Lost Seasons Mean Lost Milestones
Baseball is a statistics-heavy game, and that’s true even for those who don’t think of themselves as being part of the saber set. Because the game’s rules have had a relatively high degree of consistency across eras, the sport’s career milestones have also enjoyed a certain constancy throughout its history. That doesn’t mean that 600 homers from a player whose prime came in the 1960s are exactly the same as the 600 homers a player in the Wild Card era hit, but when you’re talking 600 homers, you’re always talking about someone who was really, really good at hitting home runs.
And while we would like to think that Hall of Fame voting is based off deep analysis and not round numbers, the fact remains that milestones still play a large part in who ends up in Cooperstown. Whether a player hits 470 homers or 520 homers still means something.
For precisely how much missed time has mattered in Hall of Fame voting, you should read my colleague Jay Jaffe’s three-part series on missed time and the Hall of Fame. In those three parts, Jay tackled how missed years due to wars and strikes were handled , and how today’s hitters and pitchers might be treated in Cooperstown terms. So go read those first. I’ll wait.
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Meg will probably now inform me that I don’t need to actually insert punctuation that represents foot-tapping, so let’s get to some data! That’s what a projection system is for, after all.
Given the world that we’re in, one of my many research projects this spring has been trying to better gauge how missed seasons ought to be treated. Forecasting those seasons is difficult in the best of times; the missed time is typically due to injury or suspension or war. Now, everyone is hanging out at home trying to not catch the current super-virus or crippling ennui.
And I wasn’t entirely sure whether the long layoff would affect all types of players to the same degree. Re-projecting stars for 1982 and 1995 using ZiPS — I didn’t have ZiPS in 1995 though I assume you’ll excuse me for not having a projection system when I was four — I tried to gauge whether missed time affected players of different qualities in different ways. Together with other data (suspensions, premature retirements, and war), I found that my normal missed time algorithm slightly overrated stars’ “return” projections. Apparently, the elite do have more to lose with lost time.
With those results in mind, and to get an idea of how the projections would change in a missed season, I projected the probabilities of some of the active players with the best chances of hitting major milestones doing so. These projections reflect both the lost season and the slightly decreased projection relative to the rest of baseball upon return. These projections also contain an algorithm that makes it more likely a player nearing a milestone will return for an additional season. Further, I told ZiPS that veterans will finish their contracts:
| Player | 700 HR | No 2020 | 600 HR | No 2020 | 500 HR | No 2020 |
|---|---|---|---|---|---|---|
| Albert Pujols | 22% | 5% | 100% | 100% | 100% | 100% |
| Mike Trout | 17% | 9% | 58% | 47% | 80% | 72% |
| Gleyber Torres | 9% | 5% | 26% | 19% | 55% | 47% |
| Ronald Acuña Jr. | 8% | 2% | 25% | 17% | 53% | 45% |
| Cody Bellinger | 6% | 2% | 25% | 18% | 51% | 41% |
| Miguel Cabrera | 0% | 0% | 1% | 0% | 50% | 20% |
| Edwin Encarnación | 0% | 0% | 6% | 0% | 47% | 26% |
| Bryce Harper | 8% | 2% | 25% | 18% | 54% | 44% |
| Nelson Cruz | 0% | 0% | 0% | 0% | 34% | 10% |
| Giancarlo Stanton | 4% | 1% | 20% | 12% | 48% | 40% |
| Rafael Devers | 1% | 1% | 12% | 9% | 42% | 36% |
| Manny Machado | 2% | 1% | 18% | 10% | 40% | 32% |
| Francisco Lindor | 0% | 0% | 15% | 11% | 37% | 28% |
| Pete Alonso | 2% | 1% | 21% | 16% | 41% | 35% |
| Nolan Arenado | 0% | 0% | 14% | 9% | 32% | 25% |
| Juan Soto | 1% | 1% | 24% | 20% | 40% | 34% |
With a full 2020 season, there was approximately a 57% projected chance that one of these 16 players would finish their careers with at least 700 home runs. A single missed year drops that by more than half, to 26%. A lot of that is Albert Pujols. 44 homers isn’t a lot, but he’s only signed through 2021 and let’s be honest, if he wasn’t a future Hall of Famer with a big contract, he’d have spent his summers playing golf the last three or four years. Losing 2020 washes out most of his probability of hitting 700.
