Meanderings

Print Baseball Encyclopedias

As I grow older, I try to stay alert to warning signs of old-fogeyism. One or two such signs are not particularly concerning--they can just be written off as personal quirks/eccentricities, which we all possess to one degree or another. A prime example for me is cell phones. I hate the things, and I always have. I finally got one, only because it was cheaper than paying for a landline, and if there's one thing I hate more than cell phones, it's spending money on any type of phone.

When it comes to baseball, one of the possible signs I've noticed is my continuing love for print encyclopedias. I think it's great that we have Baseball-Reference, Retrosheet, the National Pastime Almanac, the Baseball-Databank, and the like, and obviously there are countless advantages to computerized data that you and I take advantage of every day. Still, I have yet to warm up to the idea of going to Baseball-Reference, clicking on a page, following a link somewhere else, and wasting an hour or two just wandering in the statistical record of the game. I still do this all the time with print encyclopedias. This post is a tribute/review of them.

Of course, the print encyclopedia is a dinosaur. It always was a bit of a wonder that one could publish a multi-thousand page book, carrying a hefty hardcover price, and sell enough of them to make it a worthwhile business endeavor, especially with annual or semi-annual editions. Perhaps they never really earned their keep anyway, but they should have.

The advent of computerized equivalents has driven the print encyclopedia out of existence (although apparently the erstwhile ESPN Baseball Encyclopedia is still being shopped to publishers). If that is the inevitable cost of progress, then so be it--I wouldn't give up my Lahman database to get a new edition of Total Baseball if that was what it would take. Still, I miss the print encyclopedias--and it seems as if other people do to.

As I write this (New Year's Eve), the current cheapest prices listed on Amazon.com for a copy of the final edition of each of the printed encyclopedias (new or used) are:

* Macmillan (10th edition, 1996): $44.99

The 9th edition is available for as little as $25.

*Sports Encyclopedia: Baseball (2007 edition): $123.08

The 2006 edition is available for as little as $3.31.

*Total Baseball (8th edition, 2004): $99.65

The 7th edition is available for as little as $3.58.

*ESPN Baseball Encyclopedia (5th edition, 2008): $95.80

The 4th edition is available for as little as $1.73.

*STATS All-Time Baseball Handbook (2nd edition, 2000): $3.99

The exception, and not really an iconic book as it only went through two editions and presumably had the most limited printing run of any of the five.

I'm not sure if these prices reflect actual demand for the books in question, or whether sellers think they have something valuable and are setting the price above the intersection of the demand and supply curves. Assuming that it is a real phenomenon, it suggests that there are a fair number of people who miss the print encyclopedias so much that they are willing to pay a high price just to have the final update.

I have at least one copy of each of the big four (excluding the STATS book from that designation) on my bookshelf at all times. Of the four, the two that I use most are ESPN and Sports Encyclopedia: Baseball. Of all of the encyclopedias, I have to count SE:BB as my favorite. It's certainly not the most statistically complete or the best-edited, but it's the only one of the four that breaks from the career register format and instead presents a season rosters format.

I've always felt that the season rosters lend themselves better to browsing than the career registers. (This is the part where the readers scream, "With a computer you can have both!") Not only does it allow one to look at team composition and track changes from year-to-year, it allows one to view an entire league-season on 2-4 pages, making it much easier to get the big picture for a season.

The SE:BB is not without flaws, of course. The book is filled with typos, many of which were presumably there from the first edition to the last. Two quick examples, both from the 1994 edition (although I'd be very surprised if they were corrected in later updates):

* Johnny Kling is listed as "Johnny King" with the roster for the 1901 Cubs, and in the 1901-19 Batter Register (later Cub seasons correctly list him as "Kling").

* The header for the 1972 NLCS says "Cincinnati (west) 3 Pittsburg (East) 2". Perhaps if this was a listing for 1882, it could be considered authentic to the times.

There have to be dozens of similar errors throughout the book, none of which are damning to its utility as a baseball reference but all of which do build up to an uneasy feeling of neglect. Still, the charms of the book overcome that for my money.

Like its cousin, the Macmillan, the statistical selection in SE:BB was formed at its first publication (1969 for Big Mac, 1974 for SE:BB). OBA is nowhere to be found, nor is CS or pitcher home runs allowed. Fractional innings pitched are rounded, an the typesetting varies throughout the book, making some sections more difficult to read. Sometimes space requires severe truncating of batting lines--Dick McAuliffe went 7-27 as a 20 year old left-handed hitter for the 1960 Tigers, but that's all you can find out.

