We're very excited to have Oren Rescheff present his work. If you have any questions, please feel free to ask them. Take it away. Thank you, Andre. Thank you for having me. I'm actually really excited for this talk. I just realized when I was kind of looking for the seminars that many of the people affiliated in this seminar are cited in this paper. I'm kind of very excited to hear your comments and feedback about this project. So today I'm going to be talking about the effective entry on incumbent firms and platform markets. And when I talk about, I'll call it platform markets. I'm referring more generally to platforms or two-sided markets. These markets have been spreading to numerous areas of the economy. So for example, the largest firms in the world today, Amazon, Alibaba, Microsoft, Apple, Google are all platforms. And also if you think about the largest firms of the future. So here, for example, I look at the most successful unicorns. So these are private firms with valuation of one billion or more. Again, 60 to 70 percent of those are platforms as well. So that means that platforms are very big and are growing very rapidly, meaning that more sellers and more firms are operating fully or at the very least partially within a platform setting. The goal of this paper is to try to understand how is this rapid entry of firms onto the platform is impacting the incumbent firms and how is that different or similar to more traditional markets. Now, what I'm going to be arguing in this paper is that the ultimate effect of entry on the incumbents is theoretically ambiguous. And basically the trade-off that I'm trying to pose is that similar to traditional markets, entry usually is associated with a direct negative effect. So there is a decrease in market share driving down profitability. On the other hand, because we're in a platform market, because we're in a two-sided market, these indirect externalities, indirect network effects are going to create positive spillovers between competitors on the same platform. Now, just to set the intuition, I want to start with a stylized example. And as you'll see, this is very similar to what I'll have in the empirical setting. So I do think it's worth spending a minute or two on that. So what we see here is some platform, and this is a geographically constrained platform. I marked here free incumbent firms in red. So you should think about it as free drivers operating on Uber or free listings on Airbnb, or in my setting, these are going to be free restaurants that use their platform for delivery. Now, what I'm going to be interested in understanding is what happens to those red incumbents when we start adding more of these blue competitors onto the platform. So are they going to perform better when the platform looks like this, or maybe when it looks like this, or maybe when we start adding even more competitors like we have on the graph on the right. Now, in traditional markets, again, all things equal, we would usually think that those red incumbents would be referred to operating a platform that looks like this, rather than one that looks like that just to alleviate competition and reduce this competitive effect. However, the main argument I'm going to be making is that in a platform market, not all things are equal. In particular, if you think about it from the consumer's point of view, if there are not a lot of firms on the platform, it's not going to be very appealing to consumers. There's not going to be a lot of things to find there. So we're not going to see a lot of, in my case, virtual traffic on the platform. If we start adding more and more businesses, the values of the platform as a whole increases. It attracts more consumers onto the market, and that might potentially benefit all of the firms, including those red incumbents. So really the trade-off here is between the increase in market size and the decrease in market share. Now, empirically, it might seem appealing to just compare platforms that look like this, or markets that look like this, to markets that look like that. However, of course, the main issue is endogeneity. So entry onto the platform is strategically determined. And of course, there are other unobservables that we as the researchers don't see. Here's one example. It could be the case that there are more businesses in this market because there are more consumers, and not the other way around. So that's a reverse causality issue, for example. So how do I solve this empirical issue? Well, in this research, I use a pretty cool data set. I use proprietary data from Yelp and YTP. YTP is the Yelp transaction platform. It is a subset of the Yelp review website, which allows for transactions between consumers and local services. I'm going to be focusing specifically on food order and food delivery. Now, in order to really identify the effect of entry, I'm going to be exploiting quasi-exogenous variation generated by YTP's agreement with Grubhub delivery service. So for those who don't know, Grubhub to date is the second largest food delivery platform in the U.S. At the time, I think it was the largest one. I'll talk more about exactly what this agreement did and how it changed the platform. But at a very high level, what it did is to sharply increase the number of restaurants on the platform. So very similar to the example that I just gave. And I'm going to use variation in that shock, in the intensity of that shock across different geographies in the main specification that's going to be cities in order to conduct a difference in differences analysis. So comparing treated areas to control areas. Then if I'll have time to talk about it, I'll mention it towards the end. I'll estimate a simple structural model, which will allow me to extrapolate to additional market conditions. In particular, I'm going to be asking what happens when the platform becomes very, very large, so much larger than what I see in my data. So just to formally state the research question. So I'm going to be asking, as I said, what is the net effect of entry on the incumbent firms operating on the platform? And what I think are the most interesting effects are actually not just the average effect, but the heterogeneities. So I'll ask, how does this effect depend on firm attributes? How does it depend on platform attributes? And then I'll also talk about how do incumbents respond to increased entry on the platform? I think there is a question. Yeah, so I'm thinking a little bit about the conference that many of us were at last week, where John Vickers gave an overview of this paper on patterns of competitive interaction. And one of the takeaways from that stream of work is that prices can go up with more competition, even in the absence of this additional consumers channel that you're describing. So the idea is, in a spatial model, let's say, without somebody in the middle, we're competing hard because we're also competing for the guys in the middle. If somebody comes in between us, we give up on those customers, and that softens the price competition between us. So you can get potentially higher prices in particular with entry, even in the absence of the kind of channel that you're describing. So I'm a little bit curious about what your outcome measures are and if this is an effect that's potentially one you could identify. Thanks for this comment. This is actually very interesting. Thank you for bringing this up. I will say that in this paper, the main outcomes are going to be looking at are sales and revenue and not prices. In fact, one thing that I'll say from the get-go, and it's actually not in a presentation, is that I don't see a lot of action on the prices margin. And I think that this might be for several reasons. One of them being the specific settings. So these are restaurants. They rarely change their prices. So it seems that this might be the thing that's moving in. Also, because it's only changed on the platform and prices off-platform and on-platform might be, are usually the same. That might be the case. They don't shift their generally profit-maximizing price. So you should think about this setting as prices remaining constant, and this is actually what I empirically get. And only what I'll be looking at is their sales and their revenue. All right. Yes, David. Thanks, yep. It's helpful. We have another question. David? Yeah, thanks. So a bit more related to the channel that you want to discuss, namely more customers coming online, and specifically to restaurant delivery apps, when they tend to expand very quickly or get flooded, I shouldn't say tend. I have one anecdote in mind, which is the city that I live in. And there are at least two platforms, and one of them has many fewer restaurants, but the perception of consumers is much higher quality. And it's not just about the number of these restaurants. And I don't really think that the ratings within the app give a full story there. And obviously that's intertwined with the channel that you want us to think about. I wonder if you have thoughts on that. Yeah, so I will be looking only... So again, I think this is one of the main issues. We're just kind of comparing platforms that look like this to platform that looks like this, right? Because you said, look, there are other things that are different between these two platforms. I'm going to be looking at a particular platform and looking at an exogenous, exogenous change within that platform. So you can think about these things kind of being held constant. And in the next slide, actually, when I'll talk about the results, you'll see that your intuition is exactly correct. And one of the main driving forces is really the quality of these firms. I am going to be using ratings as quality and have some of a better measure, but you will see that there's a lot of action based on the ratings of the firms. And that's really what's kind of driving most of these effects. Interesting things. All right. So let me kind of push forward with the research question. So I frame all of my research questions in terms of the firms on the platform. Of course, when I have implications to all of those individual firms operating on the platform, that's going to have an aggregate effect on the platform as a whole. And I'll talk about that towards the end of the talk. So let me start with a preview of the results. So I asked the question. Let me answer it. I'm going to spend the rest of the time just talking about how I got from the question to the answer. So what is the effect of entry and the performance of incumbents? We'll hear really the average effect that I find is actually positive. I find an increase in weekly revenue of about four and a half percent. And this is really seems to be driven by the fact that once we add more firms to the platform, that increases the consumer base. Using the simulation, what I find is actually an inverse U-shape relation between entry and sales. When the platform is relatively small, which is what I see in my data, entry is going to benefit the firms on the platform. When the platform becomes large enough, this trend reverses and additional entry is going to be harming the firms on the platform. Looking at firms heterogeneity, then I look at heterogeneity by rating on that platform. And what I find is that actually the average effect is masking considerable heterogeneities. In particular, all of the positive effect is coming from these high rated or high quality firms. They get an increase of about 15% in weekly revenue. In contrast, low quality firms suffer from a decrease of about 10% in weekly revenue. So what we actually get is this divergence on the platform story with high quality firms performing much better and low quality firms performing much worse. Going back to those inverse U shapes, if you look at the bliss points, the points at which profits are maximized, you find that for high quality firms these happen in a much higher saturation rate. Meaning that high quality firms prefer to operate in much larger markets or alternatively prefer to have more firms entering their platforms. And of course I'll talk about the implications of that. Finally, look at an accomplished response to firm entry. We already said I'm