Movielens: The Reliable Alternative

In previous posts I’ve expressed my concern with corporate interests impacting the integrity of movie recommender algorithms. IMDB is owned by Amazon. Rotten Tomatoes is owned by Fandango. Netflix is owned by, well, …Netflix. Criticker isn’t corporately owned but is partially funded by commercial advertising. Now I present to you, Movielens, which isn’t owned by a corporation and doesn’t advertise on its website. Movielens is operated by GroupLens Research at the University of Minnesota. It exists for the benefit of students at the University who are researching predictive modeling. In other words, it exists because it wants to build the best recommender of movies that you will “really like” that you can possibly build. There is no corporate bottom line. There is just the goal of building a better mousetrap.

So far, it’s done a pretty good job. My benchmark for movies that I will “really like” is 4 out of 5 stars, or 7.5 out of 10, or 75 out of 100, depending on the rating scale used. When I calculate the probability that I will “really like” a movie that meets that benchmark for each individual website, I get the following results:

Website	Recommend Criteria		Probability I Will “Really Like”
Netflix	> 3.8		94.4%
MovieLens	> 3.75		93.7%
IMDB	> 7.4		90.2%
Criticker	> 76		89.8%
Rotten Tomatoes	Certified Fresh		89.0%

Movielens holds its own with Netflix and, unlike Netflix, its algorithm is not held hostage to their corporate interests, and its free. All you have to do is click on the MovieLens link above, sign up, and begin rating movies that you’ve seen. Even though MovieLens uses a five star scale, you can enter half stars. You will, at times, be torn between whether  you “really like” a movie or just “like” it. MovieLens lets you enter 3 1/2 stars for that situation.

I encourage you to use MovieLens. You can pat yourself on the back for making a contribution to science.

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Geek Alert!! Geek Alert!!

If you look at the Movie Lists I updated yesterday, you may be puzzled why so many movies have the same probability. Each month I recalibrate the probabilities in my Bayesian model. I’m constantly experimenting to get the right balance between a model that has many probability differences among movies, but more uncertainty about reliability, and a model that has fewer probability differences among individual movies but is more reliable. Too many probability groups create the risk of randomness creeping into the probabilities. The Bayesian Model recognizes this and shifts the probabilities closer to the probability of a random movie selection. This happened last month when I used 20 groups. When fewer groups are used, the larger groups that result  are more credible and produce less randomness. The model recognizes this and allows for probabilities closer to the tendencies of the group. This month I went back to 5 groups which produces more reliable probability results but with more movies with the same probability. Just in case you were wondering:)

Netflix Streaming: The Other Story

When you think about it, the story of Netflix is rather remarkable. They slayed one industry, video rental stores. They no longer exist. Since Netflix began streaming their entertainment properties in 2007, cable TV companies have been struggling to stay relevant. Many millennials have never been cable customers, preferring streaming options like Netflix, and more and more existing cable customers are cutting the cord. Now, Netflix is in a pitched battle with the producers of movie and television entertainment. On February 1, 2013, Netflix premiered House of Cards to rave reviews and signaled their intent to become the first worldwide streaming network of original content, for both TV and cinematic films. Their powerful rivals in this battle are responding (In response to Netflix’ announcement of 600 hours of original programming in 2016, HBO announced 600 hours of their own.) and the outcome is still in doubt, but, if track record means anything, don’t bet against Netflix.

In each of these Netflix inspired industry revolutions, Netflix has had their finger on the pulse of consumer frustration. They understood how frustrating it was to do business with video stores; the inability to find movies that you would “really like”, the wasted expense when you didn’t get a chance to watch the movie you rented before it had to be returned, and the additional fees for returning the video late. Netflix had an answer for all of these DVD rental frustrations. Netflix understood the frustration of expensive cable bills that supported obscure channels that never got watched and responded with an internet alternative. And, now, Netflix is using all of the data they’ve collected that indicate what their customers enjoy watching and producing original content that their customers should enjoy watching.

So, how does all of this Netflix history impact our quest to find movies that we will “really like”? Put simply, the gold standard algorithm used by Netflix-DVD may be an endangered species. Netflix’ vision of creating a world wide streaming network filled with their own content is being successfully executed, with 75 million subscribers in 190 countries. Of those 75 million subscribers, only 5 million are DVD subscribers. While the DVD business contributes profit to the bottom line of Netflix, it is a part of its past, not its future.