But even for the future immortals with more time remaining, it’s a pretty big deal. To hit 700 homers, a lot has to go right; otherwise, we’d have more than three players in history beyond that threshold. Losing a year is a significant loss, even for a younger player. Lopping 40 homers off the career totals of Cody Bellinger or Ronald Acuña Jr. presents a significant handicap.
For the lighter milestones, it’s less of a kneecapping since you don’t need to be quite as fortunate to hit 500 homers. The exception is Miguel Cabrera. ZiPS was already looking at him askance given that his offensive profile has confusingly become Really Slow Craig Counsell, and sees hitting 500 as being difficult if 2020 is lost. That would have been a surprise a few years ago!
| Player | 3000 Hits | No 2020 | 2500 Hits | No 2020 | 2000 Hits | No 2020 |
|---|---|---|---|---|---|---|
| Albert Pujols | 100% | 100% | 100% | 100% | 100% | 100% |
| Miguel Cabrera | 85% | 77% | 100% | 100% | 100% | 100% |
| Robinson Canó | 30% | 15% | 100% | 100% | 100% | 100% |
| Jose Altuve | 40% | 28% | 81% | 70% | 99% | 99% |
| Mike Trout | 36% | 25% | 70% | 61% | 97% | 96% |
| Nick Markakis | 32% | 17% | 90% | 84% | 100% | 100% |
| Francisco Lindor | 28% | 20% | 61% | 51% | 85% | 78% |
| Freddie Freeman | 35% | 29% | 70% | 60% | 94% | 91% |
| Mookie Betts | 30% | 24% | 60% | 53% | 92% | 90% |
| Ozzie Albies | 25% | 21% | 56% | 50% | 87% | 82% |
| Xander Bogaerts | 27% | 22% | 52% | 46% | 90% | 85% |
| Starlin Castro | 30% | 26% | 50% | 43% | 99% | 98% |
| Rafael Devers | 22% | 20% | 48% | 46% | 80% | 76% |
| Manny Machado | 31% | 22% | 47% | 44% | 90% | 87% |
| Christian Yelich | 25% | 19% | 46% | 41% | 92% | 89% |
| Elvis Andrus | 20% | 16% | 42% | 36% | 98% | 97% |
| Gleyber Torres | 11% | 10% | 32% | 30% | 86% | 84% |
| Nolan Arenado | 25% | 19% | 37% | 32% | 94% | 91% |
| Ronald Acuña Jr. | 18% | 17% | 35% | 34% | 79% | 77% |
| Joey Votto | 1% | 0% | 20% | 12% | 95% | 94% |
| Yadier Molina | 0% | 0% | 18% | 9% | 99% | 99% |
For the 21 hitters listed here, ZiPS projects that, on average, two who would have achieved the 3,000-hit feat will now fail to do so as a result of a lost 2020 season. For players near 2,000 hits like Joey Votto (1,866) and Yadi Molina (1,963), that’s unlikely to matter; Molina might retire in that case, but ZiPS isn’t capable of modeling this decision of his. The larger hits are taken by mid-career players like José Altuve and Manny Machado, players who are in their prime but old enough that the calendar is a concern.
It would be especially bad news for Nick Markakis in his quest to be the worst 3,000-hit player in major league history. Johnny Damon fell short in his quest to sneak up on 3000 hits and no 2020 could dive-bomb Markakis’s quest for the last 600ish hits:
| Player | 300 Wins | No 2020 | 250 Wins | No 2020 |
|---|---|---|---|---|
| Justin Verlander | 32% | 14% | 90% | 85% |
| Zack Greinke | 24% | 8% | 72% | 54% |
| Clayton Kershaw | 30% | 22% | 60% | 55% |
| Jon Lester | 6% | 1% | 46% | 30% |
| Max Scherzer | 9% | 3% | 40% | 35% |
| Gerrit Cole | 12% | 9% | 36% | 30% |
| Stephen Strasburg | 10% | 8% | 25% | 20% |
| Rick Porcello | 6% | 3% | 22% | 16% |
| Cole Hamels | 0% | 0% | 2% | 0% |
| Chris Sale | 2% | 1% | 15% | 12% |
| David Price | 5% | 3% | 12% | 10% |
Despite the introduction of the five-man rotation, we’ve been blessed with a surprisingly large number of 300-game winners in our lifetime, most recently the impressive Hall of Fame crew of Greg Maddux, Tom Glavine, and Randy Johnson, with Roger Clemens on the outside for non-baseball reasons. We’re now in an era, however, when the careers of our elite pitchers did not brush up against the end of the pre-sabermetric era, and starting pitchers get fewer decisions than ever before.
