So Te Punaha Matatini, which is a new centre of research excellence, which is kicking off next week. This is something that we put together about a year ago when we were lucky enough to get funded by the tertiary education commission. So I just want to start off and give you a little bit of an introduction to the things that we're interested in. First of all, we are a network, right? Actually, this is actually how I got interested in networks, was by looking at centres of research excellence. And in New Zealand, these tend to be distributed across multiple sites and multiple institutions and I got interested in networks because I was interested in knowing that we're people actually really working together across different cities in New Zealand. So we have a collaboration that is centred here at Auckland in some sense, but we reach out to Waikato, Massey University, Motu in Wellington, Victoria University, also in Wellington and the University of Canterbury. So we're a network of researchers that come together across the country, literally, Matatini means the meeting place of many faces in Māori and so we think that describes us quite well. We're quite a diverse collection of people from a diverse range of institutions and actually a diverse range of disciplines. And we're also interested in complex systems broadly, including complex networks and in Māori, Matatini or many faces is actually a metaphor for complexity, right, which actually we kind of all get today. We're here to look at social networks, how people interact and actually that's a very apt metaphor for complexity. We've got three areas we're interested in. First of all, complex data analytics. So we're interested in solving data analytics problems in industry or for government. And I've got a little picture here of logs sitting on the wall. And I think Tava is over there. So Tava is very interested in supply chain networks.  And of course the logs on the, sitting on the wall for New Zealand are the sort of the classic example we like to use when we're thinking about how we fail to add value to some of our products. We just, we're just shipping off raw materials rather than turning them into value-added products. We're also interested in ecosystems and complexity in the biosphere. So we've got a number of people working on, for example, predator-free New Zealand. You know, could we, can we read ourselves of some pests like possums using spread, controlled spread of disease, for example? We're also interested in the human side of that. Can we look at preventing perhaps the spread of disease in human populations? And then I guess what I'm focusing on today is complex economic and social systems, which is probably more what this grouping is interested in. We were looking in networks of people and how those, how those networks might contribute to innovation or to the economy. So let me just get on with my talk now. But if you're interested in finding out more about the center of research once then feel free to come up and talk to me in one of the breaks. So I'm interested in this trade-off between scale and diversity. And so I use this example, Detroit versus the San Francisco Bay Area. So when you think about the economic history of different parts of the world, say over the last 50 years, well, you know, if we rolled back 50 years ago, we would have seen that, well, Detroit was the part of the world that dominated automobile manufacture, right? It had all the scale you could want. It had the three largest car manufacturers in the world. And so when it came to scale, right, you know, if scale was sort of predictive of future success, if that's what you wanted, you would have said, well, Detroit is home and hosed. You know, this is going to be an economically successful region. Of course, it hasn't been, right? It's been, you know, it's failed to compete with other parts of the world. And so even though it sort of had scale 50 to 60 years ago, that hasn't been a recipe for long-term economic success. Whereas the San Francisco Bay Area has a very diverse range of industries, right? So, you know, these days we probably know it best for the software industry. But of course, we, you know, the semiconductor industry is still doing very well. And actually, you know, the San Francisco Bay Area is also the biggest hub for biotechnology in the United States. So there's a sense in which the San Francisco Bay Area is very diverse, right? There's a lot of different types of products being developed there and a lot of different types of knowledge that resides in the San Francisco Bay Area. And so there's this idea that sort of the diversity of capabilities or knowledge that resides in San Francisco Bay Area might be better for long-term economic success. And this is something that's sort of been around an idea that's been kicked around for a long time. And I guess what I'm going to do today is look at this using patents. We can have a look at who has the widest range of patents at a regional level and compare that and try and look and see whether that is a good thing or not. And, you know, this is important to New Zealand because we're a very, very specialized economy. And, you know, we're often, even though we're small, we're often trying to chase scale, right? We, you know, these are our exports compared to Denmark. You know, Denmark's a similar sized country, but it has a much greater diversity of products that it produces and exports and sells to the world. And, you know, New Zealand, you know, we're, although we have a similar sized primary sector to the Danish, right? We're very dependent on that, much more dependent on that primary sector. And if we look over the last, say, 20 years, right, that, you know, what's happened is we've actually become less diverse in the types of products that we export, whereas the Danes have become more diverse in the types of products that they export. So this is a problem, this is a question that's of interest to New Zealand. And this is just a breakdown of our exports back in 1995. So, you know, back in 1995, our top export by value was lamb meat, right? This is actually something we built our economy on over 100 years, sending frozen lamb to all parts of the world. And the Danish, right, they had a similar, you know, their export, their largest export by value, was similar to ours, right, Danish bacon, right? So we can, if we roll back 20 years, there's kind of a similarity that we're both producing primary sector goods. You know, if we fast forward to 2010, right, then actually, you know, of course both economies would have changed, and today, of course, it's dairy output, right? So our biggest export by value is now milk powder, right? Whereas in Denmark, their biggest export by value is now pharmaceuticals. And so there's a sense in which, you know, you can say both economies have reacted to the marketplace, both economies have changed over time. But we've sort of moved further into our primary sector, and you'll see that this is now 13%, whereas lamb meat used to