YouSaid · the spoken record

Jeff Ma

lines on the record: 58
first: 2019-12-10
most recent: 2019-12-10
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Yeah, it's interesting because when I think about my own life now, they say each generation were becoming more and more friends with our kids. And my parents, I love them to death, but we were not friends. They were my parents and they pushed me really hard. I mean, the obvious thing is education, but I think really the thing that they taught me the most is that in life there are times you have to do things that you don't want to do. That's just life. And you do things because you know that you're supposed to do them and you know them because it's the right thing to do. And life isn't always fun.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Always showing up for everything for me, both from her social and a professional level. It's a guy that has had all the success in the world. And they always say the classic thing of how you know about someone is how they treat someone that can't really do anything for them. Everything he's done for me has been out of kindness and out of love.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“And so I think clearly my dad and my mom would be my answer, but that's the easy answer. And the other answer is a guy by the name of Kevin Compton. So Kevin is close with Sam Hinkey and that he's the operating partner at Kleiner Perkins for the heyday and now runs a company called Radar Partners. I invited him to my wedding and he at the time owned the San Jose Sharks and was this tremendously passionate owner about the sharks. The weekend that we were getting married down in Newport Beach, the sharks had game three against the Detroit Red Wings in Detroit. And rather than going to that game, he came to my wedding. And because the game was Friday night when he needed to be traveling, instead of flying, he drove from San Jose down to Newport Beach. So he and his wife could listen to the game on the radio so they wouldn't miss it, but they could still make all the events in time and have it all timed perfectly. And that idea of him coming, and he's a guy that's...”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I'm going to make a little bit of a long answer on this because I have listened to your podcast and I've thought about this a lot. I think anyone that's born of immigrant parents who came here basically to make their kids have a better life, their answer has to be their parents. My dad and mom came here from Taiwan. They were originally from China when the communists took over. My dad, they never had cable in their lives. And if you talk about a great lesson about compounding, my dad was a professor. My mom was a nurse anesthetist. Great jobs, but none of them are what you would consider to be jobs that would set you up for the rest of your life in terms of, but they never had cable. The only money they really ever spent was on our education. Both my sisters and I went to Phillips Exercise Academy and then to MIT. So that's not a cheap amount of money at that level. But yet when my mom got sick, I sort of turned to my dad and I was like, hey, dad, how are you doing financially? Are you okay? And he was like, well, you know, this is.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“And so, how do you make that content or that experience the same? And a lot of times, the other problem is that people conflate something like eSports with sports, and it's not. It's a totally different thing. It's not like because I'm a sports fan, I'll also want to go watch League of Legends at HP Pavilion or something like that. That's not true. I think there is a real disconnect right now in terms of media consumption and media companies. Ideal media consumption and media companies, what media companies believe it to be.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think so. And I don't know what it looks like, so I don't want to sit on my high horse and say, I understand this. I just know that it isn't what it is right now. The idea, millennials, none of us are millennials, but millennials in Gen Z, like when I think about sports, I don't see a world where they're going to sit through a three and a half hour football game.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, even Quibby, from what I hear, the changes are going to be very not what you would call groundbreaking. They're going to be shot a little bit differently, optimize their phone. They're going to be shorter, that kind of thing. It'll be interesting to see true interaction or true evolution of media, I think, will be one of the things to watch because my guess is that there are going to be winners that come out of this world of the Netflix and the Hulus and the HBO Max. But it's going to be people that really embrace the idea of Silicon Valley meeting Hollywood. That's what everyone wants. Everyone talks about, but there isn't really an example yet of a company that's done that, I think, where there really is evolved media or evolved media experience.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Of what were physical goods is interesting. I think the idea of the shared economy of renting and whatnot, I think we've only sort of touched the iceberg on that, the idea of delivery and just one idea is probably cities, how cities function and how that sort of idea. I don't really know if I had something that I was really, really interested in. I'd probably go out. I guess one of the things I'm interested right now is the evolution of media. Okay. So if you think about Netflix or Hulu or Apple Plus and all this kind of stuff, they're all like quote unquote evolving media and Quibi. Let's take Quibi for the Meg Whitman and Jeffrey Katzenberg company. These are all this sort of evolution of media, but they're not evolving media at all. You're literally watching the exact same thing you could have watched on your television 25 years ago. It's just better, better quality. It's better written. There's better actors.