FilmStruck Fills in the Gap for Classic Movie Streaming

Splendor in the Grass is just one of the hard to find Hollywood Classics available on FilmStruck

Those of you who follow this blog are familiar with the fact that I am pretty obsessive in my collection of movie data. Before I’ll even consider watching a movie its data must suggest that there is a fairly high probability that I will “really like” it. I have a rolling list of around 360 movies that meet this high probability threshold and from these 360 movies I select 5 movies each week for my Watch List. And, finally, from these 5 movies comes the 3 or 4 movies I actually watch each week. Crazy, right?

One of the things I’ve been noticing is that, when you watch over a hundred movies each year, you end up watching most of the “really like” contemporary movies because that’s what is most available on the movie channels and the popular streaming services. That doesn’t mean that those services ignore the classics. It’s just that their inventory tends to be limited to the most popular classics. The end result is that my list of 360 “really like” movie candidates is becoming top heavy with Hollywood classics, not because there are more “really like” movies from that era, but because there are fewer of those movies being shown on the traditional sources of movie streaming.

At last, I’ve found a streaming service to fill in the gap. Filmstruck combines movies from the Turner Classic Movie library and the Criterion Collection. These movies range from Hollywood classics to independent and art house films. If you click on the link above it will take you to the home page. At the bottom of that page is a link to their library of available content. One of the things you will notice is that the library of movies is pretty extensive and eclectic.

For me, FilmStruck provides 30 movies available to watch right now of the 360 movie candidates on my list. If you go to my Watch List for this week, you’ll see 5 of those 30 movies. All 5 movies are Oscar nominated with 5 wins and 24 nominations within the group. The Year of Living Dangerously boosted the early careers of Mel Gibson and Sigourney Weaver. In Splendor in the Grass, Warren Beatty made the successful jump from the TV show The Many Lives of Dobie Gillis to the Silver Screen. His co-star Natalie Wood was nominated for Best Actress for Splendor in the Grass, following up on her previous Best Supporting Actress nomination for another movie on the list, Rebel Without a Cause. The remaining two movies on my Watch List, Separate Tables and Seven Brides for Seven Brothers, were each nominated for Best Picture among their, combined, 12 Oscar nominations.

If these five highly-credentialed movies were contemporary movies, they would be on everybody’s “must see” list. Now they are rarely seen because of limited availability. With FilmStruck on my Roku these five movies and other classics like them are  “really like” opportunities with the same availability as their contemporaries.

 

 

The Art of Selecting “Really Like Movies: Older Never Before Seen

Last week I stated in my article that I could pretty much identify whether a movie has a good chance of being a “really like movie” within six months of its release. If you need any further evidence, here are my top ten movies that I’ve never seen that are older than six months.

Last week I stated in my article that I could pretty much identify whether a movie has a good chance of being a “really like movie” within six months of its release. If you need any further evidence, here are my top ten movies that I’ve never seen that are older than six months.

My Top Ten Never Seen Movie Prospects 
Never Seen Movies =  > Release Date + 6 Months
Movie Title Last Data Update Release Date Total # of Ratings “Really Like” Probability
Hey, Boo: Harper Lee and ‘To Kill a Mockingbird’ 2/4/2017 5/13/2011          97,940 51.7%
Incendies 2/4/2017 4/22/2011        122,038 51.7%
Conjuring, The 2/4/2017 7/19/2013        241,546 51.7%
Star Trek Beyond 2/4/2017 7/22/2016        114,435 51.7%
Pride 2/4/2017 9/26/2014          84,214 44.6%
Glen Campbell: I’ll Be Me 2/9/2017 10/24/2014        105,751 44.6%
Splendor in the Grass 2/5/2017 10/10/1961        246,065 42.1%
Father of the Bride 2/5/2017 6/16/1950        467,569 42.1%
Imagine: John Lennon  2/5/2017 10/7/1998        153,399 42.1%
Lorenzo’s Oil 2/5/2017 1/29/1993        285,981 42.1%

The movies with a high “really like” probability in this group have already been watched. Of the remaining movies, there are three movies that are 50/50 and the rest have the odds stacked against them. In other words, if I watch all ten movies I probably won’t “really like” half of them. The dilemma is that I would probably “really like” half of them if I do watch all ten. The reality is that I won’t watch any of these ten movies as long as there are movies that I’ve already seen with better odds. Is there a way to improve the odds for any of these ten movies?

