Again, you conflating. The facts I already know are that a pitcher's BABIP regresses to the mean and each pitcher has little to no control over their values. What I was wrong about was that Maddux's values were 9 points below league average. But that still doesn't mean he is able to control his BABIP. If you look up his teammates, they too all beat the league average BABIP. In other words, the ballpark, pitching in the NL, and the defense behind him was responsible for most, if not all, of his ability to beat it.

As far as Kershaw goes, it appears to be the same thing. I just looked up 5 or 6 of his teammates over the years in LA to check their BABIP values. Grienke, Urias, Buehler, Jansen, Baez, all of them are 20 to 40 points below league average BABIP. Again, this means it is their defense, the fact that they all pitch in the NL, and the ballpark that account for the differences, not some magical ability that Kershaw possesses.

Or perhaps more likely, he was simply a realist.

Is it also the case that pitchers regress to the mean in extra base hits and home runs against?

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

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

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

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

No. They will regress to their own individual expected means, but not to the league averages. Bad pitchers serve up more meatballs than good pitchers. This is not contradictory to the discussion above.

If a pitcher like Maddux was better at keeping the ball in the park, and/or could limit extra base hits better, then that seems at least some evidence he could in fact control where/how hard the ball was hit against him, even if not reflected in batting average itself. Do you agree?

Take a hypothetical at bat, a bad pitcher hangs a curve and the batter hits it over the wall. Maddux paints the corner with a slider and the batter gets a bloop single off the end of the bat. Same BABIP but different (in most cases) outcome.

In Sandy's own words:

"I became a good pitcher when I stopped trying to make them miss the ball and started trying to make them hit it."


And the whole idea of 'weak contact' is within the pitcher's control - Are they consistently ahead or behind in the count: are they grooving a ball down the middle of the plate, or painting the corners; are they disrupting a batter's timing??? Great pitchers consistently pitch ahead in the count, paint the corners and keep batters off balance - and induce weak contact.

I don't know about doubles and triples. I missed that part of your question. I'd have to look at that. My gut would tell me that they likely regress. But HR rates definitely do not regress to league averages.

That's also when his BB/9 rate fell though. And when his strike zone grew.

What we need, if it doesn't already exist, is a slugging average for balls in play stat.

On plain old SLG against, Maddux over his career was some 55 points below the average. That sounds meaningful? Especially since his BA against was 14 points better than average. A non-statistician would conclude from that he was limiting extra base hits pretty well.

Great stuff guys. This is a fun thread.

A few things:

Snowman, if that model could be created it would be pretty cool. Obviously it would take a lot of work. I have a practical question. Sorry, I don’t know all the terminology, and I really have no idea how a model like that works. If that model were to be created, how would information get processed through the model? For say 1953 or whatever year, would every stat for that year have to be manually input into the model?

The idea of how athletes evolve is interesting. Of course humans have slowly gotten bigger, faster, stronger etc over the past 130 years. However, for quality of play in baseball, I’m not sure it is as simple as every year we go forward the quality of play gets a little better. Obviously, there have been social changes that impact this greatly. Quality of play clearly went down during the war years of the early 1940s, and clearly went up in the late 1940s with integration. This is only a guess, but it seems to me, just brainstorming, that quality of play seems especially strong in the 1950s / early 1960s, and also from the late 1980s to around 2000. A high number of very elite players entered MLB in the 1950s. Mantle, Mays, Aaron, Clemente, Jackie Robinson, Frank Robinson, Snider, Berra, Campanella, Banks, Matthews, Koufax, Gibson etc. The upper tier HOFers are seemingly endless for the 1950s and moving to the 1960s for the end of their careers. But it seems like there were far less upper tier HOFers starting out in the 1960s. Brock, Rose, Morgan types are not nearly as impressive as the 1950s list. Similarly, upper tier HOFers starting out near 1970 and early to mid 1980s are not nearly as impressive as the 1950s list. 1970s you have Reggie, Schmidt, Brett, early to mid 1980s you have Rickey Henderson, Ripken, etc. but no where near the top end talent starting out in the 1950s. But then in the mid to late 1980s you add Bonds, Clemens, Griffey, Randy Johnson, Maddux, Pedro, Arod, Jeter, Frank Thomas etc., just a lot of top tier HOFers and it would seem like very high level of play. I guess my question is how much impact do high end HOFers have on the level of play for a time period? The flip side of the argument would be that the “average” type players increased in skill greatly over time, and the “average” players in the league getting better over time could be more impactful than the amount of top end talent at any one time. Anyway, fun stuff to think about.

Finally, my understanding is that a high or low BABIP generally is a lucky/unlucky stat. An unusually high (and out of line) BABIP for a pitcher would entail bad luck where a bunch of line drives and grounders happen to get hits. And an unusually low BABIP for a pitcher would be good luck where line drives seem to be hit right at guys etc. How much of BABIP is “good situational pitching” or “good situational defense”? Who knows. But this being said, Maddux is a fascinating pitcher. His control is obviously elite and close to best of all time for control. And not just throwing strikes, but the ability to nibble at the edges of the strike zone. This makes it very hard to make solid contact and should equate to a lower BABIP. That’s just the eye test from watching him. Strikes that are on the corners are difficult to hit hard. It you rarely throw a meat ball and get lots of strikes on the corners then you’d think stats should follow the eye test, just because Maddux was so good with his control.

