Fitness + Health + Wisdom + Wealth

Consensus: Evidence Based Search Engine – Eric Olson


Guest: Eric Olson

Release Date: 11/07/2022

Welcome to Trulyfit the online fitness marketplace connecting pros and clients through unique fitness business software.

Steve Washuta: Welcome to the Trulyfit podcast where we interview experts on fitness and health to expand our wisdom and wealth. I am your host, Steve Washuta, co-founder of truly fit and author of Fitness Business 101.

On today’s episode, I interview Eric Olson He is the founder of consensus. What is consensus? It is an ad-free specialized search platform that utilizes NLP, which will explain natural language processing. It’s a form of artificial intelligence to provide aggregated scientific insights for users from peer review sources.

Typically, when you go to a search engine like Google, let’s say, and you type in who is the 23rd president, no problem populates the answer you believe it? When you ask a more intricate science-based question, how exactly do you know that that answer is right? There’s a lot of, you know, esoteric and nuanced things going on there.

Well, Eric decided to build a search engine, a platform in which you’re using peer-reviewed sources and papers that are populating for the science questions. He had the itch to say, Okay, if I want to ask a science-based question, how do I know I’m getting the right answer back, other people must be thinking this too and needing this too. So he’s going to talk about both the sort of marketing side of consensus why it’s important, and why we need it.

Also the technical side of consensus how exactly it goes about and works. I think it’s a fantastic thing that he’s putting together. And I think also, from a sort of a macro standpoint, what we can take from this conversation, we talk about everything, honestly, from football, to, to artificial intelligence in this conversation, but what you can, what you can take from this also is we don’t know what’s coming down the road at any given time, especially when it comes to technology, whether that’s a new social media being developed, or whether that’s new artificial intelligence and technology.

So you have to make sure that you’re prepared for this in your business models. And sometimes I know you can’t and this sounds contradictory, you can’t prepare for what’s to come. If you don’t know what’s to come, I get that. But having, you know, well-rounded skill sets, and not putting all your eggs in one basket, not just from a financial perspective, but from a business perspective. And from certainly a technology perspective, I always talk about you should have your own website because you have autonomy over you and can control your own website.

At any given time, social media can pull the plug on something, and they can change the game, they can change the rules. And if your whole business model is built off of Instagram, or Facebook or LinkedIn, and they changed the rules, maybe they don’t allow you to put links up or they don’t allow you to monetize well, then you’re in trouble. So it was again, it was a great conversation.

I really enjoyed speaking with Eric about a little bit of everything from technology to football. With no further ado, here is the founder of consensus, Eric Olson. Er, thank you so much for joining the Trulyfit Podcast. Why don’t you give my audience and listeners a little background on us your credentials and your intellectual pursuits? And what brought you to do what it is that you do today?

Eric Olson:  Yeah, no, Thanks for Thanks for having me, Steve. So yeah, my my background is I went to Northwestern for undergrad where I was actually a football player. My undergrad degree was in a pretty traditional like business, organizational change degree. Then it was in my fifth year there, I got my masters in predictive analytics. That kind of started me on the journey of falling in love with data science and analytics.

From there, I worked a job at DraftKings for a little over three years, I worked in the analytic analytics data science department. Bbasically, the most interesting thing I did there was I built our player skill models. So basically taking all of like the user bet history data, and using that to predict the skill level of player so like, what is your expected margin if you were to bet, you know, a million bets. So you know, to kind of parse through them variants or when on a parlay because that is not likely to continue over time.

You know, that kind of four-year period of getting that master’s working at DraftKings kind of made me fall in love with all things data, and the general idea of taking giant sets of complex data and using that, to spit out actionable and usable outputs, and consumable outputs. And that is kind of the thread that somewhat led me to a consensus, which is what I do now. So consent, I’m the CEO and co-founder of consensus been working full-time for a little over a year now.

And what it is, is a search engine that uses artificial intelligence to find answers in scientific research. And you know, the being qualified to do it kind of came from the data science world, but the background the much further back kind of like backstory of how it all started is. I come from a family of academics and researchers and I was the jock in the family. So I’ve kind of always had this, this outsider complex to science, but I always loved science, but I was never an expert myself.

