1 00:00:01,040 --> 00:00:04,120 Speaker 1: I have not seen the movie yet, so normally I 2 00:00:04,160 --> 00:00:06,679 see the movie and then I come up with something 3 00:00:07,400 --> 00:00:11,439 short and pithy or clever or however I'm feeling inspired, 4 00:00:11,560 --> 00:00:13,800 and then I lead into the name of the movie, 5 00:00:13,800 --> 00:00:17,000 and then I say the intro. But again, I have 6 00:00:17,079 --> 00:00:19,760 not seen the movie yet, but I'm traveling. But I 7 00:00:19,760 --> 00:00:22,039 wanted to make sure that I had an introduction for 8 00:00:22,239 --> 00:00:27,239 this episode. So enjoy the episode. We'll see what I 9 00:00:27,239 --> 00:00:29,600 thought about the movie in a couple of seconds. This 10 00:00:29,760 --> 00:00:35,759 is The Sheep Detectives. I'm Ronald Young Junior, and I'm 11 00:00:35,840 --> 00:00:36,920 leaving the theater. 12 00:01:29,280 --> 00:01:29,760 Speaker 2: All right. 13 00:01:29,959 --> 00:01:32,760 Speaker 1: This is Ronald and I am leaving the theater after 14 00:01:32,959 --> 00:01:38,799 seeing The Sheep Detectives. The Sheep Detectives written by Craig Mason, 15 00:01:39,159 --> 00:01:44,560 directed by Kyle Balda, starring Hugh Jackman, Nicholas Brown, Nicholas 16 00:01:44,560 --> 00:01:50,000 gallat Zine, Molly Gordon, Hong Chow, Emma Thompson, Julia, Louis Dryfiss, 17 00:01:50,040 --> 00:01:54,719 Brian Cranston, Chris O'Dowd, and Regina Hall per usual. For 18 00:01:54,799 --> 00:01:56,920 complete listing of the cast and crew, you could go 19 00:01:57,040 --> 00:01:59,959 to the linked in our show notes. I would strongly 20 00:02:00,079 --> 00:02:02,439 encourage you to do that for this film because there 21 00:02:02,480 --> 00:02:05,120 is a large voice cast as well as in person cast, 22 00:02:05,319 --> 00:02:08,000 and I did not include everyone, so you can go 23 00:02:08,039 --> 00:02:10,080 look at that at the link in our show notes. 24 00:02:10,199 --> 00:02:12,120 Speaker 2: I am not here alone. 25 00:02:12,199 --> 00:02:15,000 Speaker 3: I am here with Michael Paris Davis. 26 00:02:15,159 --> 00:02:21,240 Speaker 1: Michael Paris Davis. All right, The Sheep Detectives. This is 27 00:02:21,280 --> 00:02:26,400 a film about a shepherd played by Hugh Jackman, who 28 00:02:26,639 --> 00:02:31,000 is made. If you've seen the trailer, you know that. 29 00:02:31,120 --> 00:02:34,840 Then the sheep have to set about the journey of 30 00:02:34,960 --> 00:02:37,280 solving the mystery. He is a good shepherd, He is 31 00:02:37,360 --> 00:02:40,199 kind to a sheep. He reads them a lot of 32 00:02:40,560 --> 00:02:46,120 murder mystery novels, so they are apt to solve the crime. Specifically, 33 00:02:46,199 --> 00:02:48,759 one sheep, Lily, who I believe is played by Julia 34 00:02:48,840 --> 00:02:53,520 Louis Dreyfus, who is setting about finding out why this 35 00:02:53,680 --> 00:02:57,479 poor shepherd was murdered and unfolds in a town I 36 00:02:57,520 --> 00:03:00,439 believe it's called Denbury, Denberry something like that. 37 00:03:00,520 --> 00:03:02,280 Speaker 3: The town is called Denbrook. 38 00:03:01,960 --> 00:03:05,400 Speaker 1: Denbrook, Yes, Denbrook. It is set in the town of Denbrook. 