Right now there are only nine active pitchers with 150 wins and only two, Justin Verlander and Zack Greinke, who have passed 200 wins. Still, ZiPS thought that there was a 79% chance that one of those 11 pitchers would win 300 games, whether because of Verlander or Greinke being durable, Clayton Kershaw getting that last 5% back, Gerrit Cole establishing 2019-2020 as his new baseline, or maybe even Rick Porcello working his way to 300 wins by virtue of eating innings and having amassed 149 wins through age-30 thanks to an early start.
With a lost season, that becomes a coin flip (46%). The two best candidates, Verlander and Greinke, see the calendar flip unfortunately. It could still happen, but a lot more good fortune is needed.
The one thing the modern era is good for is strikeout records, because there are a lot of punch outs. What does a lost year do for those chases?
| Player | 4000 K | No 2020 | 3000 K | No 2020 |
|---|---|---|---|---|
| Justin Verlander | 38% | 30% | 100% | 100% |
| Max Scherzer | 45% | 38% | 98% | 98% |
| Clayton Kershaw | 38% | 32% | 97% | 96% |
| Zack Greinke | 7% | 2% | 94% | 92% |
| Chris Sale | 24% | 18% | 88% | 84% |
| Gerrit Cole | 40% | 37% | 82% | 76% |
| Cole Hamels | 0% | 0% | 67% | 58% |
| Jack Flaherty | 15% | 12% | 62% | 56% |
| Stephen Strasburg | 12% | 6% | 60% | 55% |
| Trevor Bauer | 16% | 11% | 49% | 44% |
| Jon Lester | 0% | 0% | 47% | 28% |
| Aaron Nola | 10% | 8% | 45% | 37% |
| Madison Bumgarner | 1% | 0% | 42% | 35% |
| Robbie Ray | 5% | 3% | 41% | 36% |
| Shane Bieber | 4% | 3% | 35% | 32% |
Nobody has established even a 1% chance of catching Nolan Ryan’s 5714 — and nobody will because of the almost 5,400 innings Ryan needed to reach that mark. The odds of catching the 3,000 and 4,000 strikeout marks, still relatively exclusive thresholds, aren’t as impacted as some of the other milestones.
Dan Szymborski is a senior writer for FanGraphs and the developer of the ZiPS projection system. He was a writer for ESPN.com from 2010-2018, a regular guest on a number of radio shows and podcasts, and a voting BBWAA member. He also maintains a terrible Twitter account at @DSzymborski.
“With a full 2020 season, there was approximately a 57% projected chance that one of these 16 players would finish their careers with at least 700 home runs.”
The individual percentages from the chart add up to 80%. Am I missing something?
You’re missing high school math, apparently.
No reason to be a jerk about it. Probability is often counterintuitive. And I’m pretty sure I didn’t learn that one until somewhere in college. It’s obvious if you’ve learned how this works, but it’s not the kind of thing that’s easy to figure out on your own.
The short answer to the actual question is that adding the probabilities of the individual players isn’t how you figure out the probability of at least one of them hitting the mark. Unfortunately, I don’t think I can explain how to do it and why it works that way within the space of this comment, in part because I’m not an expert either.
[edit] I should have kept reading the comments… see I was “sniped” several hours ago 🙁 [/edit]
” Unfortunately, I don’t think I can explain how to do it ”
The short version is that the odds that at least one of them gets to 700 HR is easier described as 100% minus the odds that none of them get to 700 HR.
That is pretty easy math- take each of the percentages and subtract them from 100% and then multiply them all together. I get 42% and 74% for with 2020 and with 2020 respectively. Now just subtract those from 100% again and you have the odds off at least one hitting 700 HR (58% and 26%; I assume the variance is that I’m working with figures rounded to the nearest percent)
Lets double-check ourselves, with some Excel-based simulations… each pass simulates the 16 players 16380 times:
#1 58%
#2 58%
#3 57%
#4 57%
#5 58%
#6 57%
#7 57%
#8 58%
#9 57%
#10 57%
… probably right 🙂
[edit] Did a couple of runs for the “no 2020” 700HR percents: 27%, 26%, 26%, 27%, 26%…
Hi rosen380,
What kind of simulations did you run? What’s the underlying math?
