Best lefty off all time? My vote is Koufax!

[Mark17]

Originally Posted by Snowman

As far as how it could be calculated, there are several options. My preferred approach would be to build a hierarchical mixed-effects model (which controls for both fixed-effects and random-effects simultaneously). These models are extremely powerful. You could create time blocks for various periods where something of note happened (like 1942-1946 when the talent pool was heavily diluted due to players leaving for WW2), or pre-1950 for larger strike zones, or 1950-62 for larger strike zones, etc. You would hard code those into your data and treat them as fixed-effects. We could also control for offensive efficiencies of each era by measuring the delta between runners left in scoring position, among countless other ways (offenses were considerably less efficient when Grove was pitching)..........

All of this is just listing many variables involved, and I'm sure many more can be added. The obvious difficulties that remain are:

1. How do these variables play together? Are they additive, multiplicative, subtractive, and to what degree. How do you combine and weigh them?

2. How do you value them, with respect to specific players?

For example, let's say you are comparing 2 pitchers who both have a right fielder with a .985 fielding average. But one has a weak throwing arm and the other is Clemente. How much does having Clemente help, with his reputation discouraging runners taking an extra base?

First you'd need to give a weight to the variable - what impact does the right fielder's reputation play? Second, you have to value Clemente.

Suppose there are two catchers with equal fielding percentages, and throw out equal percentages of baserunners. But one is a very astute signal caller and the other is a dolt. Take Grove having Cochrane for example. First, how much can a smart, observant catcher help a pitcher? Second, what value do you assign to Cochrane (or Roseboro?)

All you have done is thrown out a bunch of factors to consider. The real trick would be to come up with an algorithm that can effectively combine and weigh the variables, and then, there's the (sometimes subjective - like the brains of a catcher) value you assign to each specific player involved.

In short, the above is not anywhere close to an actual predictive model.

[Snowman]

Originally Posted by Mark17

All of this is just listing many variables involved, and I'm sure many more can be added. The obvious difficulties that remain are:

1. How do these variables play together? Are they additive, multiplicative, subtractive, and to what degree. How do you combine and weigh them?

2. How do you value them, with respect to specific players?

For example, let's say you are comparing 2 pitchers who both have a right fielder with a .985 fielding average. But one has a weak throwing arm and the other is Clemente. How much does having Clemente help, with his reputation discouraging runners taking an extra base?

First you'd need to give a weight to the variable - what impact does the right fielder's reputation play? Second, you have to value Clemente.

Suppose there are two catchers with equal fielding percentages, and throw out equal percentages of baserunners. But one is a very astute signal caller and the other is a dolt. Take Grove having Cochrane for example. First, how much can a smart, observant catcher help a pitcher? Second, what value do you assign to Cochrane (or Roseboro?)

All you have done is thrown out a bunch of factors to consider. The real trick would be to come up with an algorithm that can effectively combine and weigh the variables, and then, there's the (sometimes subjective - like the brains of a catcher) value you assign to each specific player involved.

In short, the above is not anywhere close to an actual predictive model.

The answers to most of your questions are explained above. The fact that you don't understand how a hierarchical mixed-effect model works (or even my high-level explanation of it) does not mean that it in fact does not work. I was responding to AndrewJerome, who asked, "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? If you want the coeffecients (or "weights") from such a model, you'd have to build one. But it's a LOT of work, and I don't see anyone here volunteering to pay me for my efforts. I'm simply explaining, at a very high level, how one could solve for it. I have better things to do with my time than to prove to you guys that Lefty Grove benefitted greatly from pitching in an era where his competition was lacking or that Babe Ruth was effectively swinging at home run derby "pitches" a significant percentage of the time. That much should be obvious to anyone operating on the right side of the bell curve.

[G1911]

Originally Posted by Snowman

The answers to most of your questions are explained above. The fact that you don't understand how a hierarchical mixed-effect model works (or even my high-level explanation of it) does not mean that it in fact does not work. I was responding to AndrewJerome, who asked, "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? If you want the coeffecients (or "weights") from such a model, you'd have to build one. But it's a LOT of work, and I don't see anyone here volunteering to pay me for my efforts. I'm simply explaining, at a very high level, how one could solve for it. I have better things to do with my time than to prove to you guys that Lefty Grove benefitted greatly from pitching in an era where his competition was lacking or that Babe Ruth was effectively swinging at home run derby "pitches" a significant percentage of the time. That much should be obvious to anyone operating on the right side of the bell curve.

