Best lefty off all time? My vote is Koufax!

[G1911]

Originally Posted by Snowman

Maddux also pitched in the NL, in a pitcher's park, and with one of the greatest defensive center fielders of all time catching balls for him. His BABIP would be expected to be lower than MLB average. If you look at Smoltz and Glavine's numbers during the same time, they also both beat league average MLB BABIP.

Perhaps you should read up on BABIP? I somewhat excuse the level of ignorance on these topics by the non data savvy people in this thread because it's not exactly their job to understand numbers. But if you are serious about being a data analyst, your perpetual ignorance displayed throughout the entirety of this thread with respect to just basic statistics and simple statistical concepts is remarkably embarassing. You should be ashamed of yourself. Go read a book. Or three.

The only person being embarrassed in this thread is you. You’ve progressed into actually having some points beyond claiming to be infallible and have a statistical model you can’t show that proves your claims, but any good point in it is lost by the constant insults of everyone else here and the childish immaturity of your ‘over the top brag - insult’ pattern that never ceases. I’m well aware of what BABIP is and already said the defense behind the pitcher needs to be adjusted for. Regardless of what you claim, great contact pitchers find success at not giving up many runs, often equal to or even better than great K pitchers. Dismissing all non K centric pitchers, which seems to be your implied basis for ignoring Spahn but including his exact contemporary Koufax, is not supported by the data. It does not appear to be random luck, and they tend to have lower BABIP’s over large sample sizes.

But I’m illiterate and homeless, among many other things.

[Snowman]

Originally Posted by G1911

The only person being embarrassed in this thread is you. You’ve progressed into actually having some points beyond claiming to be infallible and have a statistical model you can’t show that proves your claims, but any good point in it is lost by the constant insults of everyone else here and the childish immaturity of your ‘over the top brag - insult’ pattern that never ceases. I’m well aware of what BABIP is and already said the defense behind the pitcher needs to be adjusted for. Regardless of what you claim, great contact pitchers find success at not giving up many runs, often equal to or even better than great K pitchers. Dismissing all non K centric pitchers, which seems to be your implied basis for ignoring Spahn but including his exact contemporary Koufax, is not supported by the data. It does not appear to be random luck, and they tend to have lower BABIP’s over large sample sizes.

But I’m illiterate and homeless, among many other things.

In one breath, you claim to understand BABIP and its implications, and in the very next breath you use the completely nonsensical term of "great contact pitchers" as if such a thing exists. This is what I'm trying to tell you. There is no such thing as a "great contact pitcher". They are the Loch Ness Monster of baseball. A myth. If you don't understand this, then you don't understand BABIP and why it is important.

This isn't exactly news either. Every franchise in the league today knows this. You might find some old school uneducated managers here and there who still reject it, but the front offices and owners across the league all accept this fundamental truth. It's been well known for the better part of 20 years now.

You should read this. It's a link to the original research article by the guy who discovered this fundamental truth about pitchers not being able to control contact after the pitch.

https://www.baseballprospectus.com/n...-hurlers-have/

[G1911]

Originally Posted by Snowman

In one breath, you claim to understand BABIP and its implications, and in the very next breath you use the completely nonsensical term of "great contact pitchers" as if such a thing exists. This is what I'm trying to tell you. There is no such thing as a "great contact pitcher". They are the Loch Ness Monster of baseball. A myth. If you don't understand this, then you don't understand BABIP and why it is important.

This isn't exactly news either. Every franchise in the league today knows this. You might find some old school uneducated managers here and there who still reject it, but the front offices and owners across the league all accept this fundamental truth. It's been well known for the better part of 20 years now.

You should read this. It's a link to the original research article by the guy who discovered this fundamental truth about pitchers not being able to control contact after the pitch.

https://www.baseballprospectus.com/n...-hurlers-have/

And yet, throughout the entirety of baseball history, we have great pitchers who are not strikeout pitchers (and thus getting their outs on contact) having very long careers and performing far above most pitchers. If there is no such thing as a great contact pitcher, how are pitchers like Maddux great? Or do you think Maddux and the numerous other pitchers like him are all sheer luck?


