Where did Bob Gibson’s Z go?

There was a recent thread on the APBA Baseball Facebook page about the 1968 reissue set, namely that Bob Gibson no longer has a Z.  For those who may not be familiar, 1968 was the year of the pitcher, and it was a season APBA had a little difficulty creating the first time around.

In the original set, there were three starters who were graded A&C: Bob Gibson (plus an XZ), 31-game winner Denny McLain (XZ), and AL ERA champ (1.60) Luis Tiant (XYZ).  In the older formulas, McLain was an “automatic” A&C since he had 30+ wins.  At this point, the A&C as a starting grade had only been given once before — two years earlier to Sandy Koufax — although it had been given in the past to OFAS pitchers.  To somewhat counter the high number of A and A&C pitchers, cards were given an extra 7.

It would be pretty impossible to get a 1.12 ERA from an A&C, even in the deflated year of 1968.  A baseline was set with Tiant getting the A&C for 1.60, and you would think there would need to be some sort of improvement to get to 1.12.  The one basic game replay I could find had Gibson’s ERA at 1.55.

In the reissued set, Gibson got an upgrade to A&B (MG 25), but he lost the Z.  McLain also got a bump to 25, kept his Z but gained an L (he did give up 31 dingers to lead the AL).  Tiant stayed at A&C (MG 20) but lost his Z as well.

Continue reading

26 on the Move

I started playing some games with the 2015 baseball set this week.  One thing I begin to notice was a red 26 on dice roll 21.  I had never really seen that before.  So I began to wonder:

  • Did APBA assign more 26s?  Nope, the average card in 2014 had 1.28, and the average card in 2015 had 1.29.
  • Can players get a third 26?  Nope, not a single card in either 2014 or 2015 had a third 26.

So as seen below, the second 26 had merely swapped positions, leaving 62 for 21 (the x-axis representing the play result and the y-axis representing the percentage of plate appearances with that particular play result):



However, on a swap, you would expect the now much lower counts of 30 and 32 to take the place the relocated 26.  However, although there are some, it’s not complete.  The sum of the 2014 fly balls on 21 is .51 vs the 2015 sum of .24, but the sum of the 2015 fly balls on 62 is .07, only a .04 increase from 2014’s .03.

Do we have less 30s and 32s?  A touch less, but not much.  The 30 saw a decrease of 1.51 to 1.44, and the 32 1.52 to 1.49.  However, that’s only a total of .10.  So where did they go?

It looks like 24 and 26 became more popular destinations, although their increased presence is more than overshadowed by the 13:



Since the number of 13s remain largely unchanged (5.93 in 2014, 5.94 in 2015), the ones displaced from 21, 24, and 26 scattered to other numbers, especially 42 and 56.



Before wrapping up, I just thought I would mention one other curiosity, that even though the play numbers of 2432 on the cards in the 2015 set aren’t that different from 2014, the ratios of them seem to be somewhat different.  Here’s a list of the top 10 sets from 2014 and 2015:

2014 Out NumbersPercentage
2015 Out NumbersPercentage

The #8 combination in 2014 disappears entirely, and #3 is barely used in 2015.  I don’t know if this is something that happens every year, or just something unique to the 2015 set.  Something to explore for another day.


Calculating the Hockey Card, Part 3

After spending our time figuring out how to calculate an APBA hockey card not once, but twice, we’ll move onto the final installment, the goalie.  Just like the relief pitcher in baseball, they can be unstoppable one day and a disaster the next, and finding a good one long term is next to impossible.  So instead of finding one, let’s just try to calculate one.