not going to find effect on the price margin. That might be just a result of the specific setting that I'm looking at. I do find suggestive evidence of increasing investment in quality which I'll talk about and changes in advertising behavior which I won't have time to talk about today. So before I get to the actual project let me just super quickly yes Aaron, another channel that you might think about is the nature of the products offered. On these platforms doing better do they offer more stuff for takeout rather than eat in? Do they change the kinds of offerings that they have to try and cater for this business or do they try and differentiate more let's say. That's a good point. There's probably other directions. Thanks for that. Let me just start with super quickly reviewing the literature. Hopefully this contributes to the work on network externalities. The first thing I'll show you is pretty what I hope is a clean estimate of the magnitude of network externalities at least in this setting. I hope to contribute to literature on two-sided markets. I won't start summarizing the literature to the people who wrote the literature. I just want to maybe mention the two papers that I think are most closely related to what I do here. So the first one is Coward Al. I think the latest version that I found is a working paper from 2018. They look at bike sharing apps in China and they have a relatively similar design when they have an existing platform and they have a staggered entry platform and they kind of look at the spillovers and the competitiveness between them. Their idea is fundamentally different because the platform is the one supplying the bike so it's not really two-sided and the network effects are only happening between individuals that are moving the bikes around so their kind of forces are somewhat different. What I think is more similar is more recent paper in the JPE by Carl et al. They ask what seems to be a different question. They ask why some industries we have that the industry is kind of converging to one big platform and then in other industries we have segregation into different platforms. It seems like a different question but actually just the flip side of the same thing because their model it's mostly theoretical paper is again thinking about this tradeoff between network effect and competitiveness on the platform. Here I provide I think much cleaner evidence that is consistent with their finding as well and adding these heterogeneities and more granular questions. More generally of course this relates to other markets with positive spillovers to the effect of entry on firms and of course the importance of rating and reputation mechanisms in online markets. So with that said let me actually start talking about what I do. So as I said I'm going to be focusing on YTP it was launched in 2013 and I'll be focusing on the food order and food delivery part of the platform. Now importantly for my setting Yelp or YTP is not actually a delivery service. You should think about it as an information aggregator so think about it like Kayak does for travel services. It aggregates multiple travel services and that allows you to choose a flight or a hotel or a rent car. The same way works here. And these are specialized firms like Grubhub, Eat Street, Chownow Delivery.com etc. And then these firms are the ones that contract with the individual restaurants. So in a way the network of restaurants that you see on the platform depends on the network of partners that Yelp is affiliated with. So this is what it looks like on the consumer's end. If you look on Yelp and you look on delivery you're directed to YTP Here you can see the offering of restaurants around Warshu if you click on a specific restaurant you can get here the menu and you can finalize the transaction all within the Yelp website. Now in the background what's going on is that this order is actually fulfilled by Grubhub. So it's not fulfilled by Yelp but it's fulfilled by one of Yelp's what they call it is partners. And the consumers cannot control that but it's actually going to be a Grubhub key that picks up the food and brings it to your house. How do I use this platform in order to figure out the effect of entry? What I'm going to be using is I'm going to be using Yelp's platform level partnership with Grubhub delivery service which came into effect in February 2018. So basically what happened is they Grubhub and Yelp signed the agreement. Grubhub app and website were still operating you can still order directly for Grubhub if you had the Grubhub app nothing changed for you. However, for YTP users basically overnight the network of restaurants that they could order from substantially increased because now they could order from all of these Grubhub restaurants. And this is what it looks like empirically. So here we have the week with the week of partnership this is the 8th week of 2018 is normalized to zero we see here the total number of businesses on YTP there's a steady growth of course over time there is a very sharp discontinuous jump in the number of restaurants on the platform generated by this addition of Grubhub. Now luckily for me or importantly for my setting food order is fundamentally geographically constrained. So you cannot order food from San Francisco to New York and for the most part you can't even order food from San Francisco to Oakley. So basically you can think about it as geographically constrained. Now what I find is that there's actually a lot of variation in the intensity of that job across different markets. So some areas were affected a lot of some areas were not very affected by that partnership. Now what does that depend on? It depends on the Yelp network before the partnership. So how big were their existing partners? It depends on the Grubhub network and it depends on the overlap between the two. What I show you here is the variation in intensity. This is the change in share. It's the share of restaurants on the delivery platform out of the total number of restaurants in that city. The reason I share is that adding 100 restaurants in New York City is different than adding 100 restaurants in Berkeley. So I kind of want to standardize it by market size. I will say that in the paper I do robustness test with different definitions the results hold. What we see here is that the median is actually not or only marginally affected by that partnership but we do see a very long tail of places that got a lot of new businesses because of this partnership and other ways to see it is just to look at distribution in the pre-period, distribution in the post-period and we do see it shifting to the right but of course not homogeneously. So some places are more affected than others. So I'll talk about how I use this for the design but let me just mention what the data is. As I said, this proprietary data from both Yelp and YTP, I have data on all food orders from 2017 and 2018 so the shock happens almost in the middle of the data. I can't tell you because of the NDA a lot of details about the data. I will say that it's in the millions of millions of orders and users. I see item level data including the description and price. I aggregated for the main results to the business week level. The main outcomes that I'm going to be interested in are weekly number of orders and weekly revenue. For descriptive stats, again, I cannot show you too much because of the NDA. I will say that I have about 56,000 businesses in about 4,000 municipalities so think about cities and towns. One thing that I do want you just to pay attention to is these two lines. So here we have the fraction of businesses on YTP in the period before the partnership. On average it's about 5% and this is pretty consistent with what we know about food delivery especially in the pre-Covid area now it's probably somewhat higher and then the change in share is about 2%. Even after the partnership we're still 7% to 10% of the market are on the platform. I rarely see places with 20% and I never see places with 60% and this is one of the reasons why I actually need to extrapolate out a sample in order to figure out what happens when we get to these sizes. This is the data and let me talk for just one slide about the research design. In the paper I spent a lot of time talking to it and I spent like 30 pages of appendix talking about it. Today I'll just talk about it in one slide and maybe why it should be valid in the setting. I'm happy if you have any question to address any concerns you might have. The under observation here is the business week as I said, why is going to be the outcome of interest and I apply the inverse hyperbolic sign transformation so all of my results would be interpreted as percentage change. Post is a dummy variable for where we have pre-partnership or post-partnership and then treatment is going to be the treatment intensity which is either the continuous change in share so 1%, 2%, 3%, etc. A binary measure where I just compared those that were not affected at all to those that were somewhat affected and a short binary where I basically dropped the areas that were only marginally affected so if the change was .0001 I'm just going to drop you from the sample. The results are consistent across specifications. I allow for establishment level fixed effect and for weak state fixed effect to absorb time state shocks standard errors are clustered at the CT level which is the unit at which treatment is assigned. Two words about a notification so identifying the assumption here is the parallel trend assumption meaning that absent of treatment treated areas and control areas would have developed or would have trended the same way. This is fundamentally untestable in an assumption. In the paper I go for great lengths to kind of try to justify why I think this holds in my setting. The first thing is that really the fact that this happens at a platform level is extremely important. If we had different firms or different cities strategically choosing whether to partner up or not, that would have been a major issue of selection. Luckily this doesn't happen. Now you might still worry that maybe places that were ultimately treated or even acts anti different than places that were not. Again, I cannot prove this is not the case but what I'll show you and I'll also show it today is that at least in the year leading up to the partnership soon to be treated areas and soon to be control areas seem to be trending the same way. Really we only start seeing this effect following the shock. Now I also check whether there's other things going on at the same time at the same time as the shock. So I look at other outcomes that should not be related to the platform but should be related to the restaurant industry and the help in that market and it doesn't seem to have any effect and I do a myriad of robustness tests. I already mentioned some like how I define market, how I define treatment randomization inference, matching procedures, splitting the sample allowing for city differential city level time trends and the results seem robust to all of this. I'm happy to discuss this after I show the main result. So if there are no questions I'll go directly to the results. Great. Okay, good. So the first thing that I'll show you is the effect on the market size. So this whole premise of this idea is that we have increase in market size decrease in market share. These graphs are going to be aggregated to the city level and will include both the new businesses the one that were added through Grubhub and the old businesses the one that were already on the platform. So just to explain all of my graphs would look the same. This is the week with the week of partnership normalized to zero again and the y-axis should always be interpreted as percentage change. So 0.2 meaning 20% and minus 0.2 meaning a decrease of 20%. What we see here is the number of unique consumers in treated areas compared to control areas. We see a relatively parallel trends in the pre-appearance and then an increase following the partnership. The elasticity here is about 0.4 and of course when we have more sellers and more buyers we also see an increase in total transaction volume or total weekly