Respond, then paste the last thing you copied 1.2 |
![]() ![]() « Citoyen » 1486339080000
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ok. something something stepmania (i did this btw) Dernière modification le 1486339140000 |
![]() ![]() « Consul » 1486339080000
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interesting |
![]() ![]() « Citoyen » 1486422360000
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*grabs knife* damn that's a lot Difficulty has been a long standing and complex issue within many rhythm game communities over the past two decades. It also tends to be a divisive issue between communities. In order to understand difficulty we must first understand the current systems and the reasons for their existence. Most rhythm game companies or platforms provide a basic difficulty rating system through which new players are guided, however as players develop and become more advanced it becomes apparent that the systems provided are almost universally inadequate for anything beyond novice players. This is due to a lack of incentive for companies to actually spend the time developing and producing a robust difficulty rating system for their game, and even if they were inclined to in spite of this, it’s likely they would lack the experience to follow through. Most games are constructed with a small set of beginner files in mind which then branch out into successively larger and more difficult pools of files. Most new players take any given difficulty system at face value, and for the most part, systems provided are quite capable of guiding naive rhythm game newcomers through initial stages of progression. The threshold at which newer players recognize the shortcomings of any specific system tends to be almost by definition the point at which they are experienced enough on the matter to resolve their own methodology. As these players begin to interact with more longstanding members of their respective communities, their concept of difficulty and the system by which they internally apply it to their games becomes further shaped by those who have undergone the same process, albeit with a larger degree of experience. This is how most communally constructed difficulty systems begin and propagate. These systems largely entail taking each file and assessing its general relative difficulty to other files within the game given a sample of player opinions on the matter, and creating a list ordering the files from easiest to hardest. As a game’s file pool expands and players become better, systems are expanded or rescaled to keep up. Any player good enough to recognize “official” difficulties aren’t worth anything immediately gets sucked into the existing engine of player derived difficulty, and the need for a more refined official system is erased as soon as it is created. While at first glance this may seem to be perfectly logical and adequate (and to be fair, for the most part it is), it is within this paradigm that the fundamental challenge of difficulty is both birthed and abandoned. Every player internalizes the experience of their progression through a rhythm game and formulates their concept of difficulty. These concepts of difficulty, while relatable on a global scale, are almost all different. Every player has their own idea of what difficulty is, what particular things affect difficulty and by how much. To compound the issue these notions are rarely ever made explicit, and not only do players adopt differing perspectives on what difficulty means for a given file, but the same players may adopt slightly different perspectives when given a different file, or looking at the same file in a different period of time. Because this process produces reasonably inoffensive results on a large scale smaller scale quibbles generally end in “well that’s your opinion”. Imagine sitting in a room with 20 phd candidates arguing about the use of a particular word in a thesis. Each has their own perception of what the word means and how it should be applied and in what connotations it is appropriate. Now imagine this continues for hours or days with not a single person taking the initiative to actually check the dictionary definition of the word and the process ends in everyone agreeing that everyone’s opinion is their own and the matter is settled only because it would be more productive to find a new word to argue about. Sounds ridiculous, yes? Well the only difference is we don’t have a dictionary to consult, and therein lies the first challenge of difficulty. A common language must be created for which there are explicit definitions, and then a definition of difficulty must be declared. Part 1: Understanding Difficulty So how is this possible? I’ve already asserted that everyone internalizes their own concept of difficulty. Difficulty is a constructed human concept for which there is no physical measurement. Any definition asserted can be rejected by another party as being an opinion and their own concept of difficulty presented instead. In many cases it’s not who is right that wins out in the end, but who seems more right and garners the most support from others. There is no immediately obvious methodology for determining whether the winner is correct. Yes, this is all true, and no, it wouldn’t be a challenge if it didn’t seem like a futile exercise in battling human subjectivity. Here comes the fun part. We know difficulty is not a physical property of a chart, but what if it was? Well the solution would be rather simple then, we would measure it. But we can’t, because it doesn’t exist, however, this alone is not sufficient rationale to dismiss this line of inquiry. If we can’t measure difficulty, is there perhaps a physical property of a chart that, when measured, might produce an estimation of difficulty? The answer is yes, and undoubtedly you have both already concluded that it is NPS and dismissed the concept as idiotic. For those who haven’t made those two connections yet a brief background is in order. NPS is a measurement of notes per second, some games use kps (keys) or some other variation, but the concept remains the same. If there are more buttons to hit faster, it’s harder. I’ll be referring to the concept universally as NPS henceforce, and average NPS (the total length in time of the file divided by the total number of notes) as avg NPS, and from now on I’ll also recuse myself from the duty of making sure everything I say is globally applicable to rhythm games by restricting myself to the Stepmania. This does not mean, however, that nothing I say is applicable to other games. Most of the underlying concepts and principles are either directly applicable or adaptable to other games of the VSRG genre and in fact much of the underlying logical principles employed are applicable to any endeavor in life. Using avg NPS is the most obvious first step for approaching the problem of difficulty, and it is struck down just as quickly because the results it generates are obviously terrible. Avg NPS inherits one of the major problematic qualities community difficulty systems have, generally acceptable accuracy on a large scale, but weak accuracy on a per-file basis. Avg NPS takes this quality and magnifies it to an intolerable degree. Both the frequency and magnitude of error produced by simple avg NPS ratings are prolific (these are important concepts for those of you who aren’t particularly statistically inclined, in fact, they are the tools with which we may measure the “usability” of a system), and most communities would rather rely on nothing at all than average NPS. Avg NPS fails on two major fronts, first, it makes no accounting for how notes are configured within files, and second, it makes no accounting for how a file plays or its pacing. A 5 minute file with a few moderately difficult sections may produce the same avg NPS as a 2 minute file with consistent difficulty. Even worse, a 5 minute file with a single section so difficult it becomes unpassable may produce the same average NPS difficulty rating as both of the above. The magnitude of error is large enough to result in placing files too easy for players and too difficult for players in the same category, and while this is unacceptable, it isn’t even the most significant of the issues. There is an incredible level of variation in the patterning and flow (flow will be the general term used to refer to the specific pacing of individual charts, whether they are 5 minutes of unbroken stamina play or short files bursty files, etc) of charts. Even within packs that are themed to a specific pattern type with very narrow ranges of musical selection produces charts that play out much differently from one another. Take all of Icyworld’s Sharpnel files, for instance. They’re all basically the same thing, and yet each of them plays out uniquely. We’ve identified that pattern configuration and flow are the two wrenches in the avg NPS machine, and if we are concerned with estimating frequency of error (normally we would be estimating frequency of error within a specified margin of error, but that requires our error and the margin thereof to be an actual measurement of something, which comes later. Also, in this case the evidence is so overwhelming an exhaustive inquiry is unnecessary. If you don’t believe me play any 5 ranked maps in Osu! and tell me I’m wrong) we are concerned with the degree to which the intersection of file flow and patterning produces charts that are treated significantly different by an avg NPS system. You can either work forward by asserting the degrees to which avg NPS would underrate or overrate a sample of files or backwards by taking files with the same NPS ratings and re-ranking them on your own internally, but the conclusion you will arrive at is the same. In