I’ve always loved consuming content that was effectively experts going through what the research says about subjects, and I wish I could have that on demand. I wanted to be able at the tips of my fingers, be able to you know, whatever But I have a question. So what does the science actually say about this? And that was an idea I had six or seven years ago and then decided to finally attack it during COVID.

Steve Washuta: Well, most people think that’s not an issue. They think that they can already do that, right? They think, okay, if I need the science, I just go to my search engine, I type in what I need to and it comes up. So what is the problem that you see that you guys are actually solving that the general public doesn’t understand?

Eric Olson: Yeah, no, great, great question. The problem is that the ways that we traditionally consume information like your Google is not designed to give us good information. It is an advertising business at heart. And there are a ton of ramifications of that there is the explicit ramification of if you say, what is the best supplement for anxiety into Google, the first thing you’re gonna get back is a bunch of advertisements from athletic greens.

And you know, that is hurting your experience very explicitly like you’re seeing the ads. But there’s actually a TON TON more going on behind the scenes that also is making it not be designed to get into the best information. Because they’re selling ads. Their incentives are to keep you engaged and show you things that you’re most likely to click on.

That means they’re prioritizing popularity. They’re not prioritizing reputability. So nothing that you’re shown on Google is really the goal has really been to give you good information. And it’s mostly editorialized content, and blogs and articles, as opposed to peer-reviewed studies.

Steve Washuta: And you can gain the system by having proper SEO search engine optimization as a company, right. So I can go ahead and pay for backlinks and spend a lot of money to climb the ladder. So you guys find a way to kind of circumvent that do not allow people to do that.

Eric Olson: Yeah, we don’t show any ads at all. So nothing that decides how things are ranked on our search engine has anything to do with the popularity of the SEO ranking or the domain trust of any of anything. And then beyond that, it is also only a peer-reviewed study. So it’s only scientific documents that are under search engines, and we have no blogs or editorialized articles.

Steve Washuta: Interesting. So what so is your term? So my next question is gonna be you know, science is kind of a, you know, a blanket or umbrella term. Yeah. That’s, that’s number one. Are there other criteria that you have that are like musts, if it makes it into your algorithm, for example, if I type in something, let’s say I wrote a paper on something, and it doesn’t show up on your search engine? Why not? What did I potentially not have in my paper that didn’t make the cut for your search engine?

Eric Olson: Yeah, so that isn’t exactly the right way to think about it. Because the way that our data is populating our search engines, we have a partnership with the data, scientific data aggregator, so we have a partnership with an organization called semantic scholar, which is a research data aggregator out of the Allen Institute for artificial intelligence, which is the Microsoft co-founders Phyllanthus, philanthropic research organization, and they have access to about 200 million peer-reviewed papers.

And so we have a license with them, we have a partnership, but we’re actually able to have all those papers ourselves and use them in our product. So what exists in our product is what symmetric scholar has access to which the criteria for that I don’t know, every in and out of this, they’re all peer-reviewed papers.

But it isn’t specifically, you know, you did something wrong. If a paper that you’ve published it might be there might be copyright lights rights to that at Elsevier, and they haven’t let some index scholar have access to it. So that’s why it wouldn’t be in our search engine.

Steve Washuta: Okay. And then once that populates, is everything free? Or are there paywalls to get into each individual article based upon where the article is coming from?

Eric Olson: Yeah, so you will still see the results, even if the underlying article is paywalled. The majority of our database is open-access. But there are somewhere if you follow the link to the full text of the paper, if you don’t have a subscription yourself, you don’t get it just from using us. And we want to continue to prioritize Open Access papers. And the good news for us is, the trend is on our side.

Here are a few you’ve seen in the past few weeks, there was legislation that was passed, that every scientific study that has been funded by federal dollars has to be open access by elite, but by I believe 2025. So the trend in science is to make things more open, which, you know, is the way that it should have always been and in our opinion, it’s a public-funded enterprise and be able to be available for anybody to consume. And we want to help make that easier for people.

