Data Mining of Deck Archetypes in
Hearthstone?
Pablo Garcı́a-Sánchez1[0000−0003−4644−2894] , Antonio
Fernández-Ares2[0000−0001−9774−0651] , Alberto P. Tonda3[0000−0001−5895−4809] ,
and Antonio M. Mora2[0000−0003−1603−9105]
1
Department of Languages and Computer Systems, University of Granada, Spain
pablogarcia@ugr.es
2
Department of Signal Theory, Telematics and Communications, University of
Granada, Spain
{antares,amorag}@ugr.es
3
UMR 518 MIA, INRAE, Paris, France alberto.tonda@inrae.fr
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 Andalucı́a), RTI2018-102002-A-I00 (Ministerio Español de Ciencia, Inno-
vación y Universidades), projects TIN2017-85727-C4-1-2-P (Ministerio Español de
Economı́a 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. Garcı́a-Sánchez 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 Hearthpwn4 , 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. Garcı́a-Sánchez 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’175 and AAIA’186 ) 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. Garcı́a-Sánchez 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.
70083
65379 66306
60000 62418
57961 57184
53435 54129 52885
Number of decks
40000
20000
0
MAGE WARLOCK PALADIN PRIEST DRUID ROGUE SHAMAN HUNTER WARRIOR
Class
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
3000
2000
count
1000
0
2014 2015 2016 2017 2018 2019
Date
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. Garcı́a-Sánchez et al.
Frost Nova 28.62 % Bloodreaver Gul'dan 30.02 % Righteous Protector 23.74 %
Blizzard 38.69 % Twisting Nether 31.06 % Spikeridged Steed 24.52 %
Ice Block 39.27 % Soulfire 32.65 % Sunkeeper Tarim 25.08 %
Sorcerer's Apprentice 39.65 % Power Overwhelming 32.68 % Divine Favor 30.54 %
Polymorph 41.7 % Flame Imp 36.72 % Blessing of Kings 34.2 %
Mana Wyrm 44.96 % Doomguard 43.17 % Aldor Peacekeeper 45.22 %
Flamestrike 57.35 % Siphon Soul 44.69 % Tirion Fordring 50.19 %
Fireball 70.1 % Voidwalker 48.36 % Equality 55.19 %
Arcane Intellect 71.93 % Mortal Coil 50.49 % Truesilver Champion 67.16 %
Frostbolt 76.33 % Hellfire 54.3 % Consecration 69.3 %
0% 25% 50% 75% 100% 0% 25% 50% 75% 100% 0% 25% 50% 75% 100%
Percentage of MAGE decks using the card Percentage of WARLOCK decks using the card Percentage of PALADIN decks using the card
Cabal Shadow Priest 26.07 % Ultimate Infestation 20.58 % Vilespine Slayer 29.6 %
Auchenai Soulpriest 26.43 % Living Roots 21.17 % Edwin VanCleef 38.1 %
Dragonfire Potion 28.53 % Power of the Wild 21.47 % Deadly Poison 39.74 %
Circle of Healing 31.63 % Savage Roar 28.94 % Shadowstep 42.41 %
Shadow Visions 33.78 % Druid of the Claw 29.1 % SI:7 Agent 55.69 %
Holy Nova 49.35 % Nourish 43.35 % Preparation 58.52 %
Shadow Word: Pain 62.57 % Wild Growth 51.92 % Sap 59.39 %
Northshire Cleric 73.2 % Innervate 53.95 % Fan of Knives 60.48 %
Shadow Word: Death 74.81 % Wrath 57.71 % Eviscerate 72.79 %
Power Word: Shield 77.54 % Swipe 67.74 % Backstab 82.29 %
0% 25% 50% 75% 100% 0% 25% 50% 75% 100% 0% 25% 50% 75% 100%
Percentage of PRIEST decks using the card Percentage of DRUID decks using the card Percentage of ROGUE decks using the card
Thing from Below 29.82 % Deadly Shot 30.94 % Ravaging Ghoul 31.14 %
Rockbiter Weapon 30.66 % Freezing Trap 38.51 % Whirlwind 32.25 %
Maelstrom Portal 34.38 % Hunter's Mark 39.27 % Armorsmith 35.19 %
Fire Elemental 36.84 % Explosive Trap 40.86 % Grommash Hellscream 36.69 %
Feral Spirit 37.54 % Houndmaster 46.47 % Slam 44.25 %
Lightning Bolt 39.83 % Savannah Highmane 56.85 % Shield Slam 49.3 %
Mana Tide Totem 48.23 % Unleash the Hounds 58.29 % Brawl 58.77 %
Flametongue Totem 54.29 % Eaglehorn Bow 64.12 % Shield Block 59 %
Lightning Storm 67.38 % Kill Command 69.56 % Fiery War Axe 72.13 %
Hex 71.92 % Animal Companion 75.82 % Execute 73.48 %
0% 25% 50% 75% 100% 0% 25% 50% 75% 100% 0% 25% 50% 75% 100%
Percentage of SHAMAN decks using the card Percentage of HUNTER decks using the card 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. Garcı́a-Sánchez et al.
