Will I "Really Like" this Movie?

Navigating Movie Website Ratings to Select More Enjoyable Movies

Archive for the tag “Netflix”

This One Is All About You and Movielens

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.

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.

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

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. 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.

 

When It Comes To Movie DNA, Do Directors Have It and Has Netflix Mapped It?

I can’t wait to see Christopher Nolan’s next movie, Dunkirk, which is due to reach the theaters in 2017. I’ve seen 8 of Nolan’s 9 feature films and have given those movies an average rating of 87.5 out of 100, my highest average rating for any director with at least 8 movies seen. On the other hand, I’m bored to tears by Wes Anderson’s movies. I’ve seen 2 of his 8 movies and I’ve awarded them an average rating of 43.5 out of 100. Not included in the two movies I watched were Rushmore and Grand Budapest Hotel, both of which I tried to watch but couldn’t finish. Each of us has a distinct movie taste that guides our movie selection. It is our own unique movie DNA, if you will.

Do movie directors have a movie making DNA?  Do movie directors make movies with common traits,  a movie making DNA, that particular viewers might be drawn to or repelled by? Netflix has made millions of dollars by identifying movies and TV shows that we are predisposed to enjoy.Does Netflix, indirectly or directly, draw you to favorite directors and push you away from directors you just don’t get? These are the questions I’ve been researching the past week.

I haven’t successfully come up with a broad systematic answer to these questions yet. But, by looking at a couple of directors, one I like and another I don’t, I can begin to develop a hypothesis. The two directors I looked at have a sizeable body of work. The director who I enjoy is Ron Howard. In the last 15 years I’ve seen 9 of the 21 feature films he has directed.. The director I just don’t get is Stanley Kubrick. Everyone praises his genius but I don’t “really like” his movies. Here are my average ratings for these two director’s movies that I’ve seen over the last 15 years compared to the average ratings of all Netflix customers for the same movies. For purposes of apples to apples comparison, Netflix ratings have been converted to a 100 point scale (e.g.  3.8 out of 5 Netflix Rating is 76 on a 100 point scale).

My Avg Rating Netflix Avg Rating My Rating Difference
Ron Howard 77 76 +1
Stanley Kubrick 52 76 -24

My enjoyment of Ron Howard is fairly consistent with everyone’s enjoyment of Ron Howard. He makes movies that appeal to the general audience. This probably suggests that well done mainstream movies are in my movie DNA. On the other hand, there is a clear difference between my taste for Kubrick and everyone else’s taste for Kubrick. He is not mainstream.

So does Netflix recognize the different appeal that these two directors have for me?  Here’s the same table as the one above, except with the Netflix Best Guess average rating for how I’ll rate the movies instead of how I actually rated the movies..

Netflix Best Guess Avg Rating for Me Netflix Avg Rating Netflix Best Guess Difference
Ron Howard 83 76 +7
Stanley Kubrick 72 76 -4

Directionally it is consistent with my ratings. It is more bullish than my ratings for Ron Howard’s movies and less bearish for Kubrick’s movies. Interestingly enough it is most bullish for Ron Howards best movies as displayed below:

Ron Howard’s Movies I’ve Seen
Netflix Best Guess Avg Rating for Me Netflix Avg Rating Netflix Best Guess Difference
Netflix Avg Rating > 76 92 78 +14
Netflix Avg Rating < 76 72 73 -1

Netflix highly recommends Ron Howard’s best movies to me while taking a neutral position toward his middle of the road movies.

If there is such a thing as a director’s movie making DNA and if Netflix is successfully factoring it into the Best Guess Ratings developed for me, then that DNA relationship should exist in the movies I haven’t seen in the last 15 years as well. Here’s a look at the sample for those movies:

Netflix Best Guess Avg Rating for Me Netflix Avg Rating Netflix Best Guess Difference
Ron Howard 67 70 -3
Stanley Kubrick 55 70 -15

Again, the results are consistent with the results for the movies I’ve seen. My additional observation is that the director I like gets a Netflix recommendation boost for the movies that the Netflix universe rates the highest. Conversely, Netflix more aggressively drives me away from the movies rated lowest by the Netflix universe for the director I don’t like.

Without a broader study, I can’t say for sure that there is such a thing as movie DNA specific to a movie director, or that Netflix’ algorithm indirectly recognizes it in their recommendations. But, based on this isolated comparison, it sure looks like there is and Netflix might have it well mapped.

 

 

While I Was Away, I Had a Thought or Two

Last Friday my wife and I moved into our new place. Not all of my time this past week was spent wandering through the maze of boxes to be unpacked and wondering which one contained our toaster. Every now and then random ideas for movie studies and articles popped into my head and I’m back to share them with you.

For example, a couple of weeks ago I saw the movie Sing Street. This is the third movie directed by John Carney that I’ve seen, Once and Begin Again being the other two, and I’ve “really liked” all three. There is an identifiable DNA to the movies that certain directors make. In Carney’s case, all three movies are about making music and the not always easy interrelationship the process has with love. There is also a certain DNA to the movies we enjoy watching. I think sites like Netflix and Movielens do a pretty good job of linking our movie watching DNA with a director’s movie DNA. In the coming weeks I plan to explore Movie DNA further.

