Bad Movie Twins Bad Movie Data Analysis #2

This is the second is a series now where I’ve been breaking down wide-release bad movie data through time. The previous installment can be found here. The conclusion there was that one should split Rotten Tomatoes data at around 1998 since pre-1998 and post-1998 (when Rotten Tomatoes was established) behaved much differently. At the end I suggested I wanted to start looking at more recent trends in bad movie releases. This analysis focuses on whether studios have become better at recognizing bad films and either not releasing them (or releasing them to Video on Demand (VOD), which to us is equivalent) or dumping them into the classic bad movie dump months (January, February, August, etc.).

Once again, let’s briefly describe the data set. I collected every film released to over 600 theaters from Box Office Mojo. I only included films released to more than 600 theaters (“wide” according to Box Office Mojo) in this analysis as that is one of our qualifying metrics. I had collected the Wikipedia, IMDb, and Rotten Tomatoes links for these films prior to the previous analysis. This analysis ended up being the first step in thinking about a model based on this data, a model that could, eventually, tell us things like e.g. “Here is a set of eight films being released this February, which films are most likely to be bad, should we watch one of those films, or should we wait until March.” It could also tell us a “fair yield” for a years worth of bad movies, and eventually help identify VOD films which should qualify according to their properties (e.g. “In 2010 this film would have been released to 2000 theaters, but in 2020 it is released successfully to VOD”).

Initially I was curious about whether there was an identifiable trend (outside of general yearly trends) in bad movie releases by month. My initial prior was: I think it makes sense that classic bad movie dumps like January are getting worse in general, and that previous semi-dump months like February and March and getting better, and thus reinforcing the monthly differences we’ve seen previously. This was based on the recent discovery that zero wide release films received less than forty percent on Rotten Tomatoes during June and July 2018, which makes it extremely likely that 2018 will become the first year since the establishment of Rotten Tomatoes to not release at least 52 films with 40% or lower on Rotten Tomatoes widely (a requirement for the continued existence of BMT for all eternity, naturally).

The first question to be asked though is the above: Are we just seeing less bad movies recently? Are these films just being released to VOD (or not released at all)? I split the released by Rotten Tomatoes score to get a sense of how the groups have been changing in the last 20 years:

RawYearData

So the answer to whether there have actually been a lot less bad released recently is no I think, the number of bad releases in the past ten years has been rather stable, but there are a few crazy things of note in this data. First, that in 2007 there were over 50 wide release films to get below 20%, which is insane. The collapse of the bad movie industry coincides with the financial collapse, which I don’t think is a coincidence, I think that studios making films like Redline with ill-begotten fortunes went out of business in 2008 and simply have not come back. Second, the number of films with Rotten Tomatoes scores above 80% has ballooned. I think that is more likely a case of multiple compounding factors, namely: (1) the MCU and other franchises are now consistently releasing good-to-great films multiple times a year; (2) more consistent wide releases for independent films; and (3) Rotten Tomatoes has become bigger and in general the largest films in a year are getting more and better reviews (as we saw in the last analysis). I do think it is a combination of all of those things. Regardless we can use these films per year numbers to produce adjusted films per year (in order to prevent general year-to-year trends obfuscating the monthly trends I’m interested in):

badMovieAdjustmentFactorSplit

Easy enough. I’m generally interested in three things. First, the average number of films released in a given month in each Rotten Tomatoes category. Second, the trend in these same numbers. And finally, the trend in the bad movie share for a given month. With these three plots I think we can get a clear picture of the traditional bad movie dump months, the trend in those months, and the trend in our bad movie probability in order to better inform our BMT Live! choices in the future.

totalFilmSplit

I just wanted to get an idea of good and bad months traditionally. So this is the average films released across all 20 years in each TomatoMeter category. The dotted lines are the average films released across all five categories, and if you draw a line along the category values you can get a general sense of how much a month released good or bad films. Notably January, February, April, and very slightly August generally release bad films. November and December are the big months for good films. So how has this been changing (adjusting for yearly trends in general)?

adjTotalFilmPercentile

Here it is quite interesting. Most months are in general a wash, specifically from May to September really doesn’t have much of a trend. But it looks like January is getting worse, April is getting better, October is getting slightly worst, November is getting a lot better, and December is getting a lot worse. Perhaps November is become the main month where Oscar films are being pushed, and December is starting to clear out for larger fish (namely Star Wars) leaving bad Christmas kids’ films? April getting worse could also be a product of more summer films getting released in February and March (a la the MCU). Note that these trends are formed using the yearly adjustment factors in the second plot. Interestingly this is getting mighty close to how one would form a Rotten Tomatoes score model, so … that could be coming down the pipe.