I'm familiar with McCracken's article and Bill James' positive take on it. I think some of the points are true indeed. But I also am aware that some contact pitchers have high inning careers of greatness. These sample sizes seem unreasonable to chalk up to sheer dumb luck. If it was purely the team defense behind them, pitchers like Maddux and the number 5 starter on the team who isn't a strikeout pitcher would chalk up about the same numbers on the whole. Maddux is a good example, he wasn't a great K pitcher. He pitched to contact. And he won 4 ERA crowns, 4 FIP crowns, led the league in fewest hits per 9 once. How do we explain his 5,000IP career if contact pitchers are all bad or mediocre?


Are you capable of making any argument whatsoever without insulting anyone? I think you've actually started to bring up good points that can coalesce into a coherent, rational argument, but your absurd egotism and propensity to just resort to the ad hominem at every single turn obscures even your good points.

[Carter08]

Originally Posted by G1911

And yet, throughout the entirety of baseball history, we have great pitchers who are not strikeout pitchers (and thus getting their outs on contact) having very long careers and performing far above most pitchers. If there is no such thing as a great contact pitcher, how are pitchers like Maddux great? Or do you think Maddux and the numerous other pitchers like him are all sheer luck?


I'm familiar with McCracken's article and Bill James' positive take on it. I think some of the points are true indeed. But I also am aware that some contact pitchers have high inning careers of greatness. These sample sizes seem unreasonable to chalk up to sheer dumb luck. If it was purely the team defense behind them, pitchers like Maddux and the number 5 starter on the team who isn't a strikeout pitcher would chalk up about the same numbers on the whole. Maddux is a good example, he wasn't a great K pitcher. He pitched to contact. And he won 4 ERA crowns, 4 FIP crowns, led the league in fewest hits per 9 once. How do we explain his 5,000IP career if contact pitchers are all bad or mediocre?


Are you capable of making any argument whatsoever without insulting anyone? I think you've actually started to bring up good points that can coalesce into a coherent, rational argument, but your absurd egotism and propensity to just resort to the ad hominem at every single turn obscures even your good points.

Plus one. And I without looking at stats I will just say the eye test can tell a great pitcher. It’s fun to watch a guy where no one can touch the ball - thinking DeGrom when he’s actually healthy - but it’s also fun to watch a guy that paints corners and throws junk down the middle that ends up with dribblers.

[cardsagain74]

Originally Posted by G1911

I'm familiar with McCracken's article and Bill James' positive take on it. I think some of the points are true indeed. But I also am aware that some contact pitchers have high inning careers of greatness. These sample sizes seem unreasonable to chalk up to sheer dumb luck.

The article did have some good points, but I agree that its whole "FIP is all that matters" conclusion is too simplistic and goes too far. And some of the points were really grasping at straws; the quotes from Maddux and Pedro were an especially poor attempt to help prove the merits of the study (of course a long scoreless innings streak will have a lot of luck...what does that have to do with that specific discussion?)

I've noticed that when it comes to sports and gambling, statisticians love to claim as many "this is completely random" findings as they possibly can. A lot of that probably has to do with being the devil's advocate about the general public's often faulty attempts to find reason in trends or insufficient statistics.

And with having such a passion to do so, it's easy for them to go too far in the other direction (and be too quick to dismiss the possible meaning in some numbers)

[Snowman]

Originally Posted by cardsagain74

The article did have some good points, but I agree that its whole "FIP is all that matters" conclusion is too simplistic and goes too far. And some of the points were really grasping at straws; the quotes from Maddux and Pedro were an especially poor attempt to help prove the merits of the study (of course a long scoreless innings streak will have a lot of luck...what does that have to do with that specific discussion?)

I've noticed that when it comes to sports and gambling, statisticians love to claim as many "this is completely random" findings as they possibly can. A lot of that probably has to do with being the devil's advocate about the general public's often faulty attempts to find reason in trends or insufficient statistics.

And with having such a passion to do so, it's easy for them to go too far in the other direction (and be too quick to dismiss the possible meaning in some numbers)

