-
+
-
-
-
-
-
-
-
-
-
- {!wideFormat && {plugs} }
-
-
- Tools
-
-
- {TOOLS.map((tool, index) => {
- if (tool.href || tool.disabled) return null;
-
- return (
- event.preventDefault()}
- >
-
- {tool.title}
-
-
- );
- })}
-
-
-
-
-
-
-
+
- setShowHamburgerMenu((state) => !state)}
+ >
+ {showHamburgerMenu ?
-
+
+
-
-
-
-
+ {wideFormat && plugs}
- {login && (
-
-
+
+
-
-
+
+
-
-
-
-
-
- )}
+ {login && (
+
+
+
+
+
- {/* TODO: re-enable in the future */}
- {!login && false && (
-
-
-
-
-
-
-
- Choose your region
-
-
- {REGIONS.map((region) => (
-
- {UNLOCALIZED_REGION_NAMES[region]}
-
- ))}
+
+
+
-
-
-
- )}
+
+
+ )}
+
+ {/* TODO: re-enable in the future */}
+ {!login && false && (
+
+
+
+
+
+
+
+ Choose your region
+
+
+ {REGIONS.map((region) => (
+
+ {UNLOCALIZED_REGION_NAMES[region]}
+
+ ))}
+
+
+
+
+ )}
+
+
+
+
+ {TOOLS.filter((tool) => !tool.href).map((tool) => (
+ {
+ if (tool.disabled) event.preventDefault();
+ }}
+ >
+
+ {tool.title}
+
+
+ ))}
+
+
-
-
+
+
);
}
diff --git a/src/constants/tools.ts b/src/constants/tools.ts
index d6ea5187d..e9ddbe0e4 100644
--- a/src/constants/tools.ts
+++ b/src/constants/tools.ts
@@ -25,18 +25,11 @@ export const TOOLS: Tool[] = [
},
{
id: 'rating',
- title: 'Rating leaderboard',
+ title: 'Rating',
description: 'Full rating gamemode leaderboard',
pageDescription:
'Live and archived full rating leaderboard for World of Tanks Blitz',
},
- {
- id: 'session',
- title: 'Session tracker',
- description: 'Live session performance tracker',
- pageDescription:
- 'Track your stats in real time as you play World of Tanks Blitz',
- },
{
id: 'compare',
title: 'Compare',
@@ -44,6 +37,13 @@ export const TOOLS: Tool[] = [
pageDescription:
'Compare tanks statistics and loadouts in World of Tanks Blitz',
},
+ {
+ id: 'session',
+ title: 'Session tracker',
+ description: 'Live session performance tracker',
+ pageDescription:
+ 'Track your stats in real time as you play World of Tanks Blitz',
+ },
{
id: 'discord',
title: 'Discord bot',
diff --git a/src/icons/Blitzkit.tsx b/src/icons/Blitzkit.tsx
new file mode 100644
index 000000000..c8425ef4e
--- /dev/null
+++ b/src/icons/Blitzkit.tsx
@@ -0,0 +1,180 @@
+import { ComponentProps } from 'react';
+
+export function Blitzkit(props: ComponentProps<'svg'>) {
+ return (
+
+ );
+}
diff --git a/src/stores/app.ts b/src/stores/app.ts
index 38fbdff15..cb036b2db 100644
--- a/src/stores/app.ts
+++ b/src/stores/app.ts
@@ -12,7 +12,6 @@ interface Login {
export interface App {
devBuildAgreementTime: number;
developerMode: boolean;
- darkMode: boolean;
login?: Login;
}
@@ -21,7 +20,6 @@ export const useApp = create
+ 
+
+
+## Preface
+
+> [!CAUTION]
+> This is a working draft and is incomplete. Do not implement.
