Equal Rights

The two strongest arguments for allowing transgender athletes to compete as the gender they identify are the argument from biological diversity and the argument from human rights. When I was outlining the latter case, I settled for merely establishing the right to self-identify existed and just assumed everyone would agree to indivisibility.

Human rights are indivisible. Whether they relate to civil, cultural, economic, political or social issues, human rights are inherent to the dignity of every human person. Consequently, all human rights have equal status, and cannot be positioned in a hierarchical order. Denial of one right invariably impedes enjoyment of other rights. Thus, the right of everyone to an adequate standard of living cannot be compromised at the expense of other rights, such as the right to health or the right to education.

Now that I’m some distance from the argument, I can better picture someone rejecting indivisibility. I mean yes, as I pointed out back then, rejecting indivisibility also rejects decades of legal precedent, but leaning entirely on the letter of the law makes for an iffy argument. I should have propped up the argument by pointing out how devaluing one right harms the ability to enjoy every other right.

Trans people routinely face challenges to their basic humanity every day. Their very existence is being contested. How much rights they should be allowed to have is considered a topic for debate. When some group of people are seen as equal in dignity and rights, the rest of the society doesn’t argue about whether they should have the same rights that everybody else takes for granted. […]

Trans people are routinely discriminated by landlords and potential employers. For example, one of Freethoughblogs bloggers is a trans woman who is forced to dress as male at work, because nobody will hire her as a woman. Cis people aren’t forced to present themselves as a gender they are uncomfortable with in order to find a job. […]

I personally have been refused access to healthcare, because several transpobic doctors felt like kicking me out of their offices. Here you can read the full story about that. I am a European Union citizen, The European Court of Human Rights has ruled that trans people have a right to obtain various medical procedures that would change their gender. Nonetheless, transphobic doctors and bureaucrats still figured out a loophole how to de facto deny me the surgery I requested.

Fortunately, Andreas Avester has my back. As one of his debut blog posts, he’s done an excellent job of pointing out all the consequences of rejecting indivisibility. It’s well worth a read, all on its own.

How Was Your Boycott?

I was planning on signal-boosting the YouTube boycott, thanks to a message by Great American Satan, until everyone else beat me to it. For an awareness campaign like this, it’s more useful to space out your messages than doing one big blast, so I deliberately held back. But what is there to do once the boycott’s done, you ask?

Well, some of you might be tempted back to YouTube. There are alternatives out there, though. For instance, Intransitive posted an animated short about the Le Mans crash of 1955. Problem: it was host on YouTube. Solution: it was also on Vimeo! Rather than blindly follow that YouTube link, do a bit of digging to see if any other site is hosting it. I’d also like to plug the Internet Archive, which hosts everything from Democracy Now! to classic cartoons.

You could also contact Google/YouTube directly. Yeah, Google’s support ranges from byzantine to bad, but did you know they post a mailing address for YouTube? Track down that pen that’s migrated to the back of your desk, fish out a blank sheet of paper from the printer tray, and send them a polite but firm message about their new terms of service.

If that all sounds like too much work, why not hit them in the pocketbook? There are multiple YouTube ad blockers available, all of which can be installed with a single click, and these tools are popular enough to keep up with Google’s countermeasures. Just be sure to uninstall it if or when Google relents! It’s what I’ll be doing, now that I can watch PyData videos again.


[HJH 2019-12-14] With the benefit of hindsight, I can see an objection to my last bit of advice. Yes, blocking ads will hurt Google’s bottom line, but it also might hurt the bottom line of YouTube creators. Aren’t I taking money out of their pockets?

For the most part, people aren’t making money off YouTube ads. Some big channels rely on Patreon to keep afloat, while others use paid sponsorships, and neither is significantly effected by a YouTube ad blocker. In both cases it’s easy to make up for any lost revenue due to your ad block.

The entities who do make genuine money off YouTube ads either have a second revenue stream you can drop money into, were already famous and don’t need the cash, or are gaming the system in some way. This last category is the one most hurt by removing ad revenue, and while that would prevent a Baby Shark it also prevents Elsagate. Ironically, this gamification is also the cause of YouTube’s draconian new Terms of Service, because the old one could not satisfy video creators, advertisers, and viewers at the same time. The new one solves the issue by allowing YouTube to crack down on creators however they see fit, should bad press float their way.

Blocking ads does not prevent quality content creators from surviving on YouTube, but it does harm those hoping to game the system and pocket a quick buck. So long as that remains true, blocking YouTube ads is perfectly moral.

Deep Penetration Tests

We now live in an age where someone can back door your back door.

