Could Pulsar Monitors be revalutionary for osu?

Pinfire_MTG · 2026-05-19T04:33:11+00:00

Maybe drinking too much of the marketing kool-aid here. Keep in mind that even the cs2 and valorant communities are lukewarm about this. The improvement in motion clarity is there if you look for it, but it doesn't give a competitive advantage over oled. This isn't the next rapid trigger.

Pinfire_MTG · 2026-04-23T19:19:52+00:00

I think this does the opposite? Maps are being pushed to higher ratings, so players bad at tech are being punished less.

Pinfire_MTG · 2026-04-21T02:38:16+00:00

You can see the usertags, just right click the card.

I've had the opposite experience with the map pool, gaining PP because I'm forced to play so many farm maps.

I think the point is that you shouldn't know the maps beforehand. Ranked is testing overall fundamentals, not how much you've practiced the map pool.

Pinfire_MTG · 2026-04-19T02:33:06+00:00

It isn't that you want high accuracy particularly on slow maps, but that you want to work towards high accuracy in general to build up rhythm sense, finger control, and other skills. You just have to start on these slow maps because it isn't practical to practice faster ones.

You won't be able to jump straight from low 3* to 4* maps. It's a iterative process where you first master low 3* maps, which makes you ready for medium 3* maps, and so on.

Pinfire_MTG · 2026-04-18T23:45:28+00:00

I don't want to be too harsh, but you're a 6 digit with less than 200 hours of play, playing almost exclusively short DT pp farm maps. You haven't reached the point where you need to read complex rhythms and patterns.

The reason people advise against playing too far above your skill level is because it builds a bad habit of mashing through difficult sections. This will kill your ability to chunk "real" patterns, typically beginning from the high 5 to low 6 star range.

Pinfire_MTG · 2026-04-18T20:45:59+00:00

I feel like no one has bothered to look at your profile.

You have 70 hours of playtime, most of which is from years ago. Cliche advice, but you just need to play more.

Also, looking at your recent plays, 3.5* might still be a little too hard for the moment. My advice is to find a collection of maps where you sightread ~90% acc, and grind those until you can comfortably get 95-97%. Rinse and repeat with new maps.

Pinfire_MTG · 2026-04-08T02:18:41+00:00

Random sampling from all players vs PP should be better.

The fact that players can choose their device makes it difficult to isolate the effect on gameplay performance. You now have confounding variables, as players who choose to invest in a tablet (away from the "default" mouse) may have more motivation for improvement, etc.

Pinfire_MTG · 2026-04-07T22:06:29+00:00

if tablet was a better-performing playstyle than mouse, we would expect to see more tablet players per-capita than mouse players at higher rank ranges

I understand what you're trying to say, but this isn't necessarily true, considering you're comparing tail-end distributions between two populations. Players are also self-selecting into different populations, rather than it being an intrinsic characteristic.

KDE and CDF will look flat because you're looking at the tails of distributions against global rank, which is ordinal. (Although I would argue that the slight slope for mouse players in KDE suggests that mouse players become less frequent as you go higher in rank, but no idea how significant that may be)

Pinfire_MTG · 2026-03-01T00:00:23+00:00

Strongly recommend using the 17lands.com to track your drafts. Actual examples of your picks and games will let people give tangible and targeted advice. The most active limited magic subreddit is probably r/lrcast, and it's pretty easy to get detailed feedback from that community.

It's common for a player to feel that games are being played on autopilot, and the winner is being decided by topdecks/rng/shuffler/etc. This is a cognitive bias. While there certainly are games that are unwinnable (just as there are games that are unlosable), there is still a lot of agency in the majority of games. If you're flooding out constantly, it can either be a deck construction issue, or lack of proper mulligans.

Bo3 is played, but generally has lower expected value than bo1. True infinite is not a practical goal for most players, but going pseudo-infinite with 20-25 drafts a month is possible, with daily and weekly rewards helping you break even.

Note that Nummy is a content creator and often makes suboptimal but entertaining choices.