+
+The goal of this paper is to deliver a numerical metric for judging player performance by converging all statistics down to a single, easy-to-consume, human-readable value. This must be a mathematically elegant process that yields a reasonable number that asymptotes under extreme situations. More specifically, this value should equal 1, on average across all players and tanks. Extreme cases should be mapped between 0 and 2. This value will be multiplied by 100 and rounded to the nearest integer for readability purposes.
+
+Wargaming provides cumulative statistics for players and tanks. The big idea is to average all provided statistics together to get one unified value. We will also incorporate other composite values based on these default statistics into the average. However, averaging this way poses two problems:
+
+1. All statistics do not scale equally. For example, good players performing with 3,000 damage per battle could also attain a kill-to-death ratio of 3. But averaging them as-is would imply damage per battle has weight 1000 times that of the kill ratio. In other words, damage per battle would totally diminish the impact of kill ratios on the average. Hence, there’s a need for normalization. Normalization, in most cases, simply means dividing by the corresponding averages.
+2. All statistics are not equally important. For example, dealing damage is arguably far more important than base capture; hence, it does not make sense to average them together. But this does not mean we totally discard this value as it does make an impact albeit very small. Instead, we will be incorporating weighted averages where the weights will be calculated through correlational analysis against win rate. Specifically, the weights of the statistics will be the corresponding correlation coefficient, r. One caveat to look out for is that r can be negative for statistics like damage received which must be minimized. Fortunately, negative weights are okay and make intuitive sense; after all, bleeding health should punish the metric.
+
+## Constants
+
+A few constants that the rest of the paper depends on. Available under the “constants” object.
+
+```ts
+players_per_team = 7;
+```
+
+## Wargaming-Provided Statistics
+
+Wargaming provides the following pieces of statistics for each player and tank. These are cumulative; in other words, they simply add up over time and it is up to the recorder to contrast two snapshots of said statistics in time to get periodical statistics. Available under the “all” object.
+
+```ts
+battles;
+capture_points;
+damage_dealt;
+damage_received;
+dropped_capture_points;
+frags; // kills
+frags8p; // frags against tanks at or above tier 8
+hits;
+losses;
+max_frags;
+max_frags_tank_id;
+max_xp;
+max_xp_tank_id;
+shots;
+spotted;
+survived_battles;
+win_and_survived;
+wins;
+xp;
+```
+
+## Composite Statistics
+
+Raw statistics from Wargaming can be combined into more meaningful numeric snippets. Most of these values are divided by all.battles indicating they are the average across all battles in the given period. Assigned under the “composite” object.
+
+```ts
+win_rate = all.wins / all.battles;
+capture_points = all.capture_points / all.battles;
+damage_dealt = all.damage_dealt / all.battles;
+damage_received = all.damage_received / all.battles;
+dropped_capture_points = all.dropped_capture_points / all.battles;
+frags = all.frags / all.battles;
+hits = all.hits / all.battles;
+shots = all.shots / all.battles;
+spotted = all.spotted / all.battles;
+survival_rate = all.survived_battles / all.battles;
+xp = all.xp / all.battles;
+damage_ratio = all.damage_dealt / all.damage_received;
+accuracy = all.hits / all.shots;
+damage_per_hit = composite.damage_dealt / composite.hits;
+```
+
+## Normalized Statistics
+
+The goal is to adjust all values such that each value individually averages out to 1 across all players. Most normalizations are simple. The more complex ones will be represented mathematically. Assigned under the “normalized” object.
+
+```
+win_rate = composite.win_rate / average.win_rate
+damage_dealt = composite.damage_dealt / tier_average.health
+damage_received = composite.damage_received / tier_average.health
+```
+
+Now the complex normalizations.