Analysts believe there are currently on the order of 10 billions Internet of Things (IoT) devices out in the wild. Sometimes, these devices find their way up people’s butts: as it turns out, cheap and low-power radio-connected chips aren’t just great for home automation – they’re also changing the way we interact with sex toys. In this talk, we’ll dive into the world of teledildonics and see how connected buttplugs’ security holds up against a vaguely motivated attacker, finding and exploiting vulnerabilities at every level of the stack, ultimately allowing us to compromise these toys and the devices they connect to.

Writing about this topic is hard, and not just because penises may be involved. IoT devices pose a grave security risk for all of us, but probably not for you personally. For instance, security cameras have been used to launch attacks on websites. When was the last time you updated the firmware on your security camera, or ran a security scan of it? Probably never. Has your security camera been taken over? Maybe, as of 2017 roughly half the internet-connected cameras in the USA were part of a botnet. Has it been hacked and commanded to send your data to a third party? Almost certainly not, these security cam hacks almost all target something else. Human beings are terrible at assessing risk in general, and the combination of catastrophic consequences to some people but minimal consequences to you only amplifies our weaknesses.

There’s a very fine line between “your car can be hacked to cause a crash!” and “some cars can be hacked to cause a crash,” between “your TV is tracking your viewing habits” and “your viewing habits are available to anyone who knows where to look!” Finding the right balance between complacency and alarmism is impossible given how much we don’t know. And as computers become more intertwined with our intimate lives, whole new incentives come into play. Proportionately, more people would be willing to file a police report about someone hacking their toaster than about someone hacking their butt plug. Not many people own a smart sex toy, but those that do form a very attractive hacking target.

There’s not much we can do about this individually. Forcing people to take an extensive course in internet security just to purchase a butt plug is blaming the victim, and asking the market to solve the problem doesn’t work when market incentives caused the problem in the first place. A proper solution requires collective action as a society, via laws and incentives that help protect our privacy.

Then, and only then, can you purchase your sex toys in peace.

The Crossroads

Apparently I know the solar system very well?

I attended a lecture on Carl Sagan, hosted by the Atheist Society of Calgary, and part of the event was a trivia challenge. While I wasn’t the only person at my table offering answers, my answers seemed to be the ones most consistently endorsed by the group. Assisted by some technical issues, our team wound up with a massive lead over the second-place finisher. The organizer from ASC surprised us all by saying everyone at our table could pick up a free T-shirt. I wasn’t terribly keen on wearing their logo, but I wandered over to the merch table anyway.

Sitting among the other designs was one that stopped me cold.

[Read more…]

The Crisis of the Mediocre Man

I was browsing YouTube videos on PyMC3, as one naturally does, when I happened to stumble on this gem.

Tech has spent millions of dollars in efforts to diversify workplaces. Despite this, it seems after each spell of progress, a series of retrograde events ensue. Anti-diversity manifestos, backlash to assertive hiring, and sexual misconduct scandals crop up every few months, sucking the air from every board room. This will be a digest of research, recent events, and pointers on women in STEM.

Lorena A. Barba really knows her stuff; the entire talk is a rapid-fire accounting of claims and counterclaims, aimed to directly appeal to the male techbros who need to hear it. There was a lot of new material in there, for me at least. I thought the only well-described matriarchies came from the African continent, but it turns out the Algonquin also fit that bill. Some digging turns up a rich mix of gender roles within First Nations peoples, most notably the Iroquois and Hopi. I was also depressed to hear that the R data analysis community is better at dealing with sexual harassment than the skeptic/atheist community.

But what really grabbed my ears was the section on gender quotas. I’ve long been a fan of them on logical grounds: if we truly believe the sexes are equal, then if we see unequal representation we know discrimination is happening. By forcing equality, we greatly reduce network effects where one gender can team up against the other. Worried about an increase in mediocrity? At worst that’s a temporary thing that disappears once the disadvantaged sex gets more experience, and at best the overall quality will actually go up. The research on quotas has advanced quite a bit since that old Skepchick post. Emphasis mine.

In 1993, Sweden’s Social Democratic Party centrally adopted a gender quota and imposed it on all the local branches of that party (…). Although their primary aim was to improve the representation of women, proponents of the quota observed that the reform had an impact on the competence of men. Inger Segelström (the chair of Social Democratic Women in Sweden (S-Kvinnor), 1995–2003) made this point succinctly in a personal communication:

At the time, our party’s quota policy of mandatory alternation of male and female names on all party lists became informally known as the crisis of the mediocre man

We study the selection of municipal politicians in Sweden with regard to their competence, both theoretically and empirically. Moreover, we exploit the Social Democratic quota as a shock to municipal politics and ask how it altered the competence of that party’s elected politicians, men as well as women, and leaders as well as followers.