DRUID − Cluster 1 ( 5340 decks) DRUID − Cluster 2 ( 3814 decks) DRUID − Cluster 3 ( 3467 decks) DRUID − Cluster 4 ( 5997 decks) DRUID − Cluster 5 ( 4150 decks)
Wrath 70.58% Vicious.Fledgling 48.09% Wrath 85.15% Wrath 54.56% Wrath 97.93%
Wild.Growth 75.71% Swipe 37.15% Wild.Growth 93.74% Wild.Growth 30.12% Wild.Growth 93.66%
Ultimate.Infestation 52.87% Savage.Roar 96.43% Ultimate.Infestation 95.73% Swipe 64.33% Swipe 97.01%
The.Lich.King 35.79% Power.of.the.Wild 95.7% Swipe 90.51% Starfire 21.59% Raven.Idol 80.87%
Swipe 67.45% Patches.the.Pirate 64.84% Spreading.Plague 89.24% Starfall 34.75% Nourish 88.22%
Spreading.Plague 28.46% Mark.of.Y.Shaarj 84.35% Nourish 95.93% Savage.Roar 21.71% Mulch 40.48%
Primordial.Drake 27.27% Mark.of.the.Lotus 95.78% Mire.Keeper 54.43% Nourish 31.45% Moonfire 37.25%
Card
Card
Card
Card
Card
Nourish 76.16% Living.Mana 75.41% Malfurion.the.Pestilent 83.16% Naturalize 40.17% Mire.Keeper 65.76%
Mire.Keeper 40.06% Innervate 60.33% Jade.Spirit 65.21% Mark.of.the.Wild 22.04% Living.Roots 87.93%
Malfurion.the.Pestilent 46.22% Golakka.Crawler 45.46% Jade.Idol 90.71% Keeper.of.the.Grove 20.98% Innervate 97.88%
Jungle.Giants 33.26% Fire.Fly 90.98% Jade.Blossom 99.28% Innervate 45.64% Feral.Rage 64.43%
Innervate 67.15% Enchanted.Raven 86.6% Jade.Behemoth 82.92% Healing.Touch 22.76% Fandral.Staghelm 73.71%
Fandral.Staghelm 27.38% Druid.of.the.Swarm 43.6% Innervate 47.76% Ferocious.Howl 22.53% Emperor.Thaurissan 50.22%
Elder.Longneck 27.9% Crypt.Lord 42% Fandral.Staghelm 68.3% Druid.of.the.Claw 23.9% Azure.Drake 71.9%
Earthen.Scales 34.29% Bloodsail.Corsair 66.15% Aya.Blackpaw 75.48% Coldlight.Oracle 21.93% Ancient.of.War 44.41%
100%
100%
100%
100%
100%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
0%
0%
0%
0%
0%
Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card.
