I was inspired to read this book by an interview with Botsman on a podcast I listen to. It’s based on a compelling overall narrative… how have we reached a point where no one trusts the President of the United States, but people agree to stay in spare rooms of complete strangers on AirBnB and will literally put their life in the hands of a self-driving car? Botsman came to fame writing about the sharing economy, so it makes sense to hear her perspective on the topic.
The book isn’t as bold as I would have liked in predicting the future. In books like these, I prefer to hear an experts opinion on what is likely to happen, whether it is good or not, and what that means we could/should be aware of. Botsman prefers to focus only on the third of these topics; the dangerous trends we are working towards. That said, she does offer a couple compelling cliffs that we appear to be confidently striding towards:
Not Evaluating Bot’s Motives – Botsman weaves together stories of her daughter interacting with Alexa and scientific studies to show that as people become more comfortable with technology, we are too trustworthy of its “answers”. Her concern is that these bots that we “trust” are not there as a public service, but to influence us in to certain behaviors.
Trusting Technology Before It’s Ready – Botsman also points out that there are a LOT of areas that artificial intelligence and algorithms have not figured out how to do well, but we seem to be willing to trust them without testing them. There are several good academic studies where this behavior is exhibited.
Algorithmic Trust (Blockchain) Can Be Manipulated – Botsman covers the Ethereum DAO hack very well. If you don’t know much about Block Chain you’ll be able to follow it. She points out two concerns with the hack. First, that the hacker didn’t really “break” the system, just found a way to use it that was unintended… imagine if Ethereum had become an international currency before that happened. Second, that a relatively small group of people decided to effectively “undo” the change. In this case they were doing “good”, but it still served to demonstrate that Blockchains can be manipulated.
Reputation Based Trust Getting Out of Hand – The topic of the Social Credit Score in China is covered in more detail than I’ve ever read before and it is terrifying! It’s essentially a credit score system that doesn’t just evaluate your wealth and default history, but you friends, your political leanings, the places you’ve gone, your grades in school, your hobbies, and more. Essentially this will give the government access to incentivize all manner of behavior. The consequences will be real too, “people with low ratings will have slower internet connectivity; restricted access to more desirable night clubs, restaurants, and golf courses. To quote a government document, “allow the trustworthy to roam every where under heaven while making it hard for the discredited to take a single step.” While it is easy to say, that’s just China… we already evaluate people based on the number of followers they have, little blue check marks, star ratings on AirBnB or Uber, etc…
Overall, I recommend the book, but only if you are interested in these topics and would like a little hand book of the current state of them. I also don’t think this book will age well as these topics are evolving quickly. If you happen to come across this blog post after 2019… I doubt I’d still recommend it.
Whether you care about baseball or not. Whether you wanted to hear about it or not. If you’ve had more than three conversations with me in real life, I have told you that one of my favorite things is the fact that in baseball a player’s “Wins Above Replacement” (WAR) can be calculated. Statisticians can say, with a fairly high level of precision, how valuable (in terms of wins) a particular player is to a baseball team over a season. Imagine being able to calculate your colleagues value to the organization with math!
One of the problems with WAR though, is that it does not translate cleanly to fantasy baseball. There’s definitely a correlation between WAR and fantasy value, but it is far from perfect. So, this season, in preparation for my Fantasy Baseball draft, I set out to calculate how valuable all the potential players in Fantasy Baseball were. Settle in, this post is going to be long and worth it. I’ll start with a really quick overview of WAR in real baseball, then discuss the major sections of it that I had to change for fantasy baseball, finally I’ll talk about how the results were somewhat surprising.
WAR in “Real” Baseball.
At a high level, to calculate a player’s WAR we figure out the number of runs a player can be expected to add to a team with his bat/base running and the runs that he can prevent with his glove or pitching arm. We then divide that number by an approximation of the number of runs that it takes to win a game. Finally, we subtract the number of wins that a “replacement” player could have achieved. This is done by looking at the runs contributed/saved by the best player who would typically be available on waivers at that position.
