The ROAR Podcast: Max Mitchell

# ROAR Podcast: Max Mitchell
**Guest:** Max Mitchell
**Date:** 2026-01-21
**YouTube URL:** [https://www.youtube.com/watch?v=BSusOdwmP2Q](https://www.youtube.com/watch?v=BSusOdwmP2Q)
**Source:** YouTube auto-generated captions (no speaker diarization)

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(0:04) College football's at a tipping point, an existential threat to the future of the game. Players can transfer and be immediately eligible. Makes it really convenient and easy to think the grass is always greener. Those quotes, the first from commentator Paul Finebomb and the second from legendary coach Nick Sabin, echo what's often considered conventional wisdom about the current state of college football. Yet unprecedented changes the transfer portal and name image image and likeness policies have led to record player movement and compensation.

(0:35) Fans, media personalities, and coaches believe that college football has entered the wild west. Is that actually true? A closer examination of player movement data often tells a largely different version of the conventional story. Instead of running from competition and development, players are seeking programs with better opportunities consistent with more traditional labor markets. In particular, studies have found movement to be consistent with what a functioning matching labor market looks like. High mobility with clear and consistent pathways to achieving retired professional outcomes. Our guest today, Max Mitchell, is a recent graduate of the Northwestern MSA program. And in this conversation discusses with Adam his in-depth thesis work to answer that question. Is the conventional story actually true? I'm Bryce Clinton and this is Revenue Above Republic.

(1:29) Welcome to the Revenue Above Replacement podcast. I'm your host, Adam Gman. With me today is Max Mitchell. Max, welcome to the podcast. >> Thank you. Oh, thank you for having me. >> Yeah, it's great to have you. I'm excited to talk about the thesis that you wrote in the article that we wrote about the thesis that we wrote together with Bryce. But first, before we jump into in detail about what we're talking about, which is covering the NIL transfer portal, player movement specifically in college football, tell us more about how you came up with this idea and how the thesis started.

(2:01) >> Sure. This stems back from a previous research project which came in the back from a Clemson versus Florida State 2023 football game in which Clemson lost in overtime. and my dad and I were having a conversation about how the average age of the Florida State players was about 2 years older. And from there we wanted to do some sort of study finding the average ages throughout college football and mobility from how far players travel to go to college. And from there we gathered data from 247 sports for all college high school recruit for all college football players and CFB stats. And basically we matched high school recruits to college rosters. And within that we found that there was thousands of elite recruits that were missing. And we weren't sure if this was a data issue or kind of what was going on. So I did an independent study my senior spring at Clemson University and found that in the initial sample there was about 300 players out of 34,000 recruits that did not make it because of academic issues, disciplinary problems, changing sports, just a bunch of other reasons that they couldn't play. And from there, I basically tested it against their high school quality in their zip code and basically found that the players that didn't transfer seamlessly to division one came from lower quality schools and lower income areas. And so I guess the main takeaway there was that elite talent doesn't always overcome these structural barriers before college. And because the data was so good, we decided to scale it up into a large scale paper which I've been working with my dad who's a professor at Chicago and Andrew Hansen who was a professor at Clemson. So we expanded this to all recruits nationwide and eventually we have modeled it based off of Raj Cheddy paper and we've added county level opportunities and again found similar results that derailed players attend worse schools in poor areas and overall just come from lower mobility counties and within that we got two main takeaways. There's the investment effect and the resource effect and that's that low mobility areas produce more elite athletes and within that the lowest mobility areas produce sorry the lowest mobility areas block academic progression. So that paper kind of explains the players that never entered college labor market and given we had all this data. The next step was what happens to the players that do enter the labor market. And because of that I had three original options to do the thesis with the first one was modeling employer value based off of stories in high school to how much players getting paid in the NFL.

(4:51) Unfortunately something like that has already been done. So I had to scratch that option. Then I wanted to talk about how players were going bankrupt in the NFL and relating that back to their hometown statistics. So something like that has also been done and it was super difficult to scrape and find all the data. And then we had a conversation Adam and we thought about what is super timely in the college football industry and then we decided to do a thesis about NIL and college football and that's how everything started. >> Yeah. Before we get into it, the main finding that you discovered and that we talked about in this article is that there's this conception that the NIL and the transfer portal is like the wild west. It's very chaotic. It's unsistatic. But what we found is actually and you found and then we adopted for the article is actually looks more like a traditional labor market. And you mentioned labor markets in your previous paper. Can you just describe for our audience who may be not familiar what do you mean by labor markets and how labor markets in general and what they how would you apply them in this specific college sports context?