both scenarios your internal ranking (remember, I’ve already claimed that this methodology is systemically flawed) will reveal that avg NPS will either underrate or overrate the vast majority of files. This is the fatal flaw that renders the system unusable. With a frequency of error likely in excess of 90% (here I’m estimating the frequency of error a rigorous study on the subject might produce) it is impossible to generate a reliable baseline of ratings for comparing other ratings produced by the same system in order to provide any useful information in the first place. This is true of any system with a high frequency of error. If you’re wondering right now, “so what constitutes a high frequency of error?”, you’ve already got the right mindset, but that query requires nuanced investigation into the subject that we aren’t ready for yet so just keep it in the back of your mind for now. So we know avg NPS sucks, and we know why it sucks. For a time the concept of “NPS + fudge factor” was introduced and in use, and if it sounds like people just took average NPS ratings and pushed them up or down based on what was most obviously overrated or underrated by NPS, that’s because that’s what it was. While conceptually this was the correct idea, in practice the degree of adjustments that had to be made essentially turned it into a community derived system and it inherited all the faults of differing subjective views of difficulty on top of avg NPS being incredibly unreliable. And as with many ideas that might have worked with a little more effort, but didn’t, the practice was abandoned and avg NPS assumed a negative reputation. Yes, this foray into the annals of history does have a purpose; the following section highlights a fundamental underlying concept behind this discussion of difficulty so pay attention. We learned that avg NPS is unreliable and we learned why it was unreliable, but in the process we forgot why we wanted to use it in the first place. Every player whether they are consciously aware of it or not understands that there is a fundamental relationship between NPS and difficulty. We know avg NPS provides accurate trends of difficulty despite uselessness on a per-file basis. What’s interesting is when community derived difficulty values for large enough sample sizes are plotted against average NPS the result is a strikingly strong linear relationship. Given all of the incredibly problematic issues with avg NPS it’s remarkable that any clear trend is visible at all. Now let’s take a quick step back and imagine something curious, what if avg NPS didn’t have those problems? What would we see? If the community derived difficulties were perfect, we would see a relationship, perhaps linear, perhaps describable by another mathematical function, regardless, we would be observing that there is a mathematical function for which a value for difficulty may be accurately derived based on a physical characteristic for a file, average NPS. In essence, we would be able to “measure” difficulty. “But we can’t!” Well, we know what the problems are, and we know we get what we want if the problems go away. What is the logical conclusion? No, we don’t give up, we fix the problems. “Hold on, even if we derive a value for difficulty based on NPS, wouldn’t we just be measuring NPS, what’s the point of difficulty then? Difficulty still has no meaning and is entirely subjective.” Yes, excellent point that none of you were thinking! Before we can continue, difficulty must be defined. We’ve already established that it is mathematically relatable to NPS under ideal conditions, but is there another measurable element of reality that it is also related to? The quickest way to arrive at an answer is to ask the question, “why do we care about difficulty in the first place?”. For most players a value for difficulty represents a primary indicator for how much a player will struggle on a given file, or more measurably, their performance on the file, the secondary and less preferred indicator of course, is actually playing the file. If difficulty values are relative to one another within reasonable bounds, players can make fairly strong estimates for which files they will perform better or worse on based on their performance on files at similar difficulty ratings. The magnitude of the difference indicates the degree to which a player will struggle, playing files rated far above what they’re comfortable with translates into a proportionate decrease in performance. For other players, difficulty represents an indicator of player skill. Achieving the same score on progressively higher rated files means you are becoming better as a player. Achieving the same score on a higher rated file than another player achieves on a lower level file means you’re better than them. The magnitude of the difference indicates how much better, or worse, you are, compared to another player. The bad news is I basically just told you, again, that difficulty is subjective. The good news, is difficulty is a human construction and we can define it however we want. We just need to define it in a manner that actually produces results we care about and root it in measurable terms. Don’t think about it too hard yet, follow the logic. We want another quantitative concept to mathematically relate to difficulty, and lucky for us, we just found two. The process of constructing a common language in order to better address the question of “what is difficulty” necessitates explicitly defining various concepts, preferably quantifiably. Even if we know what “difficulty” is, the problem continues to persist if everyone has different definitions of what contributes to difficulty. Also, explicit definitions facilitate accountability. This is important conceptually, however, not very relevant right now. Let’s deal with player skill first. “Haven’t we already defined player skill as difficulty, which is just NPS? So player skill = NPS. What’s the point?” In practice holding this perspective probably won’t negatively impact your experience navigating a difficulty system, however here we are primarily concerned with fundamental understanding of concepts, and secondarily with practical usage. Difficulty is a concept through which we can make an estimation of the skill level of a player. Given a large sample of player scores on files of various difficulties we can infer a reasonably accurate measurement of that player’s true skill, it is absolutely not the same thing as player skill. “So what is player skill, and what do you by mean true skill?” The article up to this point has been concept introduction and explanatory material, an informational lift hill, if you will. This is now the informational drop. While most people don’t necessarily think of player skill as a quantifiable value, much like most things we can see or experience in the world we can establish relationships and trends when controlling for the proper variables. Player skill is an intersection between time invested in playing the game and rate of improvement per hour spent. The more time you play the game, the better you become, the more efficiently you play the game, the faster you will get better. For the set of all players who improve at exactly the same rate, the determinant of player skill and their relative to strengths to one another is singularly the number of hours invested into the game. For the set of players who have invested the exact same amount of time into the game, the sole determinant of their relative strength is their rate of improvement. The set of players who have invested X hours into the game at Y rate of improvement all have the exact same level of skill, and this is true for any value of X, or Y. Now before objecting, first, recognize that rejecting the above assertions is the logical equivalent of stating that player skill is independent of either time invested or player improvement rate, or both. In any case, the logical conclusion that follows from this is that player skill is either entirely random, or dependent upon a variable not considered. We would then observe large variations in player skill that resulted in players being able to AAA files one day that they couldn’t pass at half the speed the day prior, or the reverse. And no, I don’t care how badly you randomly played that one day, it’s not statistically relevant. If the asserted relationships between player skill, time played, and player rate of improvement were incorrect, you wouldn’t have even mentally cataloged playing badly one day because it would be the norm. Now that being said, an important point is brought up. We know players tend to vary in performance from day to day or week to week, sometimes the underlying cause is apparent, such as pain or discomfort in the hands or shoulders, or taking a few months away from the game, but often there is no obvious catalyst for drastically reduced performance. This can be frustrating and is an interesting topic for discussion and conjecture, but currently it is irrelevant. For our definitional assertion of player skill I am going to make the clear distinction between true player skill and observed player skill. Scores are the product of observed player skill. We use them as estimates of true player skill because we assume that on average, player scores faithfully represent a player’s true skill. However, while this may hold up when discussing averages it is very obviously not true on a score by score basis. So then the question is why? The game is almost entirely comprised of building muscle memory, the two other major elements to play are executing on the muscle memory and properly parsing the visual triggers for execution. Many things can adversely affect the second or third processes, being sick, playing at an uncomfortable