Expect scattered posts to continue for the next two weeks. I’m off to the Great White North-of-the-Pennsylvania-Border.
My recent blogging has been light; I’m in my fantasy football league Superbowl for the first time, and these players require constant monitoring. (Two weeks ago I was way behind and implored my defense (Cardinals): rather than give up a score and increase my deficit, they should get a turnover and run it back for a TD. Which they did, just a minute later. See?)
The Physics of Surfing (Part Two: Tubes and Barrels)
But why do some waves break as hollow grinding tubes while others crumble more gently and forgivingly? Let’s examine a little wave dynamics in order to assess the situation. Ocean waves are created by wind blowing over the ocean surface, as the kinetic energy of the air is converted into potential and kinetic energy of the water. The biggest and most powerful waves are created in massive storms. As the swells generated by these storms travel over the open ocean, the originally chaotic “victory at sea”-type wave motion is gradually organized into cleaner lines. As with all waves, it is not the actual material (water in this case) that travels any distance through the medium — it is the energy of the wave. As the wave energy passes through a point in the ocean, the water molecules rise and fall in a circular pattern but remain in the vicinity as the disturbance passes by.
Previously: Dude, It’s Physics
The Physics of Surfing (Part One: Dropping In)
[P]addling by itself doesn’t get you into the wave, because you actually cannot paddle as fast as the wave is moving, and you need to match the speed of the wave if you want to ride with it. In order to catch up to that wave speed, it’s necessary to use the gravitational potential energy of the wave. The trick is to obtain sufficient speed by paddling that, as the wave travels under you, your board begins to fall down the face. As you drop down the front of the wave, the gravitational potential energy you gain is converted into kinetic energy. Soon you are travelling as fast as the wave. In fact, if you continue to drop to the bottom of the wave, you’ll be moving faster than the wave — and if you don’t cut into the face, you might temporarily outrun it.
Check out yesterday’s “PhD Comic” comparing the average salaries of University administrators, professors, grad students and football coaches.
Here’s a different graphic, focusing on the professors and coaches
(sort of my own mini cartoon-off, like between Randall Monroe and Farley Katz)
A runner dressed as a white rabbit caught 12,000 runners on the hop – by winning a half marathon in London.
The Lake Wobegon Distribution at The Universe of Discourse
[T]he remark reminded me of how many people do seem to believe that most distributions are normal. More than once on internet mailing lists I have encountered people who ridiculed others for asserting that “nearly all x are above [or below] average”. This is a recurring joke on Prairie Home Companion, broadcast from the fictional town of Lake Wobegon, where “all the women are strong, all the men are good looking, and all the children are above average.” And indeed, they can’t all be above average. But they could nearly all be above average. And this is actually an extremely common situation.
To take my favorite example: nearly everyone has an above-average number of legs.
The post goes on to use some baseball statistics, in a way that probably won’t give Chad apoplexy, arguing that professional baseball players shouldn’t follow a normal distribution, because they are not selected at random from the population. They should represent the part of the distribution several standard deviations above the average.
One flaw in the reasoning is that not all highly skilled athletes with the right abilities become baseball players, but I think the basic argument is sound.
Of course, college students probably aren’t a normal distribution. Schools screen their applicants, and there can be further skewing within that population; students drop out of classes, and not all courses are created equal. Take physics, for example — there are typically different levels of introductory physics: a so-called physics-for-poets class, a class that require just algebra, and one that requires calculus. Generally speaking your physics ability would correlate somewhat with the class you are taking. Even if the physics-taking population as a whole comprised a normal distribution, each individual class should not: the easiest class should be deficient in students at the high end, and the hardest class should be missing the low end.
Chad’s got a post up about how Baseball Statistics Are Crap. I’ve got a different beef.
(There are, certainly, a lot of dubious statistics in baseball. I just don’t agree that things are as bad as Chad says but maybe it’s just that I’m used to the idiosyncrasies. I do understand the infield fly rule, after all. If that weirdness makes sense, maybe the weird statistics do, too.)
Anyway, my objection is that even with these simplified statistics, the sportscasters and writers read too much into them. They don’t understand what the statistics are saying, and the value of statistics is to be able to compare players. In baseball it’s not so bad — even if the stats are flawed, a player hitting .356 is objectively a better hitter, by this measure than one who is hitting .290. But what does “by this measure” mean? In baseball, you can hit for average or for power — there are different skills and abilities useful to the team, and you want to find the statistic that is appropriate to the skill you are trying to quantify.
In this regard, I think, football is an example where the reporters are a great abuser of statistics. And this goes beyond saying “turnover ratio” when “differential” is meant (one thing that’s gotten better over the years). The main abuse, I think, is saying that accuracy is measured by completion percentage, and this seemingly happens all the time.
Accuracy is your ability to hit a target, and if you want to compare apples-to-apples, the target should be the same one. A stationary target at 10 yards is easier to hit than a moving one at 40 yards. A better receiver, who can get open, is easier to hit, and also affects the ability for other receivers to get open. You can have a receiver who drops the ball even though it’s “right at the numbers,” or one who catches everything thrown his way. When nobody’s open and he’s trapped, a quarterback can take a sack or throw the ball away, giving him an incompletion. All of that affects completion percentage, and none of it reflects accuracy.
Chad Pennington is touted as an “accurate quarterback” by many sports journalists, who, in the next breath, mention he has a weak arm and dumps the ball off quite often. Short passes. Connection? I think so!
My favorite example is Donovan McNabb. When Terrell Owens was about to join the team, analysts were all cautious about how Owens would tolerate the inaccurate McNabb, who had never completed 59% of his passes. Until that year, when he completed 64%, and everyone was saying how accurate he had become. Owens leaves, and the completions percentage drops back down. (It’s up again this season, and last — he’s got better receivers, and he dumps the ball off to Westbrook when he has to)
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When the hell did I identify myself as a Ravens fan?
DOLPHINS! (Say, how are things up in New England these days? Having fun?)