The ESPN encyclopedia, edited by Gary Gillette and Pete Palmer, is my favorite of the three career register works. Mostly this is because it is the most recent, superceding Total Baseball. For the most part, the statistical selection is the same as TB. In both cases, I'd love to have a better offensive rate than OPS+, and I think they tried to hard with respect to fielding categories, but both give the basic categories necessary to build standard statistics.

Total Baseball is unique because of the volume of the text that accompanies the statistics--short biographies of notable players, team histories, a history of sabermetrics, and a bunch of other articles that changed from edition-to-edition. More than any of the other encyclopedias, the article turnover created a reason to buy each new edition (other than, of course, the updated statistics).

The MacMillan must be given respect due to its status as the pioneer; the research that went into producing it has been incorporated by every serious baseball historical work of any stripe since that time. As an encyclopedia, though, it's heyday was the first edition. It soon had SE:BB as a competitor, and with the two including essentially the same basic data, the (IMO) superior format of SE:BB made it an unfair fight. MacMillan also played fast and loose with changing statistics for silly ends. Later editions cut this out, and added some interesting data like team home/road splits and sketchy Negro League records, but by that time Total Baseball was on the scene.

The STATS All-Time Major League Handbook was the most thorough encyclopedia for individual statistics, but as such it is the one that has taken the biggest hit from the existence of Baseball-Reference. No other encyclopedia offered complete batting, pitching, and fielding data (including all of the minor categories like GDP and sacrifice hits allowed), but the sheer volume of data sapped the book of any character it might have otherwise had. While Big Mac has standings and playoff records and the like, and Total Baseball had all of that and the articles, there was no room in the Handbook for anything other than the player career register. The ancillary material was shuffled off into an equally large All-Time Sourcebook.

While the massive print encyclopedia may be something of a relic, I do think it would be wonderful if it could live on. Obviously I know nothing about the real-world feasibility of what I am about to spout, but it would be great to see an organization like SABR step up to the plate and subsidize an updated print encyclopedia (even if it had to be in PDF format, as SABR has done with the Emerald Guide) every half-decade or so. Eventually the desire for such a tome might be foreign to even the crustiest old baseball historians, but I think it's safe to say that day is still several decades off into the future.

Standard Deviation of Franchise W%

Speaking of electronic encyclopedias, this is the type of exercise that they make a breeze, which previously would have been an arduous chore. I figured these a while ago with the intent of using them in some other discussion, but that never materialized so I'll dump them here.

These charts simply show the standard deviation of full-decade W% for each major league franchise. I have criticized the use of decades as a line of demarcation for baseball statistics in the past, but this is not a through analytical endeavor and they do provide an easy, straightforward manner of categorization. I have defined the decade here as 1901-1910, 2001-2010, etc, not because I have any particularly strong feelings on the matter of decade division but because it works better since 1) it includes 2010 and 2) the first decade thus defined corresponds with the American League's 1901 ascension to major league status.

There are four different standard deviations shown for each decade--"whole" is the StD for teams that completed the entire decade. This is fairly arbitrary, as it allows the 1961 AL expansion teams but excludes the 1962 NL expansion teams (the four 1969 expansion teams are obviously excluded as well). "All" is the StD for all franchises that played in the decade, even if it was for as little as one season (actually, the shortest in-decade tenure is two years for the 1969 expansion teams). "1901s" is the StD for the sixteen franchises that have played continuously since 1901. While they now make up just over half of MLB, they at least provide a constant frame of reference throughout the century. "Expan", as you might figure, is the StD for whichever of the fourteen expansion franchises competed in a given decade.



By this measure, the 1980s and 90s stand out as very competitive periods in the game, and the 2000s were a step back from that. However, the standard deviation of franchise W% in the last decade were essentially the same as the 1950s and 60s, and still well under the norm for most of history.

The next chart gives the average W% for teams by decade broken down into 1901s and expansion teams. It also lists the best and worst franchise W%s for the decade, but those lists include only the teams that played ten seasons in each decade:



In the 1980s, expansion teams actually had a slightly better record than the 1901s, but they have lost ground in the last twenty years. Of course, most of the big city teams are 1901s, with the major exception being the Angels. The spread between the best team W% and worst was higher in the 2000s than it had been since the 1960s, but I wouldn't attempt to make anything out of it.

Two Team Cities

During a bout of encyclopedia browsing, I noticed that the two Boston teams both had dreadful 1906 seasons. The Braves were 49-102, but the now-Red Sox were even worse, losing three more games (49-105). I made the mistake of pointing this out on Twitter and saying that it "had to be the worst" such record.