(5:54) >> Um for transfer players I think within the labor markets basically saying that players have opportunities and they're making rational decisions based off of how much money they can get their playing time and just a bunch of other decisions that they weigh throughout their career. And within that, they're acting like an employee where they make these rational decisions to further along their collegiate career. And it's similar for the players coming out of high school. They go to colleges. They think they have the best opportunities. And overall, there's just this notion that there's some sort of labor market even though this hasn't been created until NIL.

(6:31) >> Yeah. Even taking a step step back from what actually labor markets are. There's been economic studies of how employees move throughout their careers, move throughout their move throughout their careers to different jobs, different opportunities based on their incentives or rationale or trying to find if there are structural ways that employees move throughout their career. We're not saying that athletes are employees or should be employees, but that there are similarities to labor markets based on previous studies. And that's what we're trying to say is instead of this looking more chaotic where people are moving around in ways that are hard to define or not align with a structural or incentive structure, there are a lot of similarities to other existing labor markets. So from your perspective, why were you interested? You mentioned a little bit from a your past data, but from a particularly from a labor market perspective and looking at it systematically, what interested you and what did you know what did the data originally suggest when you started the research?

(7:28) I think the original question was just talking about what are the motivations for players transferring and I was interested about this because at the time my alumni university had no transfers and we weren't winning. So I was trying to figure out why are players transferring to other schools and is it beneficial and because of that I took the entire sample of collegiate players and basically ran a matching process on did they chase schools in backto-back years and from that basically got all the transfers and then as I said the main question was why are they actually doing this? What are the motivations?

(8:04) And my original hypothesy was that they would be transferring for a couple core reasons. you'd be transferred for playing time, for winning, and to increase your value as in with your NIL deals. And from there, basically just built the data set and tried to in a way model the motivations or as we could say the labor market expectations of why they were transferring. >> Yeah. Well, one just to clarify, it's not like it wasn't winning at all. college is probably at the level that it went historically particularly >> 100% after being a powerhouse for about 10 years and having that couple year dip after not entering the transfer portal it's definitely raised the question is does investing in transfers correlate with winning and that's something we've also discussed in our study saw with Indiana last night they've had one of the biggest NIL collectives with Mark Cuban and that's led to the first 16 and0 season so it's been interesting to see how the landscape has evolved there >> yeah I think we want to delve into Does NIL correlate with winning? Obviously, we put a study from open doors in the paper. But before we do that, you talked about data matching and this seems like a relatively simple problem, right? If you're starting to say this is systematic for the reasons you articulated, that seems like a relatively simple problem, but it actually isn't. Like how do you define what is a better program? How do you define what how do you find all these data? One of our challenges that we articulate when we were talking is obviously this is a relatively short amount of time that player movement has existed in this format. So I maybe start there. What did player move for people who are familiar with college football or the transfer portal or player movement prior to 2018 in 2021 where there was discrete changes in NIL and transfer? What did the data say? What did it look like? And what kind of was the fundamental shift post 2018?

(9:53) So prior to the transfer portal, if you wanted to move schools specifically from a divi division one school to another division one school, which has been the emphasis of the study, you had to sit out a year and lose your eligibility. And there was about 500 transfers a year. Most of them were graduate students because that's the only time where you didn't lose a year of eligibility. And then by 2018 they started to change the rules saying you can enter the portal and you don't lose that year of eligibility anymore. And from 2015 to 2018 we saw about 3x in transfers. And then from 2018 to 2021 this trend has continued because there's less restrictions on transferring.

(10:38) players can play immediately and it's just created a lot easier of access for players to switch schools and appeal to their motivations. So it was very difficult in the beginning for players to transfer and although this study doesn't cover that I have data showing that transferring was players were not transferring at a very strong level prior to 2018 and that's all to do with the transfer portal >> and then what happened in 2021 to help >> yeah in 2021 the NCIA introduced NIL which is name image and likeness which provided a way for collegiate athletes to be compensated based off of their brand their on on the field play and get endorsements and sign deals with colleges. And because of that, there's been a lot more money in college football and it's led to players transferring schools in order to receive higher paychecks and also more opportunities. But the main reason has been through additional income that they can get. And because of that, the transfer portal has simply exploded.

