When the Facts Get in the Way of a Good Story

Normally I’m not bothered by moviemakers taking some story-tellers license when making a movie based on a true story, some adjusting of the timeline, or adding a fictitious character to better tell the essential story. In those instances, though, the essence of the story isn’t compromised. In the instance of McFarland USA, you end up with a 50% untrue story based on a true story. The story of the team is true but the story of the coach is 90% false

Friday night is movie night for my wife, Pam, and I. One of the neat features Netflix provides is the capability to add separate profiles to your account for up to five members of the family. This has allowed Pam to input her own ratings of movies which produce her own Netflix recommendations based on her taste in movies. So, on Friday nights we seek out movies that are recommended for both of us and settle in for an enjoyable movie night.

On a recent Friday night we watched McFarland USA from the Disney Studios. It is the kind of movie we both enjoy. Netflix would probably group it in the “inspirational coach of underdog kids sports movies based on a true story” group. We both loved the movie. Pam gave it five stars and I gave it a nine out of ten, which if you read my last post converts to a rating of five stars on Netflix.

As a general practice, I don’t read critics reviews of a movie until after I see the movie. For McFarland USA, the critics review at the top of the IMDB list of external reviews referenced a website I had never visited before, historyvshollywood.com. It’s a niche movie website that specializes in fact checking movies based on a true story. When I read the History vs. Hollywood fact check of McFarland USA, I discovered that a critical chunk of the McFarland USA story was a fabrication. Frankly, I felt cheated. Normally I’m not bothered by moviemakers taking some story-tellers license when making a movie based on a true story, some adjusting of the timeline, or adding a fictitious character to better tell the essential story. In those instances, though, the essence of the story isn’t compromised. In the instance of McFarland USA, you end up with a 50% untrue story based on a true story. The story of the team is true but the story of the coach is 90% false.

One of the self-imposed posting rules that I intend to keep is that I won’t discuss details of a recent movie, no spoilers (Classic movies, like Saturday Night Fever, which have been around for years, however, are fair game for discussion). If you have already watched McFarland USA, or you don’t mind “spoilers”, you can link to the Hollywood vs. History fact check of the movie here.

Rather than getting into the details of the movie, I’d like to address the issue of whether the discovery of the fact that Disney engaged in blatant manipulation should be cause to go back and rerate the movie. After all, if the rating was influenced by the inspiration provided by a true story, shouldn’t the rating reflect the different view of the movie that exists when you discover the story is full of holes. The answer is an emphatic No. Predictive modeling is a science. Check your emotions at the door. The reality is that, despite the fabrications implanted in this particular movie, Pam and I still like to watch well-made “inspirational coach of underdog kids sports movies based on a true story” and we’d like to see more of them. Despite being a little less inspired after learning about the facts behind McFarland USA, it is a well-made and entertaining story and we certainly don’t want facts to get in the way of a good story.

 

 

 

 

 

 

 

 

 

 

 

 

 

Rating Movies: If You Put Garbage In, You’ll get Garbage Out

Netflix, MovieLens, and Criticker are predictive models. They predict what movies that you will like based on your rating of the movies you have seen. Just like the predictive model discussed above, if the ratings that you input into these movie models are inconsistent from movie to movie, you increase the chances that the movie website will recommend to you movies that you won’t like. Having a consistent standard for rating movies is a must.

In my prior life, I would on occasion find myself leading a training session on the predictive model that we were using in our business. Since the purpose of the model was to help our Account Executives make more effective business decisions, one of the points of emphasis was to point out instances when the model would present them with misleading information that could result in ineffective business decisions. One of the most basic of these predictive model traps is that it relies on data input that accurately reflects the conditions being tested in the model. If you put garbage into the model, you will get garbage out of the model.

Netflix, MovieLens, and Criticker are predictive models. They predict movies that you might like based on your rating of the movies you have seen. Just like the predictive model discussed above, if the ratings that you input into these movie models are inconsistent from movie to movie, you increase the chances that the movie website will recommend to you movies that you won’t like. Having a consistent standard for rating movies is a must.

The best approach to rating movies is a simple approach. I start with the Netflix guidelines to rating a movie:

  • 5 Stars = I loved this movie.
  • 4 Stars = I really liked this movie.
  • 3 Stars = I liked this movie.
  • 2 Stars = I didn’t like this movie.
  • 1 Star = I hated this movie.