Besley, Timothy. “Gender Quotas and the Crisis of the Mediocre Man: Theory and Evidence from Sweden.” THE AMERICAN ECONOMIC REVIEW 107, no. 8 (2017): 39.

We can explain this with the benefit of hindsight: if men can rely on the “old boy’s network” to keep them in power, they can afford to slack off. If other sexes cannot, they have to fight to earn their place. These are all social effects, though; if no sex holds a monopoly on operational competence in reality, the net result is a handful of brilliant women among a sea of iffy men. Gender quotas severely limit the social effects, effectively kicking out the mediocre men to make way for average women, and thus increase the average competence.

As tidy as that picture is, it’s wrong in one crucial detail. Emphasis again mine.

These estimates show that the overall effect mainly reflects an improvement in the selection of men. The coefficient in column 4 means that a 10-percentage-point larger quota bite (just below the cross-sectional average for all municipalities) raised the proportion of competent men by 4.4 percentage points. Given an average of 50 percent competent politicians in the average municipality (by definition, from the normalization), this corresponds to a 9 percent increase in the share of competent men.

For women, we obtain a negative coefficient in the regression specification without municipality trends, but a positive coefficient with trends. In neither case, however, is the estimate significantly different from zero, suggesting that the quota neither raised nor cut the share of competent women. This is interesting in view of the meritocratic critique of gender quotas, namely that raising the share of women through a quota must necessarily come at the price of lower competence among women.

Increasing the number of women does not also increase the number of incompetent women. When you introduce a quota, apparently, everyone works harder to justify being there. The only people truly hurt by gender quotas are mediocre men who rely on the Peter Principle.

The like ratio for said talk. 47 likes, 55 dislikes, FYI.Alas, if that YouTube like ratio is any indication, there’s a lot of them out there.

Texas Sharpshooter

Quick Note

I’m trying something new! This blog post is available in two places, both here and on a Jupyter notebook. Over there, you can tweak and execute my source code, using it as a sandbox for your own explorations. Over here, it’s just a boring ol’ webpage without any fancy features, albeit one that’s easier to read on the go. Choose your own adventure!

Oh also, CONTENT WARNING: I’ll briefly be discussing sexual assault statistics from the USA at the start, in an abstract sense.

Introduction

[5:08] Now this might seem pedantic to those not interested in athletics, but in the athletic world one percent is absolutely massive. Just take for example the 2016 Olympics. The difference between first and second place in the men’s 100-meter sprint was 0.8%.

I’ve covered this argument from Rationality Rules before, but time has made me realise my original presentation had a problem.

His name is Steven Pinker.

(Click here to show the code)

Forcibe Rape, USA, Police ReportsHe looks at that graph, and sees a decline in violence. I look at that chart, and see an increase in violence. How can two people look at the same data, and come to contradictory conclusions?

Simple, we’ve got at least two separate mental models.

(Click here to show the code)
Finding the maximal likelihood, please wait ... done.
Running an MCMC sampler, please wait ... done.
Charting the results, please wait ...

The same chart as before, with three models overlaid.

All Pinker cares about is short-term trends here, as he’s focused on “The Great Decline” in crime since the 1990’s. His mental model looks at the general trend over the last two decades of data, and discards the rest of the datapoints. It’s the model I’ve put in red.

I used two seperate models in my blog post. The first is quite crude: is the last datapoint better than the first? This model is quite intuitive, as it amounts to “leave the place in better shape than when you arrived,” and it’s dead easy to calculate. It discards all but two datapoints, though, which is worse than Pinker’s model. I’ve put this one in green.

The best model, in my opinion, wouldn’t discard any datapoints. It would also incorporate as much uncertainty as possible about the system. Unsurprisingly, given my blogging history, I consider Bayesian statistics to be the best way to represent uncertainty. A linear model is the best choice for general trends, so I went with a three-parameter likelihood and prior:

p( x,y | m,b,\log(\sigma) ) = e^{ -\frac 1 2 \big(\frac{y-k}{\sigma}\big)^2 }(\sigma \sqrt{2\pi})^{-1}, ~ k = x \cdot m + b p( m,b,\log(\sigma) ) = \frac 1 \sigma (1 + m^2)^{-\frac 3 2}

This third model encompasses all possible trendlines you could draw on the graph, but it doesn’t hold them all to be equally likely. Since time is short, I used an MCMC sampler to randomly sample the resulting probability distribution, and charted that sample in blue. As you can imagine this requires a lot more calculation than the second model, but I can’t think of anything superior.