No one's paying you to claim Ruth played in a home run derby (there were less home runs when he played than now) or that Spahn was mediocre or that Grove sucked because of his birth year. Yet here you are, incessantly making unsupported claims. Your time doesn't seem to be all that valuable either.

[Snowman]

Originally Posted by G1911

No one's paying you to claim Ruth played in a home run derby (there were less home runs when he played than now) or that Spahn was mediocre or that Grove sucked because of his birth year. Yet here you are, incessantly making unsupported claims.

The notion that modern athletes are far superior to those of a century ago isn't exactly a controversial statement in the real world. This might be the only community on earth who wishes to pretend otherwise.

Your time doesn't seem to be all that valuable either.

It takes minutes to respond to your ignorant drivel. It would take multiple weeks of full time effort to build out a statistical model like the one I described above. I build statistical models for people who are capable of understanding and appreciating them; of which there is no shortage.

[brianp-beme]

I am not following this thread, but thought I would chime in...I am tired of seeing the misspelling on the title thread "Best lefty OFF all time".

The thing is I can't shame the OP to change it because he is banned...maybe a moderator or Leon can make my life a little more 'of' and little less 'off'.

Brian (best Lefty is Lefty Grove, because he was obviously better than Lefty Gomez).

[G1911]

Originally Posted by Snowman

The notion that modern athletes are far superior to those of a century ago isn't exactly a controversial statement in the real world. This might be the only community on earth who wishes to pretend otherwise.



It takes minutes to respond to your ignorant drivel. It would take multiple weeks of full time effort to build out a statistical model like the one I described above. I build statistical models for people who are capable of understanding and appreciating them; of which there is no shortage.

I've never argued that modern athletes are not superior in many ways. You take it to extreme lengths, and apply it very inconsistently where it is true for one pitcher and not true for his exact contemporary. All you do is make up some crap, fail to back up any of it, and insult people.

[Snowman]

Originally Posted by G1911

I've never argued that modern athletes are not superior in many ways. You take it to extreme lengths, and apply it very inconsistently where it is true for one pitcher and not true for his exact contemporary. All you do is make up some crap, fail to back up any of it, and insult people.

You interpret my claims as being extreme because I say something like "I'm taking Hyun Jin Ryu over Warren Spahn any day". Obviously, I said that knowing it would get a rise out of you, but I'm also not joking. Hyun Jin Ryu has better stuff than Warren Spahn had. Ryu is a pretty good pitcher. He was 2nd and 3rd in CYA voting in 2019 and 2020, and he led the league in ERA+ with 179 in 2019 as well, and he had an ERA+ of 198 the year before that. In contrast, Warren Spahn's top 3 ERA+ seasons were 188, 170 and wait for it... 130! Yes, that's right, the year Warren Spahn won the CYA his ERA+ was 130. That's a staggering statistic. 130 is NOT great. In fact, I wouldn't be surprised to learn that no other pitcher since has won the CYA with a lower ERA+ than that. And ERA+ is better than ERA (though still not as good as xFIP or SIERA). ERA+ adjusts for ballparks and peers. It's less useful when comparing across different eras, as you'd need to control for other variables too, but it's still useful when examining performance within a single season. You can interpret an ERA+ of 130 as meaning he was 30% better than an average pitcher that season. However, it fails to account for luck. But you can get a pretty good sense of that just by looking at a pitcher's BABIP during that season as well. It should come as no surprise then, that the two seasons where Spahn had his best ERA+ values of 188 and 170 were also his two luckiest seasons where batters only hit 0.243 against him on balls in play (pretty damn lucky). In contrast, Ryu wasn't so lucky when he had 198 and 179 ERA+ values, as his BABIPs were 0.282 and 0.303 those seasons. This means that Ryu's two best seasons were not just a little bit better. They were better despite Spahn benefitting from being extremely lucky those two seasons and Ryu not. Add in that level of luck to Ryu, and his ERA+ jumps significantly. Or conversely, take away the luck that Spahn benefitted from those two seasons and his ERA+ values drop signficantly. And remember, these values are relative to the overall talent level of the league that season.

