gs.publisher, (r.critic_score + r.user_score)
HAVING (r.critic_score + r.user_score) IS NOT NULL;

Is there a
correlation
between sales
and quality?
I decided to rank the critic score and
sales. Then I compared the diff
between both rankings and if =>
-diff less than 10 ‘close’
-diff between 10-30 ‘medium’
-diff more than 30 ‘huge’
Huge = 81%
Medium =13%
Close = 6%
Nothing suggest that biggest sales
make for better cs for this dataset
Best games overall_score
WITH gap_cte AS (
SELECT subquery.year, subquery.rank_sold, subquery.rank_cs,
CASE
WHEN ABS(subquery.rank_sold - subquery.rank_cs) <= 10 THEN
'close’
WHEN ABS(subquery.rank_sold - subquery.rank_cs) <= 30 THEN
'medium’
ELSE 'huge’
END AS gap_sold_cs
FROM (
SELECT gs.year,
RANK() OVER(ORDER BY gs.games_sold DESC) AS rank_sold,
RANK() OVER(ORDER BY r.critic_score DESC) AS rank_cs
) AS subquery
)
SELECT gap_sold_cs, COUNT(gap_sold_cs)
FROM gap_cte
GROUP BY gap_sold_cs

SQL-PBI Portfolio - Game sales and critics DB

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Editor's Notes