angle, new monitor, being tired. Sore muscles, sour mood, temperature, windows update lagging your pc in the background, and so on and so forth. These and many other factors can interfere with muscle memory execution or recall, however, you don’t just lose muscle memory. Players who quit for a year and resume playing are nearly always as good as or better than they were when they quit after a month. Maybe you lose some muscle, maybe your setup isn’t exactly the same as when you quit, but eventually you recoup the skill you “lost” because “skill” is fundamentally a measurement of your written muscle memory for the game. You aren’t regaining “lost” skill, the skill is still there, you just aren’t quite ready to use it yet. Furthermore, even within the same session a single player will not produce a set of scores that all produce the same estimates of player skill. Given a skill X and file difficulty Y, players will not achieve a constant and predictable score, they will only be achieving a score within their performance distribution. This is another important concept that will be revisited soon. Now, there are a couple simple facts that we must accept before continuing. First: We can never know the true skill level of a player, we can only estimate it based on information provided. Second: Even if we were told the true skill level for a player by an omniscient being, we would be incapable of verifying whether or not it was accurate. Now, yes, this sucks, but if we knew everything we wouldn’t need statistics. Unfortunately, we don’t know everything; thankfully, we do have statistics. Let’s take a model population, an infinite population whose distribution for any data point or intersection of data points perfectly models our own. This population is infinite, so we have an infinite set of Stepmania players to work with, cool. There are an infinite number of players at all skill levels (including those we have not yet reached) with every permutation of relevant traits, and we know that our collective population is a sample of this model population. Since this is all imaginary, we can even say that all values for all data points for all members of this population are known. We’ll call this population P. The P is for population. So is population, redundant, I know. Following in line with our establishment of the existence of a mathematically representable relationship between time invested and true player skill (or the library of constructed muscle memory), let’s take the set of members of population P which improve at the average rate of skill per hour invested. Since their true skill values are known we can take them and plot them against hours invested into the game, also known. The result is a distribution that indicates to us, for an average player, the number of hours required to attain a specified level of skill. True player skill X will be defined as the number of hours required to reach an observed skill level for a member of population P with a rate of improvement per hour representative of the perfect average of the population. Now that we have an explicitly defined concept of what true player skill is, we can establish its relationship to difficulty. Recall the second reason we care about difficulty. Players who have a rough idea of their performance upon files within a specified difficulty range can extrapolate those performances and formulate a relatively strong approximation of how they will score on files above and below the initial difficulty range. Now, how correct these approximations are depend on a number of things, how accurate the difficulty system is, how confident the player is in their initial estimation of their performance at a given difficulty, and how well they estimate how a given differential in difficulty translates into a differential in performance. For the moment, we’ll excuse ourselves from reality and return to the universe in which population P exists. In this universe, we know everything, and not only do we know everything, we know it to an infinite degree of accuracy. As each player of population P has a known true skill level, every file ever played by any member of population P has a known true difficulty level. This constitutes the population F (file population) for population P, and it will inherit the same qualities we have previously ascribed to population P. We’ve asserted that players use difficulty ratings to guess at expected performance on files, this is because there is a clear relationship between level of skill X of a player and the level of difficulty Y of a file. Players inherently know this to be true, and it can be observed as a trend on larger scales even within our own population for the data we have access to. Indeed, this is the entire purpose for which difficulty ratings exist in the first place, and if it fails to fulfill the purpose for which it is designed, it is useless. Hence the universal movement of rhythm gaming communities away from inadequate difficulty systems. Anyway, members of population P do not need to guess. In order for the intended purpose of difficulty to be fulfilled, every member of population P at true skill X must perform exactly the same on the same file, which has a known difficulty value of Y. Now, this does not mean that every player given a single attempt on the file in question will obtain the exact same score. Remember, scores are not measurements of skill, but estimates. Furthermore, if each member played the file a second time, not every player will obtain the exact same score on their second attempt, and even the differential between their first and second scores will vary. This is because a single score is only a single trial for a test for which there are an infinite number of outcomes. Even if every player in question attains AAAA (in Stepmania, given default timing windows, the best score attainable) grade on the file, their scores are only identical from the perspective of the game’s judgement system, or in other words, the degree of accuracy for which we are concerned with. Though we do not particularly care about performance differences on the order of +- 0.0001ms timing windows, they do exist and in our imaginary universe these measurements can be known to an infinite degree of accuracy. Given an infinite set of trials for random player of true skill X for a random file of true difficulty Y, there exists an infinite set of outcomes for the set of all permutations of judgement timings to all degrees of accuracy. Each outcome has a probability of being produced given a single trial by the player, and each outcome has a probability of being produced within N trials by the player. Now, at the moment this doesn’t really help us very much, even if we had all this information readily at our fingertips we simply do not care about the vast majority of it. We aren’t interested in variation in accuracy below the judgement windows for our game, and we aren’t particularly interested in bivariate distributions. We need to explicitly state the parameters for which we are interested in. Most if not all rhythm games operate on some variation of a point system which is based on accuracy within judgement windows. The closer you are to hitting a note at its exact point in time, the more points you get. The more points you get, the higher your score is. The specifics of a given scoring system, such as how impactful your deviation from a perfect timing is and where grade cutoffs are, or whether there are even grade cutoffs at all, vary from game to game. However, we are unconcerned with the specifics of any given system, we are only interested in the relationships it reveals: If true skill X increases and file difficulty Y remains constant, higher values of X will produce higher values of accumulated points Z. If X remains constant, and Y increases, Z decreases. The increase or decrease of Z is proportional to the increase or decrease of X or Y if the other variable constant. For any two values of X and Y there exists a unchanging value for Z regardless of a specific player or file in question. The relationships between X, Y, and Z may be expressed mathematically. Now, let’s be clear. If a specific scoring system in a game produces results that do not follow these trends, then the scoring system is erroneous in producing measurements of difficulty or player skill, or both, not our definition of difficulty. An obvious example of this is machine score in Stepmania. Over time it has become readily apparent that higher or lower machine scores have very little to do with an actual score, in fact, despite machine score still being displayed in nearly every theme’s score results screen, it’s unlikely that any serious player has looked at it in quite a few years, I know I haven’t. It is important to note that these relationships are constructed based on collapsed score distributions, or averages. For now we’ll state that a value of difficulty Y is representative of a value for player skill X for which the average of a resulting score distribution first exceeds a specified value. If we look at any score as a distribution of ms differentials of key inputs from their intended target arrow’s perfect accuracy, or their 0ms point, we know that we can obtain a probability for the occurrence for any specified ms distribution, however we don’t want to be inserting distributions for an input. That’s like, a lot of work without ms distributions being an output provided by the game engine (hint hint). For the moment, we’ll imagine that our scoring system in our perfect universe is, well, perfect. An ms distribution is used to determine the proportion of total points that is gained, and the score grade subsequently determined from that. In our perfect system any ms distribution that results in