Unfocused Does Not Mean an Absence of Ideas.

I’ve been very unfocused this week. Perhaps it’s the jetlag from my return trip from the West Coast. Perhaps it’s because my granddaughter not only shared her love but also her cold (believe me, the love is worth the cold). Perhaps it’s the Springtime in February weather we’ve been experiencing on the East Coast this week. For whatever reason, I’ve been unable to focus on a single theme for this week’s “really like” post. But, that’s okay. I can make unfocused, half-baked ideas about “really like” movies work.

I’ve been very unfocused this week. Perhaps it’s the jetlag from my return trip from the West Coast. Perhaps it’s because my granddaughter not only shared her love but also her cold (believe me, the love is worth the cold). Perhaps it’s the Springtime in February weather we’ve been experiencing on the East Coast this week. For whatever reason, I’ve been unable to focus on a single theme for this week’s “really like” post. But, that’s okay. I can make unfocused, half-baked ideas about “really like” movies work.

I was going to write something insightful about Black Panther only to discover that the airwaves and the internet have been saturated with stories about this cultural phenomenon. Anything I might have to say would get lost in the wave of Black Panther mania. I’d guess that this isn’t the last time that the hype machine will take over our cultural conversation. Some of it will be deserved. It might even be deserved for Black Panther. Its cultural significance is unquestioned. Its greatness as a movie has to meet the test of time. As I did last year for Dunkirk, by throwing down a “great” movie benchmark (Saving Private Ryan) for comparison, we can benchmark Black Panther’s greatness over time. An appropriate benchmark for Black Panther is the gold standard of Comic Book inspired movies, The Dark Knight. That gold standard includes an IMDB average rating of 9.0, a 94% Certified Fresh rating from Rotten Tomatoes, an 82 Metascore Rating, an “A” from Cinemascore, and 8 Academy Award nominations including 2 wins. So far Black Panther is exceeding the standard based on scores from Rotten Tomatoes, Metacritic and CinemaScore and lagging pretty significantly the IMDB standard. We’ll need to wait until next year’s awards season to see how much Oscar love there is for Black Panther. Today, Black Panther is a well established “really like” movie. I’m looking forward to seeing it. Let’s give it a little time to see how it measures up to the established greats like the Dark Knight series.

I also thought about posting an Academy Award related theme but decided to hold off a week on that one. I am doing an special Oscar study for next week (That’s a tease folks!). But my unfocused mind has been thinking about this year’s Oscar awards. Last week I watched two Oscar nominated movies, The Shape of Water and Mudbound. I “really liked” Shape of Water but I didn’t love it. I think for this movie to work you need to care about the creature. Don’t get me wrong I cared that the creature was being treated inhumanely. I just didn’t find out enough about the creature to care about him as an individual. On the other hand, I really cared about Elisa (Sally Hawkins) which is why I liked the movie. But, to really care about a movie relationship I think you need to care about both people in the relationship. Thus, my ambivalence about the movie.

Mudbound, on the other hand, was a revelation. I loved it. With as many movies that have been made about American race relations, it is difficult to find a story that is fresh. Mudbound is fresh and well told. I have not seen this story on the screen before. After seeing Mudbound, I began to think about how underrepresented it is in the Academy Award nominations. Is it because it is a Netflix movie? The Netflix model is to release movies in theaters overseas and on its streaming platform in the United States. Does Hollywood penalize movies owned by Netflix because of this model? I’m just wondering.

Finally, I was thinking about the movie wasteland that exists between now and the beginning of blockbuster season in May. It is not historically a good time for new “really like” movies to get released. Some ” really like” movies do, though, and I make it my personal mission to pan for that nugget of movie gold worth watching. This weekend I have my eye on two new releases, Annihilation and Game Night. Early Rotten Tomatoes reviews are promising for both. Stay tuned.

So, as you can see, I was a little unfocused this week. Just don’t mistake that for an absence of ideas.

