stanford-cs336/owt-sample · Datasets at Hugging Face

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If we never had leap years, today would be .*

This date is calculated by adding previous leap days to today’s date.

That assumes you start counting from the time of Julius Caesar and Cleopatra in 46 B.C. Through Cleopatra, Caesar met Egyptian astronomers on his trips to Alexandria. The Egyptians had figured out something the Romans missed: Days don’t fit neatly into a year.

There are about 365.2422 days each year. And that number is shrinking as tidal forces slow Earth’s orbit ever so slightly.

"The timepiece that we use, the Earth, is not as accurate as we need it to be in modern society," said David Ewing Duncan, author of the book “Calendar: Humanity's Epic Struggle to Determine a True and Accurate Year.”

"Natural objects have always been our timepiece, but we've always had to adjust," he said.

Hence the leap year and leap day on February 29th.

With Caesar’s new calendar, the Roman Empire would have three 365-day years followed by one with 366 days. This would “leap” the calendar forward, aligning it with the solar year before the two got too far out of whack.

Each year would be 365.25 days.

To get ready for the switch Caesar had to get the Roman calendar caught up. Until then, the Romans had been using a lunar calendar. So in 46 B.C., Caesar added 80 days to the calendar, in what became known as the “year of confusion.”

Unfortunately, Caesar’s plan to add a leap year every four years overshot the mark.

The difference between the extra .2422 of a day in the solar year and the extra .25 of a day in the calendar year amounted to 11 minutes and 14 seconds, according to Duncan.

It may not sound like much, but after four years the calendar was off by about 45 minutes. After about 125 years, the calendar was off by a day.<\|endoftext\|>post and was surprised to see him praising the aesthetic value of VB.NET.

Am I mistaken or has the collective self-esteem of millions of VB.NET programmers just risen considerably?

Perhaps the word 'just' is a bit of a stretch since the post was actually written several weeks ago. I would have come across it sooner, but I am currently living in mortal fear of my RSS Reader which has somehow managed to swell in size to a couple of hundred subscriptions and now spends all of my computer's spare CPU cycles calculating evil plots to take over the world. But I digress...

For those of you who have never worshiped at the alter of Don, here are his

. Besides being one of the original designers of SOAP, he's also authored of a

in the DevelopMentor "Essential" series that I wasn't quite smart enough to fully grok the last time I tried reading them. If you've been around long enough to have had the pleasure of working with COM, then you probably recognize the "COM is Love" phrase that he coined

To put it in historical context, he was a full-fledged geek rock-star when

was still busy popping zits.<\|endoftext\|>Baseball Prospectus director of technology Harry Pavlidis took a risk when he hired Jonathan Judge.

Pavlidis knew that, as Alan Schwarz wrote in The Numbers Game, "no corner of American culture is more precisely counted, more passionately quantified, than performances of baseball players." With a few clicks here and there, you can find out that Noah Syndergaard's fastball revolves more than 2,100 times per minute on its way to the plate, that Nelson Cruz had the game's highest average exit velocity among qualified hitters in 2016 and myriad other tidbits that seem ripped from a video game or science fiction novel. The rising ocean of data has empowered an increasingly important actor in baseball's culture: the analytical hobbyist.

That empowerment comes with added scrutiny -- on the measurements, but also on the people and publications behind them. With Baseball Prospectus, Pavlidis knew all about the backlash that accompanies quantitative imperfection. He also knew the site's catching metrics needed to be reworked, and that it would take a learned mind -- someone who could tackle complex statistical modeling problems -- to complete the job.

"He freaks us out." Harry Pavlidis

Pavlidis had a hunch that Judge "got it" based on the latter's writing and their interaction at a site-sponsored ballpark event. Soon thereafter, the two talked over drinks. Pavlidis' intuition was validated. Judge was a fit for the position -- better yet, he was a willing fit. "I spoke to a lot of people," Pavlidis said, "he was the only one brave enough to take it on."

Judge was more than brave enough -- he proved capable at the ensuing wonk work, allowing BP to unveil their revamped catching metrics in February 2015. Months later, Judge topped this accomplishment by revealing an unusual metric, Deserved Run Average (DRA), that could change life for the fan and hobbyist alike by fundamentally altering how baseball is analyzed.

Specifically, Judge has introduced to the community a framework that allows us to view baseball as more complicated than the usual one-on-one affair between a pitcher and a batter. The sport has always been more complex than that, even when evaluative tools suggested otherwise.