Well, that is where the troll reference may become applicable. LOL It would seem he deep down must really get into these back and forths at some level, unfortunately, maybe more so than just the desire and interest in discussing such topics themselves that we typically end up doing on here from time to time. In other words, maybe he comes on sites like this looking for the arguments because that is what his psyche wants and needs, and doing so over the internet, he can stlll remain removed, somewhat anonymous, and thus feel safe. Which is kind of the definition for being a troll when you think about it. LOL

For example, it was much earlier in this thread that he appeared to get frustrated when people pushed back and didn't simply accept what he was saying, or the implied or overt insults. So he clearly and emphatically stated he was done with this, which pretty much every intelligent, normal person would take to mean he was done with responding and interacting with everyone on this thread anymore. Had he actually stuck to his word, I wonder if he wouldn't have garnered a little more respect from the crowd on here. But instead, it was just a few posts later, and he was right back at it without missing a beat. So does that point to some deeper, psychological urge or need, who knows?

On the positive side, even though I simply ignore and no longer waste my time reading his posts, in looking at what others are posting ang saying in this thread, it appears he's finally admitting the he may have made same errant statements and that his statistical assumptions and conclusions may not in fact always be infallible. And if I'm right, good for him. He does have and makes some very intelligent and interesting points and comments. It's just that he doesn't seem to realize, or doesn't want to admit, that as good as statistical analysis can appear to be, in the end it is nothing more than a tool to hopefully allow someone to more accurately predict an outcome, like who's going to win the Super Bowl. Unfortunately, when their ability to predict outcomes like the winner of a Super Bowl begins to have some success, such people may then try to extend that tool to possibly use it for something else that is not a totally objective question, like deciding who the greatest lefty pitcher of all time is. That is clearly not an objective question, and has no absolutely certain outcome we can then actually measure the effectiveness that some statistical analysis may have in predicting it, at least not like knowing there will be an actual Super Bowl winner. And also extremely important (and maybe the MOST important thing of all), everyone knows, AND AGREES, on exactly what the definition of and how you decide on who the Super Bowl winner is. In the case of the greatest lefty of all time, we haven't even begun to decide on the correct definition of "greatest" yet, let alone the actual measures we will then use to POSSIBLY decide an answer, if it can even be done. And untill that has been determined, everything is just someone's opinion, INCLUDING someone's statistical analysis.

And in regards to referring to statistics as just a tool.........

A statistician's wife has been bugging him for weeks to replace a light fixture on the ceiling, and he's finally going to get around to doing it (And without her having to pay him to do so, go figure!). Unfortunately, he needs a screwdriver to remove a few screws to get the job done, but doesn't have one. Well, he's up on the ladder already, so before getting down and then having to drive all the way to the store to buy a screwdriver, he goes digging around in his pocket and finds his penknife, and promptly uses that to remove the screws and complete the task. So he gets the job done using a tool that wasn't actually meant for what he ended up using it for. But he took a chance on guessing it might work and got lucky, like he got lucky to also just happen to have the penknife in his pocket when he most needed it to begin with. But before you go applauding the statistician for his fine work in completing the given task, and he triumphantly goes riding off into the sunset on his noble, white steed, with his beautiful and now forever grateful wife astride behind him, I have to finish the rest of the story.

Turns out that for maybe what little the statistician knew about tools, he knew even less about electricty. For while using his penknife to remove the screws and then replace the light fixture, he accidently knicked some wires in the ceiling and unknowingly got them crossed. So once he had the fixture replaced, he joyously called his wife to come and flip the switch to see the new fixture working, and what a great job he had done. Unfortunately, the knicked and crossed wires created a short, which blew out the fuse box, and resulted in having to call in an electrician to fix everything, at a very hefty cost. And as a result, our woebegone hero ended up sleeping on the couch for the rest of the week. So much for our happy ending!

And as for statistics always being able to measure and actually predict human nature and outcomes, go read some Asimov!

He loves the attention. He will take any side of an debate simply to argue and be the contrarian. On many threads he has ended up having to back down, back off or admit he was wrong. It is truly hysterical. For him it is the sport of it. He does not care who he annoys or even how he comes off. Even our talking about him gets his juices flowing. An absolutely massive ego and has narcissism down pat.

Sure he is bright but he has no people skills and I would guess that it is just not here but out in the wild too. I cannot ignore him...I admit I have a weakness for his rants. Endlessly amusing to watch him carry on the same exact way each time.

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


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


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

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

Back to the original Grove vs. Koufax line, can you please use your statistics to explain Koufax's widely disparate home vs. away records??