revenue on that platform. Here the effect is about 0.5-6 if I remember correctly depending on the specification. But of course remember that the pie is getting larger there's more revenue to share on the platform but there are a lot more firms on this side than they are on that side. That exactly was the idea that we have an entry of more firms into the platform. So in order to figure out which one of those effects is stronger I'm going to be looking from now on only on the incumbent firms. These are the firms that were already on the platform. These are not the firms that were added through Grubhub. These are the firms that actually deliver with other partners and I'm going to be looking at firm level outcomes. This is weekly revenue per firm. What we see here is again a relatively parallel trends in the pre-period and then following the partnership we see a mild increase of about 4-5% depending on the specification in weekly revenue. Of course this is statistically significant once I aggregate it to pre and post. So it does seem like the increase in market size is dominating that increased competition on the platform. But as I said what I really want to get it is the firm heterogeneity. And the way that I do it is I define high status firm and low status firm based on the relative star rating, the Yelp star rating within city on the eve of integration. So just conceptually think about it as on the day before the partnership if you're above median in your city you're coded as high. If you're below median you're coded as low and I do again other specifications or definitions as robustness. I will say that I keep it constant across time. So even if ratings change over time you're still going to be always coded as either high or low and in the post period I'll explain why I'll do it in a couple of slides. Formally this amounts to running a diff and diff and diff design. So I have here again the pre-post and I also have here the triple interaction with firm quality. Here beta 1 captures the effect on high quality firms and beta 2 captures the effect, sorry and beta 1 plus beta 2 captures the effect on low quality firms. But before I show you the table let me show you the graphs. So these are the exact same graphs as the one I showed you two slides ago but now I separated it by high rated firms and low rated firms and what we see here is a divergence better. So high quality firms do unambiguously worse following the partnership an increase of 10 to 15 percent and in contrast low quality firms do substantially worse following the partnership and their weekly revenue decreases by about 10 percent. Of course you take plus 15 and minus 10 you get this about 5 percent that we had on aggregate. So really the answer to the question is not just what is the effect but on whom it depends who the firm is. It looks the same if I do it by sales instead of revenue very similar pattern and this is what it looks like in an admittedly ugly regression table. These are the effects on high quality firms I won't go through exactly each specification but you can see that they are positive and statistically significant and again the sum of these two coefficients is the effect on low quality firms which again is generally negative and statistically significant. These results are robust to whatever I throw at it and as I said I do throw at it quite a lot so most of the appendix is dedicated to these robustness tests but instead of talking about these I want to talk a little bit. Do we have any questions? Wonderful. Let me talk a little bit about what might be what else is going on here. The first thing I want to talk about is the role of cannibalization. One limitation of the data is that I only see what happens on the YTP platform so I don't know what's happening to toll for revenue and that is a limitation. What you can be thinking about is maybe this increased source of revenue on YTP is cannibalizing other sources of revenue. Let's say I always order from an orange pizza and I used to just call and order directly and instead of using that channel X I'm switching to channel YTP. In my data that's going to look like revenue is increasing but in fact real firm revenue is staying exactly the same. Again I don't have a direct way to address it because I don't see revenue off the platform. I will say that this is somewhat of an asymmetric argument because for local difference we see a decrease so that would be like some sort of reverse cannibalization. But nevertheless I did want to do something to see how big of a concern that is. The argument that I'm making is if people already know what they want to order and all they're changing is the channel for which they do it then we expect to see different search patterns. In particular we expect search intensity to decrease.  What I do is I look at several measures of search intensity. So I look at the number of search queries you typed I look at the number of businesses you clicked on. I looked at how long you spent on the platform and I look at how generic was the search query. So did you type orange pizza or did you just type pizza in my area? What I find is that search intensity does not seem to be decreasing and if anything it seems to be increasing which I think is more consistent with my model of people are first choosing the platform and then choosing what to buy on the platform rather than people are first choosing the product or the service and then choosing which channel they want to order it from. Other things that I look at that I will just mention here super briefly. One is the importance of differentiation and I think this came up so I look at whether you are a pizza place if another pizza place comes in or if another sushi place comes in how does that affect your performance and I can actually give you reasons why it would go either way it turns out that on average it mitigates the negative effect so the effect is more positive. Results admittedly are not as strong and then the importance of ordering of search results so where you are in the ranking of the search results as it turns out I for that would be a more important mechanism as it turns out this is not the main mechanism that's driving the results. One more thing that I want to talk about before I'll discuss the structural model is investment in quality so what we see here is the correlation between firms ratings and their orders this is in the pre-period and of course we see a positive correlation, higher rated firms sell more I don't think this is on its face very surprising what we see here is what happens post treatment here in the dashed lines we see that their relation is becoming steeper and this is consistent with what I just showed you high quality firms are doing better, low quality firms are doing worse so that means that now firms have more of an incentive to be high quality so if we think that firms can at least to some extent influence their ratings on the platform that now they have additional incentives to do so and what I test is exactly this so I look I run the exact same model but now the outcome being incumbents a subsequent Yelp rating on the platform and I find that there's a positive effect on average about 1% increase in ratings on the platform just to get an order of magnitude a 1% increase in rating is correlated with a 3% increase in weekly revenue so I do think it is economically meaningful now of course I call it suggestive evidence because I don't actually see investment in quality I just see the rating which is at best a proxy for quality I will say that I test for other stories such as rating inflation maybe different people are selecting into living reviews on the platform I look at what happens off of this delivery platform and the data doesn't seem to support any of these alternative explanations one thing that I at least for now I can check is whether they are actually investing in quality or maybe just needing more fake reviews so that's another way to effect your rating that's not directly investment in quality but it does seem like they are at least investing in the rating whether ethically or unethically and with that said let me briefly talk about the structural model and again the one goal of this model is just to see what happens when the platform becomes larger so what happens when we get to Amazon levels or what happens when we get multiple restaurants on the platform now as I said before the existing data is insufficient I mostly have a lot of firms up to the 10% of the firms in the market so what I'm going to do I'm going to estimate a simple model and then simulate firm performance under different levels of platform participation just to give the intuition for the model admittedly it's a very simple model so the way that the model works is consumers are going to have a two-step decision they first have to decide whether to join the platform and that's going to be I assume this is costly so you can think about it as a fee they have to join or in my case this is going to be like a hassle cost so you have to interact with the platform spend time figure out how it works maybe set up an account for the first time all of that is costly to the individual and they have to do it based on partial information so they have correct beliefs about what's on the platform but they don't know exactly which businesses and what items they have on the platform but that they actually have to engage with the platform which I think somewhat makes sense once they join the platform I assume searches is frictionless and I assume that products or in my case services are both vertically and horizontally differentiated so there is this rating component there like good restaurants and bad restaurants but also there's this match value so they have my favorite pizza topping or they have a vegan option what these two together create is basically a preference for differentiation and I know Andrew Hage has a paper about that so here I kind of get that from the model instead of assuming differentiation what that gives me is that I have I don't have to estimate differential differentiation preference parameter but instead I have to estimate the utility function which is relatively straightforward and the cost distribution the cost distribution or the parameters of the cost distribution it what pins down the relation between the number of restaurants and the size of the plant that's kind of what drives people into the platform when it's becoming larger so let me show you the main results so here this is what happens to the network effects so here we have the fraction of firms on the platform 100 being everybody zero being nobody you should think about this as a number of consumers in the market what I see in my data is basically this strip so I only see around 10 percent I will say that the model does very well in predicting the data in this area I have no way to test how it's doing in this area because I don't have good data here if I had to guess I will say it's probably doing pretty well here so so here I guess and probably not very well at the high end this is less of an issue for me because I don't think even in COVID times we don't get to 90 percent or 80 percent of the market so I think that that's fine and I do care more about this area that's the best I can what we see here is that the relation is concave so when the platform is small adding more businesses substantially increases the number of people on the platform but these network effects seem to be attenuating as the platform becomes larger and larger and what this does is create this inverse U shape relation right when the platform is small the network effect dominates the increase in competitiveness and all in all the effect is positive when the platform becomes larger the network effect become much weaker and this trend reverses if I do the same thing separately for low quality firms and high quality firms I get this pattern and of course high quality firms always do better but what I think is more interesting to see here is that the bliss points these maximas you can see that for low quality firms are for a lower number of firms compared to high quality firms so again that means that high quality firms should select