Murray Chass has left the New York Times.  This is neither timely nor earth shattering news in the grand scheme of things, but it does mark the end of a long and colorful writing career.  At least, in print.

Mr. Chass has ditched paper for kilobites, starting his own website where he and possibly other retired writers will post articles dealing with the more human element of the game. 

Don't call it a comeback.  Don't call it a blog, either.  Blog is a term and phenomenon that Mr. Chass has openly criticized and shown disdain towards.  I can understand his perspective, him being a newspaperman with forty plus years under his belt.  Anyone with an email address can start a blog, but it takes a wonderful talent for writing to sustain a career for such a long period of time.

That doesn't make these bloggers opinions any less relevant than Mr. Chass' or yours or mine.  What I write here on this site isn't a blog, but it is opened up to comments and conversation.  I wouldn't have it any other way.  My opinion is no more important than yours and if my writing stinks, I expect to hear about it.

There is another area Mr. Chass and I disagree and that is statistics.  Chass' articles have always told the story of the people who play the game, which is a great perspective behind what we see on TV.  Humanizing players is what highlights the differences between Mickey Mantle and Ty Cobb; both were great ballplayers with human flaws, but ultimately it's their personalities that make one more endearing than the other.

This is an important aspect of the game and one that shouldn't be ignored.  However, Mr. Chass dismisses the developments in statistics over the last thirty years as an attempt to strip the human element from the game and turn the players into the simple numbers on their backs.

I won't use this space as a forum to criticize Mr. Chass' point of view.  It's been done many times over by much funnier people and I don't believe it's necessary or appropriate.  He is not alone in his opinions, and, like everyone else (bloggers included), he is entitled to them.

Whenever I read this type of criticism of sabermetrics and newer baseball statistics, I am always baffled by the resentment shown by those who disregard the importance of such developments.  Most of the time, it seems like a disinterest in taking the time to understand what new statistics have been developed, and rather than express curiosity, they express disdain.

I've run into this dozens of times in my travels across the world wide internet and the argument always comes down to one essential question: why do I need all of these statistics when the game is played by unpredictable human beings?

That question touches directly on the misconceptions surrounding statistics. I'm no sabermetric maven, certainly not compared to the host of this site.  I've dabbled and played with some things and I certainly use numbers besides batting average to determine a hitters worth.  The reason I sought out those numbers was because of my enjoyment of the game.  I wasn't looking for a statistical project to pass the time, an excuse to take a non-descript dataset and distill it down to two or three values.  Instead, I wanted to understand another perspective on my favorite sport, an ability to appreciate it on a level I couldn't before.

Studying players, understanding trends, and using precedent to try and predict the future doesn't dehumanize the sport.  It helps us recognize the variations from the norm.  It highlights when a player is unique and draws attention to them in a way that basic wins and loses aren't able to do.  We know generally at what age a hitter peaks, when a typical drop-off may occur, how numbers can translate from the minors to majors.  When a player deviates from those general trends, it is cause for attention, not scorn.

A good example is a pitcher like Chien-Ming Wang.  Wang pitched very well his first couple of seasons with the Yankees despite a low average of strikeouts per nine innings.  It is generally accepted that a low strikeout ratio means less command, more balls in play, and therefore a higher earned run average.

Wang was the anomaly.  He's a sinkerball pitcher who gets a decent amount of ground balls.  Even with the Yankee defense behind him, he was having success by putting the ball in play.

That's the type of insight we wouldn't have had on a pitcher like Wang way back when.  He would have been just another pitcher with a bunch of wins to his credit.  Having this extra insight makes us appreciate what Wang is doing because he is unique, the very definition of the human element.

Another argument has been made that these newer statistics aren’t practical.  What is a Win Share?  What does VORP mean and how do I calculate a replacement player?  The sport has survived for years on the basis that hitters are judged by batting average, home runs, and runs batted in.  What does batting average mean?  In theory, it’s the percentage of hits a batter will get over a given number of at-bats.  But that’s misleading because at-bats doesn’t include walks or sacrifices.  So, if Joe Baseball has a .300 batting average, he may get three hits for every ten at-bats, but he won’t get three hits per ten plate appearances.

Batting average was created in the late 1800’s at a time when walks and sacrifices were treated differently.  Over a hundred years later, it’s still a prominent statistic, used every day in newspapers and telecasts across the country.  I think about this and wonder how many inventions from the 1800’s are still used in their native form now.  The light bulb came about through various inventions through the 1800’s but has undergone some reworking.  The telephone went through a similar path, as did the steam engine.  All of these inventions have been augmented and improved upon.

Why not baseball statistics?  Is it reasonable to assume that al of the knowledge needed to analyze a baseball player was developed over a hundred years ago?  I wouldn’t use medicinal practices from the 1800’s.  Why should batting average and earned run average be my main source of information?