Will I "Really Like" this Movie?

Navigating Movie Website Ratings to Select More Enjoyable Movies

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There Are No Turkeys in the Objective Top Seven Movies From 1992 to 1998

Shall we call it “The Drive for Twenty Five”? If so, this installment of our journey to the Objective Top Twenty Five Movies of the last Twenty Five years begs the question which of these Cinematic Seven will survive to Twenty Five. By adding 1998 to the Objective Database more discrete groupings of data are statistically viable. As future years are added the number of groupings will grow resulting in many changes to this list. From the initial Top Six list that was published just two weeks ago, only three movies remain in the Top Seven. I think we can expect this kind of volatility with each year we add. How many of these movies will be in the Top Twenty Five at the end? Fewer than we’d expect, I’m sure.

Here’s our significant seven:

7. Scent of a Woman (IMDB 8.0, Certified Fresh 88%, CinemaScore A, Major Academy Award Win)

This movie is a favorite of mine. It produced Al Pacino’s only Academy Award win after being shut out for his seven previous nominations.

6. Good Will Hunting (IMDB 8.3, Certified Fresh 97%, CinemaScore A. Major  Academy Award Win)

One of my followers wondered why his favorite movie didn’t make the list. Good Will Hunting is a good illustration of what it takes. It requires high ratings from all feedback groups, movie watchers, movie critics, opening night moviegoers, and peer movie artists.

5. The Shawshank Redemption (IMDB 9.3, Certified Fresh 91%, CinemaScore A, Major Academy Award Nomination)

Another one of the original Top Six. The Achilles Heel for this movie from an objective rating standpoint is its failure to win a major Academy Award despite three major nominations.

4. The Usual Suspects (IMDB 8.6, Certified Fresh 88%, No CinemaScore rating, Major Academy Award Win)

Because this is an objective ranking rather than subjective, Kevin Spacey movies are still considered. In the long run, I wonder how much the absence of a CinemaScore rating will hurt this movie and, if so, should it.

3. The Lion King (IMDB 8.5, Certified Fresh 83%, CinemaScore A+, Minor Academy Award Win)

A few weeks before the release of this picture, Elton John was given a private screening of the movie. He noticed the love song he wrote wasn’t in the film and successfully lobbied to have it put back in. That song, Can You Feel the Love Tonight, won Elton John an Academy Award for Best Original Song.

2. Saving Private Ryan (IMDB 8.6, Certified Fresh 92%, CinemaScore A, Major Academy Award Win)

The only movie from the just added 1998 year to make the list. It is also the only movie on the list to be the top grossing movie for the year it was released.

1. Schindler’s List (IMDB 8.9, Certified Fresh 96%, CinemaScore A+, Major Academy Award Win)

According to the Objective “Really Like” algorithm, a 76.98% “really like” probability is the highest score that can be achieved with the algorithm. So far, Schindler’s List is the only movie with that perfect score.

***

Disney animated movies rule Thanksgiving weekend. According to Box Office Mojo, Disney owns 9 of the 10 highest grossing Thanksgiving movies of all time. Coco, which opened in theaters yesterday, is this year’s entrant into their tradition of Thanksgiving dominance. Early IMDB ratings give it a 9.1 average rating to go along with its 96% Certified Fresh Rotten Tomatoes rating. This morning CinemaScore gave it an A+ rating.

Also, two more Oscar hopefuls go into limited release this weekend. Darkest Hour is the perfect bookend to Dunkirk. It follows Winston Churchill’s response to the events at Dunkirk. Gary Oldman’s portrayal of Churchill has him on everyone’s short list for Best Actor. Also worth considering is a festival favorite, Call Me By Your Name, which was nominated this week for an Independent Spirit Award for Best Picture.

Happy Thanksgiving to you and your families.

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Add a Year Here. Tweak a Formula There. And, the Objective Top Twenty Looks Very Different.

I was able to add 1998 to the Objective Database last weekend. The extra data allowed me to factor in Oscar wins to the algorithm. But, it was one little tweak to the Oscar performance factor that dramatically altered the 2017 Objective Top Twenty this week.