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, I think this is in many ways the journey that I'm going through right now on a personal level because I'm trying to figure out what I do next with my life. And so I think I told you the last time I was at the stage was about a year and a half ago. And that's when I discovered your podcast. And I would sort of walk around in San Francisco because it's really hard to get around the city now. And if I had go from meeting to meeting, I just popped a podcast on. I would sort of discover many things from your podcast around the way that people were using AI or machine learning. I mean, I think last year you had much more of a focus on that. And it was interesting to think through, I mean, I think obviously the obvious answers are things like blockchain and what blockchain enables obviously crypto gets way more attention than the underlying technology of blockchain, but the underlying technology of blockchain and tokenization of assets and tokenization.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, I think that that's one of the things that Twitter struggled with a lot when I was there and we realized it was a problem, which was this echo chamber that you create by who you follow and ultimately it creates this almost self-fulfilling prophecy of what you believe. I think one of the things as we're talking about this idea of reach being earned versus bought there is a way that people can basically buy followers over time or can use advertising to promote their own account over time. So even probably some of the reach that political people have has been bought at some level. So it's challenging, but you do have to draw the line. And I definitely applaud Jack for the decision he made.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Many ways that you can fool people into believing things. And in many ways, this is not new, right? This is like media has been biased since back when they said you didn't have to report both sides. And all of a sudden now this has become a thing that technology has made even more challenging. So I think I'm in favor of it as long as they're able to really, they're trying to crack down on many things. They have a whole team right now that works on election manipulation and tries to understand it, not just in the US, but all around the world. And the idea of sort of like this overcoming political ads, which clearly have some level of fake news attached to them, I think it's important. I think I'm in favor of it. I don't know. How do you feel about it?”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's a hard question. And one of the things that's amazing about Twitter, and I loved my time there, is just how inspirational Jack is in many ways about being fundamentally principled in how he thinks about the world. There was not a day or a moment that Jack is not thinking about what is Twitter's role in the world. He's not thinking about how to Twitter make money. He's thinking about how does Twitter continue to shape public conversation in a meaningful and important way. So I think the decision he made ultimately to not have political ads was through very thoughtful process of what role should we play. His statement that reach is earned, not paid for is a very interesting thing to say, the sort of ultimate democratization of reach Twitter provides is something that he was interested in. I think it's really challenging right now. And this is the whole deep fake thing.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Their behavior was around, I think a lot of it was around amplification versus creation. So a lot of these accounts were amplifying and not creating. And people that were doing that. And one of the things that was interesting was we found that many of their behaviors were similar to behaviors that we actioned on. There was almost like, okay, certain things are a violation of terms of service. And then certain things are sort of in the gray area that aren't quite violations of terms of service, but are not potentially acceptable. And those things that end up being in the gray area, which weren't violations of terms of service, but were clearly coordinated tax were the things that we found were probably the things that were leading to manipulation.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“And have those two feed in because the Russians are doing all this crazy stuff on Twitter where they're creating these fake accounts and creating coordinated efforts. And this is nothing that hasn't been in the news in terms of just the amount of stuff that they did for manipulation. But my team did a lot of the foundational work to sort of help figure that out. And that was fascinating. And then there was also some work that the team did around understanding how coordinated misinformation was happening on Twitter and literally how people were trying to create fake news and trying to get fake news to bubble up in surface areas where search algorithms or algorithms would drive what you saw and they were clearly understanding the algorithm and using different types of coordinated attacks to try to move things up. And that's what caused Twitter, the work that my team did caused Twitter to change the sort of algorithms around some of these surface areas where they were able to”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, all of this data science led to saying, hey, guys, let's really start using daily active users as a target metric. And the concept of how important target metrics are to incenting the right type of growth or the right type of behavior at a company, but constantly reevaluating those target markets because once you really start getting good at that, you need to reevaluate them because probably your optimizations have caused you to almost bastardize that target metric. So that was some interesting work. We did really interesting work around the elections and around understanding Russian interference in the elections. I had a team that did a lot of the foundational work for what we reported to Congress and sort of all the different things that you could find within the network. And so it was interesting because so much of what we needed to do was work in lockstep with sort of user research to understand both almost like an anecdotal versus an analytics level what's happening.