Bedtime Reading: Tennis Anthologies

It was once said by someone (I don't know by whom -- maybe Frank Deford? I ought to check on that) that sportswriting follows a basic rule: the smaller the ball, the better the writing. Hence the quality of baseball writing exceeded that of football or basketball writing; golf writing was better still; and the best of all was writing on boxing, which had no ball whatsoever. By this metric, tennis writing should rank quite highly. In fact, tennis does inspire excellent prose, not because of the size of the ball but perhaps because of its gladiatorial nature. The one-on-one battles render the sport a sort of non-violent (or non-contact) boxing. The fact that the sweaty visages of professional tennis players are displayed for all to see in close-up television images allows viewers (and writers) to imagine quite naturally the inner demons they are facing throughout a grueling match.

The fact that I play the sport, of course, biases my judgment a bit in favor of tennis writing. In this series of postings, I'll review some of the best tennis books I've read. In the first installment, I'll examine two great anthologies of tennis writing and reporting.
Lawn tennis (which is essentially the modern tennis game we know, as opposed to the ancient game of court tennis) was invented by Major Wingfield in 1874. Soon after that, apparently, folks commenced writing about it, as The Fireside Book of Tennis proves. This collection was edited by the great tennis journalist Allison Danzig, and it covers the period from the birth of the game into the beginnings of the open era, right up to the 1971 U.S. Open, when Chris Evert introduced herself to the world. Many of the pieces are spot reporting of events, accounts which ran in fine newspapers such as the New York Times. These are typically stellar pieces of work from the top tennis journalists of their times.

The anthology also includes long profiles of players that originally ran in magazines. Many of these go into detail about the playing styles of great players. I learned about the strategies of Jack Kramer -- how he would approach down the line virtually every time, even if his opponent knew it was coming -- a legend whom I have never seen play and only knew from his days as an ex-promoter and commentator. Tilden, Budge, Connolly, Rosewall, Emerson, Laver and many more are profiled, from the perspective of observers contemporary to the players themselves and often unafraid to discuss chinks in a given player's armor.

It's an extremely long book, and though the contents are laid out in chronological order, the book is best taken in short bursts. Many of the pieces are only three or four pages long, and several of these can easily be read in half an hour.

As comprehensive as The Fireside Book is, my favorite tennis collection is the 1981 anthology The Tennis Book. Edited by Michael Bartlett and Bob Gillen, this tome is divided into five parts. The first focuses on long descriptions of the famed tournaments and venues of the world, from Wimbledon and the other major events to the minor-league pro circuit. Most of these are magazine articles or excerpts from books. Legendary scribes such as McPhee, Bodo, Bellamy, Herbert Warren Wind, and Gordon Forbes make appearences here, poetically describing various stops (and personalities) along the tennis circuit.

The second part is composed of profiles (again, usually magazine pieces or excerpts from biographies) of the great players, from McLauglin and Tilden to stars of the late '70s like Vilas, Borg, and Evert. This part includes pieces by luminaries like Deford, Danzig, Dick Schaap, Bud Collins, Richard Evans and Mike Lupica. It concludes with a fine pair of news articles by Neil Amdur about the Borg vs. McEnroe battles that captivated the tennis world during the summer of 1980.

The next section includes essays (often written or ghost-written by famous players) about various technical aspects of the game. Laver and Budge offer their thoughts on the backhand, Gonzales discusses the serve, and modern gurus Tim Gallwey and Vic Braden chime in with advice that appeals more to the average club player. The fourth part offers three short stories related to tennis, showing that not all great tennis writing must be nonfiction. Finally, the last section contains some more statistically based works, such as discussions of the greatest matches, top players, and record-setting feats in tennis. The book concludes with a roll call of major titlists through 1980.

For the tennis historian, the first-person accounts in The Fireside Book offer meatier material. But from a literary perspective, the writing in The Tennis Book is nonpareil, deeply rewarding the reader who returns to these pages again and again.

Favorite Albums: The 1960s

This has gotta be a first on the Internet: I'm going to post a list of my favorite rock albums! Well, maybe it's not totally original, but I'll at least break up the list in an interesting way. The first post will be my 10 favorite albums from the 1960s, the next my 10 favorites from the 1970s, then my 10 favorites from the '80s, then the '90s, and finally the 2000s. It goes without saying that all these lists are subject to changes, rearrangements, editing, etc.

I'm going to cheat a little for the '60s list, though. Were I to list my favorites straight-up, nearly all of the list would be taken by those colossal titans, the Beatles and Bob Dylan. To be fair to the other musicians from probably the greatest decade of rock music, I'll post two separate lists for the '60s, one with only Beatles and Dylan albums, and one list for everyone else.