For the Oscar performance part of my algorithm I created five groupings of movies based on their highest Academy Award achievement. If a movie won in a major category it went in the first group. If it was nominated for a major but didn’t win, it went in the second group. If it wasn’t nominated for a major but won in a minor category, it went into the third group. If it was only nominated in a minor category but didn’t win, it went into the fourth group. Finally, if it wasn’t nominated in any Oscar category, it went into the fifth group.

In terms of what percentage of the movies in each group that had an average IMDB rating of 7 or better, here are the results:

Best Oscar Performance: %  7+ IMDB Avg. Rating
Major Win 90.3%
Major Nomination 87.7%
Minor Win 79.7%
Minor Nomination 71.7%
No Nominations 59.8%

Wins seem to matter, particularly for the minor categories. Major nominations clearly are better “really like” indicators than minor nominations. It’s the no nominations grouping that’s most revealing. If a movie doesn’t get at least one nomination, the odds of it being a “really like” movie are dramatically reduced. This led to my discovery of some faulty thinking on my part.

If movies like DunkirkLady Bird, and Three Billboards Outside of Ebbing, Missouri, all movies headed towards major Oscar nominations in January, are treated in my algorithm as if they failed to earn a single Oscar nomination, those movies are being unfairly penalized. It was this flaw in my system that needed fixing. Now, those movies that haven’t gone through the Oscar nominating process are designated as Not Applicable. No Oscar performance test is applied to them. Without the weight of the No Nomination designation, many of the movies that didn’t get their first release until 2017 have risen significantly in the 2017 Objective Top Twenty rankings.

***

Get ready for a Thanksgiving treat. Now that 1998 has been added to the Objective Database, we can reveal the Objective Top Seven Movies from the years 1992-1998. Adding Academy Award Wins to the mix will shake up those rankings as well. Check in next Thursday after you’ve taken your post-turkey dinner nap.

***

The wide releases this weekend are Justice LeagueThe Star, and Wonder, but it’s the limited release, Mudbound, that I’ll be watching closely . This movie, set in the post-WII rural American South, is being mentioned as a Best Picture contender. Here’s the thing though. Most people won’t see it in the movie theater since it opens simultaneously on Friday on Netflix streaming. Can a movie that is more widely viewed at home than in the theater gain Academy Award traction? Stay tuned.

 

Why Does CinemaScore Leave Out So Many Good Movies When Issuing Grades?

The 2017 Academy Awards will be forever remembered as the year that La La Land was awarded Best Picture for about a minute before they discovered that Moonlight was the actual winner. Those two movies have something else in common. Neither movie received a CinemaScore grade despite, arguably, being the top two movies of 2016.

I’m thinking about this issue this week because three movies with Oscar buzz, StrongerBattle of the Sexes, and Victoria and Abdul,  went into limited release last weekend. None of them were graded by Cinemascore. There is a valid reason for this but that doesn’t make it any less disappointing to movie pre-screeners like myself.

For me, Cinemascore is appealing because it measures only opening night reaction. Most people who go to the opening night of a movie are there because they really want to see that movie. The pre-release buzz has grabbed their attention to such an extent that they can’t wait to see it. They walk into an opening night movie expecting to love it. When they walk out of the movie and respond to CinemaScore’s survey they are probably grading based on expectations. So, when a movie receives an “A” from Cinemascore, it tells us that the movie lives up to the hype. Anything less than that suggests that the movie experience was less than they expected.

CinemaScore gets stuck when it comes to movies that are released in a limited number of theaters prior to them being released widely in most theaters. CinemaScore samples locations throughout the U.S. and Canada to establish a credible unbiased sample. When a movie goes into limited release, it is released in some of their sample locations but not in most of their sample locations. Last weekend, Stronger was released in 573 theaters, Battle of the Sexes was released in 21 theaters, and Victoria and Abdul was released in 4 theaters. To provide some perspective, Kingsman: The Golden Circle opened in 4,003 theaters last weekend and earned a “B+” grade from CinemaScore. When Stronger and Battle of the Sexes goes into wide release tomorrow, does word of mouth reaction from moviegoers who’ve seen the movie in the last week disturb the integrity of any sample taken this weekend? When Victoria and Abdul goes into wide release on October 6, is its release into just 4 theaters last weekend sufficiently small to not taint the sample? I don’t know the answers to these questions. I’ll be looking to see if these movies get graded when they go into wide release. In Box Office Mojo’s article on last weekend’s box office performance they indicate that CinemaScore graded Stronger an “A-” even though it hasn’t been officially posted on their website. Perhaps they are waiting to post it after wide release?

