If you’re like me, every so often you use a quarter of a day or so to fiddle with all the various strategy settings the game gives you, budging things this way or that, and all the while wondering if they make any difference whatsoever. Right? I mean, what’s a Quick Hook do in situation A vs. situation B, and does the game even get it “right,” whatever “right” is?
I never know what to think, but I put the time in simply because I hate the idea that it might matter. I don’t want to let my guys down, now, do I? Or, I guess, the better way of putting it is that I spend way too much time on these things because I have a distinct tendency to over-think it all.
One of those things is aggressiveness on the basepaths. Specifically, that team and individual settings for how aggressive we want to get with player A or player B. A compatriot in the old world used to call the strategy settings “Play Better” knobs—complaining that they were even in the game to begin with. The concept, in his mind was that while playing the game on full sim mode, the game should simply use the best options for the personnel he put on the field, and leave it at that. Otherwise, the work to push “steal often” up on a fast player and “don’t get your ass picked off” on the slugs was just busy work.
Yeah. I get it.
Still, the settings are the settings.
What I’m eventually getting around to is that, as I put on Discord earlier today, I’ve developed a script today that pulls the data from our box scores, and tabulates then in a way that we can actually do a little real GMing. In other words, we can now look across the league and see how often our runners are getting thrown out on the basepaths (either stretching hits too far or trying to take one extra base too many)
This is only a partial bit of the data we’d really need—and arguably only a partial bit of the data real GMs would have at their fingertips. To make more robust assessments we’d need information on how often the players make it safely to a base also (as a minimum). In other words, this tool is looking at the opposite of survivor bias. But a start is a start.
Though the sample size is very small at this point of the season, it’s still interesting to look at.
For example:
Which Team’s Baserunners Has Been Throw Out Most Often?
Note, this is another one of those weird questions where leading the league may not be a bad thing. In other words, in order to get thrown out on the basepaths, you have to first get onto the basepaths—hence bad teams may arguably be artificially look better than they are here, and teams who hit a lot of singles and take a lot of walks might score more poorly than you’d think.
That said, it’s interesting that the answer to this question is:
San Fernando – 11
Phoenix – 10
San Antonio – 9
Interesting collection, eh? Another interesting bit of trivia right now is that Rosenblatt leads the league in getting cut down at home plate, having done so five times already (while only having run into a total of six extra outs). Is this an indicator that Ahamatu Njiru (The Bomber’s third base coach), is too heavy-handed on making the big wave? You tell me. (See what I mean about overthinking?)
On the other hand, the Bombers lead the league with ten outfield kills. So if you look at it as an offense/defense balance, Justin’s club is at a +4 on the giveth/taketh away scale.
Here is the data across the league I’ve got so far (sorted by overall Kill-Out Delta. I have done a medium-fair job of spot-checking it, but if you see something off, let me know. Note again, the issue here is sample saize.
| Running> | 2B | 3B | H | Outs | Fielding> | 2B | 3B | H | Kills | Tot> | Outs | Kills | Delta |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CLG | 1 | 1 | 1 | 3 | CLG | 2 | 3 | 4 | 9 | 3 | 9 | 6 | |
