Data Mining of Deck Archetypes in
Hearthstone
?
Pablo Garc´ıa-S´anchez
1[0000−0003−4644−2894]
, Antonio
Fern´andez-Ares
2[0000−0001−9774−0651]
, Alberto P. Tonda
3[0000−0001−5895−4809]
,
and Antonio M. Mora
2[0000−0003−1603−9105]
1
Department of Languages and Computer Systems, University of Granada, Spain
[email protected]
2
Department of Signal Theory, Telematics and Communications, University of
Granada, Spain
{antares,amorag}@ugr.es
3
UMR 518 MIA, INRAE, Paris, France [email protected]
Abstract. Computer games have become a very interesting environ-
ment or testbed to develop new algorithms in many of the branches of
Artificial Intelligence. In fact, collectible card games, such as Hearth-
stone, have recently attracted the attention of researchers because of
their characteristics: uncertainty, randomness, or the infinite and unpre-
dictable interactions that can occur in a game. In this game each player
composes decks to face other players from a pool of more than 3,000
cards, each one with its own rules and statistics. This implies a great
variability of decks and card combinations with rich effects. This paper
proposes the use of clustering techniques to extract information from
data provided by Hearthstone players, i.e. a Game Mining approach. To
do so, more than 500,000 decks created by game players (both experts
and just enthusiasts) have been downloaded from Hearthpwn website.
Thus, a descriptive analysis of this dataset, along with Data Mining tech-
niques, have been carried out in order to understand which archetypes
(or deck types) are the favourites among the community of players, and
what relationships can be identified between them. The results show that
it is possible to use clustering algorithms such as K-Means to automati-
cally detect the archetypes used by the players.
Keywords: Game Data Mining · Collectible Card Games · Hearthstone
· Archetypes · Clustering Algorithms · K-Means · Agglomerative Hierar-
chical Clustering
1 Introduction
Although there is a large amount of work devoted to the use of AI in video games,
most of it is focused on making agents that play, or allow to generate content.
?
This work has been supported in part by projects B-TIC-402-UGR18 (FEDER and
Junta de Andaluc´ıa), RTI2018-102002-A-I00 (Ministerio Espa˜nol de Ciencia, Inno-
vaci´on y Universidades), projects TIN2017-85727-C4-1-2-P (Ministerio Espa˜nol de
Econom´ıa y Competitividad), and TEC2015-68752 (also funded by FEDER)
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 P. Garc´ıa-S´anchez et al.
However, a very interesting area of application is the modeling of players. That
is, starting from information related to how the human player interacts with the
game to obtain useful knowledge. Furthermore, understanding and modelling
the interaction between the player and the game can be considered a holy grail
for game developers and designers [22].
The interaction between players and games is particularly challenging in the
area of Collectible Card Games (CCGs), such as Magic The Gathering. This type
of games involves a lot of human interaction not only during the game, but also
during the creation of the decks to be used from a pool of thousands of cards.
These decks are usually shared and commented on the internet, so many players
use them as a basis to create their own versions. In addition, the appearance of
new cards and expansions makes players have to adapt their decks to the current
meta-game, that is, to the players’ behavior at a given time.
One of the most popular Digital CCGs (DCCGs) nowadays is HearthStone,
Heroes of Warcraft (HS), with over 40 million players. In addition, this game is
becoming a de facto benchmark for researchers in artificial intelligence branches,
due to the enormous amount of combinations when creating decks, along with
the randomness of the effects of the cards, and the hidden information [9].
In HS, players build a deck of 30 cards from a card pool (that can be expanded
buying random packs). To win, players must reduce the health of the opponent’s
Hero from 30 to 0, using the two types of cards available: spells, that affect the
battleground and are then discarded, and minions, that stay in play and can
attack the enemy’s Hero or other minions. There are also, weapons, a sub-set of
spells that allow the hero to attack other characters during several turns using
special abilities. Each card has an associated cost (in number of mana crystals),
that is reduced from the player’s bunch after a card is played. This amount of
crystals of each player is replenished at the beginning of the turn and increased
in one up to a maximum of 10.
In HS, deckbuilding is limited to the neutral card pool and the cards that
belong to the class of the Hero chosen for the game: Druid, Mage, Hunter,
Paladin, Priest, Rogue, Shaman, Warlock, or Warrior. Every Hero class comes
with a different Hero Power (costing 2 crystals to use), that in conjunction with
their card set, matches every Hero to different deck archetypes. For example,
Priest’s healing abilities are a very powerful choice for decks that attempt to
control the board, but not so convenient for aggressive ones, that aim to quickly
end the game.
Due to its popularity, players share the list of cards they use in their decks
publicly on websites such as Hearthpwn
4
, where users and game enthusiasts vote
for, copy and comment on the most popular decks. Currently, this website has
a huge amount of data: over 600,000 decks in total for all Hero classes, and
game modes. The data obtained by crowdsourcing, like those on this website,
allows for a dynamic, extensive and organic study of user-generated data [16].
The created decks can be entered into archetypes: that is, decks with a specific
behavior and use. For example, the Jade Druid archetype is one in which the
4
https://www.hearthpwn.com/
Data Mining of Deck Archetypes in Hearthstone 3
Druid class uses Jade Idols and other cards with the Jade keyword to obtain
stronger and stronger effects. Players are familiar with these archetypes and
often create other archetypes to counteract them.
The aim of this paper is to demonstrate whether it is possible to extract in-
formation from large user-created datasets within the scope of the DCCGs, i.e.
conduct a Game Data Mining [5] study. Specifically, the application of clustering
algorithms will allow us to detect groups of decks with common features, and
check if they are included within known archetypes. This can be useful for re-
searchers in Artificial Intelligence, for example, since by detecting certain cards
in the opponent’s deck, the corresponding archetype can be inferred, and thus
the agent could adapt its actions accordingly in order to face the predicted be-
haviour. This can also be useful for game developers that want to study how
the players are using the game resources and how they adapt to changes such as
new expansions or card updates.
The process that we are going to follow in this work consists of downloading
the dataset and pre-processing to remove unnecessary information. Next, a de-
scriptive analysis of the dataset will be performed to obtain relevant information
before applying clustering algorithms. An expert player will analyze the different
clusters to confirm that they correspond to different decks archetypes.
The rest of the paper is structured as follows. After the state of the art
in section 2, Section 3 describes the methodology used to obtain the dataset,
preprocess and analyze it. In the following section a descriptive analysis of the
dataset is made and the results of the clustering method are discussed. Finally,
in Section 5 the conclusions and future lines of work are presented.
2 State of the art
Game Data Mining [5] is one of the multiple research lines that videogames have
brought. This is understood as the application of Data Mining techniques to
datasets related to any videogame, such as telemetry measures, user-monitoring
data, player-generated information, play recordings, etc. Normally the aim is
the extraction of knowledge, mainly focused on getting some conclusions about
any of the game factors related with player experience [20], such as: enjoyment,
playability, engagement or balance; which could help the designers to improve
the game mechanics. Other approaches are centered on modelling the player’s
behaviour itself [3], which is very useful in the creation of non-player characters,
for instance.
Obtaining the dataset is the main bottleneck, thus, even if this research line
has been widely studied in several papers, the games analysed are just a few -
those for which there are available data -.
For instance, Thurau and Bauckhage [18] analysed more than 190 million
records (from 4 years) of World of Warcraft game and found different tendencies
in the evolution of guilds. Weber and Mateas [19] applied classification techniques
in order to forecast enemy behaviour in StarCraft. Also Madden NFL [21] and
(Infinite) Super Mario [20] have been studied from this perspective.
4 P. Garc´ıa-S´anchez et al.
However the most prolific game so far has been Tomb Raider: Underworld,
which has been deeply analysed in many papers. Drachen et al. have several
works applying different data mining and machine learning techniques to more
than 1300 records of players that have finished the game, such as [3], where the
authors applied Self-Organizing Maps to identify player models (archetypes), or
[15] in which the researchers used classification methods in order to predict the
players behaviour with respect to their game finishing time (or their potential
withdraw).
The objective of the present paper is also to analyze data to find archetypes,
but we are considering Hearthstone, which, to our knowledge, has not been
analyzed with this purpose yet.
This DCCG, anyway, has been one of the most prolific games/environments
for research in the last years. The studies have been mainly focused on the
creation of competitive agents to play autonomously the game [2, 17, 9], but
there are in addition other works centered on the design part, such as the game
mechanics analysis or the game balance testing [8].
Data mining has also been applied to HS. Indeed there have been two Data
Mining Challenges (AAIA’17
5
and AAIA’18
6
) using this game as a testbed.
However, the 2017 Challenge and the derived papers [13, 10] was devoted to help
AI to win the game, whereas the 2018 edition and related papers [14, 12] had as
aim to predict win-rates for specific decks.
Thus, in this study we will apply clustering methods to a big dataset, but
instead of trying to model player behaviour as in [4], we aim to discover key
features (cards in this case) in predefined decks which could lead us to identify
a cluster or set of decks as belonging to an archetype. This would help to (auto-
matically) identify game ‘profiles’ in those decks belonging to the same cluster
as an already known archetype, which could be useful for developers (to evaluate
game mechanics or the impact of an expansion) and also for autonomous agents
(to decide the best strategy to face an opponent), as already mentioned in the
Introduction.
3 Methodology
3.1 Obtaining the dataset
As the objective of this work is to analyze the decks that players create, it is
necessary to obtain a large amount of data. In our case we have used the data
available on a repository: the HearthPwn website (https://www.hearthpwn.
com/). This database contains information about all the cards available in the
game, and offers to its users the possibility to create and share decks built
from those cards. Currently there are more than 600,000 decks created, allowing
filtering by expansion, hero class, or type of game, among others. Users can view
other users’ decks and copy them into the game to use against other players.
5
https://knowledgepit.ml/aaia17-data-mining-challenge/
6
https://knowledgepit.ml/aaia18-data-mining-challenge/
Data Mining of Deck Archetypes in Hearthstone 5
Typically, the most popular and proven powerful decks are copied, or variations
are created from them.
To download the data we have made a script in Python that allows to iterate
by deck id to get the URL of that deck and download the specific deck webpage.
That web in HTML format is parsed using the BeautifulSoup
7
library to obtain
the list of cards, the date, the class and the game type of deck (Game types
in Hearthstone are: Ranked, Tavern Brawl, Arena and Adventures). With the
name of the cards it would also be possible to access to more information, such
as the cost of making the complete deck with Arcane Dust (the virtual currency
of the game), the mana cost of each card, or the card type: Spell, Minion or
Weapon. Other information such as the Rarity of cards, can als be extracted.