The underlying problem is that every statistic you read really should come with a confidence interval attached to it. But of course that's just too confusing for most people, and it would probably just annoy everyone. Plus, it's just impractical. But the reality for most of these statistics is that they are actually estimates of the athlete's underlying "true" abilities. Mike Trout's "true" batting average is some unknowable number, but we can estimate it using statistics. And that's precisely what we do. After the first game, he goes 3 for 4, we estimate it to be 0.750. Well, that's not going to fool anyone, because nobody hits 0.750, so we wait for more data. After a month, he's still hitting 0.414 though. Hell, by the all-star break, he's still hitting 0.392. That's after nearly 100 games and 400 at-bats! Surely, that's a large sample, right? Has he turned a corner? Rumors start spreading about him "putting in work in the off-season". They say he's "really focused now", etc. But none of this fool's the statistician, because we don't read his batting average as 0.392. We understand that 0.392 is just an estimate of his "true" batting average and that we can calculate a 95% confidence interval around this estimate by looking at the standard deviation and sample size associated with it. So, instead of reading it as being 0.392, we more appropriately read it as something like 0.392 +/- 0.130. In other words, his "true" batting average is 95% likely to be in the range of 0.262 to 0.522, which ultimately, just isn't all that helpful. Because we know this, we are hesitant to say things like "Trout is a better hitter this season than Harper since Trout is hitting 0.392 and Harper is only hitting 0.333 at the all-star break". The truth is, we just don't have enough data to make that determination. The sample sizes are simply too small, the standard devaition is too large, and thus the confidence intervals are too wide to be able to make claims "with confidence" about that statistic.

The same is true for something like ERA from season to season. It is a highly volatile statistic. When we say something like "it has too much variance", we mean that literally. Mathematically speaking, variance is the square of the standard deviation. Some statistics have extremely wide standard deviations, like ERA, batting averge, OBP, etc. Whereas other statistics have MUCH lower variance/standard deviations. Stats like FIP vary far less than ERA. This means we can compare two pitchers at the all-star break with much greater confidence by comparing their FIPs than we can by comparing their ERAs. It is a mathematical property of the inherent differences between those statistics. The same is true of K/9 and BB/9. They have lower variance than ERA, and thus have much narrower confidence intervals. A statistician might be able to read Koufax's K/9 rate at the all-star break with a fairly narrow confidence interval because of this. So they might read his K/9 of 10.1 as being something like 10.1 +/- 0.4, making comparisons against other pitchers much more possible. If two pitchers' statistics do not overlap when taking into consideration their confidence intervals, then you can say that you are 95% confident that Koufax is a better strikeout pitcher because his 10.1 +/- 0.4 K/9, or as an interval, read (9.7, 10.5) is greater than some other pitcher whose K/9 confidence interval is (8.8, 9.6). Note the bottom of Koufax's range (9.7) exceeds the top of the other pitcher's range (9.6), so we can state with confidence that he is indeed better. However, this is rarely possible to say with ERAs. The confidence intervals with those are just absolutely massive. Even after an entire season. One pitcher's ERA of 3.05 may look quite a bit better than someone else's 2.64, but we just can't state that with confidence because their intervals might be something like 3.05 +/ 0.65 and 2.60 +/- 0.75 resulting in ranges of (2.40, 3.70) and (1.85, 3.35). And since those intervals overlap, we cannot state with confidence that they are truly different or that one is clearly better than the other. This is why an asshole like myself says something along the lines of, "that doesn't mean shit", whereas someone more tolerant might say something like, "the standard deviations of that statistic are too wide and the sample sizes are too small for us to be able to make a determination about the differences between those two data points". One of the most fascinating aspects about baseball, which is probably a big part of why I love the game as much as I do, is that the game truly is subject to a MASSIVE amount of variance. Great hitters can hit 0.348 one season and 0.274 the next. People will come up with all sorts of explanations about what is causing the slump, whether his home life is affecting him too much, if he's injured or just experiencing a mental lapse, etc. However, the informed fan knows that this is simply within expectations, and looks to statistics like BABIP to help shed light on what the actual underlying cause is (the guy just got some lucky bounces last season and some favorable ones this season. Or perhaps he didn't. Perhaps his BABIPs are the same, and there actually really is something going on in his personal life or he really is injured. But variance/luck needs to be ruled out first, because if it's present, then you already have your answer). This is also precisely why I stated earlier that I see no reason to believe that Randy Johnson was tanking games in Seattle in 1998 before being traded to Houston that season. At first glance, his numbers appear to tell a significantly different story (ERA of 4.33 in Seattle and 1.28 in Houston). But when you dig in closer and look at the confidence intervals associated with those deltas, and look at his FIP, K/9, and BABIP values, and the confidence intervals around those, you'll see that they all overlap. We simply don't have enough data to say that those numbers are truly different, even though they certainly appear to be, and read that way to the non-statistician.