Steve Washuta: Is there a fix for the other big tech-related industries, whether that be sort of your search engines, or maybe just your social media platforms? Do you think there is going to be a fix? Are there going to be more companies like yours coming out? trying to solve that problem? Or do you think there’s just there’s nothing you can do? It’s, it’s already here to stay. And unless the government goes and takes control over it, which we probably don’t want, there’s, there’s just a, it’s a free market, they control their business.

Eric Olson: Yeah, great, great question. So they’re not going away. Right? There’s such a part of the public ecosystem. You know, they’re, they’re not inherently evil by any means. Google shows potentially showing that the information you it isn’t on purpose, per se. It’s not this back-channel conspiracy. It’s just, they’re all advertising-based companies. And that just makes them fall into their incentives to try to drive engagement.

It just so happens that sometimes the most engaging content is misinformation, right. Like, that’s why things get prioritized on Facebook that is misinformation, because their algorithm note is saying, This is what you’re most likely to engage with. And that’s what they prioritize. But they do really, really great at other things, right? Like, if you want to just type into Google, just ask him a widely known fact and say, you know, when was cleaning Queen Elizabeth’s birthday, like, that’s what Google should exist for? That’s great.

And we’ll give you that answer. But it is our opinion, that there as there will be disruption in a lot of these for a lot of these big tech players. And the disruption will come from companies that have a value prop that is antithetical to them. So you know, I’ll speak to search, I can speak a little bit to some of the other social media and some of the things we’re seeing, we think that premium ad-free subscription search is going to disrupt Google over the next, you know, 10 to 20 years.

Yeah, not saying that Google’s gonna go away, they’ll still be a multibillion-dollar company 10 times over. But as more and more people learn about the filter that they get their information through. And as, as the technology that allows for this to happen, the world of natural language processing, which is exploding right now continues to get better and better.

The offering of these premium search engines can become more viable and more enticing for people who they’d be willing to pay a small fee for it, where they no longer will have to see ads and the product can do you know what it’s really intended to do, which would be in our case, given you good scientific information, but there’ll be other premium search engines that will pop up.

And, you know, I think the first step of this was like the privatized subscription search, like DuckDuckGo, and is another one, those are giant companies already. And all they really do is make it DuckDuckGo is just privatized. Niva is like a user-curated search, which also is private with no ads. And they’re, you know, billion-dollar companies just because their value prop is, hey, we’re not Google that showing you stuff.

Then I think there’s going to be this new way of avoiding what we hope to be a part of, which is the next step, which is where they’re really helping you accomplish some specific task. And they do that really well, where you pay a small fee. And then in social media, you’re starting to see this too. The, the, two hottest apps right now, are one of them’s called be real. And the other one is called gas. And both of their value props are basically just humane social media.

So be real, if you’re familiar with be real, it’s this like kind of Instagram knockoff, in some ways where you can only post in a two minute window per day. It’s meant to just be like, you can’t, you know, set up this intricate Instagram, beautiful post that just like shows, show your friends what you’re doing.

It’s exploding, like, all my friends have it and it’s got millions of users. Then gas just released recently.I believe it’s like the number one app in the world right now. And it’s a kind of go into the Facebook route. They’re targeting young people first and only partnering with schools. And it’s like a, an anonymous compliment app. And they’re kind of intermixing some things like Facebook with some things about Tinder, like you can have secret crushes and admirers.

But they only let you say it’s a gas you up, like is you know, the young people phrase. And it’s all about like, compliments. So anyway, I think the interesting part of all that is there are these new things popping up that their whole value prop is just, hey, we’re not big tech, and we don’t have these, you know, our ethos is like, promote, you know, goodness in the world. And people are loving it.

Steve Washuta: Yeah. And you know, it’s such a moving target. When you think about the demographics coming down the road, what programs are they using now with social media? That sort of annoying them? Right? I would have never thought that nobody was going to use Facebook. When I first got to college, that’s when Facebook exploded. And now you know ask a kid who’s 18 years old what you know if he’s on Facebook and no is typically the answer more often than not right.