We have limited the decks to those belonging to the “Ranked” category. This
game mode is the one where players prepare their decks in order to compete
against other players, because it is the most popular game mode. It also is the
most common in the whole dataset, with a proportion of 62%.
Each sample (row) will be a deck identifier, and each feature (columns) will
be a card from the entire collection. A 1 in a position indicates that the deck
has that card, a 2 indicates that it has 2 copies (the maximum for non legendary
cards) and a 0 indicates that it is not included in the deck.
3.2 Method of analysis
Initially we will perform a descriptive analysis of the dataset, to see the number
of decks per Hero Class, the date of creation, or the most common cards of each
class. This can be useful as an initial overview of the whole dataset, and will
help to understand further analyses.
Then, a clustering analysis have been conducted using two techniques:
– K-Means [11], a classic method which starts from a set of patterns and tries
to separate them into k different groups, according to their features.
– Agglomerative Hierarchical Clustering (AHC) analysis [7], an algo-
rithm which, starting from samples, pairs two by two similar clusters and
builds a binary tree, called dendrogram, representing their similarity.
The first technique has been applied because it is very fast but also very
effective, as it has been proved in hundreds of studies with all kinds of data. On
the other side, Hierarchical clustering offers a very simple visual output, that
could be interpreted easily by a human expert, as this is the case in this work.
The input of both algorithms is the dataset. While in K-Means we want to
detect if we can extract archetypes (clustering decks), in the AHC we want to
extract information about how cards are related (clustering cards). That is the
reason in the AHC the input is the transpose of the array: now each card is a
row, and each feature (column name) is the ID of the deck the card belongs.
Since each hero has a subset of specific cards that only that class can use,
it does not make sense to do the clustering analysis with all the cards/decks,
7
https://pypi.org/project/beautifulsoup4/
6 P. Garc´ıa-S´anchez et al.
as the clusters obtained would be the classes themselves, considering they have
disjoint features - their exclusive cards -.
In the case of K-Means we have focused on three classes: Druid, Mage and
Warrior, as they have a very wide range of archetypes to play.
The obtained results of the analysis are presented and discussed in the fol-
lowing section.
4 Results
4.1 Descriptive Analysis of the Dataset
Figure 1 shows the distribution of dataset decks by hero class. Although they
have a similar number, there is a 32% difference between the class with the
highest number of decks (Priest) and the one with the least (Warrior). The most
common classes (Priest, Mage and Druid) are also more oriented to control and
long-term strategy, so it can explain the variability of user-created decks.
65379
57961
62418
70083
66306
57184
53435
54129
52885
0
20000
40000
60000
MAGE WARLOCK PALADIN PRIEST DRUID ROGUE SHAMAN HUNTER WARRIOR
Class
Number of decks
Fig. 1: Number of decks by hero class.
Figure 2 shows the creation of decks over time. The spikes that occur im-
mediately after a new expansion emerges can be seen clearly. Therefore, many
of these new decks may not adapt to the meta-game during the season of that
expansion, but are the basis for more refined decks.
Figure 3 is particularly interesting, as it shows that, despite having more
than 3000 cards available in the pool, all classes have one particular card with
more than 50% chance. Even, the probability of some classes is extremely high,
like Backstab in Rogue decks (with 80% chance of appearing). Classes like Mage
or Priest have up to 3 cards with a percentage of appearance of more than 70%.
The Warlock class is perhaps the least predictable with respect to its top ten,
however, there is a minimum of a 30% chance of getting one of the 10 cards
right.
4.2 Clustering Analysis
K-Means algorithm has been applied to the decks in order to see how they are
related. We have set to 10 the number of clusters for each class, a value expected
Data Mining of Deck Archetypes in Hearthstone 7
0
1000
2000
3000
2014 2015 2016 2017 2018 2019
Date
count
Fig. 2: Number of decks introduced in the data bases analyzed, over time. It is in-
teresting to observe how the spikes in the number of decks are in correspondence
to the release of a new expansion, that added more cards to the available pool
while at the same time often removing some of the previously popular cards.
to produce enough variety of archetypes, while delivering a reasonable amount
of data to be analyzed.
After applying K-Means, we extracted the 15 most common cards from the
decks of each cluster. Figure 4 show the percentage of each one for each cluster.
One of the authors, a HearthStone player that reached the highest rank
(Legend) in the competitive ladder, manually inspected the clusters and provided
an expert analysis for three classes, selected because of an anticipated larger
variety of deck archetypes: Druid, Mage, and Warrior. In the following, the
notation used for clusters is the initial of the hero class, plus the cluster id (e.g.
M2 indicates the second cluster for the Mage class). Also, Figure 4 shows the
ten most common cards in each cluster.
Druid Clusters D1, D5, D7 all present cards that provide advantages in the late
game (such as Wild Growth and Nourish); but while D1 and D7 have control
cards (such as Starfall), D5 exploits the late-game advantage to close combos,
using potentially one-turn-kills like Malygos or Aviana. Clusters D2 and D6, on
the contrary, have none of these cards, but feature weak, cheap creatures such
as Arcane Raven and Fire Fly, plus cards that enhance all friendly creatures on
the board, such as Savage Roar, thus grouping decisively Aggressive archetypes.
D3 and D9 show a preponderance of Jade cards (Jade Idol, Jade Spirit, Jade
Behemoth), thus placing these decks in the category of Jade Druid, a special-
ized midrange archetype. Cluster D10 presents mostly cards with the C’Thun
keyword, identifying the decks belonging to this cluster as variants of the combo
C’Thun Druid archetype. Clusters D4 and D8 are harder to categorize, as they
seem to either be mid-range variations of aggressive decks, or present poor co-
hesion, possibly representing outliers.
Mage Clusters M3, M4, M6, and M9 all represent aggressive archetypes, fea-
turing cards such as Fireball and Frostbolt. M3 exploits synergies with secrets
(Arcanologist, Counterspell, Medivh’s Vallet), M4 relies upon Flamewaker and
cheap spells to damage to opponent, M6 shows a strong presence of Mech minions
8 P. Garc´ıa-S´anchez et al.
76.33 %
71.93 %
70.1 %
57.35 %
44.96 %
41.7 %
39.65 %
39.27 %
38.69 %
28.62 %
Frostbolt
Arcane Intellect
Fireball
Flamestrike
Mana Wyrm
Polymorph
Sorcerer's Apprentice
Ice Block
Blizzard
Frost Nova
0% 25% 50% 75% 100%
Percentage of MAGE decks using the card
Hellfire
Mortal Coil
Voidwalker
Siphon Soul
Doomguard
Flame Imp
Power Overwhelming
Soulfire
Twisting Nether
Bloodreaver Gul'dan
0% 25% 50% 75% 100%
Percentage of WARLOCK decks using the card
69.3 %
67.16 %
55.19 %
50.19 %
45.22 %
34.2 %
30.54 %
25.08 %
24.52 %
23.74 %
Consecration
Truesilver Champion
Equality
Tirion Fordring
Aldor Peacekeeper
Blessing of Kings
Divine Favor
Sunkeeper Tarim
Spikeridged Steed
Righteous Protector
0% 25% 50% 75% 100%
Percentage of PALADIN decks using the card
77.54 %
74.81 %
73.2 %
62.57 %
49.35 %
33.78 %
31.63 %
28.53 %
26.43 %
26.07 %
Power Word: Shield
Shadow Word: Death
Northshire Cleric
Shadow Word: Pain
Holy Nova
Shadow Visions
Circle of Healing
Dragonfire Potion
Auchenai Soulpriest
Cabal Shadow Priest
0% 25% 50% 75% 100%
Percentage of PRIEST decks using the card
67.74 %
57.71 %
53.95 %
51.92 %
43.35 %
29.1 %
28.94 %
21.47 %
21.17 %
20.58 %
Swipe
Wrath
Innervate
Wild Growth
Nourish
Druid of the Claw
Savage Roar
Power of the Wild
Living Roots
Ultimate Infestation
0% 25% 50% 75% 100%
Percentage of DRUID decks using the card
82.29 %
72.79 %
60.48 %
59.39 %
58.52 %
55.69 %
42.41 %
39.74 %
38.1 %
29.6 %
Backstab
Eviscerate
Fan of Knives
Sap
Preparation
SI:7 Agent
Shadowstep
Deadly Poison
Edwin VanCleef
Vilespine Slayer
0% 25% 50% 75% 100%
Percentage of ROGUE decks using the card
71.92 %
67.38 %
54.29 %
48.23 %
39.83 %
37.54 %
36.84 %
34.38 %
30.66 %
29.82 %
Hex
Lightning Storm
Flametongue Totem
Mana Tide Totem
Lightning Bolt
Feral Spirit
Fire Elemental
Maelstrom Portal
Rockbiter Weapon
Thing from Below
0% 25% 50% 75% 100%
Percentage of SHAMAN decks using the card
75.82 %
69.56 %
64.12 %
58.29 %
56.85 %
46.47 %
40.86 %
39.27 %
38.51 %
30.94 %
Animal Companion
Kill Command
Eaglehorn Bow
Unleash the Hounds
Savannah Highmane
Houndmaster
Explosive Trap
Hunter's Mark
Freezing Trap
Deadly Shot
0% 25% 50% 75% 100%
Percentage of HUNTER decks using the card
73.48 %
72.13 %
59 %
58.77 %
49.3 %
44.25 %
36.69 %
35.19 %
32.25 %
31.14 %
Execute
Fiery War Axe
Shield Block
Brawl
Shield Slam
Slam
Grommash Hellscream
Armorsmith
Whirlwind
Ravaging Ghoul
0% 25% 50% 75% 100%
Percentage of WARRIOR decks using the card
76.33 %
71.93 %
70.1 %
57.35 %
44.96 %
41.7 %
39.65 %
39.27 %
38.69 %
28.62 %
Frostbolt
Arcane Intellect
Fireball
Flamestrike
Mana Wyrm
Polymorph
Sorcerer's Apprentice
Ice Block
Blizzard
Frost Nova
0% 25% 50% 75% 100%
Percentage of MAGE decks using the card
Hellfire
Mortal Coil
Voidwalker
Siphon Soul
Doomguard
Flame Imp
Power Overwhelming
Soulfire
Twisting Nether
Bloodreaver Gul'dan
0% 25% 50% 75% 100%
Percentage of WARLOCK decks using the card
69.3 %
67.16 %
55.19 %
50.19 %
45.22 %
34.2 %
30.54 %
25.08 %
24.52 %
23.74 %
Consecration
Truesilver Champion
Equality
Tirion Fordring
Aldor Peacekeeper
Blessing of Kings
Divine Favor
Sunkeeper Tarim
Spikeridged Steed
Righteous Protector
0% 25% 50% 75% 100%
Percentage of PALADIN decks using the card
77.54 %
74.81 %
73.2 %
62.57 %
49.35 %
33.78 %
31.63 %
28.53 %
26.43 %
26.07 %
Power Word: Shield
Shadow Word: Death
Northshire Cleric
Shadow Word: Pain
Holy Nova
Shadow Visions
Circle of Healing
Dragonfire Potion
Auchenai Soulpriest
Cabal Shadow Priest
0% 25% 50% 75% 100%
Percentage of PRIEST decks using the card
67.74 %
57.71 %
53.95 %
51.92 %
43.35 %
29.1 %
28.94 %
21.47 %
21.17 %
20.58 %
Swipe
Wrath
Innervate
Wild Growth
Nourish
Druid of the Claw
Savage Roar
Power of the Wild
Living Roots
Ultimate Infestation
0% 25% 50% 75% 100%
Percentage of DRUID decks using the card
82.29 %
72.79 %
60.48 %
59.39 %
58.52 %
55.69 %
42.41 %
39.74 %
38.1 %
29.6 %
Backstab
Eviscerate
Fan of Knives
Sap
Preparation
SI:7 Agent
Shadowstep
Deadly Poison
Edwin VanCleef
Vilespine Slayer
0% 25% 50% 75% 100%
Percentage of ROGUE decks using the card
71.92 %
67.38 %
54.29 %
48.23 %
39.83 %
37.54 %
36.84 %
34.38 %
30.66 %
29.82 %
Hex
Lightning Storm
Flametongue Totem
Mana Tide Totem
Lightning Bolt
Feral Spirit
Fire Elemental
Maelstrom Portal
Rockbiter Weapon
Thing from Below
0% 25% 50% 75% 100%
Percentage of SHAMAN decks using the card
75.82 %
69.56 %
64.12 %
58.29 %
56.85 %
46.47 %
40.86 %
39.27 %
38.51 %
30.94 %
Animal Companion
Kill Command
Eaglehorn Bow
Unleash the Hounds
Savannah Highmane
Houndmaster
Explosive Trap
Hunter's Mark
Freezing Trap
Deadly Shot
0% 25% 50% 75% 100%
Percentage of HUNTER decks using the card
73.48 %
72.13 %
59 %
58.77 %
49.3 %
44.25 %
36.69 %
35.19 %
32.25 %
31.14 %
Execute
Fiery War Axe
Shield Block
Brawl
Shield Slam
Slam
Grommash Hellscream
Armorsmith
Whirlwind
Ravaging Ghoul
0% 25% 50% 75% 100%
Percentage of WARRIOR decks using the card
Fig. 3: Most commons cards in decks, by hero class. Interestingly, some of the
cards that appear most frequently has been banned or ‘nerfed’ (cost increased
and/or effectiveness reduced) over time. Notable examples are Power word:
Shield for Priest, Innervate for Druid, Hex for Shaman, and Fiery War Axe
for Warrior.