An easy way to see it is to consider the case where there are only two players, and each has a 50% chance of reaching 700 HR. I think anyone can understand that doesn’t mean that one of them definitely will.
It’s just like flipping two coins, and asking, what’s the probability of (at least) one head? It’s not 100%, obviously. The two coins are flipped independently, and that’s pretty much the same with the two hitters.
funny to read comment 🙂
given my students are juniors in college, they either weren’t taught probability or remember none of it from high school. Shit, something like 80% of participants get at least one question on the CRT wrong. Math is hard, probability is counterintuitive, and even if you more or less memorized bayes theorem and its extensions (how to get from choose to bayes and vice versa) you could always use the wrong one.
That’s not how percentages work. Say there were 4 players who each had a 25% chance of getting 700 home runs. You don’t add them up and say there’s a 100% chance that one of them gets to 700.
For the benefit of the original commenter, the way I learned to do it is to multiply the chances that each of them fails to get to 700 to get the chance that they all fall short. So .75^4 equals a roughly 32% chance that none of them hit 700, or a 68% chance that at least one of them does.
The key here is that to find the chances of multiple things happening you need to multiply, not add. Somebody smarter than me will have to explain why, or if there’s a more elegant way to do it.
this is the good way. Most of the time people fail to invert the problem and just take 1 minus the product of all the probs. 1-π( Pplayer1, Pplayern)
You want to know the joint probability of one person getting to 700. Which is:
1- no one getting to 700,
which is the product of 1 MINUS the probability of getting to 700, so π( 1-Pplayer1, 1-Pplayern)
probability of one getting to 700:
1-π( 1-Pplayer1, 1-Pplayern)
(Pi notation sucks, apologies)
The simple version of the why is that *at least one* incident occurring is another way of saying *not none*. Negative occurrences of a binary event are impossible; it either happens or doesn’t, it can’t un-happen. So to find at least one, you need to know the odds of not none.
If you assume the individual calculate odds in the table are correct, then we know two things already:
A: the odds of each individual incident happening (each entry in the table)
B: the odds of each individual incident not happening (B = 1-A, for each entry)
For any set of independent outcomes (like who will/won’t hit 700 HRs), you can get the odds of ALL happening by multiplying all instances of A above. Or you can get the odds of NONE happening by multiplying all individual B above.
Since *at least one* is equivalent to *not none*, the way to find it is:
1. Multiply all B together
2. Subtract that number from 1
just to note that I find people tend to fail to compute the B number, and just take 1- the product of the A’s.
Why do you multiply instead of add?
Flip two coins, what are the chances of…
2 heads:
Lets say we flip the first coin, and get tails. Now there’s no reason to flip the second coin, we already know we won’t get 2 heads. So lets say we flip a first coin 100 times, and it comes up heads 50 times (because its a 50% chance). Now we take just those 50 times it came up heads, and flip a second coin for those. Half of those flips will be heads, meaning we will have 25 sets of 2 flips for heads.
Lets repeat in matheyer terms…
50% = .5
100% = 1
100 * .5 = 50
50 * .5 = 25
so
100 * .5 * .5 = 25
so
(.5* .5)=(25/100)=.25=25% chance of getting 2 heads on 2 coin flips
1st flip heads, 2nd flip tails:
We do this the same way as 2 heads. The only difference is that now we’re looking for tails on the second flip
50% chance our first flip is heads, OF THAT 50%, there is a 50% chance our second flip is tails
.5*.5=.25=25% chance of 1st flip heads and second flip tails
No heads:
We also do this the exact same way. In fact, no heads is the same as saying “2 tails). Since heads and tails have the same 50% chance, there is no difference for us here. If we were dealing with any other split though, our multiplication math would still work…
P1 = Probability of heads on flip 1
P2= Probability of heads on flip 2
P3= Probability of 2 heads flips
P1*P2=P3
We can use this for any discreet combination of flips. but what if we don’t care about the order?
At least 1 head:
This is where things get hard.
First, lets realize that getting at least one head, means any time you are not getting tails on every flip
So, we already know the chances that we DONT get at least one head… they’re the same as the chances we get 2 tails, which is .5*.5=.25=25% chance
And we know the chance we get… something/anything… it’s 100%, this is the full universe of outcomes we can possibly have while flipping two coins.