 

Is MoviePass the Next Big Thing? Or Just One More Thing.

Netflix put DVD rental stores out of business. Amazon changed how we buy books (and almost everything else). Uber has placed taxi companies on a path to obsolescence. On August 15th, MoviePass, a fledgling movie theater subscription service with 20,000 subscribers, lowered their monthly subscription price from $14.95 to $9.95. Two days later they had 150,000 subscribers and had drawn a panicked response from AMC, the top theater chain in America. Is a seismic shift occurring in the first run movie delivery system as well?

Netflix put DVD rental stores out of business. Amazon changed how we buy books (and almost everything else). Uber has placed taxi companies on a path to obsolescence. On August 15th, MoviePass, a fledgling movie theater subscription service with 20,000 subscribers, lowered their monthly subscription price from $14.95 to $9.95. Two days later they had 150,000 subscribers and had drawn a panicked response from AMC, the top theater chain in America. Is a seismic shift occurring in the first run movie delivery system as well?

Rather than go into a long explanation of what MoviePass is, I’ll link you to its Wikipedia page to fill you in. I’m more interested in whether it makes sense for the movie consumer to subscribe to MoviePass. Here’s the economics of it. At $9.95 a month, the annual cost of a MoviePass card is $119.40. According to AMC, their average ticket cost for the first quarter of 2017 was $9.33. If you see 13 movies annually it would cost you $121.19. So to save money with a MoviePass you would have to typically go to the movies more than 12 times a year. It doesn’t seem like a lot but it actually is. I would consider myself an above average consumer of movies. But when I went back and tallied how often I actually go to the movie theater, here’s what I discovered:

Year # Seen in Theater Avg Cost Total Cost
2017 6  $       9.33  $    55.98
2016 6  $       9.33  $    55.98
2015 6  $       9.33  $    55.98
2014 3  $       9.33  $    27.99
2013 5  $       9.33  $    46.65
2012 11  $       9.33  $  102.63

In the five years before 2017, I would have lost money using MoviePass. I would have to go to the movies more than twice as often as I normally do to make it financially viable.

This is the “gym membership” pricing model. You enthusiastically use your gym membership in the beginning. Over time, though, life gets in the way and you use it less and less even though you continue to pay the same monthly membership fee. In one of the articles I read to prepare for this post, Stacy Spikes, the CEO and co-founder of MoviePass, indicated that 10% of moviegoers buy 50% of the movie tickets sold. According to Spikes, it was those movie theater patrons that they were targeting with this price decrease. I don’t buy it. They wouldn’t have to reduce the price to get those consumers. It is more likely they are targeting the movie fan that thinks that they go to close to a movie a month when actually they don’t.

As consumers of movie and television programming, we are witnessing the splintering of our venues to watch this programming. One other big piece of news that came out over the summer was Disney’s announcement that they will end their arrangement to provide content to Netflix in 2019. Disney intends to launch its own streaming service. Remember that Disney includes the Marvel and Star Wars franchises as well as their stable of Disney classics. Netflix, Amazon, Hulu, HBO, Showtime, Starz and soon Disney have exclusive entertainment that we probably want to see. MoviePass should be viewed as one more subscription service to fit into our entertainment budget if we so choose. But, can we afford it all?

There could be a place for MoviePass in this equation. Here are the totals of all of the movies released in the last 5+ years that I’ve seen:

Year Total # Seen # Seen in Theater % Seen in Theater
2017 9 6 67%
2016 35 6 17%
2015 51 6 12%
2014 44 3 7%
2013 41 5 12%
2012 59 11 19%
Total 239 37 15%

I eventually watch many more of the movies released in a given year on the platforms I subscribe to, whether it be cable, Netflix DVD, or a streaming service. Currently, I subscribe to all of the streaming options, either directly or through cable, mentioned above except for Hulu. I do this to give me enough good movie options to access each week. What if I watched more of the movies I end up watching with subscription services in theaters using MoviePass instead. I might then think of my subscription services as primarily for television entertainment. Since I can only binge watch a show or two at a time, why not limit my cost to the venues I’m watching at the time. If I just finished watching Game of Thrones until the next season in 2019 and now I want to watch Ozark, I can suspend my HBO subscription and reopen my Netflix streaming account. At the same time I could suspend my Showtime and Starz subscriptions too until I get around to watching Billions or Outlander. This would free up the cash for MoviePass and save me a little more as well. My wife Pam thinks that this sounds like a lot of work. The subscription services are banking on you feeling that way as well.

