[00:00:00] Ben: People are taught at a very young age that they are good at math, or they are bad at math. And once you're told that it's really hard to believe otherwise.

[00:00:13] Ben: If I am doing my job well in writing these articles every year and doing predictions, it should be something that if you're just a math person is interesting, even if you don't like movies, and if you're just a movie person, is interesting even if you don't like math. 

[00:00:27] Vanessa: Hi, I'm Vanessa Vakharia, AKA The Math Guru, and you're listening to Math Therapy, a podcast that helps guests work through their math traumas one problem at a time. Whether you think you're a math person or not, you're about to find out that math people don't actually exist, but the scars that math class left on many of us definitely do. Oh, and don't worry: no calculators or actual math were involved in the making of this podcast. 

Intro

[00:00:52] Vanessa: Well, holy shit. Here we are. Season four, episode one, and we are all glammed up and kicking off the season straight from the red fucking carpet, which is actually a straight up lie, but like whatever we're gonna be talking about the Oscars so that's basically the same thing. And what, you wonder, does the world's most famed awards show have to do with math? Well, according to today's guest, Ben Zauzmer, the answer is: everything. 

[00:01:18] Vanessa: Ben Zauzmer has been using stats to predict the Oscars and write about awards shows for the past decade. He is the author of Oscarmetrics, The Math Behind the Biggest Night in Hollywood, which totally went viral when it came out. In fact, so viral that he now makes award season predictions for places like the Hollywood Reporter. Oh, and no big deal, but he's also the director of baseball analytics for the New York Mets. 

[00:01:40] Vanessa: Today, he's here to show us, as I have been saying for fucking ever that math and pop culture do not exist in two different worlds. And that just because you're good at math, that doesn't mean you're like a total nerd who doesn't know who Borat is. 

Ben's path to prediction

[00:01:54] Vanessa: Ben, welcome to Math Therapy! We are so excited to have you.

[00:01:58] Ben: Thank you so much for having me!

[00:01:59] Vanessa: You started Oscarmetrics while you were studying applied math at Harvard, not to brag, I'm doing it for you, no big deal. But most math undergrads don't think to apply their degree to movie prediction. So why, like, how did this all start? 

[00:02:13] Ben: I've always been a big movie fan and a big Oscars fan, that goes back well before I got to college, and it was that first year in college, my freshman year, that I was wondering who was gonna win that year. This was the year that The Artist went on to win best picture. And it was a few months before the Oscars, and I just started Googling. Being a math guy, I wanted to see if there was a model out there that actually predicted the odds every nominee won in every category. And I was surprised I couldn't find one.

[00:02:39] Ben: And so I decided to just build it myself. I spent a month in the library sitting on Google and finding, you know, one data point at a time and filling out an Excel spreadsheet to get all the data, and threw together some models, threw together a very quick website, put up the predictions. And I was surprised, it got some traction, people were into it and this whole project has grown out of that.

[00:03:02] Vanessa: Okay. Wow. Oh my God. I love that. You just said it so casually. You're like, yeah, badda boom, badda bing, I put together this whole model. Like no big deal. Now I predict the Oscars, it's cool. (laughing) Okay. I, I mean, why, why, like, yes, I'm also shocked how had no one done this before? 

[00:03:17] Ben: There were a few things out there that were maybe just predicting best picture, or just looking at one or two other categories. I was interested all the way from top to bottom, down to the sound and tech categories, all the way up to best picture and director and acting. And I wanted to gather as much data as I could in all the categories and figure out who was gonna win. At this point, I'm sure there's plenty of other people who enjoy doing this as well. For me, it's become a very fun annual hobby. 

[00:03:43] Vanessa: Oh my God. Yeah, but you're like the OG. And so you do stats for, like in sports, right? 

[00:03:49] Ben: Yes. So my full-time job, I work for the Mets in their analytics department. 

[00:03:53] Vanessa: Okay. So was this, so before that though, was Oscarmetrics, right? Like the Oscars thing started before this job. Did you like use that in a job interview? Were you like, look guys I'm predicting the Oscars, hire me. 

[00:04:05] Ben: So I, I never remember it coming up in an interview. I do recall hearing a story though. So my first job in baseball was with the Dodgers, and I'm told that the way that my resume got picked from the pile was that the person sifting through resumes in Los Angeles happened to be a really big movie fan, and they saw this on there and they're like, "oh, this guy seems interesting". I don't know if that's true. That's the story I've heard.

