In today's episode, we talk about a statistical phenomenon that might change the way you think about comebacks and falling stars.
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Transcript (Generated by OpenAI Whisper)
If you're like many people around the globe, then around the time that this episode is coming out, you're probably watching some of the Olympics, or at least you know about them. World class athletes from around the world have traveled to South Korea, and they're currently competing. They're competing in winter sports, and in this morning I was watching the snowboarding event, and I noticed that one of the athletes had a particularly good score. And it reminded me of a statistical phenomenon that is really important for us as Developer To understand. And it's one that's not very intuitive, unfortunately. This is something that certainly causes us to think wrong things, and to get into a bad rut of thinking as well, and to make poor decisions, make poor bets. We're going to talk about all of that in today's episode. My name is Jonathan Cutrella, listening to Developer Tea. My goal on the show is to help you become a better developer, to help driven developers connect to their career purpose, so that you can go and do better work. And that better work is going to have a positive effect on the people around you, on the people that are using the stuff that you're making, and on the people who are working with you on making that thing. So I'm really excited to do that every day on the show, or I guess three times a week on this show. And I love talking about subjects like this, because they truly change the way you see the world. This idea that we think in a particular way, and maybe we can think in a better way. Maybe if only we knew the ways that our thinking is not serving us well, then maybe we can correct that. Maybe we can actually approach our work with a better way of thinking. And that's why we talk about so many of these kinds of things on the show, the biases, the ways of learning, the ways of understanding the brain, all of these kind of neurology and behavioral economics. These are all subjects that are so tightly woven into our working lives and into the decisions we make as developers. So that's why we talk so much about this kind of thing. In this particular phenomenon, of course, all of these things that we talk about, the way the brain works, that's true for not only developers, but for pretty much everyone else in the world. Our brains are all wired quite similarly. And so you can share this episode with your friends that are not developers. They're going to get something out of it. But going back to my Olympic story, there's some studies that have been done on these world class athletes. And one of the studies showed that after winning a gold medal, many of these athletes end up kind of taking a dip in their athletic careers. The next year or the next Olympics, they don't win as well as they were winning the year they won the gold medal. And this is an interesting phenomenon because it kind of breaks with what we would think. Someone is getting better and better and better. All the way up to the point of winning the gold medal and you'd think that they continue that trajectory. They've become the best and they're going to continue bettering themselves and therefore they're going to continue getting, well, better. Another interesting part, kind of the flip side of this same phenomenon, is that many times when you see a very bad season for a sports team or when you see a poor performance for an otherwise good athlete, the next year they get significantly better. They go the opposite direction than you thought they would. And we call these comebacks, but it's the same mechanism, the same statistical mechanism, it causes the dip for the gold medalist. We're going to talk a little bit more about this concept right after we talk about today's sponsor, Linode. A lot of the time that we've been highlighting features of Linode, we've focused on ones that empower Developer To actually use their services to get in and get your hands dirty, get involved with API tools like their command line interface. We've shown you stack scripts in the last episode. Today we're going to talk about something a little bit different. We're talking about their managed services. Linode's managed services allow you to focus solely on your code. In other words, you're building a product. You're very busy building a product. If you're like most developers, DevOps is actually kind of an expensive use of your time. It takes up a lot of time and it's not something that most developers really need to spend a significant portion of their time understanding because most of the time you're working on a product. You're working on something that is a user-facing or maybe it's other developer-facing, maybe it's an API. The DevOps portion of your work really ends up being pretty costly to you and to the company that you're working with. That's for most developers. If you're a freelancer especially, then you probably don't have the time or the energy to put into managing a product at this level. But the engineers at Linode have your back. With managed services, they will manage your infrastructure so you can get back to focusing on your code and focusing on the business side of what you do. If you're like me, then you've probably had something go down at a pretty inopportune time, maybe in the middle of the night or on a holiday or on the weekend. Linode, once again, they have your back. They have an incident response team for your managed services. So any time something goes down, they're tracking the uptime and the responsiveness for every registered system and service that you have with them. So their experts will take immediate steps. And I'm taking this directly off of Linode's website by the way. You can go and learn more about it. But they will take immediate steps to get your systems back online as quickly as possible. This is an excellent service. It's especially useful for those of you who have systems and services that you need to be highly available. And that when they go down, you start losing money. You start losing productivity. And here's the reality. This is available to all Linode customers. This is just another add-on to your plan. Go and check it out, spec.fm-linode. You can get started and let's say you're building your product from a ground up. You may not need managed services yet. When you need it, you can have that. Add it onto your account. Head of respect.fm-linode. Use the Code Developer Tea2018 that check out for $20 worth of credit, which you can use on any service that Linode provides. Thank you, Ginalinode, for sponsoring today's episode of Developer Tea. So why do gold medalists take a dip? And on the other hand, why do really poor performing teams bounce back the following season? This phenomenon is actually the result of some pretty simple statistics. If you think about taking a pool of athletes, even if they're world-class athletes, they're going to have something that really acts as an average. What is the average performance of the world-class athletes? What's the average performance of even the Olympic level athletes? And when you have these individuals competing against one another, because the large sample size that the Olympics has, specifically the world of athletes, then those athletes at the top are going to tend to perform very close to that high average. You can see this when you look at the split times, for example, in a sprinting sport or in a racing sport. Very often, those split times are going to be very close together. But every once in a