Q Points
What is it exactly ?
Q Points is a mathematical formula Paper for body weight adjustment that would reduce or eliminate biases, with a transparent and reproducible formula and code, and that can be freely used by all nations.
The Nordic Weightlifting Federation formed a committee to discuss the development of the Q Points. This committee comprised : Tryggve Dunn, Ásgeir Bjarnason, Tom Goegebuer, Patrik Helgesson, Marianne Huebner, David Meltzer, Kim Eirik Tollefsen
This new scaling model has been peer-reviewed and published in the renowned scientific journal Medicine & Science in Sport & Exercise.
Authors
Co-authors of the scientific paper with peer review regarding the methods and more detailed comparisons:- Marianne Huebner, Department of Statistics and Probability and Department of Kinesiology, Michigan State University, East Lansing, Michigan, United States of America
- David Meltzer, College of Integrative Sciences and Arts, Arizona State University, Mesa, Arizona, United States of America
- Ásgeir Bjarnason, Star-Oddi Ltd, Gardabaer, Iceland
- Aris Perperoglou, School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, United Kingdom
Purpose of Q Points
Numerous countries rely on Sinclair or Robi points to identify top male/female competitors in weightlifting contests or to choose members for their national teams. Q Points can allow those countries federation to select, compare their athletes fairly.
Q Points does take into account the bodyweight of the athlete but, unlike Sinclair and Robi points, it is not based on world records or weight categories that can change frequently.
How it works ?
- The team used data from international and continental IWF Olympic qualifying championships between 2017 to 2021. They sourced this information from the IWF database. Importantly, they removed results from athletes with known doping violations and kept only one result for each athlete per year in each bodyweight category. In total, this gave them 3 559 competition results: 1 973 from men and 1 586 from women.
- The statistical method is called “quantile regression” Read Koenker R. Quantile Regression, hence the name “Q Points.” This method not only has resilience against outliers but also has demonstrated success in weightlifting data. For example, it's effective in taking into account age factors for women or performance development, peak age, and body weight differences for 'youth' and 'seniors' :
- Asystematic process was used to test a range of possible functions (fractional polynomials) to identify the best relationship of body weight and weightlifting performance in quantile regression models for the 90th percentile curves.
Athlete total * ( 463.26 / ( 416.7 - 47.87 * ( BW / 100 )^(-2) + 18.93 * ( BW / 100 )^2 ) )
Athlete total * ( 306.54 / ( 266.5 - 19.44 * ( BW / 100 )^(-2) + 18.61 * ( BW / 100 )^2 ) )
Advantages of Q Points
The need to establish a more fair comparison system in Olympic weightlifting becomes clear. Directly comparing weight lifted and bodyweight ratios doesn't provide an accurate reflection of an athlete's capabilities. Thus, the team embarked on a systematic investigation of the relationship between bodyweight and weightlifting performance.
- This is a systematic investigation ofthe appropriate relationship of bodyweight with weightlifting performance in the statistical model. This function selection procedure is a popular tool for flexible parametric modeling and it covers a wide range of functions that have not been considered before. The resulting function was then used for the quantile regression models.
- Quantile regression is an established statistical model that is robust to outliers compared to other regression-based models. The choice of the 90th percentile estimates the performance curve with respect to different body weights for elite athletes without the sensitivity to changes in world records, especially at the heaviest body weights. This method has more power than truncated regression models that only use a subset of the data.
- The code to calculate the formula from data is available, updates are possible, independently from the developers on this proposal (Documents on Open Science Framework). We recommend establishing a 4-year cycle coinciding with the Olympic Games for stability purposes and appropriate long-term comparisons. This considers the changing participation rate in this sport, which has seen an unprecedented growth in the recent years for women, and corresponding improvements in performances.
- The formula removes or reduces the bias of the Sinclair points and the Robi points.