The concept of the goalie card is simple.  The card is called upon to redirect a saved puck or say a goal has scored.  About 80% of the time, it will always be a redirection.  About 20% of the time it will determine whether a goal has scored, and even a 0.2% difference is different from one card to the next.  Each card contains the following sets of numbers:

  • 16: the always save numbers
  • 718: the sometimes a save, sometimes a goal numbers – a card will at most have one of these
  • 1945: the always a goal numbers

The stats you will need to calculate the card are not much and are available for many of the seasons:

  • Games Played
  • Minutes Played
  • Save Percentage
  • Assists Scored
  • Penalties Caused
  • Team Shots Allowed
  • League Average Save Percentage

Continue reading

Calculating the Hockey Card, Part 2

In the previous post, I discussed how to create the non-play number portion of a hockey card.  In this article, we move on to the actual play numbers.  Unlike the familiar baseball card, this is somewhat straight forward and transparent.  And like the previous post, this is not perfect, there will be some deviance as not all of the parameters are known.

Before we can go placing numbers on the card, we need to calculate a few things.  All of the stats you will need were listed in the previous post.

  • You will need to figure out the penalty numbers.  How to do that is listed in this post.
  • Next is the number of 9’s (power play-only shots) needed.  This is calculated by the percentage of goals that are power play goals:  1-25%=one 9, 26-34%=two 9s, 35-49%=three 9s, 50-59%=four 9s, 60-74%=five 9s, 75-80%=six 9s, 81-89%=eight 9s, 90-99%=nine 9s, 100%=ten 9s.
  • Determine the number of 30s desired to award penalty killers.  These are based on reputation, a good method would be to sprinkle anywhere between 0 and 4 extra 30s among the team depending on the overall effectiveness of the team’s penalty kill unit.  Forwards mostly get the extra 30s, defensemen getting them are rare.
  • Determine the shots on goal taken per 60 minutes, otherwise known as S/60M.
  • You will also need the number of games played and the Assist rating.

Continue reading

Calculating the Hockey Card, Part 1

In my previous article, I tried to figure out the method APBA uses to calculate the penalty numbers on the hockey cards. I then wanted to tackle the passing numbers (10-15), but I noticed a pattern. Basically, when it came to passing numbers, there was no pattern.

Well, initially there was no pattern. Once I finally broke down and decided to figure out the whole card, a pattern then emerged. So I figured if I was going this deep, I might as well try to write an article on how to calculate a card. Or, in this case, three articles. The first article will go over the top and bottom of the card, i.e. all of the ratings. The second article will go over how to calculate the actual play numbers for the skaters, while the third article will go over the goalies.

For this, I had used a data file I had made a few years ago of the Western Conference players of the 2010-11 set. Using this data, I was able to figure out most of the patterns and calculations. I had to rely on a few other cards from the other seasons I had to figure out some of the outliers.

Please note that this will not be 100% accurate. There were some things I could just not figure out, or there were things that may have been mistakes on the card maker’s part. However, this will get you most of the way there.

The stats you will need:

  • Games played
  • Minutes played: Most stat sites will have this by ice time per game, so just simply multiply that by games played to get the minutes for the season
  • Shots
  • Goals
  • Assists
  • Power Play Goals
  • Minor Penalties
  • Major Penalties
  • Misconduct + Game Misconduct Penalties
  • Faceoffs Won
  • Faceoffs Lost
  • Blocks

And here we go:

Position Ratings: This is based on reputation, with some correlation to ice time.  Higher ice time players tend to get the higher ratings.  Somewhat based on the shots allowed per team, the higher ranked teams (those with the fewest) will have their players average around 3.5 while the lowest (those with the most) will average around 2.5.  This is also somewhat tempered by the block ratings for the defensemen, which are calculated purely by stats.  Teams with a high amount of blocks should have a slightly lower average than those with a low amount of blocks.  For example, 2010-11 Detroit had a very low block rate, but had a higher than expected average defense to compensate.

Forecheck: There is no rhyme or reason to what players get, this is strictly on reputation.  Only forwards get this rating.  A handful will get a 5, about four players per team will get a 4, eight players will get a 3, about one per team will get a 1 and the remainder will get a 2.

Shot Frequency: Take the number of shots, multiply by 163.8, divide by the number of minutes played, and subtract one (unless it is already zero).