39 00:03:05,840 --> 00:03:09,039 That is where we find all the players who find 40 00:03:09,120 --> 00:03:13,080 out about the murder. All right, enough of that, Michael 41 00:03:13,120 --> 00:03:15,479 Paris Davis, what did you think of this film? 42 00:03:15,800 --> 00:03:18,120 Speaker 3: Honestly, going into it, I thought it was going to 43 00:03:18,159 --> 00:03:23,000 be incredibly cheesy, but for a detective film, it was 44 00:03:23,039 --> 00:03:25,800 actually a great film. I had a great time. I laughed, 45 00:03:26,080 --> 00:03:28,199 I cried, and I got really mad. 46 00:03:29,400 --> 00:03:32,039 Speaker 2: I agree. I think that in terms of. 47 00:03:33,560 --> 00:03:36,960 Speaker 1: What to expect from this film, I think you're expecting 48 00:03:37,039 --> 00:03:40,400 a stupid film about sheep solving a crime. 49 00:03:40,840 --> 00:03:44,759 Speaker 2: But what it gives you is a top tier I'm. 50 00:03:44,560 --> 00:03:50,439 Speaker 1: Talking Knives Out, I'm talking Poiro whatever that those movies 51 00:03:50,439 --> 00:03:52,319 are now called. They have three different ones that came 52 00:03:52,360 --> 00:03:55,719 out that are all starring that Kenneth Branna and them. 53 00:03:55,800 --> 00:03:58,240 Those ones, y'all know what I'm talking about. A Murder 54 00:03:58,240 --> 00:04:00,840 on the Nile and the Orient, Murder on the Orience Press, 55 00:04:00,840 --> 00:04:04,520 all of those. It is giving the best version of 56 00:04:04,599 --> 00:04:08,479 those mystery films. You just have to if you saw 57 00:04:08,520 --> 00:04:11,080 this and said not for me, but you are a 58 00:04:11,080 --> 00:04:15,159 person that watches British detective films and reads British detective books, 59 00:04:15,520 --> 00:04:18,879 this is firmly in that category and it does it 60 00:04:19,000 --> 00:04:19,600 very very well. 61 00:04:19,800 --> 00:04:20,040 Speaker 2: Yeah. 62 00:04:20,079 --> 00:04:22,160 Speaker 3: I fully agree. This is I didn't want to like 63 00:04:22,199 --> 00:04:25,600 it because it was It's an Amazon Studios film, and I. 64 00:04:25,560 --> 00:04:28,800 Speaker 1: Was like, shout out to Daryl Murphy, you know who 65 00:04:28,800 --> 00:04:29,079 you are. 66 00:04:29,560 --> 00:04:30,360 Speaker 2: That's our project. 67 00:04:30,439 --> 00:04:33,680 Speaker 1: Hill Mary correspondent who definitely went on about a four 68 00:04:33,720 --> 00:04:37,279 minute rant about that movie being an Amazon original. 69 00:04:38,319 --> 00:04:40,240 Speaker 2: Michael Paris Davis tagging in. 70 00:04:40,160 --> 00:04:43,360 Speaker 3: For Daryl Murphy, this one won't be as long. I 71 00:04:43,399 --> 00:04:44,839 didn't want to like it, and I thought that the 72 00:04:44,879 --> 00:04:48,600 sheep Ai was going animation was going to be bad 73 00:04:49,079 --> 00:04:53,000 believable from beginning to end. Characters I immediately was on 74 00:04:53,079 --> 00:04:56,879 board with. And there was not a second that went 75 00:04:56,879 --> 00:04:58,560 by in the film that they didn't grip me. 76 00:04:59,240 --> 00:05:02,079 Speaker 2: Yeah, it grabbed you. It grabs you from the beginning. 