When I’ve used this standard to guide others in rating movies, the feedback has been that it is an easily understood standard. The primary complaint has been that sometimes the rater can’t decide between the higher and lower rating. The movie fits somewhere in between. For example, “I can’t decide whether I “really like” this movie or just “like” it. This happens enough that I’ve concluded that a 10 point scale is best:

  • 10 = I loved this movie.
  • 9 = I can’t decide between “really liked” and “loved”.
  • 8 = I really liked this movie.
  • 7 = I can’t decide between “liked” and “really liked”.
  • 6 = I liked this movie.
  • 5 = I can’t decide between “didn’t like” and “liked”.
  • 4 = I didn’t like this movie.
  • 3 = I can’t decide between “hated” and “didn’t like”.
  • 2= I hated this movie.
  • 1 = My feeling for this movie is beyond hate.

The nice thing about a 10 point scale is that it is easy to convert to other standards. Using the scales that exist for each of the websites, an example of the conversion would look like this:

  • IMDB = 7  (IMDB uses a 10 point scale already)
  • Netflix = 7 /2 = 3.5 = 4 rounded up.  (Netflix uses 5 star scale with no 1/2 stars)
  • Criticker = 7 x 10 = 70 (Criticker uses 100 point scale).
  • MovieLens = 7 /2 = 3.5 (MovieLens has a 5 star scale but allows input of 1/2 star)

Criticker, being on a 100 point scale, gives you the capability to fine tune your ratings even more. I think it is difficult to subjectively differentiate, for example, between an 82 and an 83. In a future post we can explore this issue further.

So from one simple evaluation of a movie you can generate a consistent rating across all of the websites that you might use. This consistency allows for a more “apples to apples” comparison.

So throw out the garbage. Good data in will produce good data out, and a more reliable list of movies that you will “really like”.

 

Netflix Streaming: The Other Story

So if you go to streaming Netflix with a list of movies in mind that you will “really like” you are bound to be somewhat disappointed, and Netflix doesn’t want you to be disappointed.

When you think about it, the story of Netflix is rather remarkable. They slayed one industry, video rental stores. They no longer exist. Since Netflix began streaming their entertainment properties in 2007, cable TV companies have been struggling to stay relevant. Many millennials have never been cable customers, preferring streaming options like Netflix, and more and more existing cable customers are cutting the cord. Now, Netflix is in a pitched battle with the producers of movie and television entertainment. On February 1, 2013, Netflix premiered House of Cards to rave reviews and signaled their intent to become the first worldwide streaming network of original content, for both TV and cinematic films. Their powerful rivals in this battle are responding (In response to Netflix’ announcement of 600 hours of original programming in 2016, HBO announced 600 hours of their own.) and the outcome is still in doubt, but, if track record means anything, don’t bet against Netflix.

In each of these Netflix inspired industry revolutions, Netflix has had their finger on the pulse of consumer frustration. They understood how frustrating it was to do business with video stores; the inability to find movies that you would “really like”, the wasted expense when you didn’t get a chance to watch the movie you rented before it had to be returned, and the additional fees for returning the video late. Netflix had an answer for all of these DVD rental frustrations. Netflix understood the frustration of expensive cable bills that supported obscure channels that never got watched and responded with an internet alternative. And, now, Netflix is using all of the data they’ve collected that indicate what their customers enjoy watching and producing original content that their customers should enjoy watching.

So, how does all of this Netflix history impact our quest to find movies that we will “really like”? Put simply, the gold standard algorithm used by Netflix-DVD may be an endangered species. Netflix’ vision of creating a world wide streaming network filled with their own content is being successfully executed, with 75 million subscribers in 190 countries. Of those 75 million subscribers, only 5 million are DVD subscribers. While the DVD business contributes profit to the bottom line of Netflix, it is a part of its past, not its future.