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

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

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

Andrew,

Some very insightful points. In particular about the measure of "luck" in regards to BABIP. Kind of like predicting the outcome of flipping a coin and whether it lands heads or tails. That outcome is always a 50/50 probability. And so over time, and all other things constant and equal and assuming a sufficient sample size, anyone flipping coins would eventually expect to see them ending up with exactly half heads, and half tails. To me, I've always thought of this as kind of what is meant by "regressing to the mean", in this case ending up 50/50 on heads or tales. But what is interesting is say you start out flipping coins to test this, and everything being constant and nothing abnormal with the coin, the first 9 flips all come out tails. Now the absolute probabity of a head or a tail is still just 50/50 on that next, 10th flip, or is it? Since over a large enough sample size we expect the number of heads or tails to come up to regress to that expected mean of 50/50 for each of the two possible outcomes, if in starting out with getting tails 9 times in a row, you know you eventually have to start flipping heads, but the probability of each and every single flip is still always going to be just 50/50. So now you have somewhat of a paradox on what the actual probability of flipping a head or tail on all future attempts should be, at least it seems like one to me.

So now back to BABIP. The fact that you have some pitchers that appear to consistenly be above or below the league average BABIP, all the time, leads me to believe there is something other than simple "luck" involved with them being able to do that. At what point (ie: sample size) will a statistician be comfortable in finally admitting there may be some other factor(s) or variable(s) that they haven't been able to effectively measure, quantify, and account for, and as a result just refer to it as "luck". For wouldn't it be true that if they had been able to somehow measure and include all the pertinent factors and variables in their formulas, such as a pitcher like Maddux's ability to have batters consistenly not hit the ball hard or cleanly, that those formulas would in fact show where all things do eventually regress to a mean. Just like they do in the case of flipping coins where it will eventually always come back to show a 50/50 heads or tails probability. In other words, in the case of BABIP, if the statisticians could effectively factor in ALL variables and factors, there would be no outliers, like a Maddux maybe, sitting significantly outside the mean, unless expainable by some other variable or factor, like a lack of a sufficient sample size. But to just simply explain these outliers by attributing those differences to such an amorphous concept or idea as luck, leads me to believe there is an inability, or unwillingness, on the part of those performing the statistical analysis to effectively be able to find and include all the pertinent variables and factors in their formulas. Thus making BABIP maybe the best statistical tool for it's intended purpose they can do for now, but ultimately not the best and closest statistical measure or tool currently out there for use that it could be.

Chase,

Agree, agree, agree. Plus, you just made a very enlightening comment I had thought about as well, but hadn't shared yet. You mentioned a possible lack of people skills, which can often go along with others factors, like sitting in an office all day just running numbers and never really interacting with anyone to ever be able to develop such people skills. Isn't it often true that people will tend to gravitate towards work and professions that most often mirror, or at least coincide with a large part of, their personalities? Assuming so, maybe he just needs to get out more. The next Net54 dinner/get together at the upcoming National would be perfect, don't you think? :D

If he's as good at building predictive models as he says, I'd rather take him to Vegas. :)

Very true

Bob I think he should be a guest speaker. He can berate, mock and chastise everyone in attendance. His message really gets lost because of his method of delivery. Not sure what happened on Blowout but shortly after arriving here he made it clear he was only here to mix it up. One thing is absolute and that is he is great for page views. Not a single thread he has posted on has been boring.

LOL

I don't know if he can work them up fast enough for each hand of blackjack, roll of the dice, or spin of the roullette wheel though. Plus, he'll probably want you to pay him up front, win or lose.

O------M------G!!!!!!

That's it. He must have been hired to increase posts and site hits. That's brilliant! :D

His away numbers are worse, for sure, but I don't know that I'd call them "widely disparate". There's an expected disparity for all pitchers when pitching at home and on the road. Part of the "home field advantage" in baseball comes from an umpire's subconscious bias in calling balls and strikes, just like in basketball with fouls. Even when they are trying their best to be neutral, it is somehow still human nature to call the games more favorably for the home team than the away team. The effect is small, but measurable over the course of a career. When you look at Koufax's career Home vs Away numbers, they don't really look all that out of line to me when you consider the fact that he pitched in a pitcher's park. Here's what I see. Note he had almost identical IPs for both. Also, ERA values are much more reliable over the course of a career with 1,000+ IP, so it's fair to look at those in the context of a career of this length, whereas it wouldn't be from season to season.

Home IP: 1158.0
Away IP: 1166.1

ERA Home: 2.48
ERA Away: 3.04

BB/9 Home: 2.9
BB/9 Away: 3.4

K/9 Home: 9.5
K/9 Away: 9.1

WHIP Home: 1.045
WHIP Away: 1.167

HR% Home: 2.2%
HR% Away: 2.1%

BABIP Home: 0.252
BABIP Away: 0.266

When I look at those numbers, the most interesting difference to me is the BB/9 rate. That's a significant gap, and one that definitely has an impact on his WHIP delta. Why was he walking more batters outside of LA? That's not a park effect. Some small disparity exists from umpire subconscious bias as I mentioned, but not that much, I wouldn't think. The differences in BABIP are probalby entirely explainable through park differences and his BB/9 & K/9 rates. I don't think there's much delta attributable to luck over that sample size, and the delta is narrow enough that it is within expectation. There is an expectation also though of a player's general discomfort level when on the road. People just perform better at home. I definitely acknowledge he was better at home than on the road, but I don't see anything that looks wildly out of line with expectations. The BB/9 rate is the most interesting part to me though. Pitching in Dodger stadium definitely helped too.

