Will I "Really Like" this Movie?

Navigating Movie Website Ratings to Select More Enjoyable Movies

Archive for the month “March, 2017”

The Wandering Mad Movie Mind

Last week in my post I spent some time leading you through my thought process in developing a Watch List. There were some loose threads in that article that I’ve been tugging at over the last week.

The first thread was the high “really like” probability that my algorithm assigned to two movies, Fight Club and Amelie, that I “really” didn’t like the first time I saw them. It bothered me to the point that I took another look at my algorithm. Without boring you with the details, I had an “aha” moment and was able to reengineer my algorithm in such a way that I can now develop unique probabilities for each movie. Prior to this I was assigning the same probability to groups of movies with similar ratings. The result is a tighter range of probabilities clustered around the base probability. The base probability is defined as the probability that I would “really like” a movie randomly selected from the database. If you look at this week’s Watch List, you’ll notice that my top movie, The Untouchables, has a “really like” probability of 72.2%. In my revised algorithm that is a high probability movie. As my database gets larger, the extremes of the assigned probabilities will get wider.

One of the by-products of this change is that the rating assigned by Netflix is the most dominant driver of the final probability. This is as it should be. Netflix has by far the largest database of any I use.  Because of this it produces the most credible and reliable ratings of any of the rating websites. Which brings me back to Fight Club and Amelie. The probability for Fight Club went from 84.8% under the old formula to 50.8% under the new formula. Amelie went from 72.0% to 54.3%. On the other hand, a movie that I’m pretty confident that I will like, Hacksaw Ridge changed only slightly from 71.5% to 69.6%.

Another thread I tugged at this week was in response to a question from one of the readers of this blog.  The question was why was Beauty and the Beast earning the low “really like” probability of 36.6% when I felt that there was a high likelihood that I was going to “really like” it. The fact is that I saw the movie this past week and it turned out to be a “really like” instant classic. I rated it a 93 out of 100, which is a very high rating from me for a new movie. In my algorithm, new movies are underrated for two reasons. Because they generate so few ratings in their early months, e.g. Netflix has only 2,460 ratings for Beauty and the Beast so far, the credibility of the movie’s own data is so small that the “really like” probability is driven by the Oscar performance part of the algorithm. This is the second reason for the low rating. New movies haven’t been through the Oscar cycle yet and so their Oscar performance probability is that of a movie that didn’t earn an Oscar nomination, or 35.8%. This is why Beauty and the Beast was only at 36.6% “really like” probability on my Watch List last week.

I’ll leave you this week with a concern. As I mentioned above, Netflix is the cornerstone of my whole “really like” system. You can appreciate then my heart palpitations when it was announced a couple of weeks ago that Netflix is abandoning it’s five star rating system in April. It is replacing it with a thumbs up or down rating with a % next to it, perhaps a little like Rotten Tomatoes. While I am keeping and open mind about the change, it has the potential of destroying the best movie recommender system in the business. If it does, I will be one “mad” movie man, and that’s not “crazy” mad.

A Movie Watch List is Built by Thinking Fast and Slow

In early 2012 I read a book by Daniel Kahneman titled Thinking Fast and Slow. Kahneman is a psychologist who studies human decision making and, more precisely, the thinking process. He suggests that the human mind has two thinking processes. The first is the snap judgement that evolved to quickly identify threats and react to them quickly in order to survive. He calls this “thinking fast”. The second is the rational thought process that weighs alternatives and evidence before reaching a decision. This he calls “thinking slow”. In the book, Kahneman discusses what he calls the “law of least effort”. He believes that the mind will naturally gravitate to the easiest solution or action rather than to the more reliable evidence based solution. He suggests that the mind is most subject to the “law of least effort” when it is fatigued, which leads to less than satisfactory decision making more often than not.

How we select the movies we watch, I believe, is generally driven by the “law of least effort”. For most of us, movie watching is a leisure activity. Other than on social occasions, we watch movies when we are too tired to do anything else in our productive lives. Typically, the movies we watch are driven by what’s available to watch at the time we decide to watch. From the movies available, we decide what seems like a movie we’d like at that moment in time. We choose by “thinking fast”. Sometimes we are happy with our choice. Other times, we get half way through the movie and start wondering, over-optimistically I might add, if this dreadful movie will ever be over.

