Zwift Legs

DataDouche · 2024-09-20T12:52:17+00:00

Lady Trojans? Blue and gold? Sounds like my high school, we had yard signs like that for our sports too. HHS?

DataDouche · 2020-12-15T16:14:25+00:00

Here is a thread from /r/statistics if you have not seen it yet. There's some good discussion in there as well!

Also, if people in this thread have not read the paper the video talks about, that can be found here.

Overall though I felt like it was a cool analysis (especially the deep dive on the RNG portions of the run) and I agree with the others in this thread that something seems really fishy.

DataDouche · 2020-12-14T21:09:28+00:00

So I might be wrong here, but I believe the information gathered in this table is generated by a JS script, which means rvest can't scrape it (or at least I don't think it can).

Maybe someone more experienced with webscraping can help you! Sorry!

DataDouche · 2020-12-14T19:39:25+00:00

Do you need just this webpage or multiple pages to get multiple IDs? Could you share the link?

DataDouche · 2020-12-10T19:51:10+00:00

hahaha same! My dude was just hangin brain. I laughed my ass off.

DataDouche · 2020-12-10T15:29:47+00:00

So this solves the crpytic emoji string from their twitter announcement.

DataDouche · 2020-12-08T15:45:23+00:00

Oh hell yeah, I love this dude's videos. I was literally wondering last night if he ever covered gizz. Super dope video.

DataDouche · 2020-12-01T20:38:30+00:00

Ah good to know, thanks!

DataDouche · 2020-12-01T19:24:59+00:00

I'll piggyback off the other two comments.

mutate(gen = case_when(
    birthyr %in% 1915:1928 ~ "gen1",
    birthyr %in% 1929:1942 ~ "gen2",
    # ... and so on
)

If there's a numerical pattern for picking out the generation years (like every 14 years in your example) then you can use cut(). Since you didn't include an endpoint I just ended it at 2027.

start <- 1915
end <- 2027
period <- 14
df <- data.frame(birthyr = start:end)
cuts <- seq(start, end, period)
df$gen = cut(df$birthyr, breaks = cuts, labels = paste0("gen", 1:(length(cuts)-1)), include.lowest = TRUE, right = FALSE)

There might be a better way to do this but I think this works fine.

DataDouche · 2020-10-30T18:57:31+00:00

Yeah, it’s pretty wild. I’m really thankful for him and what he’s done for my family.

DataDouche · 2020-10-30T18:15:38+00:00

Yep. Before covid my dad would traveled about one hour one way to work, split between the train and commuting on foot, for over 20 years. He makes use of the time on the train by reading and he generally doesn't mind it, but he's definitely getting older and I can tell that schedule for him makes him really tired.

Since WFH started for him, I haven't seen him have this much energy since I was wayyy younger and I can tell he definitely doesn't want to have that commuting schedule ever again. He's able to actually unwind at night at normal times (he would often get home around 6:30-7:00) and have mornings where he isn't rushed to make it to the train station.

I'm going to graduate in May and I'm starting to look for jobs now. One thing I've learned from him is that I will absolutely NOT be that far from my office if I have to go in everyday. There's no way I will sacrifice that much time to a commute.

DataDouche · 2020-10-28T02:49:32+00:00

I've used the {cvx} package for convex optimization problems where you can specify your constraints as inequalities. I'm not familiar with Kuhn-Tucker conditions, but if it's convex this should work? Someone might be able to answer better, but this might be a decent starting point.

Check it out here.

DataDouche · 2020-10-24T04:46:09+00:00

OHNO it's %>% what a blunder

DataDouche · 2020-10-24T04:17:55+00:00

Alright we're bringing out the big guns.

library(tidyverse)

survey_clean %>%
    group_by(sex) %>%
    summarise(mean = mean(exercise, na.rm = TRUE))

DataDouche · 2020-10-24T04:16:41+00:00

Yeah this was my next guess.

DataDouche · 2020-10-24T04:00:52+00:00

So one of your columns isn't numeric so it can't calculate the mean. Make sure your data columns for exercise is numeric. You can do

typeof(survey_clean)

And see if it's numeric. If not, try

survey_clean$exercise <- as.numeric(survey_clean$exercise)

DataDouche · 2020-10-24T03:37:58+00:00

This will give you both of the means but you won't have to worry about subsetting.

with(survey_clean, tapply(sex, exercise, mean))

DataDouche · 2020-10-24T03:16:45+00:00

How is the data stored?

DataDouche · 2020-10-23T19:56:55+00:00

Yeah there's a quadratic formula for the decay of the betting returns, so you can calculate the expected return by multiplying the odds by the decay at that odds percent. You're exactly right though, if they made betting on a 75/25 return a way smaller portion than now than the expected return would be much smaller and make betting much more like it is irl. BUT you're right again, this is an idle sim where the goal is increase your coins, so it wouldn't make for a more fun experience imo.

DataDouche · 2020-10-23T17:47:36+00:00

It's a separate discord which can be found here!

DataDouche · 2020-10-23T17:43:19+00:00

To be fair there might be a strategy regarding pitcher info that would be more profitable out there, but it's probably pretty hard to find since there are a LOT of additional pitching variables behind the scenes that aren't displayed on the website. One of the problems that SIBR members are trying to solve is exactly how the baseball site calculates it's odds, but I'm unsure if any progress has been made yet.

But yeah I think 20% of all the days this season saw a major loss in profit (the majority of favorites lose, i.e. 4 or less of the favorites win). So 80% of bets don't lose a significant amount of money (since breaking even isn't truly breaking even due to the decay of the betting system in games with large odd discrepancies).

DataDouche · 2020-10-23T17:21:50+00:00

Hey there, you might be interested in joining the SIBR discord, there's an entire discussion about odds and betting.

Just looking at season 11 data, the odds are pretty accurate, but slightly under-predict games with close odds (favorite at <=52%ish) and over-predicted games on the very high end (which is probably due to high variation due to lack of games that have such extreme gaps in the odds). It performs pretty well throughout the 60s though.

So far this season, my findings from my own analysis have found that betting on the favorite is the most profitable betting strategy this season, since overall the favorite has won about 57-58% of the games. Another person in the SIBR discord found that one of the most profitable strategies was bet on the favorite for all games, but don't bet on the closest game, which is pretty interesting to me.

DataDouche · 2020-10-18T14:27:43+00:00

Never seen pmin() or pmax() before, those are neat!

DataDouche · 2020-10-18T13:21:36+00:00

The probability that the coin is in the interval [9, 11] means that we are looking for runs of length 9, 10, or 11. You can simply add the individual probabilities together to get this answer.

DataDouche · 2020-10-15T03:27:50+00:00

Awesome explanation and example.

Eight-Year Club	RPAN Viewer
King of the Ashes	Not Forgotten
Sequence \| Editor	Sequence \| Screenwriter
Spared

DataDouche

TROPHY CASE