77 00:05:02,160 --> 00:05:04,800 Speaker 1: It makes you take it seriously from the beginning because 78 00:05:04,800 --> 00:05:08,000 they start off by making it. Especially everyone who's a millennial, 79 00:05:08,399 --> 00:05:10,800 you have seen a movie made in the nineties with 80 00:05:10,800 --> 00:05:13,680 a very sweet score in which they are endearing you 81 00:05:13,759 --> 00:05:16,680 to whoever the animals are in the film, whether it 82 00:05:16,720 --> 00:05:22,160 be Homeward Bound or pick another Beethoven, pick another movie 83 00:05:22,160 --> 00:05:25,160 with animals in it. There's that little bright piano and 84 00:05:25,600 --> 00:05:29,399 strings happening that's making you love what's happening. They start 85 00:05:29,439 --> 00:05:32,199 off right there, and then they kind of lean into 86 00:05:32,240 --> 00:05:36,040 the actual mystery elements of it that make it more 87 00:05:36,040 --> 00:05:36,480 of an. 88 00:05:36,399 --> 00:05:38,759 Speaker 2: Actual It draws you in. It hooks you. 89 00:05:39,040 --> 00:05:41,439 Speaker 1: That's the hook. So it draws you in with the 90 00:05:41,560 --> 00:05:43,240 something very sweet, and then it hooked you. Now I 91 00:05:43,240 --> 00:05:47,000 will say to the CGI, wow, it was very good 92 00:05:47,160 --> 00:05:49,839 when the sheep were by themselves or with other sheep. 93 00:05:50,240 --> 00:05:53,160 Every time a human touched the sheep or interact with 94 00:05:53,199 --> 00:05:55,839 the sheep, I remember being like, oh, those aren't real sheep. 95 00:05:56,000 --> 00:05:59,360 That's not and it wasn't Uncanny Valley, but it was 96 00:05:59,439 --> 00:06:00,839 just you could you could see it. 97 00:06:01,160 --> 00:06:03,360 Speaker 3: You could see that he scooped up air and not 98 00:06:03,439 --> 00:06:04,279 a real baby sheep. 99 00:06:04,439 --> 00:06:07,680 Speaker 1: That's exactly what I was talking about. 100 00:06:07,720 --> 00:06:10,199 Speaker 3: You're so right with the millennial thing because I was 101 00:06:10,319 --> 00:06:13,480 immediately hooked into it being an animal film. But then 102 00:06:13,519 --> 00:06:15,800 it comes from underneath and is like, actually, this is 103 00:06:15,839 --> 00:06:21,480 a very well structured detective movie. And they from beginning 104 00:06:21,519 --> 00:06:22,800 to end, I didn't know what was going to happen. 105 00:06:22,920 --> 00:06:24,920 You even in the theater were like, this is going 106 00:06:25,000 --> 00:06:27,959 to happen here, this is the next step. But me 107 00:06:28,519 --> 00:06:31,519 the perfect audience member because I suspend my disbelief like 108 00:06:31,600 --> 00:06:36,519 nobody's nobody's business. I that's true, had no idea what 109 00:06:36,560 --> 00:06:38,639 was going to happen. They had me by the throat. 110 00:06:39,000 --> 00:06:39,720 Speaker 2: Yeah, that's good. 111 00:06:39,759 --> 00:06:43,079 Speaker 1: This film does a very good job of making you 112 00:06:43,199 --> 00:06:48,240 forget suspects just enough for you to The movie leads 113 00:06:48,240 --> 00:06:50,519 you just a little bit like a sheep. But then 114 00:06:50,600 --> 00:06:52,439 by the time they get to oh, and this is 115 00:06:52,480 --> 00:06:55,199 who the suspect is, I did. 116 00:06:55,040 --> 00:06:57,519 Speaker 2: Not see that coming. I did not suspect that person 117 00:06:57,560 --> 00:06:57,920 at all. 118 00:06:58,040 --> 00:06:59,959 Speaker 3: You were one hundred percent shocked at the end. 119 00:07:00,000 --> 00:07:01,720 Speaker 2: And yeah, because I. 120 00:07:01,720 --> 00:07:05,480 Speaker 1: Said out loud, oh, so the film got you. 121 00:07:05,519 --> 00:07:06,240 Speaker 2: Too, it did. 122 00:07:06,600 --> 00:07:08,959 Speaker 1: I mean. I think to me, a good mystery is 123 00:07:09,040 --> 00:07:12,000 one that it allows you to think certain things, but 124 00:07:12,079 --> 00:07:14,040 doesn't quite allow you to think other things. 