A few years ago, I remember listening to people complain about their cable bills. The common complaint was that we couldn’t pay for just the channels we wanted to watch and not pay for the others. Well, that day gets closer and closer every day as subscription services replace cable. If we don’t carefully manage our options, though, we may end up paying more for the things we “want” to watch then we ever paid for cable. We might think about paying only for what we “want” to watch right now. I think MoviePass could be part of the strategy, or not.

 

This One Is All About You and Movielens

My daughter, and others like her, will no longer need to search blindly for movies on the streaming services they subscribe to if they’ve signed up to use my favorite movie recommender site, Movielens. Aside from being a very reliable movie recommender site, it is also the most useful in terms of finding movies to watch.

A few months ago my daughter texted me for recommendations for good movies on Netflix or Amazon Prime. I recommended a hidden treasure of a movie, Begin Again, but I couldn’t remember if it was on Netflix or Amazon. I knew it was on one of them. I had to go to each site to find the movie to nail down which streaming service it was on.

My daughter, and others like her, will no longer need to search blindly for movies on the streaming services they subscribe to if they’ve signed up to use my favorite movie recommender site, Movielens. Aside from being a very reliable movie recommender site, it is also the most useful in terms of finding movies to watch.

Within the last couple of months Movielens has added its best feature to date. Not only can you get pages and pages of recommended movies, once you’ve taken the time to rate the movies you’ve seen, but now you can filter them by the most popular streaming services.

Movielens allows you to filter recommendations by movies currently on Netflix, Amazon, Hulu, HBO, and Showtime. You can filter them individually or in combination. In my case, I filter by Netflix, Amazon and HBO. This means that you can get a list of movies that you can watch right now, ranked by the probability that you will “really like” them.

If I go to the Home Page of Movielens right now and go to Top Picks, I can click on the filter’s drop down menu and select Streaming Services. This will provide me with a list of the five services mentioned previously. By clicking on Netflix, Amazon, and HBO, I get a list of movies that I can watch now that I haven’t seen before. There are 5,256 movies available for me to watch right now ranked from the one I’m most likely to enjoy, last summer’s box office surprise Me Before You (Amazon), to the movie I’m least likely to enjoy, The Admirer (Amazon). You’ve never heard of The Admirer? Neither have I. It is a 2012 Russian movie based on the love between Anton Chekhov and a young writer, Lidiya Avilova. ZZZ.

More often than not my posts are about my experiences in finding movies that I will “really like”. This one’s for you. If you only have time to track one movie recommender website, go to Movielens. It will be fun and it will save you time scrolling through lines and lines of movies searching for movies that you might like.

When It Comes to Unique Movies, Don’t Put All of Your Movie Eggs in the Netflix Basket.

It is rare to find a movie that isn’t a sequel, or a remake, or a borrowed plot idea, or a tried and true formula. Many of these movies are entertaining because they feel familiar and remind us of another pleasant movie experience. The movie recommender websites Netflix, Movielens, and Criticker do a terrific job of identifying those movie types that you “really liked” before and surfacing those movies that match the familiar plot lines you’ve enjoyed in the past.

It is rare to find a movie that isn’t a sequel, or a remake, or a borrowed plot idea, or a tried and true formula. Many of these movies are entertaining because they feel familiar and remind us of another pleasant movie experience. The movie recommender websites Netflix, Movielens, and Criticker do a terrific job of identifying those movie types that you “really liked” before and surfacing those movies that match the familiar plot lines you’ve enjoyed in the past.