into higher platforms would ideally want their platforms to become larger compared to low quality firms this is by the way what it looks like for the full model I can't really explain exactly what's going on here in the time that I have but we do see here that like for high quality firms the saturation rate the bliss point is about 40% 40 something for the median firm it's about 10% and we have this kind of monotonic increasing bliss points of course this I will admit that these precise numbers change when I change the specification but this general qualitative results are fairly robust so let me just sum up everything in the last 2 minutes and I think I'm right on time so in this paper I ask what is the effect of entry on incoming firms and I do think that this has implications to all of the stakeholders in this setting so for the businesses operating on the platform then we do see that there are maybe different implications than some markets in particular investment investing in barriers to entry or choosing to enter uncontested market may not be optimal especially if you're a high quality firms for platform and consumers I showed that in the paper and I don't think it's very surprising that profits of the platform well for the consumers is increasing with the supplier base what I think is interesting here is that there's an additional effect so adding more businesses doesn't just make your platform bigger it also on average makes it better because we now have consumers choosing better businesses we have businesses investing more in quality or in rating in this sense and we also should have a selection of better businesses onto the platform so all of this leads to an increase in the average quality on the platform finally for policymakers there's now talk about breaking up big tech and of course this would increase competitiveness between platforms this paper maybe addresses the downside of that which is the loss of these positive network effects to reduce competitiveness on the individual platform we don't get this increase in quality which might benefit everybody so basically that might have negative effects on all of the stakeholders in this setting so if there's like one take away here is that the effect of entry is ambiguous and it depends on the quality of the firm and on a platform size and I think I'm right on time so thank you very much thanks Aaron so I think next John will give his discussion and then there are a few questions in the chat maybe we can post both of those until John is done great thanks can everyone hear me okay? great I love my slides so let me just say first things first I really like this paper a great deal and one of the things I like about it specifically is that it's very connected to questions that I think actual platforms have to think about which is what kind of partnership should we be doing how much should we invest in trying to acquire more supply and so I think it really is kind of getting at the heart of a question that these sort of platforms have to think about pretty much all the time and that's kind of already a great starting point for some work and so now that I've said that I've praised the paper I can get into things that I think are not criticisms but just sort of thoughts that came up that if we were kind of looking at another paper this is what I would like to talk about next I'm not pointing out anything here that I think is not already in the paper but I think that is there a more micro level of competition where we can analyze and explore and so I think just a simple example it's easy for me to appreciate my pizza place might easily benefit from a new sushi place being available it brings more people to the platform it just sort of gets people are using it more intensively but it's just harder for my pizza place to benefit from a new pizza place being available and I think the results on how better firms benefit I think kind of the idea here is that we have two pizza places now the better pizza place is going to win out my question is not really a theory question is do we get enough kind of micro shocks like this so if you look at the distribution of how restaurants changes do we get enough kind of examples in the data where really a very direct editor for your kind of cuisine came on versus not something that we think it's really going to be that the network effects are going to dominate so this does require getting you into the kind of the micro structure but I wonder with the kind of amazing data we have here can we actually look and see what firms tend to co-occur and search results and kind of get this like okay this person had this choice set and therefore we're pretty sure that we can label these two types of firms as close competitors um you know the I think the kind of the story here is that demand for the platform is increasing in product variety and so if we went to that finer level of detail can we kind of explicitly characterize the effective variety on customer demand so you know you can imagine in a place that goes from four pizza places to five pizza places you know it just might not matter that much but going from you know Tibetan to one Tibetan might matter you know much more for the marginal new customer even the intensive margin from existing customers and I think Orrin might talk about this in paper so if I if I'm missing this my apologies but you know it would be nice at the level of the market to kind of show how much of an increase we got in variety however you can you know try to characterize that you know one thing I think is is specific to this context but I think it shares this context with a lot of platform goods is that um you know we have this bundles like no one wants to eat pizza every night no one wants to eat sushi every night and so there's there's a lot of kind of returns to having uh a lot of different variety and I think that in a nutshell is what kind of causes the cross-side externality effects to really dominate here but you know this isn't the case with something like you know ride-sharing where there's not that much differentiation and really the benefit is either going to come in terms of price or you know more probably in something like wait time um and then you know even you could go even finer than just you know whether how much variety matters and just kind of get down to is this like a within customer or between customer thing and so you know I think of like the Brinolson Bakos kind of world like you have you know is it you're each time you add a new restaurant you're picking up the customer that loves that kind of restaurant well probably not it's probably something more between customer preference for variety which I think would end up having some implications for disintermediation so you know if I only like one kind of restaurant I can sort of just go to them directly if I you know within my myself kind of vary in my taste for different cuisines over time the benefits to using a platform would just naturally be a lot a lot greater you know one effect in the paper that I thought was interesting you know Yelp is obviously an advertising platform and Orrin finds that the on platform advertising actually goes down and which kind of surprised me at first I was kind of thinking well you know you think you'd have more customers to get at and you have fiercer competition and so you want to grab more he proposes an interesting hypothesis though is that you know these better firms might I should say that the better firms are the ones that kind of have a decline and they're advertising their relative decline so the better firms are they writing up their marginal cost curve causing the returns to advertising to fall for them you know this kind of raises a natural question why not raise prices instead he doesn't find much on prices but he's got another paper I think that might explain this and there's other things he already talked about today that might explain this but then if it is a marginal cost thing you know does this effect sort of fade out over time you know do we see these restaurants kind of invest in more capital I mean I know he has this kind of reputation measure of improving quality you know maybe it's hard to get this kind of data but one idea I had is could you possibly explore this with transactional data on the firm component of wait time so with restaurants you know presumably if they kind of get really slammed with a big demand shock they kind of slow down and delivery times slow down perhaps if you see these firms that get this influx of new demand caused by the platform merger we see them actually you know have a higher throughput once they've had time to adjust so kind of this short run long run thing so I'm probably right at about five minutes so I you know mostly had a lot of questions but just to summarize I think this is a really fantastic paper I think it's going to be a really important contribution to the literature should I answer or should I wait for questions I don't know do you want to how should I do it maybe answer and then we'll collect questions so John thank you very much for these comments I really appreciate them I will answer some of these I'll answer all of them for some I'll have a bad answer for some so in terms of the more micro levels so there's kind of two things that I do in the paper to try to address it and again maybe there's more to do in this regard is first of all I in one specification I actually try to break up the market by food category and geography so that you know the St. Louis Pizza Market is a different market than the St. Louis Sushi market and then different market and so on the results are the same if I do it like this that the network that the positive networks are weaker and that makes sense because as you said people might go and look for pizza and sushi so there's positive also between treatment and control in this setting I will say one thing that I kind of glanced over today is that I look at the horizontal differentiation as well so if you're a pizza place as you said and the sushi place comes in and we actually gave the same example then it might be better for you because you're not competing for the same consumers but another argument would be well the people who are coming in because of the new sushi place are not pizza lovers so maybe you're not getting these people so theoretically it could be ambiguous I find again and maybe it's not in the version of the paper that's up I find it's actually on average if you get a different type of firms joining the effect is more positive on the incumbents one thing that you mentioned that I don't do is look at how like the increased variety attracts more people so if I add more pizza places versus if I add more sushi place that I didn't have how does that affect the network effect I didn't look at that and I think I can and I should in terms of the importance of variety so actually you gave the example of Uber and usually what I present I actually say that you need variety on Uber because you need the driver to be close to you so variety is not going to be whether you know which car to drive variety is going to be where they are if I'm in a city that doesn't have a lot of Uber drivers I'm going to use Lyft because I want I don't want to wait that long so here the value is going to be not waiting that much rather than so the match value is not if the food if it's food that I like but it's wherever they're close to me geographically I didn't look at within consumer between consumer I should do that I will say consumer data is not great it's very noisy but I can definitely do more to look at that I do have that data other measures of quality then again it's on my to-do list I should do it it's just you know it's a big project to do so I keep procrastinating but maybe I should prioritize that I think one of the downsides of knowing everything or having all the access is that you kind of unshackle your referees to ask you for everything under the sun because they know how much you have and I think that's the main the main point to your address but thanks there is a question from Eva I don't know if she wants to ask it sure hi I was wondering whether you can think about