In all other cases the figure is an interpolation of existing Wilks figures. If your bodyweight were 96 lbs. The official Wilks co-efficients are 1. The interpolated value for Wilks Formula. We may earn a small commission on purchases made through our links.
Learn more. Published by Jason Hughes. What is the Wilks score used for? Comparison between men and women. So what IS a good wilks score then? What is the best Wilks score ever achieved? Ray Williams - Jesse Norris - Sergey Fedosienko - Now calculate your wilks score! Jason Hughes. It is logarithmic — whether we like it or not.
A logarithmic scale is based on orders of magnitude, rather than a standard linear scale, so the value represented by each equidistant mark on the scale is the value at the previous mark multiplied by a constant. This formula, however is not really a new formula as much as a combination of two older ones. Herb Glossbrenner contended that the Reshel too heavily favoured the very light weight lifters, and was outdated. He also contended that, while they were both okay formulas, the Schwartz formula favoured the lighter lifters and the Wilks favoured the heavier lifters.
So, he took the average of the two to balance them out. To use the Schwartz Formula, a person would use the table of numbers and correct just as was done in weightlifting. And it worked to a certain degree , lifters of all sizes could now be compared.
But then something very new appeared on the scene. The Malone Formula corrected for bodyweight when women competed against women and seemed to do a good job of it.
For a formula based on the goal of eliminating bias in order to compare the relative strength between two different lifters, it seems Wilks was only partially successful. Next, he averaged the Wilks score in every group and then plot them out on a radar chart. If there is no bias between groups we would expect to see the data points trace out a circle around the chart. Three models were compared using the same IPF data shown in figure 1.
In addition to noting biases, the coefficient of variation CV were also measured — a measure of how close the model predictions are to actual values, where lower is better — and the amount of variation in the data explained by the model R-Squared. The ratio method performs quite poorly and allometric models are a big improvement. Meanwhile, it appears Wilks does a good job in making accurate predictions and accounting for the vast majority of the variability. It does, however, have those gender and age biases we pointed out.
There is a significant gender bias favouring men, as well as an age bias favouring the Open class. Starting with the basics, here are some factors we know affect strength at least in terms of powerlifting results. So, if a new relative strength model is to be created, at least the first six variables need to be used right? Not necessarily. Yes, we do need at least six for a general population. For this population, additional training time is not going to lead to significant increases in strength.
They are already near their maximums. Training age matters for beginners, where an extra year of training matters a lot. For his sample population of 1st place finishers, training age is no longer a relevant factor. Additionally, it was deemed that they have accumulated as much muscle mass on their skeleton as they are going to. Their height has essentially determined their weight class. For this population, almost all the variability in results is explained by only the first three variables— age, gender, and weight.
So, Frederick tried to create a relative strength model that utilised these three variables from the ground up. Once again, setting to be the average 1st Place finish and hoping to see the blue data line track a circle around the chart. That now looked much better, and we are almost seeing a blue circle. There is almost no visible bias — within error — between all classes except for the age classes. However, some of the Masters 1 lifters decided to lift in the Open class.
Which lifters were those? The top half. If you place those Masters 1 lifters back into their class, the class as a whole rises and the Open class decreases a proportional amount. So, with biases off the to-do list Frederick then went back to check the other measures of model quality — the coefficient of variation and r-squared. This is what he found:. The Strength Index gives us the most accurate scores, as indicated by the lowest CV, as well as matching Wilks in terms of accounting for variation.
It seems like we could be looking at the next generation of relative strength models. It was said that at least six variables should be used for the general population. This will need more research and trials.
Even if a better way is found, the question will be; will Wilks be gotten rid of? We will wait and see. Your email address will not be published. Terms and Conditions - Privacy Policy. Close Home V2.
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