If we remove the chance of getting all tails (25%) from our full universe of possibility, we see that there is a 75% chance of getting at least one head.
Matheyer…
.5*.5=.25
1-.25=.75=75% chance of at least one head
Now to apply this to our question here…
“What are the chances of at least 1 700 homer player” = 1-“what are the chances of all 16 of these players failing to reach 700 homers”
Chance of failing = 1-chance of succes
e.g. Pujols chance of failure = 1-.22=.78
“Chances of all 16 failing” = P1*P2*P3…P16 where P is the chance of each individual player failing to reach 700 homers
1-(.78*.83*.91 … .99)=.58
Adding them is valid, too, but it answers the question “How many 700 home run hitters do we expect?”, rather than “What are the chances that at least 1 of these players hits 700 home runs?”
For example, adding the probabilities for 500 HR gets you 290%, which translates to 2.9 expected positive results. If you look back at that list 20 years from now on, you would expect that roughly 3 of the players actually made it to 500 home runs.
So for 700 HR, there is a 57% chance that someone makes it there, but we expect 0.8 700 HR hitters from this list. This is because of the small chance that multiple players reach 700.
That’s a cool bit of math that I had to think about for a few minutes. Thanks for sharing.
If you add percentages together you need to also subtract the odds that more than one person gets 700, to avoid double-counting those scenarios.
Of course, whatever happens to this season, Chris Sale won’t have a 2020 either way.
Yeah, I should have mentioned that Sale was a theoretical!
At first I thought the odds on Acuna were kind of low for hits…. but actually, given the base rate of people getting to that number, that seems kind of high. Obviously, using an informative prior means that it’s not ZERO percent, exactly, but 79% seems optimistic. I dunno. Might be time to crack Kruschke open again.
How did you choose the list? Surprised Justin Upton is not on the lists with 1,651 hits and 298 HR through his age 31 season. Are his odds that low? I know he generally doesn’t rack up enough of either most seasons so I assume his odds aren’t great, but thanks to getting such an early start (in the majors at age 19) he has still managed impressive counting totals.
At 33 years old coming off a brutal 2019, his days as an effective player are likely over
32, not 33. Yes, last year was a lost year but he was remarkably consistent the 10-11 years before last year with wOBA between .340 and .385 every year but one. Remember that Nelson Cruz’s odds were not great at 32 either and he was wayyyyyy behind Upton in counting stats since he didn’t make the majors until he was already 25. Cruz is still 99 HR away from 500, turns 40 this summer and as you can see from the table, he had a 34% chance if the season hadn’t been affected. I’m guessing Upton has a better shot than some of the guys on the list.
I was curious about same player. ZiPS seems to be pessimistic on 3yr, projecting him to <100 games each year.
Another way to look at Upton on 3000 hits: he could grab 150 hits in each of his age 32-39 seasons, and still be just under a full age 40 short. He’d almost assuredly be a DH at this point. And the problem is that throughout his 2009-2018 track record of full seasons, he only had 150 or more hits five times, peaking at 171 in 2011.
He had very little chance at 3000 pre-stoppage and no chance at all now.
The general “that’s too bad” about lack of pitchers with 300 wins should really be offset by amazement at how many strikeouts pitchers are recording. Even without a 2020, we have four pitchers with 30%+ odds of 4,000 strikeouts. That’s a number that only four pitchers in history of hit. I know some of this is a change in batter behavior, too, but to miss that many bats for that long is really incredible.
And even if none of them get there we’re at about 80% odds that all of them end up in the top 15 all-time, which would put them right up there with Johnson-Clemens-Maddux-Martinez.
That Miguel Cabrera and 500 HR really hurts. What about Buster Posey on your hits chart?
If David Ortiz gets in, Cabrera should get in (neither of them should get in but Cabrera’s case is WAY better)
Cabrera is going to be a first-ballot HOFer even if he retires right now. If we restrict to 7000+ PA, he has the 27th highest wRC in history, and 16th since 1945.
Every player above him is a Hall of Famer or would be if not for PEDs (McGwire, Bonds, Manny), except maybe for Votto, who would fall off if we put the restriction to 7500+ PA.
