Charts: Lists
This page shows you the list charts. By default, the movies are ordered by how many times they have been marked as a favorite. However, you can also sort by other information, such as the total number of times it has been marked as a dislike.
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IMDb: Highest-rated TV Episodes 1960-1969 with 100+ Votes, minimum rating 7.8
Favs/dislikes: 1:0. As of April 12, 2013 -
IMDb: Highest-rated TV Episodes 1970-1979 with 100+ Votes, minimum rating 7.8
Favs/dislikes: 2:0. As of April 12, 2013 -
IMDb: Highest-rated TV Episodes 1980-1989 with 100+ Votes, minimum rating 8.2
Favs/dislikes: 1:0. As of April 15, 2013 -
IMDb: Highest-rated TV Episodes 1990-1999 with 100+ Votes, minimum rating 8.7
Favs/dislikes: 2:0. As of April 15, 2013 -
IMDb: Highest-rated TV Episodes 2000-2009 with 100+ Votes, minimum rating 8.9
Favs/dislikes: 1:0. As of April 15, 2013 -
IMDb: Highest-rated TV Episodes 2010-2013 with 100+ Votes, minimum rating 8.9
Favs/dislikes: 0:0. as of January 4, 2014 -
IMDB list
Favs/dislikes: 0:0. -
imdb lower than 5.9, ranked
Favs/dislikes: 0:0. -
IMDb Lowest Rated Movies
Favs/dislikes: 2:0. Claim none of this work as my own - only porting IMDb's list to ICM with biweekly updates. from IMDb: [quote]The Lowest Rated Movie list only includes theatrical features. Shorts, TV movies, and documentaries are not included. The list is ranked by a formula which includes the number of ratings each movie received from users, and value of ratings received from regular users. To be included on the list, a movie must receive ratings from at least 10,000 users.[/quote] -
IMDb marius
Favs/dislikes: 0:0. Filmer fra imdb -
IMDB Most Voted
Favs/dislikes: 1:0. Top 250 most voted movies on IMDB as of January 1, 2019. -
IMDb Most Voted (on hiatus)
Favs/dislikes: 3:0. All films listed on IMDb with over 100,000 votes. In no particular order, here are The Good, the Bad, and the Indifferent. -
IMDb + AFI's Top
Favs/dislikes: 0:0. -
Imdb ratings
Favs/dislikes: 0:0. -
IMDB Ratings
Favs/dislikes: 0:0. -
IMDB Shadow Top 100
Favs/dislikes: 2:0. The Shadow Top 100 contains the titles that have disappeared from the IMDb Top 250 but have been on it longest. These are the titles with the highest recurrence. -
IMDb Top 10,000 adjusted to my specifications (Part I)
Favs/dislikes: 2:0. . A little number game. What have I done? . On April 9, 2023 I pulled data from IMDb ( www.imdb.com/interfaces/ ). Now the data does not all fit in a csv or excel file. That's why I decided to use Power Pivot to filter out the film types (movie, short, tv-MiniSeries, tv-Movie) that are most important to me. So I had a list of over 90,000 films. (I only included films with at least 250 votes and a rating of 2.5). . Basically, my focus is on the type "movie". . Therefore, in the first step of my specification, I assigned malus points (penalty points, minus points) for "shorts", "tv-MiniSeries" and "tv-Movies" as well as for the genres "Adult" and "Documentary". In the case of the "shorts", the penalty points even increased the shorter the film is. In my opinion, I only accept a film that is longer than 5 minutes. . In the second step, I gave plus and minus points depending on data ("most favorite movies" and "most official lists") on ICM ( www.icheckmovies.com/ ). . In the third step, I then also added a weighting by year to account for the significant distortion of timeliness in online vote counts. This weight was "votes x (1+((1977 minus the release year of the movie)/100)". I copied this great idea (adapted a bit at my discretion) from the Letterboxd user Prof. Ratigan. . I chose the year 1977 because from 1977 the VHS format for video recorders became more and more established and the home video recorder market became established as a result. . In the fourth step, I used the IMDb formula* for the "weighted rating" to adjust the film rating I had already specified. . *weighted rating (WR) = (v ÷ (v+m)) × R + (m ÷ (v+m)) × C where: R = my specified average for the movie (mean) = (Rating) v = my specified number of votes for the movie = (votes) m = minimum votes required to be listed in the Top 10,000 (currently 999) C = the minimum vote across the whole report (currently 4.9) . And now the following is the result, have fun with it. -
IMDb Top 10,000 adjusted to my specifications (Part II)
Favs/dislikes: 2:0. . A little number game. What have I done? . On April 9, 2023 I pulled data from IMDb ( www.imdb.com/interfaces/ ). Now the data does not all fit in a csv or excel file. That's why I decided to use Power Pivot to filter out the film types (movie, short, tv-MiniSeries, tv-Movie) that are most important to me. So I had a list of over 90,000 films. (I only included films with at least 250 votes and a rating of 2.5). . Basically, my focus is on the type "movie". . Therefore, in the first step of my specification, I assigned malus points (penalty points, minus points) for "shorts", "tv-MiniSeries" and "tv-Movies" as well as for the genres "Adult" and "Documentary". In the case of the "shorts", the penalty points even increased the shorter the film is. In my opinion, I only accept a film that is longer than 5 minutes. . In the second step, I gave plus and minus points depending on data ("most favorite movies" and "most official lists") on ICM ( www.icheckmovies.com/ ). . In the third step, I then also added a weighting by year to account for the significant distortion of timeliness in online vote counts. This weight was "votes x (1+((1977 minus the release year of the movie)/100)". I copied this great idea (adapted a bit at my discretion) from the Letterboxd user Prof. Ratigan. . I chose the year 1977 because from 1977 the VHS format for video recorders became more and more established and the home video recorder market became established as a result. . In the fourth step, I used the IMDb formula* for the "weighted rating" to adjust the film rating I had already specified. . *weighted rating (WR) = (v ÷ (v+m)) × R + (m ÷ (v+m)) × C where: R = my specified average for the movie (mean) = (Rating) v = my specified number of votes for the movie = (votes) m = minimum votes required to be listed in the Top 10,000 (currently 999) C = the minimum vote across the whole report (currently 4.9) . And now the following is the result, have fun with it. -
IMDB Top 25 Box Office of 2014
Favs/dislikes: 2:0. -
IMDB TOP 250
Favs/dislikes: 0:0. -
IMDb Top 250 (1996)
Favs/dislikes: 1:0. The IMDb Top 250 as it existed in 1996 -
IMDb Top 250 (1996)
Favs/dislikes: 2:0. This badge is awarded for rating the entire IMDb Top 250 as of December 31st, midnight PST, 1996. -
IMDb Top 250 (1997)
Favs/dislikes: 1:0. The IMDb Top 250 as it existed in 1997 -
IMDb Top 250 (1997)
Favs/dislikes: 2:0. This badge is awarded for rating the entire IMDb Top 250 as of December 31st, midnight PST, 1997. -
IMDb Top 250 (1998)
Favs/dislikes: 1:0. The IMDb Top 250 as it existed in 1998
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