be 6%, and actually it turns out New Zealand's economy has become more specialised, and it's now more dependent in some sense on the primary sector, whereas the Danes have diversified, and they've moved into products that we might traditionally think as more value-added, more knowledge content in these. And there's another important thing, difference between these two types of products, one's primary sector and one's pharmaceutical sector, is the number of different places in the world that you're competing with, right? So how many other countries can produce milk powder, and how many other countries can produce pharmaceutical products? And it turns out that there's a lot more countries that can do this than this. And so there's this idea of novelty in what you do, right? How many other places can do what you do? So there's not only this idea of diversity, but there's this idea of uniqueness in what you can do. And so that's really kind of the two things I'm going to look at in today's talk. We're going to look at, and I'm actually going to, I've put up countries here just to make it easy, but actually we're going to go down to a regional level, and we're going to look at the patents that are held by regions. But you might describe a region or a country as being very specialised, so New Zealand would be a very specialised country, for example, whereas Denmark would be quite diverse, there's a greater range of products coming out of Denmark. And so this might describe this, you know, when we look at the country or the region, we might talk about specialisation versus diversity. If we look at what it is that they're doing, then we'll talk about novelty or ubiquity. Okay, so it turns out that there's lots of countries that have a comparative advantage in milk powder. So actually this is quite a ubiquitous product, there's many different countries that can do this, whereas pharmaceuticals and, say, biotechnology, there's few regions that can do that. And I sort of, you know, my hypothesis is kind of hidden in this diagram. This idea that maybe if you're more diverse, maybe you produce a more novel set of products. And so that's really the hypothesis that I'm going to look at today. Is there some relationship between diversity and novelty? And so we're going to use patent data. So patent data has the great benefit of being, you know, freely available. When you file a patent, there's a public record that's made of what you've done. So we, you know, there are big patent databases around the world that you can work with. And then there's other information on the patent as well. So, you know, there's the patent itself, which contains some sort of idea, some sort of invention. Actually, what we're going to use is we're just going to, there are, the patent examiners will assign some descriptors, classes to each patent, which basically tells you what kind of technology class does that patent lie in, right? And some patents will have more than one technology class, and there, you know, and there is a hierarchy of technology classes that you can look at. You can look at a very fine scale of technology classes where you could look at quite a large scale. Then we also know, you know, we know something about the inventors. Now really, I guess at a social networks meeting, I should be talking more about the inventors. In fact, we have done some work on inventors and how they're connected. But actually, I'm going to ignore that, what the inventors are up to today. And actually, what we're going to focus on is the owners of the patents, which may be the inventors in some class, but in most cases these days it's the company that you work for. And actually, I'm going to aggregate across companies. I'm just going to look at regions, right? So we'll look at, for a given region, the firms in that region, what's the patent portfolio that the totality of these firms have. So that's where we'll be getting our data. And so, you know, what are we trying to get at? Well, we're trying to understand what the knowledge and capabilities that are held in a particular region, what they contribute to in terms of the things that you're able to invent, right? You imagine that within firms and the collection of inventors that work for those firms, there's a certain skills and knowledge that are held in those firms. And some of which, of course, some knowledge is going to travel easily, right? Codified knowledge, right? We can imagine that everybody's swimming in a bath of codified knowledge that you can download off the internet. And then there's some knowledge that's sticky, right? That really is contained in that region. And that other regions who don't have that knowledge can't access, right? So there's this idea that there are bits of knowledge hidden in particular regions. And I've got Auckland and Christchurch here. You know, let's say Christchurch has bits of knowledge, A, B and D, embedded in the firms in Christchurch. And if Christchurch contains bits A and B, right, then it will produce some product P. Or some, if we're looking at patents, some technology class P, okay? And then, because Auckland has this as well, then it can also, it also has the capability to produce patents in class P. But then Auckland also has piece of knowledge C. And if you take B and C, then that gives you Q, right? So we, and then Christchurch has D, and that gives you R, right? So there's some relationship between the knowledge held and the types of outputs that we can measure that are coming out of these regions. Now, we can't directly get at these things, but we can sort of infer their presence in some sense by looking at the outputs of a particular region. And we can start to say things, right? You know, that if we see that Auckland and Christchurch are both producing P, then we can maybe guess that there's bits A and B contained in both those regions. And you can think of this, right? We could write down a matrix that relates regions to patent classes or products, right? And we can potentially decompose that into a matrix that tells you what knowledge is in a particular region. And also how that knowledge translates into products, okay? And you can think that it would be the product of these two matrices that gives us this, bearing in mind, you know, this is the thing that we can observe, right, this matrix. So specifically, right, we're going to use Reveal Comparative Advantage, right? So there's all sorts of ways you could populate that matrix. We could count patents in a particular class. We're going to use Reveal Comparative Advantage, which basically says, are you producing more than your share, your fair share, right? If you imagine, given the size of your region and the sort of the patent output of that region, you might be specializing in particular types of patent classes rather than others, right? We can see that you're producing more than your fair share of a particular type of patent. And so we say that you've got Reveal Comparative Advantage in that particular type of patent. And so we