The 10 most polarizing movies on IMDB

Let's all agree that Pixar's Inside Out is great! At least that's what its IMDB ratings suggest. IMDB and similarly Rotten Tomatoes, are making it pretty easy for us to find great movies like Inside Out with their rankings (see Top 250 on IMDB). It is likewise easy to find movies that are not worthy of anyone's time, but could be interesting to take a quick look at to see how bad a movie can be -- you could checkout The 40 Worst Movies of All Time.

But then, there are those "love it or hate it" types of movies. Those movies can be hard to find among the usual movie rankings: their average scores are likely to be mediocre, and therefore hidden among those other ones that most people agree are just, yes, mediocre. We need a way to rank what movies are the most polarizing, which we can then use as a starting point to uncover the actual hidden gems that are worth watching.

I will now present you with exactly that: a ranking of movies on IMDB by how polarizing they are to viewers.

I have loaded all the IMDB movies via IMDB's database interface. Let's dig in by first taking a look at the distribution of movies ratings.

In [1]:
from IPython.core.display import HTML, display
function code_toggle() {
 if (code_show){
 } else {
 code_show = !code_show
$( document ).ready(code_toggle);
<form action="javascript:code_toggle()">
<input type="submit" value="Click here to toggle on/off the raw code."></form>''')


In [2]:
from __future__ import unicode_literals
from __future__ import print_function

import os
import re
import warnings
import pprint
import html
import pandas as pd
import numpy as np
from scipy import stats

import pylab as pl
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15, 5)

# This option is set so that long urls can be displayed with 80 chars 
# they dissapear 
pd.set_option('display.max_colwidth', 160)

def showdf(df):
pp = pprint.PrettyPrinter(indent=4)

# Run tests for many of the functions here 
run_tests = False

# If you want run this function then you have to first download the raw data from IMDB:
list_to_csv = False

"""Code to process imdb list data
Notes to loading 
* Not treating (YYYYII examples)
* I am keeping everything as strings and letting pandas later do the conversion
* I could make better episode processing, but my main interest is movies
* I choose not to not include movies with year given as (????)

p_year = re.compile('\([12][890]\d{2}\)')  # Finding year of movie   
p_episode = re.compile('\{.*\}')  # Finding episode string
p_media = re.compile('\(\D*\)')  # Finding media if tagged

def rating_process(line, min_votes=100, verbose=0):
    entries = [elem for elem in line.split(' ') if len(elem) > 0]
    out = {}
    out['distribution'] = entries[0]
    out['votes'] = entries[1]    
    out['rating'] = entries[2]
    if int(out['votes']) < min_votes:
        return None
    title_year_episode = ' '.join(entries[3:])

        year = p_year.findall(title_year_episode)[0][1:-1]
        title = p_year.split(title_year_episode)[0].strip()
        episode = ''
        other_string = p_year.split(title_year_episode)[1]
        episode_findall = p_episode.findall(other_string)
        if len(episode_findall) == 1:
            episode = episode_findall[0][1:-1]
            episode = episode_findall[0]

        media = ''
        media_findall = p_media.findall(other_string)
        if len(media_findall):
            media = media_findall[0][1:-1]
    # TODO: Change exception to be explicit on what I will except here.
    except :
        if verbose > 0:
            print('WARNING this file did not process', 
        return None
    if title[0] == '"' and title[-1] == '"':
        title = title[1:-1]
    out['year'] = year
    out['title'] = title
    out['episode'] = episode
    out['media'] = media    
    return out
examples = [
#     Real examples
    '1....521.1      10   6.3  ".hack//Tasogare no udewa densetsu" (2003) {Densetsu no yusha (#1.1)}\n',
    '      0000001212   16660   7.6  "12 Monkeys" (2015)\n',
    '      ....1.34.1       9   7.6  "1st Look" (2011) {Columbiana (#1.9)}\n',
#     This one has a different year format
    '1000011003      58   6.5  "Amas de casa desesperadas" (2006/II)\n',
    '.....161..       6   7.0  "Amateurs" (2014) {(#1.1)}\n',
#     This one stops using "" for the title 
    '030.0..1.3      21   5.8  Struggle (2002)\n',
#     Includes a (V) and uses '' in the title
    "....112.02      11   7.5  The Making of 'The Mummy: Tomb of the Dragon Emperor' (2008) (V)\n",
#   Year not given here  
    '2........7       9   8.0  "By Any Means" (????)\n',
#   Other types of movies
    '0000000017   28338   9.7  Grand Theft Auto V (2013) (VG)\n',
    '.....1.511       7   8.1  Grand Theft Auto: San Andreas - The Introduction (2004) (V)\n'

if run_tests:
    for example in examples: 
        pp.pprint(rating_process(example, min_votes=1, verbose =3)) 