But these things do in fact matter. This isn't just some statistician's "opinion". We can actually calculate these things mathematically. We can also calculate the precise probability that pitcher A will have a lower ERA than pitcher B by the end of the season based on their differences at the all-star break. And if the formula says that pitcher A is 50% likely to have a higher ERA than pitcher B, based on their current ERAs and the confidence intervals associated with them, and if we run those comparisons for all pitchers in the league, we really will be "wrong" on 50% of them at the end of the season because these confidence intervals are real-world probabilities that will play out in the future. That's the beauty of the discipline of statistics. It's all based on sound theory that has been proven mathematically.

[cardsagain74]

Originally Posted by Snowman

The underlying problem is that every statistic you read really should come with a confidence interval attached to it. But of course that's just too confusing for most people, and it would probably just annoy everyone. Plus, it's just impractical. But the reality for most of these statistics is that they are actually estimates of the athlete's underlying "true" abilities. Mike Trout's "true" batting average is some unknowable number, but we can estimate it using statistics. And that's precisely what we do. After the first game, he goes 3 for 4, we estimate it to be 0.750. Well, that's not going to fool anyone, because nobody hits 0.750, so we wait for more data. After a month, he's still hitting 0.414 though. Hell, by the all-star break, he's still hitting 0.392. That's after nearly 100 games and 400 at-bats! Surely, that's a large sample, right? Has he turned a corner? Rumors start spreading about him "putting in work in the off-season". They say he's "really focused now", etc. But none of this fool's the statistician, because we don't read his batting average as 0.392. We understand that 0.392 is just an estimate of his "true" batting average and that we can calculate a 95% confidence interval around this estimate by looking at the standard deviation and sample size associated with it. So, instead of reading it as being 0.392, we more appropriately read it as something like 0.392 +/- 0.130. In other words, his "true" batting average is 95% likely to be in the range of 0.262 to 0.522, which ultimately, just isn't all that helpful. Because we know this, we are hesitant to say things like "Trout is a better hitter this season than Harper since Trout is hitting 0.392 and Harper is only hitting 0.333 at the all-star break". The truth is, we just don't have enough data to make that determination. The sample sizes are simply too small, the standard devaition is too large, and thus the confidence intervals are too wide to be able to make claims "with confidence" about that statistic.

The same is true for something like ERA from season to season. It is a highly volatile statistic. When we say something like "it has too much variance", we mean that literally. Mathematically speaking, variance is the square of the standard deviation. Some statistics have extremely wide standard deviations, like ERA, batting averge, OBP, etc. Whereas other statistics have MUCH lower variance/standard deviations. Stats like FIP vary far less than ERA. This means we can compare two pitchers at the all-star break with much greater confidence by comparing their FIPs than we can by comparing their ERAs. It is a mathematical property of the inherent differences between those statistics. The same is true of K/9 and BB/9. They have lower variance than ERA, and thus have much narrower confidence intervals. A statistician might be able to read Koufax's K/9 rate at the all-star break with a fairly narrow confidence interval because of this. So they might read his K/9 of 10.1 as being something like 10.1 +/- 0.4, making comparisons against other pitchers much more possible. If two pitchers' statistics do not overlap when taking into consideration their confidence intervals, then you can say that you are 95% confident that Koufax is a better strikeout pitcher because his 10.1 +/- 0.4 K/9, or as an interval, read (9.7, 10.5) is greater than some other pitcher whose K/9 confidence interval is (8.8, 9.6). Note the bottom of Koufax's range (9.7) exceeds the top of the other pitcher's range (9.6), so we can state with confidence that he is indeed better. However, this is rarely possible to say with ERAs. The confidence intervals with those are just absolutely massive. Even after an entire season. One pitcher's ERA of 3.05 may look quite a bit better than someone else's 2.64, but we just can't state that with confidence because their intervals might be something like 3.05 +/ 0.65 and 2.60 +/- 0.75 resulting in ranges of (2.40, 3.70) and (1.85, 3.35). And since those intervals overlap, we cannot state with confidence that they are truly different or that one is clearly better than the other. This is why an asshole like myself says something along the lines of, "that doesn't mean shit", whereas someone more tolerant might say something like, "the standard deviations of that statistic are too wide and the sample sizes are too small for us to be able to make a determination about the differences between those two data points". One of the most fascinating aspects about baseball, which is probably a big part of why I love the game as much as I do, is that the game truly is subject to a MASSIVE amount of variance. Great hitters can hit 0.348 one season and 0.274 the next. People will come up with all sorts of explanations about what is causing the slump, whether his home life is affecting him too much, if he's injured or just experiencing a mental lapse, etc. However, the informed fan knows that this is simply within expectations, and looks to statistics like BABIP to help shed light on what the actual underlying cause is (the guy just got some lucky bounces last season and some favorable ones this season. Or perhaps he didn't. Perhaps his BABIPs are the same, and there actually really is something going on in his personal life or he really is injured. But variance/luck needs to be ruled out first, because if it's present, then you already have your answer). This is also precisely why I stated earlier that I see no reason to believe that Randy Johnson was tanking games in Seattle in 1998 before being traded to Houston that season. At first glance, his numbers appear to tell a significantly different story (ERA of 4.33 in Seattle and 1.28 in Houston). But when you dig in closer and look at the confidence intervals associated with those deltas, and look at his FIP, K/9, and BABIP values, and the confidence intervals around those, you'll see that they all overlap. We simply don't have enough data to say that those numbers are truly different, even though they certainly appear to be, and read that way to the non-statistician.

