How to use Quadrant Analysis
There is a lot of valuable information on this page. To make it easier to read, we have it broken down into three parts, but it is recommended that you read through the entire thing to get an understanding of the Quadrant Analysis feature.
Quadrant Analysis is a way to measure the neuromuscular power demands of cycling. Taken from the book, "Training and Racing with a Power Meter by Hunter Allen and Dr. Andrew R. Coggan, "Tools such as Normalized Power, Intensity Factor, and Training Stress Score explicitly recognize the seemingly stochastic nature of cycling power output and help coaches and athletes better understand the actual physiological demands of a given race or workout. Even so, to completely understand the physiological consequences of large variations in power, one must also understand how they impact neuromuscular function—that is, the actual forces and velocities that the leg muscles must generate to produce a given power output. Such effects are recognized by the algorithm used to calculate Normalized Power, but only to the extent that they influence metabolism (e.g., via altering fiber-type recruitment patterns). Although strength (or maximal force) per se is rarely a limiting factor in cycling, neuromuscular factors nonetheless can still sometimes play an important role in determining performance. Thus, we realized that it would be useful to be able to analyze power-meter data that captures this important information in a form that could readily be grasped even by nonexperts.
Neuromuscular What? “Neuromuscular function” may sound complicated, but it simply means how fast you can contract a muscle, how strongly you can contract it, and how long you can contract it before relaxing it again. When someone learns a new movement pattern—it could be anything from learning how to type on the keyboard to pedaling a bicycle—those movement patterns are governed by that individual’s ability to transfer the information from his or her brain to the muscles that are involved. We all take this for granted, and when it comes to cycling we just pedal, but in reality each of us is different in our ability to make these contractions occur. With your power meter, you can begin to understand your neuromuscular ability, and you can determine whether you are training correctly for cycling success and then begin to improve your neuromuscular power.
What are these units on the Y and X axis?
On the Y AXIS: The velocity of muscle contraction (as indicated by cadence) is only one of two determinants of power, with the other, of course, being force. Unfortunately, at present no power meter directly measures the force applied to the pedal. However, it is possible to derive the average (i.e., over 360 degrees) effective (i.e., tangential to the crank) pedal force (both legs combined) from power and cadence data. The equation looks like this:AEPF = (P*60)/(C*2*Pi*CL)
In this formula, AEPF stands for “average effective pedal force” (in newtons, or N); P is power, in watts; C is for cadence (in revolutions per minute); CL is for “crank length” (in meters); and the constants 60, 2, and pi serve to convert cadence to angular velocity (in radians/seconds). Additional insight into the neuromuscular demands of a race or training session can then be obtained by preparing a frequency distribution histogram for AEPF that is similar the one for cadence, as shown in Figure 7.3. (Note that, as with all such plots, graphs like this one do not take into consideration how long AEPF was continuously within a given “bin,” or range. This is not an issue, however, because unlike, for example, heart rate, neuromuscular responses and demands are essentially instantaneous. Indeed, it is the generation of specific velocities and forces via muscle contraction that essentially drives all other physiological responses.)
Although simply examining the frequency distributions of AEPF and cadence provides insight, it does not reveal the relationship between these two variables. This relationship can only be quantified by plotting force versus velocity.
On the X AXIS: Circumferential pedal velocity—that is, how fast the pedal moves around the circle it makes while pedaling—is derived from cadence as follows:CPV = C*CL*2*Pi/60
Here, CPV stands for circumferential pedal velocity (in meters/second); C is for cadence (in revolutions per minute); CL represents crank length (in meters); and the constants 2, pi, and 60 serve to convert the data to the proper units. Although technically, muscle-shortening velocity, or at least joint angular velocity, should be used instead of CPV, CPV has proven to be an excellent predictor of both of these. Indeed, since crank length is generally constant, especially for a given individual, one could just as well use cadence instead of CPV. However, we have used the latter here to be consistent with scientific convention and to emphasize the relationship of cycling-specific plots to the more general force-velocity curve of muscle. A scatterplot of force and velocity, such as that shown in Figure 7.4, therefore presents information that cannot be obtained from just frequency distribution plots of AEPF and CPV.However, it can be difficult to detect subtle and sometimes even not-so-subtle differences between roughly similar rides based on such “shotgun blast” patterns, especially if the scaling of the X and Y axes is allowed to vary. Furthermore, without additional information, such force-velocity scatterplots are entirely relative in nature because there are no fixed anchor points or values that can be used as a frame of reference. It is the latter issue that Quadrant Analysis was specifically developed to address.
Using QA is as simple as clicking on the the Quadrant Analysis Tab on your workout View. Understanding what it means is a different thing though!
Before we delve into the details of understanding them, we should point out the features inside the QA tab, so you can make sure you are interpreting them correctly.