can populate our matrix RP, and this defines a bipartite graph M, right, which tells us about how technology classes are linked to regions. Okay, and so if we do this for our database, and actually, again, you could weight this, right? So Reveal Comparative Advantage, you know, strictly measures the share, your share that you've got. We actually just say, if you've got an entry greater than one, then we'll just normalize that to one. And so only if you've got a Reveal Comparative Advantage, i.e. if your entry in that matrix is greater than one, will we draw an edge connecting you. And so here's the, and this is actually, we've aggregated, to make this easy to visualize, we've aggregated this up into countries rather than regions. But of course you can do this for regions as well. We can link all the regions to the different technology classes. But this is just a visualization linking countries to particular technology classes, okay? And so now we can define diversity, right? So the diversity of a particular region, in this case a country, it's just the number of technology classes that that country or region has a Reveal Comparative Advantage in. And likewise we can define the ubiquity, right? So if you're a technology class, we can look at how many regions have Reveal Comparative Advantage in that technology, right? So regions have a measure, we can assign a measure of diversity to, and technologies we can assign a measure of ubiquity to, right? So if there are fewer places that have Reveal Comparative Advantage, okay, I can see the time starting to get away on me, I won't talk through that. Okay, we can also project this map. We can project out the regions in the countries and so we can look at how different technologies are related. And this is the map that you get. You get sensible things like dairies, oils and fats are linked together over here and they're related to food manufacture. You know, interesting, there's a whole lot of sort of very ubiquitous, highly connected technologies in the middle and then sort of more specialized things sort of sit on the periphery of that diagram. We can look at particular regions of course, so this is New Zealand. We can look at what New Zealand's good at and show enough food processing and dairy show up. We can go down to a regional level, so we can go down to the North Island and the North Island looks a lot like New Zealand and that's because most of the patents in New Zealand come from the North Island. The North Island looks a lot like Auckland and that's because most of the patents in Auckland come out of Auckland. However, the South Island looks a bit different. One of the things you can start seeing is you can start to see an electronics cluster appearing and of course that's because there's a small cluster of electronics and businesses down in Christchurch. So we can sort of pick up some of the features of regions that we expect. It's quite interesting and of course this is what we wanted to do is look at the ubiquity versus the diversity. So this is the diversity of a particular set of regions, plotted versus the mean ubiquity of their patent portfolios, right? So we've looked at the comparative advantage that the products or the patents that these regions have comparative advantage in and then we've averaged the ubiquity of those products and we see this relationship and of course if you're in New Zealander you want to know who's sitting out here with the most diverse and the most unique set of patents and over the 30, we've used a 30-year data set and here Christchurch does turn out to be to have the greatest diversity and the lowest ubiquity whereas Auckland has a lower diversity and a higher ubiquity. Actually it turns out if you bin those if you look at how things have changed over time that Auckland's actually in the last 10 years has actually passed Christchurch. So these days the more recent patent portfolio that Auckland holds is more diverse and more novel than that of Christchurch. Okay so what does this all mean? Well we've run a bunch of null models to try and tease out what are the effects that we're actually seeing so I'll just take you through a couple of null models. First of all of course patent examiners typically will assign more than one technology class to a particular patent so it could just be that we're kind of seeing the mind of the collective patent examiners and the way that they think technologies should go together and so that's this null model if we just remove the regional co-occurrence between patents and just look at how patent examiners have linked technologies then we see quite a different distribution of diversity versus mean ubiquity and one of the other interesting null models and I should say the black data is the original is the real unmodified data. The red data this is if we decide what we want to you know if we preserve the ubiquity of each particular technology class and we preserve the diversity of each particular region but nonetheless we reshuffle the patents that each region has then we get a similar distribution in some sense in that the mean ubiquity is similar to the mean ubiquity of the real data set which you might expect if you're preserving this but in fact we see a different slope there's a different dependence between diversity and mean ubiquity in this null model and it's a statistically significant difference. We can also look at what's happened over time so that's the full data set we can break it down and look at how things have changed over a yearly period or over a decade and what we find is that over time you can ignore these points because the data set patents take a while to enter into the data set so you have to go back a few years before you have a complete data set but over time this relationship between diversity and the mean ubiquity of patent portfolios has got stronger so in other words regions that have the highest diversity are tending to produce the most unique patents. Okay so a little bit over time I got one minute okay so there does seem to be a non-trivial regional structure to technological innovation diversity does seem to be linked to ubiquity so diverse regional patent portfolios are less ubiquitous or more novel than you might expect and this effect seems to be getting stronger over time. Okay and hopefully I haven't used up all my question time. So there are these models of innovation we're coming up with a new idea about taking two old ideas and combining them so if you've got a more diverse set of knowledge and capabilities in a particular region then you're able to produce those more novel pieces of knowledge because of the diversity of ideas that you're able to combine to recombine so that's kind of the... So I guess you could also look at an individual where it does come down but again recombination of different skills so I guess that's the kind of the principle model the hypothesis that's out there you know it's been tested in various ways