Judge has redefined the boundaries of what analysts can quantify by demonstrating how a metric can incorporate seemingly countless variables, ranging from the catcher and umpire to the weather and stadium. A knowledge-hungry optimist can look at Judge's work and see a future in which other, previously opaque areas are explored using similar techniques.

Yet the most fascinating part of Judge's story might be the man behind the metric.

Jonathan Judge: The stathead lawyer

Jonathan Judge could be the person to revolutionize baseball metrics.

During the day, Judge is a 41-year-old litigation partner at Schiff Hardin, a prominent law firm headquartered in Chicago. The first sentence of his biography notes he "believes that analytics are an important part of cutting-edge legal advice." (One example Judge offers of his belief: evaluating the statistical equivalence and fairness of government fines imposed on companies, insurers and the like using historical data.) Mosey on down the page and you can read his papers on various subjects -- predicting consumer product safety commissions; an examination of multistate market conduct penalties; and similar gossip like that.

But when the sun drops, Judge focuses on baseball. Though that might sound like a normal cycle for most fans -- especially those who dabble in statistical research and writing -- there is a difference: Judge is possibly, kinda sorta, almost certainly a genius.

Big claims require big evidence. Consider, then, that Judge has never received certification in a mathematical field. His degrees are in law and music -- he's a trained pianist who lists Martha Argerich and Murray Perihia among his favorite performers.

How did a lawyer-cum-pianist become a significant player in baseball analytics? Mostly through self-teaching.

"A lot of Google, Wikipedia, Stack Exchange, a few textbooks and then papers I continue to find interesting," Judge said of his learning materials. "I work on it almost every night in some form or another."

Judge, who rediscovered his childhood baseball fandom thanks to the 2008 Milwaukee Brewers, tiptoed into statistical principles a few years back, when he was looking for a "non-BS" way to solve problems. From there, the relationship between the game and numbers became evident.

"Baseball and statistics kept reinforcing each other," Judge said. "I wanted to know more about how baseball worked, which required more statistics knowledge, which in turn led to being better at statistics, and then understanding more about baseball."

Perhaps that inherent reinforcement between hobbies explains how Judge picked up on sophisticated methods as quickly as he did. Or perhaps there's something else in play.

One person who spoke with me about Judge described him as an autodidact (self-taught) -- seemingly more of a fact than an opinion. Others familiar with Judge and his work expressed similar sentiments.

"He's probably the fastest learner I've ever been around," Pavlidis said. "He freaks us out."

That Judge did all this while holding down his demanding job shouldn't be lost on anyone, either.

"He picked up a huge amount of statistical and programming knowledge in a very short amount of time -- while maintaining a full-time job as a lawyer," said Rob Arthur, a former colleague of Judge's and now a staff writer at FiveThirtyEight. "How he did it, I will never know."

Judge's education went beyond running a linear regression in Excel, or learning how to code in SQL or R. His proverbial fastball is the concept of mixed modeling -- "a statistical model containing both fixed effects and random effects," according to Wikipedia. In layman's terms, it's complicated.

Though Judge wasn't the first to introduce mixed modeling to baseball -- Max Marchi, now with the Cleveland Indians, used it to quantify framing -- he has become the most visible practitioner due to DRA -- a metric that, if only in methodology, could change everything.

The metric that could change baseball analytics

Jered Weaver was the worst pitcher in baseball in 2016, per DRA. USATSI

"I think it's the most sophisticated and probably the most accurate measure of pitcher quality that's publicly available right now," Arthur said. "The mixed models that make up the core of DRA allow you to adjust for a lot of the factors that we've known to affect pitching, but haven't been able to measure or integrate into our pitching metrics. So factors like the framing skill of the catcher, the quality of the opposition, home or away, the park, et cetera.

"That level of statistical rigor hasn't been in the mainstream of sabermetrics," Arthur said about DRA's mixed modeling application. "Until now."

"We try to do the same exact thing with more sophisticated data." Front office member

You can find a full explanation of DRA (and expansive leaderboards) elsewhere. But the gist behind DRA is that pitchers are tougher to analyze than hitters due to the confluence of variables involved in every play. Even a called strike requires a passive batter, a well-positioned catcher and an accurate umpire. There's significant room for error, and the most commonly used pitching statistics -- ERA and FIP -- are flawed in large part due to their incorrect assumptions about the elements a pitcher controls.