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“To provide more value to them through the advertising chain and they provide more value to advertisers. And so one of the things that we did there and that they have done is look much more at this concept of daily active users. And that's to the credit of all the people that work with Twitter. That's the number that they've been able to move quite a bit and has led very much to the success of Twitter over the last few years. But moving the goalposts as a company from monthly active users to daily active users and even doing the data science, there's a guy named Matt Miller who is out of Boston, came out of MIT's media lab and was at a company called Bluefin, worked with a guy by the name of Deborah Roy. These guys did a lot of the foundational work where they tried to understand via modeling what true healthy usage of Twitter was. They created HMM to sort of do this. And what they ended up finding is that the healthiest users mapped really well to sort of daily active users.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, there were a few that were super interesting. I think one was simply around user growth. And it was understanding. So I was at Twitter when we were struggling quite a bit with user growth and the stock had gone from, say, 50 to 14. I think it's close to where it bottomed out. And we needed to really rewrite the story. And if you think about all the companies in the world that had come before Twitter that had gotten to a plateau or even like started, no one had recovered. Twitter is the only story of a company that's done so. And one of the things that was challenging at Twitter is that the top level metric that we sort of use monthly active users did not really indicate or wasn't really indicative of what a truly healthy Twitter user was. Healthy Twitter user does not come once a month. A Twitter user comes every day from a standpoint of monetization. You know a lot more about a user that comes every day and you're able.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Background. So, yeah, I hadn't thought about that, but that's probably very true. But you have to start with a core team or a couple people that can help these people because they won't be able to be, they're not going to feel comfortable in a situation. And that's another thing that I've learned about in the entrepreneurial world where I come from, no one sweats whether they can do a job or not. They just figure it out. But these people that have these deep analytical minds, they sweat themselves whether they can actually do a job.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“There was definitely an opportunity where people that are, oh my God, I just spent six years doing a PhD and I've decided I don't really want to do chemistry or I really don't want to do X. I mean, first of all, just the selection bias is someone that decided to do a PhD and got through it and defended a thesis. And they've run teams before. They just don't even realize it. They've run little research teams. They've run their lab or whatever. So they have all these skills that they don't realize. Classically, their whole life they've been thinking, yeah, I had never thought about the arbitrage opportunity, but I definitely think there is. And oftentimes in the data science world, I haven't really been in it for like a year or a half when I left Twitter. But back in those days, there was a cookie cutter type thing that people were often looking for. And again, back to Yuliana, one of her big things was that she always wanted. to bring in not the normal cookie cutter ones and i think it was because she came from a very nontraditional”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Much as they can because just the way that even the way that economics is evolved from sort of classical economics to now like behavioral economics and all the work that Kahneman and Taversky and all those guys have done, it's fascinating to understand how people think and interact.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“But because I have this unique approach to understanding how to interpret data for science, I can actually be much more useful than someone that has more of a math background or a computer science background and really has a lot more of the technical skills, but not necessarily a lot of the foundational skills of scientific method and things like that. So I'm always much more, when I see a resume of someone that came through a traditional PhD program in a science and social sciences to me are some of the most interesting ones because of just how hard it is to create the right environment. This woman, Esther Duflo, I don't know if you've read about her, but she does a lot of real world in the world experiments to understand causation. And it's just fascinating how they set up these sort of real world kind of experiments. I personally didn't really study any economics in college. And like I think it was a huge miss by me. And I'm going to encourage, I have two sons. I'm going to encourage them to study economics as much as”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Scientists. Hire scientists. And what I mean by that is nowadays so many people are quote unquote data scientists. And so there's a woman that worked for me at Twitter named Juliana Pascu and she was a economist, PhD from MIT, and had an undergraduate from Wellesley, just so thoughtful in how she approached the world and how she questioned things, sort of learned to be much more technical over time and was constantly challenging herself. But this idea of being very hypothesis driven and how you do data science and being, I had really strong people that were chemistry PhDs that had essentially they go through their PhD program and they realize I don't want to go into academia. So let me go take this data science boot camp where I learned how to do map reduce and data engineering and things like that. And I learned how to use R and Python and I learned how to program and whatnot. And maybe I do that for six months and then I can come in and take a job at like a Twitter or a Facebook.