As for the algorithm that Netflix offered $1 million to try to improve upon, it no longer fits into their plans. Its value to the DVD business is that it assists subscribers to find movies that they will “really like” and put them in a queue so that, even if the movies and shows you most want to watch are unavailable, the next DVD in the queue will be one that you will “really like”. On the streaming side, satisfying their customers requires a different strategy. Because of the cost to license movies and shows to stream, and because of the huge investment Netflix has made in original content, the library of entertainment that exists on Netflix is smaller. It was reported last week by Allflicks, a website that tracks what’s available to watch on Netflix, that in a little more than 2 years the number of shows and movies available to watch on Netflix has shrunk by 31.7%. The number of movies available to watch instantly went from 6,404 to 4,335 during that time. So if you go to streaming Netflix with a list of movies in mind that you will “really like” you are bound to be somewhat disappointed, and Netflix doesn’t want you to be disappointed.

Netflix is a data behemoth. Not only do they collect the ratings that you give to each movie, they know what movies you’ve browsed, when you browsed it, on what device you browsed it. They know if you started to watch a movie and stopped. From that data they have determined that if a typical viewer doesn’t find something to watch on Netflix within the first 5 minutes of browsing, they will go someplace else to find something to watch. They have created 76,897 unique ways to describe their content. They know which of those 76,897 will most appeal to you and organize them into rows of content and put them at the top of your list so that you will choose to watch one of their movies or shows available on Netflix. They even know to not show you heavy movies like Schindler’s List on a Wednesday night when you just get home from work. Yes, it is that creepy.

My recommendation is to use Netflix like any other home viewing entertainment option available. Know what you want to watch before you go there. If they have it, great. If not, go somewhere else to watch it.

 

 

Criticker: Whose Movie Recommendation do you trust?

Criticker is not as well known a movie site as Rotten Tomatoes or IMDB. Unlike those better known sites, Criticker evaluates movies based on your taste in movies. More accurately, it estimates the rating that you will probably give a movie based on the ratings of other Criticker users that have the most similar taste in movies to you.

A friend, let’s call him Jack, recommends a movie to you. You watch the movie and it is one of those movie experiences that reminds you why you enjoy watching movies. Another friend, let’s call her Jill, recommends a movie. You watch it and you have to prop up your eyelids with toothpicks to stay awake. If future recommendations from Jack and Jill follow the same pattern, you keep on watching movies recommended by Jack but stop watching movies recommended by Jill. You reach the conclusion that you and Jack have similar taste in movies and you and Jill have different taste in movies. In the end you trust the movie recommendations of Jack because you seem to really like the same movies. This is the basis for the Criticker website movie ratings.

Criticker is not as well known a movie site as Rotten Tomatoes or IMDB. Unlike those better known sites, Criticker evaluates movies based on your taste in movies. More accurately, it estimates the rating that you will probably give a movie based on the ratings of other Criticker users that have the most similar taste in movies to you. Criticker has created a tool called the TCI (Taste Compatibility Index)). It uses the index to identify moviegoers who statistically have the most similar taste in movies to you and aggregates the scores from those moviegoers to produce the probable rating, from 1 to 100, that you might give the movie you’re interested in watching.

Here’s the thing. No matter how similar Jack’s taste in movies is to yours, there will be times when Jack recommends a movie that you don’t like. If that happens you may begin to question whether Jack really does have the same taste in movies. If Jack recommended 10 movies to you and you really liked 8 of them, you can’t be sure that you will like 8 of the next 10 movies he recommends. It may be a random event that you like 8 of Jack’s recommendations. It could just as easily have been 5 or 6. If, on the other hand, Jack has recommended 100 movies and you really liked 80 of them, the chances that you will really like 8 of the next 10 movies he recommends are greater. The same is true with Criticker. The more movies that you rate on the website, the more confident you can be of the accuracy of the probable rating that Criticker provides for the movies you are interested in seeing.

To get started, use the link at the top of the page to go to the website. Set up an account. It’s free. Then start rating movies that you’ve seen. Criticker asks you to rate movies on a 1 to 100 scale. If you ask me, that’s tough to do. For example, what criteria do you use to give one movie an 86 and another movie an 87. Unless you have established criteria to differentiate movies that finely, it’s almost impossible to do without sacrificing consistency in your ratings . In a future post, I’ll outline how I established criteria for a 100 point scale. For now, I would keep your scoring simple by rating movies on a 10 point scale and converting the score to a 100 point scale for Criticker. For example, if you rate a movie 8 out of 10 on IMDB, score it as an 80 for Criticker. If, when you were rating the movie for IMDB, you had difficulty deciding whether it was a 7 or an 8, you can rate it a 75 on Criticker. The important thing is to have a consistent set of scoring rules that are applied uniformly across all of your movies.