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

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

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

This is referred to as the "Gambler's Fallacy".

From Wikipedia - The gambler's fallacy, also known as the Monte Carlo fallacy or the fallacy of the maturity of chances, is the incorrect belief that, if a particular event occurs more frequently than normal during the past, it is less likely to happen in the future (or vice versa), when it has otherwise been established that the probability of such events does not depend on what has happened in the past. Such events, having the quality of historical independence, are referred to as statistically independent. The fallacy is commonly associated with gambling, where it may be believed, for example, that the next dice roll is more than usually likely to be six because there have recently been fewer than the usual number of sixes.

As others have pointed out previously, there's a reason my nickname is "Rainman". I was a math savant as a kid, and I'm autistic. Perhaps ironically, I also spent the first half of my adult life as a professional "gambler" before joining the corporate world.

While it may be true that certain pitchers consistently outperform league average BABIP values, that doesn't mean that the pitchers themselves are responsible. As I mentioned earlier, pitching BABIP values revert to the mean, but if you want to get more specific, they actually revert to their current team's unknowable true mean BABIP against, which we can estimate with sample data. I say current team because that team's value depends on their defensive capabilities and/or motivations (the "dog days of summer" is a very real thing, and teams out of contention behave in strange ways, but that's for another thread) at any given point in time. The ballpark is also a factor, as well as playing in the NL or AL as mentioned previously.

Doing some "back of the napkin" math, here's a quick breakdown of how those numbers might look. I say "might" because I'm just using quick maximum likelihood estimates using means here from the past 3 seasons to break these values apart, but it's probably at least directionally accurate with at least a handful of outliers. We could get a much more precise breakdown of how these BABIP values vary by team with more data and by solving for it with a system of equations using linear algebra by setting them all up in a matrix and inverting it, but I'm too lazy to do that. Well, that and it takes more time. But these values were easy to find for each team over the past 3 seasons for both home and away stats, so I did some quick math to break the numbers out into various attributions.

Notes/caveats - The standard deviation in this sample data is ~0.018, or 18 BABIP points, so these are loose estimates, and the true values will vary. But it's still a useful exercise in at least understanding how some pitchers can seemingly be able to "beat the system", when in actuality, they are just benefitting from being in the right park on the right teams. We could test this theory by looking at how pitchers perform before vs after being traded (you'd have to look at the population of all traded pitchers as a whole though, not just a few of them). The column "3Y_Delta" represents the delta between a team's 3-year average BABIP and the MLB average BABIP.

Anyhow, some interesting takeaway approximations from these loose estimates are:

Pitching in the NL appears to be worth a mere -0.002 BABIP points (not sure I buy this, I'd like more data)
Home field advantage is worth around -0.006 BABIP points

Note, the data sufficiently explains the "Koufax effect". And while I didn't run the numbers for the full league back when Maddux was pitching, I did spot check several of the other pitchers on his team during that era, and it appears to sufficiently explain the "Maddux effect" as well.

Per my rough calculations, it appears as though the Dodgers' advantage is more attributable to their defensive abilities than it is to the ballpark. While not all of the values should pass the "smell test" (sample sizes, confidence intervals, blah blah blah), you'll notice that many/most? of these do (e.g., Colorado and Boston have terrible park BABIP effects while Seattle, St Louis, and San Diego all show as being a clear pitcher's parks). Also worth noting is that the teams that do not pass the smell test are more likely to be the teams whose actual BABIP values varied wildly over the past 3 seasons (like the Chicago White Sox, whose ballpark factor of -0.030 does not pass the smell test, but whose BABIP values were all over the place the past 3 seasons with 0.292, 0.268, and 0.303).

I'm not well-tuned to the current defensive abilities of each team though. Perhaps someone paying closer attention can look at these numbers and see if they look directionally accurate as a group. Although also worth pointing out is that these defensive BABIP values are made up of both the players' abilities and team strategies like when to play the shift and where to place your players. Teams that are heavily invested in analytics certainly outperform other teams that are not, with respect to these defensive BABIP values.

See my "back of the napkin math" table below which rank orders teams by their overall 3-year average BABIP performances. Note, we would expect pitchers on these teams to have BABIPs that regress to their team averages more so than to the league average.

If you know you are a little more hittable away from your home park, might you not be inclined to nibble a bit more?

Still interested in your take on Maddux having a slugging average against 55 points below average.

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

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

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

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

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

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

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

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

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

I would think an ump's bias would be such a small part, at best, of the home field advantage. There are many factors which would make a player better at home than on the road. Such as not being tired from traveling or from time change, being able to be at their houses and having those comforts of friends, family, pets, familiar surroundings, being before the home crowd and playing at a stadium they spent 1/2 of the season. To only mention ump bias, when there are other factors which obviously influence home field advantage more, is misleading.

John,

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

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

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

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

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

And if the talk of potentially one day having the balls and strikes called by machine ever comes to pass, that should significantly reduce the effect of whatever umpire bias there might actually be.

The home team that controlled the machine would just program the bias in.

LOL. Should have figured you'd be the first one to go there. :)

Yup. Machine just does what someone tells it to do. Which is why Captain Kirk was able to prevail in the Star Fleet Academy training simulation that was "impossible" to win and that had defeated everyone else: he reprogrammed the computer.

