So if there are physiological differences between racial groups and different gene expression then why train the same way regardless of race.
*black athletes have 32% greater muscle stiffness than white athletes
*black male athletes have 26% more muscle viscosity than white male athletes. Hmmm…black females have 45% greater muscle viscosity than white female athletes(Farley & Gonzalez 1996). Wonder if this explains why more white females participate in 100m finals compared to males?
*West African sprinters have lower shank inertia in which would allow them to spend less energy on moving their limbs (Rahmani 2004).
*Senegalese sprinters were less strong and less powerful at high speeds in a squat movement compared to Italian sprinters of the same 100-m times although muscle abilities involved in slow maximal contractions were similar(Rahmani 2004).
Can whites be trained to increase muscle stiffness by 32% or is it pointless and better focus should be taken in increasing strength in high velocity movements? Does the sprint community have any ideas on what might be appropriate exercise (high velocity or not) for improving sprint performance post 50m? Maybe the answer is just one thought away.
Or shall we just stop training anyone who is not of West-African descent?
Jeremy, I think you may have just turned this from a circular debate (that nobody will win – despite there being very strong evidence on one side of the argument) into a constructive dialogue.
I can’t speak to the details of the studies you cite, but if their findings are sound, it would indicate that sprint training may need adaptation based on the physiological characteristics of the athletes in question.
If not by race, then at least by physiology.
This actually gets kind of exciting to think about. IE – From a Biomechanics standpoint – should we train a sprinter with a longer femur and shorter tibia differently than one with the ideal shorter femur and longer tibia. Or tall sprinters vs. shorter sprinters, and so on.
One thing is certain to me – all of this needs more research – The “dependent variable” side of the equation of the role race plays in sprinting clearly indicates that athletes of West African descent are not only more likely to succeed, but in fact DO succeed more in sprinting.
The question of the independent variables – Physiology, training, motivation, culture, geography, etc – are much less clear – but we can safely say that all play some role – with the evidence presented here indicating (at least to this reader) that Physiology is the most strongly linked of the independent variables.
It would be very interesting to see a longitudinal study specifically on this topic, and one with a significant sample size (so many of these medical studies mentioned have very low sample sizes).
My profession is actually full time statistical research (albeit for organizations and not studies of physiology) – but in our world, margin of error is a critical factor in evaluating the efficacy of findings – and I often remind clients and colleagues that correlation (i.e. – more blacks running faster times) does not necessarily equal causation (blacks are faster than whites).
One famous example of the danger in making these logic leaps is that ice cream sales are very strongly positively correlated to urban crime rates (r squared greater than .6 if my memory of graduate psych serves me right). This does not mean that ice cream consumption causes criminal activity, but rather that the two are related and other variables (season, more contact with others on the streets, no school, etc) are the actual causal factors.
Correlation and Causation often go hand in hand, but longitudinal research, and testing multiple groups in multiple scenarios is necessary to prove causation.
To do a proper longitudinal study of this sort, one would have to randomly and representatively select groups from multiple races – at least caucasian and west african descent – with adequate sample size (with randomly selected groups of 250+ we can make conclusions that have less than 4% margin of error and with 500 each, less than 2% margin of error).
You’d want to “randomly” select a group of each race at an early age (say age 16) who perform in a certain range of performance in the 100m (say anything under 11.20), and then monitor (and in a best scenario adjust) the training routines of groups – so that you can normalize their performances and improvements.
Follow this group for 10-12 years and see what is found. You’d probably actually have to recruit 5000 of each to join, because attrition will see only 50% of these running in collegiate athletics and probably only 1-2% of those competing post collegiately.
Would be a massive undertaking, but would actually result in definitively answering the question.
Back to Jeremy’s point – it is clear to me that as coaches, we must at the very least consider differentiating training programs based upon athlete’s physiology.