I understand why grades don’t exist on CinemaScore for many limited release movies. I understand the importance of data integrity in the creation of a credible survey. I will just observe, though, that in this age of social media, using limited movie releases to build pre-wide release momentum for quality movies is an increasingly viable strategy. Just this week, A24, the studio behind the rise of Moonlight last year, decided to put their dark horse candidate this year, Lady Bird, into limited release on November 3rd after it emerged from the Telluride and Toronto film festivals with a 100% Fresh grade from Rotten Tomatoes. CinemaScore may be facing the prospect of an even broader inventory of ungraded top tier movies than it does today. It will be interesting to see how they respond to this challenge, if at all.

 

Before You See Mother! This Weekend, You Might Read This Article

As you might expect, I’m a big fan of Nate Silver’s FiveThirtyEight website. Last Thursday they published an interesting article on the impact of polarizing movies on IMDB ratings, using Al Gore’s An Inconvenient Sequel: Truth to Power as an example. This is not the first instance of this happening and it won’t be the last.

When the new Ghostbusters movie with the all female cast came out in July 2016 there was a similar attempt to tank the IMDB ratings for that movie. That attempt was by men who resented the all female cast. At that time I posted this article. Has a year of new ratings done anything to smooth out the initial polarizing impact of the attempt to tank the ratings? Fortunately, IMDB has a nice little feature that allows you to look at the demographic distribution behind a movie’s rating. If you access IMDB on it’s website, clicking the number of votes that a rating is based on will get you to the demographics behind the rating.

Before looking at the distribution for Ghostbusters, let’s look at a movie that wasn’t polarizing. The 2016 movie Sully is such a movie according to the following demographics:

Votes Average
Males  99301  7.4
Females  19115  7.6
Aged under 18  675  7.7
Males under 18  566  7.6
Females under 18  102  7.8
Aged 18-29  50050  7.5
Males Aged 18-29  40830  7.5
Females Aged 18-29  8718  7.6
Aged 30-44  47382  7.4
Males Aged 30-44  40321  7.4
Females Aged 30-44  6386  7.5
Aged 45+  12087  7.5
Males Aged 45+  9871  7.5
Females Aged 45+  1995  7.8
IMDb staff  17  7.7
Top 1000 voters  437  7.2
US users  17390  7.5
Non-US users  68746  7.4

There is very little difference in the average rating (the number to the far right) among all of the groups. When you have a movie that is not polarizing, like Sully, the distribution by rating should look something like this:

Votes  Percentage  Rating
12465  8.1% 10
19080  12.4% 9
52164  33.9% 8
47887  31.1% 7
15409  10.0% 6
4296  2.8% 5
1267  0.8% 4
589  0.4% 3
334  0.2% 2
576  0.4% 1

It takes on the principles of a bell curve, with the most ratings clustering around the average for the movie.

Here’s what the demographic breakdown for Ghostbusters looks like today:

Votes Average
Males  87119  5.0
Females  27237  6.7
Aged under 18  671  5.3
Males under 18  479  4.9
Females under 18  185  6.6
Aged 18-29  36898  5.4
Males Aged 18-29  25659  5.0
Females Aged 18-29  10771  6.7
Aged 30-44  54294  5.2
Males Aged 30-44  43516  5.0
Females Aged 30-44  9954  6.6
Aged 45+  11422  5.3
Males Aged 45+  9087  5.1
Females Aged 45+  2130  6.3
IMDb staff  45  7.4
Top 1000 voters  482  4.9
US users  25462  5.5
Non-US users  54869  5.2

There is still a big gap in the ratings between men and women and it persists in all age groups. This polarizing effect produces a ratings distribution graph very different from the one for Sully.