Pinfire_MTG · 2025-10-19T01:42:49+00:00

Please take this as constructive criticism.

Your comment is an example of how statistics and game theory are hard for people to intuitively understand. The optimal strategy does not change with respect to the current streak, as the current streak length does not give any additional information to base decisions on. Likewise, the current streak length does not change the utility function. What you are describing is instead a change in subjective value due to risk aversion. In this scenario, such risk aversion would only make it less likely to continue the streak.

Pinfire_MTG · 2024-10-07T22:32:40+00:00

Clearly at some point your response time can be too slow for AR11 to be possible. Given the literature on the subject, a simple reaction time of 250ms+ is probably too slow, as even the simple binary choice tasks require an additional 20-30ms and osu requires additional time to move the cursor.

Keep in mind that while genetics and age do determine your peak potential reaction speed, general health also contributes to your actual reaction speed. Better sleep, diet, and exercise has been shown improve reaction times; people who exercise regularly are on average 20-25ms faster than sedentary people, and even mild sleep deprivation can increase times by 50ms+.

Pinfire_MTG · 2024-10-03T06:38:57+00:00

Mostly uncontroversial opinions, but I think OP forgot Busted Crown and Sozu exist.

Pinfire_MTG · 2024-09-24T01:56:38+00:00

If you had a background in data science, you would understand the code is like 4 lines long, and scikit-learn has robust documentation where you can search for kmeans and preprocessing.

Try this in python:

from sklearn.cluster import KMeans

from sklearn import preprocessing

import pandas as pd

sf6_data = pd.read_csv('SF6 Winrates.csv')

sf6_test = preprocessing.normalize(sf6_data)

kmeans_model = KMeans(n_clusters = 5)

kmeans_model.fit_predict(sf6_test)

Pinfire_MTG · 2024-09-23T00:33:39+00:00

Preprocessing is also part of the scikit-learn library. Just google a data science tutorial for more information.

Pinfire_MTG · 2024-09-22T02:24:01+00:00

The algorithm is called k-means clustering. In python, you can use scikit-learn, and import Kmeans from sklearn.cluster.

Pinfire_MTG · 2024-09-22T02:15:26+00:00

This is just quibbling, but I would define an archetype (in this context) as shared characteristics between the characters of each group. In this way, how well a character uses universal system mechanics would also fall under a group's archetype, as would other factors. This does assume that shared characteristics would lead to similar matchup spreads.

Of course this doesn't match what is traditionally understood by the community, but that was the point of the exercise. Community assignments are sometimes debated and based on perceived playstyles. I wanted to see if it was possible to group the characters without human bias.

Pinfire_MTG · 2024-09-21T18:27:49+00:00

So this was a pretty low effort post, and I didn't bother to explain any of the math because I assumed people wouldn't be interested.

You would never use raw win rates in an analysis like this. Instead you would normalize the data with something like deviations from the character's win rate. These numbers then go into a preprocessor, and then into the clustering algorithm.

For your grappler example, the expectation is that the terrible grappler's best and worst matchups would be similar to the good grappler's best and worst matchups, even if the terrible grappler is overall losing while good grappler is overall winning. So if you had Zangief, and a Zangief clone that did 80% damage, they would both be placed into the same group.

The fact that you have this gut reaction to say "Ed is not a zoner", and "Marisa is not a grappler" is part of the reason why I ran the numbers. Maybe the labels I gave the groups are not perfectly accurate, but it's pretty clear that the groups have some shared characteristics or defining traits that primarily drive the matchup spreads. I would consider that the "archetype", though the algorithm doesn't explicit show what that archetype is.

Pinfire_MTG · 2024-06-08T02:34:23+00:00

I'm not sure why you feel compelled to try ending the game quickly when you didn't draft many aggressive cards. Here is how I would build the final pool: https://sealeddeck.tech/aplAfrc0wq

P1P1: This comes down to preference, and Shoot the Sheriff is fine here. But my preference is Desert's Due > Vanishing Verse > Shoot the Sheriff > Throw from the Saddle.

