Author: Lenny

Deep Engine 1

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.

1977 Tournament

Coop won with his 1977 Orioles. I selected the Cards and ended up 2-2, 5th place out of 6.

Game 1. Beat Coop’s Orioles 2-1 in a Forsch-Palmer pitcher’s duel. Bases were loaded three times, and no one scored.

Game 2. Lost to Duran’s Royals. The Cards were up 2-1 through eight, but McRae tripled & was knocked in by George Brett in the top of the ninth to give KC the 3-2 win.

Game 3. Led by Don Gullet, the Commish’s Yankees shut out the Cards 5-0.

Game 4. Against Amanda’s Phillies, the Cards finally got a bit of offense in the second inning to go up 4-1, which was the end of the scoring. Forsch allowed only three hits and four base runners.

Range Factor and range ratings

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 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.

Team selection for 2008 ABL expansion

At the ABL Winter Meetings on the 16th I needed to select a team to bring into the league. Going in, I figured it would be between Boston & Philly. Here’s how I decided.

Fifteen players can be selected, so I made up lists of nine position players (eight from each non-pitching position plus a “DH”), three starters, and three relievers. Minimum requirements for the ABL are 175 ABs (125 for catchers), 70 IPs for starters, and 30 IP for relievers.

I used OPS+ to rate the hitters. For pitchers I started with WHIP, then added some arbitrary factors to get something that roughly mirrors OPS+, that is, higher is better and average is 100. I came up with (2.25-WHIP)*110. (It’s listed as “WHIP+” in the tables below.) I weighted starters twice as much as relievers, based on innings pitched (six-inning starts). I then averaged the position players and pitchers separately to get team hitting and pitching ratings. The lists for Philly & Boston are shown below.

philly_boston.png

Of course, you choose the wrong guys here, and the method suffers. Notably absent are Beckett & Schilling, who are already in the ABL. Philly has no one in the ABL.

I did not consider fielding.

In addition to Boston & Philly, I ran the numbers for a few other teams to see how they compared. The graph below shows the results.

expansion_graph.png

So Philly & Boston come out equal. That was no help at all!

So the decision was down to hitting vs. pitching. I chose hitting. Also, Philly looks to have a very balanced lineup, and Utley & Rollins are a superior middle infield. The biggest temtations of Boston were Papelbon & Okajima, both of whom have sick numbers. Big Papi & Ramirez are pretty good too!

Go Phillies! Go Perfectos!

Krazy striKeout Krap

From a Newsweek article about an upcoming jounal article by a couple of psychologists:

If the preference for people, places and things that share one of your initials is conscious, then it shouldn’t work if the thing you’re choosing is basically undesirable. Strikeouts are undesirable. Yet based on data from 1913 through 2006, for the 6,397 players with at least 100 plate appearances, “batters whose names began with K struck out at a higher rate (in 18.8% of their plate appearances) than the remaining batters (17.2%),” the researchers find. The reason, they suggest, is that players whose first or last name starts with K like their initial so much that “even Karl ‘Koley’ Kolseth would find a strikeout aversive, but he might find it a little less aversive than players who do not share his initials, and therefore he might avoid striking out less enthusiastically.” Granted, 18.8% vs. 17.2% is not a huge difference, but it was statistically significant—that is, not likely to be due to chance.

Hmmm…

This comment on the Newsweek site sums up my feelings:

If you survey enough sets of numbers you get random patterns that don’t always come out even. For example, the information about batters is really incomplete. It isn’t sufficient to just compare K against everybody else. Look at the entire alphabet and you will probably see variations of one or two percent between the letters. They won’t all come out the same. Does Q do better than F? Does C do better than M? If so, what does that really mean? Why aren’t they comparing S for strikeout? And what about the name of the pitcher? If it counts for the batter, why doesn’t it also count for the pitcher? This is not a question that can even be really addressed in isolation from everything else.

bbpix

jose_cardenal_1975_toppsjpg.jpgI 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.

card conversion

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.

10up.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.

scoresheet 27

For a while I toyed with the idea of using Metapost to create a custom scoresheet, after seeing this guy’s page. Metapost was hard to find documentation for, and seemed tough to figure out.

Then I really took to the Situational Scorekeeping card. But it’s not very compact, and requires two pages. I like the linear representation in the Chadwick narratives.

Top of the 2nd
Alex Rios             0- 0 (---0): S
Aaron Hill            0- 0 (--x0): K
Eliezer Alfonzo       0- 0 (--x1): WP.1-2
Eliezer Alfonzo       0- 0 (-x-1): 8
Reed Johnson          0- 0 (-x-2): D8.2-H
John McDonald         1- 0 (-x-2): 8

The linear representation of the baserunners would make the scoring box shorter, and would allow everything to be done with rectangles.

The result so far is scoresheet 27 (PDF file). I used NeoOffice to create it. The key is to export the file as DPF. Otherwise, the lines are printed much too thick.

The little box in the top right is for out number or run scored.

Instead of the Project Scoresheet columns for play numbers of subs, I’ll try a separate set of fields to list them in order.

I’d like to try a version that’s all cyan, but it’s a tedious process to change all the borders.

XBL: St. Cloud 5 Memphis 1

The St. Cloud Cyclones continued their improbable march towards the XBL crown Wednesday night against the home-team Memphis Dawgs. Ted Lilly went the full nine for the Cyclones, allowing only one run and five hits. Four of the five Cyclone runs came off the long ball. Eliezer Alfonzo hit two solo shots, both as a result of Park Effect Deeps! Jose Contreras also went the distance for Memphis. He nothced 11 strikeouts, but the three homers he gave up put the game beyond the reach of the anemic Memphis offense. So devastating was the loss for the Dawgs, that rumors were flying that team would be disbanded after only one season in the XBL.

Injuries
St. Cloud: Gomez (0 games)
Memphis: none

The Commish avenged his previous 1973 loss to the Cardinals, when his 1972 Yankees won a nail-biting victory over the ’72 Cardinals at Yankee Stadium. Johnny Callison hit a walk-off solo homer with two outs in the bottom of the ninth to lead the Yanks to a 5-4 victory.

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