Whose ratings should you trust? IMDB, Rotten Tomatoes, Metacritic, or Fandango?

Should you watch a movie? Well, there are a lot of factors to consider, such as the director, the actors, and the movie’s budget. Most of us base our decision off of a review, a short trailer, or just by checking the movie’s rating. There are a few good reasons you would want to avoid reading reviews, or watching a trailer, although they bring much more information than a rating. First, you may want to completely avoid spoilers, no matter how small. I understand that! Second, it could be that you want an uninfluenced experience of watching that movie.

This usually applies only to reviews, which are sprinkled with frames, like “this is a movie about the complexity of the universe” or “this movie is really not about love”. Once these frames get encoded in your short- term memory, it’s really hard to stop them from interfering with your own movie experience. Another good reason is that if you’re tired or hurried, you might not want to read a review, let alone watch a 2- minute trailer. So a numeric movie rating seems to be a good solution in quite a few situations, for quite a few people. This article aims to recommend a single website to quickly get an accurate movie rating, and offers a robust, data- driven argumentation for it. Criteria for “the best”Making such a recommendation it’s a lot like saying “this is the best place to look for a movie rating,” which is an evaluative statement, resting on some criteria used to determine what is better, what is worse or worst, and what is best, in this case.

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For my recommendation, I will use one single criterion: a normal distribution. The best place to look for a movie rating is to see whose ratings are distributed in a pattern which resembles the most, or is identical to, the pattern of a normal distribution, which is this: given a set of values lying in a certain interval, most of them are in the middle of it, and the few others at that interval’s extremes.

Generally, this is how a normal (also called Gaussian) distribution looks like: What’s the rationale behind this criterion? Well, from my own experience consisting of several hundred movies, I can tell that I’ve seen: a few outstanding ones that I’ve watched several timesa couple that were really appalling, and made me regret the time spent watching themand a whole bunch of average ones, for most of which I can’t even remember the plot anymore. I believe that most people — whether critics, cinephiles, or just regular moviegoers — have had a similar experience.

If movie ratings do indeed express movie quality, then we should see the same pattern for both. Given that most of us assess the bulk of movies as being of an average quality, we should see the same pattern when we analyze movie ratings. A similar logic applies for bad and good movies. If you’re not yet persuaded that there should be such a correspondence between the patterns, think about the distribution of ratings for a single movie. As many people rate the movie, it’s not a leap of faith to assume that most often there will be many of them with similar preferences. Maan Drama Episode 14 on this page. Oliver`S Deal Full Movie Part 1 there.

They’ll generally agree that the movie is either bad, average, or good (I will quantify later these qualitative values). Also, there will be a few others who assess the movie with one of the other two qualitative values. If we visualized the distribution of all the ratings for an individual movie, we would most likely see that one single cluster forms in one of the areas corresponding to a low, an average, or a high rating. Provided most movies are considered average, the cluster around the average area has the greatest likelihood of occurring, and the other two clusters have a smaller (but still significant) likelihood. Note that all these likelihoods can be quantified in principle, but this would require a lot of data, and would have the potential to turn this article into a book.)The least likely would be a uniform distribution in which there are no clusters, and people’s preferences are split almost equally across the three qualitative values. Given these likelihoods, the distribution of ratings for a large enough sample of movies should be one with a blunt cluster in the average area, bordered by bars of decreasing height (frequency), resembling, thus, a normal distribution.

If you have found all this hard to understand, consider this illustration: IMDB, Rotten Tomatoes, Fandango, or Metacritic? Now that we have a criterion to work with, let’s dive into the data. There are a lot of websites out there that come up with their own movie ratings. I have chosen only four, mainly based on their popularity, so that I could get ratings for movies with an acceptable number of votes. The happy winners are IMDB, Fandango, Rotten Tomatoes, and Metacritic.

For the last two, I have focused only on their iconic rating types — namely the tomatometer, and the metascore — mainly because these are more visible to the user on each of the websites (meaning it’s quicker to find them). These are also shared on the other two websites (the metascore is shared on IMDB and the tomatometer on Fandango). Besides these iconic ratings, both websites also have a less- featured rating type where only users get to contribute. I have collected ratings for some of the most voted and reviewed movies in 2. The cleaned dataset has ratings for 2.

Github repo. I haven’t collected ratings for movies released before 2. Fandango’s rating system soon after Walt Hickey’s analysis, which I will refer to later in this article. I’m aware that working with a small sample is risky, but at least this is compensated by getting the most recent snapshot of the ratings’ distributions. Before plotting and interpreting the distributions, let me quantify the qualitative values I used earlier: on a 0 to 1.

Please take note of the distinction between quality and quantity. To keep it discernible in what follows, I will refer to ratings (quantity) as being low, average, or high. As before, the movie quality is expressed as bad, average, or good.

If you worry about the “average” term being the same, don’t, because I will take care to avoid any ambiguity. Now let’s take a look at the distributions: At a simple glance, it can be noticed that the metascore’s histogram (that’s what this kind of graph is called) most closely resembles a normal distribution. It has a thick cluster in the average area composed of bars of irregular heights, which makes the top neither blunt, neither sharp. Watch Hot Property Online Fandango there. However, they are more numerous and taller than the bars in each of the other two areas, which decrease in height towards extremes, more or less gradually. All these clearly indicate that most of the metascores have an average value, which is pretty much what we’re looking for. In the case of IMDB, the bulk of the distribution is in the average area as well, but there is an obvious skew towards the highest average values. The high ratings area looks similar to what would be expected to be seen for a normal distribution in that part of the histogram.

However, the striking feature is that the area representing low movie ratings is completely empty, which raises a big question mark. Initially, I put the blame on the small sample, thinking that a larger one would do more justice to IMDB. Luckily, I was able to find a ready- made dataset on Kaggle containing IMDB ratings for 4,9. To my great surprise, the distribution looked like this: The shape of the distribution looks almost the same as that for the sample with 2. The bulk of the values is still in the average area, which makes the IMDB rating worth considering further for a recommendation, although is clearly hard to rival the metascore, with that skew.

Anyway, what’s really great about this outcome is that it can be used as a strong argument to support the thesis that the 2.