[00:04:27] Vanessa: Oh my God. Yeah, you gotta just make it true. That's how memoirs work anyways. You just kind of like embellish and go from there.

Can math & pop culture be friends?

[00:04:33] Vanessa: Okay. So this is so interesting because I'm in a rock band and I'm also a mathematician. And also to compound that, my master's thesis was called imagining a world where Paris Hilton loves math and it was an entire study of popular culture, and specifically movies and TV shows and the correlation of media representation and who chooses to pursue math, specifically with regards to gender.

[00:04:55] Vanessa: So this was like, I did it in 2010 and it was basically about how in media and in movies, we never portray women as, basically, being cool and being good at math, right? Like it's always this Good Will Hunting style character, that's like this crazed man, who's so good at math that he doesn't have a social life and like, blah, blah, blah, blah, blah. Or if it's a teen movie, it's always the hot, popular girl is a cheerleader that's not good at math. And then the girl who's good at math who's her nerdy friend needs a makeover to get the guy. Like, am I right, or am I right? 

[00:05:25] Ben: No, there's countless examples along those lines. I'm sure you covered all of them in your thesis, but even as you're describing this in my head, I'm thinking of one movie after another, one TV show after another, no, it's, it's very true. There's a clear double standard there. 

[00:05:37] Vanessa: Right? And so, like, I always think personally, it's been kind of my life's work to show people that popular culture and math and, and representation in math kind of go hand in hand. I find I have trouble balancing the two identities of being, you know, a musician and a mathematician. People always make me feel like it's such a weird thing. And I really think that stigma comes from movies. Like we never see these multifaceted characters who are both good at math and a cheerleader, or good at math and also an actor or whatever it is. And I wondered if it was tough for you because you're using like this math skill in media. Do people ever say, oh, that's so weird. 

[00:06:15] Ben: It's a great point. And what's funny is it shouldn't surprise people because you know, music is, I'm not in a rock band or anything, but I do play guitar casually on the side and music is inherently mathematical, you can't have music without math. I don't know if you can always say the same thing for movies, even for sports, those are things that maybe are inherently more forms of art, forms of entertainment. And so for me, the fun in using data statistics and math is not because I'm trying to push them into this mathematical box. It's because I'm trying to predict the future of what will happen. I wanna know what's gonna win best picture, but that's not because I think that math can tell you what the best movie was. I think it can help tell you how a group of voters might behave.

[00:06:59] Vanessa: Right. 

[00:07:00] Ben: But it can't, it can't say that this movie actually makes you feel happier, feel sadder or feel angry, or that those are things that come from the emotional side of film or art or music in this case, uh, whatever it may be. 

[00:07:12] Vanessa: That's so interesting, but I mean, I'm also like, but couldn't it tell you that? Because, I mean, you could also just find stats to show what percentage of audiences felt sadder or happier or, I mean, I feel like they probably do use math in that.

[00:07:25] Ben: Right. It gets this fascinating question of can art be decided by a majority vote if more audiences like, so. Does that inherently make it better. And then what do you do when things change over time? You know, there's a lot of great examples of best picture winners, uh, you know, Citizen Kane losing and Saving Private Ryan, losing the - you go on and on, uh, Psycho, not getting nominated and Spartacus not getting nominated, you know, famous examples in the Oscars that with the benefit of hindsight, a lot of film experts, a lot of casual fans might say, "Actually I disagree with that. I now think that this other movie should have won". And I don't know if that means that people back then were wrong or were right, I mean, it might just mean that tastes change. 

[00:08:09] Vanessa: Yeah. 

[00:08:10] Ben: And art changes and how we view things changes, which things speak to us. Some movies are more timeless and some are really specific to a certain place and time, and so, yeah, I think you can use math to tell you which music's more popular, which movie is more popular, which athlete's more popular. I don't think you can use it to tell you which one speaks to somebody more, which one really makes somebody feel something. And that's the, that gray area between art and math, where I think math often takes a backseat.

[00:08:39] Vanessa: I totally agree. That is so well said. And I think, okay. In a second, I wanna get to how you, you know, a little for the listeners of how you use math to predict. But one thing I was just thinking about right there is this idea of like, I, I guess the thing is by talking about it this way, it's so interesting. I guess when I hear this, because I come from a marketing background, like just, even talking to kids, I feel like, or students about being like "do you know you could use math to predict the Oscar winners?" Like as soon as you start talking about, you know, celebrities and you're naming movie names, it instantly makes it so much more interesting.