Shot Rating: Surprisingly, even though this is based off shot percentage, it is not entirely linear.  Use the shot percentage (Goals / Shots) and assign accordingly:

Shot %RatingShot %RatingShot %RatingShot %Rating

Minutes: Ice Time per game rounded to the nearest minute.

Faceoff: Somewhat based on reputation and somewhat based on stats:

  • All 5’s took at least 48 faceoffs per 60 minutes and had a success rate of 52.8%  However, not everybody who crossed this line got a 5
  • Most of the 4’s took at least 40 faceoffs per 60 minutes and had a success rate of 48%
  • 3’s are either infrequent faceoff takers with good percentages or frequent faceoff takers with mediocre percentages
  • 2’s are either infrequent faceoff takers with below average percentages or frequent takers under 45%
  • 1’s are everybody else who took at least a few faceoffs a game.
  • 0’s those who took less than a few faceoffs a game.

However, there are ratings that make no sense.  Here are a few:

PlayerRatingFO %FO/60 Min
Koivu (MON)552.956
Fiddler (PHO)453.967
Kopitar (LAK)449.943
H. Sedin (VAN)352.053
Backes (SLB)344.444
Backlund (CAL)248.045

Min/Maj/Mas: I detailed how to calculate the penalty ratings in this article.

Clearing: Reputation, some correlation to defensive abilities, no correlation to team shots allowed.  Only defensemen get a clearing rating.

Intimidation: Reputation, no correlation to defensive abilities or penalty minutes.

J: Based off games played: 80+ for J-0, 71-79 for J-1, 60-70 for J-2, 42-59 for J-3, 1-41 for J-4.

C: This is mentioned in the Version 2 Rule Book, but if you don’t have it:

  • Left of Slash (Offensive) is the higher of (Forecheck or Clearing) plus the A Rating (described later in article) plus Intimidation Rating
  • Right of Slash (Defensive) is the highest Defense Rating plus the higher of (Forecheck or Clearing) plus Intimidation Rating

I: This is also mentioned in the Version 2 Rule Book, but if you don’t have it:

  • Left of Slash (Offensive) is the S Rating (described later in article) plus Intimidation Rating
  • Right of Slash (Defensive) is the highest Defense Rating plus the Intimidation Rating

Blk: This is derived from Blocks per game.  2.10 or higher gets you a 3, 1.26-2.09 gets you a 2, .76-1.25 gets you a 1, everything else is a 0.  Only defensemen get this rating, even if a forward warrants one.

A/S: Although this appears in the Version 2 Rule Book, the cards I looked at appear to be a little different.  Here is what I saw:

A/SAssist Rating (for A)Shot Rating (for S)

This is a lot to chew off in one article, so we’ll go over how to make the actual card for the skater in Part 2.



In The Box

In my opinion, one of the weaknesses in the APBA Hockey game is the penalty system.  Unfortunately, the original system doled out too many penalties and each successive version has tried to kludge the system rather than just starting from scratch.  This article will go into how the system works, how to calculate what numbers a player should receive and a brief exploration of a better system.

The Current System

The play numbers 33 to 40 are used to dole out the trips to the sin bin, and essentially go in order from least severe (33) to most severe (40).  Assuming that the average player spends 80% in Forecheck 2, 5% in Forecheck 1, 5% in Forecheck 3, 5% in Power Play and 5% in Shorthanded, each number should produce the following assuming you use none of the enhancements:

  • A 33 is a coincidental penalty in Forecheck 2 72% of the time and a minor in Forecheck 3 and power play, so this will produce a coincidental penalty 58% of the time and a minor 10% of the time.
  • A 34 is a minor penalty in Forecheck 2, a coincidental penalty in Forecheck 1 and shorthanded, and a coincidental penalty 72% of the time in Forecheck 3 and power play.  This is a minor penalty 80% of the time and a coincidental penalty 17% of the time.
  • A 35 is always a minor
  • A 36 is a flight 72% of the time in Forecheck 2 and always a fight in Forecheck 3 and power play, producing a fight 78% of the time.
  • A 37 is a fight in Forecheck 2, 3 and power play, or 90% of the time.
  • A 38 is a fight in Forecheck 2, 3, power play and a fight 72% of the time in Forecheck 1 and shorthanded, or 97% of the time.
  • A 39 is a fight
  • A 40 is a major 42% of the time in Forecheck 3, 28% of the time in Forecheck 2 and 8% in Forecheck 1, otherwise it is a fight.  This returns a fight 73% of the time and a major 27% of the time.

