Name: 純反應與反應跟蹤--揭開瞄準的祕密第 2 集 (Pure Reactivity vs. Reactive Tracking - Unraveling the Secrets of Aim Ep. 2)
Uploaded: 2025-09-16T14:40:20.000Z
Duration: 12 min 24 s

This is where MattyOW unraveled the mysteries of AIM training.

這就是 MattyOW 揭開 AIM 培訓神祕面紗的地方。

For sure, everyone knows Reactive Tracking.

當然，每個人都知道 "反應式跟蹤"。

It's one of the six primary categories on every AIM training benchmark, but did you know that this one category is actually its own spectrum?

這是每個 AIM 培訓基準的六個主要類別之一，但您知道嗎？

On one end of the range, we have standard Reactive Tracking.

This includes well-known scenarios like Air and Ground Plaza.

這包括眾所周知的場景，如空中和地面廣場。

These are foundational scenarios that have been around for almost as long as Kovacs and have provided immense value since their creation.

這些基礎方案的存在時間幾乎與 Kovacs 一樣長，而且自誕生以來就提供了巨大的價值。

Yet on the other end of the spectrum, as the difficulty and evasiveness of the Bosony scenario increase, Reactive Tracking turns into Pure Reactivity.

然而在另一端，隨著 Bosony 情景的難度和迴避性增加，反應式跟蹤變成了純粹的反應式。

Pure Reactivity, as a category, includes the more difficult reactive scenarios, most notably LDDH.

純反應性作為一個類別，包括難度較大的反應性情況，其中最突出的是 LDDH。

My followers definitely know that I recommend LDDH variants frequently.

我的粉絲肯定知道我經常推薦 LDDH 變體。

This is because of how different it is in nature compared to other ground tracking variants.

這是因為它在性質上與其他地面跟蹤變體有很大不同。

Not only is this scenario much harder than Ground Plaza, but it requires a much different approach, one that we've already seen.

這種情況不僅比地面廣場難得多，而且需要一種我們已經見識過的截然不同的方法。

To clear up confusion, I must admit that calling scenarios Pure Reactivity is somewhat misleading, because Pure Reactivity is not really a category.

為了消除混淆，我必須承認，把場景稱為 "純反應性 "有些誤導，因為 "純反應性 "並不是一個真正的類別。

It is an approach, a specific technique used to play these specific Reactive Tracking scenarios.

它是一種方法，一種用來扮演這些特定的 "反應式跟蹤 "場景的特殊技巧。

Everything we're discussing within this video falls under the one category title of Reactive Tracking, but we just need different labels for whether a scenario can be played in the standard way or if it can be played purely reactively.

我們在本視頻中討論的所有內容都屬於 "反應式跟蹤 "這一類別，只是我們需要用不同的標籤來標示一個場景是可以用標準方式播放，還是可以純粹用反應式播放。

Let's begin by talking about the path a new player might take to understanding this category.

首先，讓我們來談談新玩家瞭解這一類別的途徑。

Through this, we can better understand the development of specific techniques along the way.

通過這些，我們可以更好地瞭解特定技術的發展歷程。

Let's assume that a new player to AIM training will gravitate towards the Voltaic benchmarks.

假設 AIM 培訓的新學員會傾向於使用 Voltaic 基準。

They'll play Reactive Tracking in the form of relatively easy Air and Ground variants.

他們會以相對簡單的空中和地面變體形式進行反應追蹤。

What makes these easier is that they feature bots that are much larger and slower.

讓這些機器人更容易上手的原因是，它們的機器人要大得多，速度也慢得多。

This makes it easier for new players to get used to movement reading.

這讓新玩家更容易習慣動作讀取。

At this stage, players who struggle with Reactive Tracking may also struggle with smoothness.

在這一階段，在 "反應式跟蹤 "方面有困難的玩家也可能在流暢性方面有困難。

If they cannot match the target's speed well enough or react sluggishly, or if they react too quickly in such that they overflick, it will hurt their score.

如果他們不能很好地配合目標的速度，或者反應遲緩，或者反應過快以至於過閃，都會影響他們的得分。

If you are at this stage and trying to improve with these early variants, you must simply focus on training stability and getting your movement reading skills up to par.

如果你正處於這個階段，並試圖通過這些早期變體來提高成績，那麼你必須把重點放在訓練穩定性和提高動作閱讀能力上。

This means grinding Tracking scenarios on evasive bots.

這意味著要在躲避機器人的情況下進行追蹤。

You could try playing Invincible versions that acclimate you to singular bot patterns for longer periods of time.

您可以嘗試玩 "無敵 "版本，讓您更長時間地適應單一的機器人模式。

At this point, you just need to grind and learn.