[00:09:12] Vanessa: So I actually think that's one thing that really drew me to you. I was like, you know, we talk so much about making math relevant in school and people think that means you need to talk about, like, I don't know, how to shoot a rocket ship. I mean, you're not shooting a rocket ship, how to fly a rocket ship? Ride one? (laughing) Oh my God. Okay. We need to delete this guys. Okay, but, the trajectory of a rocket ship through space and you're like, yeah, I guess that's real life, but it's not relevant to a 16 year old, but what's relevant to a 16 year old is trying to figure out what movie's gonna win next year. And I think that's what's really, really cool, is that like, we're bringing math into this realm of pop culture where it isn't before. And that's what I think you're doing that could actually make math more interesting to an audience that doesn't necessarily want to like enter a STEM field, but they're curious about how math can do something cool in the area of life that matters to them.

[00:10:01] Ben: Yeah. The way I see it, if I am doing my job well in writing these articles every year and doing predictions, you know, all of that, the Twitter account, it should be something that if you're just a math person is interesting, even if you don't like movies. And if you're just a movie person is interesting, even if you don't like math. That there should be a language that somewhere halfway in between the people on both sides of that spectrum can appreciate. It's, you know, It's not gonna be for everyone, some people don't like math or movies and that's okay. Uh, but hopefully for the people that, that like one or the other, the stuff I'm able to produce each year is interesting. 

[00:10:35] Vanessa: Those might be the same people that don't like chocolate, like, have you met those people? And you're like, are you okay? (laughing)

How to predict the Oscars

[00:10:40] Vanessa: Okay, well, you clearly know what you're doing, last year you had a 65% correct predictions rate. Is that right? Oh my God, you look so humble right now, guys, he looks really humble. But that's like pretty amazing. For our listeners. I wanna walk through an example. Do you think we can do this on a podcast? Like a brief example of how you correctly predicted that Nomadland would win best picture.

[00:11:00] Ben: Sure. 

[00:11:00] Vanessa: Okay. Let's do it. 

[00:11:01] Ben: So the basic framework here is that I have data on a few decades worth of the Oscars in each category going back. And this data includes which other categories each nominee was nominated in. So a best picture nominee might also be up for best director, one of the acting awards, one of the screenplay awards, film, editing, and so on.

[00:11:22] Ben: It includes which other awards shows it received honors at - it might have been honored or nominated at the Director's Guild or the BAFTAs or the Golden Globes and so on and so forth. Uh, all these different Guild awards that come out every January and February, uh, it could have higher Rotten Tomato scores or Metacritic scores. It could be from different genres. Anything that I can put a number on, I try to include in the model. Even betting markets, uh, which are a way of incorporating what society thinks is going to win. And sometimes they're right and sometimes they're wrong. Anything I can put a number on, and anything that's not based on my own opinions of the movie or my own viewing of the film, I try to include in the model.

[00:11:58] Ben: Not everything is predictive, but you plug it all in and then the goal is to let the math decide. So anything that has done a better job in the past of predicting the Oscars gets more weight for next year's Oscars. Uh, I try to put more weight on more recent data. I try to put more weight on predictors that have a bigger sample size, but all in all the goal is to say, okay, which things have done the best job predicting the Oscars? Those are the things we think are gonna do the best job going forward. 

[00:12:26] Ben: That's not always right. There are things about the Oscars that change over time. There are things about these previous awards that can change over time. And if the whole model is based on the premise that the past is the best predictor of the future, that's not always true. And that's why, you know, historically the model has gotten 75% of the favorites, correct. Where, if a movie is considered to be the favorite three outta four times, it wins the Oscar, but that means one out of four times, a movie with a smaller chance wins the Oscar. And that's how probability is, probability isn't a guarantee, and that's because these things are not perfect predictors of the Academy, there's not a perfect poll going in of all 7,000 Oscar voters. And that's what makes this exciting. 

[00:13:07] Vanessa: Oh my God. That was so, I'm like hanging onto every word cause I'm like, that's such a good point actually. Like what's an example of something that could change, that would kind of throw your model for a loop? 