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm sure there are, but I don't know them. And I would say that I'm probably very much of a contradiction when it comes to sort of interviewing people because I do believe that I have an intuition on people and I do believe that very quickly I know whether that person's going to be good or not.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Be intellectually curious because the whole idea of matching skill sets and things like that, I don't really believe in that, especially in the world of startups and things like that, where you're constantly adapting or changing what you do. It's really much more around, hey, how do I find someone that can challenge me and can really think on their feet?”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's really fascinating because at the core, what you just said is how do we hire people? We hire people by interviewing and what is the most bias that you could ever introduce is like you and me sitting across from each other and there's this whole concept at Twitter where they made us do a lot of this unconscious bias training. And at first I was kind of like, oh, and then I went in there and I was like, man, this is really valuable to really be aware of even the concept. I definitely thought, oh, do I want to go have a beer with this person? That's a criteria for whether I want to hire them, but that's like sort of the ultimate and bias. Whether I want to have a beer with them doesn't really describe whether they're going to be good at their job. It describes whether they're like me. So it's a terrible thing that I probably was doing in an interview process. But yeah, I mean, it's, I think ultimately when you think about, for me, when I think about interviewing people, a lot of it is just around sort of this intellectual horsepower, just this ability to sort of really think creatively.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Changed literally every few months I'll get an email from an investor friend or someone that'll basically say, Hey, these guys are trying to start a company similar to 10x or can you go talk to them and things like that, I mean, I think there's an opportunity, but I think a lot of it comes down to the acceptance of the community similar to sports. There was a lot of analogies where I would sit with engineering managers or VPs and would feel literally like sitting and talking to these coaches or these general managers from sports where there wasn't a real acceptance yet of this. They're like, no, my best engineers, they do this. You can't capture what they do with data. So I think that has to change. And I don't think it's quite there yet. And then I think the data has to be better in understanding performance.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“And the idea that professional athletes are motivated very much by knowing their own statistics and how to use that as ways to set goals, that was I mean, one sad lesson we learned is that people are a lot less self-motivated than we believe. The amount of people that really want to do that is much smaller than we believed. And so therefore, what we found is that to do this and have it be effective, it would have to be more of a manager tool and have to be more top-down where it's mandated by managers for their individuals to use this. Now, another thing we learned is that anytime you look at one of these systems where work is being done that isn't sort of active work, meaning like GitHub is active work. You're checking in code, you're things like that. But something like JIRA or Pivotal Tracker, those are not active. Those are more you have to like teach someone what process they need to do to use this thing. And when you have that situation, a lot of times that hygiene of the data is bad. And so what we found is that even to start using that data, we would have to change the way that people did work so that their data was better for us to actually analyze, which is.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“The thought was, and actually I started this company with someone that has been on your podcast, Neil Robertson. So Neil was very much the formative idea for this. And he said, hey, Jeff, do you want to kind of run with this and bring your own sort of flair to this? And so the idea was that right now we create a digital footprint every day and knowledge workers especially create this digital footprint, meaning email or calendar or sales force. And this digital footprint is now accessible via API. So software engineers, just things like JIRA, Pivotal Tracker, GitHub, things like that. That data clearly means something. But what does it mean and what can you use it for? And so our goal was to figure out how you could use it. Now, I think what we really believed or hoped is that we could build a bottoms up software system, meaning individuals would want to use this because it would help their own performance and it would help them understand and get Maslow's pyramid of needs. They'd get mastery autonomy and they'd understand purpose and things like that within their own performance via data.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Sort of all of the different teams. And so one of the things I think that is fascinating because I know you have a lot of people that listen to this that are in this world is understanding how to properly structure a data science and analytics team at a company at scale. And do you have them embedded? Do you have them in a central org? If you have them in a central org, it's the best way because professionally they understand how they can move up and things like that. But there is this idea of sort of like HRBPs or things like that that they need to be part of these teams to really understand what's going on in these teams. It's an interesting thing, but we were able to create a very strong central team at Twitter. And so when I left, I got promoted to vice president of data science analytics there. And I was leading all the central data science and analytics, which was just a fascinating thing to be a part of.