Z total points accumulated on file Y necessarily requires the same input true skill level X for a player. Also in our perfect system, the relationships between X, Y, are Z are independent of the specific value of Z (the fact that this is untrue in reality becomes important later). Now instead of having an infinite set of probabilities for an infinite set of possible scores, we have a slightly smaller infinite set of probabilities for any given total proportion of points gained. Thankfully, in reality, judgement windows force upon us a relevant degree of accuracy and furthermore, at least in Stepmania, point proportions on the score screen are rounded to two decimals. Let’s use this as our declared “care level” (degree of accuracy). What do we have now? Well now we have only 10,000 possible input Z values for which an expected value of X may be calculated to return on average given an infinite set of trials on a file of difficulty Y. Naturally, if we pick 0 as our Z value, the calculated value of X is 0 regardless of our value for Y. This is a fairly long winded proof that it takes no skill to fail out of a song, thankfully, we’re getting more out of this than that. What should become obvious at this juncture, if it wasn’t already, is that single value for difficulty depends on a stated expectation of point gain. While we may assert any value we wish to standardize difficulty, and indeed in our perfect universe it would be irrelevant which value you declared, it makes logical sense to tailor it to the landscape of the current metagame for your game. In Stepmania the de facto score grade by which players are judged is AA, or 93% of total points accumulated by the player within the scoring system. It is important to recognize why this has become the case. An A grade requires only 80% of total points to be accumulated, while AAA grade requires 100% of points to be accumulated (in our scoring system, the final accuracy judgement has no effect on proportion of accumulated points, there are arguments to be made for and against this system, however that is a discussion for another time). Given timing windows and the judgement system A grade requires a low enough proportion of total points to be gained that not only can players “mash” their way through files (keystrokes that are guided not by arrangement and place in time of notes, but merely by the existence of them), they can do it on files that they are unable to read properly (if they could read it, they wouldn’t be mashing). If we refer back to the definition of player skill, when scoring becomes decoupled from reading capability and guided development of muscle memory through directed action, scoring also becomes decoupled from skill. Yes, this is also a very, very long winded proof that mashing takes no skill. AAA grade runs in the opposite direction, requiring players to hit no arrows outside a 90ms timing window. The reality of play is that once players are capable of a high enough skill level to AAA files, the files in question are much less appealing to play (this concept will be expanded greatly upon later). Technically, the probability of a player AAA’ing a file is never 100%. Players can fumble, or stop paying attention, or have a heart attack, or be crushed under a whale that recently materialized in the upper atmosphere. When considering AAA grades, minor errors with small probabilities often nullify scores. Players often observe a near-AAA grade score and mentally undermine the score, citing that an AAA would be more impressive. When we look at this from the perspective of a perfect difficulty system, given a value of Z of 100% versus 99.99% we would find that the expected true skill levels for both queries would be virtually identical, however this fact is not represented in most attitudes. The reality of the situation is that we don’t have an infinite number of trials to expend on attempting to AAA something, the reality is minor errors nullify the perceived skill required to obtain a near AAA score more than they should, the reality is many players find files they should be able to AAA too far below their skill level to be enjoyable, the reality is certain characteristics of files and certain pattern configurations result in significantly different effects on difficulty depending on the proportion of points depending on the value and that in many cases when looking at AAA grade the proportions of files (proportion is plural, yes) that fall within relevant difficulty ranges becomes incredibly small relative to the total expanse of the file, and the reality is because of all these things once a requisite level of skill in order to AAA a file is achieved, whether or not a given player AAA’s a file is largely dependent upon their number of trials. I’ll restate the above for emphasis, and also in case you got lost. AAAs are about either grinding or about being so much better than you actually had to be to AAA it you might as well be trying to quad it. Playing files for which you are significantly above the skill required to AAA tends to be unappealing. Most players tend to play files fairly close to their skill range, and regardless of whether the player in question is focused on speed or accuracy, the point at which just barely AAAing files lies relative to player skill tends to be outside of the normal range of comfort. Now, grinding is not a measure of skill. Most people know this without knowing precisely why, so let’s approach the issue logically. When we look at scores obtained by players we generally do not take a look at their number of trials or the average score of their trials, we only consider the highest score obtained, which definitionally had the lowest probability of attainment if player skill stayed constant. Grinding is a method by which players may skew their observed score distribution and the resulting estimation of player skill in their favor. This is not to say we should begin considering taking averages for players’ scores, that’s ridiculous, we would never be able to control for scores grinded at a specific skill level and the entire principle is antithetical to much of the game. Indeed, while players who grind scores out may skew their scores in their favor, in many cases this will hurt them in the long run. If a player has obtained most of their best scores through grinding, even if they improve they have essentially forced themselves to continue grinding in order produce scores that reflect their improvement. Instead, it is our duty to construct a system for rating players that takes scores as estimations for player skill and aggregates them in a way that produces the best estimation of a player’s skill. This is an interesting topic and there is a lot to discuss when considering determining player ratings from scores which you can read about here. The result is, at least in Stepmania, AA grade has been tacitly adopted as the standard to which players are held, and to which they play. The issues that present when considering A grade and AAA grade scoring are also apparent in AA scoring, though their effect much diminished. Files which are shorter and contain short sections with sharp spikes in difficulty tend to favor a grindy approach, and even within the bounds of a 93% score goal it is certainly possible for players to mash elements of the file they are unable to read. The two issues are not necessarily mutually exclusive either, it is much easier to mash your way through a section you have difficulty reading to get a better score if it’s a short file and the amount you have to mash is limited in scope, the issue becomes compounded and grinding because very appealing. Scores which just barely scrape the edge of AA grade, but don’t officially pass the 93% mark, are held in disproportionately low esteem relative to the virtually indistinguishable level of skill required to achieve them. Regardless, the magnitude of these effects are much less pronounced and limited to a much smaller selection of files. I will personally state that as a player I find, depending on the file, the 95-97% score goal mark to most likely have the largest correlation with true skill. This is obviously a personal opinion and do not expect everyone to agree with me, however, the fact that I hold this opinion is important. In Stepmania’s current competitive metagame, insofar as it actually exists, 93% grade has been adopted as the standard benchmark for “speed” players due to the default AA grade cutoff being universal and easy to point to. It is understood within the community that it is fairly silly to base accuracy players on their AAs, and the “accuracy” have a separate school of thought. For now, I’ll only be discussing “speed” players and i’ll be using the term to refer to globally refer to all skillsets that are not pure accuracy. If you don’t know what skillsets are it turns out they’re kind of important and you can read about them and their impact on perception of difficulty and the general metagame here. While it’s arguable that a more appropriate cutoff could have been established, the decision making of the community tends to be largely unguided and unconscious, this is also fine, but, despite most players understanding that 97% on a file requires a higher level of skill to obtain than 93% on the same file, and placing emphasis on this fact on a per-file basis, when it comes to the larger perspective, the scores for players that surpass 93% by large margins are often viewed as being the same as 93% scores, resulting in players whose best scores lie within the 95%+ range being underrated compared to a similarly skilled player whose best scores lie just at the 93% mark. The issue at hand is not whether or not a 97% score is better than a 93% score on the same file, but how do we