As for the algorithm that Netflix offered $1 million to try to improve upon, it no longer fits into their plans. Its value to the DVD business is that it assists subscribers to find movies that they will “really like” and put them in a queue so that, even if the movies and shows you most want to watch are unavailable, the next DVD in the queue will be one that you will “really like”. On the streaming side, satisfying their customers requires a different strategy. Because of the cost to license movies and shows to stream, and because of the huge investment Netflix has made in original content, the library of entertainment that exists on Netflix is smaller. It was reported last week by Allflicks, a website that tracks what’s available to watch on Netflix, that in a little more than 2 years the number of shows and movies available to watch on Netflix has shrunk by 31.7%. The number of movies available to watch instantly went from 6,404 to 4,335 during that time. So if you go to streaming Netflix with a list of movies in mind that you will “really like” you are bound to be somewhat disappointed, and Netflix doesn’t want you to be disappointed.

Netflix is a data behemoth. Not only do they collect the ratings that you give to each movie, they know what movies you’ve browsed, when you browsed it, on what device you browsed it. They know if you started to watch a movie and stopped. From that data they have determined that if a typical viewer doesn’t find something to watch on Netflix within the first 5 minutes of browsing, they will go someplace else to find something to watch. They have created 76,897 unique ways to describe their content. They know which of those 76,897 will most appeal to you and organize them into rows of content and put them at the top of your list so that you will choose to watch one of their movies or shows available on Netflix. They even know to not show you heavy movies like Schindler’s List on a Wednesday night when you just get home from work. Yes, it is that creepy.

My recommendation is to use Netflix like any other home viewing entertainment option available. Know what you want to watch before you go there. If they have it, great. If not, go somewhere else to watch it.

 

 

Netflix – DVD: Simply the Best…but Streaming is Another Story

In October 2006 Netflix launched a contest to improve their capability to predict which movies their customers, individually, would “really like”. They had discovered that their movie recommender, Cinematch, had become a competitive advantage to the company. By recommending movies that customers really liked, they were able to retain more customers for their DVD delivery company. Since its beginning in 1997, Netflix had reshaped the DVD rental industry by being on the leading edge of internet product delivery. It was only logical that their movie recommender should be the best. They offered $1,000,000 to anyone who could create a movie predictor algorithm that would be at least 10% more predictive than Cinematch.  It took almost three years but a team of Austrian researchers combined with a team of Bell Lab researchers claimed the $1,000,000 Netflix Prize by developing a predictor that was 10.9% more predictive than Cinematch.

Netflix didn’t adopt the new algorithm in its entirety but it did incorporate some of the discoveries made by the winning team into Cinematch. To my knowledge, no other movie recommender out there has endured the trial by fire that Netflix endured with their Netflix Prize competition. Based on my very simple analysis of my own data, Netflix is simply the best at predicting which movies I will “really like”. Using the same approach I used to compare Rotten Tomatoes and IMDB in a previous post (Rotten Tomatoes, IMDB and the Wisdom of Crowds), take a look at the following comparison with Netflix:

Rotten Tomatoes
		“Really Like”	Don’t “Really Like”		Total	% of Total	% “Really Like”
Cert. Fresh		570	310		880	44.7%	64.8%
Fresh		326	399		725	36.8%	45.0%
Rotten		91	272		363	18.4%	25.1%

Netflix provides a Best Guess of how many stars you are likely to give a movie. Best Guesses of 3.8 stars and higher are Recommended Movies. Notice below how much more effectively Netflix sorts the recommended movies, the average movies, and the below average movies compared to Rotten Tomatoes. Netflix-DVD is the best tool out there for finding movies that you will “really like”.

Netflix
Best Guess		“Really Like”	Don’t “Really Like”		Total	% of Total	% “Really Like”
> 3.8		659	218		877	44.6%	75.1%
3.4 to 3.8		267	457		724	36.8%	36.9%
< 3.3		61	306		367	18.7%	16.6%

It’s important to understand that Netflix Streaming, which brings you House of Cards and Orange is the New Black, is not Netflix-DVD. In Oct. 2011, Netflix announced that they were creating a new company for their DVD business called Qwikster. The Netflix brand would only be used for their Streaming business. The outcry (and the cancelled subscriptions) was so great and immediate that, in November 2011, Netflix backtracked and restored the Netflix brand to their DVD business. The Netflix vision still remained, though, and very quietly Netflix separated the DVD and Streaming businesses. Today, they are run as separate businesses with their own facilities, management, and profit centers. They also have different strategies for recommending movies.

