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Source: IMDb |
As databases are being constantly updated, information in this dataset might be considered out of date (eg. review rating).
There are also inconsistencies in release country and dates. While let's say Ti West's The House of the Devil was released already in 2009 in the US, this dataset tells us about its May 2012 release in Taiwan.
I have ommited the country, however for the purpose of this blog, I kept the (sometimes imprecise) dates of release.
There was also a lot of information missing for many movies, such as budget or age restriction.
It appears that horror is still seen as a rather trashy genre where even masterpieces (subjectively) like Mike Flanagan's Oculus or the already mentioned The House of the Devil rarely score a rating above 7/10.
The mean average rating stands at 5.1, while the median is 5.0 on the dot.
If we split our movies into two groups, English spoken and non-English spoken, we can see that the non-English films are being rated slightly higher: 5.5 vs 4.9.
English is the sole language spoken in 2421 movies, followed by Spanish (96), Japanese (77) and Hindi (37).
"Emily and Eden Stevens escape one violent situation only to dive head first into another. Terrified and alone they are stranded in the dark woods only to be chased into a horrific scene in a house or horrors. They must work together to get out alive. But what is worse? What is on the inside or out?"
Shared silver goes to The Carmilla Movie (Spencer Maybee) and The Theta Girl (Christopher Bickel), both 9.6.
The best non-English spoken movie is Philippine urban legend of Teniente Gimo rated at 9.1.
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Source: IMDb |
After having to abort the mission of finding out whether there is a connection between budget and review ratings (due to insufficient data), I checked whether a length of a movie affects its rating.
The answer is no, there doesn't seem to be a connection, however I still find the graph visually quite pleasant. You can see that the ratings are fairly evenly distributed across different run times.
The length of scary movies seems to be pretty standardised too, with the vast majority falling somewhere between 75 and 100 minutes.
While talking about the length of our movies, sometimes we all just want a bite-size piece of terror. I took a look at movies lasting 45 minutes or less, and these are the 3 highest rated scary nibbles:
RIP (directed by Caye Casas and Albert Pintó, run time 16 minutes, rating 8.7)
Never Hike Alone (Vincente DiSanti, 22 min, 7.8)
Ovulation (Michael Wade Johnson, 45 min, 7.5)

Some genres (such as soaps) have their dedicated actors and horror movies are no different. So who were the busiest actors between 2012 and 2017?
After a simple count, I found out that the main man Lloyd Kaufman appeared in 29 movies. I am assuming these were not major parts as that would be near impossible.So I worked on the premise that actors are listed by the importance of their role and counted only the first 3 for each movie. And there are still some true champs out there:
Eric Roberts, 20 movies
Debbie Rochon, 17
Kane Hodder, 13
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Debbie Rochon, Source: IMDb |
One of the annoying parts of being a horror movie fan is that sometimes after watching a good movie, one finds out that it was a debut after which the director either stopped filming or moved into the waters of more profitable genres. I found the highest rated directors with more than one movie under their belt. Usually I'd go for 1 or 3, however this time I'll name four of them, as they're evenly representing USA and Japan and also (not shockingly) life-action and animation.
Ladies and gentlemen, without further ado, let me introduce you the Big Four:
Emir Skalonja, average rating 8.5
Michael Wade Johnson, 8.1
Hiroaki Andô, 8.1
Toshiyuki Kubooka, 7.9
Thriller being the most popular with 1378 movies, which is not a surprise as most of teen slashers would be probably found in this group.
I am however surprised to see Drama (531) and Comedy (513) taking silver and bronze while pushing Mystery (453) out of the podium. Ghost stories might not be quite as popular.
In the graph above, please be aware that the scale is logarithmic so I could fit in Thriller with over 1300 counts while still allowing you to see subgenres with only one count.
I had a look at the most common words used in the titles and after discarding function words I was left surrounded by such a beautiful cliche, that my horror-loving heart started bouncing with joy!
107 movies have the word dead in their title, house appears in 71 of them, blood in 58 and night in 57.
55 movies have the digit 2 in their name, suggesting sequels are still a thing.
Other popular words are dark, devil, evil, zombie, last and massacre.
But what about actual movie names? There are 74 titles that appear more than once, however only one of them appears 3 times, and that is The Bride.
Brides are apparently not to be messed with in Taiwan, Japan and Russia.
If you remember the very first graph with missing data, a big chunk of filming locations was missing. So please take this paragraph with a pinch of salt.
Not surprisingly, the most popular location to shoot a horror movie is Los Angeles, followed by London and Vancouver.And finally let's have a look at when is a good time to expect a horror movie in your local cinema.
There seems to be an increasing tendency in horror movie production starting with 345 movies in 2012 and rising up to 780 in 2017.
This trend might show increasing interest in horror, but might just as well be seen across all genres and further study might be needed.

It is not very surprising that the most horror titles are being released in October for the All Hallow's Eve. Second busiest month is January.
Hi, Can you explain how you where able to separate the release dates from the dataset into months?
ReplyDeleteHi, I'm really sorry, I didn't see you comment until now! I suspect you managed to figure it out already. But in this case I converted the column with the release date into a datetime format.
Deletehorror['Release Date'] = pd.to_datetime(horror['Release Date'])
Then all I had to do was to grab it, in this case with a lambda expression:
from datetime import datetime
horror['Release Date'].apply(lambda time: time.month)