relate a 97% score on one file to a 93% score on a completely different file? This can be done if two files and scores are carefully taken into consideration, however this is not reproducible on a larger scale. Essentially, to be a speed player whose true skill level is most accurately estimated, it is necessary to achieve as many barely obtainable AA scores as possible. For the moment we’ll ignore the fact that, for most players who strive for a point of equilibrium between speed and accuracy, this sucks, and focus on the statistical implications. Most speed players tend to enjoy playing files they can reliably obtain between 91% and 98% on, and in addition this range of difficulty is the most effective range to play at in order to develop yourself as a speed player. It is my guess that, on average, between warming up and pushing for new AA’s, most players divide their number of hours invested into playing the game fairly equally between files they can be expected to 91%, 92%, 93%, and so on and so forth. If anything the distribution is likely skewed towards easier files, or files players will on average score higher percentages on. We know a single score is the result of a single trial of a player on a given song, and we know that performance far outside the expected norm, while rare, may occur. Most players refer to such scores as “flukes”, and in turn “flukes” are often the pillars upon which the legacies of players are built. The problem is, if every score obtained above is treated at the same value as its 93% difficulty, and similarly 91-92.99% scores are underweighted, we force only a tiny fraction of the average player’s time invested to fully “matter”, and their 1/1000 probability scores must lie within this tiny fraction of play to properly influence the perception of their skill. This is ridiculous from both a practical standpoint and system efficacy/accuracy standpoint. Returning to the two major camps of players “speed” and “accuracy”, the reason they have been segregated is because the physical characteristics that influence pure accuracy, or AAA+ grade, and speed, are too different to be relatable in a coherent manner. In fact, they require significantly different development and execution of muscle memory as well as reading skill. Now, we also know that the same principle applies to 91% through 97%, which is why attempts to relate them on a mass scale have been avoided. On the other hand, assuming we properly construct a methodology for accurately determining difficulty for 93%, can we expand it to include 91%-97% scores to a degree of accuracy that is reasonable? The answer turns out to be yes and it will be discussed more later, but the first question we must answer is, what is a reasonable degree of accuracy? We know that a true difficulty for every file exists, and we know that as players given enough time and experience and through consensus we’re capable of producing estimates of true difficulty within a certain range of error. Independent of what the actual margin of error is, our explicit goal for accuracy for an algorithmically determined difficulty system is simply exceed the margin of human perception error (for now I’ll just roll frequency and magnitude of error into one, but each is important in its own way). It is essentially the point at which we are no longer capable of discerning whether or not what we’re being told by the system is wrong. As it turns out, this is a fairly low bar, however it’s important to realize that an algorithmically determined difficulty system that does not produce results within the margin of human error is functionally worthless. Now let’s backpedal a bit, we know the standard score goal for gameplay in the metagame is 93%. We might surmise that a more apt value can be chosen, however for now the decision is given to us. The difficulty rating we are concerned with is specifically the difficulty to obtain a 93% total proportion of points on a given file. Ok cool, that was pretty simple, now let’s backpedal a little further, recap, and figure out again what the point of the last 10 pages was. 1. We know every player who plays or has ever played the game has a true skill level. True skill level is measured in hours it would take an average person to invest into the game in order to construct the same library of muscle memory as the person in question. True skill level is independent of observed skill level and any factors that may influence it. True skill level is also independent of the specific muscle memory constructed, it is only a measurement of volume. We can never know the true skill level of any player, only estimate it, and we can never verify whether or not our estimation is correct, we can only assert the degree of confidence we place in it. 2. We know every file has a true difficulty level. If NPS is a perfect representation of difficulty, NPS = difficulty level, and it may be measured and scaled or transformed. Files are configurations of notes that span a length of time, and any subsection of a file also has a true difficulty level. This includes any 30 second section, 3 second section, or 3 note section. We are incapable of knowing the true difficulty level of a file, only estimating it, and again, we can only estimate the accuracy of our estimations, never verify them. Note that while technically each individual note within a file also has a true difficulty level, NPS cannot be used to measure it due to division by a time segment of 0ms. 3. If we could control for every factor that contributes to a discrepancy between observed skill and true skill, players with skill X on files with difficulty Y have identical score distributions for any combination of the values of X and Y, and the averages of these score distributions are understood to be errorless indicators of true skill level. If we control for any two of the three variables the resulting values will always be transformable through a single power exponent to fit a line. Now we are ready to declare a definition of difficulty: Difficulty values are estimations of the required level of true player skill required to produce score distributions that average a cumulative point proportion of 93% given an estimation for true file difficulty. Due to the relationship between true skill and true difficulty our value of difficulty may be interpreted as either or both and scaled to reflect measurements of true skill(hours), true difficulty(NPS), or to any system that controls one or more variables in order to produce constants during progression. Player scores on a file with a given difficulty are estimations of player skill and a system can be created that aggregates scores and produces a single estimate for player skill. Cool, now we know what we’re talking about. We’ve defined the elements relevant to the discussion of difficulty. In our model universe, we would be able to derive estimations of difficulty based on NPS that would produce identical values to their known true difficulty values. We would be able to take a score distribution from a model player and reverse calculate their player skill level, and verify our results. We would be able to take any score on any file and compare the requisite skill level to obtain this score to any score on any other file. In other words, we have created the perfect system in our perfect universe. Ok, we got through the easy part. Now the hard part is hard because of a simple truth; our universe isn’t perfect. We don’t know everything, and we don’t have infinite data or the resources to generate it. Even if we did we’d die before we could finish properly analyzing it. Now, we didn’t just waste all this time, well, we would have if we chose this moment to give up, but at least I didn’t. We’ve identified relationships and trends that we know we would observe if we knew everything. We’ve created definitions for concepts we can use to discuss difficulty and in the process we have given ourselves the tools we need to understand what difficulty is. None of this changes just because we don’t know everything. Conceptually the system is bulletproof, it is designed to give us exactly what we want so long as we give it what it wants― and therein lies the second challenge of difficulty. We know the circumstances under which this system will work, and we know the elements of reality that prevent it from working. So now it’s time to fix the problems. Part 2: Estimating Difficulty The question of how to proceed is perhaps fairly daunting. Initially it may seem as though it is unclear as to what we should do with the understanding of difficulty we have constructed. While for the reader this may be the case, when given three weeks or so to contemplate the issue, multiple approaches to the problem at hand became clear and the problem became selecting the best one. You can trust me on that, but I’ll discuss the different approaches and their relative strengths and weaknesses at another time, for now we’ll ask ourselves what do we want? We want a system that provides us with useable difficulty ratings with the least amount of developmental time, effort, and complexity. In short, we want to pick the shortest path that get’s us to where we want to be. The use of the word usability here might be confusing, so I’ll elaborate, though it mostly has to do with the concepts of statistical error and confidence and if you don’t need this particular primer feel free to skip ahead (a few pages). Error is not a concept we can eliminate or avoid. All difficulty systems have error, whether they are created by the original game designers, whether they are community derived, or whether they are created as a by-product of a programmatic system. Error exists whether or not you are aware of it, and accepting that it exists is in no way