Netflix-DVD continues to use their gold-standard algorithm for predicting how many stars you are likely to give a particular movie or TV show, but one of the most popular features of the old Netflix is missing. Gone is the list of movies and shows that Netflix suggests for you. If you want to find suggestions, you need to hunt for them. For example, you can go to New Releases or the Drama genre and have those subsets of all movies sorted by suggestions. But, Netflix-DVD no longer makes it easy for you to identify all of the movies that you will “really like”. The reason might be that the Netflix business vision no longer wants you to identify the movies that you will “really like”. They want you to “really like” the movies they have.

On Monday, I’ll explore the Streaming side of Netflix and the conflict that pits the old Netflix against the new.

Criticker: Whose Movie Recommendation do you trust?

A friend, let’s call him Jack, recommends a movie to you. You watch the movie and it is one of those movie experiences that reminds you why you enjoy watching movies. Another friend, let’s call her Jill, recommends a movie. You watch it and you have to prop up your eyelids with toothpicks to stay awake. If future recommendations from Jack and Jill follow the same pattern, you keep on watching movies recommended by Jack but stop watching movies recommended by Jill. You reach the conclusion that you and Jack have similar taste in movies and you and Jill have different taste in movies. In the end you trust the movie recommendations of Jack because you seem to really like the same movies. This is the basis for the Criticker website movie ratings.

Criticker is not as well known a movie site as Rotten Tomatoes or IMDB. Unlike those better known sites, Criticker evaluates movies based on your taste in movies. More accurately, it estimates the rating that you will probably give a movie based on the ratings of other Criticker users that have the most similar taste in movies to you. Criticker has created a tool called the TCI (Taste Compatibility Index)). It uses the index to identify moviegoers who statistically have the most similar taste in movies to you and aggregates the scores from those moviegoers to produce the probable rating, from 1 to 100, that you might give the movie you’re interested in watching.

Here’s the thing. No matter how similar Jack’s taste in movies is to yours, there will be times when Jack recommends a movie that you don’t like. If that happens you may begin to question whether Jack really does have the same taste in movies. If Jack recommended 10 movies to you and you really liked 8 of them, you can’t be sure that you will like 8 of the next 10 movies he recommends. It may be a random event that you like 8 of Jack’s recommendations. It could just as easily have been 5 or 6. If, on the other hand, Jack has recommended 100 movies and you really liked 80 of them, the chances that you will really like 8 of the next 10 movies he recommends are greater. The same is true with Criticker. The more movies that you rate on the website, the more confident you can be of the accuracy of the probable rating that Criticker provides for the movies you are interested in seeing.

To get started, use the link at the top of the page to go to the website. Set up an account. It’s free. Then start rating movies that you’ve seen. Criticker asks you to rate movies on a 1 to 100 scale. If you ask me, that’s tough to do. For example, what criteria do you use to give one movie an 86 and another movie an 87. Unless you have established criteria to differentiate movies that finely, it’s almost impossible to do without sacrificing consistency in your ratings . In a future post, I’ll outline how I established criteria for a 100 point scale. For now, I would keep your scoring simple by rating movies on a 10 point scale and converting the score to a 100 point scale for Criticker. For example, if you rate a movie 8 out of 10 on IMDB, score it as an 80 for Criticker. If, when you were rating the movie for IMDB, you had difficulty deciding whether it was a 7 or an 8, you can rate it a 75 on Criticker. The important thing is to have a consistent set of scoring rules that are applied uniformly across all of your movies.

Go ahead and get started. Pretty soon you’ll find that there are many people out there whose movie recommendations you can trust. Just remember that there is no one whose taste is exactly like yours.

What Movie Are You?

This past weekend I watched Saturday Night Fever for the fourth time. Roger Ebert mentions in his Great Movies review of the film that it was Gene Siskel’s favorite movie of all-time, having seen it 17 times. I’m in the Siskel camp. It is one of my favorite movies of all-time as well. I watched it for the first time in a Chicago area theater when it first came out in 1977. I was in the first year of my new job, the first year of 35 successful years with the same company. I was within a year of meeting my future wife, married 36+ years and still going strong. And, a little less than two years prior, I had left the middle class, New England town I grew up in and moved to the Chicago area. As it turned out, it was that momentous decision that shaped my entire adult life.