But, what about those movies that are truly original. What about those movies that present ideas and plot lines that aren’t in your usual comfort zone. Will these reliable websites surface these unique movies that I might like? Netflix has 20,000+ genre categories that they slot movies into. But, what if a movie doesn’t fit neatly into one of those 20,000 categories.

Yesterday I watched a great movie, Being There.

Being There

This 1979 movie, starring Peter Sellers in an Academy Award nominated performance, is about a simple-minded gardener who never left the home of his employer over the first fifty years of his life. Aside from gardening, the only knowledge he has is what he’s seen on television. After his employer dies he is forced to leave his home. The movie follows Chance the Gardener as he becomes Chauncey Gardner, economic advisor to the President. It’s not a story of transformation but of perception. The movie is fresh.

My most reliable movie recommenders, Netflix and Movielens, warned me away from this movie. The only reason I added it to my weekly Watch List is because I saw the movie in the theater when it first came out in 1979 and “really liked” it.

Another possible reason why Netflix missed on this movie is because I hated Peter Sellers’ other classic movie Dr. Strangelove. I rated it 1 out of 5 stars.  If Being There is slotted among a Netflix category of Peter Sellers classics, it might explain the mismatch.

Is it impossible, then, to surface movies that have creative original content that you might like. Not entirely. Criticker predicted I would rate Being There an 86 out of 100. I gave it an 89. The IMDB rating is 8.0 based on over 54,000 votes. Rotten Tomatoes has it at 96% Certified Fresh. This is why I incorporate ratings from five different websites into the “really like” model rather than just Netflix.

When it comes to unique movies, don’t put all of your movie eggs in the Netflix basket.

 

 

What Am I Actually Going to Watch This Week? Netflix Helps Out with One of My Selections.

The core mission of this blog is to share ideas on how to select movies to watch that we’ll “really like”. I believe that there have been times when I’ve bogged down on how to build the “really like” model. I’d like to reorient the dialogue back to the primary mission of what “really like” movies I am going to watch and more importantly why.

The core mission of this blog is to share ideas on how to select movies to watch that we’ll “really like”. I believe that there have been times when I’ve bogged down on how to build the “really like” model. I’d like to reorient the dialogue back to the primary mission of what “really like” movies I am going to watch and more importantly why.

Each Wednesday I publish the ten movies on my Watch List for the week. These movies usually represent the ten movies with the highest “really like” probability that are available to me to watch on platforms that I’ve already paid for. This includes cable and streaming channels I’m paying for and my Netflix DVD subscription. I rarely use a movie on demand service.

Now, 10 movies is too much, even for the Mad Movie Man, to watch in a week. The ten movie Watch List instead serves as a menu for the 3 or 4 movies I actually most want to watch during the week. So, how do I select those 3 or 4 movies?

The first and most basic question to answer is who, if anyone, am I watching the movie with. Friday night is usually the night that my wife and I will sit down and watch a movie together. The rest of the week I’ll watch two or three movies by myself. So, right from the start, I have to find a movie that my wife and I will both enjoy. This week that movie is Hidden Figures, the 2016 Oscar nominated film about the role three black female mathematicians played in John Glenn’s orbit of the earth in the early 1960’s.

This movie became available to Netflix DVD subscribers on Tuesday May 9. I received my Hidden Figures DVD on that day. Something I’ve learned over the years is that Netflix ships DVD’s on Monday that become available on Tuesday. For this to happen you have to time the return of your old DVD to arrive on the Saturday or Monday before the Tuesday release. This gives you the best chance to avoid “long wait” queues.

I generally use Netflix DVD to see new movies that I don’t want to wait another 3 to 6 months to see or for old movies that I really want to see but aren’t available on my usual platforms.

