Most of what you need to know about CrossFit can be summarized in a single equation:
Power = (Force x Distance) / Time
(Note: This is a simplification of the exact formula, which considers the angle of the force relative to the angle of the movement. More on this in future posts.)
Perhaps half of all CrossFit coaching comes down to emphasizing the practical applications of this formula subject to the constraints of our anatomy and physiology.
For the moment, consider the formula as written. We know we want to maximize power, but how? Three ways:
1. For a given force (weight) and a given distance (full ROM times the number of reps) minimize the time.
2. For a given force (weight) and a given time (e.g., 20 minutes) maximize the distance (reps, rounds)
3. For a given distance and a given time, maximize the weight.
When doing any given WOD as RX’d, exactly one of these categories will apply. “Fran” falls into the first category, “Cindy” into the second, and ME (maximum effort) WOD’s into the third. But it doesn’t have to be only this way. Sometimes, it may pay to give up going RX’d and instead focus on the other two possibilities.
Consider “Fran.” The typical goal is to get to the RX’d weight as soon as possible and then work at cutting down the time. But there are two alternatives:
Get to the RX’d weight, but cut down the reps (distance) to the point where the WOD can be done unbroken. For example instead of doing (7,7,7), (5,5,5), (3,3,3) on the thrusters, consider doing a “60% Fran,” 12, 9, 6 on pull-ups and thrusters. But do it unbroken! Learn what it feels like not to rest. The first thing you may find is that a “60% Fran” does not take 60% of the time; in fact, it may take as little as 40% the time. Much of the time on a full RX’d “Fran” is typically spent resting. The transition from doing each round in three sets to doing each round unbroken eliminates 12 breaks! Once you learn that you can do a “60% Fran” unbroken in, say, 3 minutes, your entire attitude towards the WOD may change. If you can do 60% in 3 minutes, then your goal should be raised to 70% in 3:30, then 80% in 4:00, until you’re at 100% in 5:00.
The second approach is to do the full number of reps, but cut the weight (force) until the reps can all be done unbroken. Instead of doing 95 lbs in 3 sets per round, cut to a weight that allows you to do each round unbroken. If that means going to 45 lbs, so be it. If your “Fran” RX’d is, say, 7:30, what would it be with a 45 lb bar? 5:00? Less? Find out, and then make that time your goal as you steadily increase the weight back up to 95 lbs.
We’re conditioned to think that when we do a WOD like “Fran” as RX’d, we’re not scaling. Here’s how to change that perspective: Expand the definition of RX’d to include a time limit, say 5 minutes (or pick your own time). Now, with three dimensions defining the WOD (force, distance, and time) it’s easy to see that most of us are always scaling.
When the WOD is done as traditionally thought to be “RX’d” but in more than, say, 5 minutes, we’re scaling time. When the WOD is done in 5 minutes but with less than 95 lbs (or with band pull-ups) we’re scaling force. And when the WOD is done in 5 minutes with 95 lbs but less than 21-15-9 reps (or less than full ROM) we’re scaling distance. So really, we’re always scaling. (If you’ve got a 5-minute “Fran” just redefine your standard to be a 4-minute “Fran”)
It’s all just a question of which dimension we scale. CrossFit is about constant variation. Considered varying your dimensions.
Thursday, December 3, 2009
Wednesday, December 2, 2009
Ending The Mystery Around Climate Change
There are three basic steps needed to predict existence or non-existence climate change: (1) Gathering and compiling the historical temperature data; (2) smoothing and correcting the historical data (e.g., assigning relative weights to some data that may cover larger geographic regions than other data, or adjusting for changes in the locations or surroundings of temperature-recording stations over time); and (3) running the smoothed and corrected historical data through a complex computer model.
The first step involves no science. The second step involves very little science. Both of these steps can and should be open to participation and review by the general public. The mechanisms for doing so already exist and are nearly costless.
The University of East Anglia’s Climate Research Unit states that it has lost or discarded the data from step 1 after it completed step 2. It refers to the data it created in step 2 as “value added data.” It is this value-added data that it has run through its computer models to reach its predictions.
Science demands that Step 1 be verifiable and reproducible. There is an easy way to accomplish this: All the historical data should be copied (most of it exists in the form of handwritten logs) and stored on the Internet. This is exactly the sort of process that Google excels (no pun) at. All the world’s temperature-recording stations should photocopy their logs and send them to Google.