All interesting and good things to know. Finally, since it is most important to know which months might be good for BMT I also plotted the “share” of bad movies (the percentage of a year’s worth of bad movies, <40% on Rotten Tomatoes released in a given month) with a trend line:

adjBadFraction

This reinforces some of the things said above: January is, somehow, getting worse with about 12% of the bad movies released in that month; and April and November are both getting much much better in general. Other trends are a little less clear when you look at it this way, specifically with all of the noise it is pretty unclear whether October and December are actually getting worse or not. July is almost definitely a mirage, literally zero bad wide release films came out in July this year so that +41% is going to take a huge hit if I recalculate next year.

All of this is super interesting. If I were to try and fashion some rules it would be:

(1) For the first BMT Live try and get a good January and just run with it, it is very likely to be the best bet; (2) January-March and August-October are prime time for bad movies and we might want to consider doing two good Lives in each of those spans if/when they become available; (3) It seems likely that April-July are going to be very dry forevermore, so it shouldn’t be surprising when 2018 repeats itself (missing the Spring BMT Live! because nothing became available), see rule number 2;

All good guidelines. In a single sentence: We have to get a little loosey goosey with our BMT Live!s because bad movies do seem to be released predominantly during certain months, and the trend seems to be reinforcing itself.

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Crossroads Preview

Arriving in Bolivia we make our way to the Isla del Sol. It’s here that we will find what we covet, the mysterious liquid that will power the Ivory Socket. We find a tunnel leading deep into the ground but come to a fork in the road. Dusty words reveal another riddle “At the shores of the shining lake, twins must split with goals in sight. One must fight the mighty snake. The other a flower that blooms at night.” We split up …  Patrick, who is deathly afraid of rare flowers, opts to fight what must be a dangerous foe. When he comes upon a small subterranean lake he is greeted by a voice. It’s Mr. Slithers, the friendly snake! They quickly become best of friends and Patrick soon forgets himself. He no longer cares whether he is human or snake. Mr. Slithers asks him to become blood brothers but… didn’t he already have a brother? He stares at his hand as he remembers the patented Twin Chop and horrified quickly stuns the snake and steals a cup of venom. He runs, tears streaming down his face, to the entrance to the cave to await Jamie’s return …

With quaking knees I venture down the underground tunnel alone, having been separated from Patrick in our quest to power the Ivory Socket. The only solace is the fact that Patrick has chosen to fight a large and dangerous snake in my stead, leaving me to pick some flower that sounds like it’ll be NBD. As I enter a large cavern I am enthralled by the ethereal glow that lights the entire room. In the center is a gigantic flower in full bloom. Suddenly the flower uproots itself, rippling muscles glistening in the sunlight. It’s eyes gleam red, it knows nothing but death and destruction. We grapple for what seems like hours as the giant man-like flower beats the shit out of me. I play dead, hoping to momentarily appease the flower, but the flower relentlessly pummels my face as I wish only for the sweet release of death. The light … it is so harsh on my wounds. But wait! Light? A shaft of light shines down from a hole in the ceiling. That must be where the flower is getting its power! With my last bit of strength I pitch a perfect 98 mph fastball into the crevice, and with a rumble the gap closes. The flower wilts, melting into a puddle of sickly sweet nectar that burns my bent and broken body. Sure to die, I only hope Patrick wasn’t instantly killed by what must have been an even more fearsome snake and can continue our quest alone. As darkness fills my eyes I hear a distant and familiar voice: “Not yet my friend.” I awaken hours later and stumble from the cave, thrilled to find Patrick unharmed and with a cup of the snake’s venom in hand (and yet an odd distant look in his eyes).