Threshold: This should be the threshold that you set inside your Power Training zones on your Athlete Home page. If this is not correct, you can change it here, by typing in a new Threshold value, or by Creating a new power zone on your Athlete home page
Lo Threshold: this should be set at about 20-30 watts under your Threshold value, just to give you some perspective on the graph.
Hi Threshold: this should be set at about 20-30 watts above your Threshold value, again, to help you with some perspective on the graph.
T-Cadence: This is the Threshold cadence. It's your normal self-selected cadence in which you would average when you do a threshold interval.
Crank Length: The Crank length of your cranks on your bike.
Centering the Axes- This is a somewhat hidden, but very easy and useful feature. You can change the center of the axes, by just double clicking anywhere in the view. Double-click in the upper right and it pulls the axes to the upper right. Double-click to the lower left and it pulls the axes to the lower left. Simple and can really help you read the chart more clearly.
The functions of the 'Eyes' inside QA and clicking on the text (entire ride, peak 5 minutes, etc) have some specific functions, similar to in the graph page but with a few little differences.
When you click on the text for example 'Entire Workout', then the points will all be yellow. This is the default and master color in the QA tab. Note the very small Yellow Triangle in the Data set. This is the Average of all the Data. Note also the percentages in each of the corners. The First number is the percentage of data in that quadrant in relationship with the other quadrants. The percentage number in parenthesese is the percentage of data that is inside a range on the right. See a little bit farther down for an example.
Now, when you click on an 'Eye', this changes the color to red and shows you exactly where that selection of data is inside the QA. This is very useful as it allows to you see if you completed the interval, section of ride correctly as compared to your goal or workout.
Now, finally, if you click on the text word beside the 'Eyes' column, then the section of the workout that does not include the 'Eyes', turns to blue. Note that a 2nd Triangle appears now. This is the average of the data for that range that you clicked on. In the example below, there now a yellow triangle inside QIII which is the average of the data that is highlighted (in blue) on the Ranges window. The Average of all the data is still there, but now it is a red triangle.
Now, review the screenshot above more closely and you'll see that the percentages in the corners have changed. In the QIII corner, we have 33.3% which represents how much time was spent in QIII for the entire workout and then in parentheses, we have 33.1% which represents how much that range is as a percentage of all the data in QIII.
Let's look at a Comparison QA and see what the quadrants mean:
Every ride you do, every workout will have an aerobic/anaerobic (cardiovascular) component to them and a neuromuscular(the muscles!) component. As we said above, how you create the watts can be an important factor in your training. Each quadrant represents a different combination of force(how hard you push on the pedals) and pedaling velocity(how fast you push on the pedals).
QI: High Force and High Cadence- An example of this would be sprinting.
QII: High Force and Low Cadence- An example would be steep hill repeats, big gear intervals and a lot of Mt. Biking resides in QII as well.
QIII: Low Force and Low Cadence- An example would be a recovery ride or just an easy ride around town.
QIV: Low Force and High Cadence- An example would be a Criterium or fast pedaling drills.
The below screenshot compares a Criterium, Road Race, Time Trial and a Cyclo-Cross race together. This was done by creating a Multi-file/Range Analysis and then clicking on the QA tab at the top to have a Multi-File Quadrant Analysis(MFQA)
In the next example below, we see just the criterium race. Most criteriums are characterized by high speeds, fast turns and plenty of sprinting. In a criterium, you have to use a gear that allows you to quickly change speeds to keep up with the ever surging peloton. When you examine the QA below, you see that most of the time is spent in QIV, which represents Fast pedaling at a low force and this makes perfect sense for a criterium. The next largest percentage of time was spent in QI, which means this rider also had to do some sprints in order to stay in the race.
When examining a Mt. Bike race file and comparing it to a road file, you can easily discern the differences betwen the neuromuscular demands fo the two workouts. This is where the QA really shines in demonstrating to you that the demands of a race can be very different from the demands of your training ride, even though you are doing your best to simulate a race. In the case below, we see that it's very difficult to create the same neuromuscular demands on a road bike as those that are created in a Mt. Bike race.
Now, lets examine a Microburst workout on a Trainer and compare that to a Criterium. A Criterium as represented in the QA above, places a large load in the QI area. Can you simulate a criterium race by doing a workout on the trainer? More specifically, will a Micro-burst workout(15 seconds ON at 150% of FTP and 15 seconds OFF at 50% of FTP) be a rough equivalent of a Criterium race?.
From these examples, we hope you have learned more about how to use this tool. Looking at both the QA from an individual file to help you learn more about those files and the neuromuscular demands of them, along with comparing multiple workouts together in a MFQA. These will help to better align your training with your racing demands, and along with your cardiovascular training, you will be assured that your neuromuscular power demands are being met as well.