(Mechwarper, Snowchugger), and M9 is based around cheap spells (Ice Lance,
Magic Trick) plus the minion Mana Cyclone to generate new damaging spells.
Clusters M2, M5, M7, and M8 all fall under different control archetypes, us-
ing either secrets in the case of M2, a large number of board resets (Doom-
sayer, Flamestrike) for M5, or a unique late-game finisher (Dragoncaller Alanna,
C’Thun) for clusters M7 and M8, respectively. Notably, cluster M7 also includes
decks built using only odd-cost cards, exploiting the synergy with Baku, the
Mooneater, that provides a powerful effect in exchange for limiting the pos-
sibilities of deck construction. Cluster M1 encompasses decks using the syn-
ergy between Elemental minions and Jaina, Frost Lich, thus positioning these
archetypes in a mid-range position. Finally, cluster M10 includes combo decks,
based on the quest Open the Waygate, Archimage Antonidas, and Sorcerer’s
Apprentice.
Warrior Clusters W1, W2, W3, and W4 all represent variations of Warrior
Control archetypes. Decks in W1 rely upon Dead Man’s Hand to try and finish
the game through fatigue damage, W2 groups both Mech synergy (Dr. Boom,
Mad Genius, Zilliax ) and Odd Warrior (Baku, the Mooneater), W3 decks seem
to exploit older cards (Sylvanas, Justicar Trueheart) possibly representing Wild
decks, W3 is a Control version of C’Thun Warrior, with the C’Thun cards and
several other synergies. W8 is a set of decisively aggressive decks, with cards
Data Mining of Deck Archetypes in Hearthstone 9
such as Leeroy Jenkins, Patches the Pirate, Southsea Deckhand. W6, W7, and
W10 all represent combo decks: W6 includes cards that can damage all minions
on the board (Whirlwind, Death’s Bite) plus minions that benefit from being
damaged (Grim Patron, Frothing Berserker); W7 and W10 are variations of
C’Thun Warrior, with less control elements with respect to W3, and cards such
as Brann Bronzebeard to try and finish the game using a colossal amount of
damage from C’Thun. Cluster W5 groups together Quest Warrior archetypes
based on Fire Plume’s Heart, and more generic mid-range decks still based on
Taunt minions (Stonehill Defender, Direhorn Hatchling). Finally, cluster W9
shows relatively few points in common between its decks, with the most common
card being Fiery War Axe appearing in only 68% of cases, and might thus
represent a collection of outliers, or very different mid-range decks.
Once the clusters generated by K-Means have been analyzed, Agglomerative
Hierarhical Clustering has been applied. AHC can show also interesting infor-
mation about the influence of the cards. We have run the method for the three
heroes, but due to space limitations we are showing and analyzing here only the
results of the Warrior class, as they are somehow representative and interesting.
Figure 5 show the complete generated dendrogram of the Warrior cards, and
more detail of the subtrees with height=4 is shown in Figure 6. The height of the
fusion, provided on the vertical axis, indicates the similarity/distance between
two cards. The higher the height of the fusion, the less similar the cards are.
This height is known as the cophenetic distance between the two cards. Most of
the cards are in a big cluster (subtree 4), but there exist several relevant cards
(single cards) that have enough weight to appear in their own subtree, even at
level 1. Several pair of cards shown are usually used in combos, have some kind
of synergies or belong to the same expansion. For example: N’Zoth and Bloodsail
Cultist.
5 Conclusions
Understanding how players play a game is a major concern for developers, as
they can adapt elements of the game, such as the rules and content, to facilitate
the balance or fun it can provide. In this paper we propose to use Game Data
Mining [5], to obtain information about how players create Hearthstone decks.
The goal is to demonstrate if using a large set of user-created card lists it is
possible to extract deck archetypes automatically. To do this we have extracted
a dataset from the HearthPwn website and performed a descriptive analysis plus
applied clustering algorithms.
After expert analysis of the results, we have provided information on how
the cards are related to each other, and how it is possible to detect different
archetypes from the data created by the users. However, the proposed auto-
matic clustering approach also showed a few limitations: 3 out of the 30 clusters
analyzed seems to be composed of mostly outlier decks, identifying no clear
archetype (D4, D8, W9); moreover, distinct clusters in the same hero class seem
to present very similar archetypes (W7, W10); and finally, it is sometimes pos-
10 P. Garc´ıa-S´anchez et al.
76.16%
75.71%
70.58%
67.45%
67.15%
52.87%
46.22%
40.06%
35.79%
34.29%
33.26%
28.46%
27.9%
27.38%
27.27%
Earthen.Scales
Elder.Longneck
Fandral.Staghelm
Innervate
Jungle.Giants
Malfurion.the.Pestilent
Mire.Keeper
Nourish
Primordial.Drake
Spreading.Plague
Swipe
The.Lich.King
Ultimate.Infestation
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 1 ( 5340 decks)
96.43%
95.78%
95.7%
90.98%
86.6%
84.35%
75.41%
66.15%
64.84%
60.33%
48.09%
45.46%
43.6%
42%
37.15%
Bloodsail.Corsair
Crypt.Lord
Druid.of.the.Swarm
Enchanted.Raven
Fire.Fly
Golakka.Crawler
Innervate
Living.Mana
Mark.of.the.Lotus
Mark.of.Y.Shaarj
Patches.the.Pirate
Power.of.the.Wild
Savage.Roar
Swipe
Vicious.Fledgling
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 2 ( 3814 decks)
99.28%
95.93%
95.73%
93.74%
90.71%
90.51%
89.24%
85.15%
83.16%
82.92%
75.48%
68.3%
65.21%
54.43%
47.76%
Aya.Blackpaw
Fandral.Staghelm
Innervate
Jade.Behemoth
Jade.Blossom
Jade.Idol
Jade.Spirit
Malfurion.the.Pestilent
Mire.Keeper
Nourish
Spreading.Plague
Swipe
Ultimate.Infestation
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 3 ( 3467 decks)
64.33%
54.56%
45.64%
40.17%
34.75%
31.45%
30.12%
23.9%
22.76%
22.53%
22.04%
21.93%
21.71%
21.59%
20.98%
Coldlight.Oracle
Druid.of.the.Claw
Ferocious.Howl
Healing.Touch
Innervate
Keeper.of.the.Grove
Mark.of.the.Wild
Naturalize
Nourish
Savage.Roar
Starfall
Starfire
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 4 ( 5997 decks)
97.93%
97.88%
97.01%
93.66%
88.22%
87.93%
80.87%
73.71%
71.9%
65.76%
64.43%
50.22%
44.41%
40.48%
37.25%
Ancient.of.War
Azure.Drake
Emperor.Thaurissan
Fandral.Staghelm
Feral.Rage
Innervate
Living.Roots
Mire.Keeper
Moonfire
Mulch
Nourish
Raven.Idol
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 5 ( 4150 decks)
84.18%
84.17%
77.44%
58.8%
56.36%
52.4%
48.62%
44.15%
41.41%
40.81%
38.38%
34.53%
31.1%
26.79%
22.82%
Druid.of.the.Claw
Druid.of.the.Flame
Druid.of.the.Saber
Enchanted.Raven
Innervate
Living.Roots
Mark.of.Y.Shaarj
Menagerie.Warden
Mounted.Raptor
Power.of.the.Wild
Savage.Combatant
Savage.Roar
Soul.of.the.Forest
Swipe
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 6 ( 5798 decks)
90.4%
89.68%
88.4%
85.93%
79.75%
75.8%
70.92%
69.15%
58.39%
56.93%
56.17%
48.26%
43.98%
36.42%
27.29%
Branching.Paths
Ferocious.Howl
Hadronox
Ironwood.Golem
Lesser.Jasper.Spellstone
Malfurion.the.Pestilent
Naturalize
Nourish
Oaken.Summons
Spreading.Plague
Swipe
The.Lich.King
Ultimate.Infestation
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 7 ( 8486 decks)
98.81%
97.17%
97.04%
91.18%
88.4%
86.64%
72.49%
71.4%
68.95%
57.81%
49.49%
48.69%
42.33%
36.98%
36.68%
Ancient.of.Lore
Ancient.of.War
Azure.Drake
Big.Game.Hunter
Dr..Boom
Druid.of.the.Claw
Force.of.Nature
Innervate
Keeper.of.the.Grove
Loatheb