Soon Jamie stumbles up from the other path battered, bruised, and soaked in blood. With glee we combine the snake venom and flower nectar. The serum glows red with power and we pour the contents into the Ivory Socket… which opens a compartment containing another riddle. Great. “Having defeated the mighty beast, you quest is nearly won. The trophy is your final piece, once you win a famous run.” Damn… guess we gotta find some way back to the good ol’ USA to win a race. As we head to port we see a sign that reads “Karaoke inside. Cash prizes.” Patrick and I know what must be done. That’s right! We’re watching Crossroads starring Britney, bitch. Patrick and I have obviously seen this film before (who hasn’t?) but like Anaconda before it we’d like to give it the full BMT treatment. Let’s go!

Crossroads (2002) – BMeTric: 87.3 (#44 bottom IMDb)

Crossroads_BMeT

Crossroads_RV

(Huh, what an absolutely fascinating plot. I imagine it first drops because non-fans watching Crossroads is somewhat rare prior to 2010. And then rises as more casual fans enter IMDb and, well … not all of them can give the movie less than 2, and thus it rises with the vote number. That’s all I got. Maybe it has to do with some separation between when Britney had her troubles (2007ish) and when she started to recover her image a bit (2011ish)?)

Leonard Maltin – 2.5 stars –  Teenage girl who’s always followed Daddy’s wishes finally cuts loose by taking a cross-country road trip with two girlhood friends and a guy with a 1973 Buick convertible. Her goal: to meet the mother who abandoned her when she was three. Debut vehicle for pop star Spears doesn’t waste time showing her in her underwear – and also gives her a couple of songs. Innocuous, predictable teen fare with a few serious moments.

(Yeah, so … the underwear thing feeeeeeeel exploitative. A lot of this film feeeeeeels exploitative. But I like this review. Leonard is always one to apologize for innocuousness, which I’m fine with. Two and a half stars is pushing it, but it’s fine. Like a Monte Carlo maybe.)

Trailer – https://www.youtube.com/watch?v=vb398BOlv0Q

(Bad choice putting Anson Mount’s freakout with the car in the trailer. Probably the worst moment in the film. And yeah … it feeeels exploitative still. That soundtrack seems fun though, and Britney doesn’t seem like too terrible of an actress.)

Directors – Tamra Davis – (Known For: Billy Madison; CB4; Guncrazy; Future BMT: Best Men; Half Baked; BMT: Crossroads; Razzie Notes: Nominee for Worst Director for Crossroads in 2003; Notes: Married to Mike D of the Beastie Boys and was the original director of Bad Girls before getting replaced.)

Writers – Shonda Rhimes (written by) – (Future BMT: The Princess Diaries 2; BMT: Crossroads; Razzie Notes: Nominee for Worst Screenplay for Crossroads in 2003; Notes: Wait… WHAT?!? The very same Shonda Rhimes who has created some of the biggest shows on television and just signed a giant contract with Netflix. The more you know.)

Actors – Britney Spears – (Known For: Austin Powers in Goldmember; Pauly Shore Is Dead; BMT: Crossroads; Razzie Notes: Winner for Worst Actress in 2003 for Crossroads; Winner for Worst Supporting Actress for Fahrenheit 9/11 in 2005; and Nominee for Worst Screen Couple for Crossroads in 2003; Notes: She’s Britney, bitch. One of the biggest pop stars of all time and a Mouseketeer so you know she’s got the acting chops.)

Anson Mount – (Known For: All the Boys Love Mandy Lane; Non-Stop; Straw Dogs; Mr. Right; Safe; Last Night; Boiler Room; In Her Shoes; City by the Sea; Tully; The Battle of Shaker Heights; Future BMT: Urban Legends: Final Cut; Visions; Hood of Horror; Hick; The Forger; Burning Palms; Supremacy; BMT: Crossroads; Razzie Notes: Nominee for Worst Screen Couple for Crossroads in 2003; Notes: His mother was a pro golfer. Also he claimed he didn’t want to take the role in Crossroads but Robert De Niro encouraged him to do so… liar.)

