The Exchanges

Q What have you found in the data that helps you predict the outcome of, in this case, a college basketball game?

A So, it's a combination of two things. It's a combination of data, and it's people. You gotta have the best data you can get, and you gotta have the best people to analyze that data. In college basketball, we were able to get data that nobody else really has. So, when you're capping a game, you're looking at expected points, not really actual points. So, what that means is, if somebody throws up a half-court shot that's Contested. It goes in. It counts for three points on the scoreboard. We count that as like .one. It's very unlikely for that to go in. So we have all the data where other people have Ted makes a two point shot, right? Well, is that a dunk that's worth 1.99 points, or is that a contested 15 footer worth .seven points? What we have is Ted makes 15 foot shot contested one feet away by Michael Schwimmer. And so because we have that information, we're able to put together these models to then predict the likelihood of that event occurring more or less often. So it's just about eliminating the noise and really focusing on the signal. And we know if Steph Curry's got a wide open three, he's going to make it 50% of the time. That's worth 1.5 points. I don't care if it goes in or not. It's worth 1.5 points in the long run. And so if you model that out, you can then build these predictive models to predict what's going to happen in a future game.

AI assessment note: “when you're capping a game, you're looking at expected points, not really actual points”

Q you get real-time data flowing through everything from portfolio accounting to reporting to reconciliation, trading, compliance, and more. In the AI era, asset and wealth management firms moving to Ridgeline gain a decided advantage. That's why customers call it miraculous, game-changing, and an awakening. If that's not how you would describe your investment management tech, request a demo at ridgeline.ai. And now back to the show. And how about accountability?

A Financial accountability. This is what separates us. This is what no other group is doing. We are going to pay you if our picks lose. This has never happened before. And it, the longer package you sign up for, the bigger the guarantee is. So our 17 week plan that starts for the football season. Okay. It costs three dollars a pick or less, which is the cheapest of any subscription service there is out there. But There's a thousand picks, which is the most you get of any subscription service, so it costs 3000 dollars. Now keep in mind, if we win a game, we win one unit. If we lose, we lose 1.1. Overall, at the end of 17 weeks, if we're positive, meaning you bet the same amount on every single game, and we're positive units, that means you are guaranteed to win money, even after paying our fee. If we're negative units, that means if you bet the same amount on every game, and you lose money, we will give you 10 thousand dollars back. 10,000 dollars back. No one has ever done this before. It sounds crazy. It sounds too good to be true, but this is us believing in our model. It creates a short opportunity. There are people in this world like, you can't beat Vegas. There's no way. You're the number one person that should sign up. If you're right, you get 10,000 dollars. If not, you lose three.

AI assessment note: “Financial accountability. This is what separates us. We are going to pay you if our picks lose.”

Q For those people who aren't in front of you, how tall are you?

A Six, eight. Yeah, six, eight, and at the time, I was more of a shooting guard, point guard type player, which was very unique at that time. Now you see everybody is, you know, you get Kevin Durant and everybody can do anything, but I was one of those players that could really score, but really couldn't defend other teams' guards, so I was, in high school, able to guard the other team's big man, so essentially, I kind of figured for myself that I'm almost at my ceiling in basketball, and I was, you know, recruited by Duke, Louisville, really high caliber programs, and I could get better, surely, but I couldn't really be great, I thought, because of my limitations physically. My wingspan, for example, is much shorter than my height. You almost never see that in the NBA, and really quickness. I mean, guarding a guard, I was going to always struggle guarding a guard, and that was always going to be a big problem for me, and quickness and some of those types of skills are much harder to Gain than a jump shot. So in baseball, while I wasn't that great at the time, I was the perfect build for a pitcher. It's six, eight, and I could throw the ball in the upper eighties at the time, and I thought that if I could ever figure out how to pitch, then the sky's the limit for me, and so I just sort of made a bet that that's what I wanted to do, and I also loved baseball, and I figured My bigg…

AI assessment note: “Six, eight. Yeah, six, eight”

Q All right. What teaching from your parents has most stayed with you?