Piloted.Shredder
Savage.Roar
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 8 ( 9529 decks)
99.53%
98.92%
96.52%
96.3%
95.19%
94.24%
89.88%
88.75%
86.47%
77.73%
76.76%
71.61%
70.66%
50.63%
47.06%
Aya.Blackpaw
Azure.Drake
Fandral.Staghelm
Feral.Rage
Gadgetzan.Auctioneer
Innervate
Jade.Behemoth
Jade.Blossom
Jade.Idol
Jade.Spirit
Living.Roots
Nourish
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 9 ( 4428 decks)
99.72%
98.37%
93.28%
90.51%
88.58%
88.18%
82.84%
81.67%
79.18%
76.36%
71.48%
71.42%
57.75%
53.24%
52.66%
Azure.Drake
Beckoner.of.Evil
C.Thun
C.Thun.s.Chosen
Dark.Arakkoa
Disciple.of.C.Thun
Druid.of.the.Claw
Innervate
Klaxxi.Amber.Weaver
Nourish
Swipe
Twilight.Elder
Twin.Emperor.Vek.lor
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 10 ( 3257 decks)
76.16%
75.71%
70.58%
67.45%
67.15%
52.87%
46.22%
40.06%
35.79%
34.29%
33.26%
28.46%
27.9%
27.38%
27.27%
Earthen.Scales
Elder.Longneck
Fandral.Staghelm
Innervate
Jungle.Giants
Malfurion.the.Pestilent
Mire.Keeper
Nourish
Primordial.Drake
Spreading.Plague
Swipe
The.Lich.King
Ultimate.Infestation
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 1 ( 5340 decks)
96.43%
95.78%
95.7%
90.98%
86.6%
84.35%
75.41%
66.15%
64.84%
60.33%
48.09%
45.46%
43.6%
42%
37.15%
Bloodsail.Corsair
Crypt.Lord
Druid.of.the.Swarm
Enchanted.Raven
Fire.Fly
Golakka.Crawler
Innervate
Living.Mana
Mark.of.the.Lotus
Mark.of.Y.Shaarj
Patches.the.Pirate
Power.of.the.Wild
Savage.Roar
Swipe
Vicious.Fledgling
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 2 ( 3814 decks)
99.28%
95.93%
95.73%
93.74%
90.71%
90.51%
89.24%
85.15%
83.16%
82.92%
75.48%
68.3%
65.21%
54.43%
47.76%
Aya.Blackpaw
Fandral.Staghelm
Innervate
Jade.Behemoth
Jade.Blossom
Jade.Idol
Jade.Spirit
Malfurion.the.Pestilent
Mire.Keeper
Nourish
Spreading.Plague
Swipe
Ultimate.Infestation
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 3 ( 3467 decks)
64.33%
54.56%
45.64%
40.17%
34.75%
31.45%
30.12%
23.9%
22.76%
22.53%
22.04%
21.93%
21.71%
21.59%
20.98%
Coldlight.Oracle
Druid.of.the.Claw
Ferocious.Howl
Healing.Touch
Innervate
Keeper.of.the.Grove
Mark.of.the.Wild
Naturalize
Nourish
Savage.Roar
Starfall
Starfire
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 4 ( 5997 decks)
97.93%
97.88%
97.01%
93.66%
88.22%
87.93%
80.87%
73.71%
71.9%
65.76%
64.43%
50.22%
44.41%
40.48%
37.25%
Ancient.of.War
Azure.Drake
Emperor.Thaurissan
Fandral.Staghelm
Feral.Rage
Innervate
Living.Roots
Mire.Keeper
Moonfire
Mulch
Nourish
Raven.Idol
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 5 ( 4150 decks)
84.18%
84.17%
77.44%
58.8%
56.36%
52.4%
48.62%
44.15%
41.41%
40.81%
38.38%
34.53%
31.1%
26.79%
22.82%
Druid.of.the.Claw
Druid.of.the.Flame
Druid.of.the.Saber
Enchanted.Raven
Innervate
Living.Roots
Mark.of.Y.Shaarj
Menagerie.Warden
Mounted.Raptor
Power.of.the.Wild
Savage.Combatant
Savage.Roar
Soul.of.the.Forest
Swipe
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 6 ( 5798 decks)
90.4%
89.68%
88.4%
85.93%
79.75%
75.8%
70.92%
69.15%
58.39%
56.93%
56.17%
48.26%
43.98%
36.42%
27.29%
Branching.Paths
Ferocious.Howl
Hadronox
Ironwood.Golem
Lesser.Jasper.Spellstone
Malfurion.the.Pestilent
Naturalize
Nourish
Oaken.Summons
Spreading.Plague
Swipe
The.Lich.King
Ultimate.Infestation
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 7 ( 8486 decks)
98.81%
97.17%
97.04%
91.18%
88.4%
86.64%
72.49%
71.4%
68.95%
57.81%
49.49%
48.69%
42.33%
36.98%
36.68%
Ancient.of.Lore
Ancient.of.War
Azure.Drake
Big.Game.Hunter
Dr..Boom
Druid.of.the.Claw
Force.of.Nature
Innervate
Keeper.of.the.Grove
Loatheb
Piloted.Shredder
Savage.Roar
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 8 ( 9529 decks)
99.53%
98.92%
96.52%
96.3%
95.19%
94.24%
89.88%
88.75%
86.47%
77.73%
76.76%
71.61%
70.66%
50.63%
47.06%
Aya.Blackpaw
Azure.Drake
Fandral.Staghelm
Feral.Rage
Gadgetzan.Auctioneer
Innervate
Jade.Behemoth
Jade.Blossom
Jade.Idol
Jade.Spirit
Living.Roots
Nourish
Swipe
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 9 ( 4428 decks)
99.72%
98.37%
93.28%
90.51%
88.58%
88.18%
82.84%
81.67%
79.18%
76.36%
71.48%
71.42%
57.75%
53.24%
52.66%
Azure.Drake
Beckoner.of.Evil
C.Thun
C.Thun.s.Chosen
Dark.Arakkoa
Disciple.of.C.Thun
Druid.of.the.Claw
Innervate
Klaxxi.Amber.Weaver
Nourish
Swipe
Twilight.Elder
Twin.Emperor.Vek.lor
Wild.Growth
Wrath
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
DRUID − Cluster 10 ( 3257 decks)
(a) Druid
81.96%
78.52%
74.69%
73.4%
68.39%
63.24%
54.85%
54.2%
53.37%
49.14%
45.44%
44.6%
43.93%
42.64%
37.18%
Arcane.Intellect
Blazecaller
Bonfire.Elemental
Fireball
Fire.Fly
Flamestrike
Frostbolt
Frost.Lich.Jaina
Primordial.Glyph
Pyros
Servant.of.Kalimos
Shimmering.Tempest
Steam.Surger
Tar.Creeper
Water.Elemental
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 1 ( 5511 decks)
88.41%
85.49%
84.3%
75.35%
73.59%
71.64%
64.73%
57.2%
54.86%
53.56%
40.22%
38.67%
38.54%
37.51%
37.09%
Antique.Healbot
Arcane.Intellect
Blizzard
Dr..Boom
Duplicate
Echo.of.Medivh
Emperor.Thaurissan
Fireball
Flamestrike
Frostbolt
Ice.Block
Mad.Scientist
Polymorph
Sludge.Belcher
Sylvanas.Windrunner
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 2 ( 5713 decks)
95.44%
95.38%
93.17%
89.81%
88.53%
79.84%
78.92%
73.77%
73.02%
60.77%
59%
54.59%
50.24%
41.73%
40.5%
Arcane.Intellect
Arcanologist
Counterspell
Explosive.Runes
Fireball
Firelands.Portal
Frostbolt
Ice.Block
Kabal.Crystal.Runner
Kirin.Tor.Mage
Mana.Wyrm
Medivh.s.Valet
Mirror.Entity
Primordial.Glyph
Sorcerer.s.Apprentice
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 3 ( 8109 decks)
98.31%
97.66%
95.97%
95.32%
93.78%
89.64%
83.93%
74.99%
58.67%
56.64%
52.21%
49.21%
46.15%
42.57%
40.78%
Arcane.Blast
Arcane.Intellect
Arcane.Missiles
Archmage.Antonidas
Azure.Drake
Fireball
Flamecannon
Flamestrike
Flamewaker
Frostbolt
Mana.Wyrm
Mirror.Entity
Mirror.Image
Sorcerer.s.Apprentice
Unstable.Portal
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 4 ( 8232 decks)
74.21%
68.07%
65.88%
65.43%
65.06%
61.29%
59.55%
51.37%
45.77%
45.76%
43.17%
42.19%
40.86%
39.3%
38.97%
Arcane.Intellect
Babbling.Book
Blizzard
Cabalist.s.Tome
Doomsayer
Fireball
Firelands.Portal
Flamestrike
Forgotten.Torch
Frostbolt
Frost.Nova
Ice.Block
Kazakus
Polymorph
Reno.Jackson
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 5 ( 10555 decks)
96.01%
93.86%
93.68%
90.69%
89.93%
86.85%
84.49%
84.43%
83.16%
76.39%
74%
55.47%
50.42%
45.13%
42.99%
Annoy.o.Tron
Archmage.Antonidas
Clockwork.Gnome
Cogmaster
Dr..Boom
Fireball
Frostbolt
Goblin.Blastmage
Mechanical.Yeti
Mechwarper
Piloted.Shredder
Snowchugger
Spider.Tank
Tinkertown.Technician
Unstable.Portal
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 6 ( 3308 decks)
95.4%
94.42%
92.4%
88.03%
74.14%
70.11%
65.15%
61.91%
59.17%
53.28%
48.56%
44.17%
42.6%
41.72%
39.59%
Arcane.Artificer
Arcane.Tyrant
Baron.Geddon
Blizzard
Doomsayer
Dragoncaller.Alanna
Dragon.s.Fury
Flamestrike
Frost.Lich.Jaina
Meteor
Polymorph
Raven.Familiar
Stonehill.Defender
Tar.Creeper
Voodoo.Doll
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 7 ( 4195 decks)
97.53%
95.74%
91.74%
87.86%
84.32%
70.49%
67.84%
65.57%
49.56%
45.91%
44.22%
24.79%
23.79%
23.67%
23.36%
Arcane.Explosion
Arcane.Intellect
Arcane.Missiles
Azure.Drake
Faceless.Summoner
Fireball
Firelands.Portal
Flamestrike
Frostbolt
Mana.Wyrm
Mirror.Image
Polymorph
Sen.jin.Shieldmasta
Sorcerer.s.Apprentice
Water.Elemental
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 8 ( 6079 decks)
84.07%
80.67%
76.94%
70.73%
55.68%
55.18%
51.53%
32.37%
30.86%
29.24%