Which model is best depends on the context. If you were arguing just over the rate of police-reported sexual assault from 1992 to 2012, Pinker’s model would be pretty good if incomplete. However, his whole schtick is that long-term trends show a decrease in violence, and when it comes to sexual violence in particular he’s the only one who dares to talk about this. He’s not being self-consistent, which is easier to see when you make your implicit mental models explicit.

Pointing at Variance Isn’t Enough

Let’s return to Rationality Rules’ latest transphobic video. In the citations, he explicitly references the men’s 100m sprint at the 2016 Olympics. That’s a terribly narrow window to view athletic performance through, so I tracked down the racetimes of all eight finalists on the IAAF’s website and tossed them into a spreadsheet.

 

(Click here to show the code)
Rio de Janeiro Olympic Games, finals
Athlete  Result  Delta
     bolt    9.81   0.00
   gatlin    9.89   0.08
de grasse    9.91   0.10
    blake    9.93   0.12
  simbine    9.94   0.13
    meite    9.96   0.15
   vicaut   10.04   0.23
  bromell   10.06   0.25

Here, we see exactly what Rationality Rules sees: Usain Bolt, the current world record holder, earned himself another Olympic gold medal in the 100m sprint. First and third place are separated by a tenth of a second, and the slowest person in the finals was a mere quarter of a second behind the fastest. That’s a small fraction of the time it takes to complete the event.

(Click here to show the code)
Race times in 2016, sorted by fastest time
Name             Min time         Mean             Median           Personal max-min
-----------------------------------------------------------------------------------------------------
gatlin                        9.8         9.95         9.94         0.39
bolt                         9.81         9.98        10.01         0.34
bromell                      9.84        10.00        10.01         0.30
vicaut                       9.86        10.01        10.02         0.33
simbine                      9.89        10.10        10.08         0.43
de grasse                    9.91        10.07        10.04         0.41
blake                        9.93        10.04         9.98         0.33
meite                        9.95        10.10        10.05         0.44

Here, we see what I see: the person who won Olympic gold that year didn’t have the fastest time. That honour goes to Justin Gatlin, who squeaked ahead of Bolt by a hundredth of a second.

Come to think of it, isn’t the fastest time a poor judge of how good an athlete is? Picture one sprinter with a faster average time than another, and a second with a faster minimum time. The first athlete will win more races than the second. By that metric, Gatlin’s lead grows to three hundredths of a second.

The mean, alas, is easily tugged around by outliers. If someone had an exceptionally good or bad race, they could easily shift their overall mean a decent ways from where the mean of every other result lies. The median is a lot more resistant to the extremes, and thus a fairer measure of overall performance. By that metric, Bolt is now tied for third with Trayvon Bromell.

We could also judge how good an athlete is by how consistent they were in the given calendar year. By this metric, Bolt falls into fourth place behind Bromell, Jimmy Vicaut, and Yohan Blake. Even if you don’t agree to this metric, notice how everyone’s race times in 2016 varies between three and four tenths of a second. It’s hard to argue that a performance edge of a tenth of a second matters when even at the elite level sprinters’ times will vary by significantly more.

But let’s put on our Steven Pinker glasses. We don’t judge races by medians, we go by the fastest time. We don’t award records for the lowest average or most consistent performance, we go by the fastest time. Yes, Bolt didn’t have the fastest 100m time in 2016, but now we’re down to hundredths of a second; if anything, we’ve dug up more evidence that itty-bitty performance differences matter. If I’d just left things at that last paragraph, which is about as far as I progressed the argument last time, a Steven Pinker would likely have walked away even more convinced that Rationality Rules got it right.

I don’t have to leave things there, though. This time around, I’ll make my mental model as explicit as possible. Hopefully by fully arguing the case, instead of dumping out data and hoping you and I share the same mental model, I could manage to sway even a diehard skeptic. To further seal the deal, the Jupyter notebook will allow you to audit my thinking or even create your own model. No need to take my word.

I’m laying everything out in clear sight. I hope you’ll give it all a look before dismissing me.

Model Behaviour

Our choice of model will be guided by the assumptions we make about how athletes perform in the 100 metre sprint. If we’re going to do this properly, we have to lay out those assumptions as clearly as possible.