Hey, what's up YouTube, it's your boy Zeke the Crypto Mind and today I'm going to bring you the top three swing trade ideas for July 1st, the first week of July. So stay tuned and check this video out. Okay, YouTube, you're back. Go ahead and hit that thumbs up button and hit that subscribe button because we're about to get straight into it, all right? So these are my top three swing trade ideas for the first week of July. The first one I was looking at was PayPal, so that's PYPL, PYPL, okay let's make this a little bigger and go right there, all right? So PayPal has been on a uptrend lately. See the green line? The green line represents the 180 day simple moving average and it's pointing up. Whenever the arrow is pointing up, if you just put an arrow at the end of the green line, it shows where the direction of the company is going. So right now, PayPal is in an uptrend, all right? So if we look at PayPal and we zoom in on the last couple of days, the last couple of weeks, PayPal has been staying true to the 180 day simple moving average. It bounced off of it a couple of times, it's deep below, but support brought it right back up. It had a high of $119.95, so let's draw a resistance right there, bam. If we're looking at the MACD, the MACD is just about to cross over, let's zoom in a little bit. So right now, PayPal is not overbought, it's not oversold, it's somewhere right in the middle, so you're getting it for a good price for speculation. And right when the MACD cross and these red lines start to turn to green lines, that would be that it shows that it's going to be a goodbye. So when Monday comes, you got to start putting in your orders once all your confirmations hit. So we want the MACD to cross, and we want the green bar to start showing up and make sure that it's not in the overbought area. So if we buy it on Monday, we have a potential until its next resistance line to gain 4.2% until it reaches its last all-time high of $119.95. So getting in on PayPal at $114.70, I will go ahead and wait until the MACD cross and for the confirmation to be confirmed, and then you guys could go ahead and buy PayPal for a swing trade for a profit of 4%. Now I will set my stop-loss somewhere right below the 180-day simple moving average just because it's been showing, the green line has been showing so much support for this for PayPal. I will draw right below the green line. So if you guys buy, your margin of loss should be 2.8% and your margin of profit should be over 4%. So you always want your margin of profit to outweigh your margin of loss. So this will be a good time to get in on PayPal. So another good swing trade I did, I've been looking at is Shopify SHOP. Alright, so I already drew up some lines for Shopify. So I drew a, you know what, let's zoom in a little bit. So Shopify is also on the uptrend, 180-day simple moving average. It's the green line, put an arrow on the end of it right there and it's moving up. It's on the uptrend guys. Alright, so let's zoom in a little bit on Shopify. Alright, so I drew some supports and resistance. I drew a line right here. The last time it broke up above these resistance and then it came back and it did not break the resistance. So this red line right here is now the new support. Okay, so prices do not want to fall below that new support. So then it shot back up and it got rejected right there at the next resistance line. Tried again, tried again, kept getting rejected at that resistance line and then it finally broke through that resistance line, right? So it went for another uptrend and then it got rejected at the next resistance line and kept consolidating. Consolidating means when the stock price really doesn't change much, it goes sideways. So it consolidated and then bam, it broke right through the last resistance line and got rejected again at the next resistance line of the all-time high of $338.94, alright? So then we came back and then it did not follow the last resistance line. It did not support it. It actually broke down below. And then the last support, this right here is now the new support, okay? So it came back down and it bounced off the new support. So we zoomed all the way in, okay? It was, it would have been a good time to get in a couple weeks ago, I mean a couple days ago. So it was oversold. So it had been a good, I mean people were actually selling the stock for more than, for less than what the company was working, alright? So RSI too many people were selling the stock off and it would have been a great time to get in right here. But if we wanted for all our confirmations to confirm, you want the MACD crossover. So right here, the MACD did not cross and the MACD is just now starting to cross over. So if you guys were to get in, the MACD just crossed. If it stays true on Monday, you guys could get in and buy it somewhere around here at $300 and then all the way up to this last resistance right here, and that's a profit of 5.44%. And if it goes back down to this last support, that'll be five. But if you go, if it breaks through the last resistant and goes up to the next resistance level, take profit two, that is a profit gain of 12.9%, okay guys? Give me one second, let me check on the... Alright, so your margin of profit is 12% and your margin of loss is 5%. Remember, you always want your margin of profit to outweigh your margin of loss, alright? And the next one that we're going to look at is Macy's. Bam. Alright, so Macy has been on the downtrend, but his earning card did come out a couple of days ago and they paid out their dividends. So let's zoom in on Macy's and why I think it's going to be a good swing trade up. Alright, so Macy's has been trying to break his resistance. The simple moving average, the 180 day simple moving average has been a resistance for Macy's to stay true to that. It tried to zoom in a little bit more. It tried to break through the resistance of the 180 day simple moving average once, twice, three times, and it just hasn't broke through. And it shows true down here in the MACD, it's always been in the red, alright? So only if the MACD cross and it's not overbought, that mean the RSI is somewhere in the middle between 60 and 40 and not above 70, and it crosses the simple moving average, okay? It's going to cross and close above the simple moving average, the four hour chart close above the simple moving average and the MACD cross and the RSI is not overbought then our less resistance level would be somewhere around here if there's last time it got rejected and if we zoom in and we buy after it closes above the simple moving average, a margin of profit would be around 2.87% and we said our stop loss around 1.5% down here. But we wanted to break above the simple moving average first, alright guys? So example of that, I actually pulled up an example of a chart that looked just like this that was in the downtrend, INTC, this is Intel. So Intel was in the downtrend, the hour was pointing down, Intel was way below the 180 day simple moving average and if we zoom in a little bit, it finally broke and closed above the 180 day simple moving