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“But it was immensely talented. And to be able to work with those types of people and understand Twitter is this incredible, almost like a playground to do analytics. And you have this community of 300 some million people that are interacting in ways that people don't even ever understand. There's one country, I think it's in maybe in one of the stands, Kazakhstan or something like that, where they actually use Twitter like they would Uber because Uber's not allowed there. So someone will tweet out that they're at a certain place and someone will come pick them up or something like that. And just the use cases around Twitter are really incredible. And so to be able to be a part of that. And so Anthony asked me to come sort of run this team. And there was another guy over there by the name of Todd Morgenfeld, who now is the CFO of Pinterest, who I worked for. And he and I built out just this sort of incredible team at Twitter of data science and analysts. And eventually we consolidated.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Analytics to understand user growth and to understand how to grow more. But then Anthony Noto, who is now the CEO of SoFi and at that time was the CFO of Twitter, he had started a small group of data scientists and really economists that were trying to look very much at the macro type analytics at Twitter to understand user growth and to report for IR and all that kind of stuff. And he asked me to come run this team. What's interesting is the guy that originally was running the team now runs analytics and data science at Stripe. It's like this amazing group of people that I was allowed to sort of be around. And I didn't have a real traditional background to lead sort of a data science analytics team. If you look at me right now compared to most of the people in the valley that are doing this, most of the people in the valley are PhDs at some level and are much more academics. And I had come from this completely different background to do this. And the team that they asked me to lead at that time was small.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“And this was the only time that I was really excited to join the acquiring company. And so originally when I went in there, actually it was funny because the original thought was that I was going to go in and build a sports-only app. So it was a vertical app and it was an idea of can they have a multiple app strategy, especially on the consumption side. And literally a day before the deal was going to close, Kevin Wheel, who was the VP of product at the time, called me and he said, hey, Jeff, we've decided we don't really want to do this vertical strategy anymore. Do you still want to join? And we literally been coming to the office playing FIFA soccer all day because we knew that whatever we had done, we weren't going to continue building. We're going to build everything within Twitter. So at that point, the idea of restarting and killing this deal, it wasn't going to happen. So I said, yeah, I'll join. And originally, I was part of running a lot of product within the growth team. So using it.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I had started a company that was in the world of analytics for people, so for human performance, specifically in this case we were looking at the performance of software engineers. How do you evaluate their performance? Then how do you create sort of enterprise tools to better manage them or better understand them or better motivate them? And pretty early on, we realized that this was an enterprise play. Like we were going to have to sell into big companies. And I don't think any of us really had that in our DNA. And so Twitter had just started an engineering effectiveness team and they were going to try to do a lot of the same things that we were already doing. So they decided to buy us and let us build a tool at Twitter. And we took the opportunity to join Twitter in a lot of ways because we were all fascinated by the company Twitter at the time. And personally, I've been a part of a few startups that have had success and we've sold to bigger companies.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, the challenge is the liquidity, and the challenge is being able to actually deploy capital because even though there's a lot of dumb money there, there's no incentive for the market makers to allow smart money. The really good market makers, in this case, market makers are just the sportsbook operators, but the real sophisticated ones are trying to use the smart money to inform how they do pricing to try to increase their edge over the dumb money. But most of people aren't thinking about it like that. Most of the people are thinking recreationally, how do I make the most money off of people that don't know how to bet? And then I can make a higher margin that I would if I let smart money in.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the most dangerous human beings in the world is Malcolm Gladwell because he's such a great writer, but he can take these concepts that aren't always completely true. And because he's such a great writer can make us believe that they're true. And the notion of blinking an intuition over data, that it's largely not true. There are some people out there that probably don't need data because they've had such pattern recognition in their lives. But even that, people don't understand. That's their own data that they have in their mind and their own models in their mind. But again, the idea if you decide to just use blind intuition at Blackjack and I use data, I'm going to do better than over the long haul. And then another is classic results bias or whatever process of a result where if tonight if you and I decided, hey, let's go to Atlantic City and I don't know why we'd want to do that. But if we said that there's 15 versus nine, I'm standing behind you and say, Jeff, what am I supposed to do in this situation?”