As of the first quarter of 2017, Netflix reported that there are only 3.94 million subscribers to their DVD service. I am one of them. The DVD service is the only way that you can still access Netflix’ best in the business 5 star system of rating movies. It is easily the most reliable predictor of how you’ll rate a movie or TV show. Unfortunately, Netflix Streaming customers no longer have the benefit of the 5 Star system. They have gone to a less granular “thumbs up” and “thumbs down” rating system. To be fair, I haven’t gathered any data on this new system yet therefore I’ll reserve judgement as to its value. As for the DVD service, they will have me as a customer as long as they maintain their 5 star recommender system as one of the benefits of being a DVD subscriber.

The 5 star system is a critical assist to finding a movie for both my wife and I. Netflix allows you set up profiles for other members of the family. After my wife and I watch a movie, she gives it a rating and I give it a rating. These ratings are entered under our separate profiles. This allows a unique predicted rating for each of us based on our individual taste in movies. For example, Netflix predicts that I will rate Hidden Figures a 4.6 out of 5 and my wife will rate it a 4.9. In other words, according to Netflix, this is a movie that both of us, not only will “really like”, but we should absolutely “love”.

Hidden Figures has a “really like” probability of 61.4%. It’s Oscar Performance probability is 60.7% based on its three nominations. Its probability based solely on the feedback from the recommender sites that I use is 69.1%. At this point in time, it is a Quintile 1 movie from a credibility standpoint. This means that the 69.1% probability is based on a limited number of ratings. It’s not very credible yet. That’s why the 61.4% “really like” probability is closer to the Oscar Performance probability of 60.7%. I would fully expect that, as more people see Hidden Figures and enter their ratings, the “really like” probability will move higher for this movie.

Friday Night Movie Night this week looks like a “really like” lock…thanks to Netflix DVD.

 

 

The Art of Selecting “Really Like” Movies: New Movies

Over the next three weeks I’ll outline the steps I’m taking this year to improve my “really like” movie odds. Starting this week with New Movies , I’ll lay out a focused strategy for three different types of movie selection decisions.

I watch a lot of movies, a fact that my wife, and occasionally my children, like to remind of. Unlike the average, non-geeky, movie fan, though, I am constantly analyzing the process I go through to determine which movies I watch. I don’t like to watch mediocre, or worse, movies. I’ve pretty much eliminated bad movies from my selections. But, every now and then a movie I “like” rather than “really like” will get past my screen.

Over the next three weeks I’ll outline the steps I’m taking this year to improve my “really like” movie odds. Starting this week with New Movies, I’ll lay out a focused strategy for three different types of movie selection decisions.

The most challenging “really like” movie decision I make is which movies that I’ve never seen before are likely to be “really like” movies. There is only a 39.3% chance that watching a movie I’ve never seen before will result in a “really like” experience. My goal is to improve those odds by the end of the year.

The first step I’ve taken is to separate movies I’ve seen before from movies I’ve never seen in establishing my “really like” probabilities. As a frame of reference, there is a 79.5% chance that I will “really like” a movie I’ve seen before. By setting my probabilities for movies I’ve never seen off of the 39.3% probability I have created a tighter screen for those movies. This should result in me watching fewer never-before-seen movies then I’ve typically watched in previous years. Of the 20 movies I’ve watched so far this year, only two were never-before-seen movies.

The challenge in selecting never-before-seen movies is that, because I’ve watched close to 2,000 movies over the last 15 years, I’ve already watched the “cream of the crop” from those 15 years.. From 2006 to 2015, there were 331 movies that I rated as “really like” movies, that is 33 movies a year, or less than 3 a month. Last year I watched 109 movies that I had never seen before. So, except for the 33 new movies that came out last year that, statistically, might be “really like” movies, I watched 76 movies that didn’t have a great chance of being “really like” movies.