average, the MACD cross and the green bar started to show up and once it broke and closed above, Intel actually had a gain of 2.89% over the course of 10 days after it broke above the simple moving average. So I kind of suspect MACD's to do the same if it does break and close above the 180 day simple moving average, MACD's might be an actual good swing trade for the week but the one that did show the most profitability was Shopify, okay guys? Remember Shopify had that 12% profitability to the 5% loss, right there, 5% loss, almost 13% gain, so Shopify might be the deal on the week. So what I want you guys to do is go down in the comment sections, if you're going to take any of these trades, let me know, come back Friday, I'm going to have another video Friday, come back Friday and drop down in this video if you guys actually took these trades and made profit off of these short weekly swing trades for the first week of July. I suspect a lot of price movement to start happening at the beginning of the month. So yeah guys, other than that, I want you guys to hit the thumbs up button, it really helps this channel out a lot. Hit the subscribe button and go down in the comment section and subscribe. The platform that I'm using is TD Ameritrade's platform, it's called Thinkorswim, and what they do charge and commission to trade, that's why if you guys don't want any charges or commissions when you buy and sell stocks, I left a link down to Robinhood, it's an app on your phone in the website on the computer that you guys can use, it's called the Robinhood app and you can buy and sell and trade stocks on there for free, no commissions charged. I'm going to leave a link to that down in the descriptions, but yeah man, other than that, this is your boy Zeke the CryptoMiner and I'm out.


Is winter testing in Formula One important or not? Yes it is, yes it is. We just finished Bahrain testing after Barcelona. I think of three days both circuits. And the real question is why Barcelona and why Bahrain? Barcelona is a circuit where you can find high speed and low speed corners and fast and slow section. Very, very different corners. It's a really good baseline for every team in Formula One. Testing have been done in Barcelona since forever. The only thing in Barcelona is that in the winter you can get cold weather. That's why Formula One team has decided also to go testing in Bahrain. Saying that it's not straightforward. Bahrain you can get sandstorm, like we did see a little bit on day two and very hot temperature during the day, which is not necessarily representative of what you're gonna get in the season. But it's a very different circuit from Barcelona. So very interesting to follow the winter testing there. Where's the new cars? I don't know what you think of the new cars. Do you like it? Don't you like it? Make sure you leave a comment. Tell me what you wanna speak about. Also you like and subscribe. That'll be very nice. But winter testing, yes. Who is the fastest? Who is the slowest? Is winter testing important? Yes it is. It's much more nowadays for reliability where you go run and run and run. And this year because it's a change of regulation, correlation with the wind tunnel. I think that was key for the teams to make sure that what they see in the winter tunnel is actually happening on track. And then reliability. Making sure you get plenty, plenty of laps. That's why you see the teams swapping the day between two drivers. Because if you are doing one day with one driver and you want to do 100 plus laps, the driver starts to be really tired at the end of the day. And therefore you kind of lose some track time if you want. Whereas if you do morning and afternoon different drivers, if you can do the swap of drivers position and sit quick enough, you actually optimize your day. I think Mercedes starting doing that a few years ago and then everyone kind of copy Mercedes in doing that. And it's actually working really well. So yes, who is the fastest? That's a question. What do you think? Again, make sure that you leave a comment of who you think is the fastest. I believe that Red Bull as the strongest car. I heard a Ferrari, which is good. Everyone likes to see a Ferrari at the front. Mercedes has been struggling a little bit. They brought a huge update in Bahrain, but there's almost no sideboards. Quite interesting to see the car that way. But they've been struggling a little bit. Bear in mind that last year, same time, they were actually struggling as well in Bahrain testing. They were world champion and Lewis almost was world champion as well as driver. So Mercedes is going to be there. After that, I think my client looks good and my client was very good in Barcelona. Maybe a touch less in Bahrain, but Bahrain is a very, very specific track. So don't read too much in there. Then it gets a little bit more complicated to see who is who. I think Alfa Torelli looked good some day, not so much other days. Same for Aston Martin and Alpin. I think those guys are kind of in the mix. Then I see Alfa Romeo, but Alfa Romeo has been struggling with a lot of reliability issue. Gearbox twice in two days of the last few days. They haven't done much laps in Barcelona at all and in Bahrain, they struggle. And then at the back, Haas and Williams. Even though Kevin Mohsen did the fastest lap on day two in Bahrain, Bear in mind that they were running later than anyone else. So cooler track then makes a big difference and no traffic. So let's see, maybe I'm wrong, but I guess that's my picking order. So Red Bull, Ferrari, Mercedes, McLaren, then kind of a mix, Alfa Torelli, Aston Martin, Alpin, and then Alfa Romeo, Haas, Williams. But that's going to change a lot during the year. Big updates on the car. We're going to see huge improvement, big step forward, all backwards. It's going to be interesting. Another big topic that we need to speak about is something that I cannot even say properly. It's poor poisoning, but bouncing on a straight line. Let's call it bouncing. It's so much easier. So bouncing on a straight line, we've seen teams with a lot of issues. It's due to the bigger diffuser on the Formula One car being run so low. The floor sucks the ground, but then it kind of creates that big bouncing feeling or big bouncing that we could see on the car. I'd never experienced something like that, but I can tell you one thing in the car, it must be really awful. Must be a lot of headaches, just feeling rough in a straight line where it's normally the place where you can drink in the race or breath. But right now, if it bounces like that, it's going to be hard. It's going to be hard. Some teams have the issues, some others don't, but definitely something interesting. So yeah, race one coming very soon. Qualifying will give us much more of a packing order, but that's where I see it is. So enjoy the Grand Prix and make sure that you leave me a comment after the Grand Prix so we can talk about things that you want to see, want to hear, and I'll be happy to be with you as always.


