1 00:00:03,600 --> 00:00:05,680 Speaker 1: Welcome back to a numbers game. Brian Grodski, thank you 2 00:00:05,680 --> 00:00:08,360 Speaker 1: guys for being here. You know, January was my most 3 00:00:08,480 --> 00:00:12,080 Speaker 1: listened to month of this podcast in our one year history. 4 00:00:12,160 --> 00:00:15,080 Speaker 1: So I want to thank you all for who subscribe, 5 00:00:15,120 --> 00:00:17,400 Speaker 1: who liked this show, who watch and listen to it 6 00:00:17,480 --> 00:00:20,840 Speaker 1: every single episode. It means beyond what you can imagine 7 00:00:21,079 --> 00:00:23,880 Speaker 1: I am. I am very grateful for those who are 8 00:00:23,880 --> 00:00:26,960 Speaker 1: interested in data and my messiness and everything else. I 9 00:00:26,960 --> 00:00:30,520 Speaker 1: want to start by addressing a special election that I 10 00:00:30,520 --> 00:00:32,640 Speaker 1: didn't think I have to talk to talk about. I 11 00:00:32,720 --> 00:00:34,839 Speaker 1: wasn't prepared to sit there and bring this up, but 12 00:00:35,240 --> 00:00:38,000 Speaker 1: it's made a lot of news, and there's a lot 13 00:00:38,159 --> 00:00:41,320 Speaker 1: of misinformation going on, both on social media and in 14 00:00:41,400 --> 00:00:44,160 Speaker 1: the mainstream media. So let's talk about the special election 15 00:00:44,200 --> 00:00:47,959 Speaker 1: that happened for the Texas State Senate last week. There 16 00:00:48,000 --> 00:00:51,479 Speaker 1: was a special election in Texas's ninth state Senate district. 17 00:00:51,479 --> 00:00:54,640 Speaker 1: This is the Fort Worth area. It's a district that 18 00:00:54,680 --> 00:00:57,959 Speaker 1: Trump won in twenty twenty four by seventeen points. There 19 00:00:58,000 --> 00:01:00,400 Speaker 1: was a special election in the Democrat candidate one by 20 00:01:00,480 --> 00:01:04,120 Speaker 1: fourteen points. There was a tremendous thirty one point swing, 21 00:01:04,160 --> 00:01:07,600 Speaker 1: and the media has gone on a whirlwind tour. They 22 00:01:07,640 --> 00:01:10,919 Speaker 1: are talking about this as if it is the indicator 23 00:01:10,959 --> 00:01:14,880 Speaker 1: among all indicators that Republicans are doomed. Rasta sat of 24 00:01:14,959 --> 00:01:17,280 Speaker 1: The New York Times put out a whole podcast that 25 00:01:17,319 --> 00:01:20,319 Speaker 1: Trump was losing swing voters and young people and Latinos, 26 00:01:20,640 --> 00:01:24,600 Speaker 1: his coalition that delivered him the presidency. Veteran Republican strategist 27 00:01:24,640 --> 00:01:28,560 Speaker 1: Alex Costnitos Castanos I think is apparentis's last name. He said, 28 00:01:28,560 --> 00:01:31,360 Speaker 1: the Republicans who lose as many as forty seats in 29 00:01:31,400 --> 00:01:35,520 Speaker 1: the upcoming midterm elections and basically said the House is 30 00:01:35,600 --> 00:01:38,720 Speaker 1: just doomed. And conservative writer and political commentator Henry Olsen 31 00:01:38,720 --> 00:01:40,679 Speaker 1: said that they were on a path through a twenty 32 00:01:40,800 --> 00:01:45,120 Speaker 1: eighteen style blowout shalacking. This is going to make people's 33 00:01:45,240 --> 00:01:47,120 Speaker 1: this is all going to make my head explode. And 34 00:01:47,200 --> 00:01:52,720 Speaker 1: the information that they're offering is nuggets, it's grains of sand. 35 00:01:52,840 --> 00:01:56,800 Speaker 1: It is not accurate when you look at the entire picture. 36 00:01:56,960 --> 00:01:59,720 Speaker 1: So first, let's go into a deep dive in the 37 00:01:59,800 --> 00:02:03,520 Speaker 1: Tech special election, because let's talk about the address of 38 00:02:03,680 --> 00:02:08,840 Speaker 1: why this district swung so heavily for Democrats. First, Republicans 39 00:02:09,360 --> 00:02:12,080 Speaker 1: are coming out, you know, with this that it was. 40 00:02:12,240 --> 00:02:15,000 Speaker 1: This is the Republican lie that Republicans are saying. It 41 00:02:15,040 --> 00:02:18,360 Speaker 1: was just poor turnout, and poor turnout costs the special election. 42 00:02:18,760 --> 00:02:20,560 Speaker 1: It was held on a Saturday, which is the thing 43 00:02:20,560 --> 00:02:25,080 Speaker 1: that Texas does fairly often as they hold elections on Saturdays. 44 00:02:25,160 --> 00:02:27,960 Speaker 1: I don't know why. I have never seen it show 45 00:02:28,000 --> 00:02:31,520 Speaker 1: that it shows immensely high turnout. I think it's very 46 00:02:31,520 --> 00:02:33,680 Speaker 1: confusing for most people who are used to elections on 47 00:02:33,720 --> 00:02:36,800 Speaker 1: a Tuesday to show up on a weekend. I don't 48 00:02:36,800 --> 00:02:39,240 Speaker 1: think that it encourages people to show up because they 49 00:02:39,240 --> 00:02:41,200 Speaker 1: have the day off, most of them from work. A 50 00:02:41,200 --> 00:02:43,120 Speaker 1: lot of work class people do work on the weekend, 51 00:02:43,240 --> 00:02:44,760 Speaker 1: so I don't know why they do is. But it 52 00:02:44,800 --> 00:02:47,240 Speaker 1: was an election held on a Saturday, but Republicans did 53 00:02:47,680 --> 00:02:51,160 Speaker 1: show up. It wasn't just that only Democrat shout up 54 00:02:51,160 --> 00:02:53,920 Speaker 1: Republican showed up, but a lot showed up and then 55 00:02:54,040 --> 00:02:58,720 Speaker 1: voted for the Democrat. According to researcher Ross Hunt, fifty 56 00:02:58,880 --> 00:03:01,920 Speaker 1: percent of people who showed up in this election had 57 00:03:01,960 --> 00:03:05,920 Speaker 1: a history of voting for Republicans in primaries. That is 58 00:03:06,040 --> 00:03:09,400 Speaker 1: enough to get a Republican over the finish line if 59 00:03:09,600 --> 00:03:14,960 Speaker 1: they hold those voters together. Ninety five thousand people voted 60 00:03:15,040 --> 00:03:17,840 Speaker 1: in this special election, which is down in regular election 61 00:03:17,840 --> 00:03:20,040 Speaker 1: about one hundred and twenty thousand, but it's not down 62 00:03:20,440 --> 00:03:25,079 Speaker 1: that significantly. First of all, let's talk about the race. 63 00:03:25,160 --> 00:03:28,840 Speaker 1: First of all, the Republican in the election, there was 64 00:03:28,880 --> 00:03:31,720 Speaker 1: a primary, and there was a very contested primary. One 65 00:03:31,760 --> 00:03:34,880 Speaker 1: of the candidates who was running in that primary, the 66 00:03:34,920 --> 00:03:39,200 Speaker 1: former mayor of Southlake, John Hoffman, received a twenty percent 67 00:03:39,400 --> 00:03:43,240 Speaker 1: of the vote in the primary. He did not endorse 68 00:03:43,320 --> 00:03:47,240 Speaker 1: the Republican nominee in the general of that special election, 69 00:03:48,400 --> 00:03:51,880 Speaker 1: signaling to his supporters it's either okay to sit out 70 00:03:51,920 --> 00:03:54,440 Speaker 1: this election or to vote for the Democrat. So that 71 00:03:54,560 --> 00:03:57,040 Speaker 1: was the first thing that Republicans had going against them 72 00:03:57,120 --> 00:04:01,360 Speaker 1: was that one of the primary candidates told one fifth 73 00:04:01,400 --> 00:04:04,040 Speaker 1: of the primary voting base, you know, I'm not going 74 00:04:04,080 --> 00:04:08,080 Speaker 1: to support this nominee from the Republican Party. Secondly, the 75 00:04:08,120 --> 00:04:13,560 Speaker 1: Republican nominee whose name is Lee Womsing Womasing, It's an 76 00:04:13,600 --> 00:04:19,120 Speaker 1: almost unpronounceable last name. Sources of mine who were in 77 00:04:19,200 --> 00:04:22,880 Speaker 1: the district in the campaign told me that campaign staffers 78 00:04:23,240 --> 00:04:27,920 Speaker 1: could not pronounce or spell her name on election night. 