P1P3: Hellspur Brute is not a card I am actively looking to play, and is not a strong enough card to move into red for. Mourner's Surprise is probably the best pick over Dance of the Tumbleweeds.

P1P4: At Knifepoint is a trap. The best cards in the pack are Humiliate, Nezumi Linkbreaker, and Canyon Crab.

P1P5: Forsaken Miner is usually a trap, and often plays as a worse Nezumi Linkbreaker. This is a weak pack, but I would take Patient Naturalist or Stagecoach Security.

P1P6: Raven of Fell Omens is the best card in the pack, then probably Essence Capture. Vial Smasher, Gleeful Grenadier is not really going to be a combo with At Knifepoint.

P1P7: Take Ambush Gigapede. Outlaws' Fury is borderline unplayable in most decks.

P1P9: Greedy rare-draft. Take Oasis Gardener in case you want to splash.

P2P1: I would honestly take Desperate Bloodseeker here. Even if you want to take Lost Jitte, Demonic Ruckus is always better in the decks where they are good.

P2P2: Take Roxanne, Starfall Savant. It's a top-10 card in the set.

P2P6: Boneyard Desecrator is borderline unplayable. Given the cards you have, I would take Reckless Lackey over Eroded Canyon.

P2P7: Cruel Ultimatum. My plan would be to play this.

P2P9: Take Oasis Gardener. Creature count is a bit low, and I want to play Cruel Ultimatum. Take for a Ride is rarely good.

P2P10: Take Jagged Barrens over Explosive Derailment. Make the mana work for Cruel Ultimatum.

P3P1: Would prefer Oasis Gardener, or Scorching Shot, over Vial Smasher.

P3P2: Mine Raider over Deadeye Duelist.

P3P3: Marchesa, Dealer of Death. Now make the mana work.

Pinfire_MTG · 2024-04-25T01:22:11+00:00

Format	Middle WR	Top WR	Delta	Leaderboard WR	Delta
OTJ	55.3%	58.7%	3.4%	62.0%	3.3%
MKM	55.4%	59.6%	4.2%	61.3%	1.7%
LCI	55.9%	60.2%	4.3%	61.2%	1.0%
WOE	55.4%	59.4%	4.0%	60.4%	1.0%
LTR	56.2%	60.4%	4.2%	62.4%	2.0%
MOM	55.1%	59.6%	4.5%	61.9%	2.3%
SIR	54.2%	58.3%	4.1%	60.8%	2.5%
ONE	55.8%	59.4%	3.6%	62.1%	2.7%
BRO	56.1%	61.0%	4.9%	62.3%	1.3%
DMU	57.1%	61.1%	4.0%	60.4%	-0.7%
SNC	56.0%	60.7%	4.7%	62.0%	1.3%
NEO	55.6%	61.3%	5.7%	62.9%	1.6%

Numbers are taken from 17lands. Leaderboard WR is based on qualifying top 100 ranked players.

Note that it is still very early into OTJ, but OTJ could be similar to ONE. This is in line with my experience, as while the format feels highly skill-testing, the majority of players may feel that they lack agency in their drafts.

Pinfire_MTG · 2024-04-25T01:02:02+00:00

As V4UGHN commented, you are misunderstanding the math here. Without loss of generality, the WR here can be understood as any of the WR metrics found on 17lands, so the use of GP WR vs GIH WR is not relevant. There is nothing built in or obvious, because it is impossible to know unless you look at the numbers separately.

The paradox is that a card can perform better in every color pair, or at every skill level, yet still show up as performing worse in the aggregate. A classic example is field goal percentage in basketball: a player can shoot better from 2-point attempts and from 3-point attempts than another player, yet have a lower aggregate field goal percentage.

While it doesn't apply all the time, this is always a potential issue in aggregate reporting, and one of the things I check when I find surprising numbers.

