[BobC]

Originally Posted by AndrewJerome

This is a fascinating subject. I really enjoy analyzing baseball history and player performance.

There seem to be a few disconnects in this debate.

One disconnect is how much weight to place on counting stats. Pro-Spahn posters in this thread rely on longevity and counting stats with what appears to be a decent peak, with a pretty good ERA+ across his peak years etc. Anti-Spahn posters believe he was a pretty average pitcher in regard to “stuff” since his K/9 doesn’t blow your hair back and wins are team dependent. He pitched a lot of innings and a lot of years, but innings eaters can’t get to GOAT status if they don’t provide elite innings. Essentially that Spahn’s peak is not enough to be the best lefty ever, even with all the counting stats. Koufax’s stats are obviously much different. One very good year, 5 off the charts years, some mediocre years, early retirement and nowhere near the overall counting stats of Spahn. Anti-Koufax posters essentially dismiss him outright because his lack of counting stats eliminate him from lefty GOAT status. He essentially didn’t pitch long enough to even be in the conversation. I tend to agree that the weaknesses of both Spahn and Koufax as described above eliminate them from lefty GOAT status. Both clearly were great pitchers though.

Another disconnect here is how to compare players by era. Snowman appears to be arguing that Grove’s pitching competition was weak and therefore his stats should be discounted a great deal. The ERA titles, ERA+ etc is tainted by weak pitching competition. Essentially that Grove was much better than his pitching peers, but since his pitching peers were very bad, him being much better than them should not be as impressive as the stats appear. I have always wondered about this, but I have no way of figuring out how to crunch the numbers to argue one way or the other. The 1920s / early 1930s batting averages went nuts. Hitters went crazy. How much of this was a result of bad pitching during those years? Anyway, Snowman, I am curious how stats can help us figure out which time periods were strong and which time periods are weak. It has always been something of a mystery to me. On a similar note, WAR is a bit misleading to me since it seems to value relative to replacement where replacement level is determined differently every year. The value of a replacement level player could be very different in a time period where quality of play overall is very high as compared to a time period where quality of play was lower. But how in the world can we figure out relative quality of play?

Andrew,

Some very insightful points, and all make logical sense. Statistics can only tell a part of the story, and at best, can utilize hard, factual numbers to help determine probabilities. They completely ignore the human factor though, along with a myriad of other unkown factors and circumstances that can be occuring at any point in a game, and then change in the blink of an eye. For example, I wonder how the statisticians did, and have since, handled the statistical records for pitchers that came up against the Astros a few years ago. Is it fair to record data from such games and use that to compare those pitchers to those that never faced the Astros? And how would statisticians possibly adjust their data, should they even decide to, so as to be fair to all parties concerned? And that is just one of an infinite number of factors that statistics maybe can't always explain, measure, or even record properly.

The biggest problem in merely relying upon statistics to try to measure and compare things to me is the context, which I feel that despite what some statisticians will try to tell you, they do not have accurate, consistent,and reliable ways to really measure and account for all the differences that can occur. As another example, take the person that argues a good comparison can be achieved by magically transporting say Randy Johnson from his peak years as a pitcher, and suddenly dropping him back into the time that Lefty Grove pitched. That person may automatically declare that based on statistical data, the players back in Grove's day were weaker batters and nowhere near as good as the batters Johnson faced, so he's certain Johnson would blow everyone from back then away (at least almost everyone). But that kind of argument is so out of context as to be laughable. I've said before that that would be akin to taking an Indy car driver, and his car from today, and dropping them into an Indy race back in 20s or 30s, against cars and drivers from back then. To make such an excercise not be so completely out of context, wouldn't it make much more sense to have Randy Johnson be born the same year as Grove, so he could grow up and learn to pitch under at least more similar circumstances and with more comparable context? That way you could really have a more meaningful and honest comparison between them as pitchers. And to make it possibly even more fair and measurable, you'd then want to also have Grove born the same year as Johnson to then see how those two would have fared and measured up in Johnson's day. Though by all means not a perfect, or even possible, this exercise would likely be a much better and more comparably contextual way to compare two pitchers.

And in trying to name an all tlme great, I'd suggest having everyone on the list for that title being magically born, grow up, and then play in the same era/time as everyone else on the list. That way you could better compare how each player fared, when they played under at least much more similar context and conditions, in all the different times/eras that everyone else on the list played in. My guess is that if you then looked at each different time/era like a separate season, you could use everyone's statistics from just that time/era to hopefully agree on a clear winner. And then to determine the all time greatest, you see who ended winning the most times/eras measured. Will never happen though, but makes so much more sense than just plain statistics.

And another point in regards to statisticians and statistics. If the claim is made that the basis for even starting and coming up with sabermetrics and statistical analysis to begin with was for gambling purposes, I can't argue and don't disagree with that logic and thinking at all. In fact, it makes perfect sense as something humans would do to take advantage and make money off others. However, statisticians may forget to take into consideration the origins of the statistics they espouse and then attempt to apply them to situations for which they were never originally intended. For if statistics truly were created to assist people with their gambling, that generally entails one team or athlete competing against another team or athlete(s) TODAY! Not one team or athlete competing against another team or athlete(s) from an entirely different time or era. So if as claimed, statistics were created for gambling purposes, the context they were originally created under was for comparison of ONLY current teams and players going against each other. Now the fact that statisticians may have found success with current statistical comparisons for their gambling purposes is fine, and I don't argue their applicability at all. But I'm afraid some narrow-minded, narcissistic, and vain glorious statisticians may have felt that since their (or their statistical colleague's) statistics can, and have, accurately functioned to pick gambling winners (some of the time, but certainly not all of the time), that they have carte blanche to assume they must, therefore, be more intelligent than the average person, and that their statistics are the be all and end all answer for all other sports comparison type questions then. Like choosing a greatest at something between players/athletes, even though they may have been from different times/eras and, therefore, most certainly would have competed under (often radically) different context. They completely seem to disregard the context under which they have asserted that such statistics were originally created (gambling) and falsely push that they are appropos for whatever comparative argument they want to now make utilizing them. However, they may continually appear unable to provide specifics of such statistical analysis and formulas when requested (though this is supposed to be a mathematical science with a foundation in facts and details), appear to have disregarded any attempt to even account for or measure potential statistical error, and most certainly have ignored the human element and context involved in the attempt to expand the usefullness and applicability af statistical measures developed originally for something entirely different.

A long time ago I realized what I think is a cosmic truth, "The more I learned, the dumber I became!". Seems like every time I'd learned something new, I'd suddenly find out there was even so much more I didn't know. I try to keep an open mind in debates like this, and I'm the first to admit when I'm proven or shown to be wrong. But someone simply arguing with little to no proof or support for the arguments, and expecting people to swallow their continual "I'm right, and you're wrong!" rhetoric, is just asinine and juvenile. I'll sit and listen to anyone's thoughts and theories, and honestly (and civily) debate with them, and logically consider their points and positions (and the resulting merits of such), and offer what I feel is appropriate rebuttal when warranted. And I've found that the vast majority of people on this site are of a similar ilk. To bad it doesn't always include everyone..............