79 00:04:28,240 --> 00:04:30,360 Speaker 1: That is how difficult her last name was, which I 80 00:04:30,400 --> 00:04:32,800 Speaker 1: get it. It's your last name. It's difficult. I as 81 00:04:32,839 --> 00:04:36,599 Speaker 1: a consultant have had candidates who have been running, and 82 00:04:36,680 --> 00:04:38,880 Speaker 1: I say, I have one candidate one time. His first 83 00:04:38,920 --> 00:04:42,360 Speaker 1: name was Stamatas, which is Greek, and I said, call 84 00:04:42,400 --> 00:04:46,080 Speaker 1: yourself Steve. Make it easy for people to know who 85 00:04:46,160 --> 00:04:49,920 Speaker 1: you are. Right, make it easier. Don't have an unpronounceable 86 00:04:50,000 --> 00:04:53,560 Speaker 1: last name. That does play in two things, right, So 87 00:04:53,720 --> 00:04:55,599 Speaker 1: Lee is what I'll call her instead of trying to 88 00:04:55,600 --> 00:04:57,279 Speaker 1: pronounce her last name, which you guys know, I'm not 89 00:04:57,400 --> 00:04:59,760 Speaker 1: wonderful of pronouncing last name. It's began with Lee was 90 00:04:59,760 --> 00:05:03,120 Speaker 1: all a very controversial candidate. She was part of a 91 00:05:03,120 --> 00:05:06,159 Speaker 1: group called Patriot Mobile. For those of you who don't know, 92 00:05:06,240 --> 00:05:09,240 Speaker 1: Patriot Mobile is a conservative organization and they have a 93 00:05:09,360 --> 00:05:11,640 Speaker 1: pack associated with them that deal with school board elections. 94 00:05:11,920 --> 00:05:14,840 Speaker 1: My pack and theirs have endorsed many of the same 95 00:05:14,880 --> 00:05:17,840 Speaker 1: candidates over the last five years. I've worked with their 96 00:05:17,880 --> 00:05:21,520 Speaker 1: people before in that capacity. They're perfectly fine. I personally 97 00:05:21,520 --> 00:05:23,280 Speaker 1: think they spend too much money on these schooloard races, 98 00:05:23,279 --> 00:05:26,560 Speaker 1: but that's my own take. Anyway, they're very successful. Well. 99 00:05:27,160 --> 00:05:30,800 Speaker 1: Lee was his pivotal role in Patriot Mobile. And there 100 00:05:30,920 --> 00:05:34,960 Speaker 1: was an issue that when the Conservatives won the Keller 101 00:05:35,480 --> 00:05:39,360 Speaker 1: Independent School District Keller ISD, one of the wealthier school 102 00:05:39,400 --> 00:05:43,839 Speaker 1: districts public school districts in northern Dallas area near Fort Worth, 103 00:05:44,320 --> 00:05:47,919 Speaker 1: that they tried to split the school district into two. Basically, 104 00:05:47,960 --> 00:05:51,400 Speaker 1: they wanted the wealthy area to become their own ISD 105 00:05:51,839 --> 00:05:54,440 Speaker 1: and the working class area to become their own ISD. 106 00:05:54,760 --> 00:05:58,240 Speaker 1: Split Keller in half. This caused a lot of sour 107 00:05:58,320 --> 00:06:00,880 Speaker 1: grapes in the community right. A lot of working class 108 00:06:00,880 --> 00:06:03,839 Speaker 1: people felt like they were going to lose out on 109 00:06:03,920 --> 00:06:07,119 Speaker 1: tax revenue and on dollars and that this would hurt 110 00:06:07,320 --> 00:06:10,239 Speaker 1: their school district and their kids. And it's a very 111 00:06:10,279 --> 00:06:14,320 Speaker 1: personal thing public education. I know consertives don't have a 112 00:06:14,320 --> 00:06:17,080 Speaker 1: lot of conversations about public education, I promised you. When 113 00:06:17,120 --> 00:06:20,080 Speaker 1: it comes to just average voters, they have tons of conversations. 114 00:06:20,080 --> 00:06:23,600 Speaker 1: And it was deeply offensive to them, and Lee, the 115 00:06:23,640 --> 00:06:27,320 Speaker 1: Republican nominee, became the face of it to a certain degree, 116 00:06:27,520 --> 00:06:32,520 Speaker 1: and it was deeply unpopular even among rank and file Republicans, Latinos, 117 00:06:32,600 --> 00:06:35,520 Speaker 1: independents that they were trying to split this district up 118 00:06:35,880 --> 00:06:38,960 Speaker 1: that was going into this election, and it pissed off 119 00:06:39,000 --> 00:06:43,200 Speaker 1: a ton of people, and according to my contacts in Texas, 120 00:06:43,520 --> 00:06:48,080 Speaker 1: there was also a deep frustration that the party did 121 00:06:48,120 --> 00:06:51,680 Speaker 1: not pick the state representative to be their nominee State 122 00:06:51,760 --> 00:06:55,880 Speaker 1: representative Innate Schlitz Line chafts Line. He was a state 123 00:06:55,960 --> 00:06:59,080 Speaker 1: representative who represents the area and it just won reelection 124 00:06:59,440 --> 00:07:04,440 Speaker 1: by one points. He's handsome, he's young. So why didn't 125 00:07:04,440 --> 00:07:08,960 Speaker 1: they pick the very handsome, very young, very telligenic, populous 126 00:07:09,000 --> 00:07:12,600 Speaker 1: conservative candidate over this woman name Lee who has this 127 00:07:13,000 --> 00:07:16,239 Speaker 1: connections to Patriot Mobile. Well, the answer I was receiving 128 00:07:16,240 --> 00:07:20,400 Speaker 1: from people was money. Allegedly they believed that she was 129 00:07:20,520 --> 00:07:24,440 Speaker 1: buying the nomination, and it caused these were all things 130 00:07:24,520 --> 00:07:27,720 Speaker 1: leading into this election, just one thing after the other 131 00:07:27,840 --> 00:07:32,240 Speaker 1: after the other, leading to kind of a deep dislike 132 00:07:32,320 --> 00:07:34,960 Speaker 1: among rank and file Republicans who felt that she was 133 00:07:35,040 --> 00:07:38,080 Speaker 1: just trying to buy the seat, and they were ignoring 134 00:07:38,520 --> 00:07:41,880 Speaker 1: what conservatives were feeling, both on local issues and also 135 00:07:42,120 --> 00:07:44,400 Speaker 1: that they really liked the state representative. And it was 136 00:07:44,480 --> 00:07:46,400 Speaker 1: kind of crazy. Why didn't you pick the state representative 137 00:07:46,400 --> 00:07:49,200 Speaker 1: who just won by twenty points in the same area. 138 00:07:49,240 --> 00:07:52,560 Speaker 1: Why did you pick this lady with this controversial connection 139 00:07:52,640 --> 00:07:57,480 Speaker 1: to the schools with this unpronounceable last name and two 140 00:07:57,520 --> 00:08:01,320 Speaker 1: Democrats credit on top of it. They picked a normal 141 00:08:01,360 --> 00:08:04,680 Speaker 1: white guy. They recruited a normal white guy to be 142 00:08:04,760 --> 00:08:08,200 Speaker 1: their nominating a union worker who had I don't know 143 00:08:08,240 --> 00:08:11,480 Speaker 1: if he's personally working class, but had working class connections, 144 00:08:11,680 --> 00:08:13,240 Speaker 1: someone you would want to get a beer with. They 145 00:08:13,240 --> 00:08:16,320 Speaker 1: didn't nominate an AOC, they didn't nominate a Jasmine Crockett. 