在這一點上，你只需要磨練和學習。

Focused thought and practice will bring some players to a much higher playing field.

專注的思考和練習會讓一些球員達到更高的競技水準。

The advanced section of Reactive Tracking will usually feature the standard bot patterns for high-level AIM training.

Reactive Tracking 的高級部分通常會介紹高級 AIM 培訓的標準機器人模式。

It is at this point that previously simple concepts of stability, speed matching, and smoothly recovering become integral to the process and automatic.

此時，以往簡單的穩定性、速度匹配和順利恢復等概念已成為整個過程中不可或缺的一部分，並實現了自動化。

However, movement reading at this level becomes much harder.

然而，這個級別的動作閱讀變得更加困難。

Because of quicker bot movements, you need to find opportunities to make space for recoveries.

由於機器人的移動速度較快，您需要尋找機會為恢復留出空間。

This is where you must employ Edge Tracking.

這時，您必須採用邊緣跟蹤技術。

Edge Tracking, sometimes referred to as under-aiming or under-shooting, means to place your crosshair intentionally off of center mass while tracking a reactive target, specifically on the following edge of the bot.

邊緣跟蹤，有時也稱為欠瞄準或欠射擊，是指在跟蹤反應目標時，有意將十字準線偏離中心品質，特別是偏離機器人的下邊緣。

This means that if the bot is strafing right, you will follow the bot on its left edge.

這就意味著，如果機器人向右掃射，你將跟隨機器人的左側邊緣。

And if it's strafing left, then you will follow its right edge.

如果它在左側掃射，那麼你將跟隨它的右側邊緣。

What this does is create more space for a good recovery by maximizing your time on target during a strafed direction change.

這樣做的目的是在掃射變向時，通過最大限度地延長目標時間，為良好的恢復創造更多空間。

This is because, since you are on the following edge of the bot, when the bot changes its direction, it will pass through your crosshair.

這是因為，由於您處於機器人的跟隨邊緣，當機器人改變方向時，它將穿過您的十字準線。

You will still stay on target during the time it takes for you to react to the direction change and recover the track.

在您對方向變化做出反應並恢復軌道的時間內，您仍將保持在目標位置。

For most players' aim training careers, this is as far as one will go.

對於大多數球員的瞄準訓練生涯來說，這已經是極限了。

However, as standards for aim training increase, and as players continue to press themselves towards a higher difficulty, you will inevitably run into scenarios like LDDH.

然而，隨著瞄準訓練標準的提高，以及玩家不斷向更高難度挑戰，你將不可避免地遇到像 LDDH 這樣的情況。

Well, for one thing, it is a mix of many complex, fast movements.

首先，它混合了許多複雜、快速的動作。

It's almost impossible to hold a steady track on the bot for a long period of time.

在機器人上長時間保持穩定的軌道幾乎是不可能的。

Also, most importantly, LDDH bots do not have movement acceleration and deceleration, which means there is no indication of the next bot direction change.

此外，最重要的是，LDDH 機器人沒有運動加速和減速功能，這就意味著沒有下一個機器人方向變化的訓示。

I personally found this scenario almost unplayable the first time I tried it, but after learning what I needed to do to play it correctly, I could confidently say that LDDH gave me the keys to understanding pure reactivity.

我個人第一次嘗試時覺得這個場景幾乎沒法玩，但在瞭解了正確玩法之後，我可以自信地說，LDDH 給了我理解純反應性的鑰匙。

Perhaps a better way I should have transitioned myself into these scenario types is through RA reactivity, specifically strafe track, reactive sphere, and leap track.

也許我應該通過 RA 反應能力，特別是掃射軌跡、反應球和跳躍軌跡，更好地過渡到這些場景類型。

Strafe track specifically is a much easier version of LDDH.

具體來說，Strafe 軌道是更簡單的 LDDH 版本。

Once again, viewing the category of reactive tracking as a spectrum, these scenarios, along with Fugla track variants and Blink track, fall into the middle of the range.

再次，將反應式跟蹤類別視為一個頻譜，這些方案與 Fugla 跟蹤變體和 Blink 跟蹤一樣，屬於頻譜範圍的中間部分。

It is common thought that, well, since these scenarios are simply more difficult and evasive, wouldn't it be viable to emphasize more on the concepts reinforced by advanced tracking?

人們普遍認為，既然這些場景難度更大、更容易躲避，那麼更多強調高級跟蹤所強化的概念不就可行了嗎？

Because of how fast these bots move, with no acceleration and random blinks, and in seemingly perfectly random patterns, trying to apply common advice and these well-known techniques falls apart.