If we never had leap years, today would be .*

This date is calculated by adding previous leap days to today’s date.

That assumes you start counting from the time of Julius Caesar and Cleopatra in 46 B.C. Through Cleopatra, Caesar met Egyptian astronomers on his trips to Alexandria. The Egyptians had figured out something the Romans missed: Days don’t fit neatly into a year.

There are about 365.2422 days each year. And that number is shrinking as tidal forces slow Earth’s orbit ever so slightly.

"The timepiece that we use, the Earth, is not as accurate as we need it to be in modern society," said David Ewing Duncan, author of the book “Calendar: Humanity's Epic Struggle to Determine a True and Accurate Year.”

"Natural objects have always been our timepiece, but we've always had to adjust," he said.

Hence the leap year and leap day on February 29th.

With Caesar’s new calendar, the Roman Empire would have three 365-day years followed by one with 366 days. This would “leap” the calendar forward, aligning it with the solar year before the two got too far out of whack.

Each year would be 365.25 days.

To get ready for the switch Caesar had to get the Roman calendar caught up. Until then, the Romans had been using a lunar calendar. So in 46 B.C., Caesar added 80 days to the calendar, in what became known as the “year of confusion.”

Unfortunately, Caesar’s plan to add a leap year every four years overshot the mark.

The difference between the extra .2422 of a day in the solar year and the extra .25 of a day in the calendar year amounted to 11 minutes and 14 seconds, according to Duncan.

It may not sound like much, but after four years the calendar was off by about 45 minutes. After about 125 years, the calendar was off by a day.<|endoftext|>post and was surprised to see him praising the aesthetic value of VB.NET.

Am I mistaken or has the collective self-esteem of millions of VB.NET programmers just risen considerably?

Perhaps the word 'just' is a bit of a stretch since the post was actually written several weeks ago. I would have come across it sooner, but I am currently living in mortal fear of my RSS Reader which has somehow managed to swell in size to a couple of hundred subscriptions and now spends all of my computer's spare CPU cycles calculating evil plots to take over the world. But I digress...

For those of you who have never worshiped at the alter of Don, here are his

. Besides being one of the original designers of SOAP, he's also authored of a

in the DevelopMentor "Essential" series that I wasn't quite smart enough to fully grok the last time I tried reading them. If you've been around long enough to have had the pleasure of working with COM, then you probably recognize the "COM is Love" phrase that he coined

To put it in historical context, he was a full-fledged geek rock-star when

was still busy popping zits.<|endoftext|>Baseball Prospectus director of technology Harry Pavlidis took a risk when he hired Jonathan Judge.

Pavlidis knew that, as Alan Schwarz wrote in The Numbers Game, "no corner of American culture is more precisely counted, more passionately quantified, than performances of baseball players." With a few clicks here and there, you can find out that Noah Syndergaard's fastball revolves more than 2,100 times per minute on its way to the plate, that Nelson Cruz had the game's highest average exit velocity among qualified hitters in 2016 and myriad other tidbits that seem ripped from a video game or science fiction novel. The rising ocean of data has empowered an increasingly important actor in baseball's culture: the analytical hobbyist.

That empowerment comes with added scrutiny -- on the measurements, but also on the people and publications behind them. With Baseball Prospectus, Pavlidis knew all about the backlash that accompanies quantitative imperfection. He also knew the site's catching metrics needed to be reworked, and that it would take a learned mind -- someone who could tackle complex statistical modeling problems -- to complete the job.

"He freaks us out." Harry Pavlidis

Pavlidis had a hunch that Judge "got it" based on the latter's writing and their interaction at a site-sponsored ballpark event. Soon thereafter, the two talked over drinks. Pavlidis' intuition was validated. Judge was a fit for the position -- better yet, he was a willing fit. "I spoke to a lot of people," Pavlidis said, "he was the only one brave enough to take it on."

Judge was more than brave enough -- he proved capable at the ensuing wonk work, allowing BP to unveil their revamped catching metrics in February 2015. Months later, Judge topped this accomplishment by revealing an unusual metric, Deserved Run Average (DRA), that could change life for the fan and hobbyist alike by fundamentally altering how baseball is analyzed.

Specifically, Judge has introduced to the community a framework that allows us to view baseball as more complicated than the usual one-on-one affair between a pitcher and a batter. The sport has always been more complex than that, even when evaluative tools suggested otherwise.