DRUID − Cluster 6 ( 5798 decks) DRUID − Cluster 7 ( 8486 decks) DRUID − Cluster 8 ( 9529 decks) DRUID − Cluster 9 ( 4428 decks) DRUID − Cluster 10 ( 3257 decks)
Wrath 56.36% Wrath 56.17% Wrath 91.18% Wrath 96.52% Wrath 88.58%
Swipe 77.44% Wild.Growth 88.4% Wild.Growth 71.4% Wild.Growth 86.47% Wild.Growth 82.84%
Soul.of.the.Forest 40.81% Ultimate.Infestation 75.8% Swipe 98.81% Swipe 96.3% Twin.Emperor.Vek.lor 71.48%
Savage.Roar 84.17% The.Lich.King 36.42% Savage.Roar 72.49% Nourish 89.88% Twilight.Elder 79.18%
Savage.Combatant 31.1% Swipe 89.68% Piloted.Shredder 42.33% Living.Roots 50.63% Swipe 93.28%
Power.of.the.Wild 84.18% Spreading.Plague 69.15% Loatheb 36.68% Jade.Spirit 88.75% Nourish 52.66%
Mounted.Raptor 38.38% Oaken.Summons 58.39% Keeper.of.the.Grove 97.04% Jade.Idol 99.53% Klaxxi.Amber.Weaver 71.42%
Card
Card
Card
Card
Card
Menagerie.Warden 22.82% Nourish 90.4% Innervate 97.17% Jade.Blossom 98.92% Innervate 88.18%
Mark.of.Y.Shaarj 44.15% Naturalize 56.93% Force.of.Nature 68.95% Jade.Behemoth 94.24% Druid.of.the.Claw 57.75%
Living.Roots 52.4% Malfurion.the.Pestilent 70.92% Druid.of.the.Claw 88.4% Innervate 95.19% Disciple.of.C.Thun 90.51%
Innervate 58.8% Lesser.Jasper.Spellstone 79.75% Dr..Boom 48.69% Gadgetzan.Auctioneer 71.61% Dark.Arakkoa 98.37%
Enchanted.Raven 34.53% Ironwood.Golem 43.98% Big.Game.Hunter 57.81% Feral.Rage 76.76% C.Thun.s.Chosen 76.36%
Druid.of.the.Saber 41.41% Hadronox 27.29% Azure.Drake 49.49% Fandral.Staghelm 70.66% C.Thun 99.72%
Druid.of.the.Flame 26.79% Ferocious.Howl 48.26% Ancient.of.War 36.98% Azure.Drake 47.06% Beckoner.of.Evil 81.67%
Druid.of.the.Claw 48.62% Branching.Paths 85.93% Ancient.of.Lore 86.64% Aya.Blackpaw 77.73% Azure.Drake 53.24%
100%
100%
100%
100%
100%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
0%
0%
0%
0%
0%
Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card.