(11:43) from I'd say 2018 to 2020 there was about 2,000 or so transfers and 2021 alone there was about 1,800 to,900. So in that 3 year to one year one year gap there was just 3x transfers and we've seen this to continue to rise just because players want to make money and how can you blame them? I think that's not the only case as part of what you look at in the paper is that it's not just to make money, it's to maybe increase playing time. And there's a bunch of different reasons that we'll get into. >> But before we talk about that, and it is when you talk about an explosion using the Wild West metaphor, you think there's an explosion, explosions are chaotic, the Wild West is chaotic, there's all these players moving around.

(12:27) So in order to and worse, the paper's arguing that actually it's not chaotic. It's much more systematic. It's much more like a labor market. So in order to define how we came up with the system and the systematic approach, you created a novel way to look at defining what if we're saying we're transferring from a lower performing program to a higher performing program. You had to create metrics that establish what a lower performing program was or a higher performing program was. So can you define from the paper and in the article what we define as those two programs?

(13:00) >> Sure. So in terms of a lower performing and a high performing program, there's no conclusive end of season rankings besides the AP top 25. And I wanted to use a ranking system throughout all of the division one schools. And throughout some research, I found that Simon's rating provides the most conclusive end of season rankings where they use a weighted measure based off of strength of schedule, win loss, a higher emphasis towards the end of the season, and a couple other metrics. And within that, I wanted to weigh the player weigh the schools over a three-year time period with a higher emphasis on the previous year and then the second year and then the third year. And within that, we tested what was the overall combined end of season ranking. And for a player who transferred to a school with a lower ranking, they are moving up schools, also known as an upward transfer. And if you're going to a school with an overall three-year higher ranking, you were doing a downward transfer. And through that, we were able to define kind of our employers moving to better or worse programs at the Marvin.

(14:09) >> And then what were initial outcomes? One of the things particularly moving to a higher level program, one of the things we talk about is that a higher level program could mean increased competition per playing time and potentially hurt your path to to achieving the outcomes that you wanted to achieve. So, can you describe a what were the outcomes first? Describe the what was the sample size of players? Obviously, we talked about it's a relatively small sample size timewise from 2018 through 2024 which we examine the data. So, first can you tell us the number of players we looked at? Second, can you say the highlevel results and summary metrics of upward transfers and lower transfers?

(14:52) >> Sure. The entire sample was about 8,000 transfers. Again, these were only division one transfers. Within that, about 2,000 of them transferred from 2018 to 2020. And then the remaining 6,000 were from 2021 to 2024. So, pretty substantial gap there. And some of the main initial takeaways specifically regarding upward transfers were that they were transferring and making having higher values at a at a statistically significant rate and they were more than doubling their likelihood to play in the NFL. And we found those to be the two big takeaways. And then regarding downward transfers, we saw that they were trying to maximize their short-term opportunities through playing time. And those players, I think 60% of them played more games, 40% of them started more games, which was higher overall than players transferring to better schools.

(15:50) >> And then one of the counterintuitive ideas that we talked about is particularly with the upward transfers is that there is a risk involved. And what does when we talk about a risk involved in an upward transfer, what is that risk and how did that play out in the data? One risk you would say is if you're going to a better school, there's going to be better players that you're trying to compete with for your position and there's a chance that you don't have the opportunity to step on the field and you continue to be a bench player. So, there's definitely risk that you go to a school where you think you're going to have a starting spot and you end up losing it out to another guy and the transfers were a waste. But, this was only for a pretty small segment of the sample. I think about 10% of upward transfers didn't see an increase. Sorry, 10% of upward transfers didn't play at all, but we saw a majority of players having substantial increases in their playing time. So, it seems like a lot of these guys were transferring up rationally because they believed that they were too good or not too good, but they could compete at a higher level for increased visibility and thus made the move and were able to capitalize on it. Yeah, I think it's not just increased visibility, but one of the things you found, right, is obviously there's a success in professional outcomes, right? You're more likely to reach the NFL. And I think that's there's a risk if you're going to a higher level of competition and not playing as much that you might not be seen as or you're could be a decrease in likelihood of making the NFL. The data did not suggest that. Does from your perspective, does that did that align with your expectations or did that was that kind of a counterintuitive finding from your side? Yeah, it definitely did a little bit. I always assumed that players were transferring to better schools because they were better athletes and you would assume the best players are going to make the NFL at higher rates. So, that was certainly something that I expected. I didn't think that they were going to double their odds of making the NFL. That was a little bit surprising, but the overall takeaway there wasn't a huge shock to me. And then when you're talking about in the context again of a labor market from your perspective, we obviously talked about defining what those incentives were. What we are seeing from your perspective obviously in terms of pre previous research and this research is that these incentives particularly for upward or downward transfers align with what a labor market would say, right? Is here are the outcomes and players are optimizing for these outcomes. And from our case, the outcomes, as you mentioned, were more playing time, a like more NIL dollars, and a likely more likely path to professional, particularly NFL participation.