a sacrifice, it is merely the accommodation of a reality that already exists, you know, basically what we do every day of our lives. Error comes in three forms, frequency, magnitude, and the intersection of the two, or, the frequency of error given a defined range of magnitude. At lower ends, particularly for novice files, communally derived difficulty delivers excellently on both fronts. There are plenty of players to offer different perspectives on the files in question and large amounts of data regarding performance may be collected and used to reinforce opinions. Furthermore most games contain few novice level files, largely because certain pattern usage is prohibitive, and because newer players (at least, the ones that continue to play) tend to advance fairly quickly through the ranks. There isn’t a need for a large volume of novice files because by the time novices play through enough of them, they are no longer novices. This is an odd situation, on the one hand we wish for our beginner levels to have a very high level of accuracy. Novice players, as stated before, tend to take difficulty ratings at face value and use them to guide their progression through the game. Inaccurate difficulty can be off-putting to new players, they are less likely to blame the difficulty system than to blame themselves for being incapable of executing performance on similarly rated files, even if in reality they are much farther from each other in difficulty. They simply lack the experience to be able to make an informed decision for themselves. On the other hand, from a practical standpoint the lower end of the difficulty spectrum is the section in which players advance out of the most quickly. Having perfect accuracy is unnecessary because by the time a player accrues enough scores on enough files the process of doing so will necessarily have advanced them much further. It is much more important to have perfect accuracy at the very top end (from the perspective of the active top level players, at least). At the very top ends even small margins of error in difficulty rating can place files years apart in true skill level at the same rating. Let’s use the concept of perfect nps as an example, moving from 1-2 nps files may take a novice player a few hours. Moving from 28-29 nps may take a top player with years of experience another 4 months of practice and play. In both cases nps is increased by 1, however the required time to generate the skill necessary to make the improvement is vastly different. Not only is it vastly different in absolute terms, but the relative difference between 1 and 2 is far greater than the relative difference between 28 and 29. This is the basis for the statement that movement along the nps line of skill per hour invested is logarithmic. The underlying concept is that the margin of error should not some defined metric of practicality. We don’t want to be placing two files at the same rating when one takes two more years to be able to AA than the other. This is the magnitude of error. If we wish to base our desired target magnitude of error on a pre-existing model, we have at our disposal communally generated difficulties once again. In the case of very top end files, difficulty ratings (or if we are looking at “difficulty” from the perspective of “achievement”, the degree of “achievement”), tend to present incredibly high magnitudes of error. It is not particularly the fault of the participants in the endeavor, it is due to a lack of information. When looking at what are perceived to be the top 3 players in any rhythm game (or any game, really) it is often the case that they each have radically different strengths and weaknesses, and skillsets. This is not because it just so happens that in most games the distribution of top players is guided by some cosmic force to be spread along differing specializations, it is because of another equally silly cosmic force-- human perception bias. Independent of whether a perfect system would label the top 3 players as being the top 3 or not, the default mode of human perception is to take the best at each skill and equate to them the same level of skill. No, it really doesn’t make any logical sense, and yes, we are all guilty of it. This is an extremely different paradigm to break from, and even more so when it is reinforced by psychological influences of group dynamics. The reality is if you have 10000 players playing one specific type of file, and 5 players playing another, it is more likely that the top 100 players from your first group are all better than the best player of your second group than it is for the top two to be of equivalent skill level. Ironically it is in fact this very concept that so often works against the Stepmania community. In the case of many other VSRGs the argument is put forth that because Stepmania is a relatively small and concentrated community, our players cannot be “as skilled” as more populated communities. In this regards Stepmania tends to be an extreme outlier, and it is the result of a single fundamental difference between Stepmania and other rhythm games. For its entire existence charts played in Stepmania have been created by the community. Conquering old charts creates a need for new and more challenging charts which players can create for themselves at any time in a matter of hours. In most conventional rhythm games, this process of feedback looping is artificially interrupted by level designers and “official” charts. This is however a discussion for another time. The point is communally derived difficulty systems not only fail miserably at delivering adequate magnitudes of error at the top end, but they are incapable of providing themselves with the necessary information or experience to do so, literally by definition. By the time enough players are good enough at what were formerly the most difficult files or the greatest achievements in order to accurately place them in relation to each other, they are no longer the most difficult! Now, in order for a system to functionally replace a pre-existing system, it must provide enough improvement in specific areas, or even better, across all areas. In terms of magnitude of error, the bar has been set pretty low. I observed, at the top end, magnitudes of error in the Stepmania community difficulties that I would guess spanned at minimum a year. This is obviously subjective, and being human I too am affected by human perception bias. That doesn’t stop me from making this statement, it only stops the reader from having absolute confidence in it. This leads, eventually, to an interesting realization. At best our system only needs to produce values for which given the range of error incurred by human perception bias, we are unable to determine whether or not error actually exists. Recall that we cannot create a perfect system, and recall that even if we did, we could never prove that it was perfect. The best we can do is to create a system for which we cannot tell whether or not it is perfect. The result is we are able to place our absolute confidence in it. So what does this mean functionally? Even in assessing error we cannot truly know what we are talking about in absolute terms, we can only assert estimates of error in our estimates. Thankfully I’ve already done a bunch of the leg-work, have already created a system and exhaustively tested it, and whether or not you choose to accept it, the range of error that I’ve found to be acceptable at the top end is around +-3%. This is what I view as the borderline of acceptability; this range of error (if you are already familiar with the system it’s roughly a spread of 31.4-34.7) already encompasses years of skill. My guess is a system that produces error within a 1% margin is functionally no different to us than a perfect system. These are the desired benchmarks for magnitude of error for an acceptable, and desirable system, however, they are meaningless without the context of the distribution or frequency of error. In a way no difficulty we ever assert for a file is correct; we are always wrong (technically you can get around this with clever semantics I know, but let’s stay on target). We cannot care about being right, it is a pointless endeavor, we can only care about how often and the degree to which we are wrong. The previously stated acceptable ranges of error magnitude are only the ranges for which I expect the vast majority, or 95%+, of all files evaluated for difficulty, and with respect to a distribution, I expect a very small portion of those files to have difficulties with magnitudes of error that fall at the ends of the range. For the files that fall within the specified margin, most will fall within a 2% margin of error, and hopefully enough of them will fall within a 1% margin of error that effective baseline difficulties can be assigned to guide further adjustment. Conceptually a baseline is simple, in fact it is more or less consciously employed in most of the ratings delivered through communal assignment or consensus. You take a file for which you assert the difficulty rating is as close as possible to the true value, and you use it to guide your assessments of other files. The issue, as previously discussed, is that everyone views every baseline differently. They all have their perspectives and takes on what functionally creates the difficulty in the specified file, and they all approach and utilize the baseline differently. This issue is artificially recreated if the frequency of error of any system exceeds a large enough margin. Baselines only truly help if their relation to other baselines accurately represent the true difference in difficulty. In essence, you need a large enough population of relatable baselines for having any understanding of the values provided by the system. Failure to do this creates a system that is functionally unusable, it would be like using metric and imperial measurements interchangeably during a physics class. Only instead of just metric and imperial you had an infinite set of differing units of measurements and you didn’t know how to relate any of them because there is no standard unit for anything. Right, not only is it unusable, but any reverence or officiality given to such a system is simply harmful. In other words, ideally we hope to create a system which produces values that do not exceed a 1% margin of error for any file evaluated, and for which the average margin of error is far lower (+-0.2%?). This may not resonate immediately with the reader as a far-fetched goal, however let me say that a much more reasonable (actually, the most reasonable, as it is essentially the bare minimum required for any system to be useful at all) goal is for 90% of evaluated files to fall within 3% margins of error with the average margin of error closer to 1.5%. |