(a) Druid
MAGE − Cluster 1 ( 5511 decks) MAGE − Cluster 2 ( 5713 decks) MAGE − Cluster 3 ( 8109 decks) MAGE − Cluster 4 ( 8232 decks) MAGE − Cluster 5 ( 10555 decks)
Water.Elemental 54.85% Sylvanas.Windrunner 38.67% Sorcerer.s.Apprentice 59% Unstable.Portal 46.15% Reno.Jackson 45.77%
Tar.Creeper 81.96% Sludge.Belcher 71.64% Primordial.Glyph 78.92% Sorcerer.s.Apprentice 93.78% Polymorph 68.07%
Steam.Surger 63.24% Polymorph 73.59% Mirror.Entity 41.73% Mirror.Image 58.67% Kazakus 39.3%
Shimmering.Tempest 44.6% Mad.Scientist 84.3% Medivh.s.Valet 73.77% Mirror.Entity 42.57% Ice.Block 59.55%
Servant.of.Kalimos 74.69% Ice.Block 53.56% Mana.Wyrm 89.81% Mana.Wyrm 97.66% Frost.Nova 38.97%
Pyros 53.37% Frostbolt 85.49% Kirin.Tor.Mage 73.02% Frostbolt 98.31% Frostbolt 65.43%
Primordial.Glyph 43.93% Flamestrike 88.41% Kabal.Crystal.Runner 54.59% Flamewaker 95.97% Forgotten.Torch 40.86%
Card
Card
Card
Card
Card
Frost.Lich.Jaina 73.4% Fireball 64.73% Ice.Block 50.24% Flamestrike 56.64% Flamestrike 74.21%
Frostbolt 49.14% Emperor.Thaurissan 40.22% Frostbolt 95.38% Flamecannon 40.78% Firelands.Portal 45.76%
Flamestrike 37.18% Echo.of.Medivh 37.09% Firelands.Portal 60.77% Fireball 95.32% Fireball 61.29%
Fire.Fly 78.52% Duplicate 75.35% Fireball 95.44% Azure.Drake 74.99% Doomsayer 51.37%
Fireball 45.44% Dr..Boom 38.54% Explosive.Runes 40.5% Archmage.Antonidas 52.21% Cabalist.s.Tome 42.19%
Bonfire.Elemental 42.64% Blizzard 37.51% Counterspell 79.84% Arcane.Missiles 83.93% Blizzard 65.88%
Blazecaller 68.39% Arcane.Intellect 57.2% Arcanologist 93.17% Arcane.Intellect 89.64% Babbling.Book 43.17%
Arcane.Intellect 54.2% Antique.Healbot 54.86% Arcane.Intellect 88.53% Arcane.Blast 49.21% Arcane.Intellect 65.06%
100%
100%
100%
100%
100%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
0%
0%
0%
0%
0%
Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card.
MAGE − Cluster 6 ( 3308 decks) MAGE − Cluster 7 ( 4195 decks) MAGE − Cluster 8 ( 6079 decks) MAGE − Cluster 9 ( 4025 decks) MAGE − Cluster 10 ( 9652 decks)
Unstable.Portal 42.99% Voodoo.Doll 44.17% Water.Elemental 65.57% Stargazer.Luna 29.24% Primordial.Glyph 53.57%
Tinkertown.Technician 84.49% Tar.Creeper 53.28% Sorcerer.s.Apprentice 49.56% Sorcerer.s.Apprentice 76.94% Ice.Block 89.23%
Spider.Tank 76.39% Stonehill.Defender 39.59% Sen.jin.Shieldmasta 23.67% Ray.of.Frost 23.28% Ice.Barrier 73.41%
Snowchugger 93.68% Raven.Familiar 59.17% Polymorph 87.86% Mirror.Image 51.53% Frost.Nova 97.74%
Piloted.Shredder 84.43% Polymorph 61.91% Mirror.Image 44.22% Mana.Wyrm 55.68% Frostbolt 75.68%
Mechwarper 96.01% Meteor 70.11% Mana.Wyrm 70.49% Mana.Cyclone 24.35% Flamestrike 50.18%
Mechanical.Yeti 50.42% Frost.Lich.Jaina 95.4% Frostbolt 95.74% Magic.Trick 24.22% Fireball 58.3%
Card
Card
Card
Card
Card
Goblin.Blastmage 93.86% Flamestrike 94.42% Flamestrike 91.74% Ice.Lance 30.86% Doomsayer 90.3%
Frostbolt 90.69% Dragon.s.Fury 92.4% Firelands.Portal 23.36% Frost.Nova 25.47% Bloodmage.Thalnos 49.33%
Fireball 89.93% Dragoncaller.Alanna 65.15% Fireball 97.53% Frostbolt 84.07% Blizzard 93.33%
Dr..Boom 55.47% Doomsayer 48.56% Faceless.Summoner 24.79% Fireball 70.73% Archmage.Antonidas 54.46%
Cogmaster 74% Blizzard 74.14% Azure.Drake 45.91% Bloodmage.Thalnos 32.37% Arcanologist 46.02%
Clockwork.Gnome 86.85% Baron.Geddon 42.6% Arcane.Missiles 67.84% Archmage.Antonidas 27.48% Arcane.Intellect 96.5%
Archmage.Antonidas 45.13% Arcane.Tyrant 41.72% Arcane.Intellect 84.32% Arcane.Missiles 55.18% Alexstrasza 55.78%
Annoy.o.Tron 83.16% Arcane.Artificer 88.03% Arcane.Explosion 23.79% Arcane.Intellect 80.67% Acolyte.of.Pain 54.27%
100%
100%
100%
100%
100%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
0%
0%
0%
0%
0%
Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card.
