Entries Tagged 'Triple Play Baseball' ↓

The cards arrived today, one day earlier than last year.

Are the ABL reliever usage limits realistic? One way to try to answer this is to ask the question: “What percentage of 2008 MLB relief appearances would have violated the 2009 ABL rules?” If the answer is “0%,” then I’d say the ABL rules are too lenient in allowing relievers to pitch a lot. At the other extreme, if the answer is “50%,” then I’d say the ABL rules are too strict and don’t allow pitchers to pitch enough. What’s the right percentage that would make you feel that the ABL rules are just about right? 1%? 5%? 10%? More?

Here are the current ABL rules for reference:

Short
IP          REST
0-2         0**
2.1-3       1
3.1-4       2
4.1-over    3   

Closer
IP          REST
0-1         0***
1.1-2       1
2.1-3       2
3.1-over    3  

**  Cannot pitch more than 2 consecutive games
***  Cannot pitch more than 3 consecutive games
Note: Short cannot pitch more than 4 IP’s unless no other pitchers are available.
Note: Closer cannot pitch more than 3 IP’s unless no other pitchers are available.

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The new Triple Play Baseball cards arrived in upstate New York today.

A companion to the average batter card, here is the average pitcher card. This is a bit of a hack—there’s no adjustment between starters & relievers. The pool is 107 pitchers, made up from the best of the draft, the Perfectos, and most pitchers from the teams I’ve played so far.

   vs L                vs R
---------            ---------
500 - 519     WP?    500 - 519
520 - 547  Range IF  520 - 545
548 - 573  Range OF  546 - 573
574 - 599     EF     574 - 603
600 - 616     RG     604 - 617
617 - 642     1B     618 - 645
643 - 669     EF     646 - 675
670 - 686     RG     676 - 690
687 - 704     SG     691 - 705
705 - 707     HB     706 - 711
708 - 734     1B     712 - 739
735 - 753     2B     740 - 755
754 - 776    Deep!   756 - 776
777 - 803  EF/Tired? 777 - 807
804 - 862     K      808 - 867
863 - 914   K/Tired? 868 - 920
915 - 965     BB     921 - 954
966 - 999     DP     955 - 999

Here are the range numbers:

  vs L              vs R
 ------            ------
  20.0      WP?     20.0
  28.4   Range IF   26.2
  25.7   Range OF   27.6
  26.1      EF      30.0
  17.0      RG      14.5
  26.2      1B      27.8
  26.4      EF      30.3
  17.4      RG      14.8
  17.7      SG      15.1
   3.7      HB       5.7
  26.6      1B      28.3
  18.9      2B      15.8
  23.5     Deep!    21.3
  26.7   EF/Tired?  30.7
  59.0      K       60.2
  51.8    K/Tired?  52.7
  50.9      BB      33.7
  34.4      DP      45.2

Both pitching squads were sharp. Simmons homered in the 3rd to provide the edge for the Cards.

Yankees    000 100 000   1  4  1
Cardinals  011 000 000   2  5  1

W: McGlothen, L: Medich

Series stands at 3-2 New York.

Following up on the previous post about home-field advantage, I got the MLB batting splits for the last five years from BR.com, which are summarized in the table below. (Here are the 2007 splits.)

The biggest effect is that the home team strikes out less. The next largest is more walks for the home team. To capture these effects, we need at least 12 rolls, which is an almost perfect fit into the 16 Park Effects rolls, something suggested by the Commish. A possible Park Effects replacement chart appears below. Since the Park Effects range is identical for every batter, we can eliminate the second roll and simply read the result from the original roll that landed us on Park Effects.

I worked in a couple of things that don’t otherwise tend to happen in the ABL: infield pop outs (pointed out by cnc14) and extra advancement on hits.

One could argue that the home team should have a few SF rolls to account for the extra sac-fly production.

This chart would produce a little more offense. A quick calculation shows that a Park Effects roll on this new chart would yield an average of 0.6 bases per roll, whereas the TBP chart yields only about 0.3 bases per Park Effects roll.

The TPB/ABL hit & run rules can be confusing, because the various effects are scattered all over the charts & instructions. This is an attempt to gather all the ABL H&R effects into a single “cheat sheet.” (Updated 2008-05-24.)

I adjusted my rating method to take average pitcher symbols into account on the batters’ cards. As expected, the effect is most profound for guys with big walk & HR ranges, as can be seen for a few guys in the table below. “Before” means pitcher symbols were not taken into account; “after” means they were taken into account.