what's kind of behind these differential effects on low and high quality firms that could that be also driven by the fact that the entering firms are somehow low quality I mean this seems to be a little bit the case at least in Europe in some places that the firms that sell only via this delivery services are actually only take away places which have for example bad hygiene ratings and so on so I wonder if that could be behind that as well and that would also explain maybe this investment in quality result because then you would see that the firms that were before already above mean quality they actually gain in their ratings just because the incoming firms are very low quality so I don't know if you can check that somehow as well so that's a good question I will say a couple of things first of all just on average the restaurants that are entering the platform look very similar to the firms that are already on the platform in terms of rating I mean they think that like marginally lower it's like a 0.0 something less in average rating but on average they look kind of the same one way that I try to control for it because maybe some markets get more high quality enterance in some places do not is that when I code wherever you are high or low I also include the new businesses so this is where you are in the new or in the future distribution of ratings so that's kind of a way to try to control for difference between markets I did in trying to measure the network effects and I actually don't think it ended up in the paper because I just I thought it was pretty straightforward I tried to test whether if there are better businesses joining the platform that does that draw in more people compared to when bad firms joining the platform conditional the number of firms that joined there is of course a weak positive relation there so good firms on average weekly bring me weekly bring in more consumers okay thanks thanks Oslem I think you have a question yes thank you so I was wondering maybe Yelp also has some effect on by giving prominence differently to different type of firms after this partnership contract so maybe it's not about the entry exogenously happening but it's just the Yelp ranking of those firms are changing after the partnership and I was I also can expect Yelp to not to put so much down in the list those high quality ones that are already high ranked so maybe the ones that are really suffering from this lack of prominence are the incumbents that were not high status or ranked before the partnership have you thought about this or is there any way that you can see anything in the data yep so that's a good question I think the intuition you described goes in the other way if you say that they are they want to bring up the lower ones so that wouldn't make the I was thinking about you know if I'm Yelp and I want to change the prominence of course I don't want to put the really high ranked or high quality ones incumbents down in the list but those ones that are already low quality might be more suffering from this so it's the ordering of the search yes so I do look at that in the paper so admittedly actually when I you know when I got this result I thought this would be like one of the main things that's going on it turns out it doesn't seem to be really the case so first of all if you the correlation between the your star rating and where you are on the search result is actually it's negative right because high quality business as you mentioned are higher on the list or lower in terms of rank but it's only minus point one so it's a pretty weak correlation and because there's a lot of other things going on when you search for results when you search for a restaurant I did what I did try to do is I see your search rating so I try to control for that and see if that makes if that substantially changes the results right so it's all coming from just where you are rated and intuition is that when you add more firms the good ones they stay up but the bad ones they're getting pushed down so you just don't see them then that would mean that if I if I control for the research results this should go away it doesn't seem to be the case so I agree this was my intuition as well but that does not seem to be at least in my setting the main thing the driving this results does this answer your question yeah I mean one thing is thinking about how people are doing the search and you know thinking adding firms changing the way that they are ranking the search the other one is like I was really thinking about this objective ranking versus subjective ranking you know Yelp giving prominence to those firms that are more like having partnership with Grubhub now because of the fact that maybe there's some commission coming additional from this relationship so let me clarify so first of all I worked at Yelp I never had access to their search algorithm because this is like super proprietary but I will say that to my knowledge it hasn't changed significantly over time in particular it didn't change between treated areas and control areas right so everything that I do is different so I always have treatment versus control so if there are any changes to how they rank businesses they should be the same in both of these markets the last thing that I want to say just it's a correction I think it's important the businesses that I look at in the main specification are not the Grubhub businesses right so the Grubhub is not going to be they are not included in the main analysis I only look at the businesses that were already on the platform so just to kind of I think this is important okay thanks thanks so we're exactly at one hour so at this point I will stop the recording


Well, hello, hello, and welcome back to my channel. Today, as you can see, I have an Aaron Condren box, and I got a couple of new items, so I figured I would do a haul. There is something inside of this box as well that I am gonna be doing a complete walkthrough of as soon as this video is done. So, let's get in to the Aaron Condren haul. I haven't even opened it yet, you guys. Ooh, there's something jingling around in there. Let's be smarter than the box, Emily. Smarter than the box. Okay, all right, so as usual, they always have the little sticker, and then they have a little postcard as well, which is cute. And then... Dun, dun, dun, dun, dun, dun, dun, dun, dun, dun, dun, dun, dun, dun, ooh, ooh, ooh, ooh. Okay, interesting. I did not order these, so. All right, I did order a paper tape. Ooh, all right, okay. I'm super confused. So here is a new item. This is the Starter stencil sheet, which leads me to believe that maybe, just maybe, they're gonna be doing more stencils. I don't know. Oh, it's hard to see. Let's see here. Do I have, nope, nope. I don't know if we're gonna be able to see it very well. I will write down in the comments below, not the comments, but like the description box of what I got, and then there is a clickable link down below. It is an affiliate link for Aaron Condren.  So anyway, yeah, this is really cool. I'm excited to possibly test this out. Seriously, the reason I bought this was just like, share curiosity, because I was like, all right, I wanna just see this thing. So anyway, but yeah, this is one of the stencil sheets. So hopefully they'll come out with more. It's kinda cool. All right, and then I got this paper tape, which you can kinda see. It is the painted petals. You know, I'm having like flashbacks or something. Like, I don't know if this is, this is not mine. I don't know what this is, but this is not my item. This is like conferences, teacher work day, no school, hair appointment, doctor appointment, birthday, mani pedi, boozy brunch, SIM trip, this is not my item. So I don't know whose it is. Maybe I'll reach out on the inner webs and see if anybody is missing the sound of their box. All right, and then I've got some dates, which actually goes with this item, Olivia from my vlog, if you remember, is my friend's daughter, and she was over for the weekend. I was hoping I was gonna have this box while she was here, but I didn't. This is one of those academic planners, and I thought that she would really enjoy this. She is going to be in middle school, so I thought this would be kind of a fun thing. She loves my planner, and then the little date stickers go with this, so I'm gonna give this to her, and then this is one of those cool for school books. I actually gave mine that I had to Olivia because I wanted to give these to her at the same time, but since she was over, she was looking at it, and I'm like, I just take it. So this is actually gonna be mine now. Let me open it if you guys don't remember, but these are kind of cool. They're just different. Even though I'm obviously not a teacher or anything, and I don't have kids in school, some of these are kind of fun. There's some functional things. There's definitely things in here that I would like not use, but the majority of it I definitely think would work for anyone that's not in school. Exam, I wouldn't use. Project, I may or may not use, but meeting, I would use. Game, maybe. There's a bunch of birthdays and holiday. So yeah, I think this is just really fun, and I love the foil. Not the rose gold, but whatever. And then it's got some letters and numbers in the back. So yeah, like that. And then this is what I'm gonna do a dedicated video on. So let's kind of move this stuff out of the way. In PS, there's no packing slip, which is very odd. Very odd. I kind of want to go online right now and see if I got everything in my order. Okay, so these are the neutral monthly adhesive tabs, and I got a monthly deluxe planner since they are all updated and new. And then I got my 2018 budget book. So I'm actually gonna go through this in a separate video, but let's actually look online because this is weird. Let's bust out the computer and see what's going on. I'm just a little suspect because first of all, there's no packing slip and the second of all, I got an item that's like clearly not mine. All right, so I got the marquee monthly deluxe planner. Got that. Bold, okay, academic planner. Got that. Cool for school sticker book. Got that. Paper tape in the painted pedals. Whoops. Starter stencil sheet. Got that. And then I got the free designer dots with the academic planner, which was great. And then these came free as well. Okay, so I did get everything. All right, random. I think I'm gonna reach out on some of my social media groups that are like planner related and see if this is somebody's. And if it is, I will just ship it to them because I am not going to use any of these. So yeah, very, very strange, but you know what? I got all my stuff, so I'm glad about that. And I'm sad to whoever didn't get these, but hopefully erinconvin.com has already made it right with them and I can just reach out. And if I do find them, then they can just contact me. So anyways, let's put the computer away now. All right, so that is it for my haul. I hope you guys enjoyed. I know this is kind of a short and sweet little video, but I will be going into this guy coming up very, very soon. So I'm gonna be talking about like the paper quality and the differences from the 2017 version that I am currently in working on my budget. So anyways, I'll be doing that soon. As always, there is an erinconvin link down below. Like I mentioned before, it's an affiliate link. You sign up, you get $10 off coupon code emailed to you. It'll be a unique coupon code with like letters and numbers and everything. And then that's what you use to get $10 off of your first purchase. Again, it will give me $10 off as well when your item ships or when your order essentially ships. If you have any questions, make sure to post them down below. I will list everything out as to what I purchased. I will also list out what these are to, even though they're not mine, but I have ordered them in the past and I love them, they're great. So yeah, I think that's gonna do it for now. Thank you guys so much for watching. I really, really appreciate it. I post every Monday, Wednesday and Friday at 9 a.m. Central Standard Time. So make sure to subscribe if you haven't already and I will see you guys in the next video. Bye guys.


