Training Weight vs Race Weight

Alan Couzens, MS (Sports Science)

"All we need is just a little patience" - Guns and Roses

As the Summer season is on the horizon, shirts are coming off, 5K’s are being entered and it seems that everyone is feeling ‘a few pounds heavy’. I figured it timely to pen a short piece with some thoughts on the significance of identifying and sticking to your ‘training weight’.

For many, ‘training weight’ is synonomous with ‘out of shape’ weight, i.e. I’m not yet at ‘race weight’ but this diminishes the importance of identifying and holding a good training weight.

There are a couple of studies that come to mind that back up the importance of not being in too much of a hurry to get to race weight. A 1980 study that tracked the British Olympic Road Cycling team over the season found a significant difference in the weight ‘swing’ (high to low) for those who were selected for the team vs those who weren’t. The selected athletes held an ‘off-season’ training weight of ~7% greater than race weight for the first 3 months of the season. This was significantly greater than the non-selected athletes.

A more recent study helps to explain why this difference may have been important. A 2005 study on “The effect of dominant somatotype on aerobic capacity trainability” found a significantly blunted training response in ectomorphs (the skinny group) vs all other groups. The meso-ecto group displayed almost double the improvement in VO2max of the pure ectomorphs over the same period of training. In fact, even the endomorphic group (average 20.6% body fat) exhibited a significantly better training response than the ectomorphs.  I’ve found a similar effect in my own coaching experience…

I track the relationship of fitness improvement versus training load in the form of an ‘F coefficient’ for the athletes that I coach. Without exception, the athletes with the highest F number to date have been of the mesomorphic persuasion (BMI>22). The athlete with the highest F number has a BMI of 24.1! Or, looked at longitudinally, my own highest F coefficient has occurred when my bodyweight was in the range of 176-178lbs (BMI = 21.5-21.7), while my best race performances to date have occurred below a BMI of 20.75.

My larger point is that while there are certainly races in which being light/skinny is desirable (specifically those with an abundance of heat and/or hills), when it comes to getting as generally fit as possible by both handling the most load and getting the most from that load in the early season, it’s important to maintain a little ‘reserve’.

Train Smart,

AC

Time for a lactate test?

Alan Couzens, MS (Sports Science)

As we move out of early season base training into the athlete’s ‘specific prep’ period, I will often suggest that athletes get a lab test with blood lactate analysis completed. There are a couple of significant benefits to this type of test that make the 150 bucks or so that they typically cost worth it:
• Establishing individual training zones
• Determining ‘weak points’ that we can use to direct future training.

1. Establishing individual training zones.
The notion of using ‘zones’ to direct training has become a bit of a contentious topic of late, with some coaches asserting that the delineation of metabolic processes into zones is arbitrary and an over-simplification of the way our energy systems work together. While I would agree that the ‘90s saw us getting to a point where the creation of zones got a bit out of hand (at one point the Australian Institute of Sport used 9 of them), there are a few critical physiological points that need to be determined in order for the training to be at all effective.

The most significant of these is what we call the Aerobic Threshold. If you google this term you’ll see even more disagreement and confusion – Does this point consistently show up on a lactate test? Does such a physiological entity even exist, blah blah blah. Discounting the definitional problems, the practical reality is that when conducting a lactate test from a sufficiently low level of intensity, lactate will remain at a relatively low plateau through increasing work stages until a certain point is reached when it begins to rise. We deem this point the ‘Aerobic Threshold’.

The Aerobic Threshold has considerable practical significance as it represents the lowest intensity level at which aerobic energy supply is challenged. One might suggest that since aerobic energy demand is not challenged below this point that any training done significantly below this level will not disrupt homeostasis enough to elicit a training effect and is essentially a waste of training time. This has indeed been confirmed in studies that have looked at athletes who conducted very high volumes of training at low intensities (less than AeT). For example, 5.5hrs of ski touring a day for 8 weeks resulted in ZERO change in aerobic capacity of the muscle (Schantz et al. 1983). Therefore, knowing this point so that training can be directed at or above it is knowledge worth paying for!

