By Evan Peikon
This is a two part article on training powerful athletes versus enduring athletes. The first portion will focus exclusively on how to identify an athlete as powerful or enduring, while the second will focus on the programming application side of things (among other topics). So, now that we’ve gotten that out of the way and have identified the purpose of this piece we can get going…..
In most instances coaches characterize athletes as powerful or enduring based on a number of factors. However, these characterizations are often subjective and are based on relativities and comparisons rather than concrete data. But there is a better, objective, way to make these assessments. Which, is through the use of speed preservation tests.
*Note that we use specific cyclical and mixed tests/ assessments on our athletes. But, those tests and their implications are beyond the scope of this article. So, this serves as a good objective measure for those looking to asses themselves/ their clients without that knowledge base.
The way that the test works is by taking an athletes 1k, 2k, 3k, and 5k Row PR’s and finding the following ratios between them….
1. 2,000m : 5,000m
2. 1,000m : 2,000m
3. 2,000m : 3,000m
4. 3,000m : 5,000m
With on this data we can plot a line on a graph and quantitatively measure our athlete against seven theoretical avatars (which will be explained later in the article…).
*Note that this test is best done with rowing as MOST crossfit athletes are proficient enough that technique does not skew the data (versus running where VERY FEW athletes have the required technical proficiency. Seriously…. Crossfit athletes on a whole have dog shit running mechanics).
As previously stated, we will need an athletes 1k, 2k, 3k, and 5k Row PR’s to run the numbers on this test. Most crossfit athletes have 1k and 2k PR’s on hand already, so it shouldn’t be too much of an issue to throw the 3k and 5k into an athletes testing phase to get the full range of data moving forward. So, as previously stated we will need to calculate the following ratios…
1. 2,000m : 5,000m
2. 1,000m : 2,000m
3. 2,000m : 3,000m
4. 3,000m : 5,000m
(PR Pace of Distance #1) / (PR Pace of Distance #2) = ___ x100
*Note- units for pace = seconds.
So For Example…
An athlete has a 1k PR of 3:00 (1:30/500m or 90s/500m) and a 2k PR of 7:00(1:45/500m or 105s/500m).
So in this instance you would calculate their speed preservation for test #2 as follows…..
(90 seconds) / (105 seconds) = .857 x100 = 85.7%
Athlete Classification Types :
Once you calculate the ratios for all four tests you can compare them to the following “Athlete Classification Types”. While your specific numbers may not match any of them exactly, it should be clear where you fall on the spectrum. So for example an athlete with the following numbers (85%, 90%, 89%, 95%) will fall in between Type A & Type B.
Type A- 84.5%, 89%, 87%, 94%
Type B- 86.5%, 92%, 90%, 95.5%
Type C- 88%, 93.5%, 91.8%, 96%
Type D- 90%, 93.8%, 93%, 96.5%
Type E- 91.5%, 94.2%, 94.8%, 97%
Type F- 92.5%, 94.8%, 95.8%, 97.5%
Type G- 93.5%, 95.8%, 96.3%, 97.8%
*%’s represented as test 1, test 2, test 3, test 4
So what do these classification types mean…..
In Order of Increasing Power:
G< F< E< D< C< B< A
*Ie- A is the most powerful & G Is the least powerful.
In Order of Increasing Endurance:
A< B< C< D< E< F< G
*Ie- G is the most enduring & A is the least enduring.
Specialty Time Domain Per Classification Type:
Type A/ B- ~2-4 Minutes (~Lactic Endurance)
Type C/D- ~6-8 Minutes (~Aerobic Threshold/Power)
Type E/F- ~10-20 Minutes (~Aerobic Power/ Endurance)
Type G- ~20+ Minutes (~Aerobic Endurance)
Athlete Case Study
1k- 3:14.4, 2k- 7:03, 3k- 11:17, 5k- 18:47
Athletes Speed Preservation Scores:
2000m/5000m- 94% 1000m/2000m- 92% 2000m/3000m- 96% 3000m/5000m- 98%
Based on the Classifications above This athlete would match the most closely with Type F, meaning that they fall on the enduring end on the spectrum with their speciality being in the 10-20 minute range. *Note that this athlete was previously a 3200m & 5k Running Specialist.
Now that you have an idea of where you (or your athletes) fall on the spectrum of powerful –> enduring, and have a base level of knowledge on how your athlete’s “engine” operates, it’s time to explore the implications and how to properly train/ prescribe training based upon it. Which leads us to the implications section.
These are general patterns i’ve recognized based on an extended analysis of both my exclusive coaching clients and followers of the HPA Competitor’s Blog. As such, these recommendations MUST be taken in that context, and interpreted as a statistical average of multiple individuals.
Note- Individual makeup is king. Rather than applying this information directly you should use it as a starting point for future analyses or apply it to the current paradigm centered around a given athlete (ie- Don’t scrap what you already have. Apply one thing at a time, asses the results, adjust your athlete centric paradigm as needed, then start over).
Consistency/ Progression Schemes:
In my experience “Type A” athletes tend to be less consistent, and more temperamental, in terms of pure numbers from week to week (regardless how how things “feel”). Whereas you can guess with high certainty what numbers/ scores/ times a “Type G” athlete will get on a given lift/ workout. Based on observation i’ve also found that “type G” athletes tend to have a slower skill acquisition rate, though it is highly likely that this is a correlative relationship rather than causative (ie- Type G athletes tend to come from low skill endurance based sports, so there is a selection bias in place here).Knowing this you can adjust progression schemes as needed to account for ups and downs in numbers, skill acquisition rates, and individual athlete’s speeds of adaptation on given elements (ie- How quickly do they adapt to abs strength vs musc end vs aerobic threshold progressions etc).
Often times more powerful athletes fare better with short sets/ short rest, whereas more enduring athletes can string together longer sets with more moderate rest times. This especially holds true in CP-recovery, or muscular endurance, based testing scenarios. However, both approaches should be applied in training and refined to match the individual.
Note- These strategies are only applicable to a specific subset of testing scenarios as discussed above. As such they are not recommended in lighter/ higher turnover/ ES based testers.
Aerobic Base Development:
Lower (relative) intensity, cyclical, efforts should be used when trying to develop a more powerful athlete’s aerobic base to ensure they are getting the correct training stimulus (eccentric cardiac hypertrophy, mitochondrial density, angiogenesis etc). If they were to follow the same progression scheme/ prescription as a moderate → enduring athlete it would not only yield lackluster results, but may also worsen their aerobic development in some circumstances.
Conversely enduring athletes often need high(er) relative intensities to further develop their aerobic systems and get lackluster results with the typical 85% (moderate approach). Also note that there are more options both in movement selection and training methods when dealing with aerobic base development in enduring athletes (ie- tempos, fartleks, progression runs etc with less strong of a focus on low tension, and cyclical, movements).
I’ve covered this topic in depth HERE. But, as a general statement enduring athletes often need to further development their base strength level (regardless of how “good” or “bad” their CP-recovery system/ muscular endurance are. Ie- if you can’t lift the weight you can’t play the game). On the other hand stronger, more powerful, athletes often reach a point of diminishing return where additional strength gains do not yield further gains in performance. In this scenario CP-recovery/ muscular endurance must be prioritized (gross overgeneralization).
Note- There are also correlations with absolute strength/ speed development (ie- is an athlete stronger than fast or faster than strong), but I will simply defer you to an extensive article on that topic that I wrote for the performance menu journal. Which you can find HERE.