[00:13:20] Ben: Well, so one thing recently, uh, and this is something we won't fully know how it affects voting for probably quite a few years to come, but the Academy has made, to their immense credit, a concerted effort recently to expand their membership, not just in terms of, of diversity, that's the main focus, but the byproduct of that is simply in terms of numbers. There's a much larger Academy now than there was a few years ago. And when you introduce new members, they might vote in the exact same way that old members voted, they might not. And it takes a number of years to discover in the data, how they're voting and how that affects things. 

[00:13:54] Ben: On smaller levels, it can even be things like best sound editing and best sound mixing. So they were two different categories for many, many decades, and now they're back to being one category. What does that mean for predicting the Oscar? You know, anytime there's a change, that makes my job harder. Uh, and that's, part of the fun is ... 

[00:14:11] Vanessa: You look so excited about that!

[00:14:14] Ben: I am! If it were the exact same thing every year, that takes the fun out of it. 

[00:14:17] Vanessa: Well, I think that's a great lesson, cause I feel like one of the biggest things, in teaching math is kids and adults always hate when things are hard. And I remember I was reading Angela Duckworth's book Grit a few years ago and she was talking about the idea of, instead of being like, "oh my God, things are hard, they suck", of seeing things being hard and challenging as part of the fun parts of not just life, but also math. Like that idea of being like, oh my God, the challenge is the fun of it. So I just love that you have a smile on your face when you're like, "ah, it's getting so hard for me". 

Oscars so ... diverse?!

[00:14:47] Vanessa: Okay. I actually do wanna talk about diversity though, because that's something you talk a lot about, and there's a lot of controversy over the Oscars and award shows about how the winners are often white, male, and/or straight. And you wrote a great article with Rebecca Keegan at the Hollywood Reporter on this last year, analyzing the Academy's efforts to diversify its members on the Oscar winners. So, I wanna hear a bit about that. Like this, I know that we kind of on the ground level and not behind the scenes can see that visually, obviously, on our television screens when we watch the Oscars. But what is going on behind the scenes, when you talk about the membership, and how, do you think that math and stats can actually help diversify the academy? 

[00:15:27] Ben: Uh, absolutely. Ultimately when you're trying to hit certain goals in the Academy membership, which is exactly what the Academy has set itself out to do in light of the original "Oscars So White" controversy back in 2016, ultimately that is a numbers game. Uh, you're trying to reach a certain percentage of members, that are representative of different backgrounds. You're trying to make the academy more representative of both Hollywood as a whole, and eventually the population as a whole. 

[00:15:53] Ben: There's two parts of this that you brought up. One is, are they doing a good job of diversifying membership, and the other is, are they doing a good job of diversifying the winners?

[00:16:02] Ben: Membership is much easier to track right off the bat. They don't make the full list of members public, but, uh, we were able for this Hollywood Reporter article to get a certain amount of data. And the answer is they are making significant strides, to their credit. The Academy is, by any measure, more diverse than it was in 2016 by a good margin.

[00:16:21] Ben: Then there's the part about, the winners. Does this actually result in a more diverse, more representative set of winners? It's not at the level of, say, the BAFTAs, which this most recent year reached their most diverse levels ever. It does appear, I say this much more tentatively, that the winners are also growing more diverse, but the thing with that is there's only, you know, 20 something categories each year, and that's not a lot of data to work with. 

[00:16:47] Vanessa: Mm-hmm.

[00:16:47] Ben: And then keep in mind, you have to throw out the acting ones, some of them are gendered already, so that will only provide data, in terms of, you know, different backgrounds as far as ethnicity, but not in terms of gender. And so, you're not talking about that much data to work with every year. And then you have to throw in the fact that we don't know the final votes in any of these categories. This is, uh, an object of endless frustration for people who predict the Oscars, is that we don't know if the final margin was one vote or a thousand votes.

[00:17:13] Vanessa: Right? Oh my God. 

[00:17:14] Ben: And so, that's data that would tell us a whole lot about just how much this recent push is working. And in the absence of that, it just means that we need more data, and more data means more years of the Oscars. So as time goes by, I think we'll have a better and better sense of how much the Academy's intended efforts are actually working.

[00:17:34] Vanessa: Okay. Oh my God. That was such a good explanation. And it also made me think, okay, so I actually don't follow award shows that much, but was it the BAFTAs, or what show actually got rid of the gendered categories? Didn't a show do that? 