Go ahead and get started. Pretty soon you’ll find that there are many people out there whose movie recommendations you can trust. Just remember that there is no one whose taste is exactly like yours.

What Movie Are You?

In the next series of Posts, I will introduce movie recommender sites that try to answer the question “What Movie Are You” based on the movies that you “really like”.

This past weekend I watched Saturday Night Fever for the fourth time. Roger Ebert mentions in his Great Movies review of the film that it was Gene Siskel’s favorite movie of all-time, having seen it 17 times. I’m in the Siskel camp. It is one of my favorite movies of all-time as well. I watched it for the first time in a Chicago area theater when it first came out in 1977. I was in the first year of my new job, the first year of 35 successful years with the same company. I was within a year of meeting my future wife, married 36+ years and still going strong. And, a little less than two years prior, I had left the middle class, New England town I grew up in and moved to the Chicago area. As it turned out, it was that momentous decision that shaped my entire adult life.

When I mention to others that Saturday Night Fever is a favorite of mine, a typical reaction is “I hate disco”. It is so much more than a disco movie. Disco is just its milieu. It is a movie about dreams and the barriers that get in the way of realizing those dreams. It is about being stuck in your current existence and coming to the realization that you won’t like the consequences of staying stuck. It is about breaking away and giving yourself a chance.

As I watched Saturday Night Fever that first time, I began to identify with the movie. I identified with Tony Manero’s yearning to create a bigger footprint in his life than he could in his Bay Ridge neighborhood. I recognized the emotional traps that were holding him back from pursuing his dream. I felt his relief when he finally decided to make the move to Manhattan, even though he had no job to go to. I was Saturday Night Fever without, of course, the disco dance king lifestyle.

In the next series of Posts, I will introduce movie recommender sites that try to answer the question “What Movie Are You” based on the movies that you “really like”. No site can identify all of the deep down personal reasons why a movie connects with you. Under my system, for example, there was only a 28.2% chance that I would “really like” Saturday Night Fever. But, the movies that you do “really like”, do identify the types of movies that draw you in and these sites effectively select quality movies within genres you enjoy watching. The sites are all different, using a variety of assumptions and methodologies. They are all just waiting for you to start rating the movies you’ve seen, both good and bad, so that they can get to know you.

In the meantime, consider sharing a comment on your reaction to this Post. Are there any movies that connect with you on a personal level? What Movie Are You?

The Shiny Penny

You pull a pocketful of change out of your pocket and two coins catch your eye. The first is a tarnished old quarter that you can hardly identify as a quarter and the second is a brand new shiny penny. You’ll probably get rid of that old tarnished quarter in the first transaction that comes along. You have an irrational fear that its not worth the same as other quarters in your pocket. As for the shiny penny,you’ll probably use up all of the older pennies in your pocket before you give up that shiny new penny.

We have a tendency to select movies to watch on the same basis. We prefer to watch a mediocre shiny new movie rather than a much better tarnished older movie. We can’t resist the allure of the unknown experience of the new. Or, we fear that the new movie will come up in conversation at a social gathering and we’ll be left out of the discussion. Whatever the reason, it’s an emotional choice and, like many emotional choices, it comes with greater risk of regret.

If your goal is to spend a couple of hours totally engaged in a magical movie experience, then you need to be more selective in what you watch and you need to broaden your pool of movies to watch. The reality is that the number of shiny pennies that are magical movie experiences is limited. In any given year, there may only be a handful of new movies that are “wow” movies. There might be a dozen or two more that you’ll “really like”. The less systematic you are in your movie selection the more unsatisfying movie experiences you’ll need to go through before you find those dozen or so shiny new movies worth watching.