Of course, it didn't have to be anything, and it isn't. It is only the third-worst combined record by teams in the same city since 1901. While I'm sure someone has done this before, a quick search turned up nothing. I considered Brooklyn to be New York (meaning that from 1903-1957 New York had three teams), and I considered the Angels/Dodgers and Giants/A's as sharing a city (when applicable). The ten worst single season records for the two or three teams combined:



At least Boston 1906 was the worst in something, as it was the worst non-Philadelphia combined record. Philly has seen some bad records over the years, but none worse than 1919 when the Phillies were 47-90 and the A's were 36-104. The worst years for each of the two-team cities other than Boston and Philadelphia were St. Louis 1913 (108-195, .356), Chicago 1948 (115-191, .376), Bay Area 1979 (12-199, .386), New York 1965 (127-197, .392), and Los Angeles 1992 (135-189, .417).

The best records are:



Four of these top ten featured a crosstown World Series, led by the 1906 victory by the White Sox over the Cubs; the others are St. Louis 1944, New York 1951 (Giants/Yankees as the Dodgers dropped the three-game NL playoff), and New York 1952 (this time Dodgers/Yankees). The banner years for the other cities were Boston 1915 (184-119, .607), Philadelphia 1913 (184-120, .605) and Los Angeles 2009 (192-132, .593).

The overall records for each city (for years in which they had multiple teams) are:



The cities in which the combined record has been good still have two teams; the ones in which they were poor do not. Shocking but true.

Great Moments in New York Post Horse Racing Info

Great Moments in Yahoo! Box Scores

They'd better make sure this issue doesn't carry over to my fantasy team.

Great Moments in Yahoo! Box Scores

I guess the Dodgers trouble meeting payroll came to fruition sooner than anticipated.

Scoring Self-Indulgence, pt. 2: Scoring Pitches and Strikeouts

When I score a game, I almost always keep a pitch-by-pitch record of the game, unless for some reason I have to juggle watching the game with some other task, and will not have the ability to accurately record each and every pitch. Even when I set out with this as my intention, I often find myself unconsciously scoring the pitches anyway.

My system for tracking pitches only records the basics--whether it is a ball or a strike, and any of the basic subgroups contained within those two categories (intentional balls/pitchouts, swinging strikes, called strikes, fouls). Some people attempt to keep track of pitch locations or pitch types; of course, Pitchf/x has rendered this even more of a chore than it was previously, and some people (hello, nice to meet you) just aren’t good enough at observing pitch locations and distinguishing pitch types to even attempt to put in this level of effort.

The final pitch of a plate appearance is not recorded separately--it is implied by whatever event follows. For example, if a batter draws a walk, I don’t record the fourth ball independently of noting the walk. If a pitch is hit into play, you’ll see a symbol for a base hit, or a groundout, or whatever the case may be. I don’t see any reason to waste another pencil stroke on spelling this out.

The left side of the empty scorebox is used to record balls; the right side is reserved for strikes, and the very top (and on the very rare occasions when necessary, the very bottom), with much smaller letters, is where two-strike fouls are recorded. The order of pitches in indicated by letters of the alphabet--the first pitch is “A”, the second pitch “B”, and so forth.

Balls usually don’t usually need any elaboration--intentional balls/pitchouts are the only common subcategory. I do not distinguish between the two; it is usually pretty obvious which is being employed if you consider the context of the plate appearance and the pitch sequence. An intentional ball of any stripe is simply circled.

The other, much less common alteration needed to balls is the automatic ball, on the rare occasion that the umpire makes that call. The symbol for this is simply a lower case “a” in front of the usual symbol. For example “aD” would indicate an automatic ball called on what would have been the fourth pitch.

There are more modifiers needed for strikes. Called strikes receive no alteration, while a left bracket “[“ is put around the outside of a foul and a left brace “{“ is put around the outside of a swinging strike. The foul symbol is not used with two-strike fouls, since by definition they could be nothing else. Another modifier I use which can be applied to strikes of all kinds (except two-strike fouls) is circling the letter, which is used in case of a bunt attempt.
A couple of examples will hopefully make this pretty clear:



The first pitch (A) is a garden variety ball. The second pitch (B) is a called strike. The pitcher is called for a rare automatic ball (aC) before what would have been the third pitch. The fourth pitch (albeit the third actually delivered) is a foul. The fifth pitch (E) is another foul. The sixth pitch (F) is a ball, and the seventh pitch (G) another foul. Finally, the batter flies to right on the eighth pitch, for which the pitch is not explicitly noted--the occurrence of a flyout is sufficient to demonstrate its existence.



In this plate appearance, the batter shows bunt on the first pitch (A) but takes a strike. The second pitch is a standard ball (B), but the third pitch is a pitchout (C). The batter swings and misses on the fourth pitch (D), and does it again on the fifth pitch for a strikeout.