McCovey, Stargell, and Thome are right behind him with very similar offensive and defensive numbers, and all 3 went in on the first ballot. And if Cabrera’s wRC+ continues to fall meaningfully because he hangs around due to the contract, it will mean he comfortably passes 500 HR and 3000 hits.
I think dukewinslow agrees with you, as do I. Cabrera probably slots in between Frank Thomas and Jim Thome, and both of them were first ballot hall of famers. Huge mashers with limited defensive value.
On an unrelated note, Frank Thomas’s first 5-7 years or so must be some of the most dominant non-PEDs hitting performances of anyone’s career. I kept thinking that Cabrera was better than him at the plate, but when I checked over to his page I discovered that probably wasn’t true.
I agree about Thomas. If he had been able to keep his weight in check and stay a bit more healthy, he might have been one of the best hitters ever.
Thomas has very low bodyfat though iirc
Wow, I was surprised to find that Thomas’s early career actually beats out that of Pujols by measure of wRC+.
Ortiz is a tough call. His WAR is pretty low for obvious reasons, but during his 14 years in Boston only two guys in all of baseball contributed more at the plate: Pujols and Cabrera, and as dl80 makes the case for, Cabrera (and of course Pujols) should be easy HoF selections.
At some point someone needs to be The Best Hitter Who Shouldn’t Get In, but 3rd best in baseball over that kind of stretch seems awfully high to set that mark. (I’ll acknowledge here that “pick the guy we’re talking about’s full range of good seasons” is always stacking the deck in his favor a bit. But it’s a long-ass time for someone to be super good, and he was.)
Well, the DH defensive penalty in WAR is stupid, anyway. Ortiz wasn’t actually providing negative defensive value compared to a potential replacement when he didn’t play the field.
Wait, what? Cabrera shouldn’t get in the HOF? I have to wholeheartedly agree with the other poster – Cabrera would be a lock if he retired tomorrow as a 2-time MVP, 11-time AS, 4-time batting champion and Triple Crown winner with a career .315/477/1,694 line and almost 70 WAR. But he isn’t retiring and still has 3 years on his deal which makes him more or less a lock for 3,000/500 and barring a stunning collapse in his BA (basically he would have to play full-time AND hit under .200 the next 3 years to fall below .300 career), he will join Aaron and Mays as the only .300/3,000/500 guys.
It does seem shocking to me that someone could have all those math comments above that take me actual time to follow, and then say something like that regarding Miggy
I’d argue that both of them should get in easily. Two generational talents at the plate, I can’t see why they shouldn’t get in.
doesn’t generational mean the best player of his era? Or the defining player of his era? I mean, Ortiz’s only defining characteristic that really sets him apart as a player is somehow getting away with failing a PED test and everyone immediately memoryholing the incident despite having the best age 40 season on record.
Posey currently has only 1380 career hits at age 33. He has only 217 hits over the last 2 seasons, and that’s in about 900 PA.
If we are really generous, and assume he becomes a full-time first basemen and also manages to stay healthy and keep/regain most of his hitting skills, I think assuming 140 hits per year is as aggressive as I would be.
And even at 140 per year, it will take him playing 5 more seasons to even get to 2000. I would say that’s probably only 10-15% at this point. And if 2020 is canceled, maybe 5%? He’d have to play full-time to age 39.
Well, if he does move to first base, playing full-time to age 39 or longer isn’t that unreasonable for a star player
Except he is not a star at the plate any longer
Zips really loves Gleyber Torres. Assuming a full 2020, he has a 26% chance of reaching 600 homers, and more likely than not to reach 500? Only two middle infielders ever to reach 500 are Arod and Banks. Only other non-1B infielder is Schmidt. Different eras and all, but those odds seem high.
Also Eddie Mathews. But I agree, the odds seems high for a guy who only has 62 HR and 269 H to his credit. Other than Alonso, they seem high for all the guys with just 1-2 years experience since they have hardly established themselves.
Mathews! I’m embarrassed now.
No worries. Still a valid point – 500 HR is dominated by 1B, corner OF and DH. Not much from 2B, 3B, SS (& I would note that although everyone considers Banks a SS, he DID play more than half his career at 1B)
Given the Torres numbers are calculated with a league-wide adjustment for offense (If I’m getting ZiPS right), and it’s generally much rosier than it used to be, I wonder if you went back and adjusted everyone’s career up how many middle infielders would be at those numbers. Torres could end up as a hall of famer with record home run numbers for his position, but I wonder what his numbers would have looked like back then, and what some of the bigger hitters’ numbers would look like in this offensive environment.