It doesn’t have to be that way. One tool I use is a Movie Watch List that I update each week using a “thinking slow” process.. My current watch list can be found on the side bar under Ten Movies on My Watch List This Week. Since you may read this blog entry sometime in the future, here’s the watch list I’ll be referring to today:

Ten Movies On My Watch List This Week
As Of March 22, 2017
Movie Title Release Year Where Available Probability I Will “Really Like”
Fight Club 1999 Starz 84.8%
Amélie 2002 Netflix – Streaming 72.0%
Hacksaw Ridge  2016 Netflix – DVD 71.5%
Emigrants, The 1972 Warner Archive 69.7%
Godfather: Part III, The 1990 Own DVD 68.7%
Pride and Prejudice 1940 Warner Archive 67.3%
Steel Magnolias 1989 Starz 67.1%
Paper Moon 1973 HBO 63.4%
Confirmation 2016 HBO 57.0%
Beauty and the Beast 2017 Movie Theater 36.6%

The movies that make it to this list are carefully selected based on the movies that are available in the coming week on the viewing platforms I can access. I use my algorithm to guide me towards movies with a high “really like” probability. I determine who I’m likely to watch movies with during the upcoming week. If I’m going to watch movies with others, I make sure that there are movies on the list that those others might like. And, finally, I do some “thinking fast” and identify those movies that I really want to see and those movies that, instinctively, I am reluctant to see.

The movies on my list above in green are those movies that I really want to see. The movies in turquoise are those movies I’m indifferent to but are highly recommended by the algorithm. The movies in red are movies that I’m reluctant to see.

So, you may ask, why do I have movies that I don’t want to see on my watch list? Well, it’s because I’m the Mad Movie Man. These are movies that my algorithm suggests have a high “really like” probability. In the case of Fight Club, for example, I’ve seen the movie before and was turned off by the premise. On the other hand, it is a movie that my algorithm, based on highly credible data,  indicates is the surest “really like” bet of all the movies I haven’t seen in the last 15 years. Either my memory is faulty, or my tastes have changed, or there is a flaw in my algorithm, or a flaw in the data coming from the websites I use. It may just be that it is among the movies in the 15% I won’t like. So, I put these movies on my list because I need to know why the mismatch exists. I have to admit, though, that it is hard getting these red movies off the list because I often succumb to the “law of least effort” and watch another movie I’d much rather see.

Most of our family is gathering together in the coming week and so Beauty and the Beast and Hacksaw Ridge are family movie candidates. In case my wife and I watch a movie together this week, Amélie , Pride and Prejudice, and Steel Magnolias are on the list.

The point in all this is that by having a Watch List of movies with a high “really like” probability you are better equipped to avoid the “law of least effort” trap and get more enjoyment out of your leisure time movie watching.

 

Playing Tag with Movielens, Redux

Last July I wrote an article introducing my use of tags in Movielens to organize my movies. I can’t impress upon you enough how useful this tool is to someone as manic about movies as I am.

Regular readers of this blog know that I’ve shifted my focus to Oscar nominated movies. My research revealed that movies that haven’t receive a single Academy Award nomination have only a 35.8% chance of being a “really like” movie. On the other hand, even a single minor nomination increases the “really like” odds to around 55%. My algorithm now incorporates Oscar recognition.

Based on this finding, I’ve altered my tagging strategy. I created an “Oscar” tag that I attach to any movie I run across that has received even a single nomination. Many of these movies are older without enough credible data in the movie ratings websites to earn reliable recommendations. The probabilities in my algorithm for these Quintile 1 & 2 movies are driven by their Oscar performance.

Movies that pique my interest that weren’t Oscar nominated are tagged separately. Now, because these movies have no Oscar nominations, a Quintile 1 or 2 movie is going to have a “really like” probability closer to the 35% mark that reflects its “no nomination” status. It can only climb to a high enough probability to be considered for my weekly watch list if it is highly recommended by the movie websites and it falls into a high enough credibility quintile that its Oscar status doesn’t matter much.

I apply one of two tags to non-Oscar nominated movies. If they have fewer than 25 ratings in Movielens, I tag them as “might like”. Realistically, they have no chance of being highly recommended in my algorithm until the number of ratings received from Movielens raters becomes more robust.

Those non-Oscar nominated movies that have more than 25 ratings are tagged as “prospect”. Movies with the “prospect” tag that are highly rated by the websites and have enough ratings to reach higher credibility quintiles can reach a “really like” probability high enough to be considered for the watch list. For example, a quintile 5 movie like The American President can earn a 75% “really like” probability even though it was never nominated for an Academy Award.

I also have created tags for movies I don’t want to see even though they are highly rated. If I’ve already seen a movie and I don’t want to see it again, I tag it “not again”. If I’ve never seen a movie but it’s just not for me, I tag it “not interested”. Movielens also has the capability of hiding movies that you don’t want to see in any of your searches for movies. I take advantage of this feature to hide my “not again” and “not interested” tagged movies.