[00:17:47] Ben: Some have started to do it. The Oscars haven't really talked about it yet, uh, so it could be something that we see somewhere down the line on the horizon. That's an interesting one, because that can go both ways in terms of, if your goal is expanding the diversity of the academy, you can imagine two scenarios there. You can imagine one where you see a much more representative group of actors and actresses included, and there's much less bias towards, well, men have to be playing certain types of roles to get nominated and women have to be playing certain types of roles, it's just who gave the best performance.

[00:18:17] Ben: We don't split anything else up by gender. We don't split up screenplay by gender. We don't split up film editing by gender. So there's a compelling argument there. On the other hand, you can just imagine the blowback, if, say they combine them into one gender and then for the first three years, it's (Vanessa gasps) Right, yeah, you can see where I'm going with this, that it would be, actually much worse for diversity if you combine them into one category and then you see completely homogenous winners going forward. So that can go either way, and that's probably why the academy hasn't gotten there yet, but 

[00:18:48] Vanessa: Right. 

[00:18:49] Ben: 10 years from now, 20 years from now, who's to say?

[00:18:52] Vanessa: But what about like non-binary individuals? Where would they go? 

[00:18:56] Ben: It's a great, great point. And right now the answer is very unclear on that. And that's something that the Academy, at some point, inevitably, they're going to have to address. That seems like only a matter of time, that that question is going to come up and they don't have a great answer. I'm sure the current answer right now would be how someone identifies, but not everyone identifies as strictly into actor or actress, man or woman. And so that's a pretty awkward setup for the academy that they are going to have to address at some point. 

[00:19:27] Vanessa: Oh, my God. Okay. I wanna ask you a million questions that have actually nothing to do with math at all, and I really can't because we actually have to start wrapping up soon, but this is so fascinating! And I wanted to bring up one stat that like was in your book that made me so mad. And I just wanna confirm that I understand correctly. I believe you mentioned that there is a bit of, and I'm not blaming you for this okay, you're just, you're the messenger, fine. That when it comes to the best actor categories for men, there's a correlation with age in that older men are more likely to be nominated. But for women, I, I need you to say it. I do. 

[00:20:04] Ben: No, that's exactly right. The peak age for men tends to be much older than the peak age for women, uh, in terms of when they're most likely to win best actor or actress, or even when they're more likely to be nominated for best actor or actress. And what that means is the types of roles that Hollywood is giving to men and women, and also the types of roles that the Academy is honoring among male and female performances, are not the same. There is a clear bias there, whether you blame Hollywood casting directors, whether you blame the Academy and its voters, or whether it's some of both, uh, whether you blame audiences and what stories they wanna see, it's hard to know exactly how to divvy that up. What's not hard to see in the data is simply that the bias does exist regardless of where you pin the bias. And it's pretty stark, uh, and right, I go into it in the book, but there is a pretty easy argument to be made that these groups, actors and actresses are not being treated similarly, when it comes to the types of roles they get, what ages they are and who gets awarded. 

[00:21:06] Vanessa: Well, it's so interesting because that's funny when you're like, okay, who's to blame, and this is so kind of what my thesis was based on in a way of it doesn't really matter who you blame, right? At a certain point, like everything is to blame, like it's a system, it's not just one thing. But I just think that, yeah, like at the end of the day, mass media is responsible for so much of the individual's viewpoint of what social norms should be, and this and that and the other. So like, there really is, like, yes, it's so fun, we're talking about pop culture, we're talking about the Oscars, we're talking about celebrities, but we're talking about something much bigger than that. Like that's where many of us get our directives of what we should think and how people should be treated and those biases. 

[00:21:45] Vanessa: So I actually think like, in a way, like, yes, you're doing it for fun, but you're doing really important work. Because it's two things, like you're pointing out these things that aren't just anecdotal evidence, right? It's not just someone saying, "Hey, I've never seen a movie where blah, blah, blah", it's you saying, "no, the data is showing that these roles are different and their valued differently". But b) your data, like you're actually using math to show, I think, deeply embedded systemic issues that are reflective of and reflected in Hollywood.