A Really, the way they taught by example. I was very fortunate. I grew up in a, in a middle class, maybe upper middle class family. Both my parents always wanted me to follow my dreams, whatever they were, and I've always loved sports. And they, not only did they tell me to do that, but they supported me in doing that. Any kind of pitching lesson I wanted, they gave me. Any kind of team I wanted to play for, any, anything at all like that, my dad was unbelievable about that, but I do want to talk for a minute about my mom, if you don't mind, because she is my true inspiration, and I've been very fortunate to have two women in my life that are incredible, but my, my mother obviously first, she is the American dream. She is the definition of resilience. She grew up in a very tough family situation, to the point where on her 18th birthday, she, she left the house, and Had to kind of figure things out completely independent on her own. Took a job just like entering data, and just really worked really hard. Met my dad, had me, started working at the Department of Justice very low level, and ended up ascending to becoming the CFO of the Department of Justice Office of Justice Programs, which manages tens of billions of dollars. And all the while, took night school to go to college. All the while, Never missed a single one of my games. When I like realized in my, I guess using my twenti…

AI assessment note: “Really, the way they taught by example. I was very fortunate.”

Q Joining the Players Association, what did that mean for you, and how did that drive you through to the next steps?

A Yeah, so that was really cool for me. So I joined the union Fell in love with the business of baseball. I joined the licensing committee for the MLBPA and the executive subcommittee, which directly negotiates the collective bargaining agreement with ownership. And my big thing was I had thought I'd figured out a way to help minor leaguers getting paid. Minor leaguers are actually not covered under the Major League Baseball Players Association. They're not represented at all. And so I thought, okay, I got this PowerPoint together. I went to Michael Wiener, who is, in my opinion, one of the best Union leaders in baseball and really in any union around. I mean, he's, he's, he's incredible. And I said, listen, here's what we got to do. We got to give everyone that's making five million dollars or more in baseball. So very, very few. They get one percent of their salary gets taken out and it's tax free. So they don't, they never get one percent of it. The owners match it. And now instead of baseball players making 5500 dollars a month, basically two dollars an hour. Now we can pay a minimum wage. And they can make a living and they can end up becoming better. It helps. Baseball helps everybody. And he looked at me and told me, you gotta understand my position. This is a business. I represent major league players. I'm trying to get more money to players. You're, you have a proposal t…

AI assessment note: “Fell in love with the business of baseball. I joined the licensing committee”

Q How are you able to access that data?

A So you pay for it. These are millions of dollars. We spend millions of dollars a year in data. And that's why, like, if you're a regular professional better, you're a one man shop. They're all one man shops. Okay. And these are successful people, but they're making one to four million dollars a year. It's a darn good living. I mean, I give them all the credit in the world. But they can't afford to spend a million or two million dollars on data plus pay. I mean, we pay our team. We're all in more than five million bucks here, probably on an annual basis. And so if they do that, they can't win money. And so that's why they don't buy or have access to the data.

AI assessment note: “So you pay for it. These are millions of dollars. We spend millions”

Q You talked about Gambling as predicting outcomes of games, and then you start thinking about that as just a form of entertainment. So how do you think sports as entertainment for audiences will shift as a result of all this data and the way people are watching these games?

A You're going to see a major shift, Ted, a major shift, and it's going to be from regular programming. There's going to be a twenty-four-seven gambling news network. Guarantee that within the next two years. Guarantee it. Okay. Now I don't know who's going to do it, but it's going to be 2407 because lines move markets move. There's always something to talk about. There's going to be specific shows ESPN. I do a show ESPN, the daily wager that just is an hour long every day from six to seven. That all we do is talk about betting where lines are, where they should be. It's already right there. I also think it's going to these companies that have TV rights to show these games are going to be able to add a channel and it's going to be following somebody live bet. I think that's where it's going. I think that's what millennials want. I mean, if you look at what millennials are doing these days, they're watching people play video games. I mean, they're watching people play video games. They're living vicariously. That never happened to me growing up. I never knew a single person that watched anybody play video games. This is what they're doing. Now imagine watching the Sunday ticket and AT&T and direct TV has a separate channel that costs money, right? That they can sell a subscription package. They're looking at somebody live betting these games. Here's 10,000 here, 1000 here, 3000 he…

AI assessment note: “You're going to see a major shift, Ted, a major shift, and it's going to be”

Q And what have you found as you've sort of effectively doubled the number of players that you've invested with? Any new trends coming out of either finding the right deals or different rejections?

