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#1
11-16-2021, 11:58 PM
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This is also how and why the entire field of sabermetrics was developed. People wanted to bet on baseball games but they quickly realized that the standard statistics that have been used for decades were not very useful for making predictions with because many of those stats are highly subject to luck. So they engineered new statistics that account for factors outside of an athlete's control and that focus in on what they actually have power over. The aspects of their game that are within an athlete's control are the only factors that have predictive power with respect to how well (or how poorly) they will perform in the future. Any statistic that cannot accurately predict future performance is a poor choice for evaluating one's skill level. Knowing that someone is hitting 0.375 at the all-star break tells us very little about how well he will hit for the rest of the season despite it being a seemingly large sample size of 350 at bats. A deceiving statistic like batting average is another great candidate for paving the way for another heated debate between a regular baseball fan and a statistician. One could ask "who is the best hitter this season?" and the casual fan will point to the guy with the 0.375 AVG, but the statistician looks deeper and points out that he benefited from having a 0.430 BABIP while player B is hitting 0.369 with a 0.300 BABIP. In this case, player B would be the clearly better hitter despite having the lower batting average since BABIP is useful for understanding how much of a role luck played in their performances. People keep talking about wins here as ultimately being the only thing that matters. I agree. Winning games is what matters most. That's why we statisticians use Wins as the dependent (or target) variable in our predictive models. But the difference is that you guys seem to be conflating the "wins" statistic that is awarded to a pitcher with the actual wins and losses which can only be attributed to the teams. These are not the same thing. A pitcher cannot win a game. Assigning them "wins" and "losses" has always been a bad measure of performance. Not just in the modern era. And it turns out that a pitcher's win-loss record is actually an extremely poor predictor of a team's likelihood of winning a game. And furthermore that in the presence of other statistics, it is in fact not predictive at all of their likelihood of winning a game. This is why it is a poor measure of performance. It tells you nothing at all about how well they pitched or are likely to pitch in the next game. It only tells you what the outcome was of a set of prior games. If you want to know how "good" a pitcher (or hitter) is, then you have to look at statistics that only they can control. Otherwise, you're looking at how lucky or unlucky they got rather than how well they performed. This is the job of the statistician. To find the signal in the noise. To control for factors outside of their control. To remove elements of luck. I find it humorous that when I posted in the thread about the role of artificial intelligence in grading cards that everyone praised and valued my inputs when it seemed to reinforce their views about grading. But when my views are shared here, where they are in conflict with the majority opinion, everyone shits on me. 
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#2
11-17-2021, 12:15 AM
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Fantastic, so can you now make a sabrmetric argument for Koufax? Most of them don’t put Koufax very highly at all. No? Just trolling? Also if you read the thread you would know Koufax has actually had the most supporters, and his detractors have spent nearly a thousand posts requesting a mathematical argument for him. Everyone shits on you because you declared yourself God, refused to make a coherent argument, insisted on your infallibility while refusing to make any specific actual support for your bizarre statements, and then insulted everyone. At least I’ve only insulted your argument, not your person. You sure can’t say the same. Stones in glass houses complaint here. Last edited by G1911; 11-17-2021 at 12:18 AM.
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#3
11-17-2021, 12:45 AM
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#5
11-17-2021, 01:40 AM
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#6
11-17-2021, 01:56 AM
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This is a fascinating subject. I really enjoy analyzing baseball history and player performance.