+
+### `frags`
+
+On average, a player kills 1 player on the enemy team. However, the max is 7. We need to map 0 frags to 0, 1 to 1, and 7 to 2 to maintain, for the lack of a better term, an asymptotic behavior in extreme cases. $\sqrt{x}$ serves as an excellent choice as a parent function as it already features points $\left( 0, 0 \right)$ and $\left( 1, 1 \right)$. However, at $x = 7 \Leftrightarrow y = \sqrt{7}$ but we desire 2. We can achieve this by applying a coefficient of $\frac{2}{\sqrt{7}}$. This however shifts the point $\left( 1, 1 \right)$; hence, we have to linearly interpolate the coefficient between 1 and $\frac{2}{\sqrt{7}}$ on the interval $\left[ 1, 6 \right]$. Proof for the coefficient is left as an exercise for the reader.
+
+$$
+normalized.frags = \left[ \frac{composite.frags - 1}{constants.players\_per\_team - 1} \left( \frac{2}{\sqrt{constants.players\_per\_team}} -1 \right) +1 \right] \sqrt{composite.frags}
+$$
diff --git a/docs/guide/bkrl.md b/docs/technical/bkrl.md
similarity index 100%
rename from docs/guide/bkrl.md
rename to docs/technical/bkrl.md
diff --git a/docs/guide/cdon.md b/docs/technical/cdon.md
similarity index 100%
rename from docs/guide/cdon.md
rename to docs/technical/cdon.md
diff --git a/docs/guide/rts.md b/docs/technical/rts.md
similarity index 100%
rename from docs/guide/rts.md
rename to docs/technical/rts.md
From 02b5f0c45a01f8dcba734f57aad4bc176b75ff4d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tr=C3=A8sAbhi?= <43380238+tresabhi@users.noreply.github.com>
Date: Thu, 16 May 2024 20:35:12 -0500
Subject: [PATCH 04/93] feat: more!
---
.vscode/settings.json | 1 +
docs/technical/bkpm.md | 81 +++++++++++++++++++++++++++++++++---------
2 files changed, 65 insertions(+), 17 deletions(-)
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 0e37499bb..3be768de7 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -65,6 +65,7 @@
"fullstats",
"fuzzysort",
"gamemode",
+ "gameplay",
"glsl",
"GLTF",
"haha",
diff --git a/docs/technical/bkpm.md b/docs/technical/bkpm.md
index 167d2f1fa..1b3401b33 100644
--- a/docs/technical/bkpm.md
+++ b/docs/technical/bkpm.md
@@ -4,12 +4,21 @@
Revision 1
-## Preface
-
> [!CAUTION]
> This is a working draft and is incomplete. Do not implement.
-The goal of this paper is to deliver a numerical metric for judging player performance by converging all statistics down to a single, easy-to-consume, human-readable value. This must be a mathematically elegant process that yields a reasonable number that asymptotes under extreme situations. More specifically, this value should equal 1, on average across all players and tanks. Extreme cases should be mapped between 0 and 2. This value will be multiplied by 100 and rounded to the nearest integer for readability purposes.
+## Why BkPM over WN8?
+
+BkPM addresses a lot of issues with WN8:
+
+- WN8 incorporates win rate into the metric; a metric like this must be a correlate of win rate, not a dependent
+- The weights and biases are not dynamic and their formulation is not transparent
+- WN8 values do not asymptote under extreme conditions resulting in meaninglessly large or negative values
+- WN8 has too many digits; it doesn't make sense to differentiate between players with sub-one-thousandth's worth of precision
+
+## Preface
+
+The goal of this paper is to deliver a numerical metric for judging player performance by converging all statistics down to a single, easy-to-consume, human-readable value. This must be a mathematically elegant process that yields a reasonable number that asymptotes under extreme situations. More specifically, this value should equal 0, on average across all players and tanks. Extreme cases should be mapped in $[-1, 1]$. To attain the human readable value, we will be transforming the raw metric to a scale of $[0, 200]$ using $M = \text{round}(sx + s)$ where $M$ is the resulting human-readable metric, $s = 100$ is an arbitrary scaling factor, and $x$ is the raw metric.