A No, it's just been getting better and better and better. I mean, it really has. So players, especially on the American player side, what we found is there's a major cultural difference between like a Latin American player and American players. And Latin American players in general are a lot more likely to To, like, tell their friends, like, look, this is a deal that exists. This is great. While as American players in general are more likely to keep that to themselves, and nobody really knows about it, and so we were able to catch on more like wildfire in the Latin American market, but now since we've been on Sports Illustrated and Athletic, ESPN, all this stuff, then American players are now going, oh, ok, and so we're having a lot more success in the American player market.

AI assessment note: “what we found is there's a major cultural difference between like a Latin American player”

Q When you started BLA, you have this wonderful story from your experience in the minors and how difficult it was for those players to effectively invest in themselves and then how the majors only support the majors and not the minors. What was it about sports betting that got you excited to create this model?

A So we started out doing it, and I thought, like, after I saw these results, I'm like, oh my god, let's raise a billion dollar fund. Let's put ten million dollars a game on this thing. We'll be billionaires in a year. This is great. I got to prove it first, so I went to Las Vegas. 16 days, I was shut out of all the books, because I didn't realize it's not a liquid market. The sports betting market is not liquid. At all. And so what that means is at the time I was cut down to 305 hundred per bet instead of 10,000 a bet. And I was hot, but there's no way over sample size they could prove that I was winning better. They err on the side of cutting out people instead of letting somebody slip through the cracks that might not be as sharp.

AI assessment note: “after I saw these results, I'm like, oh my god, let's raise a billion dollar fund”

Q And as you're doing that, I could see how you could take those models and look at a past game and say, well, Duke was lucky that they won that game by 12 points because statistically they shouldn't have scored as much as they did. But how do you then know that they'll play the same game, that they'll take shots in the same way going forward?

A Well, there is a defense, as you say, right? So we model out offensively and defensively and tendencies, and we model out, ok, Steph Curry's gonna take 3.8 shots. From this distance, because this is how the defensive plays over the course of the game. This is what we think is going to happen. Of those 3.8 shots, he's going to make 1.6 of the 1.6. How many points is that equal to? And that's how you do it. We do that for every player and every game and every sport. And it also is really cool because the content side of it. So I'll give you one more good example. I think this is our best example. So Duke has a point guard named Trey Jones.

AI assessment note: “we model out offensively and defensively and tendencies, and we model out”

Q How do you think about the implication of your success on betting lines? So You were going to make a billion dollars and break. We can't make a billion, but maybe we can make a billion for other people. Well, I would think that would move the market.

A Yes, it definitely should. I hope it does. Now it takes time for this to happen. So with our people betting, we've been available to the public for six months and we post how we do against the closing line, meaning after the market's moved. Okay. So against the closing line, we're still destroying the Vegas. We're still up well over a hundred units. Again, at the closing line, but now what if the closing line moves more, right? How are we going to do? So we're going to continuously track that. And if the closing lines are jumping many, many points to where we're losing, we are going to shut down the subscription service because we can no longer provide positive expected value plays to our subscribers. I don't think this is going to happen for years. That means lines have to jump two to three to four points. And if that happens, think about what that means, Ted, we then become the worldwide leader of In moving the market. We are the market. We become the market. And for a company as Jambos, that puts us on a billion dollar plus pedestal, and we could do a lot of different things with that. It certainly wouldn't be the subscription service, but there are a lot of other avenues we could go and we could take.

AI assessment note: “Yes, it definitely should. I hope it does. Now it takes time for this”

Q So you have a model, a statistical model that tells you a little bit more about the likelihood of a minor leaguer making to the pros. How do you approach the first minor leaguer and get him to sell you a piece of his future?

A Yeah, so it's important to note that I started this company by players for players. I put myself in the player's shoes. So what would the player want in this? And my idea was the money we're giving them is actually going to help them succeed. I mean, me personally, if I had a 100,000 dollars in the minor leagues, maybe I go to ASMI and get my delivery with the electrodes and see how I can pitch healthier. Maybe I stay healthy. Maybe I make 1000 of millions of dollars playing. I don't know. The idea is the money that we give them should help them achieve their dreams of making the major leagues, but I want them to be as informed as possible on their decision-making, and I don't want to sell them on anything. So I never have ever gone to a player and say, hey, of the 128 players we've signed now, and that probably 300 ish we've offered, or more, you should do this, you shouldn't do this. I say, look, this is the option for you, and you need to be informed about this decision. If a player is interested in it, I say, you need to have a lawyer review it. You should talk to your agent, your financial advisor. All these minor leaguers have agents, financial advisors, talk to them. And then after that, just to make sure they understand, we videotape the signing before the signing and ask them all these questions. Do you understand if you make five hundred million dollars, you will owe …

AI assessment note: “I don't want to sell them on anything. So I never have ever gone to a player”

Q Talk about competition. So you start this, clearly you're a first mover. There aren't other people doing it. Are you still the only game in town now trying to buy stakes in minor leaguers, or have other people come in and try to compete with you?