(18:23) >> Yes. I mean, you that's what I was about to say. I think players were definitely transferring to optimize their career capital, and that's something I definitely reiterated in the thesis. They tried to balance playing time, income, visibility, their professional upside. And within the labor market, we found out they were making these rational decisions based off of optimizing these four core motivations. You know, that was definitely the biggest takeaway there. >> And then from downward transfers, one of the things that's interesting is you're also seeing those optimizations similar to a labor market. Maybe the outcome is less likely to make the NFL, but in terms of the metrics, particularly playing time as the key metric, you're seeing that and you're seeing that substantial shifts in playing time for downward transfers.

(19:05) >> Sure. There's definitely more of a short-term incentive there for the downward transfers because if you go from a big power five school and you're not really touching the field, you feel you're missing out on some of your key playing time. And a transfer down to a school where you can become an immediate starter is definitely a spot where a lot of transfers are taking because they just want to get on the field and prove that they're a competitor. And then there's on the flip side, the guys transferring upwards are incentivized by these long-term goals, which is either That's interesting. I we even talk about that directly is right the difference between short-term and long-term incentives and you had labor markets all the time, right? Is there can be a difference in terms of optimizing for short-term or long-term incentives.

(19:47) >> Sure. And that's definitely obviously something we haven't we haven't necessarily talked about, but that's been a main takeaway within the papers, these incentives, and that ties back just to the overall motivations and optimizing the career capital. >> Yeah. You talked about career capital. Can you just give a definition of from your perspective what career capital means? >> I'd say it's just balancing how players can play at the highest level, make the most money, just within how good they are in their program, what's the best decision for them and where they want to go. It's just making the best decision based off of their surroundings and their last couple years of playing. And that that kind of ties back into something we'll talk about later with with NIL and star rating recruits. But after players play for a couple years, they start to figure out what their roles are and whether or not they have an opportunity to play at a big school, a small school, and just how can they optimize their four-year college career. Now it can be five or six year with all these red shirts and different types of exemptions. But that's how I would describe it.

(20:53) >> Yeah. Let's talk about that. Now, one of the things that you found historically, if there were a predictive metrics for future NFL participation, either being drafted in particular being drafted, it was star rating. What was your star rating in high school and how did that correlate with future NFL participation? You've you found in the paper that actually NIL became a better predictor of success. Can you talk about a how you figured that out and b what did that mean and what do we think that means from a from a predictive perspective? Yeah. So, for every single transfer, I was able to match it back to their high school recruiting profile and assign them a star rating, whether it's a five, four, 32, or a zero star. And to briefly summarize, a fivestar correlates to the best 30 recruits in the country, who the recruiting committee believes will be the first round of the NFL draft.

(21:44) Four-star ratings about the next 300 correlating to the rest of the draft, undrafted. And then three star players are people who they believe will be super successful in college but not make the NFL. And from there we I wanted to run a regression based on star rating and NIL valuation as well as transfer direction and seeing how what of these main values predicts NFL success. And within that when NIL and transfer direction was included transfer sorry NIL was so much more significant than the star ratings I think star ratings had a p value of 0.5 like super high and then NIL had a p value of less than 01 and basically found that players with higher NIL values were making the NFL at much higher rates than players with higher star ratings.

(22:45) >> Yeah. Just to for people who aren't familiar with p values, you want to target a p value typically of under 05 or have a 95% confidence interval in the variable has a correlation or a statistically significant correlation with what the outcome is. So the independent variable in this case NIL ratings or NIL dollar amounts star ratings had some what is their correlation with NFL success and being drafted and if you're a p value of 0.5 means that it is not statistically significant you get a what is called a null hypothesis a null set saying you you don't know if the variable you can't say with a statistical level of certainty if that variable has a correlation with success. So again anything below 0.05 05 is typically the benchmark that is used as a way of saying something is statistically significant. And then what do you think that means that if NIL's values are have a stronger relationship or stronger correlation to NFL participation? We identified some of the things that we think it means, but from your perspective, what do you think it means that this is potentially a better or has a stronger correlation to NFL success?