125 00:07:14,319 --> 00:07:17,240 Speaker 2: And I think, particularly when you get to the color. 126 00:07:16,959 --> 00:07:19,360 Speaker 1: Schemes towards later in the film, which we won't spoil, 127 00:07:19,639 --> 00:07:21,959 I had no idea where that was going at all, 128 00:07:22,040 --> 00:07:24,839 and by the time they got there, it did feel obvious, 129 00:07:24,879 --> 00:07:26,079 and I felt a little dumb. 130 00:07:26,439 --> 00:07:27,519 Speaker 2: But also I would have. 131 00:07:27,519 --> 00:07:29,800 Speaker 1: Never I felt dumb in the sense of oh, I 132 00:07:29,800 --> 00:07:31,800 wouldn't have even thought of that. Maybe I'm just not 133 00:07:31,839 --> 00:07:33,000 as smart as I think I am. 134 00:07:33,560 --> 00:07:35,360 Speaker 3: Thank you so much for reminding me not to spoil it, 135 00:07:35,399 --> 00:07:37,680 because I was about to just start talking write about 136 00:07:37,680 --> 00:07:40,839 who the killer was. Yeah, I felt that right at 137 00:07:40,839 --> 00:07:43,399 the beginning though you know that the piece of evidence 138 00:07:43,720 --> 00:07:47,879 is important, but you don't. We can't. We couldn't figure 139 00:07:47,920 --> 00:07:51,040 it out. We absolutely there was pieces of evidence that 140 00:07:51,360 --> 00:07:53,839 we couldn't see as an audience member, so we were 141 00:07:53,920 --> 00:07:56,639 unable to figure it out at the pace of the movie. 142 00:07:56,920 --> 00:07:57,879 Speaker 2: Yeah, it was good. 143 00:07:58,160 --> 00:08:00,639 Speaker 1: It was good sleight of hand, good story to tell, and 144 00:08:00,680 --> 00:08:03,279 I thought it was well paced in a way where 145 00:08:03,319 --> 00:08:05,959 again you're led a little bit, so there's gonna be 146 00:08:06,000 --> 00:08:08,240 some stuff that you could come to a conclusion on, 147 00:08:08,480 --> 00:08:10,600 and the movie doesn't mind you coming to a conclusion 148 00:08:10,600 --> 00:08:13,639 on some of that. But even if you went from 149 00:08:13,639 --> 00:08:16,079 the beginning and said that person is the one who 150 00:08:16,120 --> 00:08:18,680 did it. And it's funny because the movie is smart. 151 00:08:18,680 --> 00:08:21,480 It lays out these clues it talks about in the beginning, 152 00:08:21,600 --> 00:08:24,160 one of the stories that he's reading to the sheep, 153 00:08:24,560 --> 00:08:26,959 Lily turns around and says, no, it's not the maid 154 00:08:27,000 --> 00:08:29,600 because this other thing happened and that's why, and sure 155 00:08:29,680 --> 00:08:32,440 enough she's vindicated there. But that's exactly what happens in 156 00:08:32,480 --> 00:08:34,759 the movie later where they said, well, we want you 157 00:08:34,840 --> 00:08:39,120 to think it's this person, but it's absolutely not this person, which. 