A So there are a couple people that have tried to compete with us, but nobody really can because nobody Goes and gets to the guys that are outside the top 300 prospects, because you can't predict which ones are going to be. And so, there were some Goldman guys that quit and tried to do it. They have a couple players, then they left, because they realized they couldn't, they couldn't do it. When they were projecting the player, okay, I know this guy's going to be good. It was too late. We already had him, because we could project him and figure it out earlier. Unfortunately, there's been some bad actors coming in the space, uh, that I'm hearing from other players, saying, sit him down in a room, hey, sign this contract right now, I'll give you money. Which is a big, big problem for me. And players aren't understanding the contracts, not in their native language. They don't have a lawyer review it. They don't understand what's going on, which again is a, is a major problem, which is actually the, a big reason why we're pushing for a law to, to make sure these players are protected. And so we, we are going to try to get that through Delaware this year.

AI assessment note: “there are a couple people that have tried to compete with us, but nobody really can”

Q So are there specific statistics that you can model in the minors that look different from the statistics in the majors?

A Well, they're all there. It's just how you analyze them. And so the other thing that I did is a lot of conceptualizing of the statistics. So let's say you and I both have 10 home runs. But you hit your 10 home runs in 10 nothing blowouts versus the team's fifth reliever. And I hit my 10 home runs against Clayton Kershaw in tie games. I have way more power than you. Major league potential power. And so in major league baseball, all pitchers are going to be very, very good compared to minor league pitchers. So what we care about at bats, a lot more at bats with position players that face higher quality pitching. If you're facing a pitcher that's not Ever going to be a major league pitcher? What do I care if you hit 10 home runs and you build all your numbers based off that? It's amazing that what translates to future success is, is really how well you do against those types of pitchers, how well, in what circumstances, what's the ballpark like? You have to conceptualize everything to get a true predictive model.

AI assessment note: “Well, they're all there. It's just how you analyze them.”

Q All right, so there's some alpha in this model, as we like to call it, but is it too proprietary to ask what are some of the things you figured out?

A Well, that's what's going into it and conceptualizing it all. What we figured out is what we talked about earlier with what actually matters to major league success and actual value of players. Now, the, the one thing that we haven't touched on yet is pitching. You know, those were all offensive stuff. Pitching my model initially was very weak compared to my offensive model because I'm saying all these pitchers are going to be great, In the minor leagues, but they weren't because they got hurt, and they have elbow shoulder surgery, and that can really, really hurt a career. So I was doing tons of research, did all these deals with all these different companies to get more than 12,000 pitching videos, and I had done so much research on injury stuff after I'd gotten hurt, and I'm looking at angles of all different kinds of parts of the delivery to try to figure out exactly how much stress pitcher's putting on his elbow and shoulder to determine What's his likelihood of getting hurt?

AI assessment note: “I'm looking at angles of all different kinds of parts of the delivery”

Q And then, of course, the most interesting aspect of this is the success of these players. So how does the data track both your models and then relative to success rates of all minor league players making it through the pros?

A It's unbelievable. It's actually better than our back test, and I think a lot of it has to do with luck. We can't really, on players' names, we have to be, you know, they're confidential, but players have Confidential because the players want it to be that way, but some players have wanted to speak publicly. We have Fernando Tatis Jr., who is, would have been the rookie of the year if he didn't get hurt. Absolute star and even a better person who's come out in support of us and what we're doing. A couple other players have as well, but we've really done, done well. Our first fund of 77 players, and keep in mind, remember, you have less than a 10% chance to make the major leagues if you're throwing a dart in the minor leagues, and we don't get first round picks. So the players that we go after, These non top 300 prospects for the most part are two to three percent chance. And we have that 38 of the 77 in the major leagues. We're expecting over 50 out of the 77 to get to me. And that's all a tribute to our data analytics and our modeling and be able to figuring this stuff out.