Votes  Percentage  Rating
20038  12.8% 10
6352  4.1% 9
13504  8.6% 8
20957  13.4% 7
24206  15.5% 6
18686  12.0% 5
10868  7.0% 4
7547  4.8% 3
6665  4.3% 2
27501  17.6% 1

It looks like a bell curve sitting inside a football goal post. But it is still useful because it suggests the average IMDB rating for the movie when you exclude the 1’s and the 10’s is around 6 rather than a 5.3.

You are probably thinking that, while interesting, is this information useful. Does it help me decide whether to watch a movie or not? Well, here’s the payoff. The big movie opening this weekend that the industry will be watching closely is Mother!. The buzz coming out of the film festivals is that it is a brilliant but polarizing movie. All four of the main actors (Jennifer Lawrence, Javier Bardem, Michele Pfeiffer, Ed Harris) are in the discussion for acting awards. I haven’t seen the movie but I don’t sense that it is politically polarizing like An Inconvenient Sequel and Ghostbusters. I think it probably impacts the sensibilities of different demographics in different ways.

So, should you go see Mother! this weekend? Fortunately, its early screenings at the film festivals give us an early peek at the data trends. The IMDB demographics so far are revealing. First, by looking at the rating distribution, you can see the goal post shape of the graph, confirming that the film is polarizing moviegoers.

Votes  Percentage  Rating
486  36.0% 10
108  8.0% 9
112  8.3% 8
92  6.8% 7
77  5.7% 6
44  3.3% 5
49  3.6% 4
40  3.0% 3
52  3.8% 2
291  21.5% 1

57.5% of IMDB voters have rated it either a 10 or a 1. So are you likely to love it or hate it? Here’s what the demographics suggest:

Votes Average
Males  717  6.1
Females  242  5.4
Aged under 18  25  8.4
Males under 18  18  8.2
Females under 18  6  10.0
Aged 18-29  404  7.3
Males Aged 18-29  305  7.5
Females Aged 18-29  98  6.1
Aged 30-44  288  5.0
Males Aged 30-44  215  5.0
Females Aged 30-44  69  5.2
Aged 45+  152  4.3
Males Aged 45+  111  4.3
Females Aged 45+  40  4.1
Top 1000 voters  48  4.6
US users  273  4.4
Non-US users  438  6.5

While men like the movie more than women, if you are over 30, men and women hate the movie almost equally. There is also a 2 point gap between U.S. and non-U.S. voters. This is a small sample but it has a distinct trend. I’ll be interested to see if the trends hold up as the sample grows.

So, be forewarned. If you take your entire family to see Mother! this weekend, some of you will probably love the trip and some of you will probably wish you stayed home.

 

It’s a Good Week To Be on Vacation 

Every now and then I wonder if anyone would notice if I didn’t blog one week. I’m a creature of habit. I update the Objective Top Fifteen every Monday. I update my Watch List every Wednesday. I publish a new article every Thursday.

This week I’m spending the week in beautiful Newport, RI. I didn’t update the Objective Top Fifteen. Did you notice? As it turns out, there were no changes. Both The Hitman’s Bodyguard and Logan Lucky received mediocre grades from Cinemascore, which kept them off the list.

I didn’t update my Watch List today. But that wouldn’t have changed much either. After watching American History X last Wednesday, I haven’t watched another movie since.

As for the movies opening this weekend, there isn’t much to talk about. August is typically weak. If you can believe it, this August is running 64% behind last August at the box office, making it a weaker than weak August. If you want to use your newly purchased Movie Pass (Its price was recently cut to $10), check out the Indies I’ve mentioned before. Good Time, the Robert Pattinson crime drama, opens to a wide audience this weekend. Positive buzz is following its limited release last weekend.

Finally, I just wanted to let you know that I wouldn’t be publishing tomorrow. I’ll be continuing to sample signature drinks throughout Newport. Given where we are in the movie cycle, it’s a very viable alternative.

Why Did “The Big Sick” Drop Out of the Objective Top Fifteen This Week?

This past Sunday my wife, Pam, and I went to see The Big Sick. The movie tells the story of the early relationship days of the two screenwriters, Emily Gordon and Kumail Nanjiani. In fact, Nanjiani plays himself in the movie. It is the authenticity of the story, told in a heartfelt and humorous way, that makes this film special.