27.48%
25.47%
24.35%
24.22%
23.28%
Arcane.Intellect
Arcane.Missiles
Archmage.Antonidas
Bloodmage.Thalnos
Fireball
Frostbolt
Frost.Nova
Ice.Lance
Magic.Trick
Mana.Cyclone
Mana.Wyrm
Mirror.Image
Ray.of.Frost
Sorcerer.s.Apprentice
Stargazer.Luna
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 9 ( 4025 decks)
97.74%
96.5%
93.33%
90.3%
89.23%
75.68%
73.41%
58.3%
55.78%
54.46%
54.27%
53.57%
50.18%
49.33%
46.02%
Acolyte.of.Pain
Alexstrasza
Arcane.Intellect
Arcanologist
Archmage.Antonidas
Blizzard
Bloodmage.Thalnos
Doomsayer
Fireball
Flamestrike
Frostbolt
Frost.Nova
Ice.Barrier
Ice.Block
Primordial.Glyph
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 10 ( 9652 decks)
81.96%
78.52%
74.69%
73.4%
68.39%
63.24%
54.85%
54.2%
53.37%
49.14%
45.44%
44.6%
43.93%
42.64%
37.18%
Arcane.Intellect
Blazecaller
Bonfire.Elemental
Fireball
Fire.Fly
Flamestrike
Frostbolt
Frost.Lich.Jaina
Primordial.Glyph
Pyros
Servant.of.Kalimos
Shimmering.Tempest
Steam.Surger
Tar.Creeper
Water.Elemental
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 1 ( 5511 decks)
88.41%
85.49%
84.3%
75.35%
73.59%
71.64%
64.73%
57.2%
54.86%
53.56%
40.22%
38.67%
38.54%
37.51%
37.09%
Antique.Healbot
Arcane.Intellect
Blizzard
Dr..Boom
Duplicate
Echo.of.Medivh
Emperor.Thaurissan
Fireball
Flamestrike
Frostbolt
Ice.Block
Mad.Scientist
Polymorph
Sludge.Belcher
Sylvanas.Windrunner
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 2 ( 5713 decks)
95.44%
95.38%
93.17%
89.81%
88.53%
79.84%
78.92%
73.77%
73.02%
60.77%
59%
54.59%
50.24%
41.73%
40.5%
Arcane.Intellect
Arcanologist
Counterspell
Explosive.Runes
Fireball
Firelands.Portal
Frostbolt
Ice.Block
Kabal.Crystal.Runner
Kirin.Tor.Mage
Mana.Wyrm
Medivh.s.Valet
Mirror.Entity
Primordial.Glyph
Sorcerer.s.Apprentice
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 3 ( 8109 decks)
98.31%
97.66%
95.97%
95.32%
93.78%
89.64%
83.93%
74.99%
58.67%
56.64%
52.21%
49.21%
46.15%
42.57%
40.78%
Arcane.Blast
Arcane.Intellect
Arcane.Missiles
Archmage.Antonidas
Azure.Drake
Fireball
Flamecannon
Flamestrike
Flamewaker
Frostbolt
Mana.Wyrm
Mirror.Entity
Mirror.Image
Sorcerer.s.Apprentice
Unstable.Portal
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 4 ( 8232 decks)
74.21%
68.07%
65.88%
65.43%
65.06%
61.29%
59.55%
51.37%
45.77%
45.76%
43.17%
42.19%
40.86%
39.3%
38.97%
Arcane.Intellect
Babbling.Book
Blizzard
Cabalist.s.Tome
Doomsayer
Fireball
Firelands.Portal
Flamestrike
Forgotten.Torch
Frostbolt
Frost.Nova
Ice.Block
Kazakus
Polymorph
Reno.Jackson
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 5 ( 10555 decks)
96.01%
93.86%
93.68%
90.69%
89.93%
86.85%
84.49%
84.43%
83.16%
76.39%
74%
55.47%
50.42%
45.13%
42.99%
Annoy.o.Tron
Archmage.Antonidas
Clockwork.Gnome
Cogmaster
Dr..Boom
Fireball
Frostbolt
Goblin.Blastmage
Mechanical.Yeti
Mechwarper
Piloted.Shredder
Snowchugger
Spider.Tank
Tinkertown.Technician
Unstable.Portal
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 6 ( 3308 decks)
95.4%
94.42%
92.4%
88.03%
74.14%
70.11%
65.15%
61.91%
59.17%
53.28%
48.56%
44.17%
42.6%
41.72%
39.59%
Arcane.Artificer
Arcane.Tyrant
Baron.Geddon
Blizzard
Doomsayer
Dragoncaller.Alanna
Dragon.s.Fury
Flamestrike
Frost.Lich.Jaina
Meteor
Polymorph
Raven.Familiar
Stonehill.Defender
Tar.Creeper
Voodoo.Doll
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 7 ( 4195 decks)
97.53%
95.74%
91.74%
87.86%
84.32%
70.49%
67.84%
65.57%
49.56%
45.91%
44.22%
24.79%
23.79%
23.67%
23.36%
Arcane.Explosion
Arcane.Intellect
Arcane.Missiles
Azure.Drake
Faceless.Summoner
Fireball
Firelands.Portal
Flamestrike
Frostbolt
Mana.Wyrm
Mirror.Image
Polymorph
Sen.jin.Shieldmasta
Sorcerer.s.Apprentice
Water.Elemental
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 8 ( 6079 decks)
84.07%
80.67%
76.94%
70.73%
55.68%
55.18%
51.53%
32.37%
30.86%
29.24%
27.48%
25.47%
24.35%
24.22%
23.28%
Arcane.Intellect
Arcane.Missiles
Archmage.Antonidas
Bloodmage.Thalnos
Fireball
Frostbolt
Frost.Nova
Ice.Lance
Magic.Trick
Mana.Cyclone
Mana.Wyrm
Mirror.Image
Ray.of.Frost
Sorcerer.s.Apprentice
Stargazer.Luna
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 9 ( 4025 decks)
97.74%
96.5%
93.33%
90.3%
89.23%
75.68%
73.41%
58.3%
55.78%
54.46%
54.27%
53.57%
50.18%
49.33%
46.02%
Acolyte.of.Pain
Alexstrasza
Arcane.Intellect
Arcanologist
Archmage.Antonidas
Blizzard
Bloodmage.Thalnos
Doomsayer
Fireball
Flamestrike
Frostbolt
Frost.Nova
Ice.Barrier
Ice.Block
Primordial.Glyph
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
MAGE − Cluster 10 ( 9652 decks)
(b) Mage
93.4%
89.42%
85.02%
82.69%
61.72%
60.97%
60.5%
59.93%
49.28%
46.59%
39.06%
36.99%
36.28%
35.95%
35.57%
Acolyte.of.Pain
Battle.Rage
Blood.Razor
Brawl
Dead.Man.s.Hand
Drywhisker.Armorer
Execute
Scourgelord.Garrosh
Shield.Block
Shield.Slam
Slam
Sleep.with.the.Fishes
The.Lich.King
Warpath
Whirlwind
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 1 ( 5755 decks)
87.63%
82.1%
78.71%
65.19%
59.13%
55.9%
54.94%
44.88%
44.86%
44.13%
42.68%
38.15%
37.87%
35.38%
31.92%
Acolyte.of.Pain
Baku.the.Mooneater
Brawl
Direhorn.Hatchling
Dr..Boom..Mad.Genius
Dyn.o.matic
Eternium.Rover
Omega.Assembly
Reckless.Flurry
Shield.Block
Shield.Slam
Stonehill.Defender
Supercollider
Warpath
Zilliax
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 2 ( 6134 decks)
98.37%
96.69%
94.4%
92.43%
91.24%
74.17%
73.09%
65.99%
65.29%
65.27%
55.55%
55.19%
54.82%
53.83%
52.25%
Acolyte.of.Pain
Armorsmith
Bash
Brawl
Cruel.Taskmaster
Death.s.Bite
Execute
Fiery.War.Axe
Grommash.Hellscream
Justicar.Trueheart
Shield.Block
Shield.Slam
Slam
Sludge.Belcher
Sylvanas.Windrunner
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 3 ( 11918 decks)
99.16%
91.55%
86.48%
84.37%
81.1%
79.09%
68.43%
63.36%
63.15%
62.72%
61.46%
59.77%
59.03%
58.39%
55.12%
Acolyte.of.Pain
Ancient.Shieldbearer
Beckoner.of.Evil
Bloodhoof.Brave
C.Thun
C.Thun.s.Chosen
Disciple.of.C.Thun
Doomcaller
Execute
Fiery.War.Axe
Ravaging.Ghoul
Shield.Block
Slam
Twilight.Elder
Twin.Emperor.Vek.lor
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 4 ( 947 decks)
92.71%
88.82%
87.75%
87.36%
83.2%
78.77%
76.66%
76.66%
65%
64.84%
62.38%
61.07%
60.6%
51.76%
51.51%
Acolyte.of.Pain
Alley.Armorsmith
Bloodhoof.Brave
Brawl
Direhorn.Hatchling
Execute
Fiery.War.Axe
Fire.Plume.s.Heart
Primordial.Drake
Ravaging.Ghoul
Shield.Block
Slam
Sleep.with.the.Fishes
Stonehill.Defender
Tar.Creeper
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 5 ( 5168 decks)
93.31%
90.3%
88.54%
82.9%
80.24%
72.04%
69.04%
68.19%
68.16%
57.44%
50.56%
47.75%
44.49%
44.49%
34.19%
Acolyte.of.Pain
Armorsmith
Battle.Rage
Blood.To.Ichor
Cruel.Taskmaster
Death.s.Bite
Execute
Fiery.War.Axe
Frothing.Berserker
Grim.Patron
Grommash.Hellscream
Inner.Rage
Ravaging.Ghoul
Slam
Whirlwind
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 6 ( 7193 decks)
99.01%
96.95%
96.46%
95.06%
92.5%
90.94%
86.41%
83.36%
81.88%
78.75%
75.45%
74.3%
69.69%
65.32%
61.86%
Ancient.Shieldbearer
Bash
Beckoner.of.Evil
Brawl
C.Thun
C.Thun.s.Chosen
Disciple.of.C.Thun
Execute
Fiery.War.Axe
Justicar.Trueheart
Shield.Block
Shield.Slam
Slam
Twilight.Elder
Twin.Emperor.Vek.lor
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 7 ( 1214 decks)
95.96%
95.67%
94.72%
94.15%
91.94%
91.28%
91.17%
90.68%
89.94%
83.51%
80.11%
68.36%
62.99%
55.09%
51.94%
Arcanite.Reaper
Bloodsail.Cultist
Bloodsail.Raider
Dread.Corsair
Fiery.War.Axe
Frothing.Berserker
Heroic.Strike
Kor.kron.Elite
Leeroy.Jenkins
Mortal.Strike
N.Zoth.s.First.Mate
Patches.the.Pirate
Southsea.Captain
Southsea.Deckhand
Upgrade.