Wargaming provides cumulative statistics for players and tanks. The big idea is to average all provided statistics together to get one unified value. We will also incorporate other composite values based on these default statistics into the average. However, averaging this way poses two problems:
@@ -73,20 +82,58 @@ damage_per_hit = composite.damage_dealt / composite.hits;
## Normalized Statistics
-The goal is to adjust all values such that each value individually averages out to 1 across all players. Most normalizations are simple. The more complex ones will be represented mathematically. Assigned under the “normalized” object.
-
-```
-win_rate = composite.win_rate / average.win_rate
-damage_dealt = composite.damage_dealt / tier_average.health
-damage_received = composite.damage_received / tier_average.health
-```
-
-Now the complex normalizations.
-
-### `frags`
-
-On average, a player kills 1 player on the enemy team. However, the max is 7. We need to map 0 frags to 0, 1 to 1, and 7 to 2 to maintain, for the lack of a better term, an asymptotic behavior in extreme cases. $\sqrt{x}$ serves as an excellent choice as a parent function as it already features points $\left( 0, 0 \right)$ and $\left( 1, 1 \right)$. However, at $x = 7 \Leftrightarrow y = \sqrt{7}$ but we desire 2. We can achieve this by applying a coefficient of $\frac{2}{\sqrt{7}}$. This however shifts the point $\left( 1, 1 \right)$; hence, we have to linearly interpolate the coefficient between 1 and $\frac{2}{\sqrt{7}}$ on the interval $\left[ 1, 6 \right]$. Proof for the coefficient is left as an exercise for the reader.
+We will be using the standard deviation and the mean of each metric supplied to this expression below to normalize each metric in the range $[-1, 1]$. The goal is to map the mean of the statistic to $0$, and the extrema to $-1$ and $1$ (whichever direction that may be in).
$$
-normalized.frags = \left[ \frac{composite.frags - 1}{constants.players\_per\_team - 1} \left( \frac{2}{\sqrt{constants.players\_per\_team}} -1 \right) +1 \right] \sqrt{composite.frags}
+\frac{x-\mu}{\left|x-\mu\right|}\left[1-e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^{2}}\right]
+$$
+
+During computation, one may pre-compute and reuse $\gamma=x-\mu$ in:
+
+$$
+\frac{\gamma}{\left|\gamma\right|}\left[1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right]
+$$
+
+Here is an example of HisRoyalFatness scoring a very high $0.988$ with the frags metric with his [XM551 Sheridan](https://blitzkit.app/tools/tankopedia/20257). $\mu=\frac{1081.164}{1405.2273}$, $\sigma=0.2492$, and $x=\frac{7310}{4837}$, HisRoyalFatness' average frags. At $x = 0$ (if the player manages to get no frags), the metric drops nearly to $-1$ and vice versa. And, at $x=\mu$, the metric is exactly $0$.
+
+
+
+### Derivation
+
+We start with the standard normal distribution formula
+
+$$
+\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^{2}}
+$$
+
+
+
+We remove the coefficient to transform the range of the function from $\left(0,\frac{1}{\sigma\sqrt{2\pi}}\right]$ to $\left(0,1\right]$. From this point onwards, I will be using the substitution $\gamma=x-\mu$.
+
+$$
+e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}
+$$
+
+
+
+Then we mirror the function across $y=\frac{1}{2}$ to allow the function to be rewarding as you reach the extrema.
+
+$$
+1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}
+$$
+
+
+
+Finally, to punish the function as you approach the left extreme, we flip the sign of the function when $x<\mu$. One may choose to implement this as a piece-wise function (especially when implementing this in a computer), but I have chosen to represent it as one unified coefficient in this paper.
+
+$$
+\frac{\gamma}{\left|\gamma\right|}\left[1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right]
+$$
+
+
+
+For cases where increased $x$ negativey impacts gameplay, like in the case of `composite.damage_received`, the sign of the coefficient can simply be flipped. In fact, the sign of the function can be determined by the sign of the corelation coefficient.