[00:22:14] Vanessa: So like, I think you're doing something important, like really important for people to actually be able to see the numbers. I also, I kind of came into this being like, "oh, this'll be so fun". And as you're talking, I'm like, "wow, this is so necessary". And it's an arena that so many people relate to like of such, you know, diverse groups, because everyone watches TV and everyone watches movies. So I think, yeah, I think this is like a real in, in changing, you know, culture, not just math wise, but in terms of representation, in terms of inclusion, in terms of diversity. So, way to go! 

[00:22:50] Vanessa: Um, that being said, I have to ask you our final two questions, which like, this just went by so fast and I'm kind of freaking out, but I'm gonna ask them to you.

[00:22:59] Ben: Okay. 

Q1: education system

[00:23:00] Vanessa: Number one, it sounds like you love math to be honest, and you went to school for math, but if there was one thing you could change about the way math was taught in schools, what would it be? 

[00:23:10] Ben: Oh, wow. That's a great question. I think that people are taught at a very young age, that they are good at math, or they are bad at math. And I'm sure this is a subject that's come up on this podcast many times. And I think once you're told that, it's really hard to believe otherwise in either direction. And so that's, that's great for people that are told they're good at math, I mean, that's, that's wonderful. That's how the world gets its future engineers and scientists and statisticians and all those other things that we really, really need.

[00:23:43] Ben: But the thing is, just about everybody needs math at some point in their lives, whether it's for their profession or their lives or taxes and the grocery store and their family budget. And it's hard to imagine going through life without needing some level of math. And if you're told from a young age, whether directly or indirectly, that you're bad at math, that can have really difficult ramifications for that individual.

[00:24:06] Ben: And so I wish there were less of a culture, even talking like elementary school, of it being about like, okay, this is something that's too hard, someone's struggling with, and more trying to meet everyone where they're at, trying to really challenge the kids that are excelling and trying to get the kids who are not up to speed. So that, everyone by the time they ultimately graduate high school and graduate college thinks of themselves as back to being good at math, regardless of where they started and how they got there. Because I think that it's just so valuable for, so, so many people, if not everybody. 

[00:24:40] Vanessa: Okay. Oh my God. I'm like freaking out inside and trying to like remain calm for two reasons.

[00:24:46] Vanessa: Okay. The first is, I always say this. I, I say math is the first thing that the majority of kids are taught they cannot do. Like early on, like people just get told that. And you're right, like, it could be indirectly, it could be by a parent being like, "oh, I never had the math gene. And you don't either". It could be by a teacher, it could be by movies being like people that look like you aren't good at math so you're probably not either. And it's true, first of all for math, but second of all, it sets you up for this lifetime of limiting beliefs of being like, oh, if I'm not a quote unquote math person, maybe I'm not a nine to five person, maybe I'm not a relationship person, right? Like what else can I not do? So I love your approach of being like, we enter it by assuming everyone can do math at some level, and we nurture that. So, yes!

[00:25:29] Vanessa: The second reason I'm so excited is my second question was gonna be, what would you say to someone who thinks they're not a math person? Because I hate the term "math person". I'm like, just like you just said, there's no such thing as a math person, we're all capable. You didn't say that, but I'm putting the words in your mouth. That's what you meant. Right? (laughing) Like we can all do math at some level. But the reason I'm excited is this frees me up to ask the bonus question that I really wanted to ask, but wasn't gonna have time for, because you kind of already answered that question. Great. 

Predictions gone wild!

[00:25:54] Vanessa: So what I was gonna say is, was there a prediction you had that just went so sideways? Like, was there a single prediction that like the winner came out and you were like, are you fucking kidding me? Was there one? 

[00:26:06] Ben: Absolutely. So like I said, you know, 25% of the time, whichever movie is in first place, is not the one that goes on to win. There have been a bunch where I'm completely shocked. Easily, the number one answer to that has to be best picture between La La land and Moonlight. So that was of course the big envelope fiasco that everybody remembers. 

[00:26:26] Ben: Much less remembered is simply the fact that that was a major, major shock even if they got the envelopes right! La La Land had won just about every award there was to win in the run up to that year's Oscars. And so that's part of what made the envelope thing so shocking. If they'd gotten the wrong envelope and it had been any other movie other than La La Land, everyone would've immediately been like, wait, something's off here. 