So in Spahn's BEST season, he was 88% better than the average 1953 MLB pitcher AFTER benefitting from a significant amount of good luck. In Ryu's best full season, he was 79% better than the average 2019 MLB pitcher WITHOUT benefitting from good luck. Once you adjust for luck and for how much better the average 2019 pitcher was than the average 1953 pitcher, then it's really not even close at all if you're asking who had the better peak or who had the best "stuff". Obviously, I fully realize that Ryu's overall career is hardly a shred of Spahn's overall career, and that there is tremendous value in being an above average pitcher for a very long time. But if you could teleport Ryu back to the 1940s and 50s, he's would absolutely terrorize the league. We'd probably all be talking about him being the GOAT right now. The same is true of any other top 10 pitcher in the league today. They would just absolutely rape hitters from the 40s and 50s.

As far as your claim about me being "inconsistent", again, that's nonsense. You're the one who keeps claiming I only discount Grove's era and Spahn's era but not Koufax's. That's nonsense. You made that assumption and keep perpetuating it. I said no such thing. Koufax's numbers would absolutely suffer from any statistical model I would build. He pitched in a pitcher's park (so did Spahn), he pitched from a high mound, he pitched from an expanded strike zone in his best 4 years, he also had a lucky BABIP (though the entire league had a low BABIP at that time). His numbers would absolutely suffer from controlling for these variables. The reason I haven't focused on that fact is because it simply doesn't matter. I don't need to discount Spahn's era in order for Koufax to have a better peak 4, 5, or 6 years. Koufax's numbers themselves are simply miles better than Spahn's, WIHTOUT demoting Spahn for having pitched in a weaker era. But even if I did make the necessary adjustment to be able to compare apples to apples, Koufax's numbers would go down, Spahn's numbers would go down even more, and Grove's numbers would go down even more than Spahn's. The talent pool of the league gets worse the further back in time you go, not better.

Here's a glimpse of a few stats from Spahn's best 5 year peak and Koufax's best 5 year peak that are actually predictive, unlike Wins and ERA.

Spahn - 136 ERA+ average
Koufax - 168 ERA+ average

Spahn - 3.21 FIP average
Koufax - 2.02 FIP average

Spahn - 1.18 WHIP
Koufax - 0.94 WHIP

Spahn - 2.8 BB/9
Koufax - 2.1 BB/9

Spahn - 5.2 K/9
Koufax - 9.5 K/9

Spahn - 1.9 K/BB
Koufax - 4.6 K/BB

These differences are remarkable. There is no amount of adjusting (sizes of strike zone, talent level of their contemporaries, mound heights, ballparks, BABIP, etc) that you could possibly implement that would put these 5-year numbers on an even remotely similar playing field. Perhaps you should read those deltas again if you're not getting this. The differences between 5-year-peak Koufax and 5-year-peak Spahn are difficult to exaggerate. I could probably find 100 pitchers between them value-wise. That's how far apart these guys were. The only possible argument anyone could ever make for Spahn is by looking at cumulative career value. He was an above-average pitcher for a very long time. Value adds up, and WAR gives him extra credit because his peers sucked. But he was never even the best pitcher in a single season. Not even when he won the CYA, and not even in his best two seasons.

[Aquarian Sports Cards]

[QUOTE=Snowman;2165342] Yes, that's right, the year Warren Spahn won the CYA his ERA+ was 130. That's a staggering statistic. 130 is NOT great. In fact, I wouldn't be surprised to learn that no other pitcher since has won the CYA with a lower ERA+ than that.

Um, not even close:

Pete Vuckovich 1982 (the worst Cy Young winner ever) 114
Steve Stone 1980 - 123
Bob Welch 1990 - 125
Mike McCormick 1967 - 118
Early Wynn 1959 - 120

and I'm going to stop because there's too many to list them all. 130 is actually lower tier of the middle of the pack.