(b) Mage
WARRIOR − Cluster 1 ( 5755 decks) WARRIOR − Cluster 2 ( 6134 decks) WARRIOR − Cluster 3 ( 11918 decks) WARRIOR − Cluster 4 ( 947 decks) WARRIOR − Cluster 5 ( 5168 decks)
Whirlwind 35.57% Zilliax 59.13% Sylvanas.Windrunner 65.99% Twin.Emperor.Vek.lor 63.36% Tar.Creeper 51.51%
Warpath 46.59% Warpath 37.87% Sludge.Belcher 55.19% Twilight.Elder 63.15% Stonehill.Defender 65%
The.Lich.King 35.95% Supercollider 35.38% Slam 52.25% Slam 61.46% Sleep.with.the.Fishes 51.76%
Sleep.with.the.Fishes 39.06% Stonehill.Defender 44.13% Shield.Slam 94.4% Shield.Block 59.77% Slam 60.6%
Slam 59.93% Shield.Slam 78.71% Shield.Block 92.43% Ravaging.Ghoul 68.43% Shield.Block 61.07%
Shield.Slam 60.97% Shield.Block 82.1% Justicar.Trueheart 54.82% Fiery.War.Axe 81.1% Ravaging.Ghoul 87.36%
Shield.Block 85.02% Reckless.Flurry 42.68% Grommash.Hellscream 73.09% Execute 91.55% Primordial.Drake 62.38%
Card
Card
Card
Card
Card
Scourgelord.Garrosh 49.28% Omega.Assembly 55.9% Fiery.War.Axe 96.69% Doomcaller 55.12% Fire.Plume.s.Heart 76.66%
Execute 93.4% Eternium.Rover 44.86% Execute 98.37% Disciple.of.C.Thun 86.48% Fiery.War.Axe 87.75%
Drywhisker.Armorer 36.99% Dyn.o.matic 54.94% Death.s.Bite 65.29% C.Thun.s.Chosen 62.72% Execute 92.71%
Dead.Man.s.Hand 61.72% Dr..Boom..Mad.Genius 65.19% Cruel.Taskmaster 53.83% C.Thun 99.16% Direhorn.Hatchling 76.66%
Brawl 82.69% Direhorn.Hatchling 38.15% Brawl 91.24% Bloodhoof.Brave 58.39% Brawl 88.82%
Blood.Razor 89.42% Brawl 87.63% Bash 55.55% Beckoner.of.Evil 79.09% Bloodhoof.Brave 78.77%
Battle.Rage 36.28% Baku.the.Mooneater 44.88% Armorsmith 65.27% Ancient.Shieldbearer 84.37% Alley.Armorsmith 83.2%
Acolyte.of.Pain 60.5% Acolyte.of.Pain 31.92% Acolyte.of.Pain 74.17% Acolyte.of.Pain 59.03% Acolyte.of.Pain 64.84%
100%
100%
100%
100%
100%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
0%
0%
0%
0%
0%
Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card.