[00:26:49] Ben: But when they said La La Land coming outta that envelope, it seemed so normal, so predictable because La La Land was the heavy favorite, and that's what made it all the more shocking, when three minutes later they realized that was the wrong envelope, Moonlight really won. And, you know, I was sitting there tweeting up a storm like I do during every Oscars, and nearly dropped my laptop onto the ground when that all came out. So, it's hard to imagine the shock of any Oscars moment ever topping that one, and I'm sure if, if some shock does ever top that one, the Academy is not gonna be happy about it, because that one already was a, a pretty rough night for them. 

Oscar for Best Keanu?

[00:27:24] Vanessa: Wow. Wow. Okay. Final, final question. I swear. Do you think Keanu Reeves will ever win an Oscar for anything ever?

[00:27:30] Ben: Mm it's hard to, how do you put a probability on that? 

[00:27:33] Vanessa: Like best citizen. 

[00:27:35] Ben: Oh, well so they, they actually do have, it's the, the Jean Hersholt Humanitarian Award. 

[00:27:39] Vanessa: What?!

[00:27:39] Ben: For people that do, yeah! That's, that's an Oscar, it's an honorary Oscar. And so it's for someone that engages in, you know, large charitable works or things like that for many, many decades.

[00:27:48] Vanessa: Okay. 

[00:27:49] Ben: But also on the acting side, there is quite a history of actors, of directors, who maybe were primarily featured in the sorts of movies that typically are overlooked by the Academy, you know, comedies and flicks like that, that are thought to be more for popcorn loving audiences and not for the true lovers in the Academy, but then they go and they take another tack. The producers and director of Green Book, uh, who were more, you know, in the like silly comedy genre, and then they go and make a drama that really touched at the heartstrings of the Academy members. 

[00:28:20] Ben: So there are examples of this. So even if someone has previously been, you know, in the case of Keanu Reeves, it's a lot of action movies, and action movies are rarely up for, you know, those types of honors. That doesn't mean that if he chooses to switch genres at some point, or if the Academy becomes more willing to consider movies from, um, genres beyond their norm, uh, that someone in that ilk couldn't find themselves up on that stage. 

[00:28:44] Vanessa: Oh my god. Maybe it'll be in the movie that we star in together and then he'll win best actor because he'll be of the peak male age. But I won't, because I'll be way too old as a woman. Or maybe the academy will change by then. BADDAM (pounds tableq) Oh, sorry. I'm not supposed to hit the table.

[00:28:58] Vanessa: Okay. That was so fun. If you ever meet Keanu I'm your gal. I've been in love with him for literally 20 years. Please tell him to call me. 

[00:29:06] Ben: I will pass it along. 

[00:29:07] Vanessa: Please pass it along. 

[00:29:08] Vanessa: Honestly, you have been so much fun, this was the best. Movies in math, let's make it a thing, and not just A Beautiful Mind, like actual cool movies, to infinity and beyond, la la la Mean Girls but the good version. Thanks for being here!

[00:29:20] Ben: Yeah, thanks so much for having me!

Outro

[00:29:22] Vanessa: Okay. I don't know about you guys, but my mind is fucking blown. Like, okay, I get it. Sometimes I talk a lot of smack and don't actually have evidence to back it it up, but our pal Ben here has given me the fuel to my fire, or like more accurately the stats to back up my rants. If it were up to me, they would name an Oscar after Ben. Oh my God. Actually, Academy people, if you're listening, which I doubt you are, but whatever, please name an Oscar after Ben! Like the Oscar for coolest use of math in real life. I'd be happy to present the award of course, and yes I already know what I'm gonna wear.

[00:29:51] Vanessa: Fine, whatever, my work here is done. Find Ben on Twitter @BensOscarMath, or grab a copy of his book, Oscarmetrics, The Math Behind the Biggest Night in Hollywood.

[00:30:02] Vanessa: If something in this episode inspired you, please tweet us @maththerapy and you can also follow me personally @themathguru on Instagram or Twitter.

[00:30:11] Vanessa: Math Therapy is hosted by me, Vanessa Vakharia, produced by Sabina Wex, and edited by David Kochberg. Our theme song is WVV by my band, Goodnight Sunrise. 

[00:30:20] Vanessa: And guys, if you know someone who needs Math Therapy or needs to hear someone else getting Math Therapy, please share this podcast and rate or review it on whatever podcast app you use. Those things actually make such a difference. 

[00:30:33] Vanessa: I am determined to change the culture surrounding math, and I need your help. So please spread the word. That's all for this week, stay tuned for our next episode out next Thursday.