That's a neat idea. They have veterans committees to consider former ballplayers for exclusion in the Hall. There could likewise be some sort of committee to retroactively award Cy Young awards for pre- 1956 seasons.

It would be fun to pick a specific season and have a poll, as you suggest, to decide who the Cy winner would've/should've been.

I like it

Yes it is just the 89 Fleer Marlboro versions that have taken off and it wasn't too long ago they went for a couple bucks each.

The chart was more in relation to Koufax....Grove was just added for peak comparison....hence not all filled in for Grove and so I could have room to emphasize that Koufax was not contributing anything while Johnson was(while Grove was too).

No question Grove had a better career than Koufax.

There is no sensible argument that puts Koufax ahead of either Grove or Johnson. They both had Koufax's peak and they added a couple more four year peaks on top of that.

It really does come down to Grove and Johnson, but when you take into account the population factor of available VIABLE humans to compete against, and the fact that Grove's era actually went out of its way to eliminate a segment of the population to compete....and when you consider that Johnson had superior physical attributes that are the only known 100% measureables, then Johnson walks away as number one.

Johnson had tougher peers to outdistance.

First, I have to say thank you for actually reading my posts and summarizing my views in a way that I could actually sign off on. You're the first person here who has even made an attempt to understand what I've said without intentionally trying to distort it.

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). We could also control for a pitcher's ability to control the ball across eras by including their K/BB ratios and capturing the interaction of that metric against K/HRs since strikeout rates are both a function of how well a pitcher pitches and what strategies are employed by the hitters. If that relationship is non-linear, we could apply a mathematical transformation (like the square root, cubed root, log, etc.) that enables us to create a linear relationship which would then have predictive power in a model like this. Worth noting is that there is an extremely strong correlation over time between strikeout rates and HRs because swinging for the fences results in striking out more often. I would also include several rate stats that contrast the ratios between batting average and OPS over time, as this has a measurable effect on pitching statistics across eras. Also worth including is the relationship between league-wide ERA and WHIP over time and looking for gaps in that ratio. If WHIP values were high, relative to ERAs, that would be indicative of pitchers ERAs having benefitted from inefficient offenses (and something that Grove and his peers on the mound surely benefitted from, perhaps tremendously). Something else worth noting (and I suppose this is a hint of sorts for something I referenced earlier) is that it's more important to know a pitcher's strikeouts per plate appearance than it is to know their K/9. There are also differences in approach over time. Ted Williams talks about just "putting the bat on the ball" and how that made him a "better hitter" than he would have been if he tried to hit home runs. While yes, it gave him a better batting average, we now know that this isn't what makes someone a "better hitter", at least not in the sense of producing more runs and winning more games. We would also need to control for mound heights at each ballpark over time. We could treat the individual players' performances as random-effects while treating the other metrics we are interested in estimating as fixed-effects, while simultaneously adjusting for age. We could also look at the differences in slopes of the age curve calculations over time and how those slopes have changed. The flatter the curve, the less skilled their peers are, and the steeper the curve, the stronger the opposition. The beauty of using this approach with the hierachical multilevel models, as opposed to using something like standard regression or econometric type models, is that it uses recursive algorithms which output extremely accurate coefficients that are capable of producing different slopes AND intercepts for each cohort as opposed to all using the same slope with different intercepts like you'd get from multiple regression models. The overlap of players playing across different eras (in aggregate, not just cherry-picking one or two players) allows us to measure the differences in the overall skill level of each time period we are interested in (again, adjusting for age and all of the other factors simultaneously). One thing worth keeping in mind is that it's not so much that hitters from the 1930s were "worse" hitters in the sense that they were less capable (although surely, this is also true), but rather that they were "worse" hitters in the sense that they employed sub-par hitting strategies (e.g., they bunted too often and just tried to "get a bat on the ball" rather than just swinging from their heels like Babe Ruth and Lou Gehrig did). We would also want to adjust for the overall talent pool of players in the league and the populations from which they were drawn from. Professional athletes are sampled from the right tail of a Gaussian (or "normal") distribution. They are the best of the best. The ratio of the number of players in the league vs the number of total possible baseball players from which they could have been drafted is extremely important, as this effectively tells us where along that normal distribution that this talent level lies. The larger that ratio, the further to the left they are on that distribution, and the smaller that ratio, the further to the right they are. And the further the league is to the left on that curve, the less skilled they are as a whole. If one era is 3 standard deviations to the left, we can extremely confident that we're effectively watching something that amounts to something like single-A ball today with a handful of star players sprinkled in. A prime example of this is the fact that I played varsity basketball at my high-school. However, the reason I was able to make the team wasn't because I was some elite athlete, but rather because there were only about 200 students in my high-school. Had I attended a much larger school, I might not have even made the JV squad. There were probably only one or two kids on my entire team, if any, who could have made the team on a much larger school. However, their stats would have certainly gone down if they did. They might have averaged 20 ppg and 8 rebounds on my team, but only 12 ppg and 5 rebounds on the team with better players and stronger opponents. Baseball is no different. Player talent pools grew over time. The earlier years, while still fun and nostalgic, were simply not nearly as strong as they are today. Just watch some of the available footage from that era. Half those pitchers look like Weeble Wobbles on the mound with their "deliveries". Those guys were not throwing heat.

I often use these sorts of models when I'm building predictions for NFL games. If a team's starting center is injured and will miss the game on Sunday, I can use these types of models to predict the impact that his absence will have on the spread (hint, it's more you'd probably think).

We could also make retrodictions about things like how fast they pitched in the 1920s by looking at the evolution/progression of other similar sports for which we actually do have data. One option could be to look at the history of javelin and discus throwing records in the Olympics over time and see how well human performance correlates to the progression of pitching stats during the periods for which we have data for both, and regress pitching stats retrodictively against those other throwing sports to yield directionally accurate estimates for the pitching stats from the eras where we didn't have radar guns. While I haven't run the numbers yet, I'm extremely confident that there's no way in hell anyone in the 1920s was throwing a baseball 100 mph. It's worth pointing out that all of these anectodal stories about players saying that Walter Johnson (or pick your favorite hero) was the hardest pitcher they ever saw don't really mean all that much. The plural of anecdote is not data. When I was in middle school, I played against pitchers who were throwing ~70-75 mph. I still vividly remember to this day, going to the batting cages during that time and entering the 90 mph cage. I just remember laughing and thinking, "how the hell am I supposed to hit that?" Speeds are all relative. Walter Johnson throwing the ball 10 mph faster than the 2nd fastest guy doesn't mean all that much when we don't know how hard the other players are throwing. Everyone just knows that he throws "heat" relative to what they're accustomed to. He very well might have been throwing the ball a mere 90 mph, but it "felt like 100 mph" to anyone standing at the plate who was used to swinging at 80 mph "fastballs".

Koufax had 23 shutouts in 85 career starts at Dodger stadium.
Koufax had 17 shutouts in 229 career starts everywhere else.

If someone looks at that and still believes that Koufax was not helped by pitching in Dodger stadium, then they are simply not taking an objective look at things.

Which like I said above...since Koufax backers like to use the "what if." What if Koufax pitched half his games in Coors field in the 1990's early 2000's....you would never hear a thing about his complete games, shutouts, or World Series wins....they wouldn't exist and neither would this thread.

^^^This guy knows what he's talking about.

And while I say that I don't "know" who was best (because I haven't run the calculations necessary), gun to my head I'm picking Randy Johnson as well.

Here you go again with the Wins and Losses records as if they mean anything at all. Who cares if he was 7-9 in the post season or if he only led the league in Wins one time? You might as well be criticizing his hair color or number of tattoos. Please stop with this nonsense. Wins attributed to a pitcher do not matter. At all.

No. Even ERA is highly subject to variance (read "luck"), which league they pitched in, and whether or not they pitched in a pitchers park.

Just go pick your favorite pitching seasons by your 10 random favorite pitchers. Then scroll down to the advanced stats section and look at the corresponding BABIPs for those seasons. I guarantee you those BABIPs will all be super low. In other words, those were the seasons they got the luckiest, not necessarily the seasons where they had the best stuff.

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.

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.

CJ

Great points and interesting thought by slightly changing the question like that. Problem I can see in answering it though is that it gives an unfair bias/advantage to modern pitchers, like a Koufax, who we may have grown up with, or maybe our Father did and told us how great he was. We can read and learn about earlier players, but I fear for the vast majority of people, they're much more likely to throw their reverence towards a player they'd actually seen and grew up watching. Just basic human nature. And you can't really base a question like this on just people here on this forum. Let's face it, we're mostly a bunch of pre-war baseball card collecting nerds, and an extreme outlier when talking about the public in general. LOL

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 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).

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.

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!

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.

You’re spot on with this take, Bob. By and large, the players we admired and revered the most are those we have a direct (firsthand) or semi-direct connection with (through a parent’s or grandparent’s direct connection). Recency bias in full effect.

But there are only a handful of players whose reverence endures across generations…even if the vast majority of us never saw them play (or if we did, only a small percentage have a vivid and meaningful recollection). Seeing Roberto
patrol RF at Forbes Field in ‘66 as a 5 year old does not really count, as cool as that may be.

IMO, the list is a short one:

Babe
Lou
Jackie
Roberto
Willie
Mickey
Hank
Sandy

Not a slight to any of the other bonafide legends, but these 8 have a staying power in our consciousness and imagination like no others.

Then again, sentimentality has no place in this thread…even if we are all just fan(atics) at heart!

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.

[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

[QUOTE=Aquarian Sports Cards;2165354]Ouch! That's disturbing. Smells like some serious politics involved.

[QUOTE=Aquarian Sports Cards;2165354]Yup. Plus Sandy led the league in the category exactly twice. Spahn led the league in it, yup, twice. Not equating the two. Just defending Spahnie from a certain Spahn hater. Again, they named the darn award after the guy.

Not politics there. Just the typical reverence for wins that the era had.

Palmer and Sutcliffe had the best seasons in the AL that year, but at 15-5 and 14-8, it was heresy to give them the Cy Young. Vuckovich 18-6 with that low ERA+ and a WHIP of 1.502. Now that is something that's probably a record for the worst WHIP of any CYA winner.

And it wasn't even close. 14 first place votes for Vuck. No one else had more than five.

But as far as politics (or would it be better described as simply popularity and reputation)....look at Steve Carlton winning the NL CYA over Steve Rogers that year ('82). Though that also included the obsession with wins. Because even though Rogers went 19-8 and had vastly superior numbers, a 23 win Steve Carlton season was all that mattered.

Oh and 20 out of 24 first place votes for that one

[QUOTE=Snowman;2165360]A lot of them were based on wins. Notice a dearth of post 2000 players on that list.

Nolan Ryan should've won in 1987 leading the league in ERA, ERA+, FIP, K/9, H/9 and K/BB but there was no way in hell an 8 - 16 pitcher was going to win an award back then. It's amazing that he finished 5th actually.

Makes perfect sense, and that is a great list. But the majority of people on your list are still recent enough that we, or a family member, would possibly still be affected by that familiarity bias. Ruth and Gehrig (and possibly Robinson) are back enough that we wouldn't likely have that familiarity bias. But those earlier players have movies and shows about them to keep them in the public eye long after they were done playing. So that helps as well.

That is true, but it's more to helpful to look at this individual case. Using ERA+ eliminates the bias of park effects. Here's Johnson and Grove (100 is average):

Career #Led League
Randy Johnson 135 6
Lefty Grove 148 9

Because FIP (Fielding Independent Pitching) only looks at strikeouts, walks, and home runs allowed, it eliminates the bias of BABIP.

Career #Led League
Randy Johnson 3.19 6
Lefty Grove 3.20 8


These two put Grove ahead.

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).

It's funny, if you were to have a discussion of (for example) who was the best midfielder ever in soccer, statistics probably wouldn't enter into the discussion at all. Baseball is unbelievably rich in statistics and even more so with all the advanced metrics, but they don't seem to settle anything.

Dead on!

If you go back and read an earlier post in this thread it was stated that sabermetrics and statistical analysis was basically developed for gambling purposes. Well that is only for predicting games between two teams today. And over time, statisticians could tweak and refine those as they'd actually get to see how well it predicted the winner of a game. But there is no outcome or winner when you try to use statistics to decide the best lefty of all time. The formulas being used don't predict anything, and there is no winner decided that allows you to prove your formula was right, or to tweak your statistical formula if it was proven wrong. Statisticians just use the numbers they pull directly from baseball, ignoring outside and human influences, and interpret those stats in how they feel they would. The stats and formulas are nothing but talking points, as they can't prove or disprove anything regarding who really was the best. You can interpret the numbers how you want.

And they are certainly not infallible for gambling purposes either, as they don't always pick the winner.

You're right, there are too many variables in play, especially when comparing people or games from different times/eras. And you can't prove who really is right or wrong. It is really no more than an educated guess.

At the end of the regular season, every statistical model worth its salt would have said that the Dodgers were the best team in baseball this season. They also would have given the Dodgers a mere 25% chance of winning the world series despite being the best team because there is a tremendous amount of short term luck involved in baseball. This doesn't happen in football, basketball, hockey, or soccer. The best teams in those sports win the championship far more often.

Let y = 2x + 3

If x = 5, then y = 13

BobC - "Well that's just like, your opinion, man."

Players in various sports serve their team best by finding the role that is most helpful for his/her team. If a guy like Grove has a 6-0 lead after 4 innings, he serves his team best by not trying for perfection, but by laying the ball over the plate and making the opposition hit it. If they get a couple base runners, then he has to bear down to prevent too much damage, but otherwise, for expediency, he'd rather throw 90 pitches and win 6-3 than throw 120 pitches, striking out 10, and winning 6-0. In the age of the 4 man rotation and no relief specialists, complete games helped the rest of the staff get through the long season, especially when rain-outs made double-headers pile up towards the end. And if a pitcher can save a little wear on his arm, that's common sense. I don't remember who it was, but some pitcher said he very rarely threw over to first to hold a runner on, because he figured he only had so many throws in his arm.

Your soccer midfielder is a great example of a player's value being non-statistical. The best way to help your team win might have nothing to do with stats.

When I was in grade school, we played a game called Battle Ball. It was like Dodge Ball except you could catch the ball. If you dropped it, or if the opposition caught your throw on the fly, you were out and had to go to the sidelines where you could still throw at the other team whenever you got the ball.

We played it during gym class, at recess, and after school. Not to mention weekends. We had about 100 kids in each grade, divided into 4 classrooms. So the first day of each school year, we'd eagerly look at all the class lists to see what room/teacher we had, and also to see what room would have the best Battle Ball team. Well, in 6th grade, I was in room 303 and we had an all star team. The first time we played another class during our 30 minute gym time, we won 4 games - wiping out their class, starting a new game, doing it again, and again, and again.

So, one of our best and smartest players, Richard Lord, started getting out on purpose at the beginning of each game, so he could move to the out sideline and set up a crossfire attack. If we'd kept stats, Lord would look like the worst player in the whole grade, getting out in the first 10 seconds of every game. But with our team loaded, there was no chance we would lose - so eliminating the opponent as quickly as possible was the goal and he figured that out and played his role superbly.

On the soccer question, if you had the discussion among the world's most knowledgeable fans, players, coaches, writers, etc., you might not get to a complete consensus, but the same few names would be in the discussion -- all without the benefit of statistics. The "witness of the eyes" as I think John Updike called it.

Then I would counter that your statistical model needs tweaking because it's not accounting for all the variables. The Dodgers weren't "The Best Team in Baseball" as they didn't win the championship. Though they may have had the best group of individuals in baseball playing for the same team. There's a difference.

Statistics may not enter the discussion, but it probably should. Interestingly, I also build predictive models for soccer that estimate the value of every midfielder, both offensively and defensively. I've used it to bet on each of the past 3 world cups, and it's been by far my best ROI of all the sports I bet on.

+1

I’d like to interrupt our regularly scheduled lefty debate by saying this thread has just become only the 7th in the main forum’s history to reach 1,000 replies. That’s no easy feat, but even more remarkably, it only took 52,000 views (and change) to achieve it. A stunning lurking/chiming ratio!

As you were… :)

https://ibb.co/C8MDVmt

Every regression model has coefficients that estimate the value of impact of each variable in the model along with confidence intervals for those estimates and an error term. That error term encapsulates both things you haven't yet accounted for but otherwise could and random chance. While it is always true that there are probably ways to account for more of the variance in the data by finding new variables to control for, and thereby reducing your error term, it is a mistake to assume that all variance is explainable if only you had built a better model with more explanatory variables. There is always an element of randomness in sports, but baseball in particular is exceptionally subject to randomness, far moreso than other mainstream sports. A slight shift in the breeze could be the difference between a grand slam and a 2nd strike, a "bad hop" is the difference between an out and a hit, the angle of the ball leaning the bat depends on whether it made contact on the seams or the leather, etc. If you set up a 5 gallon bucket at random locations on the infield and told players to hit the bucket while major league pitchers were launching 100 mph fastballs, curveballs, and change-ups at them, even the best hitters in the league would be lucky to hit the bucket 2 times out of 10. They simply don't have that level of control. It's not possible. At best, they could hit the ball within something like 10 feet of the bucket, and that's if the bucket is on the ideal side of the field for them. If they have to push to the opposite field to hit the bucket, it's even harder. A player can control directional accuracy by trying to push or pull the ball or to swing up on it or down, but once they make contact, they have little to no control over whether that ball is hit right at the shortstop or just out of his reach. They just make contact and roll the dice most of the time. The worst teams in the league can beat the best teams on any given night. A bad team often takes 3 games out of 10 against a good team. This doesn't happen in the NFL. The worst team in the league almost never beats the best teams. And the team that wins the super bowl is far more likely to have also been the best team in the regular season in football as well. Same with basketball and hockey. But baseball is different. A 7 game series is simply insufficient for determining which team is better in baseball. Luck plays too big of a role. This is evidenced by the outcomes of past seasons. Any random team can, and often does, win the world series. But that doesn't mean they are the strongest team.

Which is surprising, considering the misspelling of 'of' in the title! You would think that annoyance would keep people from posting on the thread.

Brian...I have now posted twice on the thread, both times in regard to what should have been 'of' in the title. I predict my persistence will eventually pay of (misspelled on purpose to drive home my point).

The same could be said of any sport though. Just because a player's contributions aren't currently being tracked or valued by the mainstream doesn't mean that those contributions can't be measured. The fact of the matter is that when teams look to gain an advantage over their competition, in any sport, they turn to statisticians/data scientists to tell them how they can improve. Everyone knows the Moneyball story. This is precisely how baseball changed. Some don't like the changes for various reasons, but each change that has happened was the result of someone figuring out a way to gain an edge, however small or might be, over their competition through statistical analysis. Same thing with football and when to go for it on 4th down or when to kick a field goal. And basketball as well. Watching an NBA game today is almost like watching an entirely different game than even just 20 years ago. People who don't know any better often say that "Steph Curry changed the way the game is played", but of course that's not true. It was statistical analysis that changed the way the game is played. The MIT Sloan Sports Conference, which is where most of this transformative work comes from and gets presented prior to the changes being implemented by owners, coaches, and GMs, had several talks about how the three point shot was severely under utilized in the NBA and how any team could gain a significant advantage by stocking up on 3 point shooters. It is no accident that the first team to buy into the analytics was the team from Silicon Valley, whose ownership believes in science. It's no accident that they assembled a team of multiple strong 3 point shooters and hired Steve Kerr to coach it. Steph Curry didn't change the way the game was played, data scientists did. Curry was the beneficiary. Most people think Curry is the best shooter of all time, but they aren't even aware that Steve Kerr actually had a better 3 point shooting percentage than Curry did during his playing years.

Soccer is no exception. Just because you can identify talent without statistics doesn't mean that you can't better identify talent WITH statistics. Many of the things a midfielder does to help his team win doesn't get tracked, or at least hasn't been historically. But that's changing and will continue to change in the future as more and more data savvy owners recognize the value that statistical analysis adds to their organization.

Those variances can always be determined but not necessarily quantified or built into a model, as you noted. But you seem to dismiss them out of hand. I see at least 2 things that could be problematic with your statistical model for declaring Koufax as the best left handed pitcher of all time. 1 ) You may be using a statistical probability analysis to determine a singularity (ie Koufax being the best). This is hinted at by your constant touting of the success of your other statistical probability models. If that's not the case, then those probability results are irrelevant and don't add any value to your claim for Koufax anyway. 2 ) You constantly ignore other intangible items that, while oft times not quantifiable, are still non-zero. This is demonstrated, in part, by claims such as athletes are better today than they were years ago. While true, you continue to ignore the fact that athletes years ago played and trained under different conditions with what was available at the time. You're implying that had those 'lesser athletes' been born and raised in today's environment, they would not have taken advantage of today's methods and still been throwing rocks at a chalk outline on a barn. Maybe try the reverse? Put Koufax back into the 1930s environment and do the analysis and you'll have different variables to consider. It is possible he wouldn't have made it out of the Dodgers or Cardinals 13 or so minor league teams.

Taylor, Great points, and part of what I've been saying all along.

You seem to be conflating what others claim I said with what I've actually said. I have never once claimed that Koufax is the GOAT. I just said he's in the conversation. My arguments have been that Spahn shouldn't be in the conversation at all, and that while he was great for his time, I heavily discount Grove's statistics because of the overall skill level of his contemporaries.

The model I described above is not aimed at proving Koufax is the best, or anyone else for that matter. What I described is a tool for measuring the impact that something like a change in mound heights or a widened strike zone has on performance. It can also be used to estimate something like the overall talent level decrease across the league during WW2, and pretty much anything else that you want to understand the impact of.

Then, if you want, you could use the results of that model to build a separate model to more accurately evaluate pitching performances from different eras. It would give you a better metric than WAR.

Yes, humans evolve. Advances in nutrition, kinesiology, sports medicine, and game theory are all part of the evolutionary process. We know more today than we did 100 years ago. We're also 3 inches taller and stronger, on average, today than we were 100 years ago. Should we add a few more inches to Babe Ruth then, and credit him with a few more HRs? Or what about amphetamine usage from before they started testing for it? It was rampant. You can make adjustments for whatever you want to control for. If you want to debate with BobC about how much humans have evolved over the past 100 years, or even whether or not you think it "counts" as evolution to begin with, be my guest. It's just a conversation I'm not interested in having.

I'd like to know how Statistician Snowman would factor in cortisone, regarding Koufax. When we talk about performance enhancing drugs, in the literal sense, Koufax is a great example. Without that drug his career might've had about a 2 year peak. Grove came back from injury without such aid.

But on the other hand, Koufax might have been able to extend his career with advances in surgical techniques.

It's all just too speculative when one tries to make direct comparisons.

Another point that has occurred to me, today's pitchers I presume have access to data that literally analyzes every pitch a hitter has ever taken or swung at and I presume there are people who can turn that into useful information. In Koufax's day, they probably had little more than anecdotal information to go on, and in pre-team meetings came up with brilliant strategies like smoke him inside. Counterpoint, I guess, is that batters now have the same information about the pitcfhers.

I wouldn't give Johnson TOO MUCH credit for his mental makeup and toughness. We're talking about a guy who intentionally tanked half a season to force a trade out of Seattle.

Can we really blame him for that though? The Mariners are the worst franchise in all of sports. Not just the MLB. All major sports. As someone who grew up in Seattle, he gets a standing ovation from me for that move. The Mariners are the only Seattle sports team that I don't root for. They basically gave the fans a big middle finger for decades, so I gave one back.

Great point. Forget the numbers, would teammates, and many other players, and especially fans, ever consider someone as an all time great if they had turned ttheir backs on their teammates and fans to purposely lose for their own selfish purposes? i would think it could become a very serious negative part of such considerations. But don't tell that to someone who justs looks at numbers. They'll likely tell you it doesn't matter and ignore something like that because it doesn't fit their narrative or their definition of what the "greatest" should be.