158 00:08:38,919 --> 00:08:41,840 Speaker 3: Makes it seem like it's a children's movie at the beginning, 159 00:08:41,879 --> 00:08:45,679 but then we got got to and we follow the 160 00:08:45,759 --> 00:08:48,039 narrative and we fall in love with the characters too, 161 00:08:48,120 --> 00:08:51,559 like even our main detective of the movie. Like at 162 00:08:51,559 --> 00:08:54,600 the beginning, I was like, Wow, these are silly character constructs, 163 00:08:54,799 --> 00:08:57,600 and then along the way totally built up. I'm one 164 00:08:57,679 --> 00:08:59,159 hundred percent loyal to the characters. 165 00:08:59,399 --> 00:09:01,480 Speaker 1: I think part of that is that the voice acting 166 00:09:01,480 --> 00:09:04,320 cast is amazing. You're working with Julia Louis Dryfish, You're 167 00:09:04,320 --> 00:09:07,080 working with Brian Kranston, voices that we trusted. I did 168 00:09:07,159 --> 00:09:09,799 not identify in the film until after I read them later, 169 00:09:10,000 --> 00:09:11,919 But I remember being like, all of these folks are very, 170 00:09:12,000 --> 00:09:15,279 very believable, and I realized why at one point, I'm like, oh, 171 00:09:15,320 --> 00:09:18,159 that's Regina Hall. That'sh I figured out in the middle 172 00:09:18,159 --> 00:09:19,559 of the movie and I knew she was in it, 173 00:09:19,679 --> 00:09:21,559 but I'd forgotten and I heard her voice. So I 174 00:09:21,559 --> 00:09:24,679 think credibility and voice acting, especially in a film like this, 175 00:09:25,039 --> 00:09:26,320 is extremely important. 176 00:09:26,679 --> 00:09:30,679 Speaker 2: Now, all of that being said here, at. 177 00:09:30,360 --> 00:09:32,840 Speaker 1: Leaving the theater, we rate movies out of five stars, 178 00:09:33,120 --> 00:09:37,559 all right, so one being terrible. I never this is garbage. 179 00:09:37,720 --> 00:09:41,080 Five being basically perfect for you, three meaning I would 180 00:09:41,080 --> 00:09:43,200 see it again. Of course you got your twos and fours, 181 00:09:43,759 --> 00:09:46,519 four being a great movie. A lot of Oscar bait 182 00:09:46,639 --> 00:09:51,240 films live there, and so I'm curious for you, where 183 00:09:51,240 --> 00:09:53,120 does this movie land one to five stars? 184 00:09:53,159 --> 00:09:54,840 Speaker 2: What rating do you give this film? And why? 185 00:09:55,960 --> 00:10:01,519 Speaker 3: Oh? No, okay, so he means that it's rewatchable. I 186 00:10:01,559 --> 00:10:05,159 don't know that I would rewatch this film. Wow, I 187 00:10:05,320 --> 00:10:07,080 enjoyed it, but it takes a lot for me to 188 00:10:07,120 --> 00:10:09,440 rewatch a movie. So on your official scale, I'm going 189 00:10:09,480 --> 00:10:13,519 to say two point nine to five, just under rewatchable. 190 00:10:13,759 --> 00:10:16,759 Speaker 2: Do you rewatch movies every once in a while, so 191 00:10:16,840 --> 00:10:21,360 you don't rewatch movies? No, So three is not a good. 192 00:10:21,159 --> 00:10:24,200 Speaker 1: Scale for you if you're not a rewatcher, So there 193 00:10:24,240 --> 00:10:26,240 has to be something else, all right. To say that 194 00:10:26,279 --> 00:10:28,639 three is the average number, you're saying that this movie 195 00:10:28,639 --> 00:10:30,200 is below average for you. 196 00:10:30,240 --> 00:10:32,799 Speaker 3: No, it was above average. Okay. 197 00:10:33,519 --> 00:10:35,519 Speaker 1: So in that case, now that you understand the scale 198 00:10:35,559 --> 00:10:40,639 a little bit more, did you give me what, let's say, 199 00:10:40,679 --> 00:10:42,080 three being an average film? 200 00:10:42,159 --> 00:10:44,799 Speaker 2: Like, oh I liked that, okay, versus four? 201 00:10:44,919 --> 00:10:47,080 Speaker 3: I loved that three point eight? 