  1. The Best Athlete Is the One Who Wins the Most. Our first problem is to decide what we mean by “best,” when it comes to the 100 metre sprint. Rather than use any metric like the lowest possible time or the best overall performance, I’m going to settle on something I think we’ll both agree to: the athlete who wins the most races is the best. We’ll be pitting our models against each other as many times as possible via virtual races, and see who comes out on top.
  2. Pobody’s Nerfect. There is always going to be a spanner in the works. Maybe one athlete has a touch of the flu, maybe another is going through a bad breakup, maybe a third got a rock in their shoe. Even if we can control for all that, human beings are complex machines with many moving parts. Our performance will vary. This means we can’t use point estimates for our model, like the minimum or median race time, and instead must use a continuous statistical distribution.This assumption might seem like begging the question, as variance is central to my counter-argument, but note that I’m only asserting there’s some variance. I’m not saying how much variance there is. It could easily be so small as to be inconsequential, in the process creating strong evidence that Rationality Rules was right.
  3. Physics Always Wins. No human being can run at the speed of light. For that matter, nobody is going to break the sound barrier during the 100 metre sprint. This assumption places a hard constraint on our model, that there is a minimum time anyone could run the 100m. It rules out a number of potential candidates, like the Gaussian distribution, which allow negative times.
  4. It’s Easier To Move Slow Than To Move Fast. This is kind of related to the last one, but it’s worth stating explicitly. Kinetic energy is proportional to the square of the velocity, so building up speed requires dumping an ever-increasing amount of energy into the system. Thus our model should have a bias towards slower times, giving it a lopsided look.

Based on all the above, I propose the Gamma distribution would make a suitable model.

\Gamma(x | \alpha, \beta ) = \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha-1} e^{-\beta x}

(Be careful not to confuse the distribution with the function. I may need the Gamma function to calculate the Gamma distribution, but the Gamma function isn’t a valid probability distribution.)

(Click here to show the code)
Three versions of the Gamma Distribution

Three versions of the Gamma Distribution.

It’s a remarkably flexible distribution, capable of duplicating both the Exponential and Gaussian distributions. That’s handy, as if one of our above assumptions is wrong the fitting process could still come up with a good fit. Note that the Gamma distribution has a finite bound at zero, which is equivalent to stating that negative values are impossible. The variance can be expanded or contracted arbitrarily, so it isn’t implicitly supporting my arguments. Best of all, we’re not restricted to anchor the distribution at zero. With a little tweak …

\Gamma(x | \alpha, \beta, b ) = \frac{\beta^\alpha}{\Gamma(\alpha)} \hat x^{\alpha-1} e^{-\beta \hat x}, ~ \hat x = x - b

… we can shift that zero mark wherever we wish. The parameter sets the minimum value our model predicts, while α controls the underlying shape and β controls the scale or rate associated with this distribution. α < 1 nets you the Exponential, and large values of α lead to something very Gaussian. Conveniently for me, SciPy already supports this three-parameter tweak.

My intuition is that the Gamma distribution on the left, with α > 1 but not too big, is the best model for athlete performance. That implies an athlete’s performance will hover around a specific value, and while they’re capable of faster times those are more difficult to pull off. The Exponential distribution, with α < 1, is most favourable to Rationality Rules, as it asserts the race time we’re most likely to observe is also the fastest time an athlete can do. We’ll never actually see that time, but what we observe will cluster around that minimum.

Running the Numbers

Enough chatter, let’s fit some models! For this one, my prior will be

p( \alpha, \beta, b ) = \begin{cases} 0, & \alpha \le 0 \\ 0, & \beta \le 0 \\ 0, & b \le 0 \\ 1, & \text{otherwise} \end{cases},

which is pretty light and only exists to filter out garbage values.

(Click here to show the code)
Generating some models for 2016 race times (a few seconds each) ...
# name          	α               	β               	b               
gatlin          	0.288 (+0.112 -0.075)	1.973 (+0.765 -0.511)	9.798 (+0.002 -0.016)
bolt            	0.310 (+0.107 -0.083)	1.723 (+0.596 -0.459)	9.802 (+0.008 -0.025)
bromell         	0.339 (+0.115 -0.082)	1.677 (+0.570 -0.404)	9.836 (+0.004 -0.032)
vicaut          	0.332 (+0.066 -0.084)	1.576 (+0.315 -0.400)	9.856 (+0.004 -0.013)
simbine         	0.401 (+0.077 -0.068)	1.327 (+0.256 -0.226)	9.887 (+0.003 -0.018)
de grasse       	0.357 (+0.073 -0.082)	1.340 (+0.274 -0.307)	9.907 (+0.003 -0.022)
blake           	0.289 (+0.103 -0.085)	1.223 (+0.437 -0.361)	9.929 (+0.001 -0.008)
meite           	0.328 (+0.089 -0.067)	1.090 (+0.295 -0.222)	9.949 (+0.000 -0.003)
... done.

This text can’t change based on the results of the code, so this is only a guess, but I’m pretty sure you’re seeing a lot of α values less than one. That really had me worried when I first ran this model, as I was already conceding ground to Rationality Rules by focusing only on the 100 metre sprint, where even I think that physiology plays a significant role. I did a few trial runs with a prior that forced α > 1, but the resulting models would hug that threshold as tightly as possible. Comparing likelihoods, the α < 1 versions were always more likely than the α > 1 ones.