When I mention to others that Saturday Night Fever is a favorite of mine, a typical reaction is “I hate disco”. It is so much more than a disco movie. Disco is just its milieu. It is a movie about dreams and the barriers that get in the way of realizing those dreams. It is about being stuck in your current existence and coming to the realization that you won’t like the consequences of staying stuck. It is about breaking away and giving yourself a chance.

As I watched Saturday Night Fever that first time, I began to identify with the movie. I identified with Tony Manero’s yearning to create a bigger footprint in his life than he could in his Bay Ridge neighborhood. I recognized the emotional traps that were holding him back from pursuing his dream. I felt his relief when he finally decided to make the move to Manhattan, even though he had no job to go to. I was Saturday Night Fever without, of course, the disco dance king lifestyle.

In the next series of Posts, I will introduce movie recommender sites that try to answer the question “What Movie Are You” based on the movies that you “really like”. No site can identify all of the deep down personal reasons why a movie connects with you. Under my system, for example, there was only a 28.2% chance that I would “really like” Saturday Night Fever. But, the movies that you do “really like”, do identify the types of movies that draw you in and these sites effectively select quality movies within genres you enjoy watching. The sites are all different, using a variety of assumptions and methodologies. They are all just waiting for you to start rating the movies you’ve seen, both good and bad, so that they can get to know you.

In the meantime, consider sharing a comment on your reaction to this Post. Are there any movies that connect with you on a personal level? What Movie Are You?

Is There Something Rotten (Tomatoes) in Denmark?

With apologies to William Shakespeare and Hamlet, does the influence of corporate profit incentives have a corrupting influence on movie recommender websites? Movie Ratings have become big business. Amazon bought IMDB in 1998 to promote Amazon products. There appears to be a synergy between the two that doesn’t seem to impact IMDB’s rating system. On the other hand, the Netflix business model, which began as DVD mail order business,  today is a very different business. Netflix has become heavily invested in original entertainment content for its online streaming business and is using a recommender algorithm for that business that is different than its gold-standard algorithm used for the DVD business. Does the Netflix algorithm for its online streaming business better serve the interest of Netflix subscribers or Netflix profits? I’m sure Netflix would say that it serves both. I’m not so sure. This will be a topic of interest for me in future posts. The more immediate concern is Rotten Tomatoes.

It was announced on Feb. 17, 2016 that Rotten Tomatoes, along with the movie discovery site Flixster, was sold to Fandango. For those of you who are not familiar with Fandango, it is one of two major online advance movie ticket sales sites. MovieTickets.com is the other site. For a premium added to your ticket price, Fandango allows you to print movie tickets at home to allow the moviegoer to avoid big lines at the theater.

So, why should we be concerned? Let’s start with the perception that Rotten Tomatoes has become so influential that it makes or breaks movies before they are even released. Here are a couple of articles that express the growing concern film-makers have with Rotten Tomatoes scores: Rotten Tomatoes: One Filmmaker’s Critical Conundrum and Summer Box Office: How Movie Tracking Went Off the Rails. Whether it is true or not, the movie industry believes that the box office success or failure of a film is in the hands of 200 or so critics and the website that aggregates the results, Rotten Tomatoes.

This impact that Rotten Tomatoes has on the box office each week may be a driving force behind Fandango’s acquisition. In CNN Money’s article  announcing the purchase, Fandango President Paul Yanover states “Flixster and Rotten Tomatoes are invaluable resources for movie fans, and we look forward to growing these successful properties, driving more theatrical ticketing and super-serving consumers with all their movie needs,”. Fandango makes money when more people go to the movies, particularly on opening weekends for well-reviewed movies, when lines are expected to be long. Rotten Tomatoes’ Certified Fresh designations drive opening weekend long lines. Logically, Fandango business interests would be better served by even more movies earning the Certified Fresh rating.