Of course, the league changes the ball and it all comes back to normal.
Probably not. The closest 2B/SS/3B to 500 are Beltre (played thru the end of the Steroid Era and much of his later career in favorable home parks) and Chipper (played through the heart of the Steroid Era). B-ref has a tool that lets you translate any player’s numbers to any era and ballpark. I put both of them in 2019 Yankee stadium and they both actually lose a few HR from their totals. Once you get beyond those 2, the next closest is Ripken who only had 431 HR and no adjustment is going to give him an extra 30 HR.
BTW, that neutralized batting tool on b-ref can be a lot of fun. Put everyone in 2000 Coors and you get just silly numbers. Career 1,003 HR for Aaron and single season 94 HR for Bonds. 4 different guys would clear 5,000 hits. Just crazy stuff
I would have loved to see Cobb in Coors.
The Torres numbers are absurd. I get it is a computer, but there is no way a casino wouldn’t take +110 on any amount for him to reach 400hrs, let alone 500. Comparing SS today with shifts, and optimizers to SS of yesteryear is just silly.
FWIW, only 14 players have hit more HRs as 21-22yo… here are the (retired or at least close to the end of their careers) players within 10% of that and how many HRs they totaled ages 21 onward:
655 Alex Rodriguez
548 Frank Robinson
490 Ted Williams
465 Miguel Cabrera
457 Jose Canseco
431 Cal Ripken
411 Andruw Jones
324 Boog Powell
227 Hal Trosky
195 Bob Horner
While that is just 10% of the comps making it… if you figure that Williams has a pretty good chance at 600+ HR for ages 21+, if he doesn’t miss almost five years fighting during WWII and Korea and now we’re up to 20%.
But, lets not throw away the guys that were excluded because they weren’t far enough into their careers to lump with the guys above. All three of them were on Dan’s list, so lets count them based on their odds. Mike Trout is listed with a 58% chance of 600 HRs, so he counts as 0.58 of a 600-HR player.
Continuing to include Williams, that gets us to 3.08 600 HR players (Trout, Bellinger and Harper added in) out of 13 total, 23.7% chance.
Have to dial that back a little since I was looking at age 21+ HRs, which means to use those odds, we actually need Harper to clear 644 and Trout 635… so maybe more like 22.0-22.5%
Still not quite at 26%, but we’re in the ballpark…
Technically, Pujols was a 3B his rookie year.
Through his first ten seasons, Albert Pujols was a .330 career hitter.
If he retired tomorrow he would still round up to .300, at .29963.
But two weeks into whatever season he plays next, he will no longer be a career .300 hitter. 🙁
Through age 30: 169 wRC+
… nearest neighbors, min 5000 PA, Frank Thomas and Tris Speaker and Jimmy Foxx (players banned for liife from the sport, deserving or otherwise, excluded.
Age 31 onward: 112 wRC+
… nearest neighbors, Graig Nettles, Raul Ibanez and Davey Lopes.
Ouch.
I think it’s become pretty clear that it’s actually:
Age “31” onward
Some of the hits numbers leave me scratching my head.
EG Devers 22% to get 3000 hits, and 20% if no 2020.
I don’t believe that missing a season and say the 150-200 H that comes with it only moves the needle 2%.
Well, it might move it from 99% to 97% if he was sitting on 2,800 and he was 35 years old.
Or it might move it from 2% to 0% if he were a no-glove Judy with limited speed who would get no playing time once the wheels went.
But we’re starting at 22% (an outside shot, but a real shot) and saying that a lost season would have almost no effect. I don’t buy it.
Put another way, assuming 2020 lost, this suggests a distribution of:
3000+ hits: 20%
About 2800 or 2850 to 2999: 2% only
Now look at the distribution of the 20% past 3,000. Anything over 5,000 is laughably unlikely, and anything over 4,000 is pretty much equally laughably unlikely. Cut it up into 200-hit ranges: 3000-3199, 3200-3399,… etc.
Claim that 3000-3199 is much more likely than 3800-3899, and 3200-3399 is too. Spreading out that 20%, I think 3000-3199 for 9%, 3200-3399 for 5%, then 3%, 1.5%, 0.5% isn’t awful.