The batter attempts to bunt the first pitch, but he bunts through it for a swinging strike (A). He attempts to bunt again on the second pitch, but this time he fouls it off (B). He then takes a ball (C), fouls off a pitch (D), and eventually grounds back to the box.

There are several different possible symbols for a strikeout that actually becomes an out in my system--a swinging strikeout, a called strikeout, a strikeout where the putout is something other than catcher unassisted, a strikeout on a missed bunt (that is a swinging strikeout on a bunt), and a strikeout on a two-strike foul bunt. In these examples, I will not include the pitch sequence, since that has already been explained and it would just clutter the scorebox and distract from the out itself.



This is a standard swinging strikeout. The solid dot is my universal symbol for an out; any time the batter-runner is retired at any point, the dot will appear somewhere within his scorebox. This makes it much easier to quickly see how many outs there are in the inning, and also eliminates some potential confusion in cases in which a certain code could indicate an out or could indicate something else.



And the inscrutable backwards K for a called third strike.



Sometimes the scoring on a strikeout is something other than the standard catcher unassisted. By far the most common is the catcher throwing to first for the out (23), although there are other possible and weird ways for this to occur.



This is the symbol I use for a foul bunt with a two-strike count, resulting in a strikeout. As I’ll show later, the squiggly line is my symbol for a bunt on a ball in play, so the symbol applied to that of the strikeout has a clear meaning.



If a bunt is attempted but missed for a third strike (that is, the batter offered but did not make any contact), then the brace that indicates a swing for a non-third strike is included in the symbol above to distinguish it from the more common third strike bunted foul.

Wayne Winston's Mathletics

The "book reviews" on this blog are almost always a day late and a dollar short. They are written and published long after the book, and my comments about them usually don't amount to a review but rather as a springboard from which to discuss other topics. This one is no different.

Wayne Winston is a professor of Decision Sciences at Indiana University's business school and a former consultant to the NBA's Dallas Mavericks. He published Mathletics in 2009 with the tagline "How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football."

If you are a regular reader of this blog or similar material, do not buy this book expecting to learn a lot of new things about sabermetrics. The sabermetric material is fairly standard, rudimentary type material--introductory-level discussion of run estimators, park factors, replacement level, the base/out table, win expectancy, and the like. I would also not recommend it to a novice, not because it is poor (there are elements I like and dislike, as I'll discuss below), but because there are better resources out there--internet primers, Bennett and Fluck's Curve Ball, and Lee Panas' Beyond Batting Average among others.

I am not particularly well-read on either football or basketball quantitative analysis, so I cannot definitively state the level of Winston's discussion on those topics. My guess is that the football discussion is fairly basic (with the caveat that football analysis as a field lags behind apbrmetrics), but that the basketball material is much stronger. It is certainly obvious from the writing that basketball is Winston's passion, and that the adjusted plus/minus ratings are a particular favorite.

Winston's writing is not particularly strong--he writes like someone whose favorite class was math (as do I). There are some minor slip-ups in the baseball discussion; these won't mislead the reader, but they also reflect the pedestrian nature of the material:

* Winston includes a formula for estimating batting outs that accounts for ROE by putting a multiplier on at bats. But this applies the adjustment to all at bats, including those in which we know a batter did not reach on an error (hits) and those in which the likelihood was very small (strikeouts).

* He refers to Keith Woolner's statistic as VORPP--Value Over Replacement Player Points. This makes sense in that he applies the replacement level concept to WPA points, but he also refers to Woolner's run based version as VORPP. Additionally, he credits the concept of replacement level to Woolner. In reality, Woolner did much to popularize replacement level, but the concept did not originate with him.

* Similarly, he credits the concept of park factors to Bill James. James had much to do with popularizing the notion that statistics could be corrected for park effect, but if any single person is to be credited with the concept, Pete Palmer would be an easy choice.

* There is a chapter that discusses player improvement over time by comparing annual performance, but it does so without even really addressing aging and survivor bias.

* The discussion of strategy is fairly bare-bones and deals only with basic estimates based on a standard run expectancy table.

There are positive things of similar magnitude to the list of negatives--for example, while he uses Runs Created, he explains that a theoretical team construct is necessary to make accurate player comparisons. As a whole, the baseball portion of the book is adequate without being excellent for a novice and a yawn for those well-versed in sabermetrics.

Being a novice myself when it comes to football and basketball analysis, I found the discussion in those chapters much more interesting. Focusing on a couple interesting football tidbits, Winston offers a version of the famed two-point conversion chart that incorporates the expected number of possessions remaining in the game. There is also a formula for the probability of a successful field goal in the NFL based on distance that I found interesting, although the model produces results that are clearly too high for very long kicks.

