Tag: baseball

Rather than inundate you all with posting my scorecards after each game, I’ve gone ahead and created a shared notebook in Evernote that contains scans of all of my scorecards from this season. Feel free to peruse them at your leisure.

Since posting my first few scorecards of the season, I’ve had a few questions about them so I figured I’d answer them here.

1. Why bother keeping score?

The short answer is because I like to. But I think there are two additional reasons. First, keeping score forces me to focus on the game as more than a casual observer. I can learn more about the game by doing this, and that increases my overall enjoyment of the game.

Second, and perhaps more important, is that keeping score makes the fan a participant in the game. I’ve long since passed the point where I could ever have a chance to play in the big leagues, but keeping score makes me an active participant in the game. I like trying to outguess the players, managers and announcers as to what the call will be, what the play will be, and what might come next.

2. How do you keep score? What method do you use?

I use Peterson’s Scoremaster scorebooks to keep score of the game. These are relatively cheap scorebooks, but have most of what I look for.

You don’t need to go out an buy a scorebook, however. There are some downloadable scorecards available for free online.

As far as my method, well, everyone keeps score in their own way. As you might guess, I enjoy trying to capture as much information as possible in the most efficient and compact manner. I keep score in pen, using a Bic 4-color pen. Outs are scored in red and hits, walks, and other methods of getting on base are scored in blue, making it easy to distinguish. I track pitch sequence to batters, but not overall pitch counts.

There is a charming little book called The Joy of Keeping Score by Paul Dickson which covers pretty much everything you need to know about keeping score, including some history.

When the game is over, I scan my scorecards into Evernote, and as I said, I’m now keeping them in a shared notebook that anyone who wishes can look at. The full link to the shared notebook is here:

https://www.evernote.com/pub/jamietr/baseballscorecards

And now, I’ll stop inundating you with baseball scorecards and leave you in peace. Have a great Sunday!

Just a fair warning that I am likely to be writing more about baseball this season than I have for the last few years. If this isn’t your cup of tea, I get it, and you can ignore the posts. But there are three reasons why I’ll likely be writing more about baseball and I thought I’d delineate them here so that (a) you know why, and (b) I can point people to this post when they ask.

1. It is Derek Jeter’s last season

I’m a lifelong Yankees fan and I come by it honestly, having been born in New York City and lived, in my infancy, in the Bronx. I’m a Yankees fan despite the fact that the rest of my family are Mets fans. My brother, who was a long-time Mets fan, has lived in Seattle long enough to where he might be more of a Mariners fan than a Mets fan. But I was born a Yankees fan and I’ll die a Yankees fan. And for me, this season is the end of an era. Derek Jeter is retiring after the end of the season. I’ve watched him through his entire career, and seeing him from start-to-finish is, I imagine, like watching players of previous generations–Babe Ruth, or Ted Williams, or Mickey Mantle–go through their careers.

I didn’t watch much baseball last season, and I missed it. I found myself yearning for it in the off-season the same way I yearn for spring after a long winter. I plan to follow this season a little more closely, and since I write about things that interest me, this is one reason you’ll probably see me writing more about baseball.

2. I’m back at work on my baseball simulation software

When I teach myself a new programming language, I need a practical way of doing it. I’m still delving deeper into symbolic languages (Mathematica and R) so I’m getting back to work on the baseball simulation software that I started a while back. I don’t have a lot of time to devote to this, but I figure that over the course of the baseball season, I can get in a fair amount of work on this pet project, and come out in October with a much more solid base in these languages, and have the simulator that I’ve always wanted to make.

And yes, I do know that such simulators already exist. But using existing ones does me no good when I am trying to learn a new language. This is one instance where reinventing the wheel can be quite helpful. Besides, my simulations have a slightly different goal than the ones I’ve seen.

3. I’ve got at least one more baseball story to tell

Early this year, I published a new story called “Big Al Shepard Plays Baseball on the Moon” over at InterGalactic Medicine Show. While this is a science fiction story (a retelling of the Apollo missions) it is also, at its heart, a baseball story. I had more fun writing this story than any story I’ve written before, and I’ve written elsewhere on why this is so.

But I have at least one more baseball story in me. For a few months late last year, I worked on a story called “Strays” that I never got quite right to my satisfaction. This story, even more than “Big Al Shepard” is a baseball story, and I am determined to get it right before the baseball season is out.

But these reasons can also be seen as excuses, as if I need permission to write about something that I enjoy. The truth is, I have baseball on my mind. I have it in my blood. Sometime the feelings wax and wane, but this year, I feel them strongly. And since I write about things that I feel strongly, you can expect me to write about baseball a bit more than usual this season.

Despite its ups and downs, despite the doping and the controversies, there are things about baseball that make it powerful and good. We got to see one of those things last night, when the game’s greatest closer, Mariano Rivera, pitched his final game at Yankee Stadium.

Congratulations, Mariano Rivera! The team will not be the same without you.

It is still baseball season.

That is all. Thank you.

Sincerely,

A Baseball Fan

Baseball is back and so I know it must be spring, despite the fact that it is unusually cold here (I don’t think it supposed to break 45 F today). Two days into the regular season and I haven’t watched a game yet, but I’m keeping my eye on things:

1. The Yankees lost their opener to the Red Sox, which delighted many, if not most of my friends and family. The Yankees are playing with a lot of injuries–as one might expect from an older team. I think its way too early in the season to draw any conclusions¹.
2. Houston is now in the American League, and every division now has 5 teams, meaning that we get interleague play throughout the season. The Angels play the Reds today, for instance. So that’s a bit different.
3. The Texas Rangers were one out away from a perfect game yesterday. That’s pretty remarkable.
4. And Robinson Cano fired Scott Boras and hired Jay-Z.