Logically, the probability of selecting a “really like” movie that I’ve never seen before should be highest for new releases. I just haven’t seen that many of them. I’ve only seen 6 movies that were released in the last six months and I “really liked” 5 of them. If, on average, there are 33 “really like” movies released each year, then, statistically, there should be a dozen “really like” movies released in the last six months that I haven’t seen yet. I just have to discover them. Here is my list of the top ten new movie prospects that I haven’t seen yet.

My Top Ten New Movie Prospects 
New Movies =  < Release Date + 6 Months
Movie Title Release Date Last Data Update “Really Like” Probability
Hacksaw Ridge 11/4/2016 2/4/2017 94.9%
Arrival 11/11/2016 2/4/2017 94.9%
Doctor Strange 11/4/2016 2/6/2017 78.9%
Hidden Figures 1/6/2017 2/4/2017 78.7%
Beatles, The: Eight Days a Week 9/16/2016 2/4/2017 78.7%
13th 10/7/2016 2/4/2017 78.7%
Before the Flood 10/30/2016 2/4/2017 51.7%
Fantastic Beasts and Where to Find Them 11/18/2016 2/4/2017 51.7%
Moana 11/23/2016 2/4/2017 51.7%
Deepwater Horizon 9/30/2016 2/4/2017 45.4%
Fences 12/25/2016 2/4/2017 45.4%

Based on my own experience, I believe you can identify most of the new movies that will be “really like” movies within 6 months of their release, which is how I’ve defined “new” for this list. I’m going to test this theory this year.

In case you are interested, here is the ratings data driving the probabilities.

My Top Ten New Movie Prospects 
Movie Site Ratings Breakdown
Ratings *
Movie Title # of Ratings All Sites Age 45+ IMDB Rotten Tomatoes ** Criticker Movielens Netflix
Hacksaw Ridge         9,543 8.2 CF 86% 8.3 8.3 8.6
Arrival      24,048 7.7 CF 94% 8.8 8.1 9.0
Doctor Strange      16,844 7.7 CF 90% 8.2 8.3 7.8
Hidden Figures         7,258 8.2 CF 92% 7.7 7.3 8.2
Beatles, The: Eight Days a Week         1,689 8.2 CF 95% 8.0 7.3 8.0
13th    295,462 8.1 CF 97% 8.3 7.5 8.0
Before the Flood         1,073 7.8 F 70% 7.6 8.2 7.8
Fantastic Beasts and Where to Find Them      14,307 7.5 CF 73% 7.3 6.9 7.6
Moana         5,967 7.7 CF 95% 8.4 8.0 7.0
Deepwater Horizon      40,866 7.1 CF 83% 7.8 7.6 7.6
Fences         4,418 7.6 CF 95% 7.7 7.1 7.2
*All Ratings Except Rotten Tomatoes Calibrated to a 10.0 Scale
** CF = Certified Fresh, F = Fresh

Two movies, Hacksaw Ridge and Arrival, are already probably “really like” movies and should be selected to watch when available. The # of Ratings All Sites is a key column. The ratings for Movielens and Netflix need ratings volume before they can credibly reach their true level. Until, there is a credible amount of data the rating you get is closer to what an average movie would get. A movie like Fences, at 4,418 ratings, hasn’t reached the critical mass needed to migrate to the higher ratings I would expect that movie to reach. Deep Water Horizon, on the other hand, with 40,866 ratings, has reached a fairly credible level and may not improve upon its current probability.

I’m replacing my monthly forecast on the sidebar of this website with the top ten new movie prospects exhibit displayed above. I think it is a better reflection of the movies that have the best chance of being “really like” movies. Feel free to share any comments you might have.

 

Oh, What To Do About Those Tarnished Old Quarters.

In one of my early articles, I wrote about the benefits of including older movies in your catalogue of movies to watch. I used the metaphor of our preference for holding onto shiny new pennies rather than tarnished old quarters. One of the things that has been bothering me is that my movie selection system hasn’t been surfacing older movie gems that I haven’t seen.