In order to calculate a players value for this upcoming season, we will need to get a reasonable guess of how they’ll do statistically in 2019. There are a lot of projection services, but I will be using Steamer because I can get it for free on Fangraphs. Steamer doesn’t post their exact algorithm, but I suspect that like most systems, they project a player’s statistics by assuming they will progress in a similar manner to historical players who had similar statistics at their age. So, for example, if you want to figure out how Andrew McCutchen will do this year. You look for players that have had similar careers to this point (if you’re curious… Andre Dawson, Dave Winfield, and Vernon Wells are the most similar) and then assume McCutchen’s age 32 season will be similar to what theirs was. Interestingly, Steamer was developed by a High School math teacher and two of his students in 2008 and has consistently been as accurate or more accurate than systems developed by high profile sports shops that charge for the data (ESPN, Baseball Prospectus).
Calculating “Wins” in Fantasy Baseball
If we’re going to calculate Fantasy WAR, the first thing we have to do is determine what a “win” is. Clearly, in fantasy baseball, runs scored and runs prevented don’t win games. In my fantasy baseball format, you play head to head each week against another player, but there isn’t just one winner. You effectively play 6 offensive games each week against one opponent, one for each statistical category (TB, HR, R, RBI, SB, OBP). For example, if my team has more home runs, total bases, and RBI but my opponents team has more stolen bases, runs, and a higher OBP than we each win three “games”. It’s those wins that matter at the end of the season for playoff seeding. For this reason, the “wins” that I’m counting is winning a single statistical category.
The next question is, how do I take a players projected home runs for the season and turn it in to fantasy wins in the home run category. What I need for that, is to be able to compare the player’s numbers to the average number of home runs a fantasy team will have per game in 2019. In my league their are a total of 120 players playing in each game (there are 12 teams and each team has 10 starting position players). It’s reasonable to assume that the 120 players playing will be the top 120 players according to fantasy drafts that have already been held in 2019 (this data is available in the Fangraphs data). I averaged the projected home runs that will be hit by those 120 players over the season. Since there are ~25 weeks in the season, I divided that number by 25 to get the average number of home runs per player per game in my league. I then assumed that a “win” in Home Runs can be achieved by beating one team (10 players) of those average players. For my league that means that 22.364 home runs is a “win” in home runs.
Calculating “Replacement” in Fantasy Baseball
In addition to knowing how many wins a player is worth, I also have to figure out how many wins a replacement player is worth. My league has 6 bench spots and 2 DL spots; I have assumed that teams will, on average, use half of those for position players. As I mentioned above, there are 12 teams in my league and 10 position player starters. That means that at any moment each team has spoken for 14 position players and the league has spoken for 168 total. Using that information, I have assumed that an available “replacement” player for fantasy purposes is the 169th best position player in any particular statistical category. So, for example, in HRs, the 169th best available player is Roland Guzman of the Rangers who is projected to hit 13 HRs (or .520 HR per fantasy baseball game).
Example of How it Comes Together to Get Fantasy WAR
Let’s take a look at Gincarlo Stanton’s WAR for just HRs. He is projected by Steamer to lead baseball with 45 HRs. As you saw above in the section on calculating replacement value, our replacement home run hitter is Roland Guzman and he will hit 13 HRs. Gincarlo can then be projected to hit 32 HRs above replacement. In the section on “Calculating Wins” we learned that 22.364 HRs should be enough to win the average game in my league. That means that Gincarlo is worth 32 HRs are worth an extra 1.43 wins for whatever team has him.
Comparing The Results to Average Draft Position
If you’re the curious type and have made it this far, you can find my calculations here. I have pasted the top 20 players by Fantasy WAR above. The column “P ADP” indicates what order they are selected in during Fantasy Drafts that have already happened this year. There are a few things that jump off the page:
Stolen Bases are important. You’ll notice that no one whose projected to get less than 10 stolen bases even made the top 20 and that virtually all of the speedsters are towards the top. The reason this is true is because of how top-heavy the stolen base category is. It only takes 7 stolen bases to win a typical week, so Trea Turner’s 41 steals project to be worth almost 6 wins over the course of a season. Remember Giancarlo Stanton’s 45 HRs were only worth 1.43 wins because it would take 22 HRs to win a game. Be careful with this information though (see deficiency #3 below).
Steamer likes a few of these guys better than people drafting leagues seem to. Roughned Odor and Yasiel Puig in particular show up as much more valuable than you’d expect (most of the rest of the big differences are explained by the stolen bases).
