I should start by saying that I manage my minors quite heavily by age. By that I mean that it’s very rare you’ll see players older than 23 or so down in A-ball or players 22 in Short-A or lower. I have some reasons for this, of course, not the least is that I just like my organization to feel “real.” In game-speak, though, I also want to make sure I optimize the environment’s ability to create me one of those super-bumping diamonds in the rough, and I figure a 20-year-old who looks like a dud of a prospect has a better chance of that than a 26-year-old, or whatever.
This is a bit of a long way to say that every now and again, I like to take a look at the age scatter around my organization, and compare it to the rest of the league.
As a quick dash, StatsPlus is helpful here. I like it, even though I’m not sure exactly how it calculates its averages. I’m not sure if it’s using years and days, and I’m fairly sure it’s not including players on the injured list or players in DFA. It also doesn’t report levels below A-ball. Still, it’s good. That said, for the reasons noted above, and for reasons of formatting, it’s not good enough for what I like to do because I find it’s information hard to boil down into real packets of information (or stories) that I can digest in ways that mean something to me.
You can sense a big-ole table of data coming, can’t you?
I knew you could.
Yes, I pulled an “all players” view from the game, and did a quick cut on things by age—calculating the average ages of each team and level as well as the standard deviations of those ages. I then ranked teams organizational depth by level, and colored the top ten “youngest” teams green, and the bottom ten “oldest” teams orange.
Sorted by youngest BBA Level Team, here’s what I get.
As with all these big charts, there’s a lot to take in.
For example, simply scanning the right side of the chart you can see that the youngest BBA teams, Boise, Mexico City, and Yellow Springs tend to be young all the way down the organization. Boise’s Short-A and Rookie teams, at 14th youngest, are mid-pack, but the Spuds hug the top-10 line pretty close everywhere else and are the youngest team in the league by this metric.
Fred Holmes’ Mexico City Aztecs and my own Yellow Springs Nine are the only two teams to be among the youngest teams at each level—with my Nine ranking top 3 at every level except the International Complex (which registers at #9).
I admit this makes me happy—especially since a couple levels may well go to the post season.
I think you can glean a lot from an organization based on this right side. Teams like Brooklyn, San Antonio, and Chicago are quite young in their BBA teams, but are not-so young down in their organizational realms. Montreal is young at the BBA level and AAA level, but gets old quickly.
Look at Las Vegas, too. Matt is quite old at AAA, but young everywhere else—a snapshot that supports the conversation he often seems to have regarding stashing older and possibly useful BBA guys in his AAA teams, but aggressively managing his kids.
The resolution of this metric may be low, but simply scanning down the list would say that teams like Edmonton, Charm City, Des Moines, Nashville, New Orleans, and Sacramento (as well as the Boise, Mexico City, and YS9 axis) might be likely to yield additional players.
I also think it’s interesting to see the bottom three are pretty consistent, too. Atlantic City, Phoenix, and Portland are all fielding reams older than average all the way down the line.
I admit I’m not sure what to make of profiles in the middle, but they are fun to look at anyway. Teams like Jacksonville, Hawaii, Seattle, and Twin Cities all seem “average” in nature, but score young in a couple levels. Rockville (who I’ll talk about again in a bit) is upper-half normal down to short A, but substantially older than average down in the depths of the chart.
I find it interesting that Long Beach’s Stephen Lane is older at the BBA level (which you would probably expect given his use of the free agent market), but maintains lower-level organizations at more classic ages. Again, more on the Surfers in a moment.
THERE’S MORE TO IT THAN AVERAGE, THOUGH
I always worry about using “average” as a baseline because distribution is almost always important. Hence I like to add in the standard deviation metric—which you can see in the data for each team. Larger standard deviations mean more players sit farther away from the norm, so as a rule you can get a hint of which teams are playing older players at a particular level simply by scanning down the standard deviation columns.
A Short-A, for example, Rockville (4.53), Louisville (4.10), and Chicago (3.33) lead the pack in standard deviation. Looking at the rosters of each of these teams reveals that, indeed, each of these organizations are employing several guys who are 29 years old and up. It may or may not be of interest to note that both Louisville and Chicago’s clubs are leading their Short-A divisions, but Rockville’s is likely to miss the playoffs.
And, regarding Rockville, this table shows their propensity to stack the lower levels of their club with older players has different characteristics by team/level. For example, that while their SA club is weighted down with several aging vets, their Rookie team (while older than average) is not—something you can see in their R-Ball standard deviation. A look at their Rookie team shows one player at 23 years old, and a couple 22 year olds.
I also wanted to take a second to look at Long Beach, who as I mentioned before seems to stock its teams with age appropriate players (low standard deviations across the board), but scores old at AAA and A ball levels. Does that suggest a clogging of the organization? Is a clearing out of AAA levels to make room for younger kids coming? Or are the kids just not good enough to push their way into the upper regions? I don’t know—one of some of all of these are likely in play, as well as others.
Regardless, similar scans of different levels will reveal other such questions about other clubs in question, and it’s these stories that I find most interesting about this kid of study. I’ve been kind of staring at this on and off during the time I’ve been writing this, and I keep seeinf different things. I think the whole point here is that there are thirty-two different profiles in the league, and as such thirty-two different narratives.
So, if you see something you thin is interesting, feel free to post it below.