(23:50) Yeah, given the star ratings are preemptive measures to predict their success at ages 16 to 17, not all of these players end up having super successful careers. And with NIL values, player coaches and athletic directors and just overall NIL fans are giving money to players who they believe are super successful after watching them play for a couple years. So you can have a five-star who ends up being a complete dod and doesn't make any money, doesn't go to the NFL, but then you can have a guy like Fernando Mendoza who is a nostar coming out of high school, plays super well at JMU and then Indiana signs him for about a couple million dollars and here he is now to be the number one player in the draft. So, we've seen that coaches are recruiting players who they believe are going to be NFL ready or just super high quality college athletes. And these players with the higher NFL values are transitioning to the NFL at higher rates than the high star rating recruits. And that was the main takeaway there.

(24:57) >> Yeah. I think one I think Brianna Mendoza went uh University of California, Berkeley before he went. >> You're right. You're right. I'm thinking about the rest of their team that went to GMU and then fall. Yeah. But I think the larger point is accurate, right? I think what we want to articulate is one hypothesis for why NIL dollars have a better correlation to actual better correlation to NFL successes, right? They're a dynamic variable instead of a discrete variable. Typically star ratings are done at a certain moment in time like you suggest like you say said that doesn't mean that they couldn't have a correlation with future success but NAL because it's more dynamic more real time and based on new evidence. So if you think about it even from one way of looking at it is from a basian perspective where you get new evidence and that can change your predictions of or change what you think will happen in the future based on new evidence that you're getting from the past and change the probabilities of future success. One other thing though that is interesting about that is what one thing to talk about in our article is star ratings could almost be looked at as an SAT score. An SAT score does have uh predictive validity of future success in college. But if you could look at somebody, it's not 100% foolproof and it's not dynamic. You take the SAT once, you typically get these ratings, but when you're high school, junior, senior or some point in your high school career and they stop. So they're static. And I think that NIL as a as a particular future or correlation to future success is something that's really interesting and something that you found from the paper. And it also shows that there is that I think there is a perception that maybe NIL dollars aren't there's definitely cases where NIL dollars don't necessarily translate to onfield success in specific use cases but or specific player cases. But on the whole there does seem to be the spending of dollars does correlate with success both from NFL success and then open doors finding that there's actually a correlation with onfield success in college where essentially they found that $2 million in spend translated to one win. So from your perspective looking at this what does all that again it wasn't necessarily something you were looking for but what does that all mean to you?

(27:03) What were your takeaways from all of that work? Yeah, first I'm going to backtrack by reiterating that it's not that we're discrediting star ratings. A lot of studies have been done showing that five stars go to the end at higher rates than four stars and three stars. And that's something that we've tested. It's just that given that NL is more of a current value. It shows that at a much higher rate. But regarding players, regarding schools giving more money, as you said, 2 million more for one win, that's definitely a trend that we've seen. I think it's going to continue to stay because if you're buying the best players and giving them the fat contract, it's definitely going to correlate with winning. And we've already seen that with schools like Texas Tech, Indiana that haven't historically been great programs, them spending millions of dollars has translated into initial success, a pretty immediate onfield success.

(27:50) >> Yeah. Yeah, one thing I don't think we're necessarily not saying they're buying players or saying they're employees, but they're paying athletes. paying athletes in what we would say an efficient way because it's it does seem to be having paying athletes whether via open doors or your research is seemingly one of the things we do want to talk about and you do talk about in your paper and in the article is that payment isn't just beneficial to the players it's beneficial to the university because they're particularly in the college program particularly in terms of the variable of maxing maximizing onfield success and that this does seem to have a correlation and again that the reason also we want to about paying is that does also correlate to labor markets, right? And companies are playing employees to maximize certain metrics. It can be revenue generation, it can be product development, it can be marketing or brand metrics, but they're looking to optimize that performance by paying finding and attracting the best talent. And in general, not always, you pay more money for better talent. And that's what we're seeing here as well is that you're potentially paying more money, particularly from an NIL perspective, for better talent. And I think that's something that also has been challenged in this in in the public perception because again there are definitely specific use cases where that is not something that happens. I just want to see if that from your perspective again takeaways because this was analysis that you really largely did which I think is very impressive but takeaways from looking at NIL from that perspective. Yeah, totally. As you said, I think there was the two key motivations from the programs, as you said, getting better talent and then winning. And to get better talent, need to pay more money. And that's why we've seen these guys get these $4 million checks, $5 million checks, because other schools aren't willing to offer that amount of money. And to incentivize a player to come to you, especially if you have a guy from Florida, have him go to Ohio State or California, you have to pay him a lot more money. otherwise they're not going to want to travel across the country. And that's something that we're going to do in a future study is finding how has mobility changed pre and post NIL related to college football rosters and like our players traveling farther now because they need more money. And I think that's definitely going to be the case. That's a hypothesis for a future project. But within mine, I totally believe that these programs, if they want to win, they got to pay more money. And I think it's smart. I think they're both revenue generating in the short term. They're making more money. If they win more games, you get bonuses from bowl games.