October is just around the corner and another awards season is upon us. Already buzz about Oscar worthy movies is coming out of the 2016 Toronto International Film Festival, where La La Land has been anointed a Best Picture front runner. In the spirit of the season I’ve begun a data driven study of who are the top male and female actors of each decade for which Oscars have been awarded.

As I’ve begun to look at actors who’ve been nominated for awards in the earlier years of movie history, I’ve run across a number of movies that I’ve never seen before that pique my interest. Is it possible that movie sites like Netflix aren’t as effective in collecting movie DNA for vintage movies as they are for contemporary movies? Is it possible that fewer vintage movies get recommended by Netflix because there is less data in their database for movies that predate it’s existence as a movie recommender website? Would Netflix have surfaced John Carney’s three movies for me if they were made between 1947 and 1956 rather than 2007 to 2016?

As I mentioned in my last pre-sabbatical post, I’m reducing my posts to one  post each Thursday. For me, this blog is all about sharing the results of the research ideas  I’ve involved myself in. It seemed that with two posts a week I was spending too much time writing and not enough time generating the research that you might find interesting. So that’s my plan and I’m sticking to it, at least until I need another sabbatical.

The Mad Movie Man is back and there is much to do.

When Might We See the Next Perfect Netflix-DVD Movie?

Last Thursday I posted a list of 51 movies that received a Netflix-DVD perfect score of 4.9. For anyone who has experienced the joy of seeing a movie that they absolutely love, you know that those couple of hours of cinema nirvana don’t happen every day. If I’m lucky enough to run across a movie with a Netflix-DVD Best Guess of 4.9, that I haven’t seen, I know that there is a high probability that movie heaven has arrived. So the question is, “When am I likely to discover another Netflix-DVD movie with a 4.9 rating?”

Well, of the 51 perfect score movies out there today, here is the breakdown by month of how many 4.9 movies have gone into wide release for a given month:

Dec 14
Jun 7
Oct 7
May 6
Remaining Months < 5

It is not surprising that December is far and away the most represented month. Producers that are most confident in a particular movie’s chances of winning Oscar gold, release those movies in December. If we consolidate this list down to the three movie seasons, we see that Netflix perfection isn’t limited to Awards Season.

 # of  Movies # of movies per Month
Awards Season 25 8.3
Blockbuster Season 21 4.2
Dump Season 5 1.3

While it might appear that a perfect score movie is almost as likely to be released during Blockbuster Season as Awards Season, you need to keep in mind that Awards Season (Oct – Dec) is three months long while Blockbuster Season (Mar – Jul) is five months long. Based on the monthly average a perfect Netflix movie is almost twice as likely to be released during Awards Season as opposed to Blockbuster Season. Rarely is a perfect movie released in Dump Season. One of the five movies, Million Dollar Baby, went into limited release in December to be eligible for that year’s Awards Season before going into wide release in January. It was therefore released only technically during Dump Season.

So, now we know that the most likely time of the year for a new perfect score movie to be released is during Awards Season, particularly in December. Are we likely to see one released this year? Here’s where it gets tricky. From 1992 to 2010, at least one perfect score movie was released every year. Since 2010, we’ve had three released in 2012 and one released last year. Here’s the breakdown by decade:

2010’s 6
2000’s 15
1990’s 14
1980’s 7
1970’6 5
1960’s 2
1950’s 0
1940’s 2

Does this mean that movie heaven begins and ends between 1990 and 2009? No, the answer is more mundane. The answer lies in the statistical concept of the law of large numbers. Netflix needs a large statistical base of ratings for a particular movie before its model will assign it a 4.9. It is only with those large numbers will the Netflix model be able to confidently predict that you will love a particular movie. Of the 51 perfect score movies on my list, only four have fewer than 1,000,000 ratings – the relatively recent movies, The Martian, Argo, Lincoln, and the 1946 classic, It’s a Wonderful Life. The preponderance of perfect score movies between 1990 and 2009 has more to do with the fact that they are the most seen movies by Netflix raters.

To the question, “When will the next perfect Netflix-DVD movie come along?”, the answer is that it probably already has come along and it’s just waiting for enough Netflix ratings. Based on the results from 1992 to 2010, there is likely to be a perfect score movie this year, although it probably hasn’t been released yet (the one already released movie with a shot is Captain America: Civil War). In the mean time, watch those perfect Netflix movies from my last post that may have slipped by you. Experience a little bit of movie heaven while we wait for th next perfect movie to reveal itself.

A Netflix-DVD Perfect Score Movie Is a Must See Movie

Nothing in life is guaranteed. How often have you heard that? Those who use that phrase are probably right…most of the time. But when Netflix-DVD provides you with a “Best Guess” of 4.9 for a particular movie, I can say that you are guaranteed to “really like” that movie and be pretty confident that I am right. In my database of 1,980 movies, 51 have received a perfect score of 4.9 from Netflix-DVD. That is 2.6% of all of the movies I have watched in the last 15 years. Of those 51 perfect score movies, I have given a “really like” score of 75 (out of 100) or higher to all 51 movies. I have given a “love” score of 85 or higher to 48 of the 51. If Netflix-DVD presents me with a movie with a Best Guess of 4.9, there is a 94.1% probability that I will “love” the movie, and close to 100% that I will “really like” it. That is pretty darn close to a guarantee.