The fitting process was telling me my intuition was wrong, and the best model here is the one that most favours Rationality Rules. Look at the b values, too. There’s no way I could have sorted the models based on that parameter before I fit them; instead, I sorted them by each athlete’s minimum time. Sure enough, the model is hugging the fastest time each athlete posted that year, rather than a hypothetical minimum time they could achieve.

(Click here to show the code)

100 models of blake's 2016 race times.

Charting some of the models in the posterior drives this home. I’ve looked at a few by tweaking the “player” variable, as well as the output of multiple sample runs, and they all are dominated by Exponential distributions.

Dang, we’ve tilted the playing field quite a ways in Rationality Rules’ favour.

Still, let’s simulate some races. For each race, I’ll pick a random trio of parameters from each model’s posterior and feet that into SciPy’s random number routines to generate a race time for each sprinter. Fastest time wins, and we tally up those wins to estimate the odds of any one sprinter coming in first.

Before running those simulations, though, we should make some predictions. Rationality Rules’ view is that (emphasis mine) …

[9:18] You see, I absolutely understand why we have and still do categorize sports based upon sex, as it’s simply the case that the vast majority of males have significant athletic advantages over females, but strictly speaking it’s not due to their sex. It’s due to factors that heavily correlate with their sex, such as height, width, heart size, lung size, bone density, muscle mass, muscle fiber type, hemoglobin, and so on. Or, in other words, sports are not segregated due to chromosomes, they’re segregated due to morphology.

[16:48] Which is to say that the attributes granted from male puberty that play a vital role in explosive events – such as height, width, limb length, and fast twitch muscle fibers – have not been shown to be sufficiently mitigated by HRT in trans women.

[19:07] In some events – such as long-distance running, in which hemoglobin and slow-twitch muscle fibers are vital – I think there’s a strong argument to say no, [transgender women who transitioned after puberty] don’t have an unfair advantage, as the primary attributes are sufficiently mitigated. But in most events, and especially those in which height, width, hip size, limb length, muscle mass, and muscle fiber type are the primary attributes – such as weightlifting, sprinting, hammer throw, javelin, netball, boxing, karate, basketball, rugby, judo, rowing, hockey, and many more – my answer is yes, most do have an unfair advantage.

… human morphology due to puberty is the primary determinant of race performance. Since our bodies change little after puberty, that implies your race performance should be both constant and consistent. The most extreme version of this argument states that the fastest person should win 100% of the time. I doubt Rationality Rules holds that view, but I am pretty confident he’d place the odds of the fastest person winning quite high.

The opposite view is that the winner is due to chance. Since there are eight athletes competing here, each would have a 12.5% chance of winning. I certainly don’t hold that view, but I do argue that chance plays a significant role in who wins. I thus want the odds of the fastest person winning to be somewhere above 12.8%, but not too much higher.

(Click here to show the code)
Simulating 15000 races, please wait ... done.

Number of wins during simulation
--------------------------------
gatlin                       5174 (34.49%)
bolt                         4611 (30.74%)
bromell                      2286 (15.24%)
vicaut                       1491 (9.94%)
simbine                       530 (3.53%)
de grasse                     513 (3.42%)
blake                         278 (1.85%)
meite                         117 (0.78%)

Whew! The fastest 100 metre sprinter of 2016 only had a one in three chance of winning Olympic gold. Of the eight athletes, three had odds better than chance of winning. Even with the field tilted in favor of Rationality Rules, this strongly hints that other factors are more determinative of performance than fixed physiology.

But let’s put our Steven Pinker glasses back on for a moment. Yes, the odds of the fastest 100 metre sprinter winning the 2016 Olympics are surprisingly low, but look at the spread between first and last place. What’s on my screen tells me that Gatlin is 40-50 times more likely to win Olympic gold than Ben Youssef Meite, which is a pretty substantial gap. Maybe we can rescue Rationality Rules?

In order for Meite to win, though, he didn’t just have to beat Gatlin. He had to also beat six other sprinters. If pM represents the geometric mean of Meite beating one sprinter, then his odds of beating seven are pM7. The same rationale applies to Gatlin, of course, but because the geometric mean of him beating seven other racers is higher than pM, repeatedly multiplying it by itself results in a much greater number. With a little math, we can use the number of wins above to estimate how well the first-place finisher would fare against the last-place finisher in a one-on-one race.

(Click here to show the code)
In the above simulation, gatlin was 39.5 times more likely to win Olympic gold than meite.
But we estimate that if they were racing head-to-head, gatlin would win only 62.8% of the time.
 (For reference, their best race times in 2016 differed by 1.53%.)