Statistics often ignore the human element, like this would be. How would one ever even measure and quantify something like this from a statistical standpoint to reflect the obvious negative impact such an action by a player would bring to his perception by the public at large? Actually, I take that back. Now that I think about it, I can see some statistician quantify such actions. Upon hearing some player purposely threw some gains by performing poorly on purpose, I can see a statistician go back and remove the player's performance results from those thrown games from his overall stats, because those thrown games are not a true reflection of the players actual ability, and therefore taint his statistical database. But doing that actually helps make the player statistically better and more likely to be considered the "greatest", and not less likely as I would expect to be the case in the eyes of a majority of the public upon learning what the player had done.

And if such ever did occur, it would just reflect another disconnect between the real and statistical worlds.

This is exactly what people do when they choose Hal Chase as the greatest dead ball first baseman.

One of the measures mentioned in this thread is "If you had one game to win, like a Game 7, who do you want?" I have often thought that the single guy I DO NOT want on my team, for a big game, would be Chase. I wouldn't want him within 20 miles of the ballpark. The bigger the game, the more lucrative it might be for Chase to throw.

So, there are some who call Chase the best first baseman of his day, while I'll call him the worst with Gandil not far behind.

+1 Agreed well said

Mark, walk me through this one. Interested to learn but didn’t follow solely based on a lack of knowledge on my part. Jim

Im guessing that he is referring to the 1919 Black Sox Scandal?

From wikipedia :

it was universally understood that all eight implicated White Sox players were to be banned from Major League Baseball for life. Two other players believed to be involved were also banned. One of them was Hal Chase, who had been effectively blackballed from the majors in 1919 for a long history of throwing games and had spent 1920 in the minors. He was rumored to have been a go-between for Gandil and the gamblers, though it has never been confirmed. Regardless of this, it was understood that Landis' announcement not only formalized his 1919 blacklisting from the majors but barred him from the minors as well.

Ah, yup, this is terrible but I forgot Chase was one of them!

You must not be familiar with the New York Knickerbockers.

The Orioles aren't exactly spending up to win for their fans.

Agreed, the Rockies are also horribly run. They won't spend enough to win, and refuse to tank to get better in the long term.

Ahem...

The Mariners have never made it to the World Series. I'm not talking about winning it, I'm just talking about making it there. They are the only team in the entire MLB to have never made it.

They haven't even made the Playoffs one single time in the last 20 years. And in their entire 44 year history, they've made it 4 times. Yep, that's right, they failed to make the playoffs 40 times out of their 44 seasons. All this despite having one of the greatest center fielders of all time, THE greatest shortstop of all time (and please don't come back at me with some nonsense about Honus Wagner being better), and arguably the greatest pitcher of all time in Randy Johnson ALL ON THE SAME TEAM AT THE SAME TIME.

Meanwhile, the Knicks have made it to the NBA finals 8 times, winning it twice. They've also made the playoffs 43 times! Granted, they've been around for 75 years, but even if you cut their numbers in half, hell, cut them in a fourth, they're still miles better of a franchise than the Mariners.

When I said the Mariners were the worst franchise in sports, I meant that literally. You cannot find a worse performing team than the M's in any major sport in the United States. I'm sure there's some international soccer team somewhere from some island without potable water that miiiiight have a worse record than the Mariners, so I don't know if I can say with confidence that they are the worst team on the planet in any sport ever, but they're the worst team in any sport I'm aware of, and they're definitely the worst team in any major US sport and it's really not even close.

Sounds like we have different criteria for defining "worst franchise in all of sports"–ask any New York sports fan about Jim Dolan and watch them as they try in vain to suppress their outrage...

Mariners have won 47% of their games and 0 World Series games since 1977.

Padres have won 46% of their games and 1 World Series game since 1969.

I'd say it's quite close.

I wish The Founder had included the part where Ray Kroc used the PA system to chastise the Padres during a game.

I would say if you don't blame a guy for faking an injury and intentionally tanking games, it says a lot about your character let alone the character of the guy doing it.

OMG!

CJ,

Please don't start posting all kinds of numbers and other numerical data like that. Next thing you know, some statisticians will come on here and see it, and use it to claim they have created a statistical formula or equation that will allow them to accurately predict and name the winner of every debate thread here on Net54.