You’ll note that all ten movies have probabilities based on less than 500,000 ratings. Will some of these movies improve their probabilities as they receive more ratings? Maybe. Maybe not. To explore this possibility further I divided my database into quintiles based on the total number of ratings. When I look at the quintile with the most ratings, the most credible quintile, it does provide results that define the optimal performance of my algorithm.

Quintile 5

# Ratings Range > 2,872,053

# of Movies # “Really Like” Movies % “Really Like” Movies Proj.  Avg. Rating All Sites My Avg Rating My Rating to Proj. Rating Diff.
Movies Seen More than Once 152 134 88% 8.6 8.5 -0.1
Movies Seen Once 246 119 48% 7.5 6.9 -0.7
             
All Movies in Range 398 253 64% 7.9 7.5  

All of the movies in Quintile 5 have more than 2,872,053 ratings. My selection of movies that I had seen before is clearly better than my selection of movies I watched for the first time. This better selection is because the algorithm results led me to the better movies and my memory did some additional weeding. My takeaway is that, when considering movies I’ve never seen before, put my greatest trust in the algorithm if the movie falls in this quintile.

Lets look at the next four quintiles.

Quintile 4

# Ratings Range 1,197,745 to 2,872,053

# of Movies # “Really Like” Movies % “Really Like” Movies Proj.  Avg. Rating All Sites My Avg Rating My Rating to Proj. Rating Diff.
Movies Seen More than Once 107 85 79% 8.3 8.3 0.1
Movies Seen Once 291 100 34% 7.1 6.4 -0.7
             
All Movies in Range 398 185 46% 7.4 6.9
Quintile 3

# Ratings Range 516,040 to 1,197,745

# of Movies # “Really Like” Movies % “Really Like” Movies Proj.  Avg. Rating All Sites My Avg Rating My Rating to Proj. Rating Diff.
Movies Seen More than Once 122 93 76% 7.8 8.0 0.2
Movies Seen Once 278 102 37% 7.1 6.6 -0.6
             
All Movies in Range 400 195 49% 7.3 7.0
Quintile 2

# Ratings Range 179,456 to 516,040

# of Movies # “Really Like” Movies % “Really Like” Movies Proj.  Avg. Rating All Sites My Avg Rating My Rating to Proj. Rating Diff.
Movies Seen More than Once 66 46 70% 7.4 7.5 0.2
Movies Seen Once 332 134 40% 7.0 6.4 -0.6
             
All Movies in Range 398 180 45% 7.1 6.6
Quintile 1

# Ratings Range < 179,456

# of Movies # “Really Like” Movies % “Really Like” Movies Proj.  Avg. Rating All Sites My Avg Rating My Rating to Proj. Rating Diff.
Movies Seen More than Once 43 31 72% 7.0 7.5 0.5
Movies Seen Once 355 136 38% 6.9 6.2 -0.7
             
All Movies in Range 398 167 42% 6.9 6.4

Look at the progression of the algorithm projections as the quintiles get smaller. The gap between the movies seen more than once and those seen only once narrows as the number of ratings gets smaller. Notice that the difference between my ratings and the projected ratings for Movies Seen Once is fairly constant for all quintiles, either -0.6 or -0.7. But for the Movies Seen More than Once, the difference grows positively as the number of ratings gets smaller. This suggests that, for Movies Seen More than Once, the higher than expected ratings I give movies in Quintiles 1 & 2 are primarily driven by my memory of the movies rather than the algorithm.

What does this mean for my top ten never before seen movies listed above? All of the top ten is either in Quintiles 1 or 2. As they grow into the higher quintiles some may emerge with higher “really like” probabilities. Certainly, Star Trek Beyond, which is only 7 months old, can be expected to grow into the higher quintiles. But, what about Splendor in the Grass which was released in 1961 and, at 55 years old, might not move into Quintile 3 until another 55 years pass.

It suggests that another secondary movie quality indicator is needed that is separate from the movie recommender sites already in use. It sounds like I’ve just added another project to my 2017 “really like” project list.