There is also a discussion of quarterback ratings, which have always interested me. Like every other sane person, Winston has little use for the NFL system, focusing his discussion on Berri's rating from Wages of Wins and his own adaptation of Brian Burke's regression of team categories against team wins. Isolating the categories from Burke's equation that can be related directly to individual quarterbacks, Winston offers the following as a quarterback rating:

1.543*(Yards - Sack Yards)/(Attempts + Sacks) - 50.0957*(Interceptions/Attempts)

If you factor out and ignore the 1.543 coefficient, and change the second quantity's denominator to (Attempts + Sacks), this can be rewritten as:

(Yards - Sack Yards - 32.47*Interceptions)/(Attempts + Sacks)

In this form, Winston's rating is very similar to a number of rating formulas, including the NEWS rating published by Bob Carroll, John Thorn, and Pete Palmer in The Hidden Game of Football:

NEWS = (Yards - Sack Yards - 45*Interceptions + 10*Touchdowns)/(Attempts + Sacks)

Breaking into editorial mode and stepping away from Mathletics for a moment, the treatment of a touchdown pass can be thought of as somewhat analogous to the sacrifice fly in baseball. The comparison is strained as touchdown pass is always a positive play from any perspective, while a sacrifice fly might actually reduce run expectancy.

A fairly large number of touchdown passes occur on short passes. Suppose a quarterback completes a three-yard touchdown pass. This will actually reduce his rating in Winston's ranking, as the quarterback's rating prior to the touchdown will be higher than three. By giving a positive weight to all passing touchdowns, one could ensure that a touchdown pass always increases ranking.

However, in doing so, one gives special treatment to the touchdown because it is a tracked category (like sacrifice flies). However, one could also track "sacrifice grounders" or "first down completions". These theoretical categories would also be cases in which a positive or somewhat positive outcome was achieved, but the statistics treat it as a negative (a batting out or a reduction of the passer's rating, assuming the completion was short). Giving special treatment to the recorded categories can thus be seen as unhelpful and biased by particular types of players that might be predisposed to one or the other.

Moving back to the book, most of my comments to this point have focused on the negatives. However, there are three things that Winston does really well:

1. Winston provides downloadable spreadsheets for many of the examples. This allows the reader to follow along with the work and to learn how to carry it out in Excel. Many of the Excel steps are explained in the text as well.

The drawback to this is that some of the why behind the math is glossed over in favor of a quick Excel solution. Winston's rating system for NBA and NFL teams basically boil down to finding the best-fitting solution for a system of linear equations to predict the point margin in each game. Winston doesn't explain the math in that manner, though, instead just explaining that the Excel solver is used to minimize error. While this gives the reader enough detail to produce their own ratings, and no one is actually going to solve hundreds of equations, I personally prefer a stronger emphasis on the underlying math.

2. The bibliography is excellent, as it includes not just a list of sources but descriptions of what they offer. For example, this is the description of Phil Birnbaum's Sabermetric Research blog:

This is perhaps the best mathletics blog on the Internet. Sabermetrician Phil Birnbaum gives his cogent review and analysis of the latest mathletics research in hockey, baseball, football, and basketball. This is a must-read that often gives you clear and accurate summaries of complex and long research papers.

3. Winston's description of Birnbaum's blog provides a nice transition into discussing the best thing about his approach. While Winston has excellent academic credentials (he is a professor of Decisions Sciences at Indiana and earned a PhD at Yale in Operations Research), but he does not beat you over the head with it. In fact, I don't think that his doctorate is ever explicitly referenced.

In any event, Winston mixes the research of other academics into his text, but he gives plenty of space to amateurs as well. Some academics that enter the sports arena seem to thumb their nose down at anyone who doesn't hold an advanced degree or a teaching position. Winston is not one of them. He even used one of Birnbaum's posts to offer a counterpoint to an academic paper on the NFL draft.

Winston's book provides a great example of how sabermetric knowledge generated by academics, amateurs, and everyone in between can be integrated, and how all parties can respect and learn from each other. It also gives analysts specializing in each sport a window into the work being done on other sports. Thanks to those attributes, Mathletics is a worthwhile read.

Comments on Baseball Prospectus 2011

At some point it becomes bad sport to write the same thing about an annual book--if there’s a certain characteristic of the book that you find yourself dissatisfied with several years running, it might be a you problem. It’s one thing to decide that a certain book is not for you; it’s another to continue to believe that it will when it’s obvious that the writers have something else in mind.