And I doubt a computer is going to pass the Turing Test any time soon.

It would be easy to code and calibrate machines and have the unit secure and tamper-proof, just like a safe. They could be checked and tested between, and maybe even during, each game. That is not an issue.

Unfortunately there is no sarcasm font, I was just goofing.

I have thought for a long time that balls and strikes should be called by using light beams that intersect. And my prediction is that if it was done, people would be surprised at what would be called a strike - namely, big breaking curves that cross at the knees at the front edge of the plate, that hit the ground before they reach the catcher.

Not sure I want that but I agree that huge curves are many times technically strikes but are not called that way.

Wait, did you mean font or emoji? I thought we did have a sarcasm emoij. :rolleyes::rolleyes::rolleyes::rolleyes::rolleyes: :rolleyes::rolleyes::rolleyes::rolleyes:

So we do. :eek::eek::eek::eek::eek::eek::

Don't all good pitchers have below average slugging against though?

I don't know, but if that's the case, doesn't that suggest a problem with your thesis (as I understood it) that for balls in play, all pitchers are pretty much the same and variances are just dumb luck (random) that will even out? And therefore it was a myth that some pitchers were better at pitching to contact.

So what's a guy like Maddux who doesn't strike out many batters doing with such a low SLG against? It seems meaningful? And walks don't come into the equation, so he isn't keeping SLG down by his lack of walks. That can only mean, I think, it has a lot to do with batters not getting as many extra base hits against him, which if true seems contrary to the thesis about pitchers not controlling where the ball goes after it leaves the bat.

BTW this is what you said that makes me think I am correctly characterizing your thesis:
The extent to which pitchers actually have this ability (referring to the ability to control the flight of batted balls) is miniscule at best. It's probably at least an order of magnitude less than people are thinking of when they make that claim. Maddux rarely walked hitters. He led the league in BB/9 9 times, and was probably in the top 3 15 times or more. This was his superpower.

Why not first downs in football. Bringing the chain gang onto the field seems archaic and likely adds little precision to the measurement. But I guess a booth review of the chain gang is taboo.

In general, when referring to Koufax's disparate splits, people are usually referring to his numbers from 1962-66, the years that got him in the Hall and in which he pitched in Dodger Stadium. And that's because his splits those years (as a whole - there was one where they were pretty equal) are so different.

However, there's more to it than even that. And that's because in the LA years prior (1958-1961), he pitched in a home park that was absolutely horrendous for lefties. That's why he had a 4.33 ERA there. He also had a 4.04 ERA in Brooklyn. However, it was in those two ballparks where his lack of control was also prominently on display - 1.95 K/BB in LA at the Coliseum and 2.20 at Ebbets.

So what does all that mean? Well, it means that Sandy's unreal numbers at Dodger Stadium overwhelm the 7 years of mediocre (or worse) numbers in his other two home stadiums but raising them up enough to make it LOOK like there wasn't huge splits for him.

Yes.

The yellow line on TV is probably more accurate than the chain (which is only as accurate as where it is set.) This could be done with great precision with a little technology on the field.

I believe there's a name for this phenomenon, when aggregate data shows one thing but breaking it down into distinct constituent parts (here, the Coliseum vs. the other home parks) shows something else. Simpson's Paradox maybe? Or it's something like it anyhow.

The point about Kershaw in the playoffs and sample sizes seems to be often misunderstood by both the people making the argument and those arguing against it.

It's not that we can say with confidence that his postseason performance being sub par is just bad luck and small sample sizes. The problem is that we can't say that it isn't. This is one of the most commonly misunderstood concepts in statistics. When I was in grad school, I tutored a lot of other students and this was something that many of them struggled with. The difference between "it's just luck" and "this is entirely explainable by luck" is important, and they're not the same thing. In the case of Kershaw, we simply don't have enough data to know either way (and will likely never have it). He very well could just be someone who folds under pressure, or he could just be getting unlucky. It could also be a bit of both. My guess is that he got unlucky at first, but then allowed that to get to his head because the narrative surrounding him was that he choked under pressure, and now he felt even more pressure to perform, but fell short mentally. But I don't know. That's just speculation. The statistical explanation should be that it is explainable by bad luck, but that we don't have enough data to know if that is indeed the case. I know, it's not helpful. But agnosticism in the absence of sufficient evidence should always be the default.

The problem I have is when people want to make claims like they know he chokes under pressure. Sure, it's possible, but then again, he also might not.

I understand what you're saying, but just looking at it statistically doesn't account for observations like body language, facial expression, very similar patterns of wilting (getting shelled all at once in a fateful inning), and so forth. In my view, sometimes anyhow, you don't need a weatherman to know which way the wind blows. Could I "prove" it objectively? No, probably not.

Still somewhat subjective, but I'll bet his catchers would have a worthwhile opinion as to his stuff and location in post-season vs. regular season.

But here's the thing. In a number of the games I saw where I felt he wilted under pressure, he was cruising along looking very much like his Cy Young dominant self, then just had a massive meltdown. I recall that twice, I believe, in the 7th inning against the Cardinals. Other games as well. So it's not like he just pitched poorly, it's more that he reached a point where he couldn't handle it.