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I'd rather you do something that causes my demise than me cause my own demise. And so the classic example is if I have 15 and the dealer has a nine showing, the math 100% will tell me to hit because if they have 19 that's a winning hand, they're not going to have to do anything. And there's a good chance they have 19. And if I have 15, I'm not going to win unless they bust. So I need to take a card. But a lot of people won't want to take a card there because if they get a 7, an 8, a 9, a 10, a jack, a queen, a king, they're going to lose right away. So they'd rather just say, okay, well, I don't want to be the cause of my own demise. And so they basically decide not to hit in that situation and hope that the dealer flips a low card and has to take a card and bust. But if the dealer flips a 10 and wins, well, at least in that case, they were probably going to lose anyways. That's what they're thinking in their mind. And they weren't the cause of their own demise. And then you also see these sort of ideas of, and I don't know what this bias is, but I always joke.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Basic strategy So that's a very simple example and a very good example. Essentially, the game of blackjack has been solved. You know, given what hand you have and what the dealer has as an upcard, you know what to do and you can look at this card and the card's not secret. It's available on the internet and it's available in books and whatnot. And if you just do that, if you just go from blind intuition to all of a sudden just following this matrix, decision matrix, you go from losing roughly about 3% of the money you put on the table to about half a percent of the money. So you almost become an even player against the casino, yet you will find people all the time that know basic strategy, but then when given a situation, will decide to go with their gut over what the data says. And it highlights a lot of biases that we have. One that I talk about a lot is this concept of omission bias, where people favor inaction over action when they think it may lead to their own harm, meaning”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“was written right around the same time that Gladwell wrote outliers and talent is overrated were written and those were all these very prescriptive nature versus nurture books but blindside was sort of to me the most important one because it was this nature versus nurture story that told it in just this incredibly fascinating and accessible way in many ways that was luckily what bringing down the house the book that Ben wrote in 21 the story did is it told sort of this analytic story in a very nonprescriptive way and gave sort of this opening for people to think or talk about gambling and card counting in an analytical way”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Because the casinos had banned them and they were recruiting new people to play and they would basically teach you how to play and then send you to Vegas and give you some money and you'd win money and you basically split it with them. We had a group that played and we were quite successful. And when it was all over, I approached Ben Meserick, I think it was in 2001. Ben had written six books at that time, but his career was definitely at a crossroads. He had business school applications out and was contemplating not being a writer anymore. And I said, hey, Ben, I got a really great idea for your next book. And again, remember, this is 2001. So the World Series of Poker isn't even on television yet. I don't even know if big data is a term at this time. And it was just a really interesting moment in time. This is even before Money Ball. So this is like an interesting moment in time where we told this analytic story. So Michael Lewis is a friend of mine and I talk a lot about the blindside being sort of like this amazing nature versus nurture book that”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the premise behind the blackjack stuff was relatively simple. I mean, MIT has always sort of had this cultish type group that figured out blackjack all the way back to Ed Thorpe, who was, I think, either a visiting professor or whatnot at MIT. And he actually discovered card counting on an old school IBM mainframe computer where he did simulations where he understood that if you took all twos out of a deck of cards, what would that mean in terms of win probability? So he would win, he would do simulations. And essentially, the concept of card counting is not new. Blackjack is the only game in the casino that's subject to conditional probability, meaning what you see impacts what you're going to see. And therefore, it's like this perfect thing for data-driven thinking or data-driven strategies. So the MIT Blackjack team, my involvement essentially was there were these guys that had been card counters but no longer could play”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Background sort of in blackjack and card counting, you never had to question your model and you never had to question stationarity in the data. But in sports betting, you consistently have to if you don't, you're a fool. And so with that, there's just more inherent doubt that is cast on you that you've got to overcome. In Blackjack, you have a bad out. You have a bad session and you feel pretty good next time you're going to get them. It's okay. And sports when you go through one of these bad streaks, you worry.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, I think a lot of it is just around understanding how to overcome biases. And so this is something you and I had talked about a lot and the biases around loss aversion or short-term thinking or even just the idea of process over results and being driven that way. I think whenever you see a really strong sports better, you're never like, oh, wow, he's the most strong analytical person I've ever seen or I'd want to hire. You just see this fortitude and this ability to sort of go and overcome these big swings. Because if you're a really successful sports better, you've had some really bad swings also where you've had to really doubt yourself and doubt everything that you've done and doubt your process and you have to have been able to get through that to become successful. So I'd say that that's probably the consistent thing that I see. And then, yeah, I mean, I think the other thing, and this is one of those things that when I think about my early.