A New Year’s Ritual: Looking Back to Help Move Forward
I’m a big fan of the New Year’s ritual of taking stock of where you’ve been and resolving to make some adjustments to make the coming year better. This New Year marks the completion of my third year of working with an algorithm to help me select better movies to watch. Since establishing my database, I’ve used each New Year to take two snapshots of my viewing habits.
The first snapshot is of the movies that have met the fifteen year limit that I’ve imposed on my database. This year it’s the year 2001 that is frozen in time. I became a user of IMDB in June 2000. That makes 2001 the first full year that I used a data based resource to supplement my movie selection process which, at the time, was still primarily guided by the weekly recommendations of Siskel & Ebert.
The second snapshot is of the data supporting the movie choices I made in 2016. By looking at a comparison of 2001 with 2016, I can gain an appreciation of how far I’ve come in effectively selecting movies. Since this is the third set of snapshots I’ve taken I can also compare 1999 with 2014 and 2000 with 2015, and all years with each other.
Here are the questions I had and the results of the analysis. In some instances it suggests additional targets of research.
Am I more effective now than I was before in selecting movies to watch?
There is no question that the creation of online movie recommending websites and the systematic use of them to select movies improves overall selection. The comparison below of the two snapshots mentioned previously for the last three years demonstrates significant improvement over the last three years.
|Year||# of Movies||My Avg Rating||Year||# of Movies||My Avg Rating||% Rating Diff.|
|1999 – 2001||274||6.4||2014 – 2016||326||8.1||25.1%|
One area of concern might be a pattern, or it could be random, in the 2014 to 2016 data that might suggest that there is a diminishing return in the overall quality of movies watched as the number of movies watched increases.
Am I more likely to watch movies I “really like”?
Again, the answer is a resounding “Yes”.
|# of Movies||# “Really Liked”||% “Really Liked”|
The concern raised about diminishing returns from increasing the number of movies watched is in evidence here as well. In 2014 I “really liked” all 76 movies I watched. Is it worth my time to watch another 30 movies, as I did in 2015, if I will “really like” 15 of them? Maybe. Maybe not. Is it worth my while to watch an additional 68 movies, as I did in 2016, if I will “really like” only 24? Probably not.
How do I know that I am selecting better movies and not just rating them higher?
As a control, I’ve used the IMDB average rating as an objective measure of quality.
|IMDB Avg Rating||My Avg Rating||Difference|
The average IMDB voter agrees that the movies I’ve watched from 2014 to 2016 are much better than the movies I watched from 1999 to 2001. What is particularly interesting is that the movies I chose to watch from 1999 to 2001, without the benefit of any website recommending movies I’d personally like, were movies I ended up liking less than the average IMDB voter. From 2014 to 2016, with the benefit of tools like Netflix, Movielens, and Criticker, I’ve selected movies that I’ve liked better than the average IMDB voter. The 2016 results feed the diminishing returns narrative, suggesting that the more movies that I watch the more my overall ratings will migrate to average.
My 2017 “Really Like” resolution.
My selection algorithm is working effectively. But, the combination of a diminishing number of “really like” movies that I haven’t seen in the last fifteen years, and my desire to grow the size of my database, may be causing me to reach for movies that are less likely to result in a “really like” movie experience. Therefore, I resolve to establish within the next month a minimum standard below which I will not reach.
Now that’s what New Year’s is all about, the promise of an even better “really like” movie year.