I thought I might catch the Yankees game tonight, but it looks like I might actually make it to a writers group meeting instead, but I’ll get around to watching a game soon enough. And besides…

This Sunday, the Little Man has his first t-ball practice and that should be a lot of fun. More on that next week.

Yesterday, baseball lost two of its stars. Early yesterday, it was reported that Earl Weaver, former long-time manager of the Baltimore Orioles had passed away at 82. And then as I was heading off to bed last night, I learned that Cardinal’s great, Stan Musial, had passed away at 92. It’s a sad day for baseball, losing two great members of the baseball family. On the other hand, both men lived long lives, doing what they loved. I like what Musial said to Sports Illustrated back in 1963:

Maybe one reason I’m so cheerful is that for more than 20 years I’ve had an unbeatable combination going for me — getting paid, often a lot, to do the thing I love the most.

Back in August, I wrote a post about my Baseball Century Experiment. I haven’t had much of a chance to do actual work on it, but in the months since, I’ve done a lot of reading, particularly on the science of sabermetrics, and I now have a plan, at least, for moving forward with this little experiment. I explained some of my reasons for doing this in my original post, and a few people pointed out that there was already software out there that does this, so why reinvent the wheel? I have a couple of thoughts on this:

The first, and most important reason, for me, is to learn. Sure, there is software out there that can do this, but it exposes only the results. I’m interested in the internal mechanics of how such a piece of software might work. This helps me in 3 different ways:

1. It allows me to make a deeper exploration of baseball by implementing it as a simulation myself.
2. It allows me to dive deeper into a development package–in this case, Mathematica–that I want to know better.
3. It allows me to tinker in ways that I could not do with off-the-shelf software.

Second, I’ve looked at the software that is out there. The top-of-the-line appears to be Out of the Park Baseball. Not only did I look at this, but I bought a copy for my Mac and played around with it a bit. It gets to some of what I am looking to do, but not all of it. I’m not (at the moment) interested in human management in the game. I’m currently more interested in simulating human management through some basic game AI. That is part of the fun for me.

Third, I’m not interested in developing the kind of elaborate interface that OOTP has. My simulation will be entirely text-based. My ideal output and presentation layer would be something akin to WolframAlpha, for baseball, where you could type in some natural language queries and get a boatload of results, charts, graphs, numbers, etc. But at the simplest level, I’m satisfied with producing text-based box scores, play-by-plays, rosters, lineups, standings, etc.

Fourth, I’m not interested in using real players. Part of the point is to think of this as almost an alternate history to baseball. Fictional players, randomly generated, moving through careers based on statistically valid simulations.

The ultimate goal of my initial¹ experiment is to be able to simulate 100 continuous seasons of baseball, and then look at the resulting number and see who are the leaders? Did anyone every hit .400 in a season? Did anyone break a 56-game hitting streak? Who is the home run kings and what is the record? Did any pitcher throw a perfect game?

My approach to all of this is starting very simple and layering on more and more complexity. Over the last several weeks, I have drawn up a plan for how I will approach this. It looks something like this:

1. Develop a simple player generator

Since I’m not using real players, I need a way of bootstrapping players. One of the tools I will need to create, therefore, is a player generator. As with all the tools I’ll need to develop, my plan is to start simple and layer on complexity over time. The simple version of the tool will generate names, positions, and some basic stats for the players. My present approach for generating the stats will be to assume a standard bell curve for a statistic and randomize the stats based on a normal distribution. This probabilities of such a distribution would allow for an appropriate relative generation of “average” players to “superstars” and to players who don’t perform so well. Put another way, there would be a lot of values (say, batting averages) that have small deviations from the mean average. There would be very few that are far better or far worse.

Not a perfect solution but it allows me to bootstrap some basic statistics in a fair way without the need to borrow from real player numbers.

2. Develop a simple team generator

The team generator in this instance is a way of picking out the players needed to create a roster of n people, with all of the necessary slots fills (so many pitchers, so many fielding positions, etc.) from the pool of available people. In a more complicated version, the team generator would be a kind of AI scout or GM, looking at what is available and getting the best that it could. But that is way down the line. Right now, I’m simply looking to be able to create teams out of the players generated in #1.

3. Develop a lineup generator

Again, we are talking simple here. In a more complex version, the lineup generator would be part of the manager AI function. For now, I’m looking to produce the best possible lineup with the data available. In its most simple terms, this is likely a fairly simple two-part problem:

1. Identify a team player for every position.
2. For each position, sort the players by OBP (on-base percentage) and then choose the best OBP for the given position.

At this point, the pitcher almost doesn’t matter

In future versions, I’ll probably also look at some more advanced sabermetrics statistics, but this is good enough for now.

4. Simulate a match-up using simple BLOOP methodology

BLOOP is a method of simulation that sabermetricians have used quite a bit. It’s fairly simple. It involves calculating the probability of various outcomes based on the hitters stats. A more sophisticated version normalizes these calculations based on the pitcher they are facing and across the league, but for now, I’m keeping things simple.

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