Similarly, knowing the high end of this range, i.e. the point at which blood lactate and the associated exercise limiting by-products begin to ‘over-ride’ the system is very useful information in training and racing as it sets an upper cap of the intensity level at which you can accumulate a significant amount of work, work that is limited by the energy of the athlete rather than acidosis.

2. Establishing weak points to direct the training.

This is the fun stuff!

I’ve talked before about the importance of directing the athletes training towards their physiological ‘weak points’ with respect to their event. My preferred way of determining these areas that are ripe for improvement is via the power-duration or ‘fatigue curve’. By looking at how power or pace drops off as duration increases, we are able to make some conclusions of just how strong the athlete’s fatigue resistance or endurance is when compared to their max power numbers.

The downside of this ‘critical power’ testing is that in order to make conclusions about the far end of the curve, i.e. the endurance of the athlete’s energy stores, these energy stores must be tested (i.e. depleted). There are only so many times in a season that I want an athlete to put out a best effort test of 2.5hrs or more in duration. Therefore, getting enough data points to make any firm conclusion on the far end of the curve can be practically problematic.

Fortunately, the lactate curve offers us a good proxy for the fatigue curve without the extended recovery that comes with assessing the far end of the curve.

In the same way that the fatigue curve can be altered depending on the training undertaken, the shape of the lactate curve is also quite malleable and can literally be molded into a desired shape depending of the training intensity that is emphasized. A good example of this is shown below.



The above charts show 3 of my own lactate tests from different times in the year (times with different training emphases) and how the middle of the curve 'drops' as training is shifted.

The ‘early season’ line is from March of this year, after 4 months emphasizing base training (at or below 210W – the top of my current ‘steady’ training zone. The ‘mid season’ line is from August of last year following a period of mixed intensity training culminating with a training camp. You can see a distinct drop in net lactate production in the middle of the curve (210-290W) following this training The ‘late season’ curve is from October of last year following a block of Ironman specific training. This training yielded the highest AeT point of the year at 240W before a significant rise in lactate occurred. Unsurprisingly, I was doing a lot of volume at race pace (200-240W) during that block.

If we re-frame these charts in the same way that we look at fatigue curves, we’re also able to define these charts in terms of a ‘drop off’ rate, i.e. as lactate halves, what is the % drop off in power? This is a very similar question to what we ask when assessing the fatigue curve, i.e. as duration doubles, how much does power drop off, BUT it has the distinct advantage of not having to go out and ride successively greater durations at a maximal effort! So what ‘drop off rates do we see for the charts above?’



The drop off rates for the respective curves are 21% (early season), 17% (mid season) and 15% (late season)

How does this relate to the standard fatigue curve? Based on what I’ve seen to date in comparing field & lab data of athletes, the % drop off in the lactate curve is ~2x the % drop off in the fatigue curve, i.e. a 20% lactate curve will equate to an ~10% fatigue curve in the field. I’ve said in the past that an elite IM fatigue curve is in the range of 5-6%, placing the lactate curve at 10-12%. In practice, this may look something like 360W/8mmol 330W/4mmol 300W/2mmol 270W/1mmol, i.e approx 10% less power with each 50% decrease in lactate. This would be a typical pattern for a larger elite long course triathlete.

On the flipside, an endurance athlete who requires great anaerobic capacity, e.g. a road cyclist who specializes in the sprint will require a significantly different shaped curve with a much greater ability to generate lactate at high power levels, a curve closer to 20% (La max ~16mmol/L) In this case, the amount of aerobic vs anaerobic training that we want to undertake becomes a real consideration!

This relationship between lab and field is not perfect (and is the reason that as the season goes on I move more and more to field testing centered around the demands of the race) but in terms of getting a quick, relatively painless big picture assessment of where you are with respect to the specific fitness demands of your event, lactate testing can be a very useful tool.

Train smart.

AC