146 00:08:16,600 --> 00:08:20,040 Speaker 1: They nominated someone that Republicans were okay and independence were 147 00:08:20,040 --> 00:08:24,800 Speaker 1: okay voting for. That's how this district was lost and 148 00:08:24,840 --> 00:08:28,119 Speaker 1: how it swung so heavily. When a district swings within 149 00:08:28,160 --> 00:08:30,920 Speaker 1: the normal margins, usually on special elections like ten point 150 00:08:31,000 --> 00:08:34,040 Speaker 1: twenty points, those are big swings, but those are expected 151 00:08:34,280 --> 00:08:37,440 Speaker 1: given it's a special election and national feelings towards the president. 152 00:08:37,600 --> 00:08:40,280 Speaker 1: A thirty one point swing is a much bigger deal, 153 00:08:40,320 --> 00:08:42,760 Speaker 1: and it has to do something going on in the 154 00:08:42,840 --> 00:08:46,959 Speaker 1: district with the candidates, with the nominee, and especially when 155 00:08:46,960 --> 00:08:50,560 Speaker 1: you when you calculate the Republicans outspent Democrats by a 156 00:08:50,600 --> 00:08:54,240 Speaker 1: wide margin in the district. It wasn't so much the 157 00:08:54,280 --> 00:08:58,240 Speaker 1: conditions nationwide as it was the person. What does this 158 00:08:58,360 --> 00:09:00,920 Speaker 1: mean for Republicans nation and why, because this is what 159 00:09:00,960 --> 00:09:02,800 Speaker 1: the media is getting to, is saying, this is the 160 00:09:02,800 --> 00:09:06,600 Speaker 1: evidence that we're going to see a Republican loss. This 161 00:09:06,640 --> 00:09:08,319 Speaker 1: is the evidence that forty seats are going to go 162 00:09:08,400 --> 00:09:11,800 Speaker 1: Democrat and that the Trump coalition is over a narrative 163 00:09:12,000 --> 00:09:14,880 Speaker 1: I have heard so many times in the last decade. 164 00:09:15,520 --> 00:09:18,360 Speaker 1: Let's take a step back and let's look at all 165 00:09:18,440 --> 00:09:20,840 Speaker 1: the special elections, not just from this year, but also 166 00:09:20,880 --> 00:09:24,559 Speaker 1: from last year and compare them to the first Trump 167 00:09:24,679 --> 00:09:27,559 Speaker 1: midterm in twenty eighteen and those special elections in twenty 168 00:09:27,600 --> 00:09:31,800 Speaker 1: seventeen and twenty eighteen. So in twenty twenty five, Democrats 169 00:09:31,960 --> 00:09:36,280 Speaker 1: outperformed Trump in their respected difference by an average of 170 00:09:36,400 --> 00:09:40,520 Speaker 1: twelve points right. Democrats about twelve points better on average 171 00:09:40,960 --> 00:09:44,080 Speaker 1: in all the special elections than Trump had done in 172 00:09:44,120 --> 00:09:47,960 Speaker 1: twenty twenty four. In twenty twenty that was a twenty 173 00:09:47,960 --> 00:09:51,320 Speaker 1: twenty five right So twenty twenty five twelve points more Democrat. 174 00:09:51,840 --> 00:09:56,200 Speaker 1: That's actually worse for Republicans than twenty seventeen was leading 175 00:09:56,280 --> 00:09:59,880 Speaker 1: up to his first midterm in those elections. In those specials, 176 00:10:00,280 --> 00:10:03,920 Speaker 1: Democrats out ran Trump by eight points. So is a 177 00:10:04,080 --> 00:10:07,640 Speaker 1: four point to the left of twenty eighteen that's not good. 178 00:10:08,880 --> 00:10:12,760 Speaker 1: But the difference was from twenty seventeen to twenty eighteen, 179 00:10:13,440 --> 00:10:16,720 Speaker 1: special elections, if they show any nuggets, if they show 180 00:10:16,840 --> 00:10:21,600 Speaker 1: any signs of how the nation is trending, became bluer. 181 00:10:21,840 --> 00:10:26,000 Speaker 1: T eighteen special elections leading up to the midterm were 182 00:10:26,200 --> 00:10:31,400 Speaker 1: nine points more democratic than the twenty sixteen election. So 183 00:10:31,520 --> 00:10:34,559 Speaker 1: when from eight to nine in twenty seventeen to twenty 184 00:10:34,600 --> 00:10:38,080 Speaker 1: eighteen the average was eight point seven in a midterm 185 00:10:38,160 --> 00:10:40,280 Speaker 1: that turned out to be an eight point four year, 186 00:10:41,160 --> 00:10:44,640 Speaker 1: that's a very key telling sign. Anger against Trump was 187 00:10:44,840 --> 00:10:47,720 Speaker 1: increasing as time went on. Now, let's go back to 188 00:10:47,720 --> 00:10:50,880 Speaker 1: twenty twenty five. Last year, it was a d plus 189 00:10:51,000 --> 00:10:53,520 Speaker 1: twelve year. It was a twelve point more to the left 190 00:10:53,840 --> 00:10:57,440 Speaker 1: than the twenty twenty four election. Was What has twenty 191 00:10:57,520 --> 00:10:59,680 Speaker 1: twenty six, Although it's only been a month of twenty 192 00:10:59,679 --> 00:11:02,199 Speaker 1: four five weeks of twenty twenty six, what has twenty 193 00:11:02,240 --> 00:11:06,160 Speaker 1: twenty six shown us so far? It has been seven 194 00:11:06,440 --> 00:11:10,640 Speaker 1: point five percent more to the left than twenty twenty four, 195 00:11:11,280 --> 00:11:13,199 Speaker 1: so five point It was about a four and a 196 00:11:13,240 --> 00:11:16,600 Speaker 1: half point swing to the right from the previous year. 197 00:11:16,679 --> 00:11:21,080 Speaker 1: That is not what happened in twenty seventeen. Or twenty eighteen. 198 00:11:21,559 --> 00:11:25,600 Speaker 1: These special elections actually performed better for Republicans than last 199 00:11:25,679 --> 00:11:29,280 Speaker 1: year did. Republicans in places like New York, Alabama, and 200 00:11:29,360 --> 00:11:34,040 Speaker 1: Georgia have actually outperformed Trump in a number of special elections. 201 00:11:34,480 --> 00:11:37,480 Speaker 1: The average right now, if you combine both years together, 202 00:11:37,600 --> 00:11:40,000 Speaker 1: like I said, both years for twenty seventeen twenty eighteen, 203 00:11:40,080 --> 00:11:43,000 Speaker 1: was eight point seven pro Democrat. Right now, it's deep 204 00:11:43,000 --> 00:11:46,120 Speaker 1: plus eleven. That's very good for Democrats. I don't want 205 00:11:46,120 --> 00:11:49,480 Speaker 1: to sugarcoat it. It's very good for Democrats. But the 206 00:11:49,559 --> 00:11:52,520 Speaker 1: election signs. If you're going to point a special election 207 00:11:52,640 --> 00:11:55,480 Speaker 1: and say here is the tea leaves, here is the 208 00:11:55,480 --> 00:11:59,320 Speaker 1: evidence of how the election is going. Has gotten reader 209 00:12:00,040 --> 00:12:03,480 Speaker 1: over the last six weeks. They haven't gotten blue her, 210 00:12:03,520 --> 00:12:08,760 Speaker 1: and that's very important context. This was not the election 211 00:12:09,400 --> 00:12:12,599 Speaker 1: that should have woke Republicans up that the electorate was 212 00:12:12,679 --> 00:12:16,280 Speaker 1: turning against them. The especial election that should have woken 213 00:12:16,360 --> 00:12:19,800 Speaker 1: Republicans up and we should be having a national conversation up, 214 00:12:20,120 --> 00:12:23,160 Speaker 1: was last year. There was a special in the Pennsylvania 215 00:12:23,200 --> 00:12:27,400 Speaker 1: State Senate in Lancaster County, a Republican county, a Republican 216 00:12:27,679 --> 00:12:31,679 Speaker 1: stronghold county, that moved about fifteen points to the left. 217 00:12:31,960 --> 00:12:35,040 Speaker 1: That's when a Democrat won a seat that was pretty 218 00:12:35,080 --> 00:12:37,679 Speaker 1: safely Republican in a special election, the only one by 219 00:12:37,679 --> 00:12:40,120 Speaker 1: one point, but still it was a safe seat. It 220 00:12:40,160 --> 00:12:42,719 Speaker 1: was a safe election, and that was more of a 221 00:12:42,760 --> 00:12:46,800 Speaker 1: telltale sign of the national environment than this thirty point 222 00:12:46,880 --> 00:12:49,600 Speaker 1: swing with this candidate who had all these issues with 223 00:12:49,679 --> 00:12:51,720 Speaker 1: local voters. I mean, I called a lot of people 224 00:12:51,760 --> 00:12:53,760 Speaker 1: in Texas to ask them, and this candidate had a 225 00:12:53,800 --> 00:12:57,920 Speaker 1: lot of things working against her versus the candidate in Lancaster. 