由於這些機器人的移動速度非常快，沒有加速度，隨機閃爍，而且看似完全隨機的模式，是以試圖應用普通建議和這些眾所周知的技術就會失敗。

事實證明，邊緣跟蹤毫無用處。

Instead, you simply need to try for any long period of time on target that you can possibly get.

相反，您只需在可能獲得的目標上進行長時間的嘗試。

The reason why most players fail to do this is because of predicting.

大多數玩家之所以做不到這一點，是因為預測。

Predicting is a bad habit that forms out of too much trust in predictable bot patterns.

預測是一種壞習慣，是由於過於相信可預測的機器人模式而形成的。

What this looks like most of the time is a player that is reacting to a bot strafe that never occurs.

大多數情況下，這看起來就像是玩家在對從未發生過的機器人掃射做出反應。

Other times, it manifests when a bot is in a very long strafe and the player begins to shake off of the current strafe in the opposite direction, expecting some kind of non-existent direction change.

其他時候，它表現為機器人正在進行一次很長的掃射，而玩家開始向相反的方向擺脫當前的掃射，期待著某種不存在的方向變化。

Other times, it just looks like poor speed matching and preemptive motion changes.

其他時候，看起來只是速度匹配不佳和先發制人的運動變化。

The reason why this fails in scenarios like strafe track is because these bots are so evasive, so seemingly random, and often much smaller, so not only is there much more room for failure in a prediction, but failures also hurt your score so much more.

這在掃射軌道等場景中之所以會失敗，是因為這些機器人的躲避能力很強，看似隨機，而且往往體積更小，是以不僅預測失敗的空間更大，而且失敗對得分的影響也更大。

But predicting, at least mentally, is a part of movement reading.

但預測，至少是心理上的預測，是運動閱讀的一部分。

So does this mean that movement reading goes out the window with these scenarios?

那麼，這是否意味著在這些情況下，運動閱讀就不存在了？

Well, if you look at it through the perspective of a certain reactive player, say, madbadman, that would be a reasonable conclusion.

好吧，如果你從某個反應遲鈍的玩家（比如瘋子）的角度來看，這將是一個合理的結論。

Madbadman does not believe in reading bot movements, which makes sense for these types of scenarios.

Madbadman 不相信讀取機器人的動作，這在這類情況下是合理的。

他對他們的做法純粹是反應性的。

It is the very brute force, rigid, snappy looking kind of aim that minimizes time off-target by simply reacting to and syncing your motions with bot strafes.

它是一種非常粗暴、僵硬、看起來很敏捷的瞄準方式，只需對機器人的掃射做出反應並同步你的動作，就能最大限度地減少脫靶時間。

Pure reactivity is exactly what it sounds like.

純粹的反應性就是它聽起來的樣子。

No fancy techniques, no abusing a pattern, just good old-fashioned reaction in any direction.

沒有花哨的技巧，沒有濫用的模式，只是向任何方向做出老式的反應。

And obviously, this isn't realistic, because every scenario always has a pattern that can be exploited to some degree.

很顯然，這是不現實的，因為每種情況都有一定的模式可以被利用。

Any player will have tendencies and movement that can be exploited.

任何球員都會有可以被利用的傾向和動作。

There is no pure randomness with these kinds of things, and if there were, it wouldn't really make for a good scenario.

這類事情沒有純粹的隨機性，如果有，也不會有好的結果。

Nonetheless, pure reactivity scenarios have been coined as MBM-reactive, due to his connection with aimbeast, the only place where scenarios at this far end of the spectrum can be found.

儘管如此，由於他與 aimbeast 的關係，純反應性方案被稱為 MBM-反應性方案，而 aimbeast 是唯一可以找到這種極端方案的地方。

This includes crazy scenarios like godball, close fs, and passive track evo.

這包括瘋狂的場景，如 Godball、近距離 FS 和被動跟蹤 evo。

They take the concepts reinforced by LDDH and run with it.

他們接受了 LDDH 強化的概念並加以運用。

They are the hardest form of reactive tracking that can be possibly found in any aim training environment, at least currently, and realistically, they should be at the end of any path that a new player would take through aim training.

至少在目前的任何瞄準訓練環境中，它們都是最難的反應跟蹤形式，而且從現實的角度來看，它們應該是新玩家進行瞄準訓練的所有路徑的終點。

Pure reactivity and reactive tracking have different applications when you go to make the in-game translation.

在進行遊戲內翻譯時，純反應性和反應跟蹤有不同的應用。

It is critical that you understand these applications and mappings when you decide which to prioritize.

在決定優先考慮哪些應用時，瞭解這些應用和映射至關重要。

This next segment will discuss that in detail.