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 8 ( 5847 decks)
68.79%
61.99%
61.44%
55%
37.45%
34.29%
29.75%
26.24%
25.15%
24.74%
24.61%
23.99%
22.77%
22.35%
21.3%
Alexstrasza.s.Champion
Arcanite.Reaper
Azure.Drake
Battle.Rage
Blackwing.Corruptor
Cruel.Taskmaster
Execute
Fiery.War.Axe
Frothing.Berserker
Grommash.Hellscream
Heroic.Strike
Kor.kron.Elite
Ravaging.Ghoul
Shield.Block
Slam
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 9 ( 7287 decks)
99.35%
99.28%
98.48%
98.48%
98.26%
97.9%
95.65%
93.7%
93.41%
91.81%
88.91%
75.94%
71.96%
71.67%
70.22%
Acolyte.of.Pain
Ancient.Shieldbearer
Brann.Bronzebeard
Brawl
C.Thun
C.Thun.s.Chosen
Disciple.of.C.Thun
Execute
Fiery.War.Axe
Justicar.Trueheart
Ravaging.Ghoul
Shield.Block
Shield.Slam
Slam
Twin.Emperor.Vek.lor
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 10 ( 1380 decks)
93.4%
89.42%
85.02%
82.69%
61.72%
60.97%
60.5%
59.93%
49.28%
46.59%
39.06%
36.99%
36.28%
35.95%
35.57%
Acolyte.of.Pain
Battle.Rage
Blood.Razor
Brawl
Dead.Man.s.Hand
Drywhisker.Armorer
Execute
Scourgelord.Garrosh
Shield.Block
Shield.Slam
Slam
Sleep.with.the.Fishes
The.Lich.King
Warpath
Whirlwind
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 1 ( 5755 decks)
87.63%
82.1%
78.71%
65.19%
59.13%
55.9%
54.94%
44.88%
44.86%
44.13%
42.68%
38.15%
37.87%
35.38%
31.92%
Acolyte.of.Pain
Baku.the.Mooneater
Brawl
Direhorn.Hatchling
Dr..Boom..Mad.Genius
Dyn.o.matic
Eternium.Rover
Omega.Assembly
Reckless.Flurry
Shield.Block
Shield.Slam
Stonehill.Defender
Supercollider
Warpath
Zilliax
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 2 ( 6134 decks)
98.37%
96.69%
94.4%
92.43%
91.24%
74.17%
73.09%
65.99%
65.29%
65.27%
55.55%
55.19%
54.82%
53.83%
52.25%
Acolyte.of.Pain
Armorsmith
Bash
Brawl
Cruel.Taskmaster
Death.s.Bite
Execute
Fiery.War.Axe
Grommash.Hellscream
Justicar.Trueheart
Shield.Block
Shield.Slam
Slam
Sludge.Belcher
Sylvanas.Windrunner
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 3 ( 11918 decks)
99.16%
91.55%
86.48%
84.37%
81.1%
79.09%
68.43%
63.36%
63.15%
62.72%
61.46%
59.77%
59.03%
58.39%
55.12%
Acolyte.of.Pain
Ancient.Shieldbearer
Beckoner.of.Evil
Bloodhoof.Brave
C.Thun
C.Thun.s.Chosen
Disciple.of.C.Thun
Doomcaller
Execute
Fiery.War.Axe
Ravaging.Ghoul
Shield.Block
Slam
Twilight.Elder
Twin.Emperor.Vek.lor
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 4 ( 947 decks)
92.71%
88.82%
87.75%
87.36%
83.2%
78.77%
76.66%
76.66%
65%
64.84%
62.38%
61.07%
60.6%
51.76%
51.51%
Acolyte.of.Pain
Alley.Armorsmith
Bloodhoof.Brave
Brawl
Direhorn.Hatchling
Execute
Fiery.War.Axe
Fire.Plume.s.Heart
Primordial.Drake
Ravaging.Ghoul
Shield.Block
Slam
Sleep.with.the.Fishes
Stonehill.Defender
Tar.Creeper
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 5 ( 5168 decks)
93.31%
90.3%
88.54%
82.9%
80.24%
72.04%
69.04%
68.19%
68.16%
57.44%
50.56%
47.75%
44.49%
44.49%
34.19%
Acolyte.of.Pain
Armorsmith
Battle.Rage
Blood.To.Ichor
Cruel.Taskmaster
Death.s.Bite
Execute
Fiery.War.Axe
Frothing.Berserker
Grim.Patron
Grommash.Hellscream
Inner.Rage
Ravaging.Ghoul
Slam
Whirlwind
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 6 ( 7193 decks)
99.01%
96.95%
96.46%
95.06%
92.5%
90.94%
86.41%
83.36%
81.88%
78.75%
75.45%
74.3%
69.69%
65.32%
61.86%
Ancient.Shieldbearer
Bash
Beckoner.of.Evil
Brawl
C.Thun
C.Thun.s.Chosen
Disciple.of.C.Thun
Execute
Fiery.War.Axe
Justicar.Trueheart
Shield.Block
Shield.Slam
Slam
Twilight.Elder
Twin.Emperor.Vek.lor
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 7 ( 1214 decks)
95.96%
95.67%
94.72%
94.15%
91.94%
91.28%
91.17%
90.68%
89.94%
83.51%
80.11%
68.36%
62.99%
55.09%
51.94%
Arcanite.Reaper
Bloodsail.Cultist
Bloodsail.Raider
Dread.Corsair
Fiery.War.Axe
Frothing.Berserker
Heroic.Strike
Kor.kron.Elite
Leeroy.Jenkins
Mortal.Strike
N.Zoth.s.First.Mate
Patches.the.Pirate
Southsea.Captain
Southsea.Deckhand
Upgrade.
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 8 ( 5847 decks)
68.79%
61.99%
61.44%
55%
37.45%
34.29%
29.75%
26.24%
25.15%
24.74%
24.61%
23.99%
22.77%
22.35%
21.3%
Alexstrasza.s.Champion
Arcanite.Reaper
Azure.Drake
Battle.Rage
Blackwing.Corruptor
Cruel.Taskmaster
Execute
Fiery.War.Axe
Frothing.Berserker
Grommash.Hellscream
Heroic.Strike
Kor.kron.Elite
Ravaging.Ghoul
Shield.Block
Slam
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 9 ( 7287 decks)
99.35%
99.28%
98.48%
98.48%
98.26%
97.9%
95.65%
93.7%
93.41%
91.81%
88.91%
75.94%
71.96%
71.67%
70.22%
Acolyte.of.Pain
Ancient.Shieldbearer
Brann.Bronzebeard
Brawl
C.Thun
C.Thun.s.Chosen
Disciple.of.C.Thun
Execute
Fiery.War.Axe
Justicar.Trueheart
Ravaging.Ghoul
Shield.Block
Shield.Slam
Slam
Twin.Emperor.Vek.lor
0%
25%
50%
75%
100%
Percentage of decks (in cluster) using that card.
Card
WARRIOR − Cluster 10 ( 1380 decks)
(c) Warrior
Fig. 4: Ten most common cards of each cluster for Druid (a), Mage (b) and
Warrior (c).
Data Mining of Deck Archetypes in Hearthstone 11
Blood.Razor
Direhorn.Hatchling
Stonehill.Defender
Tar.Creeper
Alley.Armorsmith
Sleep.with.the.Fishes
Dirty.Rat
Primordial.Drake
Bash
Warpath
Eternium.Rover
Dyn.o.matic
Omega.Assembly
Rabid.Worgen
Town.Crier
Militia.Commander
Darius.Crowley
Redband.Wasp
Woodcutter.s.Axe
Inner.Rage
Grim.Patron
Unstable.Ghoul
Warsong.Commander
Emperor.Thaurissan
Gnomish.Inventor
Beckoner.of.Evil
C.Thun.s.Chosen
Ancient.Shieldbearer
Disciple.of.C.Thun
Revenge
Gorehowl
Justicar.Trueheart
Sylvanas.Windrunner
Ysera
Alexstrasza
Big.Game.Hunter
Dr..Boom
Ragnaros.the.Firelord
Baron.Geddon
Harrison.Jones
Reckless.Flurry
Cornered.Sentry
Drywhisker.Armorer
Coldlight.Oracle
Bring.It.On.
Dead.Man.s.Hand
Fierce.Monkey
Drakonid.Crusher
Faerie.Dragon
Azure.Drake
Alexstrasza.s.Champion
Blackwing.Corruptor
Twilight.Guardian
Naga.Corsair
Southsea.Captain
Loot.Hoarder
Commanding.Shout
Wild.Pyromancer
Rampage
Charge
Raging.Worgen
Small.Time.Buccaneer
Sir.Finley.Mrrgglton
Leeroy.Jenkins
Patches.the.Pirate
Piloted.Shredder
Omega.Devastator
Clockwork.Goblin
Wrenchcalibur
Augmented.Elekk
Blastmaster.Boom
Seaforium.Bomber
Weapons.Project
Dragonmaw.Scorcher
Crowd.Roaster
Emberscale.Drake
Smolderthorn.Lancer
Dragon.Roar
Firetree.Witchdoctor
Scaleworm
Supercollider
Dr..Boom..Mad.Genius
Zilliax
Ironbeak.Owl
Amani.Berserker
Arathi.Weaponsmith
Cleave
Acidic.Swamp.Ooze
Sen.jin.Shieldmasta
Spellbreaker
Deathlord
King.s.Defender
Bolster
Sparring.Partner
Grimy.Gadgeteer
I.Know.a.Guy
Public.Defender
Stolen.Goods
Brann.Bronzebeard
Doomcaller
C.Thun
Twin.Emperor.Vek.lor
Crazed.Worshipper
Twilight.Elder
Argent.Horserider
Wolfrider
Abusive.Sergeant
Arcane.Golem
Leper.Gnome
Blackwing.Technician
Saronite.Chain.Gang
Fire.Fly
Lesser.Mithril.Spellstone
Spiteful.Summoner
Gurubashi.Berserker
Animated.Berserker
Val.kyr.Soulclaimer
Arcane.Giant
Blood.Warriors
Iron.Hide
Gemstudded.Golem
Deathwing
Twilight.Drake
Book.Wyrm
Netherspite.Historian
Rotface
Sudden.Genesis
Molten.Blade
Bittertide.Hydra
Captain.Greenskin
Nightmare.Amalgam
Bonemare
Cobalt.Scalebane
Amani.War.Bear
Spirit.of.the.Rhino
Sleepy.Dragon
Charged.Devilsaur
The.Boomship
Bladed.Gauntlet
Geosculptor.Yip
Woecleaver
Skulking.Geist
King.Mosh
Second.Rate.Bruiser
Stubborn.Gastropod
Bluegill.Warrior
Murloc.Warleader
Shattered.Sun.Cleric
Boulderfist.Ogre
Chillwind.Yeti
Protect.the.King.