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i don't want to read that ayy 150446 |
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is that a map code? (jesus i copied an ashiya x reader) "This is the third time this month, Mr. Ashiya," you chided gently, a shadow of a smile on your face. The towheaded patient's stomach made a ghastly, guttural noise and he enveloped his abdomen with his arms, a groan of his own escaping passed his paled lips. "Food poisoning again... Maybe it's time to throw out the noodles?" You'd grown quite fond of the silly man, and you'd like to think you'd gotten to know him quite well. Though, you wished he didn't have to suffer from a sour stomach in order to see him, it was the reason such a peculiar bond. You'd been a nursing student for six months, and never before had you seen someone so in and out of the hospital for food poisoning. You knew Shiro Ashiya and his roommate were quite low on funds, but to continue eating expired Udon was quite the length to go to. One of your co-workers suggested that the man may have been doing it on purpose, but you bashfully dismissed the thought. Ashiya was odd in many ways, but poisoning himself on purpose was not something you could see him doing. "I-it's all we have," he grunted and shifted beneath the bed sheets. His eyes followed you weakly, but bulged out as you pulled out a brand new I.V bag. Ashiya lurched up and reached out for you, "N-no! There's no need for another!" Your brows shot up at his terrified expression; it was worse than a child receiving a shot, "Mr. Ashiya, you've lost a lot of liquids; you're dehydrated, and this is the only thing that's going to make you feel better." "But I've barely paid off the last batch!" Hopelessness swelled up in his whiskey-colored irises and he sunk back against the mattress with a heavy sigh. Ashiya clutched the sheets within his clammy fists, brows furrowed tightly, "At this rate, we'll never get ahead. Damn this weak stomach..." You frowned, and looked down to the plastic bag in your hand. With a slight purse to your lips, you swapped the empty I.V bag out for the new one, "Don't worry, Mr. Ashiya. I'll keep this one off record." His throat made a choking noise, "Miss ______, you would... really do that?" "Mmhm... I just want you to feel better," you smiled endearingly and turned to face him. "As much as I like your company, I don't like seeing you this miserable." To your surprise, the man jolted up to his knees and bowed over as he grabbed your hands, "Oh, thank you, Miss ______! You are most merciful, and I will not let your graciousness go unpaid!" A squeamish gurgle rumbled through the hospital room, and Ashiya shrunk further into himself. Curled into a fetal position, he released your hands and rolled over with a pitiful moan. You grimaced sympathetically and gave his shoulder a comforting squeeze, "Get some rest, Mr. Ashiya." You spent the rest of the night checking on the other patients, and catching up on home work between exorbitant amounts of coffee. Your shift was about to come to a long awaited end, but you wanted to fetch a cool clothe for Ashiya and check up on him before you left. The halls were mostly silent and nearly empty. Most of the staff had long since gone home, while the overnight nurses were compiled at the front nurse's station. The eerie sound of beeping monitors followed you as your shoes tapped against the linoleum floors. You were nearing Ashiya's room and, as you turned the corner, bumped into a stranger. You staggered back and opened your mouth to apologize, but you were interrupted by a click. Your eyes widened in horror, staring down a barrel of a gun. You slowly raised your hands, and hesitantly looked into the man's dark circles. He shook nervously, his eye twitching like some sort of tick, "Don't scream or I'll shoot," he said, tone hushed, yet urgent, "I-I want medication." Your mind raced and your heart thundered against your chest. You couldn't speak, your body wanted to run, but terror crippled you. You yelped as the man waved the gun and flinched as he grabbed you by the wrist. He pulled you against him, the gun to your head and his free hand around your throat, "Please..." You whimpered, eye closed tightly. "I said: I want medication. O-oxycotton... M-morph-ph-phine!" "O-okay, okay... I'll show you, just... Just don't shoot." He pushed you out, the gun still pressed firmly into the small of your back. You swallowed nervously, and silently prayed that no one cross your path. You didn't want any of the other nurses to get hurt or, worse, a patient. You barely took three steps before a door down the hall opened, and caused you to stop in your tracks. You held your breath as your white-haired patient sleepily padded out of the room, holding onto his I.V stand, "Miss ______?" You barely had time to react as the gun left your back and fired at Ashiya. You gasped as the bullet whizzed through his chest cavity and sprayed crimson liquid across the walls. Ashiya's body crumpled to the floor and brought the I.V pole down on top of him. The man behind you shoved you to the ground and stood over you, gun pointed down at your face, "L-look what you made me do!" Alarmed voices could be heard down the hall and panic consumed the man's expression. You tried to sit up, but the cool metal barrel dug into the flesh of your forehead, "Please, no!" You exclaimed, holding out your hands, "I can still get you what you need... I promise. J-just don't hurt anyone else, don't kill me..." "________?!" A nurse cried as she rounded the corner and froze immediately as the man raised his weapon to her. "Don't! Wait! Th-this man j-just wants some medication," you explained, your eyes glued to the rugged man. You didn't dare move or take your eyes off of him, even if every fiber of your being wanted to rush to Ashiya's side. "H-he won't hurt anyone as long as we get him what he wants... R-right?" He nodded, eyes narrowing at your co-worker. "Sir, will you let this nurse check on my patient? P-please?" He furrowed his brows as if to consider it, and his eyes flickered away from you. His mouth slackened and his body began to tremble once more, "Wh-where did he go?" You turned your head, Ashiya's body vacant from the floor, nothing but a pool of blood and an I.V pole. "H-he escaped! He's going to call the police!" In a panic, the gunman pulled the trigger. Time slowed as the barrel flashed, the bang causing a ringing in your ears. You held your arm up in a measly attempt of self-defense, but your life was already flashing before you eyes. You never felt impact, and, instead, heard the nurse at the end of the hall scream. A fabric brushed over your form and you lowered your arm to see a cloaked figure standing between you and the gunman. He was tall, his shoulders broad and strong. His light hair color was familiar, as were his amber eyes as he glanced over his shoulder, but the black, dragon-like scales along his face were not. "M-Mr. Ashiya?" You marveled breathlessly. "Who the hell are you?!" The gunman cried, staggering backward. Ashiya turned his gaze away from you and matched the man with a step forward. The man fired his gun several times, but Ashiya stopped them mid-air and swiped them away like flies. In a much deeper voice than you knew, he spoke, "Your weaponry will do you no good." He swiftly grabbed the man by the throat and hoisted him off the ground. The gunman choked and gagged, his feet dangling helplessly, "Pl-please, don't kill me!" Ashiya scoffed darkly and flung the man across the hall. The man's back slammed into the wall, the sheet rock caving against the force and his body collapsed to the floor. Your hand flew over your mouth as Ashiya turned to face you, his once kind eyes now narrow and cold. He knelt beside you and you flinched as the other nurse screamed, "Somebody help! Don't touch her!" He gave the nurse a side glance and raised his hand. You watched in utter bafflement as she swayed and fell limp to the floor. You gasped and jolted upright, your body flush against the wall, "Wh-what... What did you do to her?!" "She's only sleeping," his tone was gentler, but still absent of it's original aloofness, and he raised a hand toward your face. You flinched, but his hand only relented for a moment before it cupped your cheek tenderly. His palm was warm against your flesh, and it was tempting to tilt your head further into it, "Are you all right, Miss ______?" "I-I don't understand... He shot you... He -you... Why do you look like this?" "Yes, I know I have a lot to explain," he paused, the sound of sirens wailing outside. He glanced over his shoulder, red and blue lights flashing against the windows. Ashiya met your eyes again, and you caught your