"Record" appears to be Jim Lonborg 1967 at 112

[Aquarian Sports Cards]

Originally Posted by Snowman

The notion that modern athletes are far superior to those of a century ago isn't exactly a controversial statement in the real world. This might be the only community on earth who wishes to pretend otherwise.

I actually don't think most here question that. They question the idea that they are somehow evolved in 3 or four generations. Their superiority is of methods and science not innate. Therefore if you could magically transport a Grove to 2021 and allow him to grow up in this era he would, in all likelihood, still be a superior player because he also would benefit from these advances.

In short, players today are of COURSE superior, but they aren't genetically any different than their forerunners, so the best way to compare across eras is to compare a player to his peers and then compare the comparisons.

Where THAT falls short is, as everyone has access to today's advances it flattens the curve of greatness and reduces outliers like Ruth or possibly Grove, because today's "lesser players" have made themselves greater through modern methods, whereas the players with greater natural advantages can only improve so much.

[BobC]

Originally Posted by Aquarian Sports Cards

I actually don't think most here question that. They question the idea that they are somehow evolved in 3 or four generations. Their superiority is of methods and science not innate. Therefore if you could magically transport a Grove to 2021 and allow him to grow up in this era he would, in all likelihood, still be a superior player because he also would benefit from these advances.

In short, players today are of COURSE superior, but they aren't genetically any different than their forerunners, so the best way to compare across eras is to compare a player to his peers and then compare the comparisons.

Where THAT falls short is, as everyone has access to today's advances it flattens the curve of greatness and reduces outliers like Ruth or possibly Grove, because today's "lesser players" have made themselves greater through modern methods, whereas the players with greater natural advantages can only improve so much.

Good post Scott. I've been saying the same thing all along trying to get people to understand that in looking at and comparing players from different times and eras, you can't just look at baseball numbers and statistics alone, and completely ignore the context of all non-direct baseball factors. As you said, there are superior methods and science, among other things, that really explain the differences in today's players to those of the past. But statisticians still try to explain everything with just the baseball numbers and stats they have. They completely ignore the human element and all the intangibles athletes bring to the table. Statisticians ignore those kinds of things because they can't measure a player's heart or their competitiveness, and they just tell you those are meaningless things anyway because their baseball numbers and stats override all. And don't ask them to prove anything as they'll just keep telling you they don't have time, and you wouldn't understand them anyway. Statistics are fine and have a very good place in predicting behaviors and outcomes, but there is no definitive outcome to a question like who's the best lefty of all time. And because there is no outcome to prove that some statistician's formula is right or wrong, they simply assert their formula is the answer. And in doing so, ignore the context of players in different times and eras, the human element, and in my opinion, commen sense. The statisticians can't prove they're right, but we can't prove they're definitively wrong. So they get away with it.

[tschock]

Originally Posted by BobC

Good post Scott. I've been saying the same thing all along trying to get people to understand that in looking at and comparing players from different times and eras, you can't just look at baseball numbers and statistics alone, and completely ignore the context of all non-direct baseball factors. As you said, there are superior methods and science, among other things, that really explain the differences in today's players to those of the past. But statisticians still try to explain everything with just the baseball numbers and stats they have. They completely ignore the human element and all the intangibles athletes bring to the table. Statisticians ignore those kinds of things because they can't measure a player's heart or their competitiveness, and they just tell you those are meaningless things anyway because their baseball numbers and stats override all. And don't ask them to prove anything as they'll just keep telling you they don't have time, and you wouldn't understand them anyway. Statistics are fine and have a very good place in predicting behaviors and outcomes, but there is no definitive outcome to a question like who's the best lefty of all time. And because there is no outcome to prove that some statistician's formula is right or wrong, they simply assert their formula is the answer. And in doing so, ignore the context of players in different times and eras, the human element, and in my opinion, commen sense. The statisticians can't prove they're right, but we can't prove they're definitively wrong. So they get away with it.

To put it another way: If a statistician's model is good at analyzing the past, then it should be reasonably good for predicting the future. Otherwise your model needs adjusting to consider other factors. That didn't seem to play out very well when 'the best team in baseball' this year didn't even get close to winning the World Series (as one example).

