## Math still not the answer

I wrote a quick (but not very elegant) `python` script to retrieve locally enough data from www.metacritic.com for pattern recognition purposes. The main goal is to help me decide how much I will enjoy a movie, before watching it. I included the script at the end of the post, in case you want to try it yourself (and maybe improve it too!). It takes a while to complete, although it is quite entertaining to see its progress on screen. At the end, it provides with two lists of the same length: `critics`—a list of `str` containing the names of the critics; and `scoredMovies`—a list of `dict` containing, at index `k`, the evaluation of all the movies scored by the critic at index `k` in the previous list.

For example:

**>>> critics[43]**

‘James White’

**>>> scoredMovies[43]**

{‘hall-pass’: 60, ‘the-karate-kid’: 60, ‘the-losers’: 60,

‘the-avengers-2012’: 80, ‘the-other-guys’: 60, ‘shrek-forever-after’: 80,

‘the-lincoln-lawyer’: 80, ‘the-company-men’: 60, ‘jonah-hex’: 40,

‘arthur’: 60, ‘vampires-suck’: 20, ‘american-reunion’: 40,

‘footloose’: 60, ‘real-steel’: 60}

The number of scored films by critic varies: there are individuals that gave their opinion on a few dozen movies, and others that took the trouble to evaluate up to four thousand flicks! Note also that the names of the movies correspond with their web pages in www.metacritic.com. For example, to see what critics have to say about the “Karate Kid” and other relevant information online, point your browser to www.metacritic.com/movie/**the-karate-kid**. It also comes in very handy if there are several versions of a single title: *Which “Karate Kid” does this score refer to, the one in the eighties, or Jackie Chan’s?*

Feel free to download a copy of the resulting data [here] (note it is a large file: 1.6MB).

But the fact that we have that data stored locally allows us to gather that information with simple `python` commands, and perform many complex operations on it.

## Sometimes Math is not the answer

I would love to stand corrected in this case, though. Let me explain first the reason behind this claim—It will take a minute, so bear with me:

Say there is a new movie released, and you would like to know how good it is, or whether you and your partner will enjoy watching it together. There are plenty of online resources out there that will give you enough information to make an educated opinion but, let’s face it, you will not have the complete picture unless you actually go see the movie (sorry for the pun).

For example, I fell for “The Blair Witch Project:” their amazing advertising campaign promised me thrill and originality. On top of that, the averaged evaluation of many movie critics that had access to previews claimed that this was a flick not to be missed… Heck, I even bought the DVD for my sister before even watching it!—She and I have a similar taste with respect to movies. The disappointment was, obviously, epic. Before that, and many a time afterwards, I have tripped over the same stone. If nothing else, I learned not to trust commercials and sneak previews any more (“Release the Kraken!,” anyone?)

The only remaining resource should then be the advice of the knowledgeable movie critics—provided you trust on their integrity, that is. Then it hit me: My taste in movies, so similar to my sister’s, could be completely different to that of the “average critic”. Being that the case, why would I trust what a bunch of experts have to say? The mathematician in me took over, and started planning a potential algorithm:

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