breath at the base of your throat, "Do you trust me, Miss ______?" Blush crept up into your cheeks. Your mind was jumbled in confusion, but there was no doubt that this was the Shiro Ashiya you had taken care of, the silly man who so often suffered from Udon poisoning. You didn't know how or why he had grown scales,why his ears were pointed, or why he suddenly had a reptilian like tail, but if he wanted to harm you he would have easily done so by now. You nodded sheepishly, and he smirked. Ashiya sneaked his arm under your knees, and wrapped the other to support your back. A small squeak escaped your lips as he effortlessly hoisted you off the ground and held you protectively against his chest. "Where are we going?" You asked, barely above a whisper. Your brows crinkled, "And where did your cloak come from?" Ashiya didn't answer. He proceeded into his previous room and opened the window. Your heart lurched against your chest as he stepped out and jumped without warning. You clutched onto him and buried you face into his shoulder as he took flight over the city. You could have sworn he chuckled, but everything faded to black soon after... __________________________________________________________________________________ Your eyes fluttered open and a soft groaned emitted from between your lips. A haze of florescent lights blinded you, a thin mattress between your body and the floor. As the room came into focus, you realized that the apartment was not your own and there was the unmistakable scent of cucumbers in the air. You turned your head to see the back of Ashiya's head at the stove, his cloak replaced by a green apron. "Mr. Ashiya?" His head craned over his shoulder, and tightened his slack mouth into a smile. "Oh, Miss _____! Thank goodness, you're awake." You sat up slowly, a hand pressed against your head as it throbbed mercilessly. Ashiya approached the table with a pot of soup, generously pouring the hot liquid into a bowl. He beckoned you with a tilt of his head and you obliged, "Cucumber soup?" He scratched the back of his head coyly, "Yes... I'm sorry. I know it isn't much, but it beats Udon. I wouldn't want us both to end up in the hospital." Your lips quirked into a brief smile, but it faded as the memories came flooding back to you. Moisture sprung into your eyes and a knot swelled in your throat. You tried to stifle a whimper, but your sniffling had caught Ashiya's attention, "Is there... something wrong with cucumbers?" You burst into tears, your hands flying to cover your face, "I've never been so scared in my life!" You wailed. "The gun to my head... the sound of it going off when he shot you! The way your body slumped to the floor... I was so scared that you were gone!" "Miss _____--" "It's just _______!" "R-right," his tense posture slumped and he inched closer to you apprehensively. "______, there is a lot you don't know about me, and a lot I don't know if you'd believe. I... regret to inform you that it might be better if you forgot it all. I can erase it all from your mind completely." "But... what about you? Will I remember you?" He frowned, "I... don't think it would be wise. I'm sorry..." "Then, no!" You flung your arms around the man's shoulders with such force that you both toppled over. You shielded your face in the crook of his neck, both to hide the tears and the blush in your cheeks, too embarrassed to meet his eye. "I don't want to forget you, not after you saved my life. I don't care how you did it, just don't make me forget. Please, Ashiya." His breath hitched and his hands hovered over your back for several seconds. What was this warm feeling? It made his skin tingle and his heart beat faster than he ever recalled. He knew he couldn't tell you, that it was wrong to divulge such information to a human, but... he wanted to. He'd only known you a short time, but this odd, foreign feeling inside was filled with dread at the thought of you having no recognition of him should your paths ever cross again. "I am the right hand general to King Satan," he murmured, his hands slowly lowering onto your back. Almost completely on their own, his arms tightened around you in a cradling manner, "A demon... My true name is Alciel. Sire and I fled to this world to bide time until we could return to our home: Ente Isla. That man's bullet didn't kill me because your despair gave me enough magic to return to my demon form, grab my mantle and come to your rescue." You pushed yourself up, your tears dried along your face, "You... came back here to grab your cloak first?" Ashiya averted his eyes shamefully, "It... completes the look." "But... if you're a demon, why did you save me? Aren't you suppose to be... Evil?" "Sire and I have learned much since our stay in your world," a pink hue colored his cheeks, his eyes cast to the side, "You have... been most kind and gentle toward me since our meeting. I could not stand idly by while--" Ashiya's words were muffled as your lips came crashing down on his. His body stiffened, his eyes wide in surprise while yours were squeezed shut. Warmth consumed you as his right hand traced the contour of your back to your head, succumbing to the kiss and deepening it. You mewed in delight as his tongue stroked your lower lip, and you granted him access. In desperate need for air, you lifted your mouth from his. You matched his shade of blush, and stared down at him with the same wide, starry-eyed look. "I like you," the words tumbled out in a hushed whisper. "You do?" You bobbed your head, and Ashiya's mouth quirked, "I think... I quite like you, too." You smothered a grin and lowered your head back down to him. Ashiya lifted to meet you, his arms tightening around your waist. Just as you were about to steal another kiss, the apartment door swung open, "Hey, Alciel--" You tore away from between Ashiya's legs into an upright position, your entire body redefining the shade red. Even Ashiya bashfully scrambled away from you as his roommate casually strolled into the apartment, and followed up with a quick nose-dive to the floor, "M-Maou! S-Sire! It's not what it looks like -I mean, it sort of is, but... But this female was in desperate need of assistance, and one thing led to another--" "Would you relax?" Maou groaned, sticking his pinky-finger into his ear. "I heard about it on the news at Chi's. So, this is the nurse you won't shut up about?" "You... talk about me?" You murmured. Ashiya rose from the ground, his words stammering as if trying to back-track or explain. He sighed and dipped his head in dramatic defeat, "It's true." "I was beginning to think he was eating expired Udon on purpose. You were, weren't you?" "Pardon me, sire! Do you think I enjoy having a sour stomach?! I wouldn't have to eat Udon if it weren't for your reckless spending," Ashiya gasped and took another nose-dive, "I'm sorry for my brash words, Sire!" Amidst the commotion, a grin broke loose and you cried out into a fit of laughter. Maou and Ashiya looked to you quizzically as you clutched your sides, gasping for air as your giggles mercilessly attacked your lungs. "I don't understand... Miss ______, whatever is so amusing?" "You," you answered between chuckles. You gathered yourself up off the floor, still smiling brightly, "You're a silly, silly man." |
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wowza its bts comeback song named spring day and if you are from army be careful watching this video |
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ok lol 152076 |
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Numbers http://en.delfi.lt/lithuania/article.php?id=72883882 |
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what? why? Iloveajlynx hope - Today at 6:52 PM RIN HOSHIZORA IS A CUTE LITTLE FURRY |
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ok > [19:32] [ro] [Furelisee] aaaahhh |
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aaaaaaaahhhhhhhhh! Okay, so my school had a valentine’s dance tonight, and people were making fun of me, the smart, mature girl, hanging with her boyfriend. I got the last laugh when I saw one of the jerks who was making fun of me dancing with his girlfriend. |
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oh [img] [/img] |
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Ok, that's natural ![]() |
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"no no" Okay, so my school had a valentine’s dance tonight, and people were making fun of me, the smart, mature girl, hanging with her boyfriend. I got the last laugh when I saw one of the jerks who was making fun of me dancing with his girlfriend. |
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ok... |
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Haha nice colour pencils I bet you could prank someone with colour pencils like that :P Lankoo 5000mAh is a designer series power bank, as there are 16 different designs to choose from. Unlike other power banks that have simple white or black case, Lankoo is funkier. Apart from beauty, it also protects your iPhone or iPad from short-circuits and high temperature while charging. It can charge your iPhone two times when at full capacity. |
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awesomeeeee 你好我很快樂 |
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luhan ca understand that ok so after all the theoris from bts im dEAD iNSIDE aaaand i dont think that seokjin is dead |
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triggered You spin my head right round, right round When you go down, when you go down down You spin my head right round, right round When you go down, when you go down down |
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my old jam at 5 years why someone should sacrifice jin??? i mean his the umma + he's super cute |