[HistoricNewspapers]

Originally Posted by Aquarian Sports Cards

I actually don't think most here question that. They question the idea that they are somehow evolved in 3 or four generations. Their superiority is of methods and science not innate. Therefore if you could magically transport a Grove to 2021 and allow him to grow up in this era he would, in all likelihood, still be a superior player because he also would benefit from these advances.

In short, players today are of COURSE superior, but they aren't genetically any different than their forerunners, so the best way to compare across eras is to compare a player to his peers and then compare the comparisons.

Where THAT falls short is, as everyone has access to today's advances it flattens the curve of greatness and reduces outliers like Ruth or possibly Grove, because today's "lesser players" have made themselves greater through modern methods, whereas the players with greater natural advantages can only improve so much.

Really has nothing to do with evolution. Population and selective breeding have produced more physically gifted and bigger players. When you have 8.5 billion people in the world to choose from compared to 2.5, it doesn't take much of a leap to see why you would have more people throwing 95+ MPH just by the that aspect alone.

When you consider that during the pre war era that the rest of the world population wasn't even used like it is in modern times(and none of the minority american population was used either), that pool of available athletes gets even more smaller.

If you take a look at the average height of a MLB pitcher from now and compare it generation by generation you will see it increasing. That isn't evolution, yet the players are indeed taller. Weight and strength have increased too and that has some aspects of nutrition and training, but height is not really something that is easily changed from what you are already programmed to be(unless maybe extreme malnourishment impedes it).

On top of the population there are many people who choose mating partners for the express purpose of producing a larger and more athletic off spring so the off spring has a better shot at scholarships and the big money contracts.

Size does matter indeed.

The median height of a pitcher in 1920 was 6 feet and 178 pounds.
The median height of a pitcher in 1960 was 6 feet 1 and 191 pounds.
The median height of a pitcher in 2000 was 6 feet 2 and 197 pounds.
The median height of a pitcher in 2019 was 6 feeet 3 and and 215 pounds.

MPH data has not always been recorded, but the the average fastball has been steadily increasing.

In 2002 the avg fastball was 88.6 MPH
In 2006 the avg fastball was 88.9 MPH
In 2008 the avg fastball was 90.1 MPH
In 2016 the avg fastball was 92.3 MPH
In 2019 the avg fastball was 93.1 MPH
In 2021 the avg fastball was 93.5 MPH

Looking at those two concrete examples of the height/weight changes, and the MPH changes, in addition to the population disparity, there is not a smidge of logic that would point to the average player in 1930 throwing anywhere near as hard as the average player in 2020, and evolution has nothing to do with it.

The size and strength of the hitters have also seen the same increase. Every hitter in the lineup can hit a home run off of a mistake. There are no weak spots where a pitcher can 'ease up'.

Baseball science plays some part in those increases in MPH, but only a part. The majority of it comes from population, more world wide players being available, and selective breeding....And no discrimination like Pre-War years.

So comparing players, when one has a weaker set of peers to be compared to, is NOT a valid comparison.

How valid can it be when Ryu has to somehow be better than everyone in the league when the AVERAGE pitcher is the same size as him and throws just as hard, and a guy from another era had to only compete against pitchers three inches smaller, 37 pounds lighter, and throwing anywhere from five to ten MPH slower on average?

Have you ever seen that photo of Nolan Ryan standing next to Randy Johnson?? He makes Ryan look like a midget. That photo alone explains everything I'm saying without the use of a single word.

This is no disrespect to the early players, because they paved the way. Ruth out homered every team in the league, not because he is that much better of a hitter than Vlad Guerroro JR, but because his environment allowed that to happen. Ruth simply could not do that today because he would have to hit 300 home runs in a season, and off of BETTER pitchers. Different environment.

People marvel at Nolan Ryan. Longevity aside, Vlad Jr. sees Nolan Ryan type stuff 'almost' every game, and most with much better command. Ryan was a freak even as late as the 1970's. Today, he is just another pitcher(again, longevity aside)...and he would be just an averaged sized pitcher too.