Much of what I could say about the Baseball Prospectus annual for 2011 is the same as I said about in 2010, and 2009…and so I’ll try to avoid saying it again. By now, it’s clear that BP is what it is, and that can either be a great thing or a bad thing or a mostly good thing, depending on your perspective. My perspective is that it’s mostly a good thing--the redeeming qualities of the book outweigh its flaws fairly easily from my perspective.

I still felt compelled to jot down a few comments on the book this year because I might have been a little unfair in nitpicking a few things in the past. Now that there is a lot of new blood on board, it’s more apparent that some of the issues (like stats not matching up between the comments and the data directly above) are systematic, and probably endemic to producing a book of this kind. To put together a tome of that size in a few months is a massive undertaking, and there are thousands of moving parts, so expecting them all to be dialed in to the same setting is unrealistic.

The cover still has the infamous phrase that I will not repeat about PECOTA; this is obviously out of the hands of the writers. They do redeem the cover with a great caption under the little photo of Albert Pujols.

That being said, I do have one major bone to pick with the new, slimmed down statistical offerings. It’s great that they stopped doubling up on metrics that measure the same thing (in the past, there have been simultaneous displays of VORP and WARP, or EqA and MLVr), and with one glaring exception the new stat lines still manage to give you most of the key metrics. That glaring exception is the lack of any kind of component ERA (or RA, which I’d prefer anyway) figure for pitchers.

It’s not simply a matter of limiting your choice to vanilla, while having to leave chocolate, strawberry, and cookies and cream aside (after all, there are a lot of flavors of component ERA). There is none whatsoever. Instead, BP has listed Fair RA, which is a fine metric constructed by Colin Wyers and the primary input for pitcher WARP. But if the choice is between having Fair RA and a component ERA in a book that is largely aimed toward predicting performance in 2011, it’s not a choice at all. Sticking with metrics under the BP umbrella, peripheral ERA and SIERA would fit the bill.

Of course, if I could strike any category from the pitcher stat line to clear space, it wouldn’t be Fair RA--it would be W-L or saves or WHIP. But since a big target audience for the book is fantasy players, that is not an option. However, it leaves everyone (including fantasy players) without a backwards looking metric that gives us the best estimation of how the pitcher’s overall effectiveness in the past. I certainly hope that they will figure out a way to include Peripheral ERA or SIERA or something similar in the 2012 edition.

PECOTA is in good hands with Colin Wyers, and I’m sure there are still some bugs to be worked out, so please take this comment as more amusement than criticism: some of the PECOTA comps seem way off. I’m sure this happened in the past, and I didn’t bother to make note of it, but two players that really stood out to me were Gregor Blanco and Nick Franklin. Blanco’s top comps are Richie Ashburn, Kenny Lofton and Freddy Guzman. One of these things is not like the other, and two of them are nothing like Gregor Blanco (Lofton was still in the process of breaking out, but had already established himself as clearly better). The Franklin comps are more understandable since he’s a younger player with less of a track record, but it’s still an odd juxtaposition to see a player ranked as the #44 prospect in MLB while his top comps are identified as Adrian Beltre (ok), Hank Aaron and Willie Mays.

There are only a few team entries that have extensive sabermetric (as opposed to applied sabermetric) content. One of these is the Arizona entry, and sadly I have a bone to pick with it. The author accepts the mainstream view that Arizona’s copious strikeout totals in recent campaigns had doomed their offense. He (or she; I still maintain it would be more interesting to know which author is responsible for the team entry) asserts that “when the majority of the lineup falls prey to empty at-bats of this sort, highly volatile run-scoring can result.”

While there have been some studies done on the relationship between shape of offense and scoring distribution, I am personally unaware of any comprehensive or well-established enough to make a statement like that without the need for supporting evidence. The only statistic brought in to support that position is that Arizona scored three or more runs per inning as much as the NL average, but scored two or less more often.

That is a very odd and not particularly helpful way to break down innings, because it lumps scoreless innings in with one and two run innings. To be absurd for a moment, if an offense never scored three or more runs an inning, and scored 0-2 in 100% of their innings, but 40% of those were one run and 10% were two runs, they would average a healthy 5.4 runs per game. It is true that Arizona scored in a smaller proportion of their innings than did the average NL offense--25.9% of Arizona innings resulted in a run scored compared to 26.5% for the league as a whole. But Arizona was more likely to have a multi-run inning (12.4%) than the average NL team (12.2%).