WARRIOR − Cluster 6 ( 7193 decks) WARRIOR − Cluster 7 ( 1214 decks) WARRIOR − Cluster 8 ( 5847 decks) WARRIOR − Cluster 9 ( 7287 decks) WARRIOR − Cluster 10 ( 1380 decks)
Whirlwind 80.24% Twin.Emperor.Vek.lor 75.45% Upgrade. 91.28% Slam 29.75% Twin.Emperor.Vek.lor 88.91%
Slam 69.04% Twilight.Elder 65.32% Southsea.Deckhand 95.96% Shield.Block 22.77% Slam 71.96%
Ravaging.Ghoul 44.49% Slam 61.86% Southsea.Captain 51.94% Ravaging.Ghoul 26.24% Shield.Slam 97.9%
Inner.Rage 68.16% Shield.Slam 95.06% Patches.the.Pirate 62.99% Kor.kron.Elite 61.99% Shield.Block 91.81%
Grommash.Hellscream 50.56% Shield.Block 86.41% N.Zoth.s.First.Mate 90.68% Heroic.Strike 22.35% Ravaging.Ghoul 95.65%
Grim.Patron 47.75% Justicar.Trueheart 74.3% Mortal.Strike 68.36% Grommash.Hellscream 37.45% Justicar.Trueheart 71.67%
Frothing.Berserker 72.04% Fiery.War.Axe 90.94% Leeroy.Jenkins 55.09% Frothing.Berserker 55% Fiery.War.Axe 93.7%
Card
Card
Card
Card
Card
Fiery.War.Axe 88.54% Execute 96.46% Kor.kron.Elite 94.15% Fiery.War.Axe 68.79% Execute 99.28%
Execute 93.31% Disciple.of.C.Thun 78.75% Heroic.Strike 83.51% Execute 61.44% Disciple.of.C.Thun 98.48%
Death.s.Bite 44.49% C.Thun.s.Chosen 81.88% Frothing.Berserker 80.11% Cruel.Taskmaster 24.61% C.Thun.s.Chosen 93.41%
Cruel.Taskmaster 57.44% C.Thun 99.01% Fiery.War.Axe 95.67% Blackwing.Corruptor 21.3% C.Thun 98.26%
Blood.To.Ichor 34.19% Brawl 92.5% Dread.Corsair 91.94% Battle.Rage 24.74% Brawl 98.48%
Battle.Rage 82.9% Beckoner.of.Evil 83.36% Bloodsail.Raider 91.17% Azure.Drake 25.15% Brann.Bronzebeard 70.22%
Armorsmith 68.19% Bash 69.69% Bloodsail.Cultist 89.94% Arcanite.Reaper 34.29% Ancient.Shieldbearer 99.35%
Acolyte.of.Pain 90.3% Ancient.Shieldbearer 96.95% Arcanite.Reaper 94.72% Alexstrasza.s.Champion 23.99% Acolyte.of.Pain 75.94%
100%
100%
100%
100%
100%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
25%
50%
75%
0%
0%
0%
0%
0%
Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card. Percentage of decks (in cluster) using that card.
(c) Warrior
Fig. 4: Ten most common cards of each cluster for Druid (a), Mage (b) and
Warrior (c).
Height
0 100 200 300 400
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.GuardianNaga.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
play the game.
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
therefore 1-2 and 3-4 are also related.
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
Cards
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
Cluster Dendrogram
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
visually using the dendrogram generated by the AHC algorithm
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
Fig. 5: Global AHC for all cards used by Warrior class.
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
Data Mining of Deck Archetypes in Hearthstone
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
features related to the decks, such as summarizing of the number of minions in
A feature extraction method could be also applied, in order to ‘generate’
decks along the time and expansion releases may help to understand how users
techniques, such as visualizing the cards networks, or studying the changes in
transversal, specialized or emerging/disappearing categories. Other visualization
nities can be plotted in a strategic diagram to see what decks belong to motor,
detected [1], and from using their centrality and density measures, these commu-
clustering algorithms, such as the Leiden Algorithm, card communities can be
cell is the number of decks that share that particular pair of cards. Using other
As future work, a card co-appearance matrix can be made, in which each
to be defined a priori by the user. Also, relationships between cards can be seen
is no ground truth, and parameters such as an arbitrary number of clusters have
issues are typical of clustering, an unsupervised learning problem for which there
sible to detect two distinct archetypes inside the same cluster (M7, W2). These
right) of a binary tree of 4 levels. So 1 is more related to 2, and 3 to 4, and
Fig. 6: Subtrees of the AHC cluster. Each id correspond to a leaf (from left to
11
12 P. Garcı́a-Sánchez 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 with Noise [6] can partially solve the issue of deciding a
priori the number of clusters; nevertheless, they feature different parameters to
be tuned.