So, I’ve tagged all of these movies. Now what do I do with them. That will be included in next week’s topic “Building a Watch List”.

And the 2018 Academy Award for Best Picture Goes To?

We are 11 days removed from the 2017 Best Picture award debacle and already Awards Circuit has projected its first list of nominees for the 2018 Best Picture race. Obviously it is way too early make predictions like this with a high degree of accuracy. Many of the movies are still being filmed and can’t be realistically evaluated. It does, though, give us an idea of the movies that evaluators believe have Oscar pedigree.

Here is the Awards Circuit list with its current production status:

2018 Projected Academy Award Nominees for Best Picture
Movie Release Status Short Description
Untitled Paul Thomas Anderson Project In Production 1950’s Drama set in London Fashion world.
Suburbicon Post-Production 1950’s Crime mystery set in small family town.
Darkest Hour Nov. 24 release Churchill biopic set in early days of WW II
The Kidnapping of Edgardo Mortara Pre-Production Historical Drama set in 19th century Italy
Battle of the Sexes Post-Production Billie Jean King-Bobby Riggs 1973 tennis match
The Current War In Production Edison-Westinghouse scientific competition.
Mudbound Post-Production Post-WW II drama set in rural Mississippi
Downsizing Dec. 22 release Social Satire about less is more.
Marshall Oct. 13 release Biopic about a young Thurgood Marshall
The Snowman Oct. 13 release Adaptation of Jo Nesbo crime thriller.

If this first list is representative of the entire year, 2018 is going to be a year of looking back in time. Only two of the ten movies listed here take place in a contemporary setting, Downsizing and The Snowman.

I’m probably most interested in Battle of the Sexes. Emma Stone plays Billie Jean King and is projected as a Best Actress nominee by Awards Circuit. Can she go back to back years as Best Actress?

I’m least interested in the Paul Thomas Anderson movie, even if it includes a rare star turn by Daniel Day Lewis. I hated There Will Be Blood and wasn’t a big fan of Boogie Nights.

In any event, that’s my gut reaction to the Best Picture projections. Is there any data to support my gut? I’m trying out a new data point called an Anticipation Score. The website Criticker provides averages of my ratings for movies involving specific Directors, Screenwriters, and Actors. By tabulating the scores for the film makers involved in each movie I can create an Anticipation Score based on my historical rating of their work. I’m including the two lead actors for each movie. For example, Battle of the Sexes is directed by Jonathon Dayton, screen written by Simon Beaufoy, and stars Emma Stone and Steve Carell. I’ve seen two of Jonathon Dayton’s movies and given them an average rating of 65.5 out of 100. I’ve seen five movies written by Simon Beaufoy for an average of 68.6. I’ve seen seven Emma Stone movies, averaging 81.57, and eight Steve Carell movies, averaging 73. When you add all four numbers together they total an Anticipation Score of 288.67. This represents my potential enjoyment of the movie if each artist entertains me at the average level that they have in the past.

Here’s the entire list ranked by Anticipation Score:

My Anticipation Score
Director Writer Lead Actor 1 Lead Actor 2 Score
The Kidnapping of Edgardo Mortara S. Spielberg T. Kushner M. Rylance O. Isaac 323.39
Downsizing A. Payne J. Taylor M. Damon K. Wiig 313.14
Darkest Hour J. Wright A. McCarten G. Oldman K. Scott-Thomas 293.39
Battle of the Sexes J. Dayton S. Beaufoy E. Stone S. Carell 288.67
Suburbicon G. Clooney E. Coen M. Damon O. Isaac 283.01
The Snowman T. Alfredson H. Amini M. Fassbender R. Ferguson 216.67
The Current War A. Gomez-Rejon M. Mitnick B. Cumberbatch M. Shannon 212.13
Untitled Paul Thomas Anderson Project P. T. Anderson P. T. Anderson D. D. Lewis L. Manville 159.13
Mudbound D. Rees D. Rees C. Mulligan J. Clarke 137.23
Marshall R. Hudlin J. Koskoff C. Boseman S. K. Brown 86.5

My gut reaction to Battle of the Sexes and the Paul Thomas Anderson movie are borne out in the data, although these movies are neither the best or worst of the rankings. The two movies at the bottom of the list are there because I have never seen movies directed or written by the two film makers involved. In the case of Marshall, although I’ve seen Sterling K. Brown on TV shows, I haven’t seen any movies that he has been in. As a result, the Anticipation Score for Marshall is based solely Chadwick Boseman’s movies that I’ve seen.