(30:19) You get more media coverage. Alumni is good to continue to donate. Yeah. And I think the mobility is a good question to look at, right? Is one of the reasons also and it's similar in a labor market, right? If you're looking for talent across the country or throughout the world, there is a mobility concern of will people move or uproot their lives or move to different locations? Is that likely to happen? And does financial considerations and economic considerations is that something that does have has NIL changed that? Has it made more likely for mobility? So definitely curious to see you pursue that project and see what the outcomes are.

(30:54) >> May go ahead. >> I would think that one reason that players often stay closer to home is to be with their families. And now if you're getting these massive checks, you can somewhat move your families with you. You can be closer to home. And I think that although it hasn't been tested yet, we're going to see that players are definitely going to farther away schools just based off of this added income that they can support through themselves and through their families. >> Yeah. And I think that's part of what we wanted to leave with in terms of next steps, right? This research with a lot of research is a starting point, right?

(31:26) I think you've done an excellent job of looking at as large of a data set as you could for this type of analysis and finding some really again looking at some really interesting things and finding signal and then what could be noise player transfers and already articulating the next use case. Last question for you is what did you learn from this process and from this experience putting aside the actual paper itself just writing the paper, going through this process, going through this experience both either with this paper or even your previous paper with your father and you obviously professor from Clemson. What have you learned from writing this paper? What do you think would be helpful for students or people who are like interested in these topics and trying to do academic research on these topics? What have you learned from this process and experience? Yeah, I think a couple main takeaways is first, it's been super rewarding seeing everything come together going from just a super massive data set that was incredibly messy. It's taken us 2 years to filter through all the observations, it's still not completely done. So to see how that's come along and then going from just a couple hypotheses to a full-blown 50page thesis and other papers about 40 pages, we're about to publish it soon. Just seeing how that's all come together has been incredibly rewarding. As I said, and didn't really think it was going to happen this way and just happy that I was in the opportunity to get all this data in the first place.

(32:49) It took hours upon hours of scraping data and just going through all of these. I spent probably 200, 300, 400 hours researching the derailed players. Obviously, within the thesis project, there wasn't as much qualitative research. A lot of it was creating the ranking system, creating the the matching process for the transfers, and I'd say the data itself probably took over half of the time. I probably spent five, four or five months working on the data for this project. And so there's definitely a massive two-part step there, working through the data and then actually saying, "Okay, I've got everything finalized. How do I want to go forward?" And it's been super excited to see how it's come across from our initial prediction about wanting to talk about NI on the transfer portal, having this. It's been great and I would definitely recommend doing the thesis project here at Northwestern over the capstone. It's been >> I've loved it. It's been great and I I wouldn't trade it.

(33:48) >> Yeah, we didn't pay Max to say that. So glad he felt that way. Glad he did say that. And I do think that's a really important point. A lot of data science data analytics work, collecting the data is really the hardest part. So sourcing the data, collecting the data, making it usable so that you actually can do the analysis that Max ended up doing for his thesis and that we help support that's a big part of it and just the man hours or person hours that you put into doing that is is laudable. But I think it's good to remember and good for other students to think about that is it is going to take a lot of time. But if you put in the time, energy and effort that Max put in, you can get a really good outcome. And I think from his paper and the article that were published, we're very excited about the results. Excited that Max had a great experience. And thank Matt, I want to thank Max for being on the podcast. So Max, thanks for being a guest and thanks for being such a great student. And now thanks for helping to be a teaching assistant in our MSA 401.

(34:40) >> Of course, I'm also helping out Bryce helping out with both of you guys. That's a good point. >> All in with the podcast, boys. >> No better way to leave it than that. Thanks, Max, for being a guest on our show. >> Thank you so much.