Just as I’ve done with books, I tried to be more precise in how I measured the best movie and television content I watched this year. For the second year in a row, I’ve been rating each film or show according to my assessment of its quality and my level of enjoyment. The best ones are the ones that have the highest combined rating of quality and enjoyment. You can see these in the graphic below.
The best movies I watched this year
- The Banshees of Inisherin
Any period piece in Ireland is going to have a certain bleakness, but this was very funny as well. In a small town like that, I imagine a grudge with a close friend could feel like a death sentence.
- The Gift
Another movie based on a grudge in some ways, but the aspect of the film that I identified with to a greater extent was the asymmetrical friendship. There’s the true horror.
- Spider-Man: Across the Spiderverse
A visual and musical spectacle in IMAX. Almost too frenetic and with a lot of teenage angst, but a very clever, entertaining movie over all.
This movie tried so hard to be good, but nothing worked particularly well. It couldn’t decide what kind of movie it wanted to be. Lurched between a film about misunderstood genius, a rivalry movie, and a romantic comedy, and succeeded in none of them. Bradley Cooper was good in it though, and it did provide one memorably dark and funny scene where he tries to commit suicide by vacuum sealing his head in a sous vide machine.
The best shows I watched this year
Finally–a star wars story for adults! Loved it. Loved the heist scenes; loved the look at the Empire. I especially liked Deidra’s character–we never get these kinds of stories told.
- Formula 1: Drive to Survive (Season 5)
Not as compelling as last season (which came down to the final race), but I’ll still watch any episode they make of this show.
- Madoff: The Monster of Wall Street
Well-made and riveting despite the outcome being known before-hand.
Last year my data suggested that Rotten Tomato scores did not matter to my enjoyment of a movie. I was curious to see if that trend held up with another year’s worth of data (essentially doubling the number of observations).
With another year’s data, it looks like they do matter to some extent. Look at the steep regression line for evidence, as the Rotten Tomato score goes up on the x-axis, so does my enjoyment on the y-axis (rescaled from 0 to 100).1 A one unit change in the Rotten Tomato score is associated with a 0.3 change in my enjoyment score. So admittedly a subtle difference. Consider this: the difference between two movies that are 20 points apart on Rotten Tomatoes is about a 6 point difference in my enjoyment. Another way to think about this is that Rotten Tomato scores are still only explaining about 15% of the variation in my enjoyment scores; other factors appear to matter much more than the Rotten Tomato scores in determining whether I enjoy a movie.2
Year of Release
Another category I tracked was the year of release for each movie. I wanted to see if I preferred newer to older movies.
Basically no relationship.3 I enjoyed older movies as much as I did newer ones; of course, there were far fewer older movies in my watchlist. Three quarters of all the movies I watched were released in the past twenty years, with a quarter of those being released in 2020 or later.
By way of further explanation, the scatter plots I’m showing in this post all come with a regression line plotted using a basic bivariate ordinary least squares regression. That line is a line of best fit; it tries to predict the relationship between the two variables (shown on the Y and X axes). I’m not going to be tedious and present the regression tables (this is for fun!), but you can just eyeball the relationships or simply look at the regression line. A negative relationship will be shown as the line sloping down from left to right, and a positive relationship will slope up from left to right. The steeper the slope, the more correlated the variables. In the case of the scatter plot above, the steep slope indicates that the two variables are well-correlated. I’m also not going to bother reporting p-values. I don’t think they’re appropriate for several reasons. First, the observations are definitely not independent (how can they be when movies have sequels?) And secondly, the data generating process is not random at all. I deliberately chose which movies to watch and which movies to avoid based on some system, which may not even be entirely clear to me. ↩︎
Rotten Tomatoes audience scores give an even more subtle effect suggesting I should pay more attention to the critic scores rather than the audience scores.↩︎
Pearson’s R=0.09, Regression coefficient = 0.005, R-squared - 0.0↩︎