Major League Baseball in Troy, NY

A friend of mine pointed out that a Wikipedia article about the Troy Trojans baseball team cited me as a reference!

The article was actually a project for a Writing for Publication class that I took in grad school. It was later republished as a feature article in a baseball preview issue published by The Spotlight News.

However, when I looked at the Wikipedia reference link, I realized that the link was an old one that I’d forgotten about, and didn’t know was still there! I figured I should give the article a new home. So I took my article and created a new page for it. You can find the new article page here!

The article is a neat history piece that dates back to a period around the Industrial Revolution. If you’re a baseball enthusiast (like I am), I hope you enjoy it!

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Don’t like reading terms and conditions? It’s not just you

During my lunch break, I came across this article in the New York Times. It talks about privacy policies for a number of companies — and the vast majority of them are nearly incomprehensible. According to the metrics in the article, comprehending privacy policies requires a minimum of a college degree — and even then, they may not be understandable. As mentioned in the article, the policies were not written to inform the public (read: you) as much as to protect the company. It brought to mind a research article that I read in grad school. It had to do with legal documents, the language of legalese, and how it was nearly incomprehensible. I don’t remember the specifics of it (grad school was a long time ago), but the gist of it was that these documents were purposely written that way in order that any ambiguous language was eliminated and things were made clear. And when I say “clear,” I mean that definitions were defined and unequivocal. Readable, however, is another story.

I could get into data security and how privacy policies exist for your protection, but that’s not why I’m writing this article. (I’ll leave it to people like Steve Jones to address that aspect.) Rather, I’m writing this because I’m a technical writer (among other things), and document readability is a big deal to me. Indeed, this is a major point of emphasis in both of my presentations about talking to non-techies and documentation, and is one of my biggest document pet peeves.

Readability is a huge deal in documentation. Legalese may be a big deal for making sure definitions are unambiguous, but it is inappropriate for something like, say, step-by-step instructions. When I’m writing instructions, I follow a rule of thumb where if an instruction takes longer than a few seconds for the reader to understand, the instruction has failed. I continue to be appalled by technologists who insist on writing every little bit of detail in their instructions and end up with a “step by step” that is one big black body of text. And I’m continually annoyed when that person says, “it’s right there in the documentation,” but the information you seek is buried somewhere in the middle of the 100+ lines of text that (s)he wrote that takes about an hour to read.

When I talk about documentation and instructing people, one tenet that I actively push is the KISS principle. But even this is not easy to do, and people take that for granted. Indeed, this is what technical writers, UX/UI developers, and instructors do; they are in the business of taking incomprehensible technical language and translating it for people to understand.

Do privacy policies really need to be that incomprehensible? I don’t have an answer to that right now; that might be another article for another time. But what I do know is, if their intent is to inform people, especially the general public, they fail miserably.

The evolution of statistics

During my lunch break, I was perusing the ESPN website and stumbled across this article. It contemplates whether or not a .300 hitter (in baseball, for those of you who are sports-challenged) is meaningful anymore. As a baseball fan, the article caught my attention. I didn’t read through the entire article (it ended up being a much longer read than I expected — too long for me to read while on a lunch break at work), but from what little I did glean from it, a couple of things struck me.

First, they talk about Mickey Mantle‘s batting average and how important hitting .300 was to him. That struck me a little funny, because (as far as I know — as I said, I didn’t get through the entire article) there was no mention of the fact that he actually finished with a batting average under .300. His career batting average was .298.

The second thing that struck me was (Yankees’ first baseman) Luke Voit saying how he felt that “feel like batting average isn’t a thing now.” Indeed, baseball is a much different game than it was, say ten, twenty, or thirty years ago. Analytics are a big part of statistics these days. A lot of stats that are prevalent now — WAR (wins above replacement), exit velocity, OPS (on-base plus slugging), etc. — didn’t even exist when I was a kid growing up, closely following my Yankees. Back when I was eating and sleeping baseball, hitting was about the triple-crown statistics — batting average, home runs, and runs batted in (RBIs). But now, we have “slash lines,” on-base percentage, slugging percentage, and so on. Even as big of a baseball fan as I am, I haven’t a clue about many of these “new age” stats. I still have no idea what WAR represents, I’m not completely sure as to what the numbers in a slash-line are, and I don’t know what constitutes a respectable OPS.

That got me thinking about how statistics have changed over the years, and whether or not that applies to statistics outside of baseball (or sports, for that matter). Maybe people who study data analytics for a living might know this better than I do, but what business statistics have a different meaning now than they did ten, twenty years ago? Are there any numbers from way back when that I should now take with a grain of salt?

I’m sure there are many examples of this outside of sports, but I struggled to come up with any. Off the top of my head, I remember how a company where I once worked made a big deal out of perfect attendance — to the point that they gave out perfect attendance awards at the end of the year. However, that had to contend with situations such as coming to work when you were sick, and so on. Do you really want someone who’s sick coming into work? These days, workplaces do not want sick people in the office, and with the advent of work-at-home provisions, perfect attendance isn’t so meaningful, anymore. (By the way, my understanding is that company no longer recognizes or rewards “perfect” attendance.)

So I suppose the takeaway is, how well do statistics age? Can they be compared with the same statistics now? What needs to be considered when analyzing statistics from years ago? It’s true that numbers often tell a story, but in order to get the full picture, you also need to understand the full context.

My favorite PowerShell references

One thing I’ve been doing to improve my skill set is teach myself PowerShell.  For those of you who don’t know what that is, here’s my description in a nutshell: it’s the command prompt on steroids.

So far, I’ve come across some references, some good, some not so good, to guide me in this endeavor.  For my own reference (and maybe yours!), listed below are some of my favorite PowerShell references.

This is only a partial list, and I fully expect it to change.  As I find more references that I like, I’ll update the above list!