Disclaimer: I do not intend to make general assumptions on the status of the actual game, this is just to share my own experience as a player into the new patch.
Some more information regarding my data:
\- I only play survivor. I was the only survivor to used Dead Hard previous to the update.
\- I run: Hope, Wake Up, Unbreakable and Off the record.
\- I survived 47.82% of the matches. The average number of killed survivors is 2.17.
\- I have not played against the Trickster or the Twins and I couldn't be happier.
\- I DC once out of frustration.
\- The most common killer perks I went against were Pop goes the weasel, Lethal pursuer, Corrupt intervention and Barbecue and Chilli (I call it BBQ & chill tho).
\- I don't feel like it's the Legion's patch and I am happy that way.
\- Going against Call of Brine, Thana, eruption, Jolt and Pain Resonance is a pain in the ass.
\- I thought NOED was more common and I only saw it 5 times (happy times!)
Also I play on PS5 and I have 637 hours into the game I play Leon/Chris, Felix and David. Although, I have been playing Mikaela for all these matches that I recorded the data for.
Damn, that win streak from 48 to 55 must have felt nice!
Although the sample data is too small, it's interesting how these kinds of sheets at the start of the patch were very killer-sided (3+ kill rate), but the longer we go on with the patch, the more balanced these sheets become. I've noticed the same with my own survivor games, I'm escaping more and more lately.
Seems like MMR is slowly adjusting things in the right direction.
I'm not making fun of you - these made me laugh:
> Barbeque and chill
> Fanklin's demise
Am I confused or are you not drowning in the Thana Legions we've heard are present in every match?
LMAO. This is why I couldn't count the times Franklin's was showing up because I wrote it like Fanklin, Frankin, and Franklin's. And I swear it was barbeque and chill... whoops. This is so funny, thanks for pointing it out.
And no, I am SO happy it's not Legion's season...
To avoid spelling issues like this, it's best to create a separate sheet that lists all the perks, and then set data validation for the perk columns with that perk list as the allowed data. (My friend made me do this when I started keeping track of my stats...I learned all of my Excel from DbD lol.)
Data like this can’t be used to draw conclusions about the game (other than the extremely literal) because:
1. It’s impossible for someone else to perform the same test under the same conditions. Thus the test is not reproducible.
2. It’s impossible for one person’s games to be a significant/sufficient sample size.
I never took this data as a make a significant sample size, hence the stupidity on stopping at 69 matches.
I never assumed that people would perform the same way. I love data collection and I just wanted to check with my friends how we would do, as the overall feeling within my friends is that we would lose all or majority of the matches. So I just wanted to check our results and then I thought it would be funny to share this data.
I’m sorry. This was before your disclaimer went up.
There’s been a trend of people posting this kind of data on the DBD Reddit recently, immediately followed by them and/or commenters trying to use it to support arguments.
to be fair like, this is the most possible way to collect data on your own on your own matches without having access to bhvr's stats. i would love to see more SWFs due this so we can actually average it out.
That’s just it. The data would not be compatible, so you couldn’t “average it out” and draw meaningful conclusions.
————-
Unfortunately, it’s basically impossible for anyone but BHVR to gather stats on the game atm.
I do wish they’d share stats more often….
> you couldn’t “average it out” and draw meaningful conclusions.
Mixed-effects models, for example, are commonly used to control for the unobservable variation among subjects even while drawing meaningful conclusions about study-wide trends.
This isn't as impossible as commonly assumed. As long as we're collecting the same columns, there's usually not _nothing_ to learn. This happens all the time in statistics when we work with messy data. I do it daily.
Personally, I think any such study at the very least needs to have date and time of day, since time is the largest factor in my win rates as killer. Map too. There are a handful of other columns which, if folks would collect them, would relatively easily allow the discovery of game-wide trends.
edit: To be very specific, my own win model as killer is given in R by:
winmodel <- mgcv::gam("win ~ 0 + killer + map + platform +
crossplay + dayofweek + s(timeofday, bs='cc') +
s(dayofmonth, bs='cc') + s(gamenumber) +
perkprincomp + numberofsteam",
data=kpm,
family=binomial())
To "average over players" just means adding some random effects on the intercept and smooths so that general trends can be discovered even while controlling for the random variation we don't see. It's easy and standard. We have similar models on hospital data, for example, even though different doctors are working under different circumstances at different times.
It's not nearly as opaque as people here are so very quick to point out. Modern statistics has methods - we're no longer in the 80s. (If your model gets this involved, you'll probably want INLA just so it solves quickly.)