It isn't a dig at old time players as the respect will always be there for them. It is however a nod to players like Vlad Jr. and company who get disrespected by fans because they strike out too much, or for whatever other reason.

When players in the 1970's faced stuff like the pitchers throw today, they struck out a lot too....when facing Nolan Ryan ....there just weren't as many guys with that stuff and size in the league, and that is why Ryan was considered a circus freak back then and that is the 1970's. Imagine doing that same exercise going back to 1930.

[BobC]

Originally Posted by Mark17

All of this is just listing many variables involved, and I'm sure many more can be added. The obvious difficulties that remain are:

1. How do these variables play together? Are they additive, multiplicative, subtractive, and to what degree. How do you combine and weigh them?

2. How do you value them, with respect to specific players?

For example, let's say you are comparing 2 pitchers who both have a right fielder with a .985 fielding average. But one has a weak throwing arm and the other is Clemente. How much does having Clemente help, with his reputation discouraging runners taking an extra base?

First you'd need to give a weight to the variable - what impact does the right fielder's reputation play? Second, you have to value Clemente.

Suppose there are two catchers with equal fielding percentages, and throw out equal percentages of baserunners. But one is a very astute signal caller and the other is a dolt. Take Grove having Cochrane for example. First, how much can a smart, observant catcher help a pitcher? Second, what value do you assign to Cochrane (or Roseboro?)

All you have done is thrown out a bunch of factors to consider. The real trick would be to come up with an algorithm that can effectively combine and weigh the variables, and then, there's the (sometimes subjective - like the brains of a catcher) value you assign to each specific player involved.

In short, the above is not anywhere close to an actual predictive model.

Mark,

I think you bring up some great points, along the same lines I was alluding to, that there are going to be so many variables to factor into answering a question like this that it is virtually (and likely literally) impossible to effectively factor them all into any statistical equation or formula. You can attempt to do it, but at the of end of the day you'll only end up with what a statistician thinks is the right answer. And who elected them to decide that their opinions and points of view speak for all of us, or automatically overide what everyone else may think. I understand they can create these great statistical equations and algorithims to come up with a predictive formula to help decide who MAY be the best at something, but how can one be so certain of the outcome of such an equation or formula until they've actually created it and been able to show and prove it works. I thought in science that is what is known as a theory, which is unproven, and remains as such till someone can actually prove it is true and works. I don't seem to remember any true scientists ignoring questions about their work in regards to such theories, and simply telling people to trust and believe them because they have neither the time nor the inclination to fully explain their position. Nor to claim they know the answer to a question based on such a formula, when that formula has yet to be created, tested, and proven.

And that goes for key assumptions that are part of such theories and thinking, like the making of a blanket statement that ballplayers from 60-70-80+ years ago are much weaker players than they are today. How, why, what empirical data is there to factually prove that? You can pull up all the numbers and speculate and manipulate them all you want. And I understand about the increases in the population and how that factors in and, and yadda-yadda-yadda. But has the human male really evolved and changed that much physically in that last 100 or so years, or is it more so from advances in science, training, nutrition, medicine, education, even economics playing a huge part, and on and on. Heck, I've even heard somewhere that overall male testerone levels have been dropping generation by generation over the last century or so, which would initially make you think that earlier male generations may have actually been considered more masculine (and by extension athletic) than they are today. So maybe those differences in how players played back then are more due to all the other cultural and outside influences that were affecting them than most people (especially statisticians) would think. And how, unless you took players (not pitchers) from today and had them grow up to play 60-70-80+ years ago, and then likewise had the players (again not pitchers) from back then grow up to be playing today, could you really even begin to tell which era's players were stronger or weaker. Now according to blanket statements and assumptions by some statistically minded people, by switching the players like this we would expect to have the transplanted players to back then hitting tons of home runs, while probably striking out more, but overall crushing the pitchers from back then. In fact, the way these statisticians may talk and seem to think, you'd expect that all of Ruth's home run records would have been easily eclipsed way back 60-70-80+ years ago, and as a result he might not be carried anywhere close to the esteem he is today. And as for the transplanted players from back then now playing today, following some statisticians thinking you'd expect them to be completely overwhelmed and effectively having their collective asses handed to them on a daily basis by today's pitcher's, and not even have the league as a whole batting even close to .200.

