With Cathy's permission, I have converted the stats from this paper into graphic form.

Read on.

First, Cathy reported the numbers and % split of UK males and females playing selected sports. Male participation is higher than female participation.
Then, Cathy used population estimates to predict the numbers of male and female athletes who would be eligible, under a selfID model, for the opposite sex category. Cathy calculated these trans athletes as % of opposite sex category.
I have calculated the trans athletes as a frequency in the opposite sex category.

Here is the data for transwomen in female sports.
Here is the data for transmen in male sports.
Having calculated the disproportionate number of males compared with females in selected sports and the disproportionate number of transwomen compared with transmen, Cathy argues that selfID would disproportionately affect female sports.
I have mapped the asymmetric effect on male and female sports here.

SelfID would have 5 x greater effect on female tennis than on male tennis. It would have 100 X greater effect on female football than on male football.
@DavidTriesman
Cathy argues that, because of this large effect on female sport compared with a small effect on male sport:

"Eligibility criteria based on gender identity, rather than biological sex in these sports do not therefore appear justified, balanced, or even possibly legal in Britain."
So I'm going to go a bit off-piste now.

Cathy calculated the numerical impact on athlete numbers based on population data. There was, because it's tricky to work out, no adjustment for relative advantage/disadvantage in the opposite sex category.
That is, transwomen, simply by dint of numbers, may reach a frequency of 1 in 40 athletes in the female category, across that entire female cohort.

I think the effect at the elite female level will higher.
We have good performance data for athletics, so let's use that example.

Cathy reports, in athletics, 137400 male and 73500 female athletes. If 1.34% of males selfID into the female category, that's 1841 males from the male cohort>female cohort.
If we represent the male and female cohorts as blocks of 5000 athletes, and assume no performance difference between males and females, we expect around 130 males to selfID into the top 5000 female cohort. This delivers the 1 in 40 frequency calculated earlier.
However, we know that elite males have a 10% advantage in running. Stretching the male range to account for this means the top 5000 female cohort might contain 260 males. This frequency is now 1 in 20.
A big assumption here is that transitioning males are equally likely across all levels of sport. I'm also open to other methods of adjusting population numbers for performance advantage. This was a quick analysis on my part.

More from Emma Hilton

@Hogshead3Au @BARBARABULL11 @boysvswomen @cbrennansports @Martina @devarona64 OK.

Fitness data from over 85k AUS children aged 9–17 yrs showed that, compared with 9 yr females, 9 yr males were 9.8% faster in sprints, 16.6% faster over 1 mile, could jump 9.5% further, could complete 33% more push-ups in 30 s and had 13.8% stronger grip.

@BARBARABULL11 @boysvswomen @cbrennansports @Martina @devarona64 Here is my full description of that data.

Example:

1.6km timed run (CV endurance)

The *best* 17 yr old girls are matched by *average* 17 yr old boys, and beaten, by some measure, by the best 9 yr old


@BARBARABULL11 @boysvswomen @cbrennansports @Martina @devarona64 Male advantage of a similar magnitude was detected in a study of Greek children, where, compared with 6-year-old females, 6-year-old males completed 16.6% more shuttle runs in a given time and could jump 9.7% further from a standing

@BARBARABULL11 @boysvswomen @cbrennansports @Martina @devarona64 In terms of aerobic capacity, 6- to 7-year-old males have been shown to have a higher absolute and relative (to body mass) VO2max than 6- to 7-year-old

@BARBARABULL11 @boysvswomen @cbrennansports @Martina @devarona64 Pre-puberty performance differences are not negligible, and could be mediated, to some extent, by genetic factors and/or activation of the hypothalamic–pituitary–gonadal axis during the neonatal period, sometimes referred to as “minipuberty”.

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The chorus of this song uses the shlokas taken from Sundarkand of Ramayana.

It is a series of Sanskrit shlokas recited by Jambavant to Hanuman to remind Him of his true potential.

1. धीवर प्रसार शौर्य भरा: The brave persevering one, your bravery is taking you forward.


2. उतसारा स्थिरा घम्भीरा: The one who is leaping higher and higher, who is firm and stable and seriously determined.

3. ुग्रामा असामा शौर्या भावा: He is strong, and without an equal in the ability/mentality to fight

4. रौद्रमा नवा भीतिर्मा: His anger will cause new fears in his foes.

5.विजिटरीपुरु धीरधारा, कलोथरा शिखरा कठोरा: This is a complex expression seen only in Indic language poetry. The poet is stating that Shivudu is experiencing the intensity of climbing a tough peak, and likening

it to the feeling in a hard battle, when you see your enemy defeated, and blood flowing like a rivulet. This is classical Veera rasa.

6.कुलकु थारथिलीथा गम्भीरा, जाया विराट वीरा: His rough body itself is like a sharp weapon (because he is determined to win). Hail this complete

hero of the world.

7.विलयगागनथाला भिकारा, गरज्जद्धरा गारा: The hero is destructive in the air/sky as well (because he can leap at an enemy from a great height). He can defeat the enemy (simply) with his fearsome roar of war.