while, you're going to have some kind of anomaly. You can call it a stroke of luck, or maybe you can just imagine that that athlete was at their very peak in that very moment. But every once in a while, you have an anomaly, and that anomaly sticks out from the average. And if you were to look at a graph showing some kind of performance metric, then you can see that that anomaly sticks out from the group. The thing about anomalies is that very often, the next data point that you encounter is not going to be another anomaly. It's very rare to have multiple anomalies coming from the same source. Instead, what you're likely to find is a data point somewhere between the anomaly and the average. Periodically, you'll find that that data point falls below the average. But even in those cases, it's very unlikely to be an anomaly in the other direction. You don't often see an extremely good performance from one athlete followed by an extremely bad performance from the same athlete. More often, you're going to see a extremely good performance or an extremely bad performance followed by a slightly less good or a slightly less bad performance. In this statistical reality, even though it's not very intuitive, it's called regression to the mean. You can think of this as returning to normal, and it doesn't only happen in sports. For example, let's say you have an excellent week at work. You're firing on all cylinders. You feel like you're solving every problem that comes across your desk. That you have understood something brand new every day. That you're learning and you're leading other people. And people tend to take your opinions and appreciate them that you're actually contributing to the group. You're getting recognition from your peers and from your leadership. You may have even gotten a raise in this week. By all accounts, this is an excellent week for you. And our brains tend to parse this kind of information incorrectly. We see a good week as a trajectory. We imagine that when we start heading down that good path, that will continue heading down the same good path. And perhaps the following week is even better. And this may actually be true sometimes. But it's also important to recognize that even though we may have a period of one or two or even five good or great weeks, we could even have a great year. We also have to prepare ourselves for a return to the norm when we have an unusually great sales month or when we have an unusually bad sales month. We shouldn't expect that that necessarily will happen the following month. Of course, statistics don't really know about time periods very much either. Unfortunately, just measuring month to month is not going to give you the perfect bucket to understand when things will swing back to the norm. You've created those data points. But it's important to recognize that eventually things will head back to the norm. Now, how can we use this in our careers? How can we become better developers and utilize this information to work better on a daily basis? Well, I mentioned a few terms that I want you to kind of zoom in on and focus on these terms for a second. The first term is anomaly. The second term is the norm or the mean. You can use the word average. And I want you to focus on these terms because identifying what the definition of these two things is for you is incredibly important. For example, if you look at the performance of athletes over the last hundred years, the average has not stayed the same. Athletes are actually getting better. So regression to the norm in 2018 means a different value than it did in even as recently as 2000. So what's the important point here? Well, the important point is that the average may change. And you have to find ways for yourself to change your average. This means focusing on habits rather than focusing on single experiences or events. The next question that you have to ask yourself is what is an anomaly? For example, if you establish bad repeated anomalies, well, it's not really an anomaly anymore. If you continuously are overworking yourself, let's say for example, that five days a week, or even three days a week, you're working extra to the point of it being detrimental to your health or to your relationships. Well, three times a week is not rare. That's no longer an anomaly. So you shouldn't expect that you're going to regress away from that behavior. If you continuously operate in a particular way, then you can't really consider that an anomaly. So it's important to identify and aware of those boundaries. What is your average working pace? What is your average output? And what would you consider to be an anomaly? And you can do this at a personal level. You can do this at a team level. And really, there's so many ways that we can apply this that there's not really an easy way to make this any more practical for today's episode. But we can talk about this more in the future because I think it's really important to start setting those upper and lower bounds. Every once in a while, you come across an athlete or a team or a developer or a company whose average has been raised to the level of continuous anomaly. In other words, for everyone else's behavior or everyone else's performance, this level of output or this level of effectiveness, would be considered an anomaly. But these people have found ways of establishing their habits and increasing their average, becoming better, improving their normal operating ability, improving their normal output. These are the kinds of people that we want to study. These are the kinds of people that we want to focus on, pay attention to. And what you'll notice is that these people are the ones who are establishing regularity. They're the people who are establishing mindfulness. They establish routines. These are the people who are creating the right environment for themselves to increase their average continuously. To be above average for a larger sample of people. Their average, their mean, sits well above the average for everyone else. And these are the kinds of people that we want to look at. So I encourage you to identify people who are consistent. Not the one-time wins, not the one-hit wonders, but the people who consistently, on average, do well. Those are the people you want to pay attention to, emulate, find ways of gaining insight from them. And this is why we don't focus on taking shortcuts on this show. This is why we don't focus on, you know, one language. We don't focus on a single framework. We don't focus on quick fixes or temporary schemes. That's not the kind of stuff that's going to increase your average, right? Those things will give you anomalies every once in a while. But they're not going to increase your average. They're not going to put you in that top tier. They're not going to make you a better developer from your base level. Thank you so much for listening to today's episode of Developer Tea. I hope it's intriguing and challenging and exciting for you to think about raising your own average. And I hope for you who are in those anomaly positions right now, you're having an extraordinarily good, or on the flip side an extraordinarily bad week or month or even year, that you'll find some hope, or you'll find a little bit of caution in today's episode that things probably will return to normal soon. Thank you so much for listening to today's episode and thank you again to Linode for sponsoring today's episode. Head over to spec.fm slash Linode to get started today. You can get $20 worth of credit for using the code Developer Tea2018 at checkout. Thank you again to Linode. Thank you so much for listening. 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