Christian Yelich looks to be the most over-valued player in the top 20, with people selecting him 5th even though his value looks to be 13th. The most over valued player overall is J.D. Martinez who ranks 33rd in Fantasy WAR, but is being drafted 4th among position players.
Deficiencies In Fantasy WAR
Please do not use this list as your draft cheat sheet and then yell at me when your team is terrible. There are several key things that Fantasy WAR and/or my calculation of it are bad predictors of:
Billy Beane is famous for his quote, “My shit doesn’t work in the playoffs”. Neither would Fantasy WAR. A team selected based only on Fantasy WAR would be designed to beat the average team. It would be likely to rack up wins in the regular season, but may not stack up well against a team that has several categories very well covered. I would expect a team built on Fantasy WAR to do really well at racking up wins against bad teams but may lose 7-5 in the playoffs without making any of the 7 particularly close.
I ignored positions for this calculation. There were a number of reasons I chose to do this; the two utility players, the daily switching of lineups, the fact that center fielders aren’t treated separately. That said, obviously the catcher position as well as probably middle infield spots need to be given special attention. Unless I missed something, the most valuable catcher is JT Realmuto at 113th!
The values listed here are only really useful for your first pick. Once you already have Trea Turner and his 41 steals on your team, Mondesi isn’t nearly as likely to win you games. For this purpose, I intend to make a version of this spreadsheet for my draft where I can ignore certain categories once I have “enough” of them on the team.
If you look through this and spot something you think I missed, feel free to comment.
2019 is starting off in a great place; I’m very happy with the friendships/relationships that I’ve formed in New York over the last couple years (and the ones I’ve maintained via distance), I have a chance to work on a really interesting project at work, and I’m in reasonably good physical shape. The past few years there have often been “low hanging fruits” in my life that desperately needed attention and where I could quickly get very rewarding progress (e.g. having let my weight go). This year I feel the need to carefully select what I want to improve because it will take a lot of work to get significantly better than where I am.
I ended up with three distinct goals for 2019 that can help take me to an even better place:
Enhance My Just For Fun Project – For the last 4 years I have run a website where my friends can pick NFL Football games (sort of like Fantasy Football). I usually use it as a way to learn the technologies that my team at work has been using at a hands-on level even though I don’t have time to keep up with all of their progress on a line-by-line basis. This year my team is working with such cool technologies that I’ve really learned a lot and built a product that’s pretty good. I’d like to spend the off-season making it robust enough that I can open it up to users who aren’t just my friends. This will require some functional enhancements; a new user interface, the ability to create and administer leagues without messing with the database, a few other odds and ends. Most of the changes though will be creating better DevOps/Testing/Monitoring/Logging so that there are less disruptions. My friends are pretty forgiving with outages and dumb mistakes… but I wouldn’t expect everyone to be.
Get a Sampling of More Cloud Technologies – My current project has me very focused on Kubernetes on premise. I think I would benefit from a more well-rounded technical background so I can help influence other decisions. With that in mind, I’m going to be working on getting a few AWS certifications and probably a GCP one.
Get Back in to Non-Technical Reading – My career has gotten a lot more technical in the last 2 years than it was before. My focus on DevOps has only required some of the highest level knowledge of what’s going on in banking, best practices in development/agile, and management techniques. I’ll be looking to modify my regular reading, get through a couple of books that talk about the industry at a much higher level, maybe even get certified in SAFe so that I still feel as at home in the developer’s scrum as I do in the devops scrum.
In addition to those 3, I want to make sure I don’t regress on a few areas of my life that are going well. Most notably continuing to nurture my relationships with people and stay in reasonably good shape. I have a few ways to measure those, but the goals are not remarkable.
I’m sure the nearly no one who almost never read it will rejoice. It’s not a cgrand re-entry; I’m as ambivelant as ever about how many people read or enjoy it. It’s back online because I have missed having a place for long form expression. I enjoy thinking through something long enough to have a coherent thought on it and I have found the best way to be sure those thoughts are coherent is to put them somewhere where someone might read them.
If you’re curious… the old blog posts, all 1050 or so of them, are gone forever. Around this time last year I was cleaning up my AWS account and inadvertently deleted the instance that had my blog on it. Any of you that have worked with me professionally will appreciate the humor in the fact that I, for a brief moment, was forced to recognize the importance of segregation of duties!