Continue reading

The Single Column Six

In the last dozen years or so, an occasional card will end up with a weird combination of 0s and 6s in the first column. Prior to this time, the only extra base hit you could have with a 0 was a 1. So is there any rhyme or reason to who gets this combo.

Let’s first take a look to see who has it.  In the 2014 set, discounting pitchers and anyone else with less than 100 PA, there are 17 players with a 0 on 66 and a 6 either on 11 or 33 or both.  Let’s take a look at the “Special 17”:

  • Two players have 6-6-0: Conor Gillaspie and Starlin Castro
  • Two players have 6-0-0: Randal Grichuk and Chris Heisey
  • The other 13 have 6-0: Corey Hart, Dee Gordon, Stefen Romero, Grant Green, Paul Konerko, Elian Herrera, Jason Kipnis, Nate Schierholtz, Allen Craig, Emilio Bonifacio, Gerald Laird, Joaquin Arias and Clint Barmes.

So what do they have in common?

  • None of the players have enough HR to qualify for a first column 1.
  • All of them have enough doubles to qualify for at least one first column 6.
Randal Grichuk and John Jaso

Two players with similar power stats, but different power numbers, or are they different?

And strangely that’s it. Some thoughts I had:

Hypothesis #1: These players all hit a homer with just a runner on 3rd, as a 6 is a HR in that situation: This does not hold water, as four of the players (Herrera, Laird, Arias and Barmes) did not hit a homer in 2014.

Hypothesis #2: These players were all out stretching a double into a triple, and a single column 6 is used as a way to have that happen in the computer game.  49 players in 2014 were out stretching a double into a triple.  Only two of the “Special 17” were in that group (Castro and Green), so it’s not that.

Hypothesis #3: It has something to do with batting handicaps.  Nope, all of the ratings except 2 are represented and one PL is included.

Hypothesis #4: It has something to do with position or bat-handedness.  Nope, every position is represented, every batting hand is represented.  Oddly, they all throw right, but that is likely a coincidence.

Hypothesis #5: They have no second-column singles.  That actually works for a lot of the players, but it does not work for three of them (Laird, Arias and Barmes).

Hypothesis #6: Something weird happens if your only extra base hits are doubles.  This actually has merit.  Three of the group (yet again Laird, Arias and Barmes) did not hit any triples or homers.  Additionally, all other players with at least 100 PA got a single column 6 or 6s except one, Jose Molina.  Jose Molina didn’t really hit enough doubles to warrant even a single column 6, so he was given a 0 with 10 double column 6s).  So we have figured out that if all of your extra base hits are doubles, you will get a single column 6 as long as you can warrant it.  So we’ll remove Laird, Arias and Barmes from the equation, and move on to the “Frustrating 14”.

Hypothesis #7: Let’s revisit #5 since the three removed were the outliers in Hypothesis #5.  So we have 14 players who have no double column singles for those players who have a double column.  There are 8 other players who also would qualify in this situation, yet do not have the first column 6.  So it’s something more than this?

Data Set Removal #1: One of the 8 players without the single column 6, Andrew Romine, does not qualify for a single column 6 since he only qualifies for one extra-base hit number and a quarter of that number should be a homer.  So he is given a single 0 and we’re not going to worry about him in the later hypothesis, so we’ll call this other group the “Silly 7”.

Hypothesis #8: Is it something to do with the double rate?  No, as the players seem to mix.  The highest ranked doubles hitter in the combination of the two groups is in the Frustrating 14 (Gillaspie) but the second is in the Silly 7 (Jayson Werth).