Zoe Saldana – (Known For: Avengers: Infinity War; Guardians of the Galaxy: Vol. 2; Guardians of the Galaxy; Avatar; Star Trek Beyond; I Kill Giants; Pirates of the Caribbean: The Curse of the Black Pearl; Star Trek; Star Trek into Darkness; The Terminal; The Book of Life; The Losers; My Little Pony: The Movie; Out of the Furnace; Center Stage; Death at a Funeral; Guess Who; Drumline; Blood Ties; Get Over It; Future BMT: Dirty Deeds; Takers; Haven; Nina; Burning Palms; Colombiana; The Skeptic; Live by Night; Vantage Point; Constellation; BMT: Crossroads; Notes: Megastar now having roles in both the Avengers, Star Trek, and Avatar franchises. That ain’t bad considering this was one of her first films.)

Budget/Gross – $12 million / Domestic: $37,191,304 (Worldwide: $61,141,030)

(That’s fine. That’s more than fine actually. That seems great. Surprising they never gave her more to do, but maybe the genre plots will tell us a little more about the state of play at the time.)

#18 for the Pop Star Debuts genre

crossroads_popstardebuts

(There it is! So this came right at a peak of making pop stars star in films. Sure, for the first time, but overall the trend for pop stars in movies is probably the same. So Britney probably didn’t get another film because by the time the cycle of putting singers in films came back she was married / having trouble with her life. And then by the time she got things back together it was too late. We’ve seen seven of these films. Spice World is the most BMT, Glitter is the worst, Cool as Ice is the raddest. Battleship is the most profitable, starring Rihanna naturally, because why wouldn’t it?)

Rotten Tomatoes – 14% (15/104): A cliched and silly pop star vanity project, Crossroads is strictly for Britney fans only.

(A vanity project?! Britney Spears was 20 at the time. This is some producers doing and has nothing to do with vanity on Spears’ part if that is what they are suggesting. This isn’t only for Britney fans either, this is for the world! Reviewer Highlight: She’s not yet an actress, not quite a singer… – Richard Roeper, Ebert & Roeper)

Poster – Sklogroads (D)

crossroads

(Nah. I appreciate the font but this straight up looks like it’s from the 90s. Also, none of the other actresses get their name at the top of the poster? Just Britney, bitch?)

Tagline(s) – Dreams change. Friends are forever. (B)

(Are they, though? Anyway, good cadence and it gets to the root of the plot. All around pretty good but just a little nonsense.)

Keyword(s) – audition; Top Ten by BMeTric: 87.3 Crossroads (I) (2002); 75.7 Daniel der Zauberer (2004); 71.5 Bewitched (2005); 69.5 Dance Flick (2009); 69.3 Confessions of a Teenage Drama Queen (2004); 64.6 Seed of Chucky (2004); 62.4 Staying Alive (1983); 58.9 Dirty Love (2005); 57.6 The Three Stooges (2012); 52.9 Hollywood Homicide (2003);

(Number one! Number one! Seed of Chucky is weirdly the most likely I think, mainly because we are always thinking of adding another rotation of classic horror film series. Surprisingly only two of the seven Child’s Play films qualify for BMT.)

Movie Stub – Crossroads (GA-class) – Ha, all of the Talk notes are about how fans wrote the entire page and made it sound like a masterpiece. There really isn’t much to do on the page, it is (naturally) quite in depth.

Notes – Anson Mount was initially reluctant to accept the role of Ben – finding the script to be “cheesy” and “lame” – but was encouraged by Robert De Niro, with whom he was working at the time on City by the Sea (2002). De Niro turned out to be a huge Britney Spears fan. They went over scenes from ‘Crossroads’ on breaks from ‘City by the Sea,’ with Mount reading his lines and De Niro reading Spears’. (Oh no …)

At one point in the movie, the girls sing “Bye Bye Bye,” which was a hit song by *NSYNC. This was an inside joke referencing the well-publicized romance between N’Sync member Justin Timberlake and Britney Spears.

When Lucy’s Dad is talking to Lucy in the hospital, a picture of Britney Spears can be seen on a copy of USA Today that a man is reading in the waiting room. (booooo.)

One of the most highest grossing movies in Japan, due to Britney Spears’ popularity in Japan.