Another odd thing about this perspective is that it makes the inning the unit by which scoring volatility is measured. It’s true that the best perspective from which to understand how runs are scored is the inning level, since the events that transpire in each inning is independent of those that occurred in previous innings in terms of scoring in runs (I hope it’s clear that I’m talking about baserunners and outs from one inning affecting each other, not lineups turning over and pitchers being removed and the like, but you never can tell) but from a win/loss perspective, it is the run distribution per game that is crucial. Admittedly, the two are very closely related, but any time you extend the time period over which such volatility is projected, its impact is reduced.

One crude but simple and reasonably sensible way to consider the win value of a team’s per game scoring distribution is a method that I call Game Offensive Winning Percentage (gOW%) and have published here for the last three years. It is based on a Bill James idea; instead of estimating an OW% from average runs scored per game, use the team’s actual distribution of runs scored. If in a given season teams that score one run win 11.8% of the time (as they did in 2010), then credit the offense with .118 wins for each game in which they score exactly one run. Repeat for all scoring levels and average and you have an alternative OW%.

There are of course flaws with this method--the unit of games doesn’t always represent the same things (i.e. there are not always 27 outs per game), the use of the actual W% by runs scored in any given season is subject to sample size fluctuations, there is no adjustment for park, etc.--yet it’s still reasonable to think that if a team’s run distribution was particularly unusual, it would manifest itself in a comparison of gOW% to standard OW% based on average runs per game (in this case, without a park adjustment so as to better match gOW%).

The Diamondbacks led the NL in strikeouts in 2009 and 2010 and were second in 2008. In 2007, they ranked eleventh (and made the playoffs, see!), so those three seasons are the relevant high strikeout seasons for the team. In 2008, Arizona’s gOW% was .485 while their OW% was .479--considering their run distribution rather than just their average suggests an additional win. In 2009, it was .484/.483--no difference. In 2010, the split was .492/.502, which is -1.6 wins. So for the three years considered together, the net total is -.5 wins.

Of course, this does not conclusively demonstrate that Arizona’s offense was as efficient as a typical offense with their scoring average, and it certainly doesn’t allow us to make any statements about the effect of high strikeout offenses generally. However, neither does anything offered or referenced in the BP essay, yet the author chose to make much stronger assertions than I would dare to here.

My comments on strikeouts should not be taken as a negative judgment of the book as a whole--my book “reviews”, such as they are, generally serve as an opportunity to discuss issues raised by the author rather than to offer a summary judgment on the book itself. By now, you already know whether BP is a book for you or not.

Scoring Self-Indulgence, pt. 1

When I have occasion to write something on paper, I usually use a pen. It’s easier that way--ball-point pens are ubiquitous and cheap; you can sign things with them; and now that the hideous scourge of blue ink has faded a bit, they no longer result in an assault on one’s sensibilities every time they are used (okay, that last one should say “my sensibilities”). In truth, I like pencil better, specifically a mechanical pencil with .5 lead. I use the real cheap Bic ones exclusively, and have for years--you know, the ones that are supposed to be disposable, but you can hold the clicker down and push the replacement lead in through the top. You can get ten of them at Wal-Mart for $2.

The pluses of the ball-point pen allow me to save that favorite writing utensil for only the most important tasks, ones that just can’t be entrusted to the terrifying permanence of ink. For most of the winter, it sits undisturbed on my bookshelf or in a pencil holder or wherever--but sometime in March, I have occasion to take it out and put it to use, and I don’t stop until mid-autumn.

You have probably surmised by now that the important task to which I refer is scorekeeping. Yes, the existence of internet gametrackers have made the collection of data for one’s own perusal something less than a necessity if one would like access to real-time information on a game, and to the extent that people do want to keep their own score, electronic applications are pushing pencil and paper aside. And admittedly, those of us who keep score not just at the ballpark but in the privacy of our own homes have always been a rare breed and prime targets for the nerd label.

Still, I have no intention of giving up scorekeeping in the foreseeable future. It is still true that if you want something done right, you have to do it yourself. GameDay may have all of the information I need, but it (cannot yet at least) be customized to display it in the exact manner I have become accustomed to. If you want to save it for posterity, a GameDay printout lacks any sort of sentimentality whatsoever. And I might be part of a dying breed, but if I want to give my full undivided attention to the ballgame, the last thing I need to be doing is puttering around on the computer between pitches.

If this reads as a half-hearted defense of scorekeeping, I have accomplished what I set out to do with this post. For one thing, I don’t really need to justify my hobby to you; I just feel compelled to put in a good word for the practice every once in a while. I’ve never understood why announcers sometimes feel compelled to give you basic information about the sequence of plays in a game--information that they are tasked with providing--by prefacing it with “If you are keeping score at home…” Of course, this is a hanging curveball set up for the announcing partner, who gets to jump in and make a snide comment about what kind of deviants would be doing that. Considering that those of us that keep score are the least likely subset of fans to turn the game off when it’s 14-2 in the bottom the eighth...