On the following day, last weekend’s blockbuster, Dunkirk, moved into the second spot in the revised Objective Top Fifteen rankings. When a new movie comes on the list another one exits. This week’s exiting movie, ironically, was The Big Sick. Wait! If The Big Sick is such a great movie why isn’t it in my top fifteen for the year? Are all of the other movies on the list better movies? Maybe yes. Maybe no. You’ll have to determine that for yourselves. You see the Objective Top Fifteen is your list, not mine.

I developed the Objective Top Ten, which became Fifteen the beginning of July and will become Twenty the beginning of October, to provide you with a ranking of 2017 widely released movies that are most likely to be “really like” movies. Because the ranking is based on objective benchmarks, my taste in movies has no influence on the list. The four benchmarks presently in use are: IMDB Avg. Rating, Rotten Tomatoes Rating, Cinemascore Rating, and Academy Award Nominations and Wins. A movie like Hidden Figures that meets all four benchmarks has the greatest statistical confidence in its “really like” status and earns the highest “really like” probability. A movie that meets three benchmarks has a greater “really like” probability than a movie that meets only two benchmarks. And so on.

The important thing to note, though, is that this is not a list of the fifteen best movies of the year. It is a ranking of probabilities (with some tie breakers thrown in) that you’ll “really like” a movie. It is subject to data availability. The more positive data that’s available, the more statistical confidence, i.e. higher probability, the model has in the projection.

Which brings me back to The Big Sick. Cinemascore surveys those movies that they consider “major releases”. The Big Sick probably didn’t have a big advertising budget. Instead, the producers of the film chose to roll the movie out gradually, beginning on June 23rd, to create some buzz and momentum behind the movie before putting it into wide release on July 14th. This is probably one of the reasons why Cinemascore didn’t survey The Big Sick. But, because The Big Sick is missing that third benchmark needed to develop a higher probability, it dropped out of the Top Fifteen. On the other hand, if it had earned at least an “A-” from Cinemascore The Big Sick would be the #2 movie on the list based on the tie breakers.

And, that is the weakness, and strength of movie data. “Major releases” have it. Smaller movies like The Big Sick don’t.

***

This weekend may be the end of the four week run of Objective Top Fifteen movie breakthroughs. Atomic Blonde, the Charlize Theron spy thriller, has an outside chance of earning a spot on the list. As of this morning, it is borderline for the IMDB and Rotten Tomatoes benchmarks. I’m also tracking Girls Trip which earned a Certified Fresh just in the last couple of days from Rotten Tomatoes and has an “A+” in hand from Cinemascore. For now, it is just below the IMDB benchmark. We’ll see if that changes over the weekend.

 

 

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.

 

 

Sometimes When You Start To Go There You End Up Here

There are some weeks when I’m stumped as to what I should write about in this weekly trip to Mad Moviedom. Sometimes I’m in the middle of an interesting study that isn’t quite ready for publication. Sometimes an idea isn’t quite fully developed. Sometimes I have an idea but I find myself blocked as to how to present it. When I find myself in this position, one avenue always open to me is to create a quick study that might be halfway interesting.

This is where I found myself this week. I had ideas that weren’t ready to publish yet. So, my fallback study was going to be a quick study of which movie decades present the best “really like” viewing potential. Here are the results of my first pass at this:

“Really Like” Decades
Based on Number of “Really Like” Movies
As of April 6, 2017
My Rating
Really Liked Didn’t Really Like Total “Really Like” Probability
 All       1,108                  888         1,996
 2010’s           232                  117            349 60.9%
 2000’s           363                  382            745 50.5%
 1990’s           175                    75            250 62.0%
 1980’s             97                    60            157 58.4%
 1970’s             56                    49            105 54.5%
 1960’s             60                    55            115 53.9%
 1950’s             51                    78            129 46.6%
 1940’s             55                    43               98 55.8%
 1930’s             19                    29               48 46.9%

These results are mildly interesting. The 2010’s, 1990″s, 1980’s, and 1940’s are above average decades for me. There are an unusually high number of movies in the sample that were released in the 2000’s. Remember that movies stay in my sample for 15 years from the year I last watched the movie. After 15 years they are removed from the sample and put into the pool of movies available to watch again. The good movies get watched again and the other movies are never seen again, hopefully. Movies last seen after 2002 have not gone through the process of separating out the “really like” movies to be watched again and permanently weeding from the sample the didn’t “really like” movies. The contrast of the 2000’s with the 2010’s is a good measure of the impact of the undisciplined selection movies and the disciplined selection.

