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Links 1 through 10 of 134 by Chad Orzel tagged statistics

There is a massive, ever-expanding class of Americans who cannot remember a connection to pro football that did not involve the drafting and owning of skill players who work on their personal behalf. And the result, I fear, has been the mild dehumanization of humans we were already prone to perceive as machines.

Now, I realize dehumanization is a melodramatic word to use when discussing millionaires. I would guess that most people reading this column would love to be "dehumanized" in any context that pays them $9 million a year. But this isn't about feeling sympathy for pro athletes. That's not my point. What I'm proposing has more to do with how a few grains of personal investment prompt normal people to think about strangers in inaccurate, twisted, robotic ways. It's about how something fun quietly makes us selfish, and it's about the downside of turning real people into algebraic chess pieces.

The person who is making me think about this is Chris Johnson.

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Congress now speaks at almost a full grade level lower than it did just seven years ago, with the most conservative members of Congress speaking on average at the lowest grade level, according to a new Sunlight Foundation analysis of the Congressional Record using Capitol Words.

Of course, what some might interpret as a dumbing down of Congress, others will see as more effective communications. And lawmakers of both parties still speak above the heads of the average American, who reads at between an 8th and 9th grade level.

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The bell curve powerfully shapes how we think of human performance: If lots of students or employees happen to show up as extreme outliers — they're either very good or very bad — we assume they must represent a skewed sample, because only a few people in a truly random sample are supposed to be outliers.

New research suggests, however, that rather than describe how humans perform, the bell curve may actually be constraining how people perform. Minus such constraints, a new paper argues, lots of people are actually outliers.

Human performance, by this account, does not often fit the bell curve or what scientists call a normal distribution. Rather, it is more likely to fit what scientists call a power distribution.

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I am not typically interested in lotteries.  They seem silly and I am seriously beginning to question their usefulness in bringing about a good harvest.  But this morning I read in the news that the Mega Millions lottery currently has a world record jackpot up for grabs.  In fact, the jackpot is so big...

Tonight Show Audience:  HOW BIG IS IT?

It is so big that I decided to do a little bit of analysis on the expected returns.  Zing!

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On Sunday, the Atlanta Journal-Constitution, a Cox newspaper published the results of its investigation into “cheating” in American schools.  The article was entitled Suspicious Scores Across the Nation, and you can read it by following the link. The article was subtitled “Cheating Our Children.”
I was immediately suspicious of the report that the Journal-Constitution published.  They have put into place an agressive team of watchdog reporters, database specialists, and investigative reporters.  In whose interest motivates this team and this newspaper?  After reading the report, I reaffirm my suspicions.  Let me explain.

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Some shots are easier than other shots; that's a basic tenet of basketball. Many factors influence the probability of a field goal attempt resulting in a made basket, but one factor in particular has been mostly overlooked in basketball analysis: location. The most common shooting metric in the NBA is field goal percentage, which measures the percentage of field goal attempts that result in made baskets. Usually this metric is applied in a non-spatial way to describe how effective a given player or team is at "putting the biscuit in the basket." However, despite its ubiquity in all levels of basketball analysis, very few people have ever sought to measure and/or visualize the spatial dimensions of FG%.

The following graphic visualizes FG% in the NBA this season; it demonstrates that some shots are easier than others, and quantifies this effect. As you can see, some fascinating insights begin to emerge:

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The annual Sloan Sports Analytics Conference, created in 2006, has become something like Bonnaroo for sports nerds. And if there was a breakout star at this year's gathering, held at MIT this past weekend, it may have been Kirk Goldsberry, an assistant professor of geography at Michigan State (and currently a visiting scholar at Harvard). At Sloan, Goldsberry—whose dissertation "investigated real-time traffic maps" and who has also used geography to examine "access to nutritious foods in urban areas"—considered the ways that sophisticated statistical mapping can illuminate the game of basketball, in a paper called "Court Vision: New Visual and Spatial Analytics for the NBA."

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C. In the past five years, no team with an animal nickname has won the Super Bowl. The odds against this are astronomical. There's only a 3 percent chance it would happen, given a random sample. And that random sample wouldn't include men like Peyton Manning.

D. In that same time period, only two animal teams even reached the Super Bowl. They both lost. One, the Arizona Cardinals, lost in mysterious last-minute fashion, and the Saints resorted to trickery to beat the Colts. Both results are more than a little suspicious.

E. In the 32 playoff games pitting an animal team against a nonanimal team, the nonanimals are 20-12. Again, the success rate is unrealistic for a random sample. If you're looking at the real world, on the other hand, humans are typically far more successful than animals, a few shark attacks aside.

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Benford's law states that real-world numbers start more often with a 1 than a 2, more often with 2 than 3 and so on. If your dataset does not follow this distribution, it might hint at human interference.

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What is a goon? In 2005 John Cheney, then the coach of the Temple University basketball team,  sent in “seldom used” forward Nehemiah Ingram in order to give hard fouls and send a message. While Ingram is long gone, are there still players like him? What players pick up quick fouls in bunches, and what if they actually played starter minutes?

To investigate, I took all players in the “Big 6” basketball conferences (ACC, Big East, Big 10, Big 12, Pac 12, and SEC), and found a small group of players that may qualify as “goons”.

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