And I wouldn't be surprised if they had their formulas somehow always pointed right back to them being the projected debate winners. :rolleyes:

Great points Peter. Though he's got other issues to deal with, it is well-known that Trevor Bauer has gone to unprecedented lengths to scientifically study pitching, aerodynamics, spin rates, and so on using equipment and technology Grove and Spahn never had. And he is also known for his somewhat unique training techniques and exercises. And he was able to parlay all that into at least one Cy Young Award.

The idea that one needs to try to win at all costs while playing for a team that has made it abundantly clear that they intend to lose at all costs is pretty silly. Especially if he was being treated like shit in the process. Kudos to Randy Johnson for getting himself out of a bad situation.

The extent to which you guys are infatuated with my posts/opinions is rather embarrassing.

Mark,

That's an absolutely great observation, agree.

And by mentioning how some people may choose to define "greatest" by whom they would pick for a single WS game, versus how they performed during their peak playing years, or alternatively over their entire career, it underscores the need for all participants in such a discussion to first come to a consensus agreement as to exactly what "greatest" means. Secondly, then deciding on what they would agree upon as the appropriate measures to make their determination. And only after all that, then would you start looking at individual player's stats and data.

And if the definition was to be defined by who you would pick to start game7 of the WS, since we're only talking about a pitcher's single best game, and not their performance over a season or their career, do you think an argument could/should be made for Don Larsen? He cleary had the greatest single game pitching performance of any WS pitcher in MLB history.

Excellent point, couldn't agree more.

And besides, Johnson did sign a contract to play, and got a lot of money for doing so. If he didn't like it, still honor the contract and leave when the contract is over, if they won't otherwise trade you, right? No one put a gun to his head to originally sign, did they? And I'm guessing he didn't decline to accept, or pay back, what he got paid for any thrown games either.

the Lions...
 

As bad as those 2 franchises might be, the Detroit Lions are not too far behind. Granted, they won a few NFL championships from the 1930's to 1950's, but they have only won 1 playoff game since 1957, and hold the current distinction as the oldest NFL franchise to never appear in a Super Bowl.

My Dad and I gave up on them a few years ago after wasting too many Sunday afternoons watching them create new and creative ways to lose games.

Season after season of watching them snatch defeat from the jaws of victory each week felt bad for our health.

We're much happier now. :)

If you take the paychecks, you owe 100% effort. Period.

This comment has made me decide to not pay Snowman $500,000 to develop that statistical analysis he brags he could build. I think he'd be likely to take the money and give a half-@ss effort.

Couldn't agree more!

And along those lines, can't remember if it was Dimaggio, Mantle, or some other player who said (and I'm paraphrasing here), that they always went out and played every game as hard/well as they could, even if they were hurting or slightly injured, because they knew some kid/person had paid for their ticket to come and watch him play that day. And that's the kind of person/player you put into a conversation of greatest of all time. It's that intangible human factor that statistics can't measure.

Half?!?!?!

I'm thinking you're being way to generous there. Maybe a third-@ss, or even a quarter-@ss effort? :eek:

My favorite give it all guys are Derek Jeter in baseball and Alonzo Mourning in basketball. They both seemed to give all they had all the time.

Randy being a quitter that year could be why he has such a small fan base and the reason his cards are dirt cheap.

Part of the reason why less than a week after Manny Ramirez was traded, there were 'Manny who?' signs going up in Boston.

Great point and comments!

The majority of players, especially the great ones, know one of the most important things they can ever do is play for their fans. And Jeter is a particularly great example. Heck, how many times in his career did he hurt himself trying to make a play he should have just let go?

All of this talk is irrelevant though if it actually never happened. I, for one, have never even heard this rumor before about Randy Johnson throwing games or faking injuries. I'm just saying kudos to him for finding a way out of Seattle if he did. But if you look at his stats from the year he got traded, and even the year before that, he didn't appear to miss any time and his stats were in line with what he was doing in the years just prior to that, so I'm not so sure I buy it. If he were actually throwing games, that would show up in his stats, and if he were faking injuries, he would have been missing starts, no?

Ah, nevermind. Bad idea. Silly me thinking statistics can help answer questions. That's just like, my opinion, man.