226 00:12:58,440 --> 00:13:00,480 Speaker 1: Wasn't a great candidate, but a lot of it was 227 00:13:00,480 --> 00:13:03,360 Speaker 1: the national environment. And it should also serve as a 228 00:13:03,360 --> 00:13:06,920 Speaker 1: wag of call too Republicans who are raising masses amounts 229 00:13:06,920 --> 00:13:08,600 Speaker 1: of money. I sold you in the last episode three 230 00:13:08,679 --> 00:13:11,040 Speaker 1: hundred million for the Trump Pack, two hundred million for Crypto, 231 00:13:11,240 --> 00:13:13,800 Speaker 1: one hundred million in AI, one hundred million in in 232 00:13:14,040 --> 00:13:19,680 Speaker 1: a pack. That money does not always buy you election doctories, right, 233 00:13:20,160 --> 00:13:23,800 Speaker 1: money matters in politics. Your first dollar matters a lot. 234 00:13:24,200 --> 00:13:28,560 Speaker 1: Your one hundred millionth dollar doesn't matter that much. If 235 00:13:28,679 --> 00:13:32,680 Speaker 1: money bought elections, we would have presidents Jeff Bush, Hillary Clinton, 236 00:13:32,679 --> 00:13:37,160 Speaker 1: and Michael Bloomberg. It's just not the case right, National 237 00:13:37,280 --> 00:13:42,160 Speaker 1: environments mean something. You know, Policies mean something, Excitement means something. 238 00:13:42,760 --> 00:13:45,840 Speaker 1: I need add just one last thing to really break 239 00:13:45,920 --> 00:13:48,200 Speaker 1: up what the media has been saying. They put up 240 00:13:48,200 --> 00:13:51,319 Speaker 1: this notion that Republicans are going to lose forty seats 241 00:13:51,440 --> 00:13:54,840 Speaker 1: or more, which is around the twenty eighteen election landslide 242 00:13:54,840 --> 00:14:00,280 Speaker 1: the Democrats had. That is not true, right, If Republics 243 00:14:00,360 --> 00:14:02,640 Speaker 1: lose forty seats, a man in my world, I will 244 00:14:02,640 --> 00:14:06,120 Speaker 1: eat crow on this podcast in livestream on an episode 245 00:14:06,400 --> 00:14:08,840 Speaker 1: that would take the Republicans total down to one hundred 246 00:14:08,880 --> 00:14:12,719 Speaker 1: and eighty seats. Now, according to every prediction market and 247 00:14:12,840 --> 00:14:16,400 Speaker 1: nonpartisan analysts and even analysts who say they're nonpartisans, but 248 00:14:16,400 --> 00:14:20,480 Speaker 1: they're really partisan, I e. Larry Kudla just saying saying, 249 00:14:20,520 --> 00:14:23,240 Speaker 1: not saying. They say that the app that Republicans are 250 00:14:23,240 --> 00:14:27,640 Speaker 1: either in safe or very likely Republican districts in between 251 00:14:27,720 --> 00:14:31,880 Speaker 1: two hundred to two hundred and three seats. That's what 252 00:14:31,920 --> 00:14:35,120 Speaker 1: they're saying. Is the floor. That's without the Florida via 253 00:14:35,800 --> 00:14:38,680 Speaker 1: Florida redistricting effort that's coming in this month. That's with 254 00:14:38,920 --> 00:14:41,880 Speaker 1: the Supreme Court cases in Alabama and Louisiana that couldnet 255 00:14:41,880 --> 00:14:44,920 Speaker 1: Republicans two to four more seats are looking at Section two. 256 00:14:45,160 --> 00:14:47,800 Speaker 1: If if Thomas writes the decision or a Lder writes 257 00:14:47,800 --> 00:14:49,960 Speaker 1: the decision, it could net Republicans twenty seats. And it's 258 00:14:50,000 --> 00:14:52,080 Speaker 1: a completely different ballgame. So if you want to have 259 00:14:52,120 --> 00:14:55,760 Speaker 1: a completely unbiased opinion, just looking at the raw numbers, 260 00:14:56,040 --> 00:14:59,480 Speaker 1: things do not look good for Republicans the midterms. It's 261 00:14:59,480 --> 00:15:02,400 Speaker 1: almost so going to be a Democrat majority. Hakkiem Jeffreys 262 00:15:02,480 --> 00:15:05,320 Speaker 1: is most certainly likely to be the first well if 263 00:15:05,360 --> 00:15:07,320 Speaker 1: he can, if they don't, if they vote for him. 264 00:15:07,360 --> 00:15:10,360 Speaker 1: But right now, Hakkiem Jeffreys is likely to be the 265 00:15:10,360 --> 00:15:12,200 Speaker 1: next Speaker of the House, the first black Speaker of 266 00:15:12,200 --> 00:15:17,280 Speaker 1: the House. But elections in this year special, the ones 267 00:15:17,320 --> 00:15:19,560 Speaker 1: that they're saying you need to pay attention to, are 268 00:15:19,600 --> 00:15:25,160 Speaker 1: actually far better than last year's, better than twenty eighteen. Right, 269 00:15:25,520 --> 00:15:29,600 Speaker 1: Republicans are not going to lose forty seats in the House. 270 00:15:29,680 --> 00:15:34,640 Speaker 1: It is extremely, extremely improbable, and that's mostly because of 271 00:15:34,840 --> 00:15:37,160 Speaker 1: just the way the districts are now. There are fewer 272 00:15:37,200 --> 00:15:40,400 Speaker 1: districts that are not safe even in the Democratic weight. 273 00:15:40,520 --> 00:15:45,680 Speaker 1: In Virginia, where the state state delegates, Republicans got shellacked 274 00:15:45,720 --> 00:15:48,240 Speaker 1: in a real way. They didn't lose any Trump plus 275 00:15:48,280 --> 00:15:50,680 Speaker 1: ten seats. They didn't lose any Trump plus nine seats. 276 00:15:50,880 --> 00:15:53,960 Speaker 1: This was not like they went into the deepest Republican districts. 