| MTL | 1 | 1 | MTL | 2 | 5 | 7 | 1 | 7 | 6 | ||||
| MEX | 2 | 1 | 1 | 4 | MEX | 4 | 3 | 1 | 8 | 4 | 8 | 4 | |
| ROS | 1 | 5 | 6 | ROS | 5 | 3 | 2 | 10 | 6 | 10 | 4 | ||
| LOU | 3 | 1 | 1 | 5 | LOU | 3 | 3 | 2 | 8 | 5 | 8 | 3 | |
| BKP | 2 | 1 | 3 | BKP | 3 | 1 | 1 | 5 | 3 | 5 | 2 | ||
| CBH | 2 | 1 | 1 | 4 | CBH | 1 | 2 | 3 | 6 | 4 | 6 | 2 | |
| HAW | 2 | 2 | 3 | 7 | HAW | 4 | 2 | 3 | 9 | 7 | 9 | 2 | |
| LBC | 1 | 3 | 3 | 7 | LBC | 1 | 4 | 4 | 9 | 7 | 9 | 2 | |
| NI | 1 | 2 | 3 | NI | 2 | 3 | 5 | 3 | 5 | 2 | |||
| SAC | 2 | 1 | 1 | 4 | SAC | 3 | 3 | 6 | 4 | 6 | 2 | ||
| AUS | 1 | 3 | 4 | AUS | 3 | 1 | 1 | 5 | 4 | 5 | 1 | ||
| VAL | 3 | 1 | 4 | 8 | VAL | 2 | 4 | 3 | 9 | 8 | 9 | 1 | |
| BOI | 1 | 4 | 1 | 6 | BOI | 1 | 2 | 3 | 6 | 6 | 6 | 0 | |
| CCJ | 2 | 2 | CCJ | 2 | 2 | 2 | 2 | 0 | |||||
| DM | 1 | 1 | 2 | 4 | DM | 1 | 1 | 2 | 4 | 4 | 4 | 0 | |
| LV | 4 | 1 | 1 | 6 | LV | 2 | 4 | 6 | 6 | 6 | 0 | ||
| NSH | 1 | 1 | 2 | NSH | 2 | 2 | 2 | 2 | 0 | ||||
| YS9 | 2 | 2 | 1 | 5 | YS9 | 3 | 2 | 5 | 5 | 5 | 0 | ||
| BIK | 4 | 2 | 6 | BIK | 1 | 2 | 2 | 5 | 6 | 5 | -1 | ||
| CPF | 1 | 1 | 1 | 3 | CPF | 1 | 1 | 2 | 3 | 2 | -1 | ||
| ATC | 1 | 2 | 2 | 5 | ATC | 1 | 2 | 3 | 5 | 3 | -2 | ||
| POR | 2 | 1 | 2 | 5 | POR | 2 | 1 | 3 | 5 | 3 | -2 | ||
| TWC | 5 | 5 | TWC | 1 | 2 | 3 | 5 | 3 | -2 | ||||
| VAN | 2 | 1 | 3 | 6 | VAN | 1 | 1 | 2 | 4 | 6 | 4 | -2 | |
| CHA | 2 | 3 | 5 | CHA | 1 | 1 | 2 | 5 | 2 | -3 | |||
| JAX | 2 | 1 | 2 | 5 | JAX | 1 | 1 | 2 | 5 | 2 | -3 | ||
| SFB | 1 | 6 | 4 | 11 | SFB | 3 | 2 | 3 | 8 | 11 | 8 | -3 | |
| MAD | 3 | 1 | 4 | 8 | MAD | 3 | 1 | 4 | 8 | 4 | -4 | ||
| SA | 2 | 3 | 4 | 9 | SA | 1 | 2 | 3 | 9 | 3 | -6 | ||
| PHX | 4 | 4 | 2 | 10 | PHX | 1 | 1 | 2 | 10 | 2 | -8 |
What About the Players?
I should note that, of course, I can get to individual players, too. I can, for example, use this data to say that Madison’s Gary Fellers, who seems like a perfectly decent little baserunner, is leading the league in getting erased, having now been thrown out on the basebpaths four times.
My own Bikini club has lost six runners on the basepaths, each of the six having been a different runner.
Is that good?
I don’t know. Time to get on the overthinking cap again, right? All I can say for certain is that Ex-Krill outfielder Júlio López is with Long Beach now, and he’s gunned us down twice all by himself. Maybe we shouldn’t run on him? Both have been at third base.
That’s another question, right?
Across the league we’ve had 52 runners thrown out at second base. One assumes those are all players trying to stretch singles to doubles. 56 players have been thrown out at third base. There is, at this point, nothing in this data to say how many were guys trying to go first-to-third, or how many were doubles stretching to triples. Maybe I can figure a way to cross-hatch data here with data from someplace else to get this, but right now it’s a big sucking black hole.
I wish OOTP could give us this kind of basic stuff. Any team worth their salt would have a high school intern (or AI agent) doing this, right?
Anyway, we’ve also had 54 runners thrown out at the plate.
So this is pretty even right now.
It that right? Is that “normal” for baseball? I have no idea.
Are the distributions of “outs” proper per the ratings? I have no idea , either. But if you want to dig into the granular data on your own players, or across the league, feel free. It would be a lot of manual work that I’m not up for right now—made harder by the fact that I can’t pull ID information from the box scores.
Anyway…this is kind of fun, though.
I need to go do some serious over-thinking about it.
If you, too, do any over-thinking, please share! In the meantime, here’s the raw data.