Hypothesis #9: Is it something to do with the triple rate?  Each group has a player with no triples, and the other players mix again, so it isn’t this.

Hypothesis #10: Is it something to do with the home run rate?  There is a weird thing where the lowest number in the Silly 7 is 0.65, but there are players in the Frustrating 14 with a higher rate.

Since I can’t think of anything else that seems to work, I’m going to go with this to determine whether a player gets a 6-0, 6-6-0 or 6-0-0 combination:

  • Player must have at least one double for each 34.25 PA.
  • Player must have fewer than one homer for each 34.25 PA.
  • If the player has hit no triples or homers, the player will have at least one first column 6 and then the requisite number of 0s.
  • If the player will not have any single column singles, the player may have at least one first column 6, but they may not.

Do you have a hypothesis of your own?  Then don’t be a stranger, leave a comment…



The Dreaded Shootout

I am an avid player of the APBA hockey game but I only play tournaments. Since I need to play playoff-style overtimes, I never had to deal with the shootout. However, when I got the 2014 Olympic set, I would have to use the shootout, because that’s what the Olympics did.  Even though my Olympic replay was going to tweak the seedings a bit by using the 1980 system rather than the 2014 system, I’m still keeping the 2014 game rules.

So in my first game, Switzerland pushed Russia to a shootout.  So I reviewed the rules:

  • Get the shot range for the shooter
  • Add 10 (a.k.a 6) to the shot range, and use either this number or 43, whichever is lower
  • Roll the dice for the shot
  • If the shot roll is within range, roll for the goalie. If you roll a 1 for the goalie, it is not a goal, otherwise, it is a goal
  • If the shot roll is outside of range, it is not a goal

Rather anti-climatic, despite the Swiss winning the shootout 3-2.  And frankly, I’m a bit worried about the realism.  Recent stat geekery has shown that shot percentages tend to be very volatile, and not a great reflection of offensive ability.  So, me being me, I had to run a few tests. Continue reading

Fielder’s Choice

A while back, I ran a simulation of the various hit numbers. I then did the same thing, concentrating on the pitching grades. This time I’m going to look at fielding, including the arm ratings for catchers and outfielders. The latter prove to be pretty surprising.

First off let’s take a look at the most important stat when comparing the fielding ratings: run differential. I had previously established a baseline of 4.04 runs scored per 36 PA for the average 2012 replay. For all of the different fielding ratings, here are the variances away from that 4.04:

Usage 1 2 3 4 5 6 7 8 9 10
P .03 -.01                
C         .07 .05 .00 -.04 -.04  
1B   .10 .02 -.01 -.06          
2B         .14 .08 .04 -.02 -.04  
3B   .08 .06 .03 -.07 -.06        
SS           .15 .10 .01 -.06 -.09
OF .05 .03 -.01              

One of the things that sticks out is that the “average” rating (e.g.: 2B-7, OF-2) are slightly worse than average run-wise. This is compensated somewhat (with the exception of 3B) by having the average rating of the position over the course of a replay be a little above average. For the outfielders, the sum of the OF divided by 3 is presented, as I had no way to isolate the simulations to just use a certain rating in one of the three fields:

Usage 1 2 3 4 5 6 7 8 9 10 Avg
P .37 .63                 1.63
C         .03 .07 .46 .37 .06   7.35
1B   .15 .35 .28 .22           3.57
2B         .00 .11 .42 .33 .14   7.50
3B   .00 .43 .37 .19 .00         3.76
SS           .05 .12 .59 .20 .03 8.05
LF .28 .53 .19               1.91
CF .01 .31 .68               2.67
RF .21 .48 .31               2.11

There are only two positions where the replay average is below the theoretical average: third base (by a lot) and leftfield (by a little). In total, the team defense averages 38.54: 31.86 in the infield and 6.69 in the outfield. With the shift over the last 20 years of 3B producing the second most errors on a team (rather than 2B), upping the 2B ratings and lowering the 3B ratings is one way to do it.