I was a pitcher/catcher in college and believe me, a catcher can tell when his pitcher starts to break down mentally, or when he begins to tire. Sometimes hitters hit quality pitches and to the average fan, it looks like the pitcher is losing it when he's actually still sharp. And vice versa.

Clayton is such a good guy that I imagine anyone who ever caught him, played with him, or managed him would never say anything against him. So we may never know. It would be a shame if his legacy is more about his post-season than about his dominant regular seasons.

Or quite possibly, his manager managed him differently in the post season than in the regular season. We certainly see managers, especially today, manage their pitching staffs differently in the post season than in the regular season - because they are managing for five or seven games, not 162.
I can imagine that when you have a pitcher as dominant in the regular season as Kershaw, if he gets in a tight spot in the post-season, there may be a temptation to leave him in a bit longer and let him work through it. Not all pitchers, but one that got you this far. A manager/pitching coach looks down at the bullpen and asks himself - 'Who do I have that is better than Kershaw at this point?' Dance with who brung ya. If I'm a manager, I want my best pitchers throwing more innings in any postseason series.
I too had noticed the same thing with Clayton - great start to a game and then one bad inning. If it was the regular season, maybe he would have been pulled instead of staying out on the mound.

Home run rates definitely account for some significant portion of slugging deltas between pitchers. And any pitcher can attempt to encourage fly balls vs ground balls simply by aiming higher or lower in the strike zone, or with breaking balls. But there's a trade off between the two, as ground balls drop in for hits at a slightly higher percentage but fly balls drop in for extra base hits at a higher rate despite being caught for outs more often (not to mention more home runs). I suspect this is probably what's responsible for any amount of control a pitcher has over their BABIP, however small that is. But they still have no control over how often those fly balls drop in for hits or how often those ground balls squeeze through gaps or become infield hits, etc. My understanding is that the tradeoff effectively balances itself out though in the grand scheme of things (e.g., run production), but I'd have to do more research.

I think there's a parallel here on the offensive side in terms of game theory strategy that we're all seeing across the league as batting averages drop but are traded off for more home runs.

Exactly how this balance effects BABIP values, you could figure out. But fly ball vs ground ball rates is almost certainly the best predictor of it. This is why I prefer using SIERA for evaluating pitchers. However, we don't have the data needed to calculate it for the prior generations of pitchers unfortunately. But for today, it's the best ERA statistic that I'm aware of, and one of the metrics I use in my models. It adjusts for fly ball and ground ball rates.

55 points difference in SLG, over a career, seems to me (admittedly without research) to reflect more than just being better at keeping down HR. But even if that's a complete explainer, it still supports my thesis that some pitchers are more difficult to hit productively even if the aggregate amount of CONTACT tends to be the same across the spectrum.

I think it's plausible that he also might have fewer doubles and triples against. If so, it's likely a tradeoff against more singles. If you could compare his singles to doubles rate against his peers and see if that ratio is higher, it would tell you.

Assuming it is the case, would you agree that it's probably not just the result of luck/randomness but is attributable to his pitching skill?

Couldn't it also partly be because of outfield dimensions/layouts, who he has playing behind him in the outfield, the decision of the manger to often (or maybe not at all) use outfield shifts, and on an on? Luck can often, erroneously, be attributed to things that are otherwise not readily or easily measured, known, or ever recognized and acknowledged.

Agree. The difference between a single and double is often simply where the batted ball lands - in front of an outfielder or between them. The difference between a double and triple is often a matter of the hitter's speed and outfielder's arm.

A line shot to center that reaches the outfielder on one bounce might be a single, while the batter who is fooled and hits one off the end of the bat and bloops it over the first baseman has himself extra bases.

On almost any ball hit in fair territory there are doubtless multiple things happening at once that contribute to the outcome, but so what? Are you really trying to argue that Maddux was a great pitcher solely because his walks were low, and everything else was attributable to these other factors? To me, they don't explain at all Maddux' greatness RELATIVE to other pitchers for whom doubtless the same variables were at play on balls hit against them. To me, that greatness is of course due in part to his low walk totals, but based on both personal observation and the SLG stats I've discussed, much of it has to do with his serving up fewer balls that were good to hit.

No, you misinterpret me, I'm actually supporting your point. Just maybe didn't get it to come across well. I added those as additional variables to counter the, "Oh it's just luck!", people out there. The ability of certain pitchers to seem to always be able to get batters to do more poorly against them than other pitchers when they do get the bat on the ball is not just primarily attributable to luck. Just like there are certain pitchers that become known as double play pitchers. They seem to have the uncanny ability to get batters to ground into double plays more often than it would seem by luck alone, and certainly more so than most other pitchers. They may change their pitching and style to induce more ground balls in those types of situations. Does anyone out there keep stats on what types of balls are put in play for pitchers when there's a man on first and less than two outs, whether it is a grounder, pop up, line drive, etc.? If so, that might be able to show at least some ability of certain pitchers to get batters to do what they want. While some pitchers might have a better chance/ability at striking out a batter to get to two outs in an inning, other pitchers may recognize they may not be able to overpower a hitter for that strikeout, so they opt to use finesse, location, and control to induce the batter to hit into a double play instead. In both instances, the pitcher's goal is to get batters out. And both types of pitchers (overpowering strikeout vs. finesse and control) have different ways of achieving those same goals. And it doesn't mean that one pitching type is necessarily better than the other, though some people/statistics seem to always skew towards the more dominant, strikeout pitcher as a better (greater) pitcher. If so, that is merely their opinion, and nothing else.