So like these things are just moving targets because of whatever is trending or too much stuff has been thrown at them. So there’s been too much of this fake sort of Instagram lifestyle. All Posts that people are just getting sick of. So now that goes the other direction, right? The younger people coming in. So we don’t want that. We just want two minutes of a quick, this is what our real lives look like. And it’s sort of hard to predict that. But it’s, it’s fun. And it’s exciting.

Eric Olson: Totally. Yeah, the Facebook thing is hilarious that somehow the app that started with students in schools, so it’s turned into the app that it’s only like six-year-olds on this trajectory,

Steve Washuta: most of the messages I get on Facebook are either former clients of mine or family members over the age of 60, telling me that their account was hacked. And what they mean by that is that they just clicked on the wrong thing, and somebody sent them over your email. So I don’t spend a lot of time on Facebook. But you mentioned and NLP Can you describe that in sort of layman’s terms for my audience?

Eric Olson: Yeah, but natural language was in. And it is a subset of artificial intelligence that has to do with human language. So it’s it’s machine’s ability to understand and process and analyze text and language, and the way that we’re seeing that pop-up, and what the reason why it’s exploding is the advent of large language models.

And I’m sure if you’ve been on Twitter, and you follow any people in the tech world, you’ve seen the tool called Dalai where you can type in a prompt, and it will spit you out an image, an AI generated picture. That is the underlying technology is large language models there. And basically, they’re these giant, giant text models that are trained on basically the entire internet worth of text, where they basically, the way that they’re they’re taught is they shown, you know, billions and billions and billions of words across the internet.

And then they’ll blank out certain words, and they’ll ask the model to try to predict what word is missing. So then over time, it learns, like the way language works, and that over basically the entire Internet. And that is it comes pre trained with that. So like the big innovation is basically these models have this underlying knowledge of that. And then you can take them in, teach them how to do specific tasks in a variety of different tasks. So that comes with this, like base layer knowledge.

And then you can fine-tune it to do specific things. Like in our case, that’s reading through scientific documents and pulling out the interesting parts where authors are stating their claims. In other cases, it could be creating marketing copy where you say, I want to describe the product with a happy tone, that is a fitness podcast, and then it will spit out you know, generated marketing copy for something like that.

Basically, yeah, it is able to do that, because it comes with this robust understanding of knowledge. And then you can train them to do your specific tasks. And yeah, it is exploding everywhere in the tech world right now.

Steve Washuta: Will there even be bloggers? I mean, what’s the point of me sitting down to write a blog, if I can simply just say what you did it can scan the Internet and write the blog for me? Does that just take that job away?

Eric Olson: Potentially, yeah, so one, yeah, well, it doesn’t do that, in real-time, scan the Internet, that is actually how it basically comes to you. Okay, and then you would give it examples of prompt and output, and then using that underlying knowledge, it would then learn how to do that task, if that makes sense. Yeah. And yeah, it’s, it’s funny that we, everyone used to hypothesize about AI. And, ya know, the last place that it’s gonna go is the subjective creative domains, you know, writing and art and, yeah, now, that’s just not true. That’s exactly what it’s doing. And it’s, you can find stories of students using it to write papers and getting A’s on papers. Wow.

Steve Washuta: Yeah. That’s amazing. And, you know, there’s so much more information now on the internet for these for this NLP sort of program to ingest and then take out, right, so every time I do a podcast, I transcribe it, I use something called It transcribes my audio, I put it on there. So it’s reading all of that and taking real conversations also into account, right, not just people writing, because there is a difference in language as far as speaking and writing, I wonder if it gets to that extent where it can tell the difference between those two?

Eric Olson: Certainly, and yeah, that way, you just gave us another example of an NLP product that can transcribe text and take in speech and turn it into text. Now, there’s, there are so many different interesting applications. And what you said is totally true. The accuracy of the internet just continues to grow, there are more and more ways for him to learn.