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I wouldn't say that. Some of them play the market, and if you call that a quant, i.e. they understand market moves. And there are, if you bet into the black market or into the legal markets, there can be big opportunities for pricing disparities. So there's some people that do that. And I wouldn't necessarily call them quants. There are definitely a handful of people that can sort of read those market moves or can look for things like that that do well over time. It's basically just arbitrage at some level. But I would say the people that originate that sort of are fundamental versus technical, they're sort of always going to be analytically driven or create models.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's hard to answer that simply because it's changing so much. I think I would have said to you, I don't think there's much of a moat, to be honest. And I think one of the reasons that there isn't much of a moat is because the top betters are constantly changing what they're doing. They're not doing the same thing over and over again. And it's not like they've established something. They're constantly having to reevaluate. I was talking to someone the other day about the MBA and I was saying people that bet the MBA with these sort of static models right now are crazy to me because the game is changing so much every year because of analytics, because of rules, because of everything that's happening, even just because of the way the schedules change. Like the schedule has changed a lot because they're trying to give players more rest. And even with that, players are getting more rest naturally. They're also sitting them out of games much more than they used to. So if you're building a model based on historical data that takes into account when someone's rested and when someone's not, it's not going to work anymore because.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it's very true. It's very true. I would say the amount of people that can consistently beat the market, i.e. become professional sports bettors, is very small. I mean, it's in the neighborhood of one percent of the people that bet, maybe even less.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I just think that's what you said. It's the sort of motivation. I think that a better would not care about, there was a lot of similarities, I guess, because ultimately you're trying to predict winning. I think there are many times where they'll be very aligned, but there's also times where a better is much more mercenary in how they're thinking about things. Yeah, I guess teams also have to think a lot more holistically about the entire season and what they're trying to do, whereas betters are often just trying to think about one individual game.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I would say that there's definitely a level of it that's easier because there aren't as many sort of confounding factors or composite things that happen. But I think even in golf, it has its challenges because courses are different and everything like that.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, well, you brought up soccer, which is interesting because soccer, soccer, and hockey are very interesting because of how little scoring there is. And so therefore, there isn't a lot of, you would think soccer is probably a perfect example of what you're saying where you want to actually come up with how to predict something beyond scoring. You want to predict scoring chances or that kind of thing. And there's actually a guy that's a good friend of mine named Ted Knutson who started a company called Statsbomb that is actually really going back and training people to go back and watch games and create new data from watching games that allows them to do these things, model out more predictive things. In terms of individual sports, my partner on the podcast that I do bet the process, Rufus Peabody, he's one of the, if not the number one golf better in the world. He's one of the top. And he definitely is for a long time was sort of looking at data that most people weren't looking at. That market has become much more efficient now, unfortunately, for him, but modeling out individual performance.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“There's the most green space to go out and figure this out because so much of what happens on field is just not really well understood. And even something like the modern day concept of not punting or never punting or never kicking field goals, you still see teams that are very smart or in a lily driven, even the Patriots, you see them kick field goals in situations where all the math and all the analytics would tell you that it's a terrible decision to do so, but you still see them. Anytime you kick a field goal inside of five yards, so any field goal that's less than, say, 22 yards is probably a really bad idea, almost inside of 10 yards is probably a really bad idea because not only you giving up the ball, but you're going to end up giving up field position and you give up the opportunity to score seven points versus three points.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source
“Baseball is the most interesting, they're all interesting in different ways. So, baseball is very interesting because at the core, it's very solved. And because it's very solved, they've had to go to very new ways, i.e. this data that I'm talking about. They've had to kind of reinvent themselves and go to the data. Basketball is interesting because it's the one that's probably getting the most attention now because of guys like Daryl Moray and because of just the general trend of what's happening in these front offices and the corner three. And it just fundamentally analytics, I think, have fundamentally changed the game the most in basketball. Like it looks fundamentally different now than it looked even five years ago. And how different it looked now teams play faster. Teams try to shoot more threes. Teams understand they need to drive more. The mid-range, that is all analytics driven. I think football is probably, I would say singly the most interesting simply because it's the one.”
2019-12-10 · Invest Like the Best · Jeff Ma – Making Decisions with Data - [Invest Like the Best, EP.151] · IDENTIFIED FROM THE TRANSCRIPT · source