# Make CSV file of the intries
def imdb_list_to_csv(min_votes=1000):
    entries = [
        'rating', 'votes', 'title', 'episode', 'year', 'distribution', 
        'media', ]

    with open('ratings.list', 'r', encoding="cp1250") as f:
        for k in range(500):
            line = f.readline()
            if 'MOVIE RATINGS REPORT' in line:
        for k in range(2):
        num_movies = 0
        with open('ratings.csv', 'w') as fcsv:
            for k in range(int(1e6)):# 
                line = f.readline()
                if len(line.split('\n')[0].strip()): 
                    d = rating_process(line, min_votes=min_votes)
                    if d:
                        line_csv = ';'.join([d[entry] for entry in entries])+'\n'
                        num_movies += 1

            print("Number of movies %i \n" % k, 
                  "movies with more than %i votes: %i" % (min_votes, num_movies))

if list_to_csv:

In [ ]:

Loading the data:

After filtering out entries that i) are obvious TV shows and computer games, and/or ii) have less than 1000 votes, we get:

In [3]:
reader = pd.read_csv('ratings.csv', 
                     header=0, na_values=[' ', ''], 
top_movies = []

df_chunks = []
for i, df_chunk in enumerate(reader):
df = pd.concat(df_chunks)
df.reset_index(inplace = True)

def get_movies_only(df):
    df_new = df[df['episode'].isnull()][df['media'].isnull()]
    return df_new

def imdb_google_link(row):
    return (
        '<a href="">%s</a>' 
        % (' '.join(['imdb', row['title'], str(row['year'])]), row['title']))

def wikipedia_google_link(row):
    return (
        '<a href="">%s</a>' 
        % ((' '.join(['wikipedia', 'movie', row['title'], str(row['year'])])), 
           'wiki: '+row['title']))

df['Title'] = df.apply(imdb_google_link, axis=1)
df['Wikipedia link'] = df.apply(wikipedia_google_link, axis=1)

with warnings.catch_warnings():
    # We get a userwarning for reindexing which we filter out here
    df = get_movies_only(df)

print('%i entries in our dataset' % len(df))

25454 entries in our dataset

Distribution of ratings

IMDB allows people to give a rating between 1 and 10 for each movie. Let's take a look at the rating distribution across all the movies in the dataset:

In [4]:
pl.title('Rating distribution', fontsize=20)
print('Mean %.2f' % df['rating'].mean(), 'and median', df['rating'].median());

Mean 6.63 and median 6.8

Finding polarizing movies

Let's take a look at the rating distribution around the average for the movies in the dataset.

In [5]:

     In this list, movies have been rated on a scale of 1 to 10, 10 being
good and 1 being bad.  For each movie, the total number of votes, the
average rating, and the vote distribution are shown.  New movies are indicated
by a "*" before their entry.

     The vote distribution uses a single character to represent the percentage
of votes for each ranking.  The following characters codes can appear:

     "." no votes cast        "3" 30-39% of the votes  "7" 70-79% of the votes
     "0"  1-9%  of the votes  "4" 40-49% of the votes  "8" 80-89% of the votes
     "1" 10-19% of the votes  "5" 50-59% of the votes  "9" 90-99% of the votes
     "2" 20-29% of the votes  "6" 60-69% of the votes  "*" 100%   of the votes
p_dist_att = re.compile('[\d*.]{10}')

def p_to_prop(p):
    if p == '.':
        r = 0.
    elif p == '*':
        r = 1.
    return r