下一部分將詳細討論這個問題。

For those of you who don't know me, I'm an aim trainer player, and I'm mostly known for my reactive tracking scores.

對於那些不認識我的人來說，我是一名瞄準訓練器玩家，我最出名的是我的反應跟蹤得分。

Outside of aim trainers, my main game is Overwatch.

除了瞄準訓練器，我的主要遊戲是《守望先鋒》。

For reference, over my seven years of playing it, I have around 3,000 hours in Overwatch, and earlier this season, I was ranked 61 on DPS.

作為參考，在我七年的遊戲生涯中，我在《守望先鋒》中的遊戲時間約為 3000 小時，本賽季早些時候，我的 DPS 排名為 61。

I mainly played Soldier and Tracer to get there.

我主要是玩《阿兵哥》和《追蹤者》才達到目的。

Overwatch arguably has the highest pure reactivity requirement out of any popular FPS game, and this is due to the fundamental nature of the game engine used by Overwatch.

可以說，在所有流行的 FPS 遊戲中，《守望先鋒》對純反應能力的要求是最高的，這是由於《守望先鋒》所使用的遊戲引擎的基本性質所決定的。

Movement in the Overwatch engine is completely different from almost every other popular FPS game in one incredibly crucial way.

在《守望先鋒》引擎中，移動與幾乎所有其他流行的 FPS 遊戲都完全不同，其中有一個極其重要的方面。

Unlike games like Apex or CSGO, Overwatch has instant acceleration.

與《Apex》或《CSGO》等遊戲不同，《守望先鋒》擁有即時加速功能。

This means instead of taking a small amount of time to ramp up to your max velocity when moving in-game, you instantly start moving at your max speed whenever you press a movement key in Overwatch.

這意味著，在遊戲中移動時，您不再需要少量時間來提升到最大速度，而是隻要按下《守望先鋒》中的移動鍵，您就會立即開始以最大速度移動。

Instant acceleration, or instant excel for short, might seem like a minuscule thing, but in reality it has pretty massive implications when it comes to aiming.

瞬時加速（簡稱 "瞬時卓越"）看似微不足道，但實際上在瞄準時卻有相當大的影響。

In a game with gradual acceleration, like Apex Legends, you have a small window of opportunity to re-adjust a target's movement whenever they change directions.

在《Apex Legends》這類漸進加速的遊戲中，每當目標改變方向時，你都有一小段機會重新調整目標的移動。

This window of opportunity occurs when the target's velocity differs from their max speed, right?

當目標的速度與最大速度不同時，就會出現機會之窗，對嗎？

If somebody starts decelerating in Apex Legends, that means they've just started strafing in the opposite direction.

如果有人在《Apex Legends》中開始減速，這意味著他們剛剛開始向相反的方向掃射。

If you're able to consistently notice this in-game, it makes it relatively easy to read movement.

如果你能在遊戲中始終注意到這一點，那麼閱讀動作就會變得相對容易。

As Matty has previously discussed, a game like Apex Legends focuses more on general reactive tracking than pure reactivity for this simple reason.

正如 Matty 之前所討論的，《Apex Legends》這類遊戲更注重一般的反應跟蹤，而不是純粹的反應性，原因很簡單。

In a game like Overwatch, it's completely different.

在《守望先鋒》這樣的遊戲中，情況則完全不同。

Instant acceleration eliminates the window of opportunity for reading strafes.

瞬時加速消除了讀取掃射的機會窗口。

This turns situations that would involve general reactive tracking into situations that exclusively rely on pure reactivity.

這就把涉及一般反應性跟蹤的情況變成了完全依賴純反應性的情況。

Another additional layer of complexity is added by the plethora of movement abilities that exist in Overwatch, like Tracer's blink, or Soldier's sprint, or Cassidy's roll, or Ball's grapple, or D.Va's thrusters, or Monkey's jump, or Rein's charge, etc.

守望先鋒》中存在的大量移動能力，如 Tracer 的閃爍、Soldier 的衝刺、Cassidy 的翻滾、Ball 的抓取、D.Va 的推進器、Monkey 的跳躍、Rein 的衝鋒等，又為複雜性增加了一層。

Granted, with all this being said, there actually are a small handful of abilities in Overwatch that have gradual acceleration, like Wrecking Ball's ball form, and there even are some individual heroes that have gradual acceleration as a part of their base movement, because either they don't touch the ground, or they have a weird form of movement like Lucio or Ekko.

當然，說了這麼多，《守望先鋒》中其實也有一小部分能力具有漸進加速，比如破壞球的球形，甚至還有一些個別英雄的基礎動作也是漸進加速，因為他們要麼不接觸地面，要麼就像盧西奧或埃科那樣有一種奇怪的移動方式。