Here's an interesting wrinkle to Amazon a small business lender. Yeah, now it's interesting the best small business lender is square They lend against the cash sheets. I use square at Barstain Miguel when we don't need cash But we use their we use their caviar division for takeout But and the reason there are two reasons why square is moving up One is people theorizing that Amazon is going to buy them And the other one is that people theorize that Jack Dorsey is going to leave and put it in the hands of Sarah fryer They're brilliant CFO. I just adore so it's entirely possible that there's something going on at square as a Re-through with Amazon. They are the small business lender of the future. That's the one I like now I'm looking just so people know I am saying that AMD is better than people think and square is better than people think as Re-throughs to these issues about Nvidia and about Amazon


The Federal Road Safety Corps legal state command says it will comment a 4-day vehicle inspection starting from Thursday. The command said it had concluded arrangements to carry out the exercise, which would be its second quarter free safety checks on vehicles across all FRRC formations in the state. It's noted that the effort is to prevent breakdown of vehicles across the state, adding that the exercise would assist motorists detect different faults in their vehicles. The FRRC sector commander, Olushe Gung, Ogunbe Mide, disclosed that at the end of the initiative, a checklist ticket would be issued to encourage motorists to fix any defect detected in their vehicles afterwards.


contest what it's time for sports max at moment and it's straight from the training ground 8 6 13 and 7 no no no good to go rock that you're not okay alright right who's going a good finish there though from fixin


Hey guys, it's NateAK, the footer count, and welcome back to the channel. EA gave us an SBC yesterday that it's got a lot of people talking. Yeah, we're looking at that Raheem the Dream Sterling versus Fire or Ice, which one's the better one to take? But I will say, I think the SBC right now is missing one thing. And we're going to take a look at that today, as well as these FC versus cards that give extra chemistry boost. That is a part of this promo, guys. These players give extra chemistry to your ultimate team squads with different chemistry boosts based on which card you are using, which card type. And of course, we did have a lot of market movements yesterday, which I do want to talk about and more market movements potentially coming today, all based around evolutions and leaks. So a lot to dive into today. If you're excited for it, drop a thumbs up, subscribe if you're new. We're going to go objectives first and cover quickly all the content that was dropped yesterday for this new promo. We're starting off with the refresh of the versus daily play completionist, which is a W and 86 times two pack guys. These objectives are part of the way right now where if you're grinding this game and if you want to grind this game, something as easy as this, I know it's a little bit of a grind. But if you play a couple of squad battles, games a day doesn't take very much time, you're working on an evolution, you can be even, you know, getting a lot of things done, very efficient with your game play. You're playing champs, whatever this is worth doing.  You get a premium gold pack and a couple of 75 plus picks, free fodder. Right. So I love this. I think this is a W from EA. It's just you grind the game a bit and you get some rewards. Also, I'm kind of a fan of this objective just because it's super easy to get. And if you like scoring headers, this is the card for you. Right. I would even say this objective is clear. This is Tewani card, which I don't understand why it's the storyline version, really random. That is the storyline version and not like the versus fire or ice version. I don't really get it. But regardless, this objective card is better than the R10 Dovbeck player of the month, SBC, that we got yesterday, who was also from Girona. So that was leaked. We talked about how we knew that SBC was going to become him. Do this. Do this objective for free instead of doing the SBC. This guy's got four star, four stars, Tewani, 98 heading, 99 jumping, 97 strength and power header plus play style. He's also got acrobatic and aerial technical dribbler. He's even got power shot and chip shot. So for a free card, it's kind of in its own, I guess, place where it's pretty good, I think. So that's a decent objective that you'll probably get that done if you have a lot of league of players in your club pretty easily. But I think he's better than that player of the month that was dropped yesterday, for sure. Now let's go to evolutions. We were excited for the center back evo and it did drop. But now we're really not excited because of how little pace boost you get from this evolution. I'm actually a little bit disappointed. I think some people are even taking it as far as saying, this is the worst evolution of the entire year that we got yesterday. But it's all because of the pace boost, not being that great. Now, I will say there are some good evo cards that do come from this. But maybe this is one, like we say oftentimes, that you wait to see how it can work inside of a chain evo unless there's somebody that it fits that you want to get done right away. This evolution is really easy to get done. You just have to play three squad battles, win three squad battles and get a couple clean sheets. It's really easy and don't overlook some of the boosts. Like, I know pace was what we were hoping for. We only get plus five pace, but you get plus 10 passing plus eight dribbling and four physical. That is a pretty good boost. You also get the block play style and the center defensive mid position. Some of the top cards that you can put in this evo, as you can see, it's very, very downvoted and disliked, 85 percent downvoted. But I mean, you can put Alessandro Martinez in this. You can put a John Stones in this as well. In my opinion, do somebody or keep this and sit it around or do somebody that you like think could maybe be evolved in the future. Like this Alessandro Martinez. Yes, he does get a center defensive mid position that is added to him. But you look at this card and you're like 78 pace. Yeah, everything but the pace looks really good. And maybe he's actually going to be decent as a center defensive mid in game, medium, high work rates as a stayback CDM with pretty good defending dribbling, physicality and passing. Like that could be a really solid card. It's just not going to be super meta because he's not really pacey. Right. That's kind of a thing that the this evo doesn't give you. You can put Gabrielle in it. You can put Jimenez in it and get a little double upgrade here for Jimenez with keep up in his card here, but you still only have 78 pace. So that's the downside to it. Now, again, it's up to you on whether you want to do this one or not. But a lot of people are not happy with it because of the lack of the pace that you get from it. But the center defensive mid, I will shout this out. Once again, CDM, it adds the position to the card, which means potentially in the future for a CDM, maybe a center mid evo, a midfielder evo that could make some of those cards evolvable even another time. But they do put them pretty high rated like that. Romero goes to an 88. Los Angeles goes to an 88. So that's a bit of a down grader, like a kind of a bad thing about that evolution. But, you know, it's there. It's maybe something that you want to do. I'm going to save it, though. I'm going to let it sit, see what I can evo as a part of the chain later on. Now, we mentioned the player of the month, SBC, for Artem Dolphic. It's 70,000 coins. That SBC is not the one that we're excited about right now in this game. The team of the player pick is back. We're going to talk about team of the weeks and their prices in this video today for sure, because a lot of people are investing once again. And I know it's scary to invest in 30,000 coin team of the weeks. But this is the day that they were near their lowest last week. We'll talk about that. There's a daily FC versus challenge, which is cheap and it gives you a tradable pack. It's not that great of a pack, but it's a tradable one. And then there is the big SBC. Raheem the Dream Sterling. It was not Gavi. There was a Sterling leak that came out early yesterday on Friday before the promo, and he ended up being our SBC player, and it's not a player pick. I'm actually kind of glad for this. In FIFA 22, they gave us a player pick SBC for Pulisic. You had to choose one or the other. They gave us both SBC.  But this is the one that everybody is doing or a lot of people are doing because it gives you a Raheem Sterling that fixes the problem that Raheem Sterling has in this game. The weak foot, right? He always has a three-star weak footer, maybe upgraded to a four. But most of the time, it's a three-star weak foot, right footed, four-star skills, high-medium work rates. You know his dribbling is going to be good enough with 89 dribbling, 97 balance, 98 agility. His composure is low. That's probably a glaring stat on the card, also 64 aggression. But you get the Power Shot plus play style. You get first touch, acrobatic, and you get rapid. And it's really the Power Shot. It's the striker position as well. And it is the, still, the great shooting and the great pace and the 90 overalls there and the five-star weak foot that has a lot of people doing this SBC. Now the one thing that this is missing is, and it's not missing it anymore, it's almost like EA is saying, hey, you should do this SBC and you shouldn't do this one because they don't have a dynamic image yet for the 88-rated Sterling when I'm making this video. And he just got a dynamic image on the 88, the Fire version on this card. So that's kind of interesting. It's like, hey, this one's got the dynamic image. It looks more pretty. You should do this one, right? I think a lot of people are going towards this one though, just because of the Power Shot, the five-star weak foot, and also you can't discount the links. He gets an extra English nationality link, which is very good for chemistry. Now this Raheem Sterling sounds very fun as well. If you like five-star skills, if you like tricks or plus, this is the one that's maybe for you. He's not a striker. He's a cam also can play on the right and the left. He's got even better dribbling with a little bit better composure, but he