But these things do in fact matter. This isn't just some statistician's "opinion". We can actually calculate these things mathematically. We can also calculate the precise probability that pitcher A will have a lower ERA than pitcher B by the end of the season based on their differences at the all-star break. And if the formula says that pitcher A is 50% likely to have a higher ERA than pitcher B, based on their current ERAs and the confidence intervals associated with them, and if we run those comparisons for all pitchers in the league, we really will be "wrong" on 50% of them at the end of the season because these confidence intervals are real-world probabilities that will play out in the future. That's the beauty of the discipline of statistics. It's all based on sound theory that has been proven mathematically.

I am completely aware of the very elementary statistical principles that you just described. But none of that has anything to do with my point, which is sometimes coming to a biased or subjective conclusion from your data (and using just the "good contact pitcher" study as an example.

As a statistician, if McCracken didn't already have a tendency to lean toward certain preferred results, he never would've used those quotes by Maddux and Pedro to supposedly "prove" his point some (and actually would have likely mocked any mention of them doing so).

More often than not, I'm with you. Am the first guy to look for that progressive royal video poker machine that's gone to 100.75% in EV with perfect play and has a high enough hourly rate to be worth playing, etc etc. Anyone who doesn't trust the math in those completely quantifiable spots is ignoring undeniable reality.

But when it comes to spots where human elements are involved, my point here (and many others' point) is that there are too many intangibles that may or may not apply in some spots to come to such sound conclusions, even when some individual smaller pieces of that particular puzzle have been proven statistically.

It reminds me of how the EMH in the financial markets is seen by statisticians. When I actually read the "proof" that the market is supposedly 100% random, I was stunned by how elementary the research was. Especially given how some elite full-time traders have been highly successful over the course of many, many thousands of trades. Results that would be impossible by chance, no less.

You see someone like Bill Russell as highly overrated and extremely lucky. I see someone who likely didn't win 15 titles in the 16 years he was the core of his team from high school on by chance (and yes everyone, I know there were two years he didn't win a ring the pros...he got hurt early in their losing playoff series during one of those two seasons.) And see someone like Chris Archer as a guy who's just gotten extremely unlikely, rather than a pitcher who give up the big hit to decide a game much more often than the norm (over a period of many years and over 200 starts).

Sometimes there might be more to these things than just being that occasional outlier on the bell curve.

Dynamic events are just a totally different ballgame to datamine proof from than anything set in mathematical stone, imo. And I know you won't agree. Though I can't prove it

[Peter_Spaeth]

John, good post. As I recall saying to someone years ago who was stat oriented in discussing Kershaw and his post-season woes, we aren't just dealing with APBA cards where players inevitably regress to their mean, we're dealing with people.

[BobC]

Originally Posted by cardsagain74

I am completely aware of the very elementary statistical principles that you just described. But none of that has anything to do with my point, which is sometimes coming to a biased or subjective conclusion from your data (and using just the "good contact pitcher" study as an example.

As a statistician, if McCracken didn't already have a tendency to lean toward certain preferred results, he never would've used those quotes by Maddux and Pedro to supposedly "prove" his point some (and actually would have likely mocked any mention of them doing so).