+
+$$
+\frac{r\gamma}{\left|r\gamma\right|}\left[1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right]
$$
From 8d697847798117c08e3e0dc4e0fb4b365d462125 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tr=C3=A8sAbhi?= <43380238+tresabhi@users.noreply.github.com>
Date: Fri, 17 May 2024 14:14:44 -0500
Subject: [PATCH 05/93] feat: more!
---
docs/technical/bkpm.md | 42 ++++++++++++++++++++++++++++++++++++------
1 file changed, 36 insertions(+), 6 deletions(-)
diff --git a/docs/technical/bkpm.md b/docs/technical/bkpm.md
index 1b3401b33..bd018283b 100644
--- a/docs/technical/bkpm.md
+++ b/docs/technical/bkpm.md
@@ -1,8 +1,6 @@
-
+ BlitzKit Performance Metric
+ Revision 1 +
-
-
+
+
+# BlitzKit Neutrality Index
> [!CAUTION]
> This is a working draft and is incomplete. Do not implement.
@@ -18,13 +16,45 @@ BkPM addresses a lot of issues with WN8:
## Preface
-The goal of this paper is to deliver a numerical metric for judging player performance by converging all statistics down to a single, easy-to-consume, human-readable value. This must be a mathematically elegant process that yields a reasonable number that asymptotes under extreme situations. More specifically, this value should equal 0, on average across all players and tanks. Extreme cases should be mapped in $[-1, 1]$. To attain the human readable value, we will be transforming the raw metric to a scale of $[0, 200]$ using $M = \text{round}(sx + s)$ where $M$ is the resulting human-readable metric, $s = 100$ is an arbitrary scaling factor, and $x$ is the raw metric.
+The goal of this paper is to deliver a numerical metric for judging player performance by converging all statistics down to a single, easy-to-consume, human-readable value. This must be a mathematically elegant process that yields a reasonable number that asymptotes under extreme situations. More specifically, this value should equal 0, on average across all players and tanks. Extreme cases should be mapped onto the range $[-1, 1]$. To attain the human readable value, we will be transforming the raw metric to a scale of $[0, 2s]$ using $M=\operatorname{round}\left(sR+s\right)$ where $M$ is the resulting human-readable metric, $s = 100$ is an arbitrary scaling factor, and $R$ is the raw metric.
Wargaming provides cumulative statistics for players and tanks. The big idea is to average all provided statistics together to get one unified value. We will also incorporate other composite values based on these default statistics into the average. However, averaging this way poses two problems:
1. All statistics do not scale equally. For example, good players performing with 3,000 damage per battle could also attain a kill-to-death ratio of 3. But averaging them as-is would imply damage per battle has weight 1000 times that of the kill ratio. In other words, damage per battle would totally diminish the impact of kill ratios on the average. Hence, there’s a need for normalization. Normalization, in most cases, simply means dividing by the corresponding averages.
2. All statistics are not equally important. For example, dealing damage is arguably far more important than base capture; hence, it does not make sense to average them together. But this does not mean we totally discard this value as it does make an impact albeit very small. Instead, we will be incorporating weighted averages where the weights will be calculated through correlational analysis against win rate. Specifically, the weights of the statistics will be the corresponding correlation coefficient, r. One caveat to look out for is that r can be negative for statistics like damage received which must be minimized. Fortunately, negative weights are okay and make intuitive sense; after all, bleeding health should punish the metric.
+## Computation
+
+$\mu$ is a simple weighted average of statistics across all players where $C_{battles}$ is the number of player battles and $x_{i}$ is the statistic for player $i$.
+
+$$
+\mu=\frac{\sum_{ }^{ }C_{battles}x_{i}}{\sum_{ }^{ }C_{battles}}
+$$
+
+$\sigma$ is the standard deviation of the statistic across all players. Below is a refresher of the formula where $x_{i}$ is the statistic for player $i$ and $n$ is the number of players (or the sample size).