Bowling for Soup, the band who features in the film and have 3 songs in the film, belong to Britney Spears’ record company, Jive Records, as do *NSYNC (the band featuring Spears’ then boyfriend Justin Timberlake) who also have a song that features in the film. (Good to know, they are the band at the party in the beginning)

The set used for the hospital scene is that from TV Series Scrubs (2001). (These facts sure are fun)

The young Lucy in the opening scene is played by Britney Spears’ younger sister Jamie Lynn Spears. (Whaaaaa, and I didn’t recognize her?!)

The pictures in Lucy’s locket are actual pictures of Britney Spears when she was younger. (I mean … what else would they be. That’s how they do)

Dan Aykroyd created a detailed back-story for his character and his life in the navy. He even had a friend draw a navy tattoo on his arm (which can be seen in the scene where he is working on the car in the garage). (You also get that from the dialogue, so duh)

The Britney Spears songs in the film all went on to become singles and were featured on her third album, “Britney,” which was released shortly before the film.

No Coca-Cola products are ever used. Instead, Pepsi products are featured prominently, e.g. Mimi drinks from a Pepsi can in the hotel room after their karaoke win. Britney Spears, of course, endorses Pepsi-Cola. (Of course)

Melissa Joan Hart was considered for the role of Mimi. (Oh, that would have been interesting)

The scene where the group stop to watch the sunset before camping is filmed in Red Rock Canyon outside Las Vegas

Ben’s car is a Buick Skylark custom convertible. (Like the two protagonists from My Cousin Vinny!)

Was originally titled, “What Friends Are For.” (hmmm, I’m rolling that around in my brain to see how it feels… I think Crossroads is still better).

Awards – Winner for the Razzie Award for Worst Actress (Britney Spears, Madonna, 2003)

Winner for the Razzie Award for Worst Original Song (Max Martin, Rami Yacoub, Dido, 2003)

Nominee for the Razzie Award for Worst Picture (2003)

Nominee for the Razzie Award for Most Flatulent Teen-Targeted Movie (2003)

Nominee for the Razzie Award for Worst Director (Tamra Davis, 2003)

Nominee for the Razzie Award for Worst Screenplay (Shonda Rhimes, 2003)

Nominee for the Razzie Award for Worst Original Song (Max Martin, Rami Yacoub, 2003)

Nominee for the Razzie Award for Worst Screen Couple (Britney Spears, Anson Mount, 2003)

Bad Movie Twins Bad Movie Data Analysis #1

Welcome to a special Bad Movie Twins bad movie data analysis post. I haven’t done one of these in a while, but it was high time I started refining my machine learning / data viz creds again. Recently I have been going through the process of collecting a large data set of movie information for use in analysis. This is the first fruit of that labor.

First, the briefly describe the data set. Collected via the BoxOfficeMojo.com yearly box office listings (e.g. For 2010) I collected the top 200 movies by theater count (ultimately this is something like every movie Box Office Mojo has on record released to 300 or so theaters back to 1980). These are Box Office Mojo links. I then meticulously collected the Wikipedia, IMDb, and Rotten Tomatoes links for all of these films (around 7500 films in total). The set ultimately included Rotten Tomatoes review / fresh counts, consensus, etc.; IMDb title, year, genres, etc.; Box Office Mojo gross and theater count, among other things. Eventually I would like to develop a model out of this data, but I wanted to first test out some hypothesis testing on it just to see how it performs.

One of the main things I’ve always been curious about Rotten Tomatoes data in particular was how the review number / freshness has changed over time. My initial priors were: (1) There are far more reviews from 2000 onward than prior to 2000, this is just my anecdotal experience; (2) More recently there have been more reviews and in general, and reviewers have been kinder. Again, just anecdotal. So why not test these hypotheses? So first a quick visualization:

RTReviewPlusFresh

So the first plot here shows two things. First, the probability of a fresh review from a rolling 12 month window for movies that have at least one review, and were released to at least 600 theaters (black line, left axis). This is calculated by summing all fresh reviews and dividing by the total number of reviews on Rotten Tomatoes for that 12 month span (so not an average TomatoMeter, although that generally has the same shape, just shifted downwards). Secondly, it shows the total number of fresh (green filled area) / rotten (red filled area) reviews in the 12 month window (right axis).

