1. Understanding
Movie
Success
Piyush Bhargava
Jason Helgren
Abhishek Singh

2. The Questions
How do we calibrate a movie’s success? What are the
indicators?
How do other variables correlate with these indicators?
Other interesting insights?

3. Methodology
Scrape 13,000
movie URLs
from Box Office
Mojo using
Python
Randomly
select 5% and
download
JSON blobs
from OMDB
Clean data and
check for
consistency.
Export to
PostgreSQL

4. Methodology
Scrape 13,000
movie URLs
from Box Office
Mojo using
Python
Randomly
select 5% and
download
JSON blobs
from OMDB
Clean data and
check for
consistency.
Export to
PostgreSQL
Problems along the way:
● Couldn’t get an API key
● Lots of missing values
● Not all URLs had JSON files

5. Methodology
Scrape 13,000
movie URLs
from Box Office
Mojo using
Python
Randomly
select 5% and
download
JSON blobs
from OMDB
Clean data and
check for
consistency.
Export to
PostgreSQL
Problems along the way:
● Couldn’t get an API key
● Lots of missing values
● Not all URLs had JSON files
Final Result:
database of
about 500
movies

6. ER Diagram

7. ER Diagram

8. Sample Query: User vs Critic Rating
SELECT AVG(metascore) AS critic_avg,
STDDEV_POP(metascore) AS critic_std_dev,
AVG(imdb_rating) AS imdb_avg,
STDDEV_POP(imdb_rating) AS imdb_std_dev,
CORR(metascore, imdb_rating) AS correlation
FROM movies;

9. User vs Critic Rating

10. Sample query: Summer blockbuster effect?
SELECT sum(1) AS count, month, genre FROM
(SELECT lhs.movie, lhs.month, rhs.genre FROM
(SELECT movie, month FROM movies) AS lhs
LEFT JOIN
(SELECT movie, genre from genre) AS rhs
USING(movie)) AS iq WHERE genre
IN ('Action', 'Comedy', 'Drama', 'Romance', 'Adventure', 'Crime')
GROUP BY month, genre ORDER BY month;

11. Sample Query: Summer Blockbuster Effect?
SELECT sum(1) AS count, month, genre FROM
(SELECT lhs.movie, lhs.month, rhs.genre FROM
(SELECT movie, month FROM movies) AS lhs
LEFT JOIN
(SELECT movie, genre FROM genres) AS rhs
USING(movie)) AS iq WHERE genre
IN ('Action', 'Comedy', 'Drama', 'Romance', 'Adventure', 'Crime')
GROUP BY month, genre ORDER BY month;
1

12. Sample query: Summer blockbuster effect?
SELECT sum(1) AS count, month, genre FROM
(SELECT lhs.movie, lhs.month, rhs.genre FROM
(SELECT movie, month FROM movies) AS lhs
LEFT JOIN
(SELECT movie, genre FROM genres) AS rhs
USING(movie)) AS iq WHERE genre
IN ('Action', 'Comedy', 'Drama', 'Romance', 'Adventure', 'Crime')
GROUP BY month, genre ORDER BY month;
1
2

13. Sample query: Summer blockbuster effect?
SELECT sum(1) AS count, month, genre FROM
(SELECT lhs.movie, lhs.month, rhs.genre FROM
(SELECT movie, month FROM movies) AS lhs
LEFT JOIN
(SELECT movie, genre FROM genres) AS rhs
USING(movie)) AS iq WHERE genre
IN ('Action', 'Comedy', 'Drama', 'Romance', 'Adventure', 'Crime')
GROUP BY month, genre ORDER BY month;
1
2
3

14. Summer Blockbuster Effect

15. Summer Blockbuster Effect

16. Most Common Genres and MPAA Rating

17. Awards/Nominations Versus Votes
*Major Win - Oscar/BAFTA/Golden Globe Wins
ρ = 0.65 ρ = 0.53
Nominations Wins Major Wins

18. Awards/Nominations vs other variables
*Major Win/Nomination - Oscar/BAFTA/Golden Globe Wins/Nominations

19. Revenue Vs IMDB Rating
● Higher rated movies earn more worldwide
than in US.
● Worldwide movies are potentially influenced
by IMDB rating/number of votes.

20. Takeaways and Next Steps
● Scrape more data to get a
normal distribution!
● Build a model to predict
movie success.