As I’ve pointed out in recent posts, I’ve made some changes to my algorithm. One of the big changes I’ve made is that I’ve replaced the number of movies that are “really like” movies with the number of ratings for the movies that are “really like” movies. After doing my decade study based on number of movies, I realized I should have used the number of ratings method to be consistent with my new methodology. Here are the results based on the new methodology:

“Really Like” Decades
Based on Number of “Really Like” Ratings
As of April 6, 2017
My Rating
Really Liked Didn’t Really Like Total “Really Like” Probability
 All    2,323,200,802    1,367,262,395    3,690,463,197
 2010’s        168,271,890        166,710,270        334,982,160 57.1%
 2000’s    1,097,605,373        888,938,968    1,986,544,341 56.6%
 1990’s        610,053,403        125,896,166        735,949,569 70.8%
 1980’s        249,296,289        111,352,418        360,648,707 65.3%
 1970’s          85,940,966          25,372,041        111,313,007 67.7%
 1960’s          57,485,708          15,856,076          73,341,784 68.0%
 1950’s          28,157,933          23,398,131          51,556,064 59.5%
 1940’s          17,003,848            5,220,590          22,224,438 67.4%
 1930’s            9,385,392            4,517,735          13,903,127 64.6%

While the results are different, the big reveal was that 63.0% of the ratings are for “really like” movies and only 55.5% of the number of movies are “really like” movies. It starkly reinforces the impact of the law of large numbers. Movie website indicators of “really like” movies are more reliable when the number of ratings driving those indicators are larger. The following table illustrates this better:

“Really Like” Decades
Based on Average Number of “Really Like” Ratings per Movie
As of April 6, 2017
My Rating
Really Liked Didn’t Really Like Total “Really Like” % Difference
 All      2,096,751.63      1,539,709.90      1,848,929.46 36.2%
 2010’s          725,309.87      1,424,874.10          959,834.27 -49.1%
 2000’s      3,023,706.26      2,327,065.36      2,666,502.47 29.9%
 1990’s      3,486,019.45      1,678,615.55      2,943,798.28 107.7%
 1980’s      2,570,064.84      1,855,873.63      2,297,125.52 38.5%
 1970’s      1,534,660.11          517,796.76      1,060,123.88 196.4%
 1960’s          958,095.13          288,292.29          637,754.64 232.3%
 1950’s          552,116.33          299,976.04          399,659.41 84.1%
 1940’s          309,160.87          121,409.07          226,779.98 154.6%
 1930’s          493,968.00          155,783.97          289,648.48 217.1%

With the exception of the 2010’s, the average number of ratings per movie is larger for the “really like” movies. In fact, they are dramatically different for the decades prior to 2000. My educated guess is that the post-2000 years will end up fitting the pattern of the other decades once those years mature.

So what is the significance of this finding. It clearly suggests that waiting to decide whether to see a new movie or not until a sufficient number of ratings come in will produce a more reliable result. The unanswered question is how many ratings is enough.

The finding also reinforces the need to have something like Oscar performance to act as a second measure of quality for movies that will never have “enough” ratings for a reliable result.

Finally, the path from “there to here” is not always found on a map.

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.

Create, Test, Analyze, and Recreate

Apple’s IPhone just turned 10 years old. Why has it been such a successful product? It might be because the product hasn’t stayed static. The latest version of the IPhone is the IPhone 7+. As a product, it is constantly reinventing itself to improve its utility. It is always fresh. Apple, like most producers of successful products, probably follows a process whereby they:

  1. Create.
  2. Test what they’ve created.
  3. Analyze the results of their tests.
  4. Recreate.

They never dust off their hands and say, “My job is done.”