That is why we’ve seen these image generator models because there’s this whole Internet of a billion images with captions on them. And that is perfect training data for those it has an image it has the description, it can now learn exactly the given description, of what to do with an image. And that’s why these image generator models are so good.

Steve Washuta: Is there anything else in the sort of health and fitness world that you think is going to be really changed over the next five to 10 years, whether it’s NLP or something consensus is doing that you haven’t already mentioned.

Eric Olson: Yeah. As far as it relates to NLP, you know, I’m going to be extremely biased because what we are doing in one of our biggest early use cases are people who are interested in evidence-based health and fitness.

I think using this technology to get layman’s, better scientific insights whenever they want is going to be a real innovation, we hope to be a part of that where, you know, there’s a huge boom, as I’m sure you know, in evidence-based health, whether it’s you know, the Huberman lab podcast or Peter Thiel has a newsletter, personalities like that, you know, those weren’t people who were famous 10 years ago, but now there’s this whole culture of them. And I think it’s great for them.

I mean, there are obviously some charlatans involved as well. But the good ones Yeah, it’s a great thing, right? It’s, it’s these experts teaching you how to, you know, be healthy based on what the research says. We want to be able to build the tools that allow people to do that at the tips of their fingers. These large language models will enable anybody can learn what does the research say about my health and fitness questions.

I think an interesting intersection will be how this will potentially interact with like wearables, because wearables is another way that technology has impacted our health and wellness space, right? There’s another big boom over the last five years, and there’s definitely gonna be some room for synergy there, where it’s like, given my metrics, what does the research say I should be doing and kind of unifying those two sets of technology?

Steve Washuta: Yeah, that’s great information. And I, you know, my, my brain was going okay. Also, where does this fuse with the, the health world, let’s say, as we start to get more of this evidence-based, whether it’s nutrition or personal training, fitness-based stuff, the does that eventually lead into, and this, this, I’m just spitting spitballing, here, there being sort of health care coverage based around this stuff, right?

Because right now, the fitness world is sort of seen as this is just a leisure product, and you go into your personal trainer, or even go into a registered dietician, and you can’t get coverage from sort of your medical sense, and insurance, but eventually, we’re going to have enough of this information where that may change that landscape may change.

Eric Olson: Yeah, and, you know, there, it’s a great point and isn’t exactly where my mind went, cuz like you just said, like, when I think of health and wellness, you think of it in all these products, you think of it on like this personal level, but one of the biggest and most powerful applications of AI that’s already starting to happen is in the more like, in the more traditional healthcare world where they’re using it, you know,

AI can be used to take in a bunch of patient data and make predictions on what you know, what the disease is, it can look at X rays and try to predict what, what you see on the X ray, and it can find tumors that the human eye can’t see. So I think there’s, there’s so much that can be done with the more advanced data we have on people and symptoms and diseases, the better technology can potentially be helping us find cures and the best treatments for certain things.

Steve Washuta: I was listening to a radiologist talk about the AI that’s involved now with X-rays and MRIs and those sorts of things. And it’s funny to see that they couldn’t even predict what the AI was going to do necessarily, so they took like, I don’t know, 10,000 images, and they put it through this AI to see if it knew what was going on. I think they were actually scanning the eyeball.

And it with a 99% prediction rate, which no person can do. It actually told the gender, it can tell the difference between a male and a female by looking at the eyeball somehow, and they didn’t. They weren’t trying to do that. Right. That wasn’t the objective. That was a sort of a side piece of information that was filled out. So I guess there are things that you couldn’t even imagine that the AI is going to learn how to discover.

Eric Olson: Certainly, well, first off, I am interested to learn what what about what qualities of our eyeballs make it able to predict if you’re male or female? It’s that’s super interesting. Yeah. Yeah. It you know, the what I said earlier, like, this is a different example. But it’s it’s similar to what you’re saying. I think there’s a lesson to be learned about these types of technologies that everyone’s prediction was that AI was going to come last for subjective, creative domains. That is just not happening whatsoever.

We like it’s, you should just never, you know, always happens in ways that we don’t think it’s going to happen. It’s always going to help us and pop up in ways that we could never have predicted. And it’s kind of a fool’s, you know, it’s fun to make predictions, but we should never think that we know the future of this technology because it’s so powerful. It can surprise us in so many Ways.