Am I being too cynical? Well, according to a study by Nate Silver’s FiveThirtyEight site  Fandango has done this before. According to FiveThirtyEight Fandango used some creative rounding to inflate their movie ratings in the past. Has Fandango learned its lesson? They claim that Rotten Tomatoes will maintain their independence within their corporate structure. Maybe, but from my experience, corporate acquisitions are made to create profitable synergies – more Certified Fresh ratings, more moviegoers, more long lines for tickets, more “theatrical ticketing” in advance, more profits.

If you begin to “really like” fewer movies that are Certified Fresh on Rotten Tomatoes you might conclude that there may be something Rotten (Tomatoes) in Fandango…if not in Denmark.

 

Rotten Tomatoes, IMDB and the Wisdom of Crowds

In the Introduction of James Surowiecki’s The Wisdom of Crowds, the author writes that “under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them”. This prescient book, written in 2004, was describing the crowd-sourcing, data driven world that we live in today. If you want information, you type a couple of words into Google and you find exactly what you were looking for on the first page of links. If you are visiting a new city and you’re looking for a good restaurant, you check Yelp to identify the highest rated restaurants. And, if you want to go to the movies, you check Rotten Tomatoes and IMDB to see which of the movies you are considering is the highest rated.

The “right circumstances” for groups to be intelligent, according to Surowiecki, is that the group has to be big enough, diverse, and individual decisions within the group need to be made independently. Rotten Tomatoes is independent enough, most of the critic reviews are made prior to the release of the movie without knowledge of how other critics are rating the movie. Diversity is an interesting question. They are all movie critics after all and most of them are men. Still, they certainly bring a diverse set of life experiences. So, diversity isn’t optimal but still exists. The biggest question mark is whether the group is big enough. Star Wars: The Force Awakens is the most reviewed movie I’ve come across on Rotten Tomatoes with a little more than 335 critics reviews counted in the rating. My database average is 104 reviews. That is not a big sample size for statistical analysis. While, logically, movies rated Certified Fresh 95% should be better than Certified Fresh 75% movies, my data doesn’t support that.

		“Really Like”	Don’t “Really Like”		Total	% “Really Like”
CF > 88%		284	155		439	64.7%
CF < 88%		283	154		437	64.8%

There is virtually no difference between movies rated higher than Certified Fresh 88% and those less than Certified Fresh 88%. On the other hand, when you just look at Certified Fresh vs. Fresh vs. Rotten movies, the group allocates the movies intelligently.

		“Really Like”	Don’t “Really Like”		Total	% of Total Database	% “Really Like”
CF		567	309		876	44.6%	64.7%
F		324	399		723	36.9%	44.8%
R		91	272		363	18.5%	25.1%

It turns out that crowds of critics are pretty smart.

IMDB certainly meets the criteria for an intelligent group. It is big enough, Star Wars: The Force Awakens has over 450,000 votes, for example. While not as diverse demographically as one might like, it is much more diverse than a crowd of critics. And, moviegoers who vote on IMDB cast their vote independently (how influenced they are by other ratings is a subject for another day). When I rank the movies in my database by Avg. IMDB Rating and allocate them in groups identical to the Rotten Tomatoes table, you get the following results:

Avg. IMDB Rating		“Really Like”	Don’t “Really Like”		Total	% of Total Database	% “Really Like”
> 7.4		552	324		876	44.6%	63.0%
6.7 to 7.4		361	362		723	36.9%	49.9%
< 6.7		69	294		363	18.5%	19.0%

Crowds of moviegoers are pretty smart as well.

Let’s go one step further. What would these results look like for movies that Rotten Tomatoes rated Certified Fresh and IMDB rated 7.4 or higher:

“Really Like”	Don’t “Really Like”		Total	% of Total Database	% “Really Like”
370	156		526	26.8%	70.3%

How about if Rotten Tomatoes rated the movie Rotten and IMDB had an average rating of 6.7 or less:

“Really Like”	Don’t “Really Like”		Total	% of Total Database	% “Really Like”
24	193		217	11.1%	11.1%

This is the basis for my rating system. When you combine movie recommender systems together, you improve your chances of selecting movies that you will “really like” and avoiding movies you won’t “really like”. It turns out that crowds of critics and moviegoers are the wisest crowds of all.