AI assessment note: “And we have that 38 of the 77 in the major leagues.”

Q So what's the modeling that you're doing to predict outcomes of baseball games?

A So we look at each specific matchup. So we take the pitcher versus every single hitter, and we do a simulation model. And we know this pitcher throws a fastball with a spin rate of 23 64. And we know this hitter has seen those fastballs With that spin rate X amount of times, and we know what he's done against those spin rates and a curveball. So we model, okay, how many fastballs, curveballs and changeups is going to see how has he done against those pitches in the past? And we go from there. So it's really intense. Now, baseball is interesting. It's not like basketball, basketball. We have data that nobody else has. Baseball is the only sport that all the data is publicly available. So it's just about how good your team is. That's all it is. It's how good is your team? Because everyone's working on the same data in NFL and college football. We have data that nobody else has. And so that's why I think those two sports will have a far better advantage than baseball. I know we've done really well predicting outcomes.

AI assessment note: “we take the pitcher versus every single hitter, and we do a simulation model”

Q And was that a fluke in the sense if you just went to Vegas for a while and you had a 59% win rate, how would they know to flag you?

A Exactly. But it's all on the app, so it's all tracked. I mean, they got your player stuff. And they look at closing line value, meaning if you're betting a game at minus three and it ends at minus five, that's good value. They're looking at all these types of stuff, but I agree. I don't think they could have had any kind of sample size. I mean, what I bet a 102 hundred games more. I mean, something like that. I mean, there's no way they could had enough sample. Even if I didn't lose a game, that's lucky still. I mean, at least how I would look at it, but it's not how they look at it. They do not like losing. And so that's why the sports market is inefficient because it's not a fair market. So that's when I had this thought that I can't bet 300,000 dollars on a game, but they can't stop a thousand people from betting 300 on a game because you can't go lower. They always allow you to bet the posted limits. That's part of the law, and so that's when I thought, okay, we'll sell the picks, and now everybody can bet these games, and then that got me into this tout service subscription service space and researching that, and it is I mean, you see a lot of industries. I'm not sure there's a more disgusting, awful industry to get into than the tout service and subscription services.

AI assessment note: “it's all on the app, so it's all tracked. And they look at closing line value”

Q That sounds great. What's been the most disappointing aspect?

A The most disappointing aspect by far was the pushback from, uh, Different groups, organizations, people about what we're doing, and it's all because they only care about the subset, the small subset of the group that makes it, right? Just because I said over half the players will make it, we're still projecting 20% to be profitable. Keep in mind, they had to play three years. Even if they do 10%, they're returning 150,000, because they make 500,000 dollars a year. In order to be successful, we got to make the big money. If we can get, you know, 10% will be very successful. I think we're going to get about 20%, which means we're going to be losing money on 80% of these players.

AI assessment note: “The most disappointing aspect by far was the pushback from, uh, Different groups”

Q an awakening. If that's not how you would describe your investment management tech, request a demo at ridgeline.ai. And now back to the show. So I got to dive into this model a little bit. So you have 12,000 videos of players that are pitching, and then you or your team go in and are modeling some statistics about either the arm angle or the wind-out. How does that work?

A Right. So we're trying to figure out throughout the delivery, how much stress are you putting on your elbow and your shoulder? So you look at the delivery, there are so many theories out there on what causes arm injuries, and I was just going to try to test for them all. Now, in the beginning, in the first fund when I was doing this, we were investing in pitchers because I was able to get the high, medium, low. It wasn't until we raised the second fund that we had a lot of money from the management fee to really get a team, and they were able to crack the code on pitching that I wasn't able to. Sam Hinckley, who ran the 70 Sixers, is in our fund. Paul D. Podesta, obviously, and now here we are with the second fund. We got a lot of capital, and I called them both, and I said, I want to turn this thing from a baseball company that invests in minor leaguers The world's greatest sports analytic company is what was my, is my dream and my vision. Now I want to hire these guys to help the baseball model, obviously, but then get into whatever else we think we should get into. And so I called Sam. I was like, who's the smartest guy we know in sports, all sports. And he goes, well, that's easy. The answer to that is, is this guy, Jason Rosenfeld, but he'll never ever do it because he got tapped by magic Johnson and he's, he'll be the Laker GM in five years or a GM of another team in very…

AI assessment note: “It wasn't until we raised the second fund that we had a lot of money”