And, you know, there’s a positive spin on that in many ways, both in terms of how cool certain applications could be, but also, everyone, you know, you can find these doomsday videos on YouTube about, everyone’s gonna be out of a job. Like the reality is we don’t know what the answer is going to be in 10 years and there’ll be a whole other sector of jobs that pop up, we probably have no idea what they are now that are, you know, these the AI hand holders or overseers or data creators, right.

Like, if with new technology comes new economic opportunities. So, you know, I think it’s sometimes a fool’s errand to try to predict like, we know what the future with this when technology is this powerful.

Steve Washuta:  Speaking of predictions and forecasting, I’m going to jump over to sports now. Vegas makes predictions and forecasts, they point spreads. I know you were doing similar things I’m sure with DraftKings, you have to write the number of points that whatever wide receiver quarterback gonna score on any given day, you know, based upon certain metrics. So you can talk a little bit about that.

But I also want to talk about football. Why is it that football maybe hasn’t caught up to baseball and some of these other sports and metrics, Northwestern, where you played a good program? Let’s go ahead and say you were playing Northeastern, you’d be a 3040-point favorite verse,

Northeastern, why wouldn’t Northeastern run hook and ladders and not punts and nip and never really run their traditional offense come out in something totally different? Because it’s a high risk, but it’s also a high reward, and they have no chance of winning, but I don’t see that I see these 40 and 30-point underdogs just playing a normal football game and always losing.

Eric Olson: Alright, yeah, you’re, you’re speaking my language. Well, first off Northeastern disbanded their football team over a decade ago. So that’d be an impossible matchup. But if we did play them, we would have been significant favorites because they were not Division one. Yeah, so there’s a lot there. So first, why football hasn’t caught up to as much as a sport like baseball? I’ll address that. If the answer is pretty simple.

There’s it’s nuanced, like the simplest thing is baseball just translates itself to being statistically analyzed better than any sport possible. And it’s like a binary game. Did you get ahead? Did you get on base or did you not everything can be gathered a spreadsheet, and there’s basically no context that needs to be layered. And you can understand the baseball game nearly in its entirety, just by looking at the statute.

Football is the most of the other sports gonna fit somewhere in between, like basketball is a really nice kind of middle ground, where it’s a little bit of both football is like the full opposite end of the spectrum, for the most part where there’s so much context and there’s so much variance in outcomes, you can look at the stats of a game, and a team can help game another team by 250 yards, and they could still lose. And there’s no equivalent to that in baseball.

So that’s the number one, it makes it a lot easier to evaluate players and do the Moneyball experience because you can have players up to watch the film and understand you know, the impact of the offensive lineman on the play. But where that is true, and where that isn’t true is engagement. Because there are many things we can quantify. And it’s one of the things that I get really frustrated with, with seeing coaches making conservative decisions on kick punting on fourth down or kicking field goals, because this is like the one part of football.

Like we have all the data, we have the wind probability models, that basically it’s a solved problem. It tells you what you should do. You have coaches still are too conservative to get me to the last point you made. About why don’t teams that are bigger underdogs play with more variance in their in their play styles. I think they 100% ship. I’ve had this conversation many times that I if I was a coach of a cultural program. I would want to know the point spread, and you should be humble about it.

And you should say, Okay, if we’re these huge underdogs, like they’re better, they’re more talented. Let’s play a game that is conducive to more variance. But the reason coaches don’t do that is because if you’ve ever played high level sports. Especially football, the coaches are very stubborn. And the idea of being humble and being like. This team is better and more talented than us is the most taboo idea on the world. In every coach ever is like, oh, we’ll just run our stuff.

And we if we execute, we can beat them. Like that is what the coach answer is. I think that’s stupid. Yeah. Because there are differences between teams. And you can make yourself more likely to win by playing with a more aggressive style. And that I think, you know, that generally is why coaches don’t follow the analytics book. Because they have this old school mentality like the average need to execute and they also are very conservative. When it comes to their job security.