So, how do you improve your chances of picking movies you’ll “really like”? First, create a watchlist. Identify the movies you want to watch before you sit down on the couch and start scrolling through the list of movies available on Netflix, Amazon Prime, or On Demand. Second, set some criteria for the movies you’ll put on your watchlist. It can be as simple as targeting movies that sound interesting to you and are Certified Fresh by Rotten Tomatoes or it can be as complex as the Bayesian probability approach that I use (more on my approach in a later post). Third, don’t limit yourself to recent movies, Cast a wider net. Movie-making has been going on for over 100 years. If you really don’t like old movies, check out movies released since 2000, for example. Finally, include great movies that you’ve seen before in the pool of movies available for your watchlist.

If you click the link on the sidebar of this page titled My Top Ten Movies to Watch, you can see my current watchlist. These are movies that I either haven’t seen before or haven’t seen in the last 15 years. These are the movies with the highest probability that I will “really like”. The list includes newer movies that I’ve never seen before and older movies that I have seen before.

Every month I remove movies from my database that I haven’t watched in fifteen years. Those that meet my selection criteria, I’ll watch again. If it’s a movie that I’ve seen only once before, it will often feel like I’m watching it for the first time. And, if it’s one of those magical movies, I’m grateful that I valued that old tarnished quarter instead of the shiny new penny.

Will I “Really Like” this Blog?

I love baseball, movies and analyzing data. Since analyzing baseball data is a well-traveled path and analyzing data from baseball movies is too narrow a path, I am left with the intersection of movies and data analysis. Specifically, I analyze data generated by five Movie Ratings websites: IMDB, Rotten Tomatoes, Netflix-DVD, Movielens, and Criticker. There are other sites I could include and maybe will include in the future. For example, Metacritic is a fairly well known website but, for now, I’ve chosen not to use Metacritic because it is similar to Rotten Tomatoes with a less robust volume of movie ratings. I focused on these five sites because they are a good cross section of the methodologies I’ve come across that are used to rate movies.

Over the past few years I have built and maintained a database of all of the movies I have watched in the last 15 years. As of January 31, 2016, my database contains 1,957 movies. For each movie, I have entered the ratings provided by IMDB and Rotten Tomatoes, as well as the personalized ratings generated by Netflix-DVD, Movielens, and Criticker. If you are unfamiliar with these sites, the links at the top of the page will get you to the home page for each site. IMDB and Rotten Tomatoes don’t require any work on your part to see ratings. Criticker and Movielens base their ratings off of the ratings you provide for the movies you’ve seen. Their value as movie guides requires some effort on your part. Netflix-DVD is also based off of your ratings but has the additional requirement that you be a subscriber to their DVD service.

At about this point you are probably asking, “Why is he doing this?”  Initially, I wanted to test which website was the best at leading me to movies that I’d “really like”. Instead, I ended up with an algorithm, using all five websites, that provides me with the probability that I will “really like” a particular movie.  For example, the two Oscar nominated movies for Best Picture this year that I haven’t seen are Bridge of Spies and The Revenant. Based on my algorithm, there is a 98.1% chance that I will “really like” Bridge of Spies while there is only a 49.5% chance that I will “really like” The Revenant. I will watch both movies but it will be with the recognition that Bridge of Spies is close to a sure thing while The Revenant is a 50/50 proposition. Where these probabilities come from is a topic for another day.

 I’ve been using some form of this algorithm to select the movies to watch over the last two years. A comparison of the last two years with the first two years used in my study would suggest I’ve been pretty successful. I watched 165 movies during the 1999 & 2000 calendar year and “really liked” 72 of them. Over the last two years, 2014 & 2015, I watched 182 movies and “really liked” 163.  I’ve gone from “really liking” 44% of my movies in the first years of my study to 90% over the last two years.                                                      

I have a friend of mine who, on occasion, will ask me what I thought of a particular movie. Mostly, I’ll tell him I “really liked” it. He then dismisses my recommendation by saying “but you like everything.” He’s right! I’ve reached the point where 9 out of 10 movies I watch I “really like”. It’s not, however, because I like everything. It’s because I’m able to identify those movies that I probably will “really like” and avoid watching those that I probably won’t like.

As to the question posed in today’s title, “Will I ‘Really Like’ this Blog?” I’ll say this. If you frequently watch movies that you wish you hadn’t, you will “really like” this blog. You won’t have to build your own personal movie selection algorithm. You will, though, gain a better understanding of various movie websites and how they can help you pick a movie to watch that you will “really like”.