Obsidian.Destroyer
Sunwalker
Grimestreet.Pawnbroker
Fool.s.Bane
Violet.Illusionist
Malkorok
Golakka.Crawler
Chillmaw
Nefarian
Bone.Drake
Flame.Juggler
Devastate
Improve.Morale
Dimensional.Ripper
Sweeping.Strikes
Akali..the.Rhino
Oondasta
Safeguard
Vicious.Scraphound
Archivist.Elysiana
Hecklebot
Argent.Squire
Plated.Beetle
Grimestreet.Smuggler
Don.Han.Cho
Doppelgangster
Brass.Knuckles
Hobart.Grapplehammer
Zombie.Chow
Knife.Juggler
Dragon.Egg
Nerubian.Egg
Phantom.Freebooter
Blackwater.Pirate
Bloodsail.Corsair
Festeroot.Hulk
Sul.thraze
Heavy.Metal.
War.Master.Voone
Wyrmguard
Hench.Clan.Thug
Fungalmancer
Vicious.Fledgling
Bomb.Lobber
Chromaggus
Hungry.Dragon
Soggoth.the.Slitherer
Deathwing..Dragonlord
Y.Shaarj..Rage.Unbound
Yogg.Saron..Hope.s.End
Clockwork.Automaton
Azalina.Soulthief
Zola.the.Gorgon
Jeweled.Scarab
Siege.Engine
Clockwork.Gnome
Mechanical.Yeti
Haunted.Creeper
Hobgoblin
Jeeves
Micro.Machine
Missile.Launcher
Skaterbot
Mecharoo
Replicating.Menace
Cursed.Blade
Orgrimmar.Aspirant
Bloodmage.Thalnos
Prince.Malchezaar
Barnes
Twilight.Summoner
Vicious.Scalehide
Blackhowl.Gunspire
Carnivorous.Cube
Dozing.Marksman
Overlord.s.Whip
Death.Revenant
Night.Howler
Corridor.Creeper
Prince.Keleseth
Explosive.Sheep
Tentacle.of.N.Zoth
Frost.Giant
Garrison.Commander
Nexus.Champion.Saraad
Arcane.Nullifier.X.21
Piloted.Sky.Golem
Sneed.s.Old.Shredder
Bull.Dozer
Damaged.Stegotron
Huge.Toad
Mechanical.Whelp
Weaponized.Piñata
Baleful.Banker
Arch.Thief.Rafaam
Aberrant.Berserker
Deadly.Arsenal
Earthen.Ring.Farseer
Murloc.Tidehunter
Murloc.Tidecaller
Coldlight.Seer
Grimscale.Oracle
Stormwind.Champion
Bloodfen.Raptor
Razorfen.Hunter
Dark.Iron.Dwarf
Worgen.Infiltrator
Ogre.Warmaul
Lone.Champion
Dragonhatcher
Master.Oakheart
Rusty.Recycler
Mistress.of.Mixtures
Frostwolf.Grunt
Pompous.Thespian
Wax.Elemental
Madder.Bomber
Stampeding.Kodo
Hogger..Doom.of.Elwynn
Kezan.Mystic
Hench.Clan.Hogsteed
Genn.Greymane
Witchwood.Piper
Hogger
Master.Jouster
Ancient.Brewmaster
Devilsaur.Egg
Tinkmaster.Overspark
Sea.Giant
Cyclopian.Horror
Faceless.Shambler
Gorillabot.A.3
Dire.Mole
Malygos
Mecha.thun
Dr..Boom.s.Scheme
Barista.Lynchen
Unseen.Saboteur
Spectral.Knight
Feugen
Stalagg
Dancing.Swords
Undertaker
Refreshment.Vendor
Tournament.Medic
Mogu.shan.Warden
Cult.Master
Target.Dummy
Tournament.Attendee
Swift.Messenger
Coppertail.Imposter
Fight.Promoter
Scaled.Nightmare
Ravasaur.Runt
Ebon.Dragonsmith
Furnacefire.Colossus
Injured.Blademaster
Frightened.Flunky
Tomb.Warden
Infested.Goblin
Into.the.Fray
Restless.Mummy
Bloodsworn.Mercenary
SN1P.SN4P
Captain.s.Parrot
Skycap.n.Kragg
Eater.of.Secrets
Angry.Chicken
Ancient.Watcher
Ogre.Brute
Glacial.Shard
Blazecaller
Servant.of.Kalimos
Tol.vir.Stoneshaper
Fire.Plume.Phoenix
Igneous.Elemental
Rend.Blackhand
Volcanic.Drake
Arcanosmith
Bilefin.Tidehunter
Saronite.Taskmaster
Rocket.Boots
Explodinator
Kaboom.Bot
Enhance.o.Mechano
Evil.Heckler
Corrupted.Seer
Marsh.Drake
Crazed.Alchemist
Marin.the.Fox
Naga.Sea.Witch
Shade.of.Naxxramas
Drakkari.Enchanter
Silver.Vanguard
Tentacles.for.Arms
King.Togwaggle
Hired.Gun
Kobold.Barbarian
Worgen.Abomination
Echoing.Ooze
Gnomeregan.Infantry
Onyxia
Corpsetaker
Demolisher
Faithful.Lumi
Magnataur.Alpha
Sea.Reaver
Kodorider
Polluted.Hoarder
Flesheating.Ghoul
Dragonling.Mechanic
Murloc.Raider
North.Sea.Kraken
Lifedrinker
Mossy.Horror
Alarm.o.Bot
Runic.Egg
Bright.Eyed.Scout
Hemet..Jungle.Hunter
Gadgetzan.Jouster
Troggzor.the.Earthinator
Fen.Creeper
Mukla.s.Champion
Silverback.Patriarch
Frostwolf.Warlord
River.Crocolisk
Ancient.Harbinger
Acherus.Veteran
The.Darkness
Finja..the.Flying.Star
Gentle.Megasaur
Rockpool.Hunter
Flying.Machine
Wisp
Blood.of.The.Ancient.One
Recombobulator
Giant.Mastodon
Stegodon
Void.Ripper
Furious.Ettin
Hungry.Ettin
Underbelly.Ooze
Hungry.Crab
Stranglethorn.Tiger
Untamed.Beastmaster
Omega.Defender
Rabble.Bouncer
Force.Tank.MAX
Foe.Reaper.4000
Junkbot
Spring.Rocket
Silithid.Swarmer
Cult.Apothecary
Friendly.Bartender
Toxic.Sewer.Ooze
Maiden.of.the.Lake
Ironforge.Rifleman
Chief.Inspector
Silver.Hand.Knight
Jungle.Panther
Thrallmar.Farseer
Windfury.Harpy
Magma.Rager
Loose.Specimen
Mojomaster.Zihi
Mindbreaker
Ticking.Abomination
Half.Time.Scavenger
Auctionmaster.Beardo
Burgly.Bully
Mana.Addict
Questing.Adventurer
Trogg.Gloomeater
Grave.Shambler
Toxicologist
Corrupted.Healbot
Ancient.of.Blossoms
Piloted.Reaper
Injured.Kvaldir
Emerald.Hive.Queen
Guild.Recruiter
Livewire.Lance
Hack.the.System
Questing.Explorer
Belligerent.Gnome
Grand.Archivist
Tunnel.Blaster
Spiritsinger.Umbra
Nat.Pagle
Shifter.Zerus
Emperor.Cobra
Nesting.Roc
Imp.Master
Blingtron.3000
Salty.Dog
Lil..Exorcist
Shieldbreaker
Wrathion
Lorewalker.Cho
Swamp.Dragon.Egg
Witch.s.Cauldron
Bolf.Ramshield
Boneguard.Lieutenant
Kvaldir.Raider
Pit.Fighter
Anubisath.Sentinel
Old.Murk.Eye
Puddlestomper
Murloc.Tinyfin
Snowflipper.Penguin
Ticket.Scalper
Jepetto.Joybuzz
Daring.Reporter
Big.Time.Racketeer
Burly.Rockjaw.Trogg
Stonesplinter.Trogg
Deranged.Doctor
Gruul
Eerie.Statue
Icehowl
Midnight.Drake
Medivh..the.Guardian
Lance.Carrier
Sunborne.Val.kyr
Steel.Rager
Sightless.Ranger
Spark.Drill
Baron.Rivendare
Skelemancer
Tomb.Lurker
Burly.Shovelfist
Venture.Co..Mercenary
Volatile.Elemental
E.M.P..Operative
Sunreaver.Warmage
Clockwork.Giant
Gazlowe
Toshley
Mosh.Ogg.Enforcer
Bone.Wraith
Injured.Tol.vir
Khartut.Defender
Armagedillo
Faceless.Lurker
Arena.Fanatic
Deathspeaker
Ancient.Shade
Goblin.Bomb
Whirliglider
Nerub.ar.Weblord
Hoarding.Dragon
Ornery.Tortoise
Portal.Keeper
Portal.Overfiend
Frost.Elemental
Maexxna
Armored.Warhorse
Shallow.Gravedigger
EVIL.Cable.Rat
Magic.Carpet
Scorp.o.matic
Fungal.Enchanter
Violet.Teacher
King.Mukla
Spawn.of.N.Zoth
Plague.of.Wrath
Mekgineer.Thermaplugg
Mimiron.s.Head
Young.Dragonhawk
Spellzerker
Darkspeaker
Blowgill.Sniper
Primalfin.Lookout
Mayor.Noggenfogger
Grand.Crusader
Corrosive.Sludge
Green.Jelly
Corpse.Raiser
Giant.Wasp
Wobbling.Runts
Dragonslayer
Bloodworm
Wailing.Soul
Weasel.Tunneler
Summoning.Stone
Spiked.Hogrider
Nerubian.Unraveler
Crowd.Favorite
Fencing.Coach
Silver.Hand.Regent
Core.Hound
Ravenholdt.Assassin
Nerubian.Prophet
Hyldnir.Frostrider
Spark.Engine
Wretched.Tiller
Illidan.Stormrage
Am.gam.Rager
Ice.Rager
Eydis.Darkbane
Priestess.of.Elune
Ravencaller
Zealous.Initiate
Arcane.Dynamo
Ogre.Magi
Mogor.s.Champion
Mogor.the.Ogre
Stormwatcher
Ultrasaur
Hench.Clan.Hag
Microtech.Controller
Lightwarden
Coliseum.Manager
Secretkeeper
Grook.Fu.Master
Saboteur
Ozruk
Thunder.Lizard
Moat.Lurker
Humongous.Razorleaf
Snapjaw.Shellfighter
Prince.Taldaram
Oasis.Snapjaw
Stoneskin.Gargoyle
Argent.Watchman
Millhouse.Manastorm
Nat..the.Darkfisher
Tanglefur.Mystic
Faceless.Rager
Potion.Vendor
Gilblin.Stalker
Goblin.Sapper
Felsoul.Inquisitor
Dollmaster.Dorian
Backstreet.Leper
Hozen.Healer
Arcane.Anomaly
Blubber.Baron