202 00:10:47,840 --> 00:10:48,200 Speaker 2: Okay? 203 00:10:48,440 --> 00:10:52,000 Speaker 1: Actually knowing you, I feel like that one fits. I'm 204 00:10:52,039 --> 00:10:55,799 going to give this film a four point one five. Yes, 205 00:10:55,919 --> 00:10:58,799 this is a great movie. I'd say. The only drawback 206 00:10:58,840 --> 00:11:00,279 that keeps this from me in a four p two 207 00:11:00,360 --> 00:11:02,519 five or four point five zero is there was no 208 00:11:02,559 --> 00:11:05,200 reason for me to be in a theater watching this film. Amazon, 209 00:11:05,639 --> 00:11:07,559 you have put this movie in the theater. Yes, it 210 00:11:07,559 --> 00:11:09,639 has a theater window. Great, people are gonna go see it. 211 00:11:09,679 --> 00:11:12,679 Maybe you'll make some money. That's fine, But I there's 212 00:11:12,679 --> 00:11:14,240 no need for me to be in the theater watching 213 00:11:14,240 --> 00:11:16,559 this film. I just felt like there were several times 214 00:11:16,600 --> 00:11:19,159 in which this type of movie, this type of mystery 215 00:11:19,159 --> 00:11:22,080 movie that doesn't have big set pieces or anything like that, 216 00:11:22,200 --> 00:11:25,320 there's no reason for me to drive to a theater, 217 00:11:25,720 --> 00:11:28,679 get out of my car, go inside, unless you're in 218 00:11:28,879 --> 00:11:30,840 Arizona and you just want to get out of the heat, 219 00:11:30,919 --> 00:11:34,240 which we are, and that's fine. But that being said, 220 00:11:34,279 --> 00:11:36,559 like that's the only reason why you'd really need to 221 00:11:36,559 --> 00:11:39,080 be out of the heat watching this movie inside and 222 00:11:39,159 --> 00:11:41,279 air conditioning. But if you have air conditioning in your house, 223 00:11:41,360 --> 00:11:43,080 you could just be watching this on the couch. And 224 00:11:43,120 --> 00:11:44,559 I think if I was watching this in the couch, 225 00:11:44,559 --> 00:11:47,600 it'd probably be like a four point five, four point six, 226 00:11:47,639 --> 00:11:48,399 four point seven. 227 00:11:48,480 --> 00:11:52,519 Speaker 3: Really, who who is the target audience for this movie? 228 00:11:52,799 --> 00:11:54,720 Speaker 2: I have no idea because it doesn't feel like a 229 00:11:54,799 --> 00:11:55,480 children's movie. 230 00:11:55,720 --> 00:11:59,320 Speaker 3: It doesn't. It feels like it was for us, It feels. 231 00:11:59,080 --> 00:12:00,519 Speaker 2: Like it was for MILLENNI. 232 00:12:01,759 --> 00:12:04,480 Speaker 1: It slips in I would say in the sophisticated circle 233 00:12:04,559 --> 00:12:07,799 that Pixar normally operates in making a film that is 234 00:12:07,840 --> 00:12:11,159 so good that parents want to watch it and dumb 235 00:12:11,320 --> 00:12:14,200 enough that kids will miss the parts about it that 236 00:12:14,240 --> 00:12:17,240 make it super emotional and resonant for parents, like a Coco, 237 00:12:17,600 --> 00:12:19,720 you know. But I'd say it's slightly elevated in that 238 00:12:19,840 --> 00:12:22,360 because there's not just there's just not a ton of 239 00:12:22,440 --> 00:12:25,000 kid elements there except for the fact that the sheep are. 240 00:12:24,840 --> 00:12:28,879 Speaker 3: Talking yeah okay. That you can bring your kid to 241 00:12:28,960 --> 00:12:29,759 this movie, you. 242 00:12:29,759 --> 00:12:32,080 Speaker 1: Absolutely can, Like this is a family film. You can 243 00:12:32,120 --> 00:12:34,960 bring a whole family to the movie. But if you're 244 00:12:35,039 --> 00:12:36,919 a family that like I remember, I was thinking, my 245 00:12:36,960 --> 00:12:39,320 mother would love this film, My sister would love this film. 246 00:12:39,480 --> 00:12:41,799 Speaker 2: My dad would be engaged enough. 247 00:12:41,840 --> 00:12:43,039 Speaker 1: I don't know if he would love it, but he'd 248 00:12:43,039 --> 00:12:44,960 be engaged enough to finish it and be like, man, 249 00:12:44,960 --> 00:12:47,559 those sheep were crazy, So there's plenty of like. 250 00:12:48,000 --> 00:12:51,080 Speaker 2: It marks a lot of quadrants. Curtis would like this film. 251 00:12:51,159 --> 00:12:54,279 Speaker 1: Curtis my action movie loving friend that always surprises me 252 00:12:54,600 --> 00:12:56,480 with a pick out of left feel Well. 253 00:12:56,480 --> 00:12:58,120 Speaker 2: It would be like, oh I liked this. I'm like, 254 00:12:58,440 --> 00:13:00,600 I didn't expect that from you. Curtis. I think he 255 00:13:00,639 --> 00:13:02,200 would like this film. If he was like, oh, all 256 00:13:02,240 --> 00:13:03,360 we got to see one movie. 257 00:13:03,480 --> 00:13:05,759 Speaker 1: I got to bring my child Evelyn and my wife 258 00:13:05,879 --> 00:13:07,720 Jen to this film, he would like this. 259 00:13:07,960 --> 00:13:11,320 Speaker 2: Marcus would not. Marcus would be like stupid she was talking. 260 00:13:11,360 --> 00:13:12,000 He wouldn't like this. 261 00:13:12,120 --> 00:13:14,000 Speaker 1: So I feel like there's ways in which it hits 262 00:13:14,000 --> 00:13:16,320 all four quadrants and also misses on all fours. 263 00:13:16,480 --> 00:13:18,000 Speaker 3: I was about to ask you to list all of 264 00:13:18,000 --> 00:13:19,960 your friends and whether or not they would like this movie. 265 00:13:20,039 --> 00:13:20,399 Speaker 2: I could. 266 00:13:20,480 --> 00:13:24,080 Speaker 1: I can go through each one of them, Maryland, Yes, Caroline, 267 00:13:24,080 --> 00:13:25,960 and John. Caroline would be like it was cute, but 268 00:13:26,000 --> 00:13:28,000 a little stupid. John would be like, what are we 269 00:13:28,039 --> 00:13:31,879 talking about here? Just start going now the list. Nor 270 00:13:31,879 --> 00:13:35,159 would be like okay, and then Nora's husband John would 271 00:13:35,200 --> 00:13:38,440 be like, I mean, he'd make a lot of dad 272 00:13:38,519 --> 00:13:41,720 jokes about lamb and sheep. So I'm not even go 273 00:13:41,759 --> 00:13:43,440 through all my friends, because I'm getting trouble for people 274 00:13:43,480 --> 00:13:46,759 I didn't mention. Tyra would not watch this. Jama would 275 00:13:46,759 --> 00:13:49,080 not watch this. He would be like, why am I 276 00:13:49,120 --> 00:13:52,399 at the movies? I could be doing something more important. Yeay, 277 00:13:52,440 --> 00:13:54,399 I could go down through a list of people I don't, 278 00:13:54,480 --> 00:13:57,399 but the point is for the most general people who 279 00:13:57,480 --> 00:14:01,360 like movies, and specifically millennials who British detective film, this 280 00:14:01,440 --> 00:14:02,080 is a film for you. 281 00:14:02,799 --> 00:14:04,080 Speaker 3: I was about to say ten out of ten, but 282 00:14:04,200 --> 00:14:05,360 it's three point eight out of five. 283 00:14:05,519 --> 00:14:07,639 Speaker 1: Yeah, I was gonna say, just multiply three point eight 284 00:14:07,679 --> 00:14:09,480 times two and you get the number that is actually 285 00:14:09,480 --> 00:14:12,840 out of ten. So with that, Leaving the Theater is 286 00:14:12,879 --> 00:14:17,159 a production of Owitz Big Round Studios. I mixed this episode, 287 00:14:17,200 --> 00:14:20,320 and per usual, I'm out here working hard. I'm currently 288 00:14:20,720 --> 00:14:25,120 in Arizona and I'm still working. I did actual work 289 00:14:25,200 --> 00:14:28,159 this week that was not related to Owits Big Round Studios. 290 00:14:28,320 --> 00:14:30,240 Speaker 2: Some of my other production work that I'm doing. 291 00:14:30,440 --> 00:14:32,960 Speaker 1: I'm actively working on a pilot that I'm set to 292 00:14:33,000 --> 00:14:34,519 release on Patreon as. 293 00:14:34,360 --> 00:14:35,559 Speaker 2: Soon as I get it finished. 294 00:14:35,759 --> 00:14:38,519 Speaker 1: It is a video and audio project, but it's coming 295 00:14:38,519 --> 00:14:41,360 out soon and hasn't even been announced on Patreon yet, 296 00:14:41,440 --> 00:14:44,000 which means by the time you decide to start helping 297 00:14:44,000 --> 00:14:47,559 Owiz Big Round Studios by becoming a patron. You could 298 00:14:47,600 --> 00:14:52,799 see that video audio pilot on Patreon. I am working hard. 299 00:14:52,840 --> 00:14:56,200 I'm out here doing hard work. This is a labor 300 00:14:56,240 --> 00:14:59,000 of love, but it is still labor. If that means 301 00:14:59,000 --> 00:15:01,320 something to you, go to patrio on dot com slash 302 00:15:01,360 --> 00:15:02,519 ODS Big Ron, or go to. 303 00:15:02,480 --> 00:15:04,039 Speaker 2: The link in our show notes. 304 00:15:04,120 --> 00:15:07,000 Speaker 1: Show art from Heather Wilder, theme music by the mysterious 305 00:15:07,039 --> 00:15:11,799 Breakmaster Cellinder. For more information about the Sheep detectives, check 306 00:15:11,840 --> 00:15:13,879 out my show notes. You can follow me on Instagram, 307 00:15:13,919 --> 00:15:17,120 bluse guy, TikTok, threads or letterboxed at odits Big Ron. 308 00:15:17,200 --> 00:15:19,799 Speaker 2: That's at O H I T S b I g 309 00:15:20,360 --> 00:15:21,279 R O N. 310 00:15:21,480 --> 00:15:23,159 Speaker 1: You found out more about this show and other ODS 311 00:15:23,200 --> 00:15:25,919 Big Ron Studio shows by following us on Instagram at 312 00:15:26,039 --> 00:15:28,960 ODS Big Ron Studios. Leaving the theater will be back soon. 313 00:15:29,240 --> 00:15:31,200 Thanks for listening and thanks for being on the show. 314 00:15:31,919 --> 00:15:35,320 Speaker 3: Absolutely anytime I could sent it to this what. 315 00:15:44,320 --> 00:15:47,679 Speaker 1: I'm Ronald Young Junior and I'm not as smart as 316 00:15:47,679 --> 00:15:48,279 I think I am.