But the other reason that it can be difficult to espouse the virtues of scorekeeping is that scorekeeping is a very personal pursuit. Everyone has their own technique, their own special symbols built around the familiar position numbers that have united the vast majority of scorecards from the 1890s or so on. (Except for the early twentieth century occasional flip-flopping of 5 and 6 for third base and shortstop). This makes it difficult to generalize--I might say that I love keeping score because I could quickly get a precise count of how many balls the hapless Ranger pitches had thrown while Neftali Feliz waited in the bullpen…but your scoresheet might not tell you that. Instead, it might tell you who won the sausage race.

There are displays of the variety and innovation in individual scorekeeping out there online, but not to an extent that I consider sufficient, so last year I asked people to send me their scoresheets for posting on my scorekeeping blog, Weekly Scoresheet. Several people graciously accepted my invitation, but I was foolish enough to make the initial request during the offseason, when even compulsive scorekeepers weren’t particularly likely to have an example sitting around. (*) So if you’re interested in sharing now, please send me an email.

Weekly Scoresheet has a whopping total of six subscribers on Google Reader, which is completely understandable--a personal scorekeeping blog is a vanity blog, plain and simple. Unfortunately, I haven’t been updating it recently because I no longer have a scanner at home, and it just isn’t a big enough priority for me to buy one even though this is 2011 and they are cheap. Eventually, Weekly Scoresheet will be back in full swing to bore the five people who read it with my own chicken-scratched records of ballgames.

In the meantime, though, I’ll be using this space to occasionally run a tutorial on my scoring system and walking through a sample game from the 2010 season. Calling it “my scoring system” is a misnomer--there's certainly nothing groundbreaking about it and most of the symbols are drawn from other people’s systems--but the great thing about scorekeeping is that the precise combination of data you record, codes you use, and the like is fairly unique. I would not encourage anyone to learn to score a game in the way I do, not because I don’t think it’s a decent system but because I would encourage you to organically develop your own that fits your needs and interests as a baseball fan. As such, an explanation and tutorial is ultimately just a way to fill up space and pad the post count. I’ll enjoy writing it; if you enjoy reading it, then much the better.

(*) Sadly, I have to admit that I spend a not insignificant free time in February doodling scoring of imaginary games, using Excel to design some new scoresheets that I’ll never use because I continue to use the same basic sheet (and I do mean basic) that I have for over a decade, wondering why the calendar can’t turn to March so that spring training games can be scored, and other such pursuits.

I will begin simple, with a look at one of my blank scoresheets--it's just a bunch of empty blocks in a 9x9 grid. I originally made this in the DOS Brief text editor prior to the 1998 season, using a lot of “_” and “|” symbols. The lines weren’t solid, so I eventually traced them down for 1999 or so. Later I would scan it as a PDF and touch it up a little bit in Photoshop, but it still has non-perfect lines which to me grants it a little character that you don’t get from using a computer to draw the lines precisely. I have a couple facsimile versions created in Excel (which I use now for any new scoresheets I make--it might not be as capable graphically as some other programs but making grids is something that spreadsheet software does very well), but there’s nothing like the original article for me.



* Why a 9x9 grid? You do realize that the average team doesn’t even use half of those scoreboxes in a game, right?

One of the great things about the Project Scoresheet is that it pioneered the use of numbered boxes rather than a box for every batter to hit in every inning. This was a great way to conserve space, but it also makes it harder to view the inning as a standalone unit. I prefer to see each inning on its own. Yes, a team batting around is a mild inconvenience, and splits up an inning into two columns, but I’ve never understood why some people freak out about and start crossing off the inning headings and pushing each inning down a column.

* No room for statistical lines (AB-R-H-BI and the like)?

Nope. I don’t think that standard compiled statistics for a single game tell you much of anything, and the sort of boilerplate box score is something that is very easy to obtain online (although some of them aren’t so accurate). I’m already sacrificing space by using the 9x9 format; I don’t want to waste any more on stat lines. Plus, if you fill them in as the game goes on, it’s another distraction and you have a bunch of ugly tallies on your sheet. If you wait until the game is over to finish your scoresheet, then it’s just work.

* No diamonds?

Nope, I’ve never liked them. They’re great if you just want a quick snapshot of where the runners are, but if you’re trying to record a lot of detail on how runners advanced, they get in the way. I split the box up into four corners for each of the bases, but I don’t think a visual aid like a diamond is necessary to accomplish this.