277 00:15:54,040 --> 00:15:56,520 Speaker 1: Democrats are fighting hard this year. They're competing in areas 278 00:15:56,520 --> 00:16:00,640 Speaker 1: that are very tough. However, all signs are not pointing 279 00:16:00,920 --> 00:16:04,440 Speaker 1: to what analysts right now are saying. There's a lot 280 00:16:04,480 --> 00:16:07,400 Speaker 1: of hyperbole. There's a lot of hype, and the data 281 00:16:07,440 --> 00:16:09,440 Speaker 1: is showing that things have actually trended a little bit 282 00:16:09,440 --> 00:16:12,760 Speaker 1: more Republican than these specials from last year. But Republicans 283 00:16:12,800 --> 00:16:16,040 Speaker 1: are in trouble. That's the data. Okay. Coming up next 284 00:16:16,080 --> 00:16:21,280 Speaker 1: is Ask Me Anything. Thank you for being part of 285 00:16:21,280 --> 00:16:23,440 Speaker 1: the Ask Me Anything segment. Guys, this is the best 286 00:16:23,560 --> 00:16:25,880 Speaker 1: part of the show. I love getting these emails. Email 287 00:16:25,960 --> 00:16:28,920 Speaker 1: me Ryan at Numbers Gamepodcast dot com. That's Ryan at 288 00:16:29,000 --> 00:16:32,360 Speaker 1: Numbers Plural, Numbers Gamepodcast dot com. I will get to 289 00:16:32,400 --> 00:16:35,440 Speaker 1: all your questions. I'm doing a longer episode today because 290 00:16:35,440 --> 00:16:37,400 Speaker 1: I'm a little backlogged on them, but please keep sending 291 00:16:37,400 --> 00:16:40,280 Speaker 1: me your emails. Ask me anything, politics, non politics, and 292 00:16:40,320 --> 00:16:42,200 Speaker 1: I will get to it for this show, or if 293 00:16:42,200 --> 00:16:44,640 Speaker 1: it's just a private conversation, I'll just email you guys back. 294 00:16:45,040 --> 00:16:48,000 Speaker 1: First one up is Michael Woods. He says, I have 295 00:16:48,000 --> 00:16:50,720 Speaker 1: a question reguarding the fourteenth Amendment that it guarantees due 296 00:16:50,800 --> 00:16:53,520 Speaker 1: process and equal protection. Since these standards apply to all 297 00:16:53,560 --> 00:16:56,640 Speaker 1: people and buying states, how can cities like Minneapolis deny 298 00:16:56,720 --> 00:16:59,600 Speaker 1: law enforcement protection to federal authorities? While I understand the 299 00:16:59,640 --> 00:17:03,080 Speaker 1: states you're not enforced federal immigration law. Police still have 300 00:17:03,120 --> 00:17:06,600 Speaker 1: a duty to protect all individuals equally, including federal employees. 301 00:17:06,840 --> 00:17:09,080 Speaker 1: How can police choose not to respond to a mob 302 00:17:09,160 --> 00:17:12,439 Speaker 1: in situations that are far from quote peaceful protests. It 303 00:17:12,480 --> 00:17:15,600 Speaker 1: appears that the city's applying different standards based on political affiliation, 304 00:17:15,640 --> 00:17:18,159 Speaker 1: refusing to protect those who do not align with the 305 00:17:18,200 --> 00:17:21,240 Speaker 1: progressive policies. Why hasn't there been a civil liberties lawsuit 306 00:17:21,280 --> 00:17:23,760 Speaker 1: against the city. This is a great question, Michael. I 307 00:17:23,840 --> 00:17:27,200 Speaker 1: actually had to call a lawyer one of my constitutional 308 00:17:27,240 --> 00:17:30,280 Speaker 1: lawyer friends. Is I didn't know the answer. So according 309 00:17:30,320 --> 00:17:32,560 Speaker 1: to my friend who's a constitutional lawyer, he said, the 310 00:17:32,600 --> 00:17:35,639 Speaker 1: premise of the question is incorrect. You believe that the 311 00:17:35,640 --> 00:17:38,639 Speaker 1: fourteen Amendment deals with powers of local police officers, and 312 00:17:38,680 --> 00:17:42,000 Speaker 1: it doesn't. It's a faulty understanding of the constitution. Police 313 00:17:42,040 --> 00:17:45,480 Speaker 1: officers don't have an affirmative duty to respond equally to 314 00:17:45,560 --> 00:17:48,160 Speaker 1: every situation. It doesn't guide them on how they should 315 00:17:48,200 --> 00:17:51,680 Speaker 1: respond to every circumstance. The Fourteenth Amendment talks about limits 316 00:17:51,680 --> 00:17:54,879 Speaker 1: of government power and not necessarily the duty of local 317 00:17:54,960 --> 00:17:57,840 Speaker 1: law enforcement on how to handle the power. Look at 318 00:17:57,880 --> 00:18:01,120 Speaker 1: Section one. It's an operative limit on the state and 319 00:18:01,160 --> 00:18:05,199 Speaker 1: not discriminatory powers. Anyway, that's when he said, I'm not 320 00:18:05,240 --> 00:18:07,159 Speaker 1: a lawyer, I don't even play one on television or 321 00:18:07,160 --> 00:18:09,200 Speaker 1: on podcasts, but that is the answer I can get 322 00:18:09,280 --> 00:18:11,600 Speaker 1: best for you, Michael. I hope that he answered your 323 00:18:11,640 --> 00:18:15,400 Speaker 1: question about why the Fourteenth i'ment doesn't guarantee police protection 324 00:18:15,520 --> 00:18:17,920 Speaker 1: for ice agents. A great question though, I really really 325 00:18:17,920 --> 00:18:20,920 Speaker 1: liked it. I actually learned something. Next question comes from Derek. 326 00:18:21,000 --> 00:18:23,560 Speaker 1: He writes, I have been reading some interesting thoughts on 327 00:18:23,600 --> 00:18:26,520 Speaker 1: how China's population may be overstaated by five hundred million. 328 00:18:26,560 --> 00:18:29,480 Speaker 1: The reasoning is for their population is to be where 329 00:18:29,480 --> 00:18:31,919 Speaker 1: it is is the total fertility rate from the nineteen 330 00:18:31,960 --> 00:18:34,800 Speaker 1: fifty to two thousand would needed to be three point 331 00:18:34,800 --> 00:18:37,600 Speaker 1: three child dumper woman in order to achieve that growth. 332 00:18:38,080 --> 00:18:40,880 Speaker 1: Is the idea that China's population is massly overstated well 333 00:18:40,920 --> 00:18:43,560 Speaker 1: grounded or am I misreading the data? Okay, I did 334 00:18:43,600 --> 00:18:46,240 Speaker 1: research on this. I had heard things, but I never 335 00:18:46,480 --> 00:18:50,480 Speaker 1: did a deep dive. Every country is individually responsible for 336 00:18:50,640 --> 00:18:54,000 Speaker 1: their population counting and their birth data. Some countries are 337 00:18:54,160 --> 00:18:58,679 Speaker 1: very good at Other countries misrepresent their information right. China 338 00:18:58,800 --> 00:19:01,000 Speaker 1: is one of the worst. Indian is also very very 339 00:19:01,000 --> 00:19:05,560 Speaker 1: bad at actually reporting data and being involved in international statistics. 340 00:19:06,320 --> 00:19:09,399 Speaker 1: China is also very very bad as far as five 341 00:19:09,600 --> 00:19:13,159 Speaker 1: hundred million goes, that is very high. That would be 342 00:19:13,200 --> 00:19:17,159 Speaker 1: like a thirty percent mark upon the population. A man 343 00:19:17,240 --> 00:19:21,080 Speaker 1: named Ye fun Jin, I'm sorry, I'm miss Madison. I'm 344 00:19:21,160 --> 00:19:23,000 Speaker 1: Ye Fujin. I think is how you pronounce it, and 345 00:19:23,359 --> 00:19:26,680 Speaker 1: obstetrician at the University of Wisconsin Madison, who conducts the 346 00:19:26,760 --> 00:19:31,760 Speaker 1: demography research, said this to Newsweek. Ye said he said 347 00:19:31,880 --> 00:19:35,360 Speaker 1: China Statistics Bureau early reports that there there have been 348 00:19:35,640 --> 00:19:38,359 Speaker 1: two hundred and three million births between nineteen ninety one 349 00:19:38,359 --> 00:19:41,280 Speaker 1: and two thousand and seventy nine million depths, giving the 350 00:19:41,320 --> 00:19:44,960 Speaker 1: population a one point two seven billion dollar sorry one 351 00:19:45,000 --> 00:19:48,160 Speaker 1: point seven billion person estimate at the turn of the century. 