There seem to be a few disconnects in this debate. One disconnect is how much weight to place on counting stats. Pro-Spahn posters in this thread rely on longevity and counting stats with what appears to be a decent peak, with a pretty good ERA+ across his peak years etc. Anti-Spahn posters believe he was a pretty average pitcher in regard to “stuff” since his K/9 doesn’t blow your hair back and wins are team dependent. He pitched a lot of innings and a lot of years, but innings eaters can’t get to GOAT status if they don’t provide elite innings. Essentially that Spahn’s peak is not enough to be the best lefty ever, even with all the counting stats. Koufax’s stats are obviously much different. One very good year, 5 off the charts years, some mediocre years, early retirement and nowhere near the overall counting stats of Spahn. Anti-Koufax posters essentially dismiss him outright because his lack of counting stats eliminate him from lefty GOAT status. He essentially didn’t pitch long enough to even be in the conversation. I tend to agree that the weaknesses of both Spahn and Koufax as described above eliminate them from lefty GOAT status. Both clearly were great pitchers though. Another disconnect here is how to compare players by era. Snowman appears to be arguing that Grove’s pitching competition was weak and therefore his stats should be discounted a great deal. The ERA titles, ERA+ etc is tainted by weak pitching competition. Essentially that Grove was much better than his pitching peers, but since his pitching peers were very bad, him being much better than them should not be as impressive as the stats appear. I have always wondered about this, but I have no way of figuring out how to crunch the numbers to argue one way or the other. The 1920s / early 1930s batting averages went nuts. Hitters went crazy. How much of this was a result of bad pitching during those years? Anyway, Snowman, I am curious how stats can help us figure out which time periods were strong and which time periods are weak. It has always been something of a mystery to me. On a similar note, WAR is a bit misleading to me since it seems to value relative to replacement where replacement level is determined differently every year. The value of a replacement level player could be very different in a time period where quality of play overall is very high as compared to a time period where quality of play was lower. But how in the world can we figure out relative quality of play?
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#7
11-17-2021, 05:40 PM
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To get to your question, any argument to be made that Koufax's peers were better than Grove's also means that Randy Johnson's were better than Koufax's. Johnson's were indeed better than Koufax's, as were Koufax's better than Grove's. The measurable's such as running speed, throwing speed, height, strength, bat speed, all show that players have gotten continually better generation after generation. This is fact. I can show more charts in another post. It is not a matter of evolution, although selective breeding is a factor. Most of it is a result from the sheer number of population growth and the addition of more parts of the world to draw players from. Realize that we are on the cusp of having 8 billion people in the world right now to draw from, compared to 2001 where there were 6.2 billion, to 1965 where there were only 3.8 billion people in the world to draw from...and in 1935 appx 2.3 billion. In reality, Grove and Koufax's population in the US and world wide viability of players to choose from, were closer in comparison. Wheras Johnson had it tougher, and anyone after Johnson even tougher. People from yesteryear don't like to hear that. I'm from yesteryear, but the reality is the reality. When you add the selective breeding of people who have found mates with the purpose of creating athletic off spring to make millions, and the advances in sports science to train them at a young age to maximize their MPH(with command) and their bat speed, that creates a vast difference between generations above and beyond what the logic of more people to draw from creates. Of course Grove's generation actually excluded minorities from the US, making his peers even more worse than Koufax's. However, in 1965 the league was still 78% white. In 2001 it was only 60% white so it is clear that the pool of players reached further out in 2001 than in even 1965. 1965 was still more homogonized than 2001. That is X many more people in the world who can throw 95 MPH(with control) for Johnson and modern players to compete against, X many more people who can hit 430 foot home runs, X many more people who can throw a cannon from the hole at SS, etc... There is more of that to expound upon and I will in a week, but Johnson does not even need that aspect to best Koufax. It really isn't that close, and I address some of the common things the Koufax camp says(and have addressed them earlier in the thread). Best ERA+ seasons: Johnson....Koufax.....Grove 197........190............217 195........186............189 193........160............185 188........159............185 184........143............175 181........122............165 176........105............160 152........101............160 135.........93 135.........Not good enough to pitch enough innings to qualify 118.........Not good enough to pitch enough innings to qualify 112.........Not good enough to pitch enough innings to qualify Johnson had unrivaled physical tools. No pitcher in MLB history can match his physical tools. He was six foot eleven and threw over 100 MPH with a ridiculous slider....WITH COMMAND(after a few year learning curve). Some pitchers had one or two of those tools, but nobody had ALL of those tools like he did. Let me explain why the physical tools are of such importance. Why would you take another pitcher over Johnson if the other pitcher was ten inches shorter, threw three miles an hour slower, had lesser command, and similar or less breaking pitches? The only other factor would be mental make up. Do they have the ability to handle being a professional player? Johnson obviously answered that question. Do they have the mental ability to thrive for a long time? Johnson answered that question YES. Environments a player plays in severely muddles or hides statistical measurements, but the tools are concrete. The tools are a known. A lot of the statistical measurements are unknowns because environment muddles them. An environment can give false perceptions of ones true ability. Six foot eleven cannot be muddled. 