The main thing that pops out to me is that when you move backwards and forwards in time from around the year 2000 the “freshness” of the reviews seems to increase. But interestingly the first reason I proposed for why this might happen, the number of reviews increasing, isn’t actually true when you move either way. The review count decreases as you move into the past, and moving forwards in time there is an initial increase, but for the most part the total number of reviews has been pretty stable over the last two decades. The plot does seem to suggest that, maybe, reviewers are getting kinder over time though, so what is happening?

My next though was that the trend is a manifestation of “review inequality”. As you move backwards in time the number of films getting after-the-fact reviews placed on Rotten Tomatoes dwindles, and thus an increasing number of reviews end up being submitted for the best films of the year for the most part, thus the reviews will become increasingly fresh. Alternatively, as you move forwards in time more and more of the “small” reviewers on Rotten Tomatoes are added and they will tend to only review a subset of the, mostly good, major releases. So again, as you move forwards the bigger and generally better regarded films will get a larger and larger share of the reviews.

And an easy test of this hypothesis is to look at something like a Gini Coefficient. There are a multitude of complaints about the Gini Coefficient (as outlined in the wikipedia page), but considering it is a general first step in income inequality analysis, I decided to toss a flyer out there and see if the Gini Coefficient could tell us anything about whether my hypothesis holds water:

RTProbPlusGini

And what do you know, the plots are eerily similar. I had figured that this was all a single phenomenon, but the two domains (before somewhere around 2000 and after) do seem to act different. Here is the same plot transformed a bit:

gini2DPlot

This actually looks like there could be a good partition around the year 2000. So where does the data partition? One (likely simplistic) way would be to develop a piece wise linear regression on the Gini plot and point out the spot where the two regressions meet:

giniPiecewiseFit

Now that’s pretty cool. July, 1998, was around the point in time (almost to the month) when Rotten Tomatoes was launched. As a matter of fact, for the analysis below I will treat the partition as specifically the launch month of August, 1998. I think the obvious hypothesis as to why the Gini coefficient correlates positively (and shockingly well) with the review probability is that the higher the number of reviews the higher your TomatoMeter tends to be. So when a high percentage of the reviews are distributed to a small number of movies that will drive the overall freshness of Rotten Tomatoes up. And just to hammer home the point as to why this partitioning is particularly interesting, here is a scatter plot of the Rotten Tomatoes reviews and the TomatoMeter for all of the films since 1980 which were released to 600 or more theaters and have at least one review on Rotten Tomatoes:

RTCompTogether

Super weird. The red squares are formed by grouping the data into 25 groups and taking the mean value. Note that this plot suggests that a movie is more likely to be good if it has around 50 reviews instead of, say, 100. Which without further testing you might think is due to independent movies or some other small-movies-are-better effect. But now let’s split the data by the Rotten Tomatoes launch date and treat movies which were released before (and backfilled) and movies which were released after differently:

RTCompSplit

And now you could come to a different conclusion. Both sides uniformly increase in quality with the number of reviews, it is just that backfilled films tend to have fewer reviews in total. Almost all movies released prior to Rotten Tomatoes launching have less than 100 reviews on Rotten Tomatoes, a number which today would represent a fairly small release.

I’ll leave the analysis there (this is already incredibly long), but I found this all very interesting and I think it shows the power of analyses like this. If I were to make a model to try and predict a film’s TomatoMeter using the total reviews as an input, for example, and wanted it to apply backwards in time, this analysis strongly suggests that you either want to (1) treat a movie released before and after Rotten Tomatoes was launched differently, or (2) Build a time dependent factor that can build this partition in for you. Again, I like the analysis because this isn’t a totally obvious result in my opinion, and it gives a simple and easy to follow guideline for eventual use in modelling.

And as for my hypotheses? There are indeed fewer reviews prior to 2000 than after, drastically so. But interestingly if you look at the piece wise regression on the Gini vs. fresh review probability plot above it actually kind of suggests that given the amount of inequality with how the reviews are being allocated, that the films are actually getting worse reviews in the last 2 years than between 2000-2016. Which is something I think I’ll want to explore more in a second installment of this series.

Hopefully I’ll get that analysis up soon, but I’ll probably start looking at building a simple predictive model as well. I will say the data set I’ve collected, while it takes ages to update, has already been incredibly useful in finding and analyzing potential BMT films.