Mukla..Tyrant.of.the.Vale
Star.Aligner
Lowly.Squire
Pint.Sized.Summoner
The.Skeleton.Knight
Silvermoon.Guardian
Lost.Spirit
Mad.Hatter
Kobold.Geomancer
Kooky.Chemist
Gnomish.Experimenter
Tortollan.Primalist
Banana.Buffoon
Gurubashi.Offering
Arcane.Tyrant
Validated.Doomsayer
Keening.Banshee
The.Beast
Mana.Wraith
Grim.Necromancer
Necrotic.Geist
Moroes
Armored.Goon
Fallen.Sun.Cleric
Linecracker
Shroom.Brewer
Kobold.Monk
Cursed.Disciple
Kobold.Apprentice
Sated.Threshadon
Faceless.Behemoth
Backroom.Bouncer
Emerald.Reaver
Arena.Treasure.Chest
Scarab.Egg
Twisted.Worgen
Helpless.Hatchling
Hemet.Nesingwary
Gadgetzan.Socialite
The.Boogeymonster
Eggnapper
Vryghoul
Toxfin
Stoneskin.Basilisk
Dragonhawk.Rider
Captured.Jormungar
Pantry.Spider
Archmage
Dalaran.Mage
Anubisath.Warbringer
Heroic.Innkeeper
Living.Monument
Elite.Tauren.Chieftain
Lost.Tallstrider
Defias.Cleaner
Sergeant.Sally
Night.Prowler
Mosh.Ogg.Announcer
Arena.Patron
Silent.Knight
Gelbin.Mekkatorque
Shrieking.Shroom
Mogu.Cultist
Madam.Goya
Feral.Gibberer
Eldritch.Horror
Duskboar
Deadscale.Knight
Red.Mana.Wyrm
Regeneratin..Thug
Street.Trickster
Worgen.Greaser
Leatherclad.Hogleader
Tanaris.Hogchopper
Gormok.the.Impaler
Siamat
Rattling.Rascal
Evolved.Kobold
Griftah
Former.Champ
Drakkari.Trickster
Rumbletusk.Shaker
Boisterous.Bard
Wind.up.Burglebot
Tainted.Zealot
Splitting.Festeroot
Fjola.Lightbane
Deathaxe.Punisher
Shimmering.Courser
Conjured.Mirage
Phalanx.Commander
Bomb.Squad
Booty.Bay.Bookie
Walnut.Sprite
Traveling.Healer
Swamp.Leech
Soldier.of.Fortune
Sideshow.Spelleater
Recruiter
Pterrordax.Hatchling
Murloc.Tastyfin
Mana.Reservoir
History.Buff
Octosari
Serpent.Egg
Electrowright
Avian.Watcher
Violet.Warden
Sneaky.Devil
Sabretooth.Stalker
Exotic.Mountseller
Ancient.Mage
Wicked.Skeleton
Sewer.Crawler
The.Voraxx
Subject.9
Harbinger.Celestia
Holomancer
Fossilized.Devilsaur
Wrapped.Golem
Temple.Berserker
Sunstruck.Henchman
Streetwise.Investigator
Recurring.Villain
Neferset.Ritualist
Ice.Cream.Peddler
Gurubashi.Chicken
Furbolg.Mossbinder
Flight.Master
Eccentric.Scribe
Cheaty.Anklebiter
Murmy
Beaming.Sidekick
Jar.Dealer
Arcane.Watcher
Dalaran.Librarian
Fel.Orc.Soulfiend
Spellward.Jeweler
Mad.Summoner
Mad.Scientist
High.Inquisitor.Whitemane
Happy.Ghoul
Zephrys.the.Great
Brightwing
Colossus.of.the.Moon
Kazakus
Brainstormer
Toothy.Chest
Waterboy
Unpowered.Mauler
Sandbinder
Light.s.Champion
King.Phaoris
Illuminator
Golden.Scarab
Dalaran.Crusader
Frigid.Snobold
Frozen.Crusher
Spellweaver
Chef.Nomi
Da.Undatakah
Genzo..the.Shark
Nozdormu
Meat.Wagon
Unpowered.Steambot
Prince.Valanar
Gravelsnout.Knight
Violet.Wurm
Volcanosaur
Majordomo.Executus
Tomb.Spider
Menagerie.Magician
Zoobot
Squirming.Tentacle
Master.Swordsmith
Young.Priestess
Arfus
Blood.Knight
Scarlet.Crusader
Grotesque.Dragonhawk
Djinni.of.Zephyrs
Dragonkin.Sorcerer
Hakkar..the.Soulflayer
Gilnean.Royal.Guard
Pumpkin.Peasant
Rummaging.Kobold
Sharkfin.Fan
Proud.Defender
Batterhead
Archmage.Vargoth
Big.Bad.Archmage
The.Boom.Reaver
Stormwind.Knight
Clockwork.Knight
Fel.Reaver
Dire.Wolf.Alpha
Gadgetzan.Auctioneer
Crystallizer
Grimestreet.Informant
Bog.Creeper
Psych.o.Tron
Goldshire.Footman
Shieldbearer
Nightblade
Lord.of.the.Arena
Voodoo.Doctor
Booty.Bay.Bodyguard
Darkscale.Healer
Stormpike.Commando
War.Golem
Ironfur.Grizzly
Raid.Leader
Stonetusk.Boar
Kel.Thuzad
Molten.Giant
Mountain.Giant
Sunfury.Protector
Explore.Un.Goro
Skeram.Cultist
Twilight.Geomancer
Mad.Bomber
Spiteful.Smith
Tauren.Warrior
Novice.Engineer
Elven.Archer
Reckless.Rocketeer
Abomination
Voodoo.Doll
Countess.Ashmore
Muck.Hunter
Defender.of.Argus
Mind.Control.Tech
Reno.Jackson
Bouncing.Blade
Crush
Iron.Juggernaut
Antique.Healbot
Youthful.Brewmaster
Axe.Flinger
Varian.Wrynn
Ship.s.Cannon
Giggling.Inventor
Galvanizer
Upgradeable.Framebot
Beryllium.Nullifier
Security.Rover
Bronze.Gatekeeper
Wargear
Loatheb
The.Black.Knight
Annoy.o.Tron
Warbot
Cogmaster
Mechwarper
Screwjank.Clunker
Spider.Tank
Tinkertown.Technician
Argent.Commander
Harvest.Golem
Cairne.Bloodhoof
Infested.Tauren
N.Zoth..the.Corruptor
Faceless.Manipulator
Elise.the.Trailblazer
Baku.the.Mooneater
Gluttonous.Ooze
Ironforge.Portal
Doomsayer
Elise.Starseeker
Mountainfire.Armor
Unidentified.Shield
Scourgelord.Garrosh
Forge.of.Souls
Gather.Your.Party
The.Lich.King
Phantom.Militia
Rotten.Applebaum
Blackwald.Pixie
Witchwood.Grizzly
Tar.Lord
Ornery.Direhorn
Fire.Plume.s.Heart
The.Curator
Blood.To.Ichor
Grommash.Hellscream
Battle.Rage
Whirlwind
Armorsmith
Cruel.Taskmaster
Death.s.Bite
Shieldmaiden
Sludge.Belcher
Frothing.Berserker
Kor.kron.Elite
Mortal.Strike
Arcanite.Reaper
Heroic.Strike
Dread.Corsair
Bloodsail.Cultist
N.Zoth.s.First.Mate
Upgrade.
Bloodsail.Raider
Southsea.Deckhand
Slam
Bloodhoof.Brave
Ravaging.Ghoul
Fiery.War.Axe
Shield.Block
Brawl
Shield.Slam
Acolyte.of.Pain
Execute
0 100 200 300 400
Cluster Dendrogram
Cards
Height
Fig. 5: Global AHC for all cards used by Warrior class.
Fig. 6: Subtrees of the AHC cluster. Each id correspond to a leaf (from left to
right) of a binary tree of 4 levels. So 1 is more related to 2, and 3 to 4, and
therefore 1-2 and 3-4 are also related.
sible to detect two distinct archetypes inside the same cluster (M7, W2). These
issues are typical of clustering, an unsupervised learning problem for which there
is no ground truth, and parameters such as an arbitrary number of clusters have
to be defined a priori by the user. Also, relationships between cards can be seen
visually using the dendrogram generated by the AHC algorithm
As future work, a card co-appearance matrix can be made, in which each
cell is the number of decks that share that particular pair of cards. Using other
clustering algorithms, such as the Leiden Algorithm, card communities can be
detected [1], and from using their centrality and density measures, these commu-
nities can be plotted in a strategic diagram to see what decks belong to motor,
transversal, specialized or emerging/disappearing categories. Other visualization
techniques, such as visualizing the cards networks, or studying the changes in
decks along the time and expansion releases may help to understand how users
play the game.
A feature extraction method could be also applied, in order to ‘generate’
features related to the decks, such as summarizing of the number of minions in
12 P. Garc´ıa-S´anchez et al.
the set, or the amount of beast cards, weapon cards, or combo cards, to cite some
examples. This information could better describe the decks for their analysis.
Moreover, other clustering algorithms such as Density-Based Spatial Clus-
tering of Applications w ith Noise [6] can partially solve the issue of deciding a
priori the number of clusters; nevertheless, they feature different parameters to
be tuned.
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