Now I won’t be so presumptuous to claim to have created something as revolutionary as the IPhone. But, regardless of how small your creation, its success requires you to follow the same steps outlined above.

My post last week outlined the testing process I put my algorithm through each year. This week I will provide some analysis and take some steps towards a recreation. The results of my test was that using my “really like” movie selection system significantly improved the overall quality of the movies I watch. On the negative side, the test showed that once you hit some optimal number of movies in a year the additional movies you might watch has a diminishing quality as the remaining pool of “really like” movies shrinks.

A deeper dive into these results begins to clarify the key issues. Separating movies that I’ve seen at least twice from those that were new to me is revealing.

Seen More than Once Seen Once
1999 to 2001 2014 to 2016 1999 to 2001 2014 to 2016
# of Movies 43 168 231 158
% of Total Movies in Timeframe 15.7% 51.5% 84.3% 48.5%
IMDB Avg Rating                   7.6                   7.6                   6.9                   7.5
My Avg Rating                   8.0                   8.4                   6.1                   7.7
% Difference 5.2% 10.1% -12.0% 2.0%

There is so much interesting data here I don’t know where to start. Let’s start with the notion that the best opportunity for a “really like” movie experience is the “really like” movie you’ve already seen. I’ve highlighted in teal the percentage that My Avg Rating outperforms the IMDB Avg Rating in both timeframes. The fact that, from 1999 to 2001, I was able to watch movies that I “really liked” more than the average IMDB voter, without the assistance of any movie recommender website, suggests that memory of a “really like” movie is a pretty reliable “really like” indicator. The 2014 to 2016 results suggest that my “really like” system can help prioritize the movies that memory tells you that you will “really like” seeing again.

The data highlighted in red and blue clearly display the advantages of the “really like” movie selection system. It’s for the movies you’ve never seen that movie recommender websites are worth their weight in gold. With limited availability of movie websites from 1999 to 2001 my selection of new movies underperformed the IMDB Avg Rating by 12% and they represented 84.3% of all of the movies I watched during that timeframe. From 2014 to 2016 (the data in blue), my “really like” movie selection system recognized that there is a limited supply of new “really like” movies. As a result less than half of the movies watched from 2014 through 2016 were movies I’d never seen before. Of the new movies I did watch, there was a significant improvement over the 1999 to 2001 timeframe in terms of quality, as represented by the IMD Avg Rating, and my enjoyment of the movies, as represented by My Avg Rating.

Still, while the 2014 to 2016 new movies were significantly better than the new movies watched from 1999 to 2001, is it unrealistic to expect My Ratings to be better than IMDB by more than 2%? To gain some perspective on this question, I profiled the new movies I “really liked” in the 2014 to 2016 timeframe and contrasted them with the movies I didn’t “really like”.

Movies Seen Once
2014 to 2016
“Really Liked” Didn’t “Really Like”
# of Movies 116 42
% of Total Movies in Timeframe 73.4% 26.6%
IMDB Avg Rating                       7.6                                  7.5
My Avg Rating                       8.1                                  6.3
“Really Like” Probability 82.8% 80.7%

The probability results for these movies suggest that I should “really like” between 80.7% and 82.8% of the movies in the sample. I actually “really liked” 73.4%, not too far off the probability expectations. The IMDB Avg Rating for the movies I didn’t “really like” is only a tick lower than the rating for the “really liked” movies. Similarly, the “Really Like” Probability is only a tick lower for the Didn’t “Really Like” movies. My conclusion is that there is some, but not much, opportunity to improve selection of new movies through a more disciplined approach. The better approach would be to favor “really like” movies that I’ve seen before and give new movies more time for their data to mature.

Based on my analysis, here is my action plan:

  1. Set separate probability standards for movies I’ve seen before and movies I’ve never seen.
  2. Incorporate the probability revisions into the algorithm.
  3. Set a minimum probability threshold for movies I’ve never seen before.
  4. When the supply of “really like” movies gets thin, only stretch for movies I’ve already seen and memory tells me I “really liked”.

Create, test, analyze and recreate.

 

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