I think my Anticipation Score formula needs some tweaking to take into account the volume of movies seen for each artist. The fact that I’ve seen 21 Spielberg movies should be recognized in addition to the average rating I give each of his movies.

In any event, keep your eye out for these movies as we get back into Oscar season, beginning in October.

 

 

Is Meryl Streep’s Oscar Record Out of Reach?

With the presentation of Academy Awards completed last Sunday, I am able to tabulate the last Actors of the Decade winners. For the male actors, the winner is Daniel Day Lewis.

Top Actors of the Decade
2007 to 2016 Releases
Actor Lead Actor Nominations Lead Actor Wins Supporting Actor Nominations Supporting Actor Wins Total Academy Award Points
Daniel Day Lewis 2 2 0 0 12
Jeff Bridges 2 1 1 0 10
Leonardo DiCaprio 2 1 0 0 9
Colin Firth 2 1 0 0 9
Eddie Redmayne 2 1 0 0 9
George Clooney 3 0 0 0 9

This result is pretty incredible when you consider that Daniel Day Lewis only appeared in three movies during the entire decade. His three Academy Award Best Actor wins stands alone in the history of the category. It might be interesting to measure Oscar nominations per movie made. I’d be surprised if we found any actor who is even close to Daniel Day Lewis.

As for the Best Female Actor, once again, it is Meryl Streep.

Top Actresses of the Decade
2007 to 2016 Releases
Actress Lead Actress Nominations Lead Actress Wins Supporting Actress Nominations Supporting Actress Wins Total Academy Award Points
Meryl Streep 5 1 1 0 19
Cate Blanchett 3 1 1 0 13
Jennifer Lawrence 3 1 1 0 13
Marion Cotillard 2 1 0 0 9
Sandra Bullock 2 1 0 0 9
Natalie Portman 2 1 0 0 9

When the 28 year old Emma Stone accepted her Best Actress in a Leading Role award, she commented that she still has a lot to learn. It is that kind of attitude, and a commensurate work ethic, for a young actress today to take a run at Meryl Streep’s Oscar nomination record of 20 nominations. Consider that the actresses that Streep chased early in her career, Katherine Hepburn and Bette Davis, received their first nominations some 45 years before Streep earned her first nomination. It has been 38 years since Meryl Streep received her first nomination. We should be on the lookout for the next actress of a generation. Is there a contender already out there?

Let’s look first at the career Oscar performance of Streep, Hepburn, and Davis.

Acting Nomination Points
Lead Actress = 1 point,  Supporting Actress = .5 points
Points at Age:
30 40 50 60 70 80
Meryl Streep 1 7 11 14.5 18
Katherine Hepburn 2 4 6 9 10 11
Bette Davis 3 8 10 11 11 11

I chose not to equate a supporting actress role with a lead actress role to be fair to Hepburn and Davis. With the studios in control of the movies they appeared in, stars didn’t get the chance to do supporting roles. Bette Davis had a strong career before age 50. Katherine Hepburn was strong after age 50. Meryl Streep has outperformed both of them before 50 and after 50. It is not unreasonable to expect more nominations in her future.

As for today’s actresses, I looked at multiple nominated actresses in different age groups to see if anyone is close to tracking her.

Age as of 12/31/2016 Comparison Age Points at Comparison Age Streep at Comparison Age
Cate Blanchett 47 50 5.5 11
Viola Davis 51 50 2 11
Kate Winslet 41 40 5.5 7
Michelle Williams 36 40 3 7
Amy Adams 42 40 3 7
Natalie Portman 35 40 2.5 7
Marion Cotillard 41 40 2 7
Jennifer Lawrence 26 30 3.5 1
Emma Stone 28 30 1.5 1
Keira Knightley 31 30 1.5 1
Rooney Mara 31 30 1.5 1

Except for the 30-ish actresses, none are keeping pace. You might argue that Kate Winslet is in striking distance but, given Streep’s strength after 40, that’s probably not good enough.

Of the young actresses, Jennifer Lawrence has had a very strong start to her career. With 3 lead actress nominations and 1 supporting nomination over the next 14 years she would join Bette Davis as the only actresses to keep pace with Meryl Streep through age 40. Then all she would have to do is average between 3.5 and 4 points every 10 years for anther 30 years or more.

Good luck with that. Along side Joe DiMaggio’s 56 game hitting streak, it may become a record that will never be broken.

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