Update: All of the Stadium Cards have been updated and can be found on this page.
The Stadium Cards released over the last fewl months have about 6-8 too many hit results on them. For example, these are the Old and New versions of Fenway Park for 1986:
If you look at the top of the right column on each table, you'll see that the old card has 8 more die rolls (666-673) that result in a hit. Eight die rolls out of 600 possible results doesn't seem like a lot, but its enough to drive up batting averages by about 15 points (e.g. pushing a league batting average from .265 up to .280). New Stadium Cards will be uploaded here before the end of the week (Friday, January 21st).
Fixing Existing Cards
If you've already printed cards (or purchased printed cards), the easiest workaround is to find 7 consecutive hits on your current Stadium Cards and change them to outs. For example on the above card you could change the rolls from 673-679 to outs. The type of out depends on how complicated you want to make things, but you could use the last digit (e.g., 673 = F3, 674 = F4, etc.)
If you're curious how this happened, I had bad data for Sacrifice Flies and Sacrifice Hits, and these numbers are needed to calculate the total number of plate appearances in each season. Again, this may seem inconsequential, but removing a few thousand plate appearances from the data changes things.
Adobe Acrobat is the default application for printing PDF files. Unfortunately, it adds an (unwanted) margin around the entire page that can make the whitespace for each Player Card somewhat asymmetrical. To get around this:
1. Select “Properties” from the File Menu (or type Ctrl-D) and click the Advanced tab.
2. Change Page Scaling from “Default” to “None”.
3. Click "OK".
Alternatively. you can open the RTF folder included with each purchase and print the ".rtf" files using Microsoft Word, which doesn't have this problem.
I recently improved the result tables for the Hit & Run rules. The Hit & Run is still resolved in the same manner, but I've made some changes to the results that are affected, and how they are affected. My goal was to improve the statistical realsim, but I also ended up simplifying the rules in the process.
In the previous rules, there were two double play results that became hits, depending on the handedness of the batter. For a right-handed batter, the second baseman would break to cover 2nd base on the Hit & Run play, leaving a hole on the right side (with the reverse being true for left-handed batters). This motion by the defense is part of the reason that the Hit & Run play raises batting averages by about 30 points in Major League Baseball, and this is the reasoning behind the old table (at right).
However, I realized that real baseball doesn't always work this way. Sometimes the defense switches things up in an attempt to fool the hitter. (A good example of this can be seen in Game 6 of the 1986 World Series, where Wally Backman covers 2nd on the Hit & Run, despite the fact that the left-handed Wade Boggs is batting).
So I looked into ways for the Hit & Run to create the correct benefits (increased batting average and prevention of the double play) without relying on the handedness of the batter. After digging into the math, I realized that the Hit & Run play in Season Ticket Baseball already increases batting averages by 30 points — by reducing the number of strikeouts. Being aggressive early in the count cuts down on both strikeouts and walks, and the rules correctly simulate this by changing both 'K' and 'BB' results into a 2-strike count that doesn't end the at-bat. In order to strike out with the Hit & Run on, you essentially have to roll a 'K' result twice. This rule, by itself, increases batting averages by 30 points — exactly the amount that I was trying to design into the system.
These are the effects of the Hit & Run with the new rules:
It's tough to confirm whether these results perfectly match real-life outcomes, mostly because it's hard to identify all the Hit & Run plays in the statistical record. Nevertheless, my research indicates that they are pretty close to the effects seen in the above table. If you'd like an idea of the kind of research I did, I recommend starting with this article at Baseball Prospectus.