Assuming each station has an average of one page of temperature readings per month for the last hundred years, and assuming there are one thousand stations, then this amounts to 1.2 million pages of data, a trivial amount for Google to handle. A page of characters requires about 2 kilobytes of storage. 1.2 million pages require less than 3 gigabytes of memory. An iphone has 32 gigabytes of memory. Even if the data were stored as photographs, requiring significantly more storage, this would still be a very small undertaking for Google. (Google already has satellite photos of virtually every square foot of the entire world available on-line for free.)
Next, the handwritten data should be put into spreadsheet form. This would significantly compress the data. (The collective public would be able to verify that the information from the photocopies of the logs was accurately transferred into spreadsheet form.) At one data point per day, one hundred years of data points would require a spreadsheet with 36,500 cells, one spreadsheet for each station.
Next step: Wikipidia. The on-line encyclopedia could create an entry for each recording station. Each entry should have a link to a Google spreadsheet with the station’s data, and whatever historical narrative the recording station has provided. For example, there would be information explaining how the location or procedures changed over the years. Was the recording station in Central Park moved over the years, etc?
This narrative would then be available for those wishing to adjust and smooth the data (i.e., add value). There may be debate about how and why the raw data should be adjusted, but at least that debate could be in plain view of the general public. Someone with great historical knowledge of Central Park might be able to provide valuable insight into questions about why the data from 1958 look different than the same year’s data from another nearby location.
In sum, the process of getting through Step 1 and Step 2 can be easily placed in the public domain where it not only belongs, but also can be better handled. The final step would be for scientists to offer the details of their mathematical models for analyzing the value-added data set and to offer their predictions. It may very well be that less than one in one thousand people are capable of understanding such models and the math and science behind them, but the planet has six billion people, six million of whom qualify as being a “one in one thousand.”
The first step involves no science. The second step involves very little science. Both of these steps can and should be open to participation and review by the general public. The mechanisms for doing so already exist and are nearly costless.
The University of East Anglia’s Climate Research Unit states that it has lost or discarded the data from step 1 after it completed step 2. It refers to the data it created in step 2 as “value added data.” It is this value-added data that it has run through its computer models to reach its predictions.
Science demands that Step 1 be verifiable and reproducible. There is an easy way to accomplish this: All the historical data should be copied (most of it exists in the form of handwritten logs) and stored on the Internet. This is exactly the sort of process that Google excels (no pun) at. All the world’s temperature-recording stations should photocopy their logs and send them to Google.
Assuming each station has an average of one page of temperature readings per month for the last hundred years, and assuming there are one thousand stations, then this amounts to 1.2 million pages of data, a trivial amount for Google to handle. A page of characters requires about 2 kilobytes of storage. 1.2 million pages require less than 3 gigabytes of memory. An iphone has 32 gigabytes of memory. Even if the data were stored as photographs, requiring significantly more storage, this would still be a very small undertaking for Google. (Google already has satellite photos of virtually every square foot of the entire world available on-line for free.)
Next, the handwritten data should be put into spreadsheet form. This would significantly compress the data. (The collective public would be able to verify that the information from the photocopies of the logs was accurately transferred into spreadsheet form.) At one data point per day, one hundred years of data points would require a spreadsheet with 36,500 cells, one spreadsheet for each station.
Next step: Wikipidia. The on-line encyclopedia could create an entry for each recording station. Each entry should have a link to a Google spreadsheet with the station’s data, and whatever historical narrative the recording station has provided. For example, there would be information explaining how the location or procedures changed over the years. Was the recording station in Central Park moved over the years, etc?
This narrative would then be available for those wishing to adjust and smooth the data (i.e., add value). There may be debate about how and why the raw data should be adjusted, but at least that debate could be in plain view of the general public. Someone with great historical knowledge of Central Park might be able to provide valuable insight into questions about why the data from 1958 look different than the same year’s data from another nearby location.
In sum, the process of getting through Step 1 and Step 2 can be easily placed in the public domain where it not only belongs, but also can be better handled. The final step would be for scientists to offer the details of their mathematical models for analyzing the value-added data set and to offer their predictions. It may very well be that less than one in one thousand people are capable of understanding such models and the math and science behind them, but the planet has six billion people, six million of whom qualify as being a “one in one thousand.”
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