+
+$$
+\sigma=\sqrt{\frac{\sum_{ }^{ }\left(x_{i}-\mu\right)^{2}}{n}}
+$$
+
+An individual atomic statistic is provided by the equation below where $x$ is the player's statistic being evaluated, $\mu$ is the mean of the statistic across all players, and $\sigma$ is the standard deviation of the statistic across all players. During computation, one may pre-compute and reuse $\gamma=x-\mu$.
+
+$$
+A=\frac{x-\mu}{\left|x-\mu\right|}\left(1-e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^{2}}\right)=\frac{\gamma}{\left|\gamma\right|}\left(1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right)
+$$
+
+The raw metric is the weighted average of all atomic statistics.
+
+$$
+R=\frac{\sum_{ }^{ }wA}{\sum_{ }^{ }w}
+$$
+
+The final human-readable metric will be passed through the following transformation where $s=100$.
+
+$$
+M=\operatorname{round}\left(sR+s\right)
+$$
+
## Constants
A few constants that the rest of the paper depends on. Available under the “constants” object.
From 61db42d2250824f9f0b259b7ccc6678f729f8ac0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tr=C3=A8sAbhi?= <43380238+tresabhi@users.noreply.github.com>
Date: Fri, 17 May 2024 20:23:51 -0500
Subject: [PATCH 06/93] feat: more!
---
docs/technical/bkpm.md | 22 ++++++++++++++--------
1 file changed, 14 insertions(+), 8 deletions(-)
diff --git a/docs/technical/bkpm.md b/docs/technical/bkpm.md
index bd018283b..479f9628e 100644
--- a/docs/technical/bkpm.md
+++ b/docs/technical/bkpm.md
@@ -37,12 +37,24 @@ $$
\sigma=\sqrt{\frac{\sum_{ }^{ }\left(x_{i}-\mu\right)^{2}}{n}}
$$
-An individual atomic statistic is provided by the equation below where $x$ is the player's statistic being evaluated, $\mu$ is the mean of the statistic across all players, and $\sigma$ is the standard deviation of the statistic across all players. During computation, one may pre-compute and reuse $\gamma=x-\mu$.
+A normalized atomic statistic is provided by the equation below where $x$ is the player's statistic being evaluated, $\mu$ is the mean of the statistic across all players, and $\sigma$ is the standard deviation of the statistic across all players. During computation, one may pre-compute and reuse $\gamma=x-\mu$.
$$
A=\frac{x-\mu}{\left|x-\mu\right|}\left(1-e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^{2}}\right)=\frac{\gamma}{\left|\gamma\right|}\left(1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right)
$$
+$m$ is the slope of the least squares regression line. Below is a refresher of the formula where $x_i$ is the player's statistic being evaluated, $y_i$ is the player's win rate, and $n$ is the number of players (or the sample size).
+
+$$
+m=\frac{n\sum_{ }^{ }x_{i}y_{i}-\sum_{ }^{ }x_{i}\sum_{ }^{ }y_{i}}{n\sum_{ }^{ }x_{i}^{2}-\left(\sum_{ }^{ }x_{i}\right)^{2}}
+$$
+
+The weight of an atomic statistic is $w$ where $r$ is the correlation coefficient between the statistic and win rate, $\mu$ is the mean of the statistic across all players, and $m$ is the slope of the least squares regression line.
+
+$$
+w=\mu mr
+$$
+
The raw metric is the weighted average of all atomic statistics.
$$
@@ -124,7 +136,7 @@ $$
\frac{\gamma}{\left|\gamma\right|}\left[1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right]
$$
-Here is an example of HisRoyalFatness scoring a very high $0.988$ with the frags metric with his [XM551 Sheridan](https://blitzkit.app/tools/tankopedia/20257). $\mu=\frac{1081.164}{1405.2273}$, $\sigma=0.2492$, and $x=\frac{7310}{4837}$, HisRoyalFatness' average frags. At $x = 0$ (if the player manages to get no frags), the metric drops nearly to $-1$ and vice versa. And, at $x=\mu$, the metric is exactly $0$.