And, you know, a perfect example of this is. Coaches who are down let’s say they’re down by 14 points. And they have the ball in their own territory. With three minutes to go and it’s fourth and 10 at their own 20. Most coaches will still. But if you punt, your probability of winning is basically zero. If you go for it, is your probability still low of winning? Yes. But maybe it’s 8%. Now, if you go for it and get it, yet, most coaches will still punt.

And the reason why is because they’re like. Oh, 21-point loss is better than it was worse than a 14-point loss. It’s still a loss. That’s all that really actually matters. But coaches are so conservative about, you know, what makes them look bad, and job security. And they come into default to this conservative way of thinking. It is changing, it is changing for the positive. But not changing fast as it should be.

Steve Washuta: Well, also, too, if you’re in that position, where you’re down 14. There’s a certain amount of time you have the metrics. The idea would be on third down to run a play. In which now you’re the fourth down is a shorter, right. So and third down, I’m going to run a draw. I’m not going to throw the ball on third and eighth. So now my fourth and four, I have a higher chance of getting that. And I shouldn’t be doing that, too. And I don’t think coaches are always thinking two downs ahead. That seems to be another problem. But yeah,

Eric Olson: you definitely see coaches starting to do that more of planning ahead and saying. We’re gonna have four dancers that can dictate some of the blank on whoever would push back. Because I do think that it is the data always says the best way to achieve a first down. Is to throw past the sticks. And team should stop throwing frickin screens on third 15 Because they don’t work.

Steve Washuta: Okay, well, I’ll trust you and trust the data there. But I do think it is really a mentality thing like you said. Because you will have coaches who are huge underdogs. Who decide, let’s go ahead and say they run the spread, they’re gonna you know what. The first drive, we’re going to come out, we’re gonna run the triple option, they won’t be expecting it. We’re gonna get understand we’re going to do this. But that’s it after the first drive, whether it stalls or

Eric Olson: doesn’t really plan and then they default to what they know.

Steve Washuta: And it’s like, well, if you if you’re really breaking that mentality down. If the whole purpose of that is to confuse them, why not stick with the confusion plan. And make sure you continue to do things differently, but like you said, it’s a mentality thing. It’s a stubborn thing.

Eric Olson: The number of times you will hear if you spend time around a football program. It’s like, just run our stuff. And all we got to do is execute and we’ll win, like, just in a perfect world. Yes. But that’s not the way the world works. And you need to be more self, you need to be more self-aware than that. Yeah,

Steve Washuta: not when Alabama was playing Louisiana Tech,

Eric Olson: maybe I don’t care what you execute, you’re going to lose. The only way you’re gonna win. Some weird stuff happens play a style that’s going to possibly allow for that weird stuff to happen.

Steve Washuta: Eric, this was fantastic information. Can you give my audience some more information as far as where they can find consensus. Where they can maybe find you if they want to reach out to you for a question. Whether that’s your own personal social media and whatever other realm you have to drive us to?

Eric Olson: Yeah, so go visit consensus. It’s It is completely free right now to create an account and search. And you know, one of our best early use cases are people who are interested in evidence-based health and wellness. Now you can go to our engine and type in a plain English research question. Say, Hey, does creatine help with athletic performance? Or is magnesium good for sleep?

You know, you can type in these questions in plain English and we will return you insights directly from research. So if that’s something that sounds interesting, go over to consensus dot app. Create your free account and start asking questions. Definitely, also follow us on Twitter. That’s the best way to reach me because I’m on Twitter all day at consensus, NLP. Give us a follow-up if you have any questions. DMS or DMS are open. And yeah, we’d uh, we’d love to get some of these listeners in our community.

Steve Washuta: My guest today has been Eric Olson. Eric, thank you so much for joining the truly fun podcast.

Eric Olson: Thanks, Steve. It’s great.

Steve Washuta: Thanks for joining us on the Trulyfit podcast. Please subscribe, rate, and review on your listening platform. Feel free to email us as we’d love to hear from you.

Thanks again!




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