What's going on, everyone? Sam here with late in sports cards. I've been 22, 23, putting in a Prism basketball hobby. 4 box break, number 94. Pick your team style. Read off the list of our names on our teams. We've got Antonio B with the Wizards. That is going to be Bob H. Lakers. Chris H. Pistons, Warriors, Thunder, Magic Blazers. Chris D. Pacers Jazz. Daniel E. with the Spurs. Javante J. That is Bulls and Sons. Joseph R. Bucks-Nix. Justin C. Rockets. Pelicans. Mike H. with the Cavs. Heat Raptors. Nets. Nicholas M. Grizzlies. Paul G. Nuggets. Richard G. with the Hornets. Rory C. Hawks. Ryan B. Clippers. T. Maeda with the Timberwolves. Thomas C. Celtics Mavs and Victor R. Kings and Sixers. Here we go, guys. Let's do it. Let's do it. And that base will be split into all spots. You will get all your rookies, inserts, collards, silvers, all that good stuff. Just kind of go. Red Wave. Lamello. Trey Young. Silver Al Horford. Wendelmore Jr. Rookie Ray Allen and Kennedy Chandler. So I'll keep two separate piles next to me of the non-sleeved stuff. That is Lamello Fireworks, Silver Simons, Isaiah Mobley Rookie, Magic Johnson, Dyson Daniels. And Kyle over on Channel Two Rip and Personals. Isaiah Red, Austin Rivers to 299. Got Second-Year Auto. Josh Christopher. Signatures go into the Rockets. Hyper is Jalen Suggs. Peyton Watson Rookie. Desmond Bain and Vince Carter. Vin Sanity. Dylan Brooks, Red Wave. Prismatic, Scotty Barnes. Hyper, Aaron Gordon. Hakim Elijahwan. A.J. Griffin. Seal of Draft, A.J. Griffin. And Jaren Jackson, Jr. Little J. Blue Ice. Dominique to 125. Nice one there for the Hawks. Steph Currie, Inser. Silver Trey Young. A. Johnny Davis. Chris Weber and J. Dubb. Jalen Williams for the Thunder. Purple to 99. Patrick Baldwin Jr. Rookie. Nice one there for the Warriors. Oshai Baji, Silver Daniel Tice. Blake Wesley Rookie. Bryce McGowan's Rookie. Dominique Wilkins. That is going to be a Red Wave Ant-Man for the Timber Wolves. Prismatic KD. Hyper, Malik Beasley, Jazz. B. Clark. Musa Diabate and T.J. Warren. Who's ready for next year's national and good ol' Cleveland. Purple Ice, Luke Canard. Nice one there too, 149 for the Clippers. Still a Clipper in this. Luke Hyper, David Robinson. Nikolay Jovic. Here's Diabate Graham and Tim Duncan. Hyper's Luka's got to be sorted. What's up, Toke? Silver is Kawhi. Damian Lillard. Silver, Isaiah Roby. Patrick Ewing. Scotty Pippin Jr. and Steve Nash. Silver, Yannis on the Fearless base there. Silver, Jason Kidd. Wallace, Barkley and Terry Eason. Prismatic Yokoche, Trey Mann, Pulsar. The other Jalen Williams. Bill and Brooks, Jeremy Sohan Rookie. And last pack here. That's going to be a blue Johnny Davis. Nice one there for the Warriors. 2199. Nice one here for the Hawks. How about four of ten gold sensational signatures? A.J. Griffin. Still a Draft, A.J. Griffin. Steel of the Draft. A.J. Griffin. Pulsar, Dougie McBuckets, Isaiah Roby, Jason Kidd and Malachi Brandt. I think he said it three times. Steel of the Draft, A.J. Griffin. You got to know it's the Steel of the Draft. A.J. Griffin. Get him out. Tim won't be the last time, buddy. Be the last time there, friendo. All right, on to the next one. They have laser tag right next to it. The Dulles Expo. Is that in? That's in Northern Virginia, right? Orange Wave, LeBron James. Nice. Going to Lakers. 27 of 60. That's well-centered too. I would say probably tag that one. Just be safe. Silver, Boyan, Bogdanovich, Jalen Williams, Dylan Brooks, Jeremy Sohan. So, William. William, what up, dude? Steve from Crash on Making the Trash with. Yeah, Steve's a weekly participant there. There's a Kuzma to 75, widescreen, Tatum, Silver, Reggie Jackson, Roby, Kidd and Brannum. Jake's usually there. You were out of class this week. To select few. They're on there every week? Yeah. Red Wave, Sadiq Bay. Sadiq the Freak. Kyrie Irving. There's Norman Powell. Stottemeyer. Keegan Murray-Rookie. Nice one there. And a Danny Green. I am not toke. Neither of us are now. They'll be staying here holding down the fort. Oh yeah. Someone's got to run the shop. Grayson Allen to $2.99, right? Yes, $2.99 for the Bucks. Marta