+Here is an example of HisRoyalFatness scoring a very high $0.988$ with the frags metric with his [XM551 Sheridan](https://blitzkit.app/tools/tankopedia/20257). $\mu=0.7694$, $\sigma=0.2492$, and $x=1.5113$, HisRoyalFatness' average frags. At $x = 0$ (if the player manages to get no frags), the metric drops nearly to $-1$ and vice versa. And, at $x=\mu$, the metric is exactly $0$.
@@ -161,9 +173,3 @@ $$
$$
-
-For cases where increased $x$ negativey impacts gameplay, like in the case of `composite.damage_received`, the sign of the coefficient can simply be flipped. In fact, the sign of the function can be determined by the sign of the corelation coefficient.
-
-$$
-\frac{r\gamma}{\left|r\gamma\right|}\left[1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right]
-$$
From 87b44d1c27bf1cc192bced289a4f5e1a4265b83f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tr=C3=A8sAbhi?= <43380238+tresabhi@users.noreply.github.com>
Date: Fri, 17 May 2024 20:29:27 -0500
Subject: [PATCH 07/93] feat: r
---
docs/technical/bkpm.md | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/docs/technical/bkpm.md b/docs/technical/bkpm.md
index 479f9628e..f8403ad27 100644
--- a/docs/technical/bkpm.md
+++ b/docs/technical/bkpm.md
@@ -43,6 +43,12 @@ $$
A=\frac{x-\mu}{\left|x-\mu\right|}\left(1-e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^{2}}\right)=\frac{\gamma}{\left|\gamma\right|}\left(1-e^{-\frac{1}{2}\left(\frac{\gamma}{\sigma}\right)^{2}}\right)
$$
+$r$ is the correlation coefficient of the statistic across all players. Below is a refresher of the formula where $x_{i}$ is the statistic for player, $y_{i}$ is the player's win rate, $\mu$ is the mean of the statistic across all players, and $\overline{y}$ is the average win rate across all players.
+
+$$
+r=\frac{\sum_{ }^{ }\left(x_{i}-\mu\right)\left(y_{i}-\overline{y}\right)}{\sqrt{\sum_{ }^{ }\left(x_{i}-\mu\right)^{2}\sum_{ }^{ }\left(y_{i}-\overline{y}\right)^{2}}}
+$$
+
$m$ is the slope of the least squares regression line. Below is a refresher of the formula where $x_i$ is the player's statistic being evaluated, $y_i$ is the player's win rate, and $n$ is the number of players (or the sample size).
$$
From b4c7b0715865c9ab8d93390767bc144eccfe5b48 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tr=C3=A8sAbhi?= <43380238+tresabhi@users.noreply.github.com>
Date: Sat, 18 May 2024 21:12:11 -0500
Subject: [PATCH 08/93] feat: id discovery
---
.vscode/settings.json | 1 +
docs/technical/dids.md | 18 +++++
package.json | 1 +
scripts/averages.ts | 139 +++++++++++++++++++++++++++++++++++
src/core/blitz/idToRegion.ts | 12 +++
src/core/streams/dids.ts | 51 +++++++++++++
6 files changed, 222 insertions(+)
create mode 100644 docs/technical/dids.md
create mode 100644 scripts/averages.ts
create mode 100644 src/core/streams/dids.ts
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 3be768de7..34fa56e76 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -49,6 +49,7 @@
"degressive",
"devkrieg",
"diacriticless",
+ "DIDS",
"donotremove",
"drei",
"Droodles",
diff --git a/docs/technical/dids.md b/docs/technical/dids.md
new file mode 100644
index 000000000..6a02cabbf
--- /dev/null
+++ b/docs/technical/dids.md
@@ -0,0 +1,18 @@
+# Discovered Ids
+
+```c
