Best lefty off all time? My vote is Koufax!

[tschock]

Originally Posted by BobC

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

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

[Peter_Spaeth]

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.

[BobC]

Originally Posted by Peter_Spaeth

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.

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.

[Mark17]

Originally Posted by Peter_Spaeth

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.

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.

[Peter_Spaeth]

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.

[BobC]

Originally Posted by Peter_Spaeth

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.

+1

[cjedmonton]

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

[brianp-beme]

Originally Posted by cjedmonton

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

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).

[BobC]

Originally Posted by cjedmonton

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

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.

[Snowman]

Originally Posted by Peter_Spaeth

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.

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.

[Snowman]

Originally Posted by Peter_Spaeth

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.

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.

[BobC]

Originally Posted by tschock

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).

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.

[Snowman]

Originally Posted by tschock

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).

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.

[Snowman]

Let y = 2x + 3

If x = 5, then y = 13

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

[tschock]

Originally Posted by Snowman

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.

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.

[Snowman]

Originally Posted by tschock

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.

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.

[tschock]

Originally Posted by Snowman

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.

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.

[BobC]

Originally Posted by tschock

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.

[Snowman]

Originally Posted by tschock

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.

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.

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.

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.

[Mark17]

Originally Posted by tschock

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.

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.

[Peter_Spaeth]

Originally Posted by Mark17

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.