100 MPH cannot be muddled. Nasty slider cannot be muddled. Command cannot be muddled. The only other obstacle is mental make up and thrive to succeed. He obviously passed that only unknown hurdle. So when you are weighing all this, the physical tools play a vital role in solving the dilemma of cross era comparison. Johnson had the results to back it up. Johnson was umpire proof. He didn't need the inches off the plate like Maddux and Glavine often did to excel to the levels they did. He was era proof. He didn't need lineups in the league where numbers six through nine were zero threats and hit basically zero power...like which occurred in other eras where scoring was depressed, or era's like the 30's where only the elite few were legit power threats. In fact, he pitched in probably the toughest era to be a pitcher, with the live ball, DH, and steroids. Any pitcher that can handle the toughest environment to pitch in, surely would have no problem in the eras where it was pitcher friendly. He didn't need a dead ball to excel or last a long time. Johnson was stadium proof. He didn't need to rely on a certain stadium to make him dominant. Make no doubt, DOdger stadium helped Koufax tremendously. Johnson had peak dominance and longevity dominance. He was the guy that if you lined all these historic pitchers up at a local baseball field standing shoulder to shoulder, then watched him unleash what he had, he would be the guy every single coach would pick. Coaches would be drooling. If you want to play the "what if" game people do with Koufax, realize that JOhnson missed two plus seasons worth of starts in his prime too. What if johnson didn't get hurt? What if Clemens was not taking steroids and then the second place finisher(randy johnson) adds TWO MORE Cy Youngs? My favorite what if? What if Johnson got to pitch off an eight inch higher mound, and had strikes called at the chest?? What if Koufax pitched in Coors Field half his career games...then there wouldn't even be this thread because Koufax's numbers would look much different, even though his ability would not be any different 
__________________
http://originaloldnewspapers.com
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#8
11-17-2021, 05:59 PM
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Some very insightful points, and all make logical sense. Statistics can only tell a part of the story, and at best, can utilize hard, factual numbers to help determine probabilities. They completely ignore the human factor though, along with a myriad of other unkown factors and circumstances that can be occuring at any point in a game, and then change in the blink of an eye. For example, I wonder how the statisticians did, and have since, handled the statistical records for pitchers that came up against the Astros a few years ago. Is it fair to record data from such games and use that to compare those pitchers to those that never faced the Astros? And how would statisticians possibly adjust their data, should they even decide to, so as to be fair to all parties concerned? And that is just one of an infinite number of factors that statistics maybe can't always explain, measure, or even record properly. The biggest problem in merely relying upon statistics to try to measure and compare things to me is the context, which I feel that despite what some statisticians will try to tell you, they do not have accurate, consistent,and reliable ways to really measure and account for all the differences that can occur. As another example, take the person that argues a good comparison can be achieved by magically transporting say Randy Johnson from his peak years as a pitcher, and suddenly dropping him back into the time that Lefty Grove pitched. That person may automatically declare that based on statistical data, the players back in Grove's day were weaker batters and nowhere near as good as the batters Johnson faced, so he's certain Johnson would blow everyone from back then away (at least almost everyone). But that kind of argument is so out of context as to be laughable. I've said before that that would be akin to taking an Indy car driver, and his car from today, and dropping them into an Indy race back in 20s or 30s, against cars and drivers from back then. To make such an excercise not be so completely out of context, wouldn't it make much more sense to have Randy Johnson be born the same year as Grove, so he could grow up and learn to pitch under at least more similar circumstances and with more comparable context? That way you could really have a more meaningful and honest comparison between them as pitchers. And to make it possibly even more fair and measurable, you'd then want to also have Grove born the same year as Johnson to then see how those two would have fared and measured up in Johnson's day. Though by all means not a perfect, or even possible, this exercise would likely be a much better and more comparably contextual way to compare two pitchers. And in trying to name an all tlme great, I'd suggest having everyone on the list for that title being magically born, grow