 

Rotten Tomatoes: The Critics Aren’t Always Right but, collectively, they are Not Often Wrong

I lived in Chicago from 1976 to 1980. During that time I discovered a little show on WTTW, the local PBS channel, called Sneak Previews.  In the show, a couple of local film critics showed clips from recent movies and each gave their individual review of each movie.  Those film critics, Gene Siskel and Roger Ebert, were in the early years of a show that, through more than 35 years, would go through a number of name changes, would eventually be syndicated to a nationwide audience, and would endure contract disputes, the death of Siskel and the serious illness to its other originator Ebert.  People across the nation tuned in to find out if a movie they were thinking of seeing would get “two thumbs up”.  Like Roman emperors at the coliseum, the box office fate of a movie might hinge on whether Siskel & Ebert gave a movie thumbs up or thumbs down. As a viewer, if a movie got a “two thumbs up” it landed on my mental list of movies I’d consider watching.  If it got a “two thumbs down” it landed on my” don’t waste my time watching” list. But, Siskel & Ebert were competitors from rival Chicago newspapers, and, not surprisingly, they didn’t always agree about a movie. Some movies got a split decision. Siskel would give a “thumbs up” and Ebert would give a “thumbs down”, or vice versa. This left me in the quandary of having to choose which critic to put my faith in since there was no consensus opinion.

This brings me to Rotten Tomatoes. With no disrespect intended to Siskel & Ebert, or any other critic, Rotten Tomatoes is the concept of “two thumbs up” on steroids. The website aggregates the opinions of critics from around the globe. Instead of giving a “thumbs up” or a “thumbs down”, critics label a movie as “Fresh” or “Rotten”. Instead of two critics, a widely distributed movie might garner up to 300 critic reviews. Rotten Tomatoes includes reviews only from critics who have been certified by film critic associations or writing guilds. In addition, they designate some of those critics as “top critics”, well-respected critics writing for newspapers or national magazines. In fact, Roger Ebert was one of those “top critics” before his death.  If a given movie has been reviewed by at least 40 critics, including at least 5 “top critics”, and 75% of those critics designate the movie as “Fresh”, then the movie earns Rotten Tomatoes top designation of being “Certified Fresh”. If less than 60% of the critics rate the movie as “Fresh”, then the movie is designated as “Rotten”. Movies in between, for the most part, are designated as “Fresh”.

I have a lot of respect for film critics. All of the other movie recommender websites that I use rely on feedback from moviegoers after they’ve seen the movie. Movie critics form their opinion, most of the time, before the movie has been released to the general public. They don’t know whether it will be a blockbuster at the box office or a flop. They rely on their expertise without the benefit of feedback from the viewing public. In my next article, I’ll get into how effective Rotten Tomatoes has been in leading me to movies that I “really like”. For now, I’ll just say it’s amazing how often good film critics get it right. Two Thumbs Up!

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Beginning with this article, I am going to attempt to keep a regular schedule for my posts – two a week, Monday and Thursday. In addition, I plan on updating my movie lists by each Wednesday. Look for my next article, Rotten Tomatoes, IMDB and The Wisdom of Crowds to be posted March 10th.

What Shall I Watch Tonight?

The first day of each month is a big day in my obsessive quest to watch movies that I will “really like”. At the end of each month I recalibrate my probabilities and start the next month with a fresh Top Ten Movies to Watch list (see updated list) . Here’s the rub, only one of those movies is available for me to watch tonight. It therefore is really a list of the Top Ten Movies to Watch Someday.

I’m adding a new list under my Movie Lists section, Top Ten Movies Available to Watch This Month. Technically, almost any movie I want to watch is available if I’m willing to pay for it. But, I do have a budget with an already significant allocation to it. So, the movies available for me to watch in a given month are limited to “free” movies available from my cable company (Comcast), HBO, Showtime, Amazon Prime, Netflix, and Netflix DVD (2 a month limit). My list is made up of the movies that are available to watch this month on these platforms plus two movies from Netflix DVD. I’ll generally use the Netflix DVDs to make a dent in my Someday list. There are also some miscellaneous streaming channels (Crackle, Tubi TV etc.) that I’ll use on occasion.

Which brings me back to the first day of the month. While each of these platforms will make some weekly additions and deletions to their available movies, there are wholesale changes on the first day of each month. The supply and demand curve for “What Shall I Watch Tonight?” can be radically altered on the first day of each month.

So, “What Shall I Watch Tonight?” I don’t know yet but check out the list. You’ll find it there.