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OOTP 14 - General Discussions Discuss the new 2013 version of Out of the Park Baseball here! |
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12-12-2013, 10:15 AM | #41 |
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You should watch Time Team, been going a good few years. Mostly stuff in Britain but they've been to other countries a few times over the years, bit rough when it first started but once technology got better became a really good programme.
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12-12-2013, 11:35 AM | #42 | |
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12-12-2013, 11:44 AM | #43 |
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12-12-2013, 12:05 PM | #44 | |
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You obviously don't understand what we're discussing.
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12-12-2013, 12:12 PM | #45 | |
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One more time: the talent distribution is not normal, but is actually the far end of the curve.
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12-12-2013, 12:15 PM | #46 | |
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12-12-2013, 01:36 PM | #47 |
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Baseball talent is distributed on a Bell Curve, but not in the way some here are thinking.
The Bell Curve that applies to baseball (and anything else, if you think about it) encompasses the baseball potential of every adult man in the world, not just of self-selecting Major League players. The sigmas at far left end of the Bell Curve encompass the physically infirm, the elderly, the athletically disinclined, and the like, who can't play at all. The middle left, middle, and middle right sigmas encompass those athletic enough to be able to engage in the sport at some level, ranging from being able to step onto a (likely softball) field and play terribly, to playing well hardball well enough to be a very good player on an amateur level. And the sigmas at the far right end of the Bell Curve encompass those good enough to play baseball at the professional level somewhere in the world. The far right hand sigmas (probably the sixth sigmas and higher) represent the most exceptional baseball players from the entire world of adult men as potential baseball players, and the farther along you go along that continuum, the more exceptional they are, and the fewer of them there are. Here's another way to look at it: through 2013, there were 9,660 non-pitcher players in the history of major league baseball. Here's a table showing the number of players broken out by their career hitting Wins Above Replacement, shown as the integer root of the WAR (WAR Int): A total of 4,872 of these players, or a little over 50% of them, ended up with career hitting WAR of 0 or less (meaning negative hitting WAR). Another 1,353 of them (15%) ended up with career hitting WAR of between 0 and 0.9. Four hundred and eight-five ended up between 1.0 and 1.9. And so on, and as you go up in WAR, there are fewer and fewer players who were good enough to achieve that. Here is what this looks like on a chart: As we can see, this looks nothing like a Bell Curve. For it to look like a Bell Curve, 16% of players would have had to end up with WARs between 0 and 56, 68% with WARs between 56 and 112, and the other 16% with WARs between 112 and 168. We don't see that. We see a huge and dramatic dropoff to almost nothing by the time we get to a WAR of 50, and that's because 98.12% of all major league hitters in the history of the game have career WARs under 50. So, if anything, The Wolf's chart, if it was meant to represent the portion of the Curve comprised of athletes who could make the major leagues, is wildly optimistic. A real representation might yield a blue area that couldn't be detected with the naked eye. So, in short, The real baseball Bell Curve takes in the baseball potential of every adult male in the entire world, and since only the very end of the curve encompasses major league hitters, there's no way that part could ever be a Bell Curve. If anything, that part is a cliff. |
12-12-2013, 02:03 PM | #48 | |
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That chart was a generalized illustration. Thank you for more precise data.
Not surprisingly, if you plotted that data in centered layers with the X and Y axes reversed, you'd get a pyramid shape.
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12-12-2013, 02:05 PM | #49 | |
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And here's the takeaway: within the MLB, relative scarcity of players increases proportionately with talent level.
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12-12-2013, 03:37 PM | #50 | |
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That data is pretty misleading, though, because you have no idea what career WAR players would have accumulated had they all played for 15 seasons. It's not a fair comparison. Obviously if you compare everyone who has ever played baseball, most people will have a career WAR near 0, because most people don't play very many games. |
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12-12-2013, 03:44 PM | #51 |
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All this talk about regular people playing baseball sure is making me miss my days as a (very average) high school player
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12-12-2013, 03:51 PM | #52 | |
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Imagine being a perfectly competent and successful college player and realizing your senior year that your talent level falls just to the left of chucksabr's chart, and you're simply not talented enough to ever even be a replacement player.
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12-12-2013, 04:00 PM | #53 |
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Sorry to hear that Wolf, that had to be a tough pill to swallow. But hey, at least you got to play competitive ball longer than 99% of people do.
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12-12-2013, 04:29 PM | #54 |
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Let me clarify that the comment in the interview was specifically about statistical output distribution within MLB. What I wanted to convey was that within the group of players already in MLB the distribution of results is too wide. I wonder if that's impossible to control due to the randomness built into development/aging.
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12-12-2013, 05:18 PM | #55 |
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Presumably those players didn't play fifteen seasons because they didn't have the talent to support a playing career that long (barring injuries or other non-sport related causes). The fact that only a very small number of players currently in the minor leagues will ever play at the major league level, and of those who do most will have a career only a few seasons long, is a testament to the fact that only a vanishingly small number of persons have a talent level high enough to support a long career.
Last edited by Le Grande Orange; 12-12-2013 at 05:19 PM. |
12-12-2013, 06:06 PM | #56 | |
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One major issue with any study like this is that players who put up good seasons (and therefore players who tend to have longer careers) have been, more often than not, lucky. This is why Tango, when working out the aging curves that OOTP uses in its player development model, has to regress performance to the mean. Just as a thought experiment, imagine you take 1,000,000 coins. You flip each coin up to 15 times, counting how many times you get Heads. But the instant you get Tails twice in a row, you stop. Then look at how the number of Heads is distributed. A full 25% of coins give you 0 Heads (you get Tails twice straight from the jump). Another 18.75% give you exactly 1 Head (you get HTT or THTT). So almost 44% of your coins give you 0 or 1 Head, while of course a few are going to give you 8 or more. But of course no coin is any better than any other, despite the fact that they produced very different results. Some were just luckier than others to survive long enough to put up good results. The same thing definitely happens in baseball. |
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12-12-2013, 07:00 PM | #57 |
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And I kind of forgot to emphasize the main point of my coinflip analogy. If you graph the number of times you got 0, 1, 2, etc Heads, you get a heavily left-tailed distribution, similar to but obviously not quite as extreme as the one from the WAR chart above. And this is in a situation where everyone has exactly the same 'skill'.
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12-12-2013, 07:20 PM | #58 | ||
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12-12-2013, 07:22 PM | #59 | |
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^ This
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12-12-2013, 07:23 PM | #60 |
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I will also say that I'm not Tom Tango, in that I do not have the wherewithal to do an exhaustive bulletproof study of this. But I believe my conclusion gets us 90% the way there, and I would be shocked if someone like a Tom Tango doing an exhaustive study of this would arrive at a substantially different conclusion than I have.
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