But somehow I don't think all that would happen. Because humans are affected by and react, change and evolve to fit the situations and circumstances that surround and are constantly changing around them. No one can say with any meaningful certainty how a Grove or Spahn would pitch today. No statistician can honestly measure a person's drive, ambition, competitiveness, and any other intangibles that truly make them the player/athlete they are. And in demeaning and putting down an entire era or generation's ballplayers, without at least trying to factor in all the potential contextual differences between players from different times/eras, is simply insulting to those players. Especially since there is no truly effective way to account for, measure, and quantify all of the infinite number of cultural, contextual, and human differences (in addition to the differences in the game of baseball itself) that would need to be included in such a comparative and predictive formula. But a statistician can get away with saying they can in fact create such a formula or equation to accurately say who or what era/generation was better than another, even though they can't actually or empiracally prove they're right, because they know you or I, for the exact same reasons, can't definitively prove them wrong either.

To illustrate how times and context can be be ignored in statical analysis, the greatest ever left handed pitcher could have been someone born in the 20's who ended up dying in WWII and never even got to play in the majors. Or, they were born in the 20's, but got hurt coming out of high school when there was no Tommy John surgery back then yet, so they never got to play in the majors either. Or what about the time Randy Johnson spent on the injured list, what if he was pitching 100 years ago and got injured, but the medical knowledge back then couldn't completely cure him and he never pitched again, or at least never pitched anyhere near as well as he could have? Or here's a good one, Johnson's in college in the early '50's, and we know from his actual career it would would take him a few years to get his pitching act together. Back in the early '50's, ballplayers didn't get the kind of money they got later on when Johnson actually played. Since he's what '6"10 - '6"11, who is to say the school's basketball coach approaches him about playing BBall, so he does and ends up good enough to make the NBA because of his natural height, and never even goes to pitch in the majors. So how does a statistician ever account for and measure any of these instances in their formulas and equations? They don't, because it doesn't fit into their formulas and equations, but these examples do illustrate how in trying to look at a particular player and how well they may perform in a different time or era, the context of playing in that other time/era could result in a dramatic change to how their career would look or end up.

One last example, though a different sport. Tom Brady graduates from college and ended up being drafted in the 6th round, with what was it, 32 teams in the league then. So what if Brady had actually graduated 40 years earlier, and with a lot fewer teams in the NFL, he never gets drafted and becomes the GOAT. Different time, different context, totally different career outcome.

Statisticians create statistical formulas and equations to predict current game outcomes for gambling purposes. And after doing so, they see what the actual outcome of their game is, and can then tweak and improve their formulas as needed. The main thing is, they can actually test and prove it by looking at how well they did gambling. So they think they have these formulas and equations down and can use them to now try and determine something else like who was a better player, looking at multiple players playing in different times and eras. The problem is, you don't have any actual game or competition that will occur to tell you who won, like you do when you bet on a ball game and their is an actual winner. So there is no way to actually test that type of statistical formula or equation in picking who's the greatest at something all time, and thus be able to prove if that statistical formula or equation is in fact right or wrong. Statisticians will tell you that their statistics are all that can be accurately used to make such decisions, but since they can't ever be proven right or wrong for this type of question, statistics in this regard are nothing more than talking points, no more and no less. Something to maybe talk about, but certainly not the final answer!

[Snowman]

Originally Posted by BobC

Mark,
I think you bring up some great points, along the same lines I was alluding to, that there are going to be so many variables to factor into answering a question like this that it is virtually (and likely literally) impossible to effectively factor them all into any statistical equation or formula. You can attempt to do it, but at the of end of the day you'll only end up with what a statistician thinks is the right answer.

You say that, yet this is precisely what the entire mathematical discipline of Statistics was developed for. It is absolutely possible to measure the impact that something like pitching mound heights or strike zone dimensions has on performance with remarkable precision. This is not "pin the tail on the donkey". It is pure mathematics. If something has an effect, it can be measured, given a sufficient amount of data. The more data you have, the more accurately it can be estimated. It's all about sample sizes. And in baseball, we have a TON of data.