Looking at the main purpose of the fielding rating (errors), there is little surprise in the correlation between the errors per 36 PA for a rating and the errors per 36 PA for an overall replay.

EDiff 1 2 3 4 5 6 7 8 9 10 Avg
P .02 -.02                 .06
C         .11 .08 .01 -.06 -.07   .10
1B   .06 .01 -.02 -.04           .06
2B         .12 .09 .01 -.02 -.04   .08
3B   .02 .02 -.01 -.05 -.07         .10
SS           .08 .05 .00 -.08 -.10 .15
LF .03 -.01 -.03               .05
CF .06 .02 -.01               .03
RF .04 .00 -.02               .04

A little surprising is that the cliff between those positions where -2 away from the norm (e.g.: C-5 and C-7), the margin between -2 and -1 away is greater than +1 and +2 away. LF-2 is a negative, and CF appears to be the least important position to have an OF-3 in, while it is the most deadly to have an OF-1. A LF-3 can pay dividends, especially by neutralizing that pesky fielding 2 LF error with the bases empty.

A look at hits is not very surprising either, with not much difference between the bad fielders and the good ones:

HDiff 1 2 3 4 5 6 7 8 9 10
P .01 .00                
C         .03 .03 .02 .02 .02  
1B   .06 .00 -.01 -.02          
2B         .05 .01 .02 -.01 -.01  
3B   .03 .03 .03 -.02 .02        
SS           .04 .03 .01 .00 -.02
OF .03 .01 -.01              

A bit of an anomaly at catcher, since the hits were up no matter what the rating. Makes me wonder if there is a little bit of an issue with the baseline I am using where the hits may have been a little low. I also have a feeling that the 3B-6 simulation was a little bit of an outlier on the offense side, as both the run and hit differentials were worse than 3B-5.

Let’s move on to the catcher’s throwing arm in respect to stopping (or enabling) the running game:

C -4 -3 -2 -1 -0 +1 +2 +3 +4 +5 +6 Avg
Rdiff .05 .04 .02 .03 .00 .01 .00 .00 -.01 -.01 .00  
SB% .85 .82 .80 .78 .75 .73 .71 .69 .69 .69 .67 .80
SBA/36 .66 .65 .65 .65 .65 .63 .57 .50 .45 .37 .33 .63
Usage .18 .31 .11 .12 .11 .05 .00 .02 .04 .03 .03 -1.21

This was probably the largest surprise of this simulation. First off, that the average Throw rating was -1.21 (and a median of -3), contributing somewhat to a higher than expected 80% steal success rate. The real-life rate for 2012 was only 74%. I have a feeling that APBA is still basing the ratings off a strict percentage and not curving them. The rate when the MG first came out was in the mid-60s, and a difference between what used to be an average of +1 in older sets going down to -1.21 roughly correlates to a 6% higher success rate.

Also noted that the success rate does go down as the throw rating gets better until about +3, at which it levels off. Taking over is a go/no-go call of sending the runner a lot less. I don’t have a Micromanager utility to check on Duke Robinson to be sure, but that would be my gut feeling. Duke also tended to steal more when then pitcher was higher rated, and laying off when lower rated.

And on the other end of the surprise, it turns out that the OF arm ratings actually have more influence than the defense ratings. Again, I could not split the run differences, but I could check on assists and the playing time of the individual ratings. To keep this from being too ridiculous, I only checked Arm ratings 20, 25, 30, 35 and 40. The usage figures below are based on 20-22, 23-27, 28-32, 33-37 and 38-40.

OF 20 25 30 35 40
Rdiff .08 .07 .03 -.01 -.05
LF A/36 .07 .05 .06 .07 .08
CF A/36 .06 .06 .06 .05 .06
RF A/36 .07 .07 .09 .10 .12
Usage LF .00 .03 .75 .22 .00
Usage CF .00 .01 .38 .59 .01
Usage RF .00 .00 .30 .67 .03

An arm of 40 has more benefits than an OF-3. And an arm of 20 has a higher penalty than an OF-1. Interestingly, in the 2012 set the arm ratings only range from 25-38. When it comes to assists (like errors, runs and hits), the centerfielder has the least influence of the three spots, with the rightfielder having the most. Just a little quirk of APBA to keep in mind when putting together your lineup.