In the spirit of Elvis Costello's quote ("Writing about music is like dancing about architecture"), I won't really write anything about these albums. Nothing I could write would really do this great music justice, anyway. Rather, I hope that anyone who reads this might be encouraged to check out (or revisit) some of this music. If your tastes are anything like mine, it will enrich your life.

My Favorite Beatles and Bob Dylan Albums from the 1960s
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The Beatles -- Sgt. Pepper's Lonely Hearts Club Band
The Beatles -- Abbey Road
Bob Dylan -- Highway 61 Revisited
The Beatles -- The Beatles ("The White Album")
Bob Dylan -- Blonde on Blonde
The Beatles -- Revolver
Bob Dylan -- Bringing It All Back Home
The Beatles -- Magical Mystery Tour
The Beatles -- Rubber Soul
Bob Dylan -- Another Side of Bob Dylan

My Favorite Albums from the 1960s (non-Beatles-and-Bob-Dylan category)
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Love -- Forever Changes
The Kinks -- Arthur (or the Decline and Fall of the British Empire)
The Velvet Underground -- The Velvet Underground and Nico
The Band -- The Band
The Who -- Tommy
The Kinks -- The Village Green Preservation Society
The Rolling Stones -- Beggars Banquet
The Byrds -- Sweetheart of the Rodeo
The Kinks -- Something Else
The Doors -- The Doors

EDIT: I need to include an album that I hadn't heard before making this list, but which is now one of my favorite of all time:
The Zombies -- Odessey and Oracle
Amazingly perfect pop.  I'd put it around 3rd or 4th on the second list above.

Actually, taking away the Beatles and Dylan left enough room for about all of my most deeply beloved '60s albums to make the top ten. The albums that were considered but left out were some that I admire a great deal but only love a little, e.g.: Pet Sounds, Astral Weeks, Music From Big Pink, John Wesley Harding, Surrealistic Pillow, Led Zeppelin II, The Who Sell Out, Everybody Knows This is Nowhere. Fine albums, but none that I really regret not including. I imagine the 1970s and '80s lists, based on sheer numbers, might force some harder calls.

Crunching Numbers

We begin by examining the daily life of a statistician. One of the most common tasks a statistician must undertake is the calculation of a sample mean. Let's work through an example to illustrate this.

We begin with a set of numbers, which statisticians often call "the data". (A common question ordinary laymen have is how to pronounce the word "data". It's an apt question, as I have personally heard the word pronounced in no fewer than two ways! Fortunately, there's a simple rule to help you remember the correct pronunciation of the word: Data rhymes with "strata".)

Suppose these are the numbers: 4, 8, 15, 16, 23, 41, 17, 26.923, 1. (An immediate question is, where have these numbers come from? Lengthy tomes could be written on this subject, but it suffices to say that they are usually on a piece of paper handed to you by the boss.) In initially perusing the numbers you may believe you see a familar pattern; ignore it! -- it can only lead to disappointment when the pattern goes askew. Professional statisticians are best equipped to handle this, having been trained from an early age to scan numbers dispassionately.

Let's return to the task of the calculation. The first step is to sum the numbers. This is most easily done with electronic calculators, which are commonly found, e.g., on the faces of today's fashionable wristwatches. (In this manner, we can achieve the sum without any carrying of ones, twos, or threes: Statisticians avoid manual labor whenever possible.) In this example, the sum of all of the numbers is easily seen to be 151.923. We're nearly halfway done.

The next step is to count the number of data values. Go ahead, count them. (To minimize the chance of error, you should actually place your finger on top of each number on the computer screen as you count. Don't worry about smudging the screen; modern computer monitors are easily cleaned with a scouring pad or something.) Done? I hope you agree that there are nine values in the data set. A neophyte would finish the calculation by dividing the previously attained sum of 151.923 by 9. While this technically works, it's kind of like riding a tricycle. Any thrill-seeking statistician would scorn that approach, instead being much more likely to MULTIPLY the sum by 1/9. Either way, the result is identical: approximately 16.8803. At long last, we have our sample mean! Some folks colloquially call this "the average", but such people are Philistines who, as the avian biologists like to say, "couldn't tell a Sphyrapicus thyroideus thyroideus from a Sphyrapicus thyroideus nataliae".

With the sample mean safely calculated, the statistician can head home, content in the afterglow of a hard day's work done. In our next posting in this series, we'll examine another common statistician's task: the development of asymptotically unbiased (order root-n) confidence bands for the hazard function of a doubly-censored random vector. Until then, good night!