My APBA playing involves a solo league I put together 16 years ago.  The premise is I make either 6, 8, 10 or 12 teams out of that major league season and play a 30 game schedule using the Master Game and the approximately one million innovations I’ve added through the years.  So far I’ve played 35 seasons with every season since 1996, most of the 60s, and some scattered years from the past.  One of the things I have to do is come up with the base lineup(s) for each team.  In the modern area, it usually involves two lineups because of the more pronounced platoon splits.

One decision I had to come up with this year for a team is the rightfield position that came down between Jay Bruce and Torii Hunter.  Normally I would look at a formula that is essentially modified OPS (with an addition/subtraction for SB/CS) and make sure the platoon situation wasn’t terribly weird in one way or another.  My formula had a very close call between the two, showing that classic tough call on do you go complete player over raw power.

So I began to wonder: what is the true value of an 9, or a 14, or a penalty for a 24, in the context of runs?

Thinking about it for a minute, an APBA card is a “blank” slate.  Meaning that if you managed to go 0-for-whatever with no strikeouts, no double plays, and no other method of reaching base during the season, your card would have a 12, a 25, a 31 and a 35, one or two error numbers (depending on your position), one or two rare play numbers (again depending on your position) plus the “common out numbers” (26-30, 32, 33 and 34) distributed among the remaining 29-30 numbers.

Any hits, walks, double plays, hit by pitches and strikeouts take the place of those common out numbers.  Similarly, a good performance may turn some of the less desirable out numbers (33 and 34) into more positive ones (26, 28, 30, 31 or 32).  So what is the value of a “blank slate” card, and how much better can it get with hits, walks and hit by pitches, and how can it get penalized with strikeouts (since they’re unproductive outs) and double plays (since they’re really unproductive outs).  Fortunately for me, Jim McLaughlin for the 1932 Browns had 1 at bat and managed to get a card.  APBA was nice enough to give him 3 hit numbers, but I took them away to make a hypothetical “blank slate” card.

In making a card, you are basically calculating the singles, doubles, triples, homers, walks, strikeouts, hit by pitch and double plays and substituting those numbers for the outs on the “blank slate” card. So in theory, if I added a number the card would normally not have, this would represent the value for that number if it’s distributed to everybody.

So I devised a plan.  Take this year’s computer disk and play a normal season.  That would be the baseline.  After establishing that baseline, find the most common out number on the card between 26 through 30 and 32, and substitute a different number for every player.  The first test would give every player an extra 1, then an extra 2, and so on.  When it got to the out numbers, you couldn’t replace yourself.  So if your most common out was a 27 and I was giving an extra 27, a different out number would be substituted.  I would play a season for each of those numbers.

I quickly learned a few things when starting this plan.  First, a season it is not enough of a sample.  I figured this out when initial results showed a 4 more valuable than a 3, even though the latter should produce more total bases given an average distribution of base situations.  Also, my testing outs concept really didn’t work, so when it came time to check those, I had to use a different method.

So to counter that, I had to make a larger schedule.  I had actually figured out a rubric for a 954-game schedule per team that was balanced.  I had the schedule thingy generate it, and then I played a season.  Immediately I noticed a problem — the number of runs scored was way down in the baseline — at least a half a run a game.  Additionally, when I finished with the extra 1 trial, the difference was less than a run a game when compared to the baseline, so a lot of 1’s were getting knocked down to doubles.  I quickly learned that the computer when unencumbered from playing a typical schedule will overuse the good pitchers.

After trying various other combinations, I figured out that the best thing to do was to simply play each trial multiple times, so I settled on 5, which made the trial roughly 12,000 games (I didn’t replay any rain outs).  I also made sure that every team had any player that had at least 100 PA or 40 IP on their rosters, meaning some players were on two teams.  Plus I scaled any player whose OPS was under .675 to have the equivalent playing time of 650 PA.  I was getting sick and tired of always having to adjust Boston and Oakland’s lineup to keep David Ortiz from playing short or Johnny Gomes from catching.

Now that I’ve given a good introduction I’m going to break down the card number findings into three groups: the on base numbers, the error/rare play numbers and the out numbers.  You’ll see that there were some interesting finds along the way.  Future articles will look at how this is different in a non-stock situation (such as the one I am playing), effects of pitching, defense and most of the subratings (I don’t really give a hoot about BK0).

Read Part 1