Judge has redefined the boundaries of what analysts can quantify by demonstrating how a metric can incorporate seemingly countless variables, ranging from the catcher and umpire to the weather and stadium. A knowledge-hungry optimist can look at Judge's work and see a future in which other, previously opaque areas are explored using similar techniques.

Yet the most fascinating part of Judge's story might be the man behind the metric.

Jonathan Judge: The stathead lawyer

Jonathan Judge could be the person to revolutionize baseball metrics.

During the day, Judge is a 41-year-old litigation partner at Schiff Hardin, a prominent law firm headquartered in Chicago. The first sentence of his biography notes he "believes that analytics are an important part of cutting-edge legal advice." (One example Judge offers of his belief: evaluating the statistical equivalence and fairness of government fines imposed on companies, insurers and the like using historical data.) Mosey on down the page and you can read his papers on various subjects -- predicting consumer product safety commissions; an examination of multistate market conduct penalties; and similar gossip like that.

But when the sun drops, Judge focuses on baseball. Though that might sound like a normal cycle for most fans -- especially those who dabble in statistical research and writing -- there is a difference: Judge is possibly, kinda sorta, almost certainly a genius.

Big claims require big evidence. Consider, then, that Judge has never received certification in a mathematical field. His degrees are in law and music -- he's a trained pianist who lists Martha Argerich and Murray Perihia among his favorite performers.

How did a lawyer-cum-pianist become a significant player in baseball analytics? Mostly through self-teaching.

"A lot of Google, Wikipedia, Stack Exchange, a few textbooks and then papers I continue to find interesting," Judge said of his learning materials. "I work on it almost every night in some form or another."

Judge, who rediscovered his childhood baseball fandom thanks to the 2008 Milwaukee Brewers, tiptoed into statistical principles a few years back, when he was looking for a "non-BS" way to solve problems. From there, the relationship between the game and numbers became evident.

"Baseball and statistics kept reinforcing each other," Judge said. "I wanted to know more about how baseball worked, which required more statistics knowledge, which in turn led to being better at statistics, and then understanding more about baseball."

Perhaps that inherent reinforcement between hobbies explains how Judge picked up on sophisticated methods as quickly as he did. Or perhaps there's something else in play.

One person who spoke with me about Judge described him as an autodidact (self-taught) -- seemingly more of a fact than an opinion. Others familiar with Judge and his work expressed similar sentiments.

"He's probably the fastest learner I've ever been around," Pavlidis said. "He freaks us out."

That Judge did all this while holding down his demanding job shouldn't be lost on anyone, either.

"He picked up a huge amount of statistical and programming knowledge in a very short amount of time -- while maintaining a full-time job as a lawyer," said Rob Arthur, a former colleague of Judge's and now a staff writer at FiveThirtyEight. "How he did it, I will never know."

Judge's education went beyond running a linear regression in Excel, or learning how to code in SQL or R. His proverbial fastball is the concept of mixed modeling -- "a statistical model containing both fixed effects and random effects," according to Wikipedia. In layman's terms, it's complicated.

Though Judge wasn't the first to introduce mixed modeling to baseball -- Max Marchi, now with the Cleveland Indians, used it to quantify framing -- he has become the most visible practitioner due to DRA -- a metric that, if only in methodology, could change everything.

The metric that could change baseball analytics

Jered Weaver was the worst pitcher in baseball in 2016, per DRA. USATSI

"I think it's the most sophisticated and probably the most accurate measure of pitcher quality that's publicly available right now," Arthur said. "The mixed models that make up the core of DRA allow you to adjust for a lot of the factors that we've known to affect pitching, but haven't been able to measure or integrate into our pitching metrics. So factors like the framing skill of the catcher, the quality of the opposition, home or away, the park, et cetera.

"That level of statistical rigor hasn't been in the mainstream of sabermetrics," Arthur said about DRA's mixed modeling application. "Until now."

"We try to do the same exact thing with more sophisticated data." Front office member

You can find a full explanation of DRA (and expansive leaderboards) elsewhere. But the gist behind DRA is that pitchers are tougher to analyze than hitters due to the confluence of variables involved in every play. Even a called strike requires a passive batter, a well-positioned catcher and an accurate umpire. There's significant room for error, and the most commonly used pitching statistics -- ERA and FIP -- are flawed in large part due to their incorrect assumptions about the elements a pitcher controls.