INTRO
This story began at the end of April 2024, when I completed the London Marathon, clocking 2:48:31 in chip time and 2:52:13 in gun time.
The training process was extremely challenging and grueling: balancing motherhood, sleep deprivation, extra weight, and hip injuries. It wasn't my fastest race, but it marked my first marathon since giving birth six months earlier. And by that result, I had set the new National Armenian Record, breaking the one that had lasted for over 20 years. I hope you feel how significant this result is to me. Despite the modest timing, it was so essential for me, my nation, and my boys.
Four months after the race, I'm writing this article. I’m still surprised by the time and energy needed to have my legitimate result from the Platinum Labeled TCS London Marathon reflected on my World Athletics profile.
It's crucial for the recognition of this result as the National Record and to add some scoring points to my account for qualification for upcoming races. It's now August 13th, and it's still not there.
I also feel that through the countless emails exchanged with the London Marathon Team and the World Athletics Team, I uncovered a more significant issue with the WA scoring system, which raises concerns about the reliability of the World Ranking and Time Standards.
The purposes of World Athletics are to:
....preserve the right of every individual to participate in Athletics as a sport, without unlawful discrimination of any kind undertaken in the spirit of friendship,
solidarity and fair play.
4.1.j World Athletics Constitution
THE PROBLEM
The World ranking is a crucial part of the reality of World Athletics. For their results in the competition, athletes get scoring points, which they can use to qualify for the main athletics competitions, such as the Olympic Games, the World Championship, and the Euro Championship. Last years, the World Athletics pushed World Ranking and scoring as one of the primary sources of qualification criteria. They significantly raised the time of the standards to have enough spots for qualification by ranking tables.
In the official documentation from the World Athletics regarding the Ranking system, there is a statement that to have scoring points, the race must be in the World Athletics Global Calendar. And nothing more, no words about exceptions, no words that your result from the official race can be eliminated.
“From 1 January 2023, World Athletics will only process results (for all its statistical purposes including World Rankings) which have been achieved in a competition listed in the World Athletics global calendar. ”
But in practice, at least a third of Platinum races submitted the restricted list of results to the World Athletics. For the 2023 year, it’s (7 of 15): Seoul Marathon, Xiamen Marathon, London Marathon, Sydney Marathon, New York City Marathon, Shanghai Marathon, and Budapest Half Marathon.
The criteria of selection are unclear. It can be only elite field results, only national championship results, or for New York City Marathon 2023 women’s results ended by Shalane Flanagan's result (118th place, 3:04:55 (WA profile), 3:04:52 (NY City Marathon result))
You can check this here:
I hope you also feel that there’s something wrong here.
Lack of information
There is no publicly available description for athletes on which races they can get scoring points and what the hidden criteria of selection are.
The documentation is available only in English. Even regarding the World Athletics Constitution, the official languages are English and French.
"The Constitution, Rules and Regulations, minutes reports and other communication from World Athletics shall be in English and French" (77.1 and 77.2)
Violation of the Right to participate
If you are not in the elite field, you are not guaranteed to get your time and scoring points in the system at some races. The Official Regulation of Labeled Races covers only athletes' rights in the elite field. (C1.6 18.2)
At the London Marathon, the last elite women's athlete's time was 2:44:00. 15 women from the mass start were faster that time but didn’t get their scoring points.
Math statistics: incorrect predictions and unreliable results
The last one is the most important and most complicated. The data selection (results) is not random, so it can hardly be used for accurate math models, estimation, or setting standards.
If you are in the elite field, the probability that your result will be in the World Athletics database is "1." If you are not in the elite field and raced the London Marathon, it's "0." These are simple words and examples of non-random selection.
More complex explanation with math words:
The prediction will likely be inaccurate if the sample is not random. This is because non-random samples can introduce bias, violating the assumptions of many statistical models, including the Law of Large Numbers and the Central Limit Theorem. These principles rely on random sampling to ensure that sample statistics (such as the mean) converge to the true population parameters. Without random sampling, the sample may not be representative of the population, leading to incorrect predictions and unreliable results.
Key Mathematical Laws and Properties:
Law of Large Numbers: This law states that as a sample size increases, the sample mean will converge to the population mean, but it assumes that the sample is random. In a non-random sample, this convergence may not occur.
Central Limit Theorem: This theorem suggests that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the original distribution of the population. However, this holds true primarily for random samples.
Bias: Non-random samples can introduce bias, which refers to systematic errors that skew results. Bias affects the accuracy of predictions by distorting the representation of the population.
Generalizability: Predictions based on non-random samples may not generalize well to the broader population because the sample may over-represent or under-represent certain groups or characteristics.
Cover photo by George Becker: https://www.pexels.com/photo/1-1-3-374916/
Comments