The 2800 or 2850-2999 range has to be more than 2%, then.
I guess you could say if he got to 2,800 he’d do whatever e could to stick around for 3,000but it doesn’t always work out (cf. Vizquel, Omar).
Maybe it’s just that ZiPS has him blowing past 3000 hits in most of those outcomes. Injury risk/down performance simulations are the cases landing him short of 3000, so missing a season doesn’t meaningfully affect the outcomes where ZiPS sees a guy who broke in at 19 playing through age 40.
That kinda makes sense. Thanks.
But the home run odds near 20% are a lot jumpier and that seems to makes sense. If we confine ourselves to younger (under 30?) guys to be fair…
Gleyber 600 drops from 26% to 19%
Acuña 600 drops from 25% to 17%
Bellinger and Harper 600 25% to 18% each
Trout missing 500 rises from 20 to 28%
etc.
Good stuff Dan!
Here’s one I found: Acuña is 33 HR away from becoming the first player in history reach 100 HR and 50 SB before turning 23. He would have been only the 4th player to reach 100 HR by that age.
Dan’s statement, “Because the game’s rules have had a relatively high degree of consistency across eras, the sport’s career milestones have also enjoyed a certain constancy throughout its history. ” Dan’s belief is the world is fixed in time and space. This insipid comment that rules over the last 120 years, by Dan’s definition, so slightly, ever so slightly, changed. Actually the strike zone has radically changed; the mound height has radically changed; the number of games per season changed; travel changed; there have been World Wars and crap wars ruining careers; the draft; the introduction of minorities; and the entire concept of pitching has evolved. BTW, umpiring has evolved for the better. Oh and salaries evolved.
Dan may be gifted at creating ZIPS but honestly what is his system worth? Let’s compare Ted Williams, doing his 2nd tour after WW2 and after multiple dogfights over North Korea for a few years, career with Frank Thomas, now doing GNC commercials for non-FDA approved pills. How many ZIP attributes does a fat slug get credited compared to a man who lost 5 years in war? Answer, Who cares? Frank Thomas is a ZIPS favorite and a laughing stock, selling penis enhancing drugs.
Or let’s examine the ZIPS insight comparing Yogi Berra and Warren Spahn living through storming the beaches in Normandy with a cheaters like Verlander and Bregman. ZIPS is useless to make any evaluation. LOL I have an opinion.
Let’s compare a cheater like Altuve with someone banned from baseball like Minoso for 6 years because of his skin color. Or let’s compare him to Mays who lost 2 years in the Army during the Korean War.
Let’s compare Shoeless Joe Jackson’s facts with those of Carlos Correa. Specific evidence, with benefit of counsel. And then let’s compare Charles Cominsky facts with what is that filth owner of the Astros? Again who cares?
Dan’s system ignores MLB sending young Spanish-speaking players to the deep south in the 1950’s. That horror altered those young men forever. He ignores their adjustments to a language and a culture. He ignores the harm MLB teams inflicted on their Caribbean signees. Felipe Alou will never make the HOF as an outstanding player and manager. Pascual, Versalles, Gilliam and many Negro League players will never be recognized . But ZIPS will help Markakis get HOF votes.
If anything in the above indicates a degree of concern for Dan’s perception of race and society, I want to reject that immediately. I’m postulating Dan just doesn’t get it. How many Caribbean teenagers signed by MLB, quit because of racial and sexual harassment? It does not matter. Old news.
He doesn’t care there are some Caribbean Series or Negro League numbers that should reevaluate the entire 1945 to 1959 period.
Dan likes remind us that his work is valuable.
Dan, I think after a reviewing your life’s work on ZIPS, your value is easily worthy of a free bottle of GNC pills.
Uh…ok?
Compared to other major North American sports leagues, it actually has remained pretty consistent since the foul strike rule was adopted by the AL in 1903.
Compare that for example to several radical rule changes and playstyle changes that have come to Pro American Football, as well as the huge percentage bump from 14 to 16 and now to 17 game seasons making most seasonal and career records and leaderboards complete jokes. Baseball has been a model of consistency compared to all of that!
Serb, come save the day!
Since starting pitchers generally only get 80% of the starts they used to get, we should probably just consider 240 Wins nowadays as the equivalent of 300 Wins from back then.
Of course, earlier bullpen hooks are also driving down Wins totals in general, but the elite starters still at least mostly avert that trend.