For comparison, FiveThirtyEight gave roughly those odds for Hilary Clinton becoming the president of the USA in 2016. That’s not all that high, given how “massive” the difference is in their best race times that year.

This is just an estimate, though. Maybe if we pitted our models head-to-head, we’d get different results?

(Click here to show the code)
Wins when racing head to head (1875 simulations each)
----------------------------------------------
LOSER->       gatlin      bolt   bromell    vicaut   simbine de grasse     blake     meite
gatlin                   48.9%     52.1%     55.8%     56.4%     59.5%     63.5%     61.9%
bolt                               52.2%     57.9%     55.8%     57.9%     65.8%     60.2%
bromell                                      52.4%     55.3%     55.0%     65.2%     59.0%
vicaut                                                 51.7%     52.2%     59.8%     59.3%
simbine                                                          52.3%     57.7%     57.1%
de grasse                                                                  57.0%     54.7%
blake                                                                                47.2%
meite                                                                                     

The best winning percentage was 65.8% (therefore the worst losing percent was 34.2%).

Nope, it’s pretty much bang on! The columns of this chart represents the loser of the head-to-head, while the rows represent the winner. That number in the upper-right, then, represents the odds of Gatlin coming in first against Meite. When I run the numbers, I usually get a percentage that’s less than 5 percentage points off. Since the odds of one person losing is the odds of the other person winning, you can flip around who won and lost by subtracting the odds from 100%. That explains why I only calculated less than half of the match-ups.

I don’t know what’s on your screen, but I typically get one or two match-ups that are below 50%. I’m again organizing the calculations by each athlete’s fastest time in 2016, so if an athlete’s win ratio was purely determined by that then every single value in this table would be equal to or above 50%. That’s usually the case, thanks to each model favouring the Exponential distribution, but sometimes one sprinter still winds up with a better average time than a second’s fastest time. As pointed out earlier, that translates into more wins for the first athlete.

Getting Physical

Even at this elite level, you can see the odds of someone winning a head-to-head race are not terribly high. A layperson can create that much bias in a coin toss, yet we still both outcomes of that toss to be equally likely.

This doesn’t really contradict Rationality Rules’ claim that fractions of a percent in performance matter, though. Each of these athletes differ in physiology, and while that may not have as much effect as we thought it still has some effect. What we really need is a way to substract out the effects due to morphology.

If you read that old blog post, you know what’s coming next.

[16:48] Which is to say that the attributes granted from male puberty that play a vital role in explosive events – such as height, width, limb length, and fast twitch muscle fibers – have not been shown to be sufficiently mitigated by HRT in trans women.

According to Rationality Rules, the physical traits that determine track performance are all set in place by puberty. Since puberty finishes roughly around age 15, and human beings can easily live to 75, that implies those traits are fixed for most of our lifespan. In practice that’s not quite true, as (for instance) human beings lose a bit of height in old age, but here we’re only dealing with athletes in the prime of their career. Every attribute Rationality Rules lists is effectively constant.

So to truly put RR’s claim to the test, we need to fit our model to different parts of the same athlete’s career, and compare those head-to-head results with the ones where we raced athletes against each other.

(Click here to show the code)
     Athlete First Result Latest Result
0      blake   2005-07-13    2019-06-21
1       bolt   2007-07-18    2017-08-05
2    bromell   2012-04-06    2019-06-08
3  de grasse   2012-06-08    2019-06-20
4     gatlin   2000-05-13    2019-07-05
5      meite   2003-07-11    2018-06-16
6    simbine   2010-03-13    2019-06-20
7     vicaut   2008-07-05    2019-07-02

That dataset contains official IAAF times going back nearly two decades, in some cases, for those eight athletes. In the case of Bolt and Meite, those span their entire sprinting career.

Which athlete should we focus on? It’s tempting to go with Bolt, but he’s an outlier who broke the mathmatical models used to predict sprint times. Gatlin would have been my second choice, but between his unusually long career and history of doping there’s a decent argument that he too is an outlier. Bromell seems free of any issue, so I’ll go with him. Don’t agree? I made changing the athlete as simple as altering one variable, so you can pick whoever you like.

I’ll divide up these athlete’s careers by year, as their performance should be pretty constant over that timespan, and for this sport there’s usually enough datapoints within the year to get a decent fit.

(Click here to show the code)
bromell vs. bromell, model building ...
year	α	β	b
2012	0.639 (+0.317 -0.219)	0.817 (+0.406 -0.280)	10.370 (+0.028 -0.415)
2013	0.662 (+0.157 -0.118)	1.090 (+0.258 -0.195)	9.970 (+0.018 -0.070)
2014	0.457 (+0.118 -0.070)	1.556 (+0.403 -0.238)	9.762 (+0.007 -0.035)
2015	0.312 (+0.069 -0.064)	2.082 (+0.459 -0.423)	9.758 (+0.002 -0.016)
2016	0.356 (+0.092 -0.104)	1.761 (+0.457 -0.513)	9.835 (+0.005 -0.037)
... done.

bromell vs. bromell, head to head (1875 simulations)
----------------------------------------------
LOSER->   2012   2013   2014   2015   2016
   2012         61.3%  67.4%  74.3%  71.0%
   2013                65.1%  70.7%  66.9%
   2014                       57.7%  48.7%
   2015                              40.2%
   2016                                   

The best winning percentage was 74.3% (therefore the worst losing percent was 25.7%).

Again, I have no idea what you’re seeing, but I’ve looked at a number of Bromell vs. Bromell runs, and every one I’ve done shows at least as much variation, if not more, than runs that pit Bromell against other athletes. Bromell vs. Bromell shows even more variation in success than the coin flip benchmark, giving us justification for saying Bromell has a significant advantage over Bromell.

I’ve also changed that variable myself, and seen the same pattern in other athletes. Worried about a lack of datapoints causing the model to “fuzz out” and cover a wide range of values? I thought of that and restricted the code to filter out years with less than three races. Honestly, I think it puts my conclusion on firmer ground.

Conclusion

Texas Sharpshooter Fallacy: Ignoring the difference while focusing on the similarities, thus coming to an inaccurate conclusion. Similar to the gambler’s fallacy, this is an example of inserting meaning into randomness.

Rationality Rules loves to point to sporting records and the outcome of single races, as on the surface these seem to justify his assertion that differences in performance of fractions of a percent matter. In reality, he’s painting a bullseye around a very small subset of the data and ignoring the rest. When you include all the data, you find Rationality Rules has badly missed the mark. Physiology cannot be as determinative as Rationality Rules claims, other factors must be important enough to sometimes overrule it.

And, at long last, I can call bullshit on this (emphasis mine):

[17:50] It’s important to stress, by the way, that these are just my views. I’m not a biologist, physiologist, or statistician, though I have had people check this video who are.

Either Rationality Rules found a statistician who has no idea of variance, which is like finding a computer scientist who doesn’t know boolean logic, or he never actually consulted a statistician. Chalk up yet another lie in his column.

And the Beat Goes On

Essence of Thought has published a timeline of the Rationality Rules affair. If you’re missed any of the last five months, it’ll bring you up to speed.

Cripes, has it been that long already?! I had a look through my archives, and all but two of my posts over the last two months have been focused on Rationality Rules, and even those two were about transphobia. I know, I know, the constant drumbeat is getting a bit repetitive and boring. But there’s a reason for it.

[11:31] Now, some of the walkouts had formed a support group, which I was later added to, and reading through their accounts is truly horrifying. Many discussed the abuse they suffered thanks to Woodford and his audience. There are numerous discussions on how their sleep was impacted, about how they’re having to see psychiatrists and other specialists. I’ve even seen [a post?] discussing suicide in relation to what had occurred. That’s the level of severity we are talking about with this issue: people discussing suicide. That’s the damage Woodford and his supporters have caused this one group, this one organization.

I don’t have any way to verify this part, but some of it tracks with comments I’ve read elsewhere, the claims have remained consistent over time, and it would explain why ACA members seem willing to talk to Essence of Thought despite the ocean between them.

One thing I do know: the odds of anyone holding Rationality Rules responsible are basically zero. Some big names in the atheo-skeptic sphere, such as Matt Dillahunty and AronRa, either agree with RR or don’t care enough to do their homework. The ACA tried to do the right thing, but it appears RR supporters elected themselves into a majority on the ACA’s board, possibly breaking the rules in the process, and promptly started kissing their abuser’s ass.

In order to remove any ambiguity in the following statement, I wish to make clear that the ACA earnestly and sincerely apologizes to Stephen Woodford (Rationality Rules) for vilifying his character and insinuating that he is opposed to the LGBTQIA+ community. The Board of Directors has officially retracted our original statement.

Rationality Rules was so confident nobody would take him to task, his “improved” video contains the same arguments as his “flawed” one. And honestly, he was right; I’ve seen this scenario play out often enough within this community to know that we try to bury our skeletons, that we treat our minorities like shit, that we “skeptics” are just as prone to being blind followers as the religious/woo crowds we critique. And just like all those other times, I cope by writing words until I get sick of the topic. Sometimes, that takes a while.

This is one of those “a while” times. If it helps, I’m actively trying to avoid covering topics other people already have, and elevating the voices of others to break up the monotony.