[image lost]

The higher-rated players will have bigger differences, simply because lots of homers & walks make them highly rated. Of course, nothing changed in the cards, it’s just that I overrated the batters. Assuming my current ratings are much more accurate, I overrated Burrell’s performance by 7.4%, not an insignificant amount!

For a calculation, I need to know the symbol content of the average pitcher. I did the starters and relievers separately, using the ABL pitchers. Say that of 53 likely ABL starters, 19 have a B symbol. That means that the average starter has 19/53 (0.36) of a B. The chart below shows the averages for all the symbols. Starters & relievers are combined in a ratio of 7:2, to estimate the pitching over an entire game.

Starters & relievers are quite comparable in terms of the walk symbols, B & L. Relievers have a definite edge for the runners-on symbols, R & H. Not surprisingly, only relievers have Fs.

This is a first step toward measuring defensive in terms of offense. The question to answer is: how much good hitting makes up for poor fielding? Or, to look at it from the other side, to what extent does superior glove work compensate for light hitting?

There are two components to defense: error and range. Let’s consider error rating only for the moment. And let’s hope my logic is correct.

For one PA of his own, a player will be in the field for approximately nine PAs by the opponent. For one opponent PA, the chance of rolling a possible error is 6.3%, which is 60 rolls on 11-70 plus about 20% of the 16 rolls on 81-96 (Park Effects). A possible error will fall on our guy’s position 10% of the time. So, over 9 opponent PAs, the average number of possible errors for our guy will be 9 * 6.3% * 10% = 0.057, which is equivalent to 57 rolls.

Now we have to consider specific positions. Let’s look at 3B/SS, where errors occur the most, and let’s consider the most extreme difference in error ratings. On a possible error, an error-20 thirdbaseman (Jeff Cirillo) will make an error 24% of the time, while an error-1 (Ryan Braun) will boot it 99% of the time. Let’s ignore the more complex outcomes (E(2), 1B+E(1), RG+) and just say that an error is equivalent to a single. In other words, an error made on defense neutralizes a single hit by the same guy on offense. The 57 rolls calculated above will work out to 14 rolls for a Cirillo error/single and 56 rolls for a Braun error/single, a difference of 42 rolls.

One could use these results to adjust a player’s offensive rating. 14 of Cirillo’s average 25 1B range are neutralized by his errors. Via linear weights, Braun’s 56 neutralized singles are equivalent to 18 neutralized HR rolls. (Braun has a 95 HR range vs. L, 35 vs. R.) You could also do a comparison and say that Braun would have to have 42 more 1B rolls (or 14 more HR rolls, or equivalent) than Cirillo to make him equal value, all other things (range, power, etc.) being equal.

These “neutralized rolls” can be similarly calculated for other positions and all the error ratings. The results are plotted in the graph below. I didn’t count E(0)s (dropped fouls) as errors on the catcher.

The numbers in the chart are just approximations, and each position’s accuracy is different. For example, all the catcher errors counted are of the two-base variety. Anyway, it’s a start.

It’s interesting that, with the exception of the catcher & outfielders, all the lines are nearly parallel. So the difference between an error-20 & an error-1 at a given position is pretty much the same for either 1B, 2B, SS, 3B, or P.

Using the rating data (the best half of the free agents plus the Titusville roster), an average batter card can be calculated.

   vs L                vs R
---------           ---------
  0 -  10    Crazy    0 -  10
 11 -  70    Error   11 -  70
 71 -  80     LO     71 -  80
 81 -  96    Park    81 -  96
 97 - 140     1B     97 - 134
141 - 152     2B    135 - 144
153 - 174     DP    145 - 166
175 - 218     1B    167 - 204
219 - 221     3B    205 - 206
222 - 229     SF    207 - 215
230 - 245     HR    216 - 229
246 - 250     HB    230 - 234
251 - 278     RG    235 - 270
279 - 324     BB    271 - 302
325 - 399     K     303 - 379
400 - 412     2B    380 - 389
413 - 441     RG    390 - 426
442 - 499     EF    427 - 499

In terms of ranges, it looks like this:

  vs L             vs R
 ------           ------
   11.0    Crazy    11.0
   60.0    Error    60.0
   10.0     LO      10.0
   16.0    Park     16.0
   43.5     1B      37.9
   12.0     2B       9.7
   22.5     DP      22.2
   44.0     1B      38.4
    2.9     3B       2.3
    8.1     SF       8.3
   16.0     HR      13.9
    4.5     HB       5.2
   28.5     RG      36.1
   46.3     BB      32.2
   75.0     K       77.1
   12.4     2B      10.2
   28.9     RG      36.6
   58.3     EF      73.1

You can see why lefty pitchers can have a hard time.

More data from the Deep Engine. All results are based on ten million trials.

Here’s the results for all 30 parks from the TPB 2007 data:

     power:     5        4        3        2        1

   homerun:   48.55%   32.38%   19.18%    9.26%    3.11%
    caught:   47.55%   63.72%   76.92%   86.84%   92.99%
      foul:    3.90%    3.90%    3.90%    3.89%    3.90%

As expected, no significant changes from 2006.

I re-ran with the 12 2008 ABL parks, using the TPB 2007 data.

     power:     5        4        3        2        1

   homerun:   40.45%   24.74%   12.86%    5.28%    1.54%
    caught:   55.65%   71.36%   83.24%   90.82%   94.56%
      foul:    3.90%    3.90%    3.90%    3.89%    3.90%

Wow, there are some big parks in the 2008 ABL! It’s much harder to homer, especially for the light hitters who will find it almost twice as hard to hit them out in the ABL compared to the 30-park circuit.

Now let’s see how the numbers look for the different hitting types. Again, this is ten million trials in the 2008 ABL parks.

Rsp
     power:     5        4        3        2        1
   homerun:   39.79%   24.05%   12.36%    5.01%    1.44%
    caught:   57.61%   73.34%   85.03%   92.38%   95.95%
      foul:    2.60%    2.61%    2.61%    2.60%    2.60%

Lsp
     power:     5        4        3        2        1
   homerun:   39.96%   24.20%   12.48%    5.02%    1.46%
    caught:   57.44%   73.19%   84.91%   92.38%   95.94%
      foul:    2.60%    2.60%    2.61%    2.59%    2.60%

Rp
     power:     5        4        3        2        1
   homerun:   40.83%   25.34%   13.35%    5.72%    1.68%
    caught:   53.98%   69.47%   81.46%   89.09%   93.11%
      foul:    5.19%    5.19%    5.19%    5.19%    5.21%

Lp
     power:     5        4        3        2        1
   homerun:   41.23%   25.34%   13.21%    5.37%    1.58%
    caught:   53.55%   69.47%   81.58%   89.43%   93.23%
      foul:    5.21%    5.19%    5.21%    5.20%    5.19%

Not surprisingly, the pull hitters end up with more foul balls. In spite of that, they still end up with a greater probability of homering.

This data can be combined with the batter’s power & the average deeps to estimate the number of home runs a batter will get with Deep! rolls against an average pitcher. Actually, the difference in home-run potential is so similar among the hitting types, that it’s not worth making a distinction. So, for example, a power-5 hitter will homer on about 40% of his 18.7 deep rolls against the average pitcher, effectively giving him an additional 7.5 home-run range.

Combine this with the power distribution, and the probability of a home run on a Deep roll works out to 20.4%. That’s an important number for rating individual pitchers against the average batter.

For the ABL draft & season I’ll develop some ratings based on the card ranges. The batter’s card is pretty straightforward. The ranges can be used to directly compute things like OBP & Slugging that are independent of pitcher-card rolls. The one vital correction, however, is the power, which will determine how many HR result from Deep! rolls. So, I need to get a handle on how power affects those probabilities. Later, I can make calculations based on Deep! ranges, either averages or against particular pitchers.

I wrote a “Deep Engine” that captures the location and distance data and allows me to run some Monte Carlo simulations. For now I’m going to ignore robbed HRs, which is a pretty small effect. For the first calculation I’ll assume a random distribution of pull types (Lsp, Lp, Rsp, Rp) and use all the 2006 parks. (Later I should whittle it down to the 2007 parks in the ABL.) I ran ten million trials for each power rating and came up with the following probabilities.

     power:     5        4        3        2        1

  home run:   48.61%   32.40%   19.17%    9.27%    3.11%
    caught:   47.51%   63.71%   76.93%   86.82%   92.98%
      foul:    3.89%    3.89%    3.89%    3.91%    3.91%

So a power-5 guy has about a 50% chance of hitting it out on a Deep! The average power-1 batter is more likely to send it foul than over the fence fair.

The Bill James Handbook lists Range Factor, which is the number of Successful Chances (Putouts plus Assists) times nine divided by the number of Defensive Innings Played. Does this statistic correlate with the TPB range ratings? I picked a couple of the more important defensive positions and compared the 2007 Range Factors for starters with the TPB range ratings from the 2007 TPB Statistics Book. Graphs for shortstops and center fielders are below.

• shortstops
• center fielders

Shortstops show a bit of correlation. It’s no surprise to me that Furcal & Vizquel are highly rated by both measurements. I’m surprised to see that Reyes has such a low Range Factor.

Center fielders are all over the place. Vernon Wells has a Superior TPB rating and the lowest Range Factor!

The red lines are the linear fits to the data. The graphs assume that the TPB ratings are linear, that is, that the difference between VG & SP is the same as between PR & WK. Whether or not that’s the intent, it’s clear that there’s no strong correlation between the Range Factor and the TPB range rating. That could mean that either 1) the two measurements are meant for different purposes, or 2) one or both of the measurements are inaccurate.

I don’t think #1 is likely. Surely each is trying to quantify the ability of a fielder to field balls that are hit in his general direction. Of course, measuring any kind of defensive ability is difficult. (See this discussion of various methods.) Whatever the case, it’s clear that the TPB ratings are not based on Range Factor.

I found a site with lots of small baseball-card pictures. The guy uses them for display in the Strat-O-Matic software.

For a lot of the old TPB cards I can’t remember the guys or have never heard of them. I thought it would be cool to have a searchable database of these photos, so I hacked together bbpix. It’s not a database, but it works.

It’s October, so let’s have some World Series action, seventies style. 1973: the Oakland A’s take on the New York Mets.

   

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I’ve got a better idea. I can tile them ten to a page, baseball-card size, and I won’t need any guide lines. 2.5+2.5+3.5=8.5

Since there are different numbers of cards on the page, need to cut them all out first (card_cutter_10up.pl), then paste them together in groups of ten (card_paster_10up.pl).

Printing from Preview, the dpi is actually about 359.1 (not 360), which is imposed by specifying the resolution when converting to PNG.

I also tried 8-up, just like as-shipped. Worked out fine, but I still like 2.5×3.5.

To do:

• Deal with odd numbers of cards at the end.
• Write out as TIFFs instead of PNGs (pnmtotiff -lzw), cat them (tiffcp), & convert to PDF (tiff2pdf -z). Will need to specify resolution at some point.

Goal: convert TPB PDFs to baseball-card size (2.5×3.5) with a layout compatible with batch cutting.

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This time I looked at how often runners on first were thrown out at third on a single. Again, no other baserunners. Not counted:

• Error on the play at third. (Errors allowing the batter to advance past first are OK.)
• Runner on first out at second or home.
• Runner safe at home.
• Baserunner hit by batted ball.

The percentage I’m interested in is the number of times the runner is out at third divided by the number of times the runner is out or safe at third. Again, I counted all the years available in the Retrosheet event files. Graph below. The line is the least-mean-squares linear fit.

I’m surprised how seldom the runner is out at third. There’s a clear downward trend, which indicates that runners and/or third base coaches have become more conservative. Perhaps stronger arms in the outfield are also a factor.

The whole exercise makes me question the role of TPB’s “sending runners” in these situations. (TPB out of the box, not ABL rules.) Why is this a manager’s decision? Runners will try for the extra base on their own, or take guidance from the third base coach. Would it be more realistic to roll for an advancement that is explicitly specified on a chart? Such a chart should be roughly:

• 26%: runner safe at third
•  2%: runner out at third
• 72%: runner holds at second

Then you could sprinkle in some potential errors & such. Of course, there would be a dependency on where the single was hit (as there is now).

The ABL simplifies runner advancement on singles. I think the only way to go from first to third on a single is on a hit-and-run. This made me wonder about how often runners advance past second on a single. Here’s what I got from the Retrosheet event files for 2006. (single_first.pl) These are singles with a man on first and no other base runners. Advancement on fielding errors counts, but getting thrown out doesn’t.

first to second    4101   (73.5%)
first to third     1473   (26.4%)
first to home         8   ( 0.1%)

About a one-in-four chance to move the man to third. That sounds about right.

Here are the numbers from 1973:

first to second    3270   (68.9%)
first to third     1468   (30.9%)
first to home        10   ( 0.2%)

Why did more guys go from first to third back then?