def dist_to_props(dist):
    if type(dist) is float:
        dist = int(dist)
    dist = str(dist)
    if len(dist) < 10:
        dist = '0'*(10-len(dist))+dist
    # testing if the dist ahere to the format:
    if p_dist_att.match(dist) and len(dist) == 10:
        props = np.array([p_to_prop(p) for p in dist])
        # adding what is not accounted for when we take
        # the lower bound of all the percentages: 
        # i.e. 10%-19%: take it as 10% 
        if props.sum() > 1.:
            print('This is not a valid distribution %s' % dist)
            props = None
            props += (1. - props.sum())/10. 
        props = None
    return props

if run_tests:
    print(dist_to_props('4310000000') is not None)
    print(dist_to_props('4378234782234') is None)
    print(dist_to_props('234') is not None)
    print(dist_to_props(21100121.0) is not None)
    # This one should fail but it is OK for now 
    print(dist_to_props('.........*') is not None  )
    print(dist_to_props('..1......*') is None) # sum to > 1

In [6]:
def props_to_avg(props):
    W_scores = [(i+1)*prop for i, prop in enumerate(props)] 
    return np.sum(W_scores)

def props_to_std(props):
#     Weighted standard deviation / 2nd moment: 
    std = np.sqrt(np.sum(props*(range(1, 11)-props_to_avg(props))**2))
    return std

def dist_to_avg(dist):
    props = dist_to_props(dist)
    if props is not None:
        r = props_to_avg(props)
        r = np.NaN
        print('problem with ', dist)
    return r

def dist_to_std(dist):
    props = dist_to_props(dist)
    if props is not None:
        r = props_to_std(props)
        r = np.NaN
        print('problem with ', dist)
    return r

if run_tests:
    print('testing distribution stats:')
    print('std:', dist_to_std(df['distribution'][0]))
    print('dist avg', dist_to_avg(df['distribution'][0]))
    print('real rating', df['rating'][0])

In [7]:
# adding new stats
df['dist_std'] = df['distribution'].apply(dist_to_std)
df['dist_avg'] = df['distribution'].apply(dist_to_avg)

In [8]:
# How bad is the estimating of the rating

pl.title('Distribution rating-avg', fontsize=20);

And next, let's see the distribution of standard deviations:

In [9]:
pl.title('Distribution of standard deviation for IMDB ratings', fontsize=20);

Here we see that the movie distribution has a medium around 2.25, and with a small ramp up at small standard deviations, and a fat tail of movies with high standard deviation. Let's get to the fun part: which ones are the most polarizing movies?

10 most polarizing movies

A polarizing movie will have ratings with large standard deviation, so let's start by ranking all the movies by their standard deviation, here abbreviated as dist_std.

In [10]:
df[['Title','Wikipedia link', 'rating', 'year', 'votes', 'dist_std']
   ].sort_values('dist_std', ascending=False)[:10]

Title Wikipedia link rating year votes dist_std
10311 Audacity wiki: Audacity 4.7 2015 3978 4.341371
22450 Night Train to Mundo Fine wiki: Night Train to Mundo Fine 2.7 1966 5832 4.224926
31338 Troppo belli wiki: Troppo belli 2.9 2005 4404 4.224926
19025 Kis Vuk wiki: Kis Vuk 2.2 2008 7292 4.150602
30266 The Starfighters wiki: The Starfighters 2.3 1964 2995 4.127953
15134 Fat Slags wiki: Fat Slags 2.6 2004 3549 4.127953
21760 Monster a-Go Go wiki: Monster a-Go Go 2.3 1965 6200 4.127953
31008 Titanic - La leggenda continua wiki: Titanic - La leggenda continua 2.1 2000 7653 4.127953
12813 Cool Cat Saves the Kids wiki: Cool Cat Saves the Kids 3.7 2015 2071 4.127953
10904 Ben & Arthur wiki: Ben & Arthur 2.2 2002 6915 4.127953

Movies with the highest standard deviation tend to have quite low average ratings, with the exception of number 1. Let's take a look at it:

There is a huge difference in the average rating of men and women, which explains a lot of the polarization here. The IMDB rating dataset unfortunately doesn't include the distribution of male vs female votes -- it would have been interesting to find movies that are the most divided by gender, and for that matter also country, age, etc.

Take a look at these movies; you might find something interesting. I have also added a link for each movie that looks up its Wikipedia entry in case you are curious (it doesn't work for the first one though).