In one of my early articles, I wrote about the benefits of including older movies in your catalogue of movies to watch. I used the metaphor of our preference for holding onto shiny new pennies rather than tarnished old quarters. One of the things that has been bothering me is that my movie selection system hasn’t been surfacing older movie gems that I haven’t seen. Take a look at the table below based on the movie I’ve watched over the last 15 years:

Movie Release Time Frame # of Movies Seen % of Total
2007 to 2016 573 29%
1997 to 2006 606 31%
1987 to 1996 226 11%
1977 to 1986 128 6%
1967 to 1976 101 5%
1957 to 1966 122 6%
1947 to 1956 109 6%
1937 to 1946 87 4%
1920 to 1936 25 1%

60% of the movies I’ve watched in the last 15 years were released in the last 20 years. That’s probably typical. In fact, watching movies more than 20 years old 40% of the time is probably unusual. Still, there are probably quality older movies out there that I’m not seeing.

My hypothesis has been that the databases for the movie websites that produce my recommendations are smaller for older movies. This results in recommendations that are based on less credible data. In the world of probabilities, if your data isn’t credible, your probability stays closer to the average probability for randomly selected movies.

I set out to test this hypothesis against the movies I’ve watched since I began to diligently screens my movies through my movie selection system. It was around 2010 that I began putting together my database and using it to select movies. Here is a profile of those movies.

Seen after 2010
Movie Release My
Time Frame Average Rating # of Movies Seen % of Total Seen
2007 to 2016 7.2 382 55%
1997 to 2006 7.9 60 9%
1987 to 1996 7.9 101 15%
1977 to 1986 7.8 57 8%
1967 to 1976 7.9 23 3%
1957 to 1966 8.2 26 4%
1947 to 1956 8.2 20 3%
1937 to 1946 8.4 17 2%
1920 to 1936 6.9 4 1%

It seems that it’s the shiniest pennies, that I watch most often, that I’m least satisfied with. So again I have to ask, why aren’t my recommendations producing more older movies to watch?

It comes back to my original hypothesis. Netflix has the greatest influence on the movies that are recommended for me. So, I compared my ratings to Netflix’ Best Guess ratings for me and added the average number of ratings those “best guesses” were based on.

Movie Release Time Frame My Average Rating Netflix Average Best Guess Avg. # of Ratings per Movie My Rating Difference from Netflix
2007 to 2016 7.2 7.7    1,018,163 -0.5
1997 to 2006 7.9 8.0    4,067,544 -0.1
1987 to 1996 7.9 8.1    3,219,037 -0.2
1977 to 1986 7.8 7.8    2,168,369 0
1967 to 1976 7.9 7.6    1,277,919 0.3
1957 to 1966 8.2 7.9        991,961 0.3
1947 to 1956 8.2 7.8        547,577 0.4
1937 to 1946 8.4 7.8        541,873 0.6
1920 to 1936 6.9 6.1        214,569 0.8

A couple of observations on this table;

  • Netflix pretty effectively predicts my rating for movies released between 1977 to 2006. The movies from this thirty year time frame base their Netflix best guesses on more than 2,000,000 ratings per movie.
  • Netflix overestimates my ratings for movies released from 2007 to today by a half point. It may be that the people who see newer movies first are those who are most likely to rate them higher. It might take twice as many ratings before the best guess finds its equilibrium, like the best guesses for the 1987 to 2006 releases.
  • Netflix consistently underestimates my ratings for movies released prior to 1977. And, the fewer ratings the Netflix best guess is based on, the greater Netflix underestimates my rating of the movies.

What have I learned? First, to improve the quality of new movies I watch, I should wait until the number of ratings the recommendations are based on is greater. What is the right number of ratings is something I have to explore further.

The second thing I’ve learned is that my original hypothesis is probably correct. The number of ratings Netflix has available to base its recommendations on for older movies is probably too small for their recommendations to be adequately responsive to my taste for older movies. The problem is, “Oh, what to do about those tarnished old quarters” isn’t readily apparent.