So, after providing all of these guarantees, it would be just cruel of me not to share with you the 51 perfect score movies. Here they are:

Netflix-DVD Perfect Score Movies
American President, The King’s Speech, The
Apollo 13 L.A. Confidential
Argo Lincoln
Batman Begins Lord of the Rings: The Fellowship of the Ring
Bourne Identity, The Lord of the Rings: The Return of the King, The
Bourne Ultimatum, The Martian, The
Braveheart Million Dollar Baby
Casablanca Mystic River
Cinderella Man Raiders of the Lost Ark
Dark Knight, The Rocky
Departed, The Saving Private Ryan
Few Good Men, A Schindler’s List
Field of Dreams Shawshank Redemption, The
Forrest Gump Silver Linings Playbook
Fugitive, The Sixth Sense, The
Gladiator Sleepless in Seattle
Glory Social Network, The
Godfather, The Sound of Music, The
Godfather: Part II, The Spider-Man 2
Gone Baby Gone Star Trek
Good Will Hunting Star Wars IV: A New Hope
Hoosiers Star Wars V: The Empire Strikes Back
It’s a Wonderful Life Star Wars VI: Return of the Jedi
Jerry Maguire Sting, The
Juno To Kill a Mockingbird
When Harry Met Sally

Those of you who are movie lovers probably have seen all or most of these. If not, you probably can’t go wrong sampling some movies from this list. The list is also a peek at my taste in movies. Netflix-DVD is uncanny in its capability to look into the depths of my movie soul and pick out the perfect movie. I’ll just mention again that I’m not referring to the recommendations that you get on Streaming Netflix. It seems like they give five stars to everything. The perfect scores for this post is from the DVD recommender.

We all strive for perfection at different times in our lives. Netflix 4.9 movies define perfection for movie recommendations.

Until That Next Special Movie Comes Along

Happy 4th of July to all of my visitors from the States and, to my friends to the North, Happy Canada Day which was celebrated on this past Saturday. It is a good day to watch Yankee Doodle Dandy, one of those special movie experiences I’m fond of.

This past weekend I watched another patriotic movie,  Courage Under Fire with Denzel Washington, Meg Ryan, and a young Matt Damon among others in a terrific cast. It was one of those special movies that I yearned for in my last post on July movie prospects. It was a July 1996 release that wasn’t nominated for an Academy Award (how it didn’t get an acting nomination among several powerful performances astounds me). It earned a 94 out of 100 score from me. I loved this movie. The feeling I get after watching a movie this good is why I watch so many movies. It is the promise that there are more movies out there to see that I will love that feeds my passion for movies.

As I was thinking about special movies the last few days, a question occurred to me. Can I use my rating system to find movies I’ll “love” rather than just “really like”? Of course I can. Any movie that earns a rating of 85 out of 100 or higher meets my definition of a movie I will “love”. An 85 also converts to a five star movie on Netflix. I can rank each of the movie rating websites that I use in my algorithm from highest rating to lowest. I then can take the top 10% of the rankings and calculate the probability that a movie in that top 10% would earn a score of 85 or higher. Regular readers of this blog shouldn’t be surprised by the results.

Top 10% Threshold Actual % of My Database Probability for “Love” Movie
Netflix >  4.5 9.5% 81.4%
Movielens >  4.2 10.7% 76.9%
Criticker >  90 10.3% 55.4%
IMDB >  8.1 10.8% 45.8%
Rotten Tomatoes >  Cert. Fresh 95% 10.4% 41.7%

High Netflix and Movielens scores are the most reliable indicators of “love” movies. Here’s my problem. There are no movies that I haven’t seen in the last fifteen years that have a Netflix Best Guess of 4.5 or higher. There are fewer than 10 movies that I haven’t seen in the last fifteen years with a Movielens predicted score of greater than 4.2. Here’s the kicker, the probability that I will “love” a movie with a Movielens predicted score of 4.2 or better that doesn’t also have a Netflix Best Guess greater than 4.5 is only 62%. It seems the chances to find movies to “love” are significantly diminished without the strong support of Netflix.

On the 1st of each month Netflix Streaming and Amazon Prime shake up the movies that are available in their inventory. The July 1 shakeup has resulted in a couple of new movies being added to my list of the Top Ten “Really Like” Movies Available on Netflix or Amazon Prime. This list is actually mistitled. It should be the Top Ten “Love” Movies Available. Take a look at the list. Perhaps you haven’t seen one of these movies, or haven’t seen it in a while. It is your good fortune to be able to watch one of these movies the next time you are in the mood for a special movie experience.

As for me, I’m still hoping that one of the movies released this year rises to the top of my watch list and is able to captivate me. If it were easy to find movies that I will “love”, I would have named this blog Will I “Love” This Movie?. For now, I will continue to watch movies that I will “really like” until that next special movie comes along.

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