When determining the whole value of a card by including the hit factors from the hitting articles, note that these are not on the same scale. The numbers in this article are based on every play on the field, whether or not the rating was used. The batting numbers only refer to the time that batter is hitting, so a factor must be either multiplied to the hitting numbers or divided from the fielding numbers depending on the expected batting position of the player (a batter batting 1st bats more often than a player batting 9th). If batting 1st-3rd, multiply/divide (whichever you chose) by .12, 4th-6th use .11 and 7th-9th use .10.

Another way to look at it: the difference between a SS-9 and a SS-7 (a dilemma often seen when trying to determine who to play) is roughly equivalent to an extra 1 for a SS-9:

  • The difference between run differentials for a SS-7 (.10) and a SS-9 (-.06) is .16.
  • Just assuming the player bats in the middle of the lineup, you would divide .16 / .11 to get 1.45, which is roughly equivalent to the value of a 1 (1.50).

The other positions on the difference between the standard Fielding 3 and 1 are a little wacky compared to the defensive spectrum we’re used to, basically, don’t worry about 2B and OF so hard and the corners are pretty important:

  • Third Base (difference between 3B-5 and 3B-3): .13 (equivalent to two extra 7s)
  • First Baseman (difference between 1B-4 and 1B-2): .11 (equivalent to an extra 3)
  • Second Baseman (difference between 2B-8 and 2B-6): .10 (equivalent to an extra 6)
  • Catcher (difference between C-8 and C-6): .09 (equivalent to an extra 8 and a 9)
  • Outfielder (by arm, difference between 35 and 25): .08 (equivalent to an extra 9 and a 10)
  • Outfielder (difference between OF-3 and OF-1): .06 (equivalent to an extra 7) (RF is likely most important, a 1 should stay out of CF)
  • Catcher (by arm, difference between +3 and -3): .04 (equivalent to an extra 9)

The C and OF differences are cumulative. For example a C-8/Th+3 would be worth .13 better than a C-6/Th-3 (similar to the 3B-5/3 split) and an OF-3/35 would be .14 better than an OF-1/25 (equivalent to an extra 6 and 9).

Next up will be the other things that can be a factor: batting handicaps, speed ratings and steal ratings .

Making the Grade

In the original National Pastime game, the only thing that a pitcher contributed was hitting.  There were no grades, no subratings, no handicaps.  When APBA came out in 1951, the concept of the grade was added, so now pitchers fell into six classes: A&C, A&B, A, B, C and D.  The grade was pretty much determined by two factors: ERA and innings.  Six years later, the subratings were added: 3 for control and 4 for strikeouts.  You now had 72 different classes of pitchers, but in reality you really had 12: 4 grades and 3 control subratings.  The strikeout subratings were of little overall effect and the A&C and A&B grades rarely came into play.

When the Master Game came into play, the number of grades increased from 6 to 30, with each grade divided into 5 parts.  There were no additional control or strikeout subratings, but additional subratings were added for homers, wild pitches, balks, hit by pitch and holding runners.  The only ratings that really made a material difference where the homer ratings: 5 additional ratings and approximately 20 effective grades jumped the classes to 1,500.  In a quirk of the computer vs. Master Game, the latter now has an additional control rating and 4 additional strikeout ratings.

A while back, I ran a simulation of the various hit numbers.  I then did the same thing, this time concentrating on the pitching grades.  Instead of drowning you in numbers, I’m going to go the pretty route and drown you with charts.  First up I simply ran a 5-season simulation on every grade from 1 through 30 with all of the ‘neutral’ subratings: no control, no strikeout and no homerun.  Here is a comparison of a few of the stats for each simulation:


Continue reading