More often than not, I'm with you. Am the first guy to look for that progressive royal video poker machine that's gone to 100.75% in EV with perfect play and has a high enough hourly rate to be worth playing, etc etc. Anyone who doesn't trust the math in those completely quantifiable spots is ignoring undeniable reality.

But when it comes to spots where human elements are involved, my point here (and many others' point) is that there are too many intangibles that may or may not apply in some spots to come to such sound conclusions, even when some individual smaller pieces of that particular puzzle have been proven statistically.

It reminds me of how the EMH in the financial markets is seen by statisticians. When I actually read the "proof" that the market is supposedly 100% random, I was stunned by how elementary the research was. Especially given how some elite full-time traders have been highly successful over the course of many, many thousands of trades. Results that would be impossible by chance, no less.

You see someone like Bill Russell as highly overrated and extremely lucky. I see someone who likely didn't win 15 titles in the 16 years he was the core of his team from high school on by chance (and yes everyone, I know there were two years he didn't win a ring the pros...he got hurt early in their losing playoff series during one of those two seasons.) And see someone like Chris Archer as a guy who's just gotten extremely unlikely, rather than a pitcher who give up the big hit to decide a game much more often than the norm (over a period of many years and over 200 starts).

Sometimes there might be more to these things than just being that occasional outlier on the bell curve.

Dynamic events are just a totally different ballgame to datamine proof from than anything set in mathematical stone, imo. And I know you won't agree. Though I can't prove it

John,

Excellent post, great points, and I'm one of those people that keeps bringing up the variables, like you. Also like you, I understand the principles and such behind the math, and their use, especially in the example you gave with regard to the video poker machine. Great example.

What is funny is how statisticians seem to really despise the word "opinion" when used in reference to what it is they do. But you know right away in dealing with statistics, such as provided by baseball, that you're going to have recognized deviations and varying levels of confidence in the stats of a player as to what his "true" statistical measures (stats) should be. And since by a statistician's own understanding there is never going to be a 100% certainty as to a player's stats being their "true" stats then, the use of that player's stats for some comparison is never going to be 100% accurate, and therefore it is NOT an undeniable fact. And if a statistician uses something that is not an undeniable fact as even part of the basis for determing some answer, then that answer is their opinion, and can never be fact. They should look up the definition of the word "opinion". Putting it a simpler way, if someone says water is wet, that is a fact. But if a statistician says they are 95% confident water is wet, therefore I'm going to say it is wet, then that isn't a fact, that is their opinion. Simplistic, maybe, but I hope it drives my point home.

And using current stats to compare players all playing MLB at the same time is one thing, and obviously hard enough to do based on a lot of what has been discussed already. But to expand such comparisons to now include several players over a number of different years and dramatically different eras of the game, and to then think that statistics alone could ever provide a definitive answer to such comparisons, is sheer lunacy. And making and using a blanket statement that pitchers like Grove and Spahn played back in the day when players were weaker and nowhere near as good as today's ballplayers, and therefore Grove and Spahn are nowhere near as good as today's pitchers, as part of such a statistical analysis to compare pitchers, is not just wrong, it is downright insulting to Grove, Spahn, and everyone playing MLB back when those two were pitching. Just taking a pitcher today and throwing him back in Grove's day, completely ignores the context of the different times, different game, and on and on.

Often when people talk about financial matters from different periods or eras, they may at least try to reflect or account for those different eras by adjusting numbers for inflation so they can say something that cost $XXX in 1921, would cost or be worth $YYY in 2021, as "adjusted for inflation". It likely is not a perfect way to account for the different eras, but at least it lends for the information being more relevant and comparable to people today. What is needed to even begin to make blanket statements about how good (or bad) players from a certain era were, is some type of "adjusted for inflation" equation or formula to at least try to account for ALL the differences and variables faced by players from different times and eras. I doubt a formula/equation to perform such an "adjusted for inflation" calculation even exists. But I wouldn't be surprised if you asked 10 different statisticians to independently of each other work up such a formula/equation, assuming you could find 10 to even try, that they'd all (or at least a majority of them) come up with something at least slightly different. In other words, they'd each probably have their own, differing "opinion" as to what this "adjusted for inflation" calculation should.

And the very last line you typed highlights maybe the biggest problem of all. In the case of a subjective question like who's the greatesty lefty pitcher of all time, the statitisticians can't definitively prove they are right. But then unfortunately, we can't definitely prove them wrong either.