References
1. Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., Herrera, F.: SciMAT: A
new science mapping analysis software tool. Journal of the American Society for
Information Science and Technology 63(8), 1609–1630 (2012)
2. Da Silva, A.R., Goes, L.F.W.: Hearthbot: An autonomous agent based on fuzzy art
adaptive neural networks for the digital collectible card game hearthstone. IEEE
Transactions on Games 10(2), 170–181 (2017)
3. Drachen, A., Canossa, A., Yannakakis, G.N.: Player modeling using self-
organization in tomb raider: Underworld. In: 2009 IEEE symposium on compu-
tational intelligence and games. pp. 1–8. IEEE (2009)
4. Drachen, A., Sifa, R., Bauckhage, C., Thurau, C.: Guns, swords and data: Clus-
tering of player behavior in computer games in the wild. In: 2012 IEEE conference
on Computational Intelligence and Games (CIG). pp. 163–170. IEEE (2012)
5. Drachen, A., Thurau, C., Togelius, J., Yannakakis, G.N., Bauckhage, C.: Game
Data Mining, pp. 205–253. Springer London, London (2013)
6. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for
discovering clusters in large spatial databases with noise. In: Kdd. vol. 96, pp.
226–231 (1996)
7. Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Hierarchical clustering. Cluster
analysis 5, 71–110 (2011)
8. Garcı́a-Sánchez, P., Tonda, A., Mora, A.M., Squillero, G., Merelo, J.J.: Automated
playtesting in collectible card games using evolutionary algorithms: A case study
in hearthstone. Knowledge-Based Systems 153, 133–146 (2018)
9. Garcı́a-Sánchez, P., Tonda, A.P., Leiva, A.J.F., Cotta, C.: Optimizing hearthstone
agents using an evolutionary algorithm. Knowl. Based Syst. 188 (2020)
10. Grad, L.: Helping ai to play hearthstone using neural networks. In: 2017 federated
conference on computer science and information systems (FedCSIS). pp. 131–134.
IEEE (2017)
11. Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm.
Journal of the royal statistical society. series c (applied statistics) 28(1), 100–108
(1979)
12. Jakubik, J.: A neural network approach to hearthstone win rate prediction. In: 2018
Federated Conference on Computer Science and Information Systems (FedCSIS).
pp. 185–188. IEEE (2018)
13. Janusz, A., Tajmajer, T., Świechowski, M.: Helping ai to play hearthstone: Aaia’17
data mining challenge. In: 2017 Federated Conference on Computer Science and
Information Systems (FedCSIS). pp. 121–125. IEEE (2017)
14. Janusz, A., Tajmajer, T., Świechowski, M., Grad, L., Puczniewski, J., Ślkezak, D.:
Toward an intelligent hs deck advisor: Lessons learned from aaia’18 data mining
competition. In: 2018 Federated Conference on Computer Science and Information
Systems (FedCSIS). pp. 189–192. IEEE (2018)
Data Mining of Deck Archetypes in Hearthstone 13
15. Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., Yannakakis, G.N.: Predict-
ing player behavior in tomb raider: Underworld. In: Proceedings of the 2010 IEEE
Conference on Computational Intelligence and Games. pp. 178–185. IEEE (2010)
16. Rughini, C.: Citizen science, gallaxies and tropes: Knowledge creation in im-
promptu crowd science movements. In: 2016 15th RoEduNet Conference: Net-
working in Education and Research. pp. 1–6 (Sep 2016)
17. Świechowski, M., Tajmajer, T., Janusz, A.: Improving hