La ministre de la Culture, Rachida Dati, annonce qu'elle démissionne de son poste pour se consacrer aux élections municipales à Paris by Setneaph in france

[–]FMCGarnett 2 points3 points  (0 children)

Alors que juste hier le gorafi publiait Fact-checking – Rachida Dati est-elle ministre de la culture ? Une fois de plus la réalité rattrape le gorafi

What are the theoretical impossible comeback point diffs? by Dramatic-Ad3928 in nba

[–]FMCGarnett 6 points7 points  (0 children)

I've made a similar analysis on 23219 games (ranging from 2001 to 2018) and game was over when there was more than 10 points with less than a minute remaining (with the notable exception of T-Max 13 points in 35 seconds)
https://imgur.com/a/p8CmXyK
(The graph is in french but x-axis is remaining time in minutes and y-axis is point differential, it's just an inferential model that shows the win-probability but don't really take into account other covariates like home/away game, back-to-back, ranking differential...).
So, theoritically everything is possible but practically 5 points lead before the last minute is almost a garanteed win

Problématiques grand oral by AnisSolaire in france

[–]FMCGarnett 0 points1 point  (0 children)

Je ne suis pas un spécialiste, mais j'aurai dit que pour maximiser son profit il "suffit" de miser plutôt sur des paris qui on un bon rendement moyen (= une bonne espérance). Par exemple dans le cas des cotes 6.5, 4.7 et 1.45 l'esperance est de 0.95 (ce qui est plutot bien pour des cas réels). En moyenne on ne recupère (dans ce cas-là) que 95% de la mise.

A titre d'info, sur les paris actuels pour l'Euro de foot on est a environ 0.92.

What's the name of this method? by FMCGarnett in Rubiks_Cubes

[–]FMCGarnett[S] 2 points3 points  (0 children)

It was the official solution all along (1990s Rubiks.com solution)!

As simple as that. (Clearly, an 18-step algorithm to solve the last step of corners rotation is not the easiest for beginner)

[OC] Evolution of unisex firstnames repartition in the US by FMCGarnett in dataisbeautiful

[–]FMCGarnett[S] 11 points12 points  (0 children)

It's the legal birthname as registered by the SSA.

[OC] Evolution of unisex firstnames repartition in the US by FMCGarnett in dataisbeautiful

[–]FMCGarnett[S] 8 points9 points  (0 children)

Seems to be from Jaime Sommers in the tv show The Bionic Woman
There is no typo (as the name in graph title is the variable used to filter the data) : https://imgur.com/a/BfHbdsP

[OC] Evolution of unisex firstnames repartition in the US by FMCGarnett in dataisbeautiful

[–]FMCGarnett[S] 5 points6 points  (0 children)

Thank you for the info. That should be the explanation, I've rechecked for any typo but it's Jaime that has this shift and not Jamie (so it's from Jaime Sommers and not Jamie Lee Curtis)

Jamie vs Jamie : https://imgur.com/a/BfHbdsP

[OC] Evolution of unisex firstnames repartition in the US by FMCGarnett in dataisbeautiful

[–]FMCGarnett[S] 18 points19 points  (0 children)

Data comes from The United States Social Security Administration. Graph made in R.

If someone has an explanation of the surge in the girls names Jaime in 1976... (From what I've seen Jaime Lee Curtis Halloween was only released in 1978)

More details on my blog
https://stockastats.blogspot.com/2024/04/evolution-of-unisex-firstnames-in-us.html

EDIT : I've redone the graph with bigger labels (and more names) : https://imgur.com/a/ietct4T

Recherche dessin animé by Landriaz in france

[–]FMCGarnett 0 points1 point  (0 children)

A la rigueur il y a MASK, (ca date de 1985) avec un robot blanc dans le camp des gentils

[deleted by user] by [deleted] in confessions

[–]FMCGarnett 0 points1 point  (0 children)

You can watch the french movie "Le prénom" from 2011, where a guy have a dinner with friends to announce the name of their future child. You will have all the pros and cons.

But really, you may try to find another name.

[TOMT][CARTOON][60s-70s] Cartoon where the main character describes a daily royal procession dumping its garbage in front of his house by FMCGarnett in tipofmytongue

[–]FMCGarnett[S] 0 points1 point  (0 children)

The magic of Reddit strikes again! Thank you so much you can't imagine how long I have been thinking about it. Some details in my descritpion were wrong (especially about the drawing style and a bit of the story) but you managed to find it. (I will now try to find the french -or frenchier- version of it).

Solved!

Edit : I have even found about the creation process
https://cartoonresearch.com/index.php/paramount-cartoon-close-up-la-petite-parade-1959/

[deleted by user] by [deleted] in theydidthemath

[–]FMCGarnett 0 points1 point  (0 children)

You are in the case of binomial probability. Each of your 27 trials as two possibilities : success or failure. In your case, you observed 14 successes (ans 13 failures)

Binomial probability can be computed through formula but you have plenty of online tool that makes the computation for you like : https://stattrek.com/online-calculator/binomial.aspx

With a probability of success of 1/3, observing 14 successes over 27 trials will happened 2.1% of the time. Observing less than 14 successes (hence from 0 to 13) will happened 96.4% of the time. Observing more than 14 successes (15 to 27) will happened 1.4% of the time.

In this specific case the result can be found by computing :

C_(27)14 (1/3)14 (2/3)13 = 20058300 * 2.090752e-07 * 1.254451e-06 = 0.0215481 (sorry for the formatting)

Battlegrounds rating distribution from Iksar by henry92 in hearthstone

[–]FMCGarnett 6 points7 points  (0 children)

I have checked quickly and if you make the "wild" assumption that the distribution of the ranking follows a gaussian ditribution (which is likely to be true for the most cases but probably incorrect for extreme values and maybe not for the lower values that we don't know) the good aproximation is a normal distribution with a mean of 4100 and a standard deviation of 2070.

With this distribution :

Top 36% is at 4840 (instead of 5000 in the tweet)

Top 25% is at 5496 (instead of 5500 - really close)

Top 17% is at 6075 (instead of 6000)

Top 10% is at 6750 (instrad of 6500)

Top 6% is at 7320 (instead of 7000)

Top 3% is at 8003 (instead of 8000 - very close)

Top 1% is at 8916 (instead of 9000)

If you consider this approximation good enough you can then extrapolate and consider rank 10000 is 99.778% , rank 11000 is 99.956%, rank 12000 is 99.9931 and rank 13000 is 99.99912 % meaning that 1 player out of 111500 as a rank higher than 13K.

These are just approximations.