up, and then play in the same era/time as everyone else on the list. That way you could better compare how each player fared, when they played under at least much more similar context and conditions, in all the different times/eras that everyone else on the list played in. My guess is that if you then looked at each different time/era like a separate season, you could use everyone's statistics from just that time/era to hopefully agree on a clear winner. And then to determine the all time greatest, you see who ended winning the most times/eras measured. Will never happen though, but makes so much more sense than just plain statistics. And another point in regards to statisticians and statistics. If the claim is made that the basis for even starting and coming up with sabermetrics and statistical analysis to begin with was for gambling purposes, I can't argue and don't disagree with that logic and thinking at all. In fact, it makes perfect sense as something humans would do to take advantage and make money off others. However, statisticians may forget to take into consideration the origins of the statistics they espouse and then attempt to apply them to situations for which they were never originally intended. For if statistics truly were created to assist people with their gambling, that generally entails one team or athlete competing against another team or athlete(s) TODAY! Not one team or athlete competing against another team or athlete(s) from an entirely different time or era. So if as claimed, statistics were created for gambling purposes, the context they were originally created under was for comparison of ONLY current teams and players going against each other. Now the fact that statisticians may have found success with current statistical comparisons for their gambling purposes is fine, and I don't argue their applicability at all. But I'm afraid some narrow-minded, narcissistic, and vain glorious statisticians may have felt that since their (or their statistical colleague's) statistics can, and have, accurately functioned to pick gambling winners (some of the time, but certainly not all of the time), that they have carte blanche to assume they must, therefore, be more intelligent than the average person, and that their statistics are the be all and end all answer for all other sports comparison type questions then. Like choosing a greatest at something between players/athletes, even though they may have been from different times/eras and, therefore, most certainly would have competed under (often radically) different context. They completely seem to disregard the context under which they have asserted that such statistics were originally created (gambling) and falsely push that they are appropos for whatever comparative argument they want to now make utilizing them. However, they may continually appear unable to provide specifics of such statistical analysis and formulas when requested (though this is supposed to be a mathematical science with a foundation in facts and details), appear to have disregarded any attempt to even account for or measure potential statistical error, and most certainly have ignored the human element and context involved in the attempt to expand the usefullness and applicability af statistical measures developed originally for something entirely different. A long time ago I realized what I think is a cosmic truth, "The more I learned, the dumber I became!". Seems like every time I'd learned something new, I'd suddenly find out there was even so much more I didn't know. I try to keep an open mind in debates like this, and I'm the first to admit when I'm proven or shown to be wrong. But someone simply arguing with little to no proof or support for the arguments, and expecting people to swallow their continual "I'm right, and you're wrong!" rhetoric, is just asinine and juvenile. I'll sit and listen to anyone's thoughts and theories, and honestly (and civily) debate with them, and logically consider their points and positions (and the resulting merits of such), and offer what I feel is appropriate rebuttal when warranted. And I've found that the vast majority of people on this site are of a similar ilk. To bad it doesn't always include everyone.............. Last edited by BobC; 11-17-2021 at 09:55 PM.
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#9
11-17-2021, 07:21 PM
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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".
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#10
11-17-2021, 02:01 AM
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#11
11-17-2021, 07:27 AM
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No, no, no, he was Linc, lol. As in Lincoln.
I wonder through the eyes of 2021 how much of that show would now appear to be stereotyping.
__________________
Net 54-- the discussion board where people resent discussions.  My avatar is a sketch by my son who is an art school graduate. Some of his sketches and paintings are at https://www.jamesspaethartwork.com/ Last edited by Peter_Spaeth; 11-17-2021 at 07:29 AM.
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#12
11-17-2021, 02:00 PM
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Still waiting for your expert statistical analysis that accounts for all factors and proves one pitcher was better than the other.
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#13
11-17-2021, 03:42 PM
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My kind of links:
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#14
11-17-2021, 06:35 PM
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__________________
Working Sets: Baseball- T206 SLers - Virginia League (-1) 1952 Topps - low numbers (-1) 1953 Topps (-60) 1954 Bowman (-3) 1964 Topps Giants auto'd (-2)
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