352 00:19:48,359 --> 00:19:51,119 Speaker 1: The two thousand census came up short, though so officially 353 00:19:51,640 --> 00:19:54,200 Speaker 1: so officials launched a campaign to add tens of millions 354 00:19:54,200 --> 00:19:57,760 Speaker 1: of quote missing people, bringing the total population close to 355 00:19:57,800 --> 00:20:00,320 Speaker 1: the initial estimate, he said, but a close look at 356 00:20:00,359 --> 00:20:04,240 Speaker 1: demographic show at glaring disparity. Around one hundred and sixty 357 00:20:04,320 --> 00:20:06,520 Speaker 1: four million people were born between nineteen ninety one and 358 00:20:06,520 --> 00:20:09,520 Speaker 1: two thousand, and after accounting for these births, attracting deaths, 359 00:20:09,560 --> 00:20:13,760 Speaker 1: and net migration, there were about forty million fewer Chinese 360 00:20:13,800 --> 00:20:17,160 Speaker 1: than reported some countries. I guess that are very good 361 00:20:17,160 --> 00:20:19,440 Speaker 1: at reporting it. He says, that are estimates about one 362 00:20:19,520 --> 00:20:22,960 Speaker 1: hundred million more Chinese counted. I believe I can see 363 00:20:22,960 --> 00:20:26,160 Speaker 1: that believable. One hundred million is still a vast over 364 00:20:26,240 --> 00:20:29,480 Speaker 1: reporting of eight to ten percent, depending on the estmenates 365 00:20:29,520 --> 00:20:34,600 Speaker 1: you're looking at. Five hundred million is wildly high, like 366 00:20:34,640 --> 00:20:37,240 Speaker 1: you're talking thirty five percent or greater. So I find 367 00:20:37,280 --> 00:20:41,719 Speaker 1: five hundred million to be inaccurate, but one hundred million 368 00:20:41,800 --> 00:20:43,919 Speaker 1: is probably really where it is. So yeah, China has 369 00:20:43,960 --> 00:20:47,320 Speaker 1: probably overestimated their numbers, but it's looking closer and closer 370 00:20:47,320 --> 00:20:49,920 Speaker 1: to one hundred million. I'll be back with more asked 371 00:20:49,920 --> 00:20:56,040 Speaker 1: Me anything after this. Okay, we're back with asking me anything. 372 00:20:56,160 --> 00:20:58,960 Speaker 1: My buddy Peter Fomo. Peter Fomo, Peter, I love that 373 00:20:59,000 --> 00:21:01,480 Speaker 1: you email me as often as you do. You definitely 374 00:21:01,480 --> 00:21:03,640 Speaker 1: add a lot to the show. Peter asked an episode 375 00:21:03,640 --> 00:21:06,359 Speaker 1: a question for the episodes ago about ICE deportations, and 376 00:21:06,400 --> 00:21:09,080 Speaker 1: he said that I misunderstood the question. The question I 377 00:21:09,160 --> 00:21:11,639 Speaker 1: thought he was saying was should ICE agents not involved 378 00:21:11,680 --> 00:21:13,640 Speaker 1: in blue states at all? He said, I think you're 379 00:21:13,640 --> 00:21:15,520 Speaker 1: misinteresting in my question. What I meant is if Trump 380 00:21:15,600 --> 00:21:18,240 Speaker 1: focused on red states rather than blue states like Minnesota, 381 00:21:18,280 --> 00:21:20,720 Speaker 1: as a result, illegals would flee to those blue states 382 00:21:20,720 --> 00:21:23,439 Speaker 1: and sanctuary areas, where eventually Trump would have to confront 383 00:21:23,440 --> 00:21:27,280 Speaker 1: blue state protesters and uncooperative governors and mayors. I asked 384 00:21:27,280 --> 00:21:29,919 Speaker 1: this because some of some have said Trump should ignore 385 00:21:29,920 --> 00:21:32,679 Speaker 1: blue states initially and focused where he can get cooperation. 386 00:21:33,080 --> 00:21:38,879 Speaker 1: I think that's a misunderstanding of where their comments are 387 00:21:38,880 --> 00:21:43,639 Speaker 1: a misunderstanding of where sanctuary their jurisdictions happen. There are 388 00:21:43,680 --> 00:21:47,440 Speaker 1: a number of sanctuary cities in red states. New Orleans, Atlanta, 389 00:21:47,560 --> 00:21:53,120 Speaker 1: Omaha all sanctuary cities or counties. Trump cannot only work 390 00:21:53,200 --> 00:21:57,000 Speaker 1: with red states and nor sanctuary cities because so many 391 00:21:57,000 --> 00:22:01,480 Speaker 1: sanctuary cities exist in red states. Right, you can't ignore Atlanta, 392 00:22:01,600 --> 00:22:04,879 Speaker 1: you can't ignore Omaha. If you're looking at Nebraska. These 393 00:22:04,880 --> 00:22:07,320 Speaker 1: are big places, not every place like Florida, which is 394 00:22:07,359 --> 00:22:11,359 Speaker 1: banded sanctuaricities. So you can't only go where you're getting 395 00:22:11,480 --> 00:22:16,320 Speaker 1: local cooperation because you're ignoring too many big population centers. 396 00:22:16,640 --> 00:22:20,520 Speaker 1: What should Trump be doing is what Tom Holman has 397 00:22:20,640 --> 00:22:23,200 Speaker 1: been saying. Tom Holman has been sidelined for a while 398 00:22:23,359 --> 00:22:27,640 Speaker 1: by Christy Nome. That was always wrong. Homan has been saying, 399 00:22:27,720 --> 00:22:30,120 Speaker 1: these rates should be taking place at night, so there's 400 00:22:30,119 --> 00:22:33,720 Speaker 1: not that many protesters. Figure out where the people are 401 00:22:33,760 --> 00:22:36,960 Speaker 1: primarily either with an existing deportation order, which there are 402 00:22:37,359 --> 00:22:41,320 Speaker 1: tons of people millions with a pre existing deportation order, 403 00:22:41,400 --> 00:22:47,119 Speaker 1: and criminal allegations, convictions or upcoming sentencing, and look to 404 00:22:47,280 --> 00:22:49,320 Speaker 1: process them first. Now, if you're if you go to 405 00:22:49,359 --> 00:22:51,800 Speaker 1: a house with an illegal alien who's committed a crime, 406 00:22:51,920 --> 00:22:55,240 Speaker 1: a major crime, especially and their spouse is illegal, and 407 00:22:55,240 --> 00:22:57,280 Speaker 1: their children are illegal, or they're living with illegals, you 408 00:22:57,359 --> 00:22:59,440 Speaker 1: arrest everybody. You don't just arrest the one person. You 409 00:22:59,520 --> 00:23:04,320 Speaker 1: arrest every but do those raids tactically at night, away 410 00:23:04,320 --> 00:23:07,679 Speaker 1: from a million cameras, away from a million different iPhones, 411 00:23:07,760 --> 00:23:11,960 Speaker 1: right and make it make the images less jarring to 412 00:23:12,040 --> 00:23:17,399 Speaker 1: American citizens, who polls show overwhelmingly support deporting illegal immigrants. 413 00:23:17,880 --> 00:23:21,440 Speaker 1: This would stop a lot of the backlash we're seeing 414 00:23:21,480 --> 00:23:25,800 Speaker 1: publicly against ice right. These videos are very, very damaging. 415 00:23:25,840 --> 00:23:29,040 Speaker 1: People's emotions are all getting tied up to it. It 416 00:23:29,200 --> 00:23:33,879 Speaker 1: also would continue to spur the level of self deportations. 