Polarizing movies with high average ratings

Let's find the polarizing movies with an average rating of more than 7, as such a search might return movies that are more likely to be worth watching.

In [11]:
df[df['rating'] > 7.0
  ][['Title', 'rating', 'year', 'votes', 'dist_std']
  ].sort_values('dist_std', ascending=False)[:10]

Title rating year votes dist_std
10102 Aquarius 7.5 2016 5793 3.822303
25111 Sausage Party 7.5 2016 1890 3.494281
30796 Theri 7.6 2016 5295 3.494281
9354 Adolf Hitler: The Greatest Story Never Told 7.7 2013 1997 3.404042
4983 Rebelde Way 7.2 2002 1555 3.389690
21311 Meet the Mormons 7.1 2014 1698 3.350746
5268 Shahrzad 8.7 2015 1741 3.350746
16293 Gopala Gopala 7.7 2015 2735 3.350746
30631 The Weight of Chains 8.3 2010 3487 3.350746
8109 Tim and Eric Awesome Show, Great Job! 7.3 2007 8433 3.316625

Number one is Aquarius: another movie that women love and men in general would love to hate.

Another interesting find on this list is number 4, about Adolf Hitler.

We see that about 20% of its votes are 1s, whereas the majority 65% are 10s. I would be curious to find out what drives the 20% to give it 1; perhaps they just voted it 1 because the movie is about Adolf Hitler. I find it hard to imagine that the movie is really that bad if so many voted it 10.

Let's see what the most polarizing movies are with an average rating higher than 8.

In [12]:
df[df['rating'] > 8.0
  ][['Title', 'rating', 'year', 'votes', 'dist_std'
    ]].sort_values('dist_std', ascending=False)[:10]

Title rating year votes dist_std
5268 Shahrzad 8.7 2015 1741 3.350746
30631 The Weight of Chains 8.3 2010 3487 3.350746
4779 Planet Earth 9.5 2006 98627 3.207803
4301 Monty Python's Flying Circus 8.9 1969 43995 3.091925
3834 Life 9.2 2009 22836 3.091925
7597 The Twilight Zone 9.0 1959 43607 3.091925
25706 Sicilian Vampire 8.8 2015 8120 3.031089
22885 Om 8.8 1995 1236 2.872281
29665 The Parlor 8.2 2001 1045 2.872281
11003 Bey Yaar 8.7 2014 1518 2.872281

Here number one, Shahrzad, is a movie that especially young people love and elderly people dislike quite a lot.

Least polarizing movies:

Let's also check out which movies are the least polarizing.

In [13]:
    df[['Title', 'rating', 'year', 'votes', 'dist_std']].sort_values('dist_std', ascending=True)[:10]

Title rating year votes dist_std
23091 Ouija: Game Never Ends 8.7 2015 2611 1.388344
1649 Den andra sporten 8.8 2013 1158 1.465435
18452 Joe Kidd 6.5 1972 12295 1.552417
11304 Blood Work 6.4 2002 34105 1.552417
28872 The Invisible Man Returns 6.5 1940 1994 1.552417
24627 Rob the Mob 6.3 2014 8565 1.552417
28841 The Indian Fighter 6.4 1955 1336 1.552417
22000 Murder! 6.4 1930 4095 1.552417
18274 Janis: Little Girl Blue 7.4 2015 1438 1.564449
13377 De nieuwe wildernis 7.3 2013 1269 1.577973

It’s interesting to note that here that we get a good mix of movies with average ratings ranging from mediocre to high. The ratings are consistently high for the first two movies, while consistently mediocre for the rest.

It’s interesting to note that here, we get a good mix of movies with average ratings ranging from mediocre to high. The ratings are consistently high for the first two movies, while consistently mediocre for the rest.