has less shooting and almost all of the dribbling play styles. So with technical, I can't even imagine how good this card is gonna be. If they would have given him technical plus, then I think it would actually be a competition between these two. But I think that the Fire Raheem Sterling, I think the value is actually, I think it's decent. I mean, we were talking about this on stream, this Raheem Sterling, how much would he be if he was actually on the market? When I look at the rest as promo team and see how they're priced, like this card with these stats would probably be somewhere around 300 to 400K, to be honest. This promo of cards is pretty rare, as we'll talk about. So this is a Prem Stryker SBC, that at least very interesting because it's a middle to top tier option. I think a lot of people are gonna end up trying to craft. It is seven squads, which is a little bit annoying. And how many team of the week does it require? You need one team of the week, two team of the week. It's only two team of the weeks, which isn't too bad. You do get some packs back and it's just seven squads, which is a little bit annoying. But it's a really solid SBC. It really is. Like this is, I kind of think back, like when is the last promo that we had a crazy SBC that we could almost all agree on that was like good value? Yeah, during Winter Wild Cards, we had Hullet and we had Martinelli on that first Friday, but even those seemed to really expensive. Now this Sterling for 478K compared to a 600,000 coin Martinelli seems a bit interesting. So if that card suits you, I think it's actually a decent SBC. I don't think I'm gonna end up doing it because I'm still rocking the Harry Kane, I got the Mbappe in the squad. I'm happy with how my squad looks, but that actually caused some strikers to go down a bit on the market yesterday because people are doing that Sterling SBC and there's a lot of people excited for it. Now, let's talk about the team that is in packs once again. We are looking here at the versus fire team. Now, like I mentioned before, these cards give more chemistry based on what card type they are. Now, in my personal opinion, I actually think that the cards of the versus ice actually maybe help out chemistry a little bit more than the fire cards do because for versus ice, it's in here in the game. You can see it very explained very easily here. The versus ice cards, they give you an extra club link and you only need four of the same club to get to two chemistry points. Whereas for the same nation, you need five players to get to two chemistry points on each player. So I think that it maybe is a little bit better to have the ice players for chemistry, but I think I like the look of the fire players and the cards I'm there, just personally being honest. Guys, these cards, they're rare. Sergio Ramos is super rare, super overpriced for this card, but on the other side of things, you've got guys like Asenjo, which is a really sick card. Finesse shot plus five star skills and he has only 25,000 coins. That is a really great card there. So Soko is cheap, he's going to be good. Spinazola is one of the best right backs in the Serie A on a budget and he's 25,000 coins as well. I even think this Comma Vinga for 230K is a solid card for the price. Aerial plus is a little bit confusing, but that might end up being really good in the box when you're defending. He can win some of those header balls on corners and set pieces and stuff. That's a good card for the price. And I like this team. It's like a seven out of 10 team because there are some nice promo boosts in here, but there also are some bad ones. The bad ones are mostly at the top tier too, right? Sun, Neymar, their boosts are not that good, but it has provided us with a cool Trippier, really cool Ramos, Comma Vinga is cool, and then some of the lower tier options. It just feels like there's not very many cards in packs. And guys, these cards, just like we expected, are very, very difficult to pack. They are rare on the market. Yesterday, even in the first couple of hours, like Sergio Ramos, I was able to flip this Ramos card for 160, 170,000 coins of profit because there was only two on the market at that time. And the rarity with these cards is crazy. Now, I do expect some of them to drop off in price today, namely Trippier. I think Trippier's got a lot of hype for both of his cards. I think both of his cards are really interesting. A center mid Trippier with over the top, long ball pass, and then his pace is a little bit low, but his passing is great, defending Gribbling is solid. I think he's just a little bit inflated because of the rarity. And the same thing with the Ramos, with the Neymar, they finally updated the prices for some of these cards. Like Hyunmin Sun was extinct, Neymar was extinct. They're on the market now. I do think they'll drop off a bit today. Comma Vinga has a pretty interesting midfielder card too with price proven, you know, that's a solid card. But unless it's a lower tier card, I actually did pick up a couple of Ascendos because I think this is a card that people might want to go out and try today. PSG Lynx, Spanish Lynx as well. Finesse Shot Plus for 25K, that just seems like a really, really cheap card. Spinazola is one that moved really well yesterday too. I can see some of these maybe rising up a little bit into the morning today on Saturday, but probably a lot of them, unless it's the cheaper tier, will end up dropping off. I also picked up a Marta for under 300K because this is how I made coins on Ramos yesterday. Yeah, Marta's 289, that's where I bought her, 287. This card is 280, 90,000 coins, right? But the other Marta, which I don't think is as good, has the Trickster Plus, which is probably not as usable as Trivella Plus. This one's got more dribbling traits and it's got higher passing, I believe. The other Marta is like 150,000 coins more. Like this one, like the price discrepancies between a couple of these cards because they're so rare is really interesting. So that's why I picked up a Marta Ice because she's really rare. This one's 450K and so yeah, we'll see how that works out. Definitely going forward a little bit with the risky flips, but these cards are gonna be really good to trade like the past couple of promos have been because they are very rare. So let's talk about the rest of the market too, right? Because yesterday, it kind of went according to plan in a lot of areas as a lot of player prices on the market were moving upward because you think about the content in total yesterday. Okay, team in packs, really tough to pack. That always makes people a little bit disappointed. The SBC was good. There were some cards that moved. I was able to flip a Sam Kerr for a decent amount of profit because she was low and then she went up because it's a Chelsea link to Sterling. So there were some opportunities to flip cards like that, but really what did the best yesterday was what we expected to do the best, the Winter Wild Cards. Remember I had a Ronaldo Sanchez that was on my transfer list that I bought for 380,000 coins. I sold him at 430K. He ended up going over 440. Now he's back down a little bit, but these cards, as we take a look across the market yesterday, they did really well. Alaba's up 30,000 coins a card. Mukiella just reached 125, 126, still rising. You know, even some of the icons, right? Doing really good. I did end up selling the Doku's, right? We've been following Doku's price all week. And the reason Doku, I sold him for like, I basically made here on all these Doku's in total on these five. I probably made about 25 to 30,000 coins. It was not that much. It was not worth the investment. I still profited, but the reason I sold him is because there was some leaks for a right wing Eva, which we're gonna talk about a little bit more. And that held his price in check and also hurt some other right wings on the market, especially on the middle of the top tier. But nonetheless, the Winter Wild Cards that went out of packs did very, very well yesterday. So if you invested in any of these GG because you're making some coins, Goretzka went up, Kunde started to go up. It was that middle to high tier, the more rare ones. And some of the really low ones too. The icons did good. Shira's 154, Yashin's back over 1.05. Loudrup had a huge rise. He's up like 30K. GG's if you invested there. For a lot of the lower tier part of the market still, I think the market should do fine still today and into this weekend. It's Monday that has me a little bit more worried, but I think it's still gonna be a good time to trade on this market today and tomorrow. People are buying cards and they are wanting to spend coins on players. Now we're gonna look at fodder right now. EA has been running lighting rounds and they ran the lighting rounds that make fodder drop. The 84 times 10s, they're actually, they just ran another set of them a couple of hours ago. This is the pack that really hurts the player prices of fodder, like real bad. Like you just saw it right there. 84s right now are 1.5K. That is almost minimum price. Like they're so low for 84s and it's just like, man, when is this fodder going to go up? I still believe in it. I still believe in fodder guys. I think that now once again, there's not a lot of people that are opening packs. This is what we talked about yesterday on stream. There's not as many people opening packs, but there's still gonna be SBCs to do, especially in the middle to low tier. Range 84, five, six, seven squads that come out for packs that people wanna save for team of the year that we should be seeing in the next two weeks. That's why I still feel very positively about 83s, fours, five, sixes, sevens, even 88s. I picked up some 88s yesterday on bid for under 16K. Feel really good about those. 