417 00:23:34,480 --> 00:23:36,760 Speaker 1: Mitt Romney Sar was the first one, I don't know 418 00:23:36,840 --> 00:23:38,960 Speaker 1: who are for president talking about self deportations, and he 419 00:23:39,080 --> 00:23:43,320 Speaker 1: was correct. Self deportations is a key part of how 420 00:23:43,359 --> 00:23:47,240 Speaker 1: you achieve mass deportations, people just leaving on their own. Now, 421 00:23:47,280 --> 00:23:50,320 Speaker 1: the administration estimates that two million people have self deported 422 00:23:50,359 --> 00:23:52,240 Speaker 1: in the first year of the Trump presidency. That seems 423 00:23:52,240 --> 00:23:54,720 Speaker 1: to be a little high. I've looked at some independent 424 00:23:55,000 --> 00:23:57,119 Speaker 1: authorities on this, and they estimate that I'm going to 425 00:23:57,160 --> 00:24:00,880 Speaker 1: be closer to two hundred thousand. I do an episode 426 00:24:00,880 --> 00:24:04,760 Speaker 1: on why there's a discrepancy between what the Administration's putting 427 00:24:04,760 --> 00:24:07,000 Speaker 1: out what these people are putting out. But let's say 428 00:24:07,000 --> 00:24:09,320 Speaker 1: it's close to the two hundred thousand estimate, which seems 429 00:24:09,600 --> 00:24:12,000 Speaker 1: pretty close to actor. Maybe it could be higher, but 430 00:24:12,040 --> 00:24:14,639 Speaker 1: it's about two hundred thousand. If you add the two 431 00:24:14,880 --> 00:24:18,159 Speaker 1: hundred thousand to the number that Trump deported in the 432 00:24:18,200 --> 00:24:22,119 Speaker 1: first term the first year of his administration of a 433 00:24:22,160 --> 00:24:25,639 Speaker 1: second term, it's basically doubling it. You go from two 434 00:24:25,720 --> 00:24:27,800 Speaker 1: hundred and fifty thousands with the New York Times site 435 00:24:27,880 --> 00:24:32,160 Speaker 1: is Trump deportations to four hundred and fifty thousand, right, 436 00:24:32,200 --> 00:24:35,040 Speaker 1: So two hundred plus about two hundred and thirty plus 437 00:24:35,240 --> 00:24:38,680 Speaker 1: two hundred and fifty thousand deportations plus two hundred thousand 438 00:24:38,720 --> 00:24:41,560 Speaker 1: self deportations, that's almost half a million. You're actually getting 439 00:24:41,640 --> 00:24:45,520 Speaker 1: to really substantially high numbers when it comes to that, 440 00:24:45,600 --> 00:24:48,920 Speaker 1: and also you're deterring millions more from making the journey. 441 00:24:49,400 --> 00:24:52,719 Speaker 1: So when you include all that information, it may be 442 00:24:52,720 --> 00:24:54,840 Speaker 1: a drop in the bucket to the overall number or 443 00:24:54,840 --> 00:25:00,359 Speaker 1: what expectations are, but you're getting towards It is the 444 00:25:00,440 --> 00:25:04,280 Speaker 1: path of mass deportations what you want, and you can't 445 00:25:04,280 --> 00:25:08,560 Speaker 1: avoid blue states. You just have to do it more tactfully. Right, 446 00:25:08,920 --> 00:25:12,520 Speaker 1: looking at people with convictions with prior deportation, you know, 447 00:25:12,600 --> 00:25:17,480 Speaker 1: deportation warnings, people you know who Ice knows about. That's 448 00:25:17,560 --> 00:25:20,280 Speaker 1: how you avoid the bad pr and that's how you 449 00:25:20,280 --> 00:25:24,160 Speaker 1: still achieve mass deportation. People know the deportations are happening, illegals 450 00:25:24,200 --> 00:25:27,440 Speaker 1: are self deporting. You're still getting the people. You're avoiding, 451 00:25:27,440 --> 00:25:31,359 Speaker 1: the video cameras, you're avoiding, you know, the shootings with 452 00:25:31,480 --> 00:25:35,160 Speaker 1: good and pretty you are getting, but you're getting the results. 453 00:25:36,000 --> 00:25:38,560 Speaker 1: So don't avoid Blue states. There are millions there. And 454 00:25:38,680 --> 00:25:40,960 Speaker 1: also one of the things you don't want to do 455 00:25:41,080 --> 00:25:43,920 Speaker 1: that because if you ignore the Blue states, here's what happens. 456 00:25:43,960 --> 00:25:46,080 Speaker 1: If you only go up to red states. Illegals will 457 00:25:46,119 --> 00:25:49,600 Speaker 1: start moving to the Blue states to avoid deportations. How 458 00:25:49,640 --> 00:25:52,000 Speaker 1: does that affect the future. We've talked a number of 459 00:25:52,000 --> 00:25:55,000 Speaker 1: times on this podcast about people moving away from Blue 460 00:25:55,000 --> 00:25:57,760 Speaker 1: states and them not being counted in the census. Well, 461 00:25:57,760 --> 00:25:59,959 Speaker 1: if you have a million people moving to California and New York, 462 00:26:00,119 --> 00:26:03,040 Speaker 1: guess what they're going to get House representative seats and 463 00:26:03,080 --> 00:26:06,159 Speaker 1: electoral college votes based on those illegal aliens. So, no, 464 00:26:06,280 --> 00:26:09,119 Speaker 1: you can't ignore the Blue states. Who's ever saying that 465 00:26:09,200 --> 00:26:11,760 Speaker 1: out loud? Just stop listening to them. They don't know 466 00:26:11,760 --> 00:26:14,960 Speaker 1: what they're talking about. You need to work everywhere to 467 00:26:15,080 --> 00:26:18,760 Speaker 1: achieve this goal of mass deportations, and Christy No needs 468 00:26:18,760 --> 00:26:20,480 Speaker 1: to be much much smart about it. And let Tom 469 00:26:20,480 --> 00:26:24,320 Speaker 1: Holman do his thing. Okay, last question for the podcast, 470 00:26:24,440 --> 00:26:26,840 Speaker 1: which comes from Bill. It's a non political question, and 471 00:26:26,880 --> 00:26:29,040 Speaker 1: you know, I love that. He says, I saw your 472 00:26:29,080 --> 00:26:31,480 Speaker 1: tweet on Sanford and Son. I thought, hey, he's like me. 473 00:26:31,600 --> 00:26:33,280 Speaker 1: His parents didn't have cable when he was a kid, 474 00:26:33,320 --> 00:26:36,040 Speaker 1: where he watched reruns on channels five and eleven. Then 475 00:26:36,080 --> 00:26:39,120 Speaker 1: I saw the replies that a cable channel, TV land 476 00:26:39,280 --> 00:26:41,720 Speaker 1: or featured Sanford and Son. Were you watching on cable 477 00:26:41,800 --> 00:26:43,919 Speaker 1: or when it aired on TV? Or what rewuns did 478 00:26:43,960 --> 00:26:46,800 Speaker 1: you watch for essential viewing as a kid. Great question 479 00:26:46,840 --> 00:26:49,240 Speaker 1: because as a child of the nineties, it is something 480 00:26:49,280 --> 00:26:53,520 Speaker 1: I am incredibly passionate about because being in the nineties, 481 00:26:53,760 --> 00:26:56,840 Speaker 1: and especially when I was growing up and the conditions 482 00:26:56,880 --> 00:26:59,239 Speaker 1: I grew up in. We only had one television, so 483 00:26:59,600 --> 00:27:04,120 Speaker 1: you had to watch things that had intergenerational entertainment value. Right. 484 00:27:04,400 --> 00:27:06,640 Speaker 1: If my grandparents were home, we had to watch them 485 00:27:06,680 --> 00:27:08,399 Speaker 1: they liked as well as what I liked. And sometimes 486 00:27:08,440 --> 00:27:11,040 Speaker 1: it was everything Everyone Loves Raymond, and sometimes it was Friends, 487 00:27:11,080 --> 00:27:13,239 Speaker 1: and a lot of times it was older shows. So 488 00:27:13,280 --> 00:27:17,120 Speaker 1: I grew up with a broad appreciation of different generations 489 00:27:17,200 --> 00:27:19,560 Speaker 1: levels of television and music and movies and all the 490 00:27:19,600 --> 00:27:21,800 Speaker 1: rest of it, and I tweeted about Sanfordancen because the 491 00:27:21,840 --> 00:27:26,000 Speaker 1: star Demon Demon passed away earlier this week, and I love, love, 492 00:27:26,080 --> 00:27:28,080 Speaker 1: love sanfrtan Son. It's like one of the best shows 493 00:27:28,080 --> 00:27:30,280 Speaker 1: that was ever created. So we had cable growing up. 494 00:27:30,320 --> 00:27:31,880 Speaker 1: I watched it on Nick and Night Nick and Knight 495 00:27:32,560 --> 00:27:35,960 Speaker 1: in the nineties. It's such a good job at putting 496 00:27:36,000 --> 00:27:39,360 Speaker 1: on classic television. They had on TV Land, on TV 497 00:27:39,400 --> 00:27:42,280 Speaker 1: Line it was nickd Night. It was every Monday, every 498 00:27:42,400 --> 00:27:44,359 Speaker 1: night of the week of the weekday, especially in the summer, 499 00:27:44,560 --> 00:27:47,640 Speaker 1: they would have a whole night dedicated to one program. 