+primary DiscoveredIds {
+ utf8"DIDS" magic;
+ Header header;
+ primary Body body;
+}
+
+Header {
+ uint16 version; // always 1
+}
+
+Body {
+ uint32 count;
+ uint32[count] ids;
+}
+```
diff --git a/package.json b/package.json
index 962bbead8..5650355f6 100644
--- a/package.json
+++ b/package.json
@@ -5,6 +5,7 @@
"main": "src/server.ts",
"type": "module",
"scripts": {
+ "build:averages": "vite-node --script scripts/averages",
"build:db": "prisma generate",
"build:bot": "vite-node --script scripts/build --production",
"build:website": "yarn build:db && yarn build:sitemap && next build",
diff --git a/scripts/averages.ts b/scripts/averages.ts
new file mode 100644
index 000000000..5d4a98316
--- /dev/null
+++ b/scripts/averages.ts
@@ -0,0 +1,139 @@
+import { mkdir, writeFile } from 'fs/promises';
+import { times } from 'lodash';
+import { compress } from 'lz4js';
+import { Region, REGIONS } from '../src/constants/regions';
+import getTankStats from '../src/core/blitz/getTankStats';
+import { idToRegion, MAX_IDS, MIN_IDS } from '../src/core/blitz/idToRegion';
+import { asset } from '../src/core/blitzkit/asset';
+import { superCompress } from '../src/core/blitzkit/superCompress';
+import { DidsReadStream, DidsWriteStream } from '../src/core/streams/dids';
+import { IndividualTankStats } from '../src/types/tanksStats';
+
+const RUN_TIME = 1000 * 60;
+const MAX_REQUESTS = 10;
+const startTime = Date.now();
+
+const list: IndividualTankStats[][] = [];
+const preDiscovered = await fetch(asset('averages/discovered.dids.lz4')).then(
+ async (response) => {
+ return [
+ 1000000048, 500000058, 500000076, 2000000096, 2000000097, 2000000102,
+ 2000000103, 2000000109, 1000000116, 500000131, 2000000138, 2000000141,
+ 2000000140, 2000000143, 2000000144, 2000000145, 500000147, 2000000150,
+ 2000000173, 2000000174, 2000000178, 2000000177, 2000000182, 2000000189,
+ 500000191, 2000000191, 2000000192, 2000000196,
+ ];
+
+ if (response.status === 200) {
+ const buffer = await response.arrayBuffer();
+ return new DidsReadStream(buffer).dids();
+ }
+
+ return undefined;
+ },
+);
+const discoveredIds: number[] = [];
+let processes = 0;
+
+if (preDiscovered) {
+ console.log(
+ `Revalidating ${preDiscovered.length} pre-discovered ids with ${MAX_REQUESTS} chains...`,
+ );
+
+ let preDiscoveredIndex = 0;
+
+ async function chainPreDiscovery() {
+ processes++;
+
+ if (preDiscoveredIndex >= preDiscovered!.length) {
+ processes--;
+
+ if (processes !== 0) return;
+
+ console.log(`Rediscovered ids in ${Date.now() - startTime}ms`);
+ discover();
+
+ return;
+ }
+
+ const id = preDiscovered![preDiscoveredIndex++];
+ const region = idToRegion(id);
+
+ if (id > MAX_IDS[region]) return;
+
+ const stats = await getTankStats(region, id, { felids: 'all' });
+
+ if (stats !== null && stats.length > 0) {
+ discoveredIds.push(id);
+ list.push(stats);
+
+ console.log(`re-discovered ${id} in ${region}`);
+ }
+
+ processes--;
+ setTimeout(chainPreDiscovery); // circumvent max call stack
+ }
+
+ times(MAX_REQUESTS, chainPreDiscovery);
+} else {
+ console.log('No pre-discovered ids found :(');
+ discover();
+}
+
+function discover() {
+ const index: Record
- BlitzKit Performance Metric
- Revision 1 -
+ {packageJSON.version}
+
+
+
- {packageJSON.version}
-
+