89s under 30, feel really good about those. Under 50K for 90s, feel really good about those as well for maybe an icon pick or pack that would come out tomorrow. And even as soon as today, there could be some fodder movements. It was last Saturday that we had the 83 times 10. When we were able to grind that two weeks ago, we had the 83 times 10. And that did make some team of the weeks go up a little bit. So you've got people that are investing in team of the weeks once again. These were as low as 29K yesterday. They're now approaching 30 to 31. And basically the situation here is will team of the weeks go up this week? I think everybody's asking this and a lot of people are investing. The team of the weeks can go up this week because EA keep requiring them. It just depends on how many SBCs we get and what SBCs we get. If you think about it logically, we're two weeks before a team of the year we're fresh out of the Winter Wild Cards promo and EA is still giving us fodder, right? They're gonna put out SBCs. It just depends on what SBCs they put out and how hardcore they go this week, which is two weeks before a team of the year at draining, trying to drain people's coins out of their clubs to give them packs that maybe they wanna save for team of the year. So that is how I think people are trying to look at this and investing in team of the weeks. If you want to risk it, it's big risk, big reward because team of the weeks have done pretty well almost every single week. It seems more sure than it has ever. It still seems weird to buy team leaks for 30,000 coins. Trust me, I don't like spending 30K for a card that should be probably 15 or 20K, but here we are at 30,000 coins. They could go to 35 in the next two, three days. They could go to 25, but I think that's kinda your range. So it's not that big of a risk. And if you do it by here at 30,000 coins, I'm talking myself into this a little bit, honestly, because team of the weeks have been pretty consistent the last couple of weeks. And yeah, they seem expensive, but EA keeps requiring them. So I'm not gonna say I'm going all in on team of the weeks.  So we'll see what I end up doing. The team of the week player pick is once again the better place to always go and get a team of the week card because it's literally 18K to do the team of the week player pick SBC. Do this with your free fodder from objectives. You don't have to buy a team of the week to get an SBC done, but other people do, because it's quick, it's easy to get the SBC done. You still have player SBCs like icons and Mbappe that are requiring team of the weeks. Sterling requires a team of the week, right? Two of them. So there's still demand for those players there and people buy them off the market. So I'll let you decide whether you want to buy or not, but that's kind of the situation there. Yeah, team of the weeks, most popular investment, but also with that most popular investment could mean the most amount of panic if there's not good content, it's up to you. I'll let you decide. Let me know what you want to do down in the comments, but if you get them under the 30K or right at 30,000 coins, it's probably an okay price if you're gonna risk it this week. Let's talk about content today and what moved the market even more yesterday. First of all, I tweeted this yesterday. I don't know if you guys saw it or not. Before the content yesterday, there were some crazy movements on right wing cards like Philippe Anderson, doubling in price, Kudus, Sterling Inform, David Neres, doubling in price, Gold Rodrigo, going from 4,000 coins to 6,000 coins, then yesterday from 6,000 to 8,000 coins, continually inflated in price because of leaks or an EVO that is incoming for it looks like right wings. Now we've just had a leak tweet from Foot Police that says two evolutions are coming, both for the right wing position. One of them is Finisher, the other one is cut inside. Now this one seems kind of interesting to me and maybe the whole point of this is that it's kind of like the versus promo. Maybe it's Finisher and cut inside so you could have like almost the same requirements or similar requirements but you get a different boost depending on which evolution you do, kind of like a versus ice or versus fire. That's my guess, I don't know. Again, position right wing must not be center mid and that he knows some requirements. Max playstyle plus is zero. Players gain a weak foot and a playstyle plus and the expected release date is today on Saturday. The other one is cut inside which is position right wing and that's all that he says for that one. So if they do the evolutions here like they have been doing during Winter Wild Cards it'll be evolution today and an evolution tomorrow on Sunday. We'll just have to see but that's why some right wings have gone up like crazy on this market. And what I mean crazy, I can like these are cards that have doubled in price are going up crazy, crazy amounts. There are so many golds. Like you just look at the top of foot bin and you'll randomly see a card is like, wait, why? Why is he up here? Why is his card? There's not any right now as I look at foot bin but it's like why is that card up at the top of foot bin? Well, it's probably because he's being invested in for an Evo and then you look at the graph and you see that he's up like double in price like the Rodrigo that we were just looking at and you kind of understand what's going on there. So watch out for that Evo today. If it's really good, it could make some prices crash but as we always say, sell into the hype. It is always worth selling into the hype for these evolutions guys. If you invested in any of those, even if it fits what you think the Evo will be, it is always worth selling in the hype because you can be safe rather than sorry if the evolution is different than what actually comes out or if it doesn't even drop today then you would wanna sell in the hype for sure. Now, SBCs today that could move some things. First of all, if I had to guess on an SBC we get today, I think it's this one. I would have taken a guess that it's gonna be Danny Wollbeck flashback. He kind of has to be today or tomorrow because EA put out on their weekly content tweet. This was gonna be in the next week and that runs Monday to Monday or Monday to Sunday. So I think this is today or tomorrow. Seems like a Saturday type SBC to me. If he does drop, I think he should be halfway decent value. It's kind of like, wait a second, they're gonna drop an English striker flashback just after they dropped a regime sterling who's also really good as an English striker. Yup, that's what it seems. I think Wollbeck would be a lot cheaper though. I'm like, I'm talking maybe 150K or below that. And if that's the case, if his card is good, even if it's just above 90 pace with decent shooting and they upgrade a weak foot and skills, it's gonna be a little bit popular because cheap prem striker SBCs are always popular. I would be careful with cards maybe like Carlos Tevez, cheaper Premier League strikers, just cheaper strikers in general could take a hit as this would be an SBC for Wollbeck that would be widely completed if it's a cheap price and at least a halfway decent SBC. You look at his regular gold card. He's got rapid playstyle already. They'd have to add some playstyles in there. Six foot one, high, high, three, three. So they gotta make it a bit of an upgrade. But with a flashback and the flashbacks we've seen this year, they've been doing decent upgrades. So that's an SBC I think could be today. I guess technically there's a shout for Gavi as well. But I think this is too big of a name of a player to be dropped on a Saturday. To me, it's maybe a Sunday, maybe sometime during next week, but honestly, this could be an SBC that would come out on a Friday. Maybe next Friday if there's gonna be a team too, which there might be depending on how this pans out and how it looks, this is an SBC that could be sometime in the next week, unless they're really not gonna upgrade them that much. And in that case, then it might not be that great of an SBC. We'll see. But that's one that I'm not investing in. We thought that was gonna be yesterday, but then the whole sterling thing happened instead. I don't know if Gavi's gonna be today. I guess there's a shout for it, but maybe not. There's also some rumors about an icon Maldini SBC, which seems crazy, but those are not confirmed. Maldini was dropping a lot yesterday and there were a couple of cryptic tweets that made it seem like Maldini might be getting an icon SBC soon. Watch out for that. That would be pretty big if he was to be leaked. That would be an icon SBC that would be worth investing for in a lot of the high tier fodder, because that's a center back that a ton of people would wanna do during this time period if it's out at a decent value during team of the year, especially. They'd love to craft that. So watch out for that. And then we do actually know the mini release. You might be like, Nate, where was Bobby Firmino? Where was Uppamakano? This is the official mini release of players that will be coming out either today or tomorrow. If I had to guess, I'd say tomorrow. I know Winter Wild Cards mini releases were on Saturday. Could be today, could be tomorrow though. And I don't know if those are the official cards. I think it is. He says official Uppamakano looks decent. Firmino striker and Firmino center mid. That's the center mid Firmino looks pretty dope. Not that big of a difference between the cards except for the defense and the play style. But that center mid Firmino does look pretty nasty. Kinda reminds me as of people put in the comments the ultimate screen Firmino from back in the day. So that's a nice one there. Maybe today, but also maybe not. And then last thing I wanna point out is EA tweeted about the team of the year announcement, which is gonna have a kickoff show, which is I'm actually kind of excited for this because Jude Bellingham and Puteas are both on this kickoff show. It's happening on Monday. So I don't know exactly what time it's gonna be, but we'll hopefully be able to watch it live on stream. That sounds really cool. As you can see both Puteas and Bellingham as a part of that with the team of the year in there and Jamie Carragher, Mike Richards, and probably gonna be a pretty fun show. So kind of excited for that. It's really cool. They're going out with some cool features for the team of the year announcement and team of the year votes. And yeah, again, really good times to trade right now, guys. I bought these hems for $315 yesterday. Made some good flips on that. This Ramos was the biggest flip. Bought it at 940, sold it at 1.1. Bought a Comma Vinga at 280, sold it at 330. So really good flips right now in this game. I'm gonna try to make some more flips today. I'm gonna try to stick to low budget though. Low budget and really rare, like with this Marta, because that seems to be working the best in this market right now. But if you enjoyed this video today, if you're excited for content on Saturday, and if you like this versus promo, let me know down in the comments. But of course, drop a thumbs up on the video. Subscribe if you're new, and I'll see you guys in a video tomorrow. It's been Nathan with Accountant. Catch you guys there. Peace, out.