So here in the end, let's take a look at the most and least polarizing movies with more than 10.000 votes:

In [14]:
df[df['votes'] > 10000][
                        ['Title', 'rating', 'year', 'votes', 'dist_std']
                        ].sort_values('dist_std', ascending=False)[:10]

Title rating year votes dist_std
22944 One Direction: This Is Us 4.1 2013 22092 4.055552
26738 Superbabies: Baby Geniuses 2 1.9 2004 25163 3.822303
18506 Jonas Brothers: The 3D Concert Experience 2.1 2009 17118 3.822303
23636 Pledge This! 1.9 2006 15272 3.822303
13202 Daniel der Zauberer 2.0 2004 12613 3.822303
29994 The Room 3.5 2003 29880 3.779881
16162 God's Not Dead 4.9 2014 30778 3.747999
24126 Ra.One 4.8 2011 31327 3.747999
13897 Dilwale 5.4 2015 21003 3.747999
23811 Prem Ratan Dhan Payo 4.9 2015 10147 3.612478

Number 1 here has 38.5% of reviewers giving it 10s and 42.5% 1s, and again a large difference between the ratings of men and women, where women tend to like it. Maybe not all that surprising for a movie about a boyband's rise to fame.

And lastly the movies with least polarization:

In [15]:
df[df['votes'] > 10000][
                        ['Title', 'rating', 'year', 'votes', 'dist_std']
                        ].sort_values('dist_std', ascending=True)[:10]

Title rating year votes dist_std
11304 Blood Work 6.4 2002 34105 1.552417
18452 Joe Kidd 6.5 1972 12295 1.552417
32721 X-Men 7.4 2000 446901 1.700000
18042 Invictus 7.4 2009 122821 1.700000
19995 Le prénom 7.3 2012 10023 1.700000
13207 Dans la maison 7.4 2012 21993 1.700000
19930 Le gamin au vélo 7.4 2011 20905 1.700000
9554 Alice Doesn't Live Here Anymore 7.4 1974 15405 1.700000
19862 Layer Cake 7.4 2004 132896 1.700000
29301 The Man Who Knew Too Much 7.5 1956 41687 1.700000

where we mostly get mediocre to good (but not great) movies.

We might be wondering whether movies have become more polarizing over time. Let's see what the trend is.

In [16]:
for func in [np.min, np.max, np.mean, np.median, np.std]:
pl.legend(loc=3, fontsize=18);
pl.title('Trend in polarization over time', fontsize=20);

This graph can be interpreted to say that newer movies are more polarizing. However, this could be driven by the larger number of recent movies on IMDB as we will see below. A lot of old movies are not listed on IMDB. I would guess that only the best movies make the cut; their mediocre counterparts could have easily been lost, destroyed, or hidden away by the movie studios.

In [17]:
df[df['year'] < 2015].groupby('year')['rating'].apply(np.sum).plot(xticks=range(1880, 2020, 10))
pl.title('Movies per year', fontsize=20);

Final Thoughts

Thank you for joining me on this journey. Here are a couple of things that I’ve learned:

  • There are definitely some very polarizing movies on IMDB that could potentially be hidden gems (I haven't watched any of the ones I found here yet). Please let me know if find something worthwhile.
  • The most polarizing movies tend to be polarized over gender.
  • The larger number of polarizing movies that are recent could be explained by the fact that there are just a lot more recent movies on IMDB, and the older ones that made it on IMDB are likely to the ones that people in general like.

There is a number of ways that I (or you) could extend this exploration. Below are a couple of things that I have thought about:

  • It would be quite interesting explore what movies tend to polarize over, e.g. gender, age, race, country, income. Currently, I don't have the data available, but IMDB have the data to do this, so we could imagine that it would be possible in the future.
  • I would also love to see if it was possible to make a predictive model that tell if a movie was polarizing or not given whatever data is available about it. I think it would be challenging to do this well with the current available data, and it would only really be interested if we could do this for movies where we didn't have the ratings.

One potential problem with this dataset is that most of the users who rate the movies are self-selected. It would probably look different if IMDB had taken a random sample of our population, asked them to watch a movie and rate it afterwards. What we have here are instead ratings of people who had been lured into watching a given movie either by trailers, commercials, or rankings like the ones we’ve seen.

Every movie, however, has a target audience, and perhaps the more polarizing ones could’ve done a better job at targeting the right segment. Either that, or maybe guys should be better at saying no to their girlfriends when they are invited to watch a chick flick.

I hope you’ve enjoyed the read. Let me know if you have ideas for other things that would be interesting to look at.


PS: I have made the code available on github.


I owe a lot of credit to my movie loving cousin Mads Lundgaard, who told me about the hidden information in the movie rating distribution many years ago.

A warm thank you also goes to Hanh Nguyen for editing.