500 00:27:47,720 --> 00:27:50,760 Speaker 1: So it was like on Mondays it was The Monsters 501 00:27:50,840 --> 00:27:53,560 Speaker 1: and the Monkeys. On Tuesdays, I Love Lucy. On Wednesdays 502 00:27:53,640 --> 00:27:56,480 Speaker 1: was Bewitched. On Thursdays, I Joined Jeanie, And on Fridays 503 00:27:56,520 --> 00:27:59,560 Speaker 1: it was Their Happy Days or Welcome Back Hodder like 504 00:28:00,240 --> 00:28:04,320 Speaker 1: television of like the fifties, sixties, and seventies, And that's 505 00:28:04,320 --> 00:28:07,159 Speaker 1: how I really got an education on classic TV. And 506 00:28:07,200 --> 00:28:10,639 Speaker 1: also back back in the nineties, a lot of shows 507 00:28:10,800 --> 00:28:14,600 Speaker 1: that were even being released them were very reverential to 508 00:28:15,600 --> 00:28:17,520 Speaker 1: older television. Right, So even if you like if you 509 00:28:17,560 --> 00:28:20,080 Speaker 1: watch like The Nanny from the nineties. That show was 510 00:28:20,080 --> 00:28:23,080 Speaker 1: on a friend Dresser. She talks about the seventies and 511 00:28:23,119 --> 00:28:25,760 Speaker 1: the sixties a lot, even like daytime TV, like a 512 00:28:25,960 --> 00:28:28,800 Speaker 1: Rosy O'Donnell would bring on stars from the seventies and 513 00:28:28,800 --> 00:28:31,919 Speaker 1: the sixties a lot. So when I would catch those interviews, 514 00:28:31,920 --> 00:28:33,960 Speaker 1: So I watched those episodes of New Things, they were 515 00:28:34,600 --> 00:28:37,879 Speaker 1: constantly referencing older stuff in a way that I don't 516 00:28:37,960 --> 00:28:40,920 Speaker 1: think happens anymore. I don't watch a lot of TV 517 00:28:41,040 --> 00:28:44,040 Speaker 1: gear towards younger people. Obviously I'm almost middle aged. But 518 00:28:45,360 --> 00:28:49,840 Speaker 1: that happened a lot more back then than it happens now. 519 00:28:50,360 --> 00:28:54,520 Speaker 1: And because gen Z and jen Elfa and Jen Beta 520 00:28:54,560 --> 00:28:57,400 Speaker 1: now the new generation is Jen Beta, because they all 521 00:28:57,400 --> 00:29:01,440 Speaker 1: have personalized entertainment. Everyone's got an or an iPad. That 522 00:29:01,560 --> 00:29:06,880 Speaker 1: intergenerational intermingling of entertainment is something that's very much loss 523 00:29:06,920 --> 00:29:09,000 Speaker 1: on the culture. Something I believe is completely lost in 524 00:29:09,000 --> 00:29:11,720 Speaker 1: the culture. And I was very lucky to grow up 525 00:29:11,760 --> 00:29:13,480 Speaker 1: at the time I grew up and where I grew up, 526 00:29:13,880 --> 00:29:18,760 Speaker 1: because I got information on entertainment. You know, everything from 527 00:29:18,760 --> 00:29:23,160 Speaker 1: my grandparents singing Dion and Frankie Valley to my parents 528 00:29:23,240 --> 00:29:25,880 Speaker 1: being kids of the seventies and eighties and playing you know, 529 00:29:26,280 --> 00:29:31,080 Speaker 1: veruse Springsteen and Rod Stewart to modern day things plus 530 00:29:31,080 --> 00:29:33,600 Speaker 1: being things being rerun, and I think that's really how 531 00:29:33,640 --> 00:29:37,160 Speaker 1: I have a very full encyclopedia of entertainment knowledge about 532 00:29:37,160 --> 00:29:40,160 Speaker 1: the decades. In my opinion, anyway, I think I'm pretty 533 00:29:40,160 --> 00:29:42,200 Speaker 1: good at it. And that's also why I'm much better 534 00:29:42,240 --> 00:29:44,880 Speaker 1: at talking to someone who's fifty five, someone who's seventeen 535 00:29:44,960 --> 00:29:46,960 Speaker 1: years older than me, than someone who's seventeen years younger 536 00:29:46,960 --> 00:29:49,200 Speaker 1: than me. Like I talked to somebody someone talking to 537 00:29:49,240 --> 00:29:52,800 Speaker 1: someone who's twenty one. I will make a joke and 538 00:29:52,880 --> 00:29:54,600 Speaker 1: sometimes I'll laugh and like do you have any idea 539 00:29:54,640 --> 00:29:56,360 Speaker 1: what I've just reference to you? And They're like, no, 540 00:29:56,400 --> 00:29:58,600 Speaker 1: not a clue, And I'm like, damn it, man, Like 541 00:29:58,680 --> 00:30:00,600 Speaker 1: you have to like work with me here. You've got 542 00:30:00,640 --> 00:30:03,160 Speaker 1: like why did your parents fail you? So sam very 543 00:30:04,240 --> 00:30:07,120 Speaker 1: very I do say it sometimes openly. I talked to 544 00:30:07,160 --> 00:30:09,120 Speaker 1: somebody who was like, oh, yeah, my wife's never watched 545 00:30:09,120 --> 00:30:11,320 Speaker 1: Sanford and so I'm like, did her parents not love her? 546 00:30:11,760 --> 00:30:15,600 Speaker 1: Like that? What is going on? People? The episode where 547 00:30:15,640 --> 00:30:19,320 Speaker 1: Red Fox is in the courtroom, and it's like I 548 00:30:19,360 --> 00:30:21,760 Speaker 1: can't repeat it on this podcast. It's going to try 549 00:30:21,800 --> 00:30:25,760 Speaker 1: to make it family friendly. But it's so funny, it's 550 00:30:25,880 --> 00:30:29,040 Speaker 1: so good. It's just so He was so brilliant and 551 00:30:29,360 --> 00:30:31,760 Speaker 1: Edna anything to do with her was brilliant. I watched 552 00:30:31,840 --> 00:30:34,680 Speaker 1: reruns actually when when the star of the show passed 553 00:30:34,680 --> 00:30:37,880 Speaker 1: away this because I was like, what a good, good show. 554 00:30:38,120 --> 00:30:42,160 Speaker 1: That's just perfect comic timing, not overly crude, and a 555 00:30:42,240 --> 00:30:44,400 Speaker 1: family could watch and now it would probably be pretty. 556 00:30:44,480 --> 00:30:47,600 Speaker 1: It's probably actually shocking now because the comedy has changed. 557 00:30:47,640 --> 00:30:50,400 Speaker 1: But that's how I grew up with an appreciation of things. 558 00:30:50,480 --> 00:30:52,280 Speaker 1: It was because of Nick and I was a big 559 00:30:52,320 --> 00:30:54,800 Speaker 1: part of it, and also my grandparents and my parents 560 00:30:54,800 --> 00:30:57,280 Speaker 1: putting on a lot of older stuff. You knew, by 561 00:30:57,280 --> 00:31:00,400 Speaker 1: the way, one last thing you knew growing up in 562 00:31:00,440 --> 00:31:03,440 Speaker 1: the nineties, if you had cable that you were up 563 00:31:03,520 --> 00:31:06,720 Speaker 1: too late. If Mash came on, that was the telltale 564 00:31:06,760 --> 00:31:08,440 Speaker 1: sign up. My brother and I were playing you know, 565 00:31:09,080 --> 00:31:11,080 Speaker 1: or cards or whatever, and we were uplated, and I 566 00:31:11,120 --> 00:31:13,800 Speaker 1: were playing a risk with my cousin and mashould come on. 567 00:31:14,200 --> 00:31:17,320 Speaker 1: You knew, guys, go to bed. It is way way 568 00:31:17,400 --> 00:31:20,560 Speaker 1: too late. Anyway, that's this episode. Thank you guys so 569 00:31:20,640 --> 00:31:22,560 Speaker 1: much for listening. I really appreciate it. Please like and 570 00:31:22,560 --> 00:31:25,880 Speaker 1: subscribing the iHeartRadio app, Apple Podcasts, YouTube, wherever you get 571 00:31:25,920 --> 00:31:27,880 Speaker 1: this podcast, and I will see you guys on Monday.