The bottom line is results. And I still ask, if the ultimate, final result in baseball is to win the games, how do you discount that factor so much in looking at pitchers? Despite all the other variables that go into determining who wins a ball game, the starting Pitcher IMO has a bigger impact on that final outcome than every other player on the field. And that is even more so when pitchers pitch mostly complete or near-complete games, like Grove and Spahn primarily did back in their days. Modern pitchers typically get pulled much earlier in games, resulting in them having less influence on their outcomes than ever before. So in arguing the greatest pitcher, why wouldn't pitchers who completed games, and thus had a greater impact on the outcomes of those games, actually get a leg up on modern pitchers who often have less to do with their team's winning. And if that's the case, then maybe none of of these modern pitchers should ever be considered as great, because none of them have as of yet truly shown they can come anywhere even close to consistently influence the positive outcomes of games, at anywhere near the level and influence, of pitchers like Grove and Spahn. To counter such thinking though, statisticians resort to declaring the players of earlier eras are flat out weaker and nowhere near as good as today's players, so they can then disparage pitcher's like Grove and Spahn, instead of recognizing and giving them credit for things they did that modern pitchers don't. Of course they can't point to any hard, actual statistics to prove it, and just suddenly fall back on their own logic and opinions to explain away whatever gets in the way of their own statistical analysis always being right. The ability of statisticians to seemingly ignore the importance and value of winning just blows my mind, especially when that is the only real reason the games are played.

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

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

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

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

Size does matter indeed.

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

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

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

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

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

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

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

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

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

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

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

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

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

OK I was confused because you seemed to be pointing to a bunch of things out of the control of the pitcher as reasons for some of their success, which seemed contrary to the thesis that yes the pitcher has a lot to do with it.

Be wary, bottom line for me, when a statistician tells you that your years of observation are not accurate. You wonder how many games some of these dudes have actually WATCHED.

I guess it depends on what you consider skill. Is it a skill to cause more ground balls than fly balls, or is it simply a preference? Some pitchers feel more comfortable throwing lower in the zone. As I said, there is a tradeoff between ground and fly ball pitchers. My understanding is that one isn't "better" than the other. Here's an article from fangraphs.com that discusses the topic.

https://library.fangraphs.com/which-...-ball-pitcher/

Along the lines of what I was talking about earlier; I mentioned that Maddux probably traded in a lower slugging percentage against for a higher batting average against. You could look up more pitchers than this of course, but a simple comparison of Maddux's numbers vs Randy's numbers certainly shows this tradeoff.

Randy Johnson
0.221 AVG, 0.353 SLG, 2.4% HR

Greg Maddux
0.250 AVG, 0.358 SLG, 1.7% HR

As you can see, they had very similar slugging percentages, with Randy's being 5 points lower despite him giving up 40% more HRs, but Maddux's batting average against is much higher than Randy's. There is a tradeoff happening here. It's a difference of approach. Throwing more ground ball pitches is going to net you more singles against than fly ball pitches, but fewer 2B, 3B, and HR.

It's a skill. It's a preference to do it but a skill to master it. Which they both did. So both had their own skill set which they mastered. The end result of them having a different style pitching and each pitching to their own strengths. Neither would have been nearly as successful of a pitcher had they tried to pitch in the style of the others strength.

As to the article, it is good in what it does, but it's comparing averages and really doesn't mean much when you are looking a pitchers on the elite end of the scale, as is the case with both Johnson and Maddux. They both were much more effective in what they do so you could probably ignore what any 'analysis' would say they should do.

My thinking on Maddux without any stats to break it up is that like Gwynn knowing where in the field he would hit a pitch, Maddux probably said to himself things like I’m going to get this guy to ground out to third and was able to accomplish that much more than most could.

All things being equal, fewer extra base hits in turn is going to yield fewer runs, no? So if you're comparing Maddux to a similar pitcher who is striking out about 6 per game but yielding the average SLG, his results are going to be much better even if the BAPIPs are similar?

That point has some merit.

Maddux and Johnson were extremely gifted at what they did and were on the extreme end of the scale.

What many forget about Maddux is that Maddux had an above average fastball in his prime. He sat in the low 90's on his fastball. League average was 88. Add in the elite movement and command, Maddux was something special and a power pitcher in his own right(in his prime).

Maddux ended up with 3,371 career strikeouts. I think many fans forget Maddux is a member of the 3,000 strikeout club.

I bet half his Ks were getting batters to swing at pitches off the plate or that broke into the dirt.