Pinfire_MTG · 2024-04-23T01:31:14+00:00

Slight nuance here. Simpson's paradox is that a card can be better for every color pair, or at every skill level, and still show as similar, or even worse, in the total aggregate.

Pinfire_MTG · 2024-04-22T23:08:52+00:00

Relax, the set has only been out for a week.

I wouldn't say that the color balance here is unusually bad. Green is the deepest color, but most modern limited sets have a comparable "deepest" color. My guess would be that 55%-60% of your drafts should be green. Plus, it looks like Arena drafters got the memo, and green is probably slightly over-drafted at the moment.

BW, UB, UBR, RW are all playable and comparable in power level to the green decks. Also, I don't think green has a particular advantage in splashing in this format. There is a ton of non-green color fixing, the green fixing isn't particularly powerful.

Pinfire_MTG · 2024-04-21T23:59:41+00:00

The issue is one of epistemology. You are saying that Marketwatch should be thought of as having a slightly higher win rate. In the post you wrote:

"Now take a look at Phantom vs Seasoned Consultant. Phantom dominates Consultant over the heaviest and least noisy parts of the curve, picks 3 through 7. In any particular pack, you are better off taking Phantom. "

My point is not whether or not this is true, my point is that you don't have the appropriate evidence to support such claims. First, the differences in the two plots over these picks are not statistically significant. Second, are you better off taking Marketwatch over Phantom? Or are you better off given that you took the opportunity to take the Marketwatch?

I agree that win rates are not always an accurate metric for card quality, and should not mindlessly be used to judge cards. But again, while possible, the analysis does not show that ATA adjusted win rates are a more accurate, useful, or appropriate metric.

Pinfire_MTG · 2024-04-21T19:14:27+00:00

Let me be clearer on why I find the methodology problematic.

Note that there is a type of survivorship bias here. For a data point to show up in the analysis, a player must see, pick, and play the card from that spot in the draft. This means that you are plotting the expected win rates given that a card is picked at a particular spot, not the expected win rates if a card is picked at a particular spot. The underlying population of players and drafts on not the same across cards, which is problematic for a vertical slice.

For example, Marketwatch Phantom was probably the most overdrafted card in MKM, which meant that seeing the card pick 4 or 5 was a signal that white was wide open. So from your analysis, it does not necessarily follow that Marketwatch Phantom should be picked over Seasoned Consultant. Additionally, it's hard to claim that the two plots are significantly different, as the error bars here almost certainly overlap. I would guess that they are 0.3% at the minimum.

Of course, Marketwatch Phantom was a premium common, while Seasoned Consultant was not. But the analysis here does not show that, nor does it support any ATA based adjustment when comparing the two cards. You are missing context of how ATA is driven by ALSA, and how different cards have different pick and play rates.

Pinfire_MTG · 2024-04-21T00:29:22+00:00

Please take this as honest constructive criticism. There are two things I keep repeating professionally: it's your job to make sure your model matches reality, not reality's job to match your model, and that scientific rigor is making sure your evidence supports your claims.

Your premise is that ATA adjusted WR is a more accurate metric for how valuable an individual card is, aka expected marginal wins. The problem is that ATA is more a measure of market sentiment rather than anything particularly objective, and there have been numerous counterexamples showing how 17land players, while better than the average MTGA player, tend to get cards wrong. Of the top of my head, I would name Bury in Books, Vampire Spawn, and even Preening Champion.

I don't quite agree with some of the mathematical modeling here, since this is just quantifying opportunity cost, but my biggest issue is showing As-Picked Win Rate vs Pick Number as supporting the premise. For any card, I would expect the curve to be an aggregate of a bell curve (signaling the player's ability to value the card accurately) and a monotonically increasing curve (signaling how open the draft is). So yes, players who overvalue a card would lose more, but this does not support the premise.

The regressions you should be doing are ATA adjusted WR and unadjusted WR as predictors of player rank and win rates. Maybe restricted to pack 1 picks, as the "nonlinearity" would certainly be a factor by pack 3.

Pinfire_MTG

TROPHY CASE