Cheerios,

The Sklogs

Righteous Kill Quiz

A quiz on the film Righteous Kill. To ace it won’t require much skill. Just patience and time. Obsess on fictional crime. And it’ll help if you’re mentally ill. – Poetry Sklog

Pop Quiz Hot Shot!

1. Detectives Cowan (De Niro) and Fisk (Pacino) intentionally do not use their names during the film (in order to not give away the big twist ending, what a twist!). What are their nicknames?

2. During the course of the film Poetry Boy (spoiler alert, it is Al Pacino) kills multiple people, but what event started it all?

3. Name or describe the five people we “see” Pacino kill during the course of the film.

4. When Pacino and De Niro send a lawyer into Spider’s club (a converted Bank, it is so Hollywood Badass I love it) to get some cocaine while wearing a wire, what does she say she needs the cocaine for?

5. We’ll end with a tough one. In the NYPD softball game we see De Niro score a run. How would you log the run in the scorecard?

Answers

Conan the Barbarian (2011) Quiz

You have battled and fought and made your body lean and sinewy, ready to cleave your enemies in two with your razor sharp sword. There is only one thing else to do, Crom demands it … it’s quiz time.

Pop Quiz Hot Shot!

  1. In the beginning of the film Conan’s father sends the youths of the camp off to compete for a spot in the hunt. What is the competition?
  2. What’s more important in forging a blade, fire? Or ice?
  3. The day of his father’s death left indelible scars on Conan’s soul. But it also left slightly less metaphorical scars on his body (something he then shows to the eeeeeevil Khalar Zym to prove he was the boy destined to kill him, how convenient …). What injury was inflicted onto Conan on that fateful day?
  4. Conan frees the thief Ela-Shan from slavery, befriending him for life (convenient, perhaps his lockpicking skills will be vitally important for the climax of this film …). But why did Conan allow himself to be enslaved in the first place?
  5. Straight up … what is the plot of this film? Who is Khalar Zym, what is he looking for, and why is he looking for it?

Answers

Alex & Emma Quiz

While dictating the next great American novel to the harried freelance stenographer you hired, your publisher called you up and said four words that would change the course of your romantic life! …

Pop Quiz Hot Shot!

  1. Throughout the movie Emma (Kate Hudson) bothers Alex (Luke Wilson) by doing what? Hint: It concerns books … I mean Luke Wilson seems perturbed throughout, this seems to offend him.
  2. Rob Reiner just loves Alex’s mojo. How many novels has Alex written in his illustrious career?
  3. In order to lure Emma to her murder … er, to be a freelance stenographer in a highly unlikely literary gambit, Alex advertised the job under what guise?
  4. What nationalities does Emma’s fictional alter-egos in the story-within-a-movie take on throughout the film?
  5. The target of Adam Shipley’s lust is the single mother Penelope Delacroix. Adam is worried that Penelope is going to wed the very rich John Shaw, to whom she owes an enormous sum. What two ways does Adam think to get her out from underneath her debt?

Answers

Behind Enemy Lines Quiz

Uh oh, you thought you would just buzz by this website and peruse our bad movie ramblings? Think again! You just got shot down by our Bad Movie anti-aircraft missiles and are stuck behind the Bad Movie enemy line. And we’re after you, this ain’t no joke, we’ve been stuck in a Bad Movie prison camp and know how to do one thing well: kill. You best get ready for a quiz!

Pop Quiz Hot Shot!

  1. There are two crewmen in Burnett’s jet, a pilot and navigator. Which is Burnett?
  2. After Burnett (Owen Wilson) and Stackhouse’s flight is cancelled (again, boo!) they go to the mess to get their chow on. What food does Burnett make quite a show of eating?
  3. After getting shot down Burnett and Stackhouse are separated from their cockpit chairs which had homing beacons. Why did Reigart subsequently order Burnett’s homing beacon to be turned off?
  4. After a close escape from the factory booby trapped with mines Burnett escaped to a nearby highway to flag down a very America-loving truck. What kind of music was the truck playing?
  5. The Serbians have a dastardly plan to fake Burnett’s death to prevent the Americans from deploying a rescue mission. What event tips off the battleship that Owen Wilson was alive and well?

Answers