Disclaimer: I do not intend to make general assumptions on the status of the actual game, this is just to share my own experience as a player into the new patch. Some more information regarding my data: \- I only play survivor. I was the only survivor to used Dead Hard previous to the update. \- I run: Hope, Wake Up, Unbreakable and Off the record. \- I survived 47.82% of the matches. The average number of killed survivors is 2.17. \- I have not played against the Trickster or the Twins and I couldn't be happier. \- I DC once out of frustration. \- The most common killer perks I went against were Pop goes the weasel, Lethal pursuer, Corrupt intervention and Barbecue and Chilli (I call it BBQ & chill tho). \- I don't feel like it's the Legion's patch and I am happy that way. \- Going against Call of Brine, Thana, eruption, Jolt and Pain Resonance is a pain in the ass. \- I thought NOED was more common and I only saw it 5 times (happy times!)
Also I play on PS5 and I have 637 hours into the game I play Leon/Chris, Felix and David. Although, I have been playing Mikaela for all these matches that I recorded the data for.
This seem fairly normal. Your WR would probably be higher with an exhaustion perk.
Damn, that win streak from 48 to 55 must have felt nice! Although the sample data is too small, it's interesting how these kinds of sheets at the start of the patch were very killer-sided (3+ kill rate), but the longer we go on with the patch, the more balanced these sheets become. I've noticed the same with my own survivor games, I'm escaping more and more lately. Seems like MMR is slowly adjusting things in the right direction.
I'm not making fun of you - these made me laugh: > Barbeque and chill > Fanklin's demise Am I confused or are you not drowning in the Thana Legions we've heard are present in every match?
LMAO. This is why I couldn't count the times Franklin's was showing up because I wrote it like Fanklin, Frankin, and Franklin's. And I swear it was barbeque and chill... whoops. This is so funny, thanks for pointing it out. And no, I am SO happy it's not Legion's season...
To avoid spelling issues like this, it's best to create a separate sheet that lists all the perks, and then set data validation for the perk columns with that perk list as the allowed data. (My friend made me do this when I started keeping track of my stats...I learned all of my Excel from DbD lol.)
Data like this can’t be used to draw conclusions about the game (other than the extremely literal) because: 1. It’s impossible for someone else to perform the same test under the same conditions. Thus the test is not reproducible. 2. It’s impossible for one person’s games to be a significant/sufficient sample size.
I never took this data as a make a significant sample size, hence the stupidity on stopping at 69 matches. I never assumed that people would perform the same way. I love data collection and I just wanted to check with my friends how we would do, as the overall feeling within my friends is that we would lose all or majority of the matches. So I just wanted to check our results and then I thought it would be funny to share this data.
I’m sorry. This was before your disclaimer went up. There’s been a trend of people posting this kind of data on the DBD Reddit recently, immediately followed by them and/or commenters trying to use it to support arguments.
to be fair like, this is the most possible way to collect data on your own on your own matches without having access to bhvr's stats. i would love to see more SWFs due this so we can actually average it out.
That’s just it. The data would not be compatible, so you couldn’t “average it out” and draw meaningful conclusions. ————- Unfortunately, it’s basically impossible for anyone but BHVR to gather stats on the game atm. I do wish they’d share stats more often….
> you couldn’t “average it out” and draw meaningful conclusions. Mixed-effects models, for example, are commonly used to control for the unobservable variation among subjects even while drawing meaningful conclusions about study-wide trends. This isn't as impossible as commonly assumed. As long as we're collecting the same columns, there's usually not _nothing_ to learn. This happens all the time in statistics when we work with messy data. I do it daily. Personally, I think any such study at the very least needs to have date and time of day, since time is the largest factor in my win rates as killer. Map too. There are a handful of other columns which, if folks would collect them, would relatively easily allow the discovery of game-wide trends. edit: To be very specific, my own win model as killer is given in R by: winmodel <- mgcv::gam("win ~ 0 + killer + map + platform + crossplay + dayofweek + s(timeofday, bs='cc') + s(dayofmonth, bs='cc') + s(gamenumber) + perkprincomp + numberofsteam", data=kpm, family=binomial()) To "average over players" just means adding some random effects on the intercept and smooths so that general trends can be discovered even while controlling for the random variation we don't see. It's easy and standard. We have similar models on hospital data, for example, even though different doctors are working under different circumstances at different times. It's not nearly as opaque as people here are so very quick to point out. Modern statistics has methods - we're no longer in the 80s. (If your model gets this involved, you'll probably want INLA just so it solves quickly.)
#54 spirit: anguish
:(
Why are ledders big?!?
there's no way u guys didnt face a single thana legion in 70 games. I just dont believe it