The point has been made several times that humans are getting bigger, taller, and stronger as time goes on. This is undeniably true, but for me this has a much more pronounced impact on other sports like basketball, football, track and field, swimming etc. It’s undeniable in basketball. NBA players are simply taller, more athletic and more skilled now than 30 years ago. Guys who play like the Greek freak, near 7 feet tall, did not exist 30 years ago. There is no modern Spud Webb or modern Mugsy Bogues. Same in football. O linemen and D linemen are significantly bigger, stronger, and more athletic now than 30 years ago. And obviously track and field times get lower and lower. Swimming times get lower and lower. All of those athletes are bigger, taller, stronger, and more athletic than the athletes that came before them in those sports. I’m not sure this exactly tracks in baseball. And that’s why I love baseball, and don’t enjoy track and field and swimming as much. It is true that some baseball players now are bigger, taller, stronger than ball players of previous times. Pitchers especially. However, this is not true across the board for elite baseball players. The best athletes simply don’t always make the best baseball players. Again, this is what makes baseball great. Michael Jordan was the best basketball player ever, and he was a pretty terrible baseball player relative to MLB stars. Bigger, taller, stronger, more athletic doesn’t always equate to better in baseball. As a hitter, you need elite hand eye coordination, eyesight, and wrist strength to create bat speed for any of the bigger, taller, stronger to matter. Little Jose Altuve at 5’ 4” has this ability as a hitter, which makes baseball great. As a pitcher, you need some semblance of control for a 100 MPH fastball to matter. You need to be skilled. You need to have control of the strike zone. The fact that modern pitchers overall throw harder does not make every single one of them all better pitchers than the pitchers who came before them. And I have no idea what more height and more weight has to do with being a good pitcher besides getting you more velocity (and giving you much more risk of blowing your arm). Anyway, you need to be able to locate the ball and get guys out for any of that to matter. Straight 98 MPH fastballs down the middle get crushed by good hitters. I have a ton of respect for Nolan Ryan. And Nolan Ryan threw really, really hard for his time. He also wasn’t nearly the best pitcher of his time. He never won a Cy Young in his 20+ years of pitching. There’s a lot more to pitching than just how hard you can throw.

Randy Johnson has freakish size for any era. He first pitched in 1988 at 6’ 10”. It’s 2021. If he’s the model of baseball evolution or whatever other phrase you want to call it, then there should be 7 foot guys now dominating the sport. It isn’t going to happen. Randy is an outlier. Once you get to about 6’ 3 or 6’ 4” that’s about it for being an elite baseball player. 6’ 8” and taller guys trying to field ground balls won’t work out so well. 6’ 8” and taller guys trying to swing at pitches at their knees won’t work out so well. It would be comedy gold though. There’s limit to how much height helps you as a baseball player.

Vlad Jr isn’t great because he’s bigger, taller, stronger and more athletic than previous ball players. He lost 40 pounds last year and he’s still squishy. He’s not some freak physical specimen. But he can smash a baseball.

It is common misconception to believe size does not matter a lot in baseball. There is a reason why the average height of a pitcher is Six foot three and not 5 foot 9. Same for hitters. The average male human is around five foot nine, and if size did not matter then the league would also be five foot nine in average and there would be several five foot three guys being MVP's

If size did not matter then how come there are no Five foot three MVPs??

There are few outliers who are small at the plate and on the mound, but small guys like Altuve still weigh 165 pounds, not 140 like some in the pre war era.

There aren't any six foot eleven pitchers dominating baseball now, but there are plenty of six foot five + ones....and go back go pre war times and show me how many six foot five pitchers were better than average(and not some stiff).

But you are right, there is a lot of natural ability to be able to hit like Vlad that is in your genetic code. There is a certain body make-up to allow someone to throw 98 MPH. When you have 8 billion people in the world you will produce more players that possess that ability as opposed to when you have only 2.5 billions people in the world.

Then when you consider that the population to draw from players in 1930 ONLY included white americans, and no minorities and no world wide players, that severly limits the player pool and why you don't have those guys league wide throwing 95 MPH, and when one came along, he was a marvel.

I will await the list of five foot five Cy Young winners and five foot three MVP players if size does not matter in baseball. If you find any, they come from when the talent wasn't nearly as good in the early 1900's, because everyone else was smaller too.

BTW Vlad is six foot two and anywhere from 230-250. That is a lot of muscle mass underneath any 'blubbler'

I understand that baseball fans want to cling to the notion that the players of yesteryear did it better, etc....but that is not reality...and I'm from yesteryear.

Go ahead and do a search of current pitchers being over six foot five and see how many of them reach the upper 90's on their fastballs. Guys like Tyler Glasnow would be an absolute monster in 1933, standing six foot eight and throwing 99 MPH, and has command and knows how to pitch.

Scherzer is only average height at six foot three right now. He averages 94 MPH and can hit the upper 90's, with movement. His command is better than ANY 'control' pitcher that pitched before 1980, without a doubt, except he threw harder than all of them and most likely taller than all of them.

Your assertion that these guys just throw harder and thats it is utterly and completely false.

Pedro wasn't exactly a big dude, maybe 5 foot 10 and 170.

I would bet more Ks were from those called third strikes way outside the Braves staff were famous for getting.:)

Maddux and Glavine were geniuses at expanding the strike zone. Pedro got a lot of those calls too.