Rosen, Hyper, Dinwiddy. I'm sure it's going to be awesome. Wendell Moore Jr., Ray Allen, Kenny Chandler. Oh yeah, that's going to be a good time. Especially since we're not breaking there. I guess a lot less stress on everybody. A lot less logistics that go into it, which is kind of nice. Katie, widescreen. Mitchell, Silver, Josh Richardson. David Roddy, Rookie. Come on. Jval and Apache Baldwin, Rookie. What, to get on the trash list, Steve? No, it is not easy. There's Tracy McGrady to $2.99 for the Raptors. Ty Ty Washington, Rookie Silverado for the Rockets. There is a little inkstain on the back there. We'll send an empty box and wrapper. Still a nice hit. Congrats on that. Moses Moody, Tony Parker, Mark Williams, and Trey Murphy. I guess that technically still works. Prismatic of K.D. Jha, Silver, Vince Williams, Jr., Rookie. Payton Watson, Desmond Bain, and Vince Carter. I'd rather be safe than sorry. Yeah, no. Steve got up to $2 this week on the trash list, which is kind of insane. That's like Donnie and Kyle. I've never even been that high. Yes, you have. Keegan Murray. Really? There's one time you were, yes. Anthony Edwards. I missed that. Johnny Davis, Chris Webber, and Jalen Williams. Maybe you were three. You were there two or three. I don't think I've ever been top two. You were definitely three one week. I know you've been as high as three. Red Wave is Pat Bev, but you might be, I don't think you were two actually, I think about it. Kade, Silver Brogdon, B-Clark, Moussa Diabatze, and T.J. Warren. Bubble Goat, T.J. Warren. That's going to be tough to knock off. I know, that is. The only time you'll ever see somebody else is if I'm not here. Chris Paul-Harward. Silver Josh Hart. I think Donnie was born. Tim Duncan. Devante Graham, yes. It just shifted up one. Yeah, everybody moved up. Like a courtesy thing. That is Vince Williams, Red Wave. There's Donovan. Hyper, A.J. Griffin. Could you imagine what are the odds of Sam not being top? I know. Like Sean said, it'd have to take like, he's like, you're like Michael Jackson Thriller at number one.  To 199, Johnathan Kaminga for the Warriors. Otto Jabari Walker. Mojo to 25 for the Blazers. Nice one there. Going to Portland. Hyper, David Robinson. Ben Wallace, Barkley, and Terry Eason. I think to be fair, I think if we had been doing this longer, that time that Donnie messed up the Papa John's order, I think he would have been number one that week. I would have to. He was so mad at Donnie. That was so funny, dude. So funny. So mad. To 149, CP3 on the Purple Ice. Nice one there. Sons, Otto, Manu Genobli. Silver signatures. Congrats, Spurs. Cool one there. Manu. Hyper, Aaron Wiggins. Trey Jones, Oshai Baji, and Kendall Brown. Johnny Davis. Silver, Robert Williams. Jalen, Howya Durin. Howya Durin. Howya Durin. I think you've kind of been around for that one. Jalen, Howya Durin. Jalen, Howya Durin. Donnie actually came up with that and told me to use this. Absolutely. Desmond Bain. What's up, Chris? Ty Ty Washington, Herbert Jones, and Ty Jones. What's going on, Chris? Big Chris M. I know you were in chat when I folded on the Immaculate Grim the other night. There's Trevor Keels. Keels with the wheels right there. You were severely disappointed at me. Definitely so. Eggs Benedict, Cumbermatch, Matheryn. Ty Jones and Caleb Euston. So we have the top 10 worst stick lists that's coming up soon over there. We got a Mojo. Mojo James Harden. Nice one there. 12 of 25. It's a Mojo James Harden, Donnie. It's a 25. Mojo. Yannis. Silver, Marcus Morris. Stockton. Shadon Sharp. And Brandon Ingram. What up, Nick? There's a Malik Monk. Manu Genobli. Silver, D'Angelo Russell. Eggs Benedict, Cumbermatch, Matheryn. Chris Mullin, Kenneth Lofton, Jr. Red, Deandre Hunter for the Hawks to $2.99. Kelly Olenek for the Jazz. Silverado. Yeah, me and Jake got to see him play live. There's KP. That was a great night. Christie does a lot of fun. McDermott and Marjan Bochamp. Blue Ice to 125. Corey Kispert. Nicola Jokic. Silver Corey Kispert. Jane and Hardy. Harry Payton and Dallin. Terry. What's going on, Nick? Yeah, how you been, Nick? What up, what up, what up? There's a Red Wave, D'Angelo Russell. Wrong one. Anthony Edwards.  You just got jingled. Joe Engels. I can't wait to get to Jersey. Me too, dude. Shaq, we're going to be running deep at LSC with Joe Engels' jerseys. We're going to be hella running deep here at LSC with Joe Engels' jerseys. Red, Jason Tatum, $2.99. Kwai Leonard. Hyper. Dennis Rodman. Shet Rookie. Oseman Zhang and Alan Iverson. I remember the first time I ever did that to Sean. He looked at me like I was legitimately insane. Insane. He was so funny. So, Widescreen, Jason Tatum. Fearless. The Honest. Silver. Nicola Jovic. Rookie. To the Heat. Josh Minott. I feel like he does it every time you do one of your six. Oh yeah. Well, that one was like, he just, he didn't, I'd never showed him the meme at that point. And he obviously had never seen it at that point. Silver. Cade. Luca. Silver. Dominique. Max Christie Rookie. Andrew Nemhard Rookie. And E.J. Liddell. Last box. Good luck here, everybody. See what we've got. A quick recap of our autos after as well. Ooh, an orange to $49. Is Alexi Pokeshevsky. Nice one there for the thunder. Zion. Silver of Paul George. Christian Brown Rookie. T-Mac and Jabari Walker. Good to hear, Nic. Chillin's never a bad thing. I love that. I love doing that. It's like my favorite pastime. Michael Porter Jr. Red Wave. Wuckle Audrey Shadon. Hyper. Jabari Walker Rookie. D-Wade. Isaiah Mobley. And Magic. Johnson. Silver. Fearless. Is Lamello. Prismatic. A.D. Silver. Max Christie Rookie for the Lakers. Bison Daniels. Elijah Wan. And a steal of the draft. A.J. Griffin. Do you know who was the steal of the draft? I didn't. Thanks for that. I'm not at me. Absolutely. The Dorcher Chamber. The $199. Lee Dort. Ryan Rollins. Rookie Otto. Nice one there for the warriors. Got a Pulsar Ant-Man. Triple J. Lake Wesley. And Bryce McGowan's. And we'll probably have an E.J. Laddell Rookie somewhere because they all follow each other. I believe. Cade. Second-year Cade. Red Power to 75. James Harden. Silver Wendell Carter Jr. Steve Nash. Jaylin Williams. And Dylan Brooks. Red Froms. Wagner to 299. DeMar. Hyper. Kemba. Sohan Rookie. Wendell Moore Jr. And Ray Allen. A wedge steal of the draft. Confirmed steal of the draft. Chris, confirmed steal of the draft. How about you? I want a legend. Ant-Man. Chalmerant. Silver Nemhard Rookie. Nice one there. Kennedy Chandler. Peyton Watson. Desmond Bain. Confirmed steal of the draft. A.J. Griffin. McAlarie Keegan Murray. Fearless Moniu. That's not a number it is. Wow. Yes it is. One of one. Manu Ginobili. Fearless. Yeah dude that like you could tell because the background's a little different. That's nasty. Crazy. Thank god I caught that one. Congrats on that one. Spurs. Uno de uno. Is Manu. Look at that into a mag. V.C. That's really cool. Like the color match too. Yeah dude that's kind of insane. That's an insane card right there. Good lord. It's almost like a black tie right here. Chris Paul. Silver KCP. Dominique. Jovic. And Tim Duncan. That is really sweet. Let's go Mets and Red Sox baby. What a match. Let's go. Clint Capella. Swiss Bank. Prismatic Cade. Silver K. Love. Jalen Williams. Brandon Clark. And Musa Diabate. So two packs to go here. We got an auto coming up as well. It can be Purple Ice of Kenyon Martin Jr. To 199. Nice one here for the Mabs. The Jet. Jason Terry. Sensational Signatures. These are really good. Immaculate great answer. Hyper Dennis Rodman. TJ Warren. Ben Wallace. And Charles Barkley. They have a, it's called Hoops Grid. That's what I use. Yeah. That's what Bryson showed me. Yeah. That one's kind of fire. Red Wave KG. Donovan Mitchell Fearless. Marcus Morris Hyper. Devonte Graham. Patrick Ewing. And Scotty Pippen Jr. Do a quick recap here guys. That was a fun, fun break right there. Autos. Josh Christopher. Jason Terry. Ryan Rollins. Mono Ginobili. Kelly Olenek. Ty Ty Washington. Jamari Walker. Hyper. Stila Draft. AJ Griffin. Gold. LeBron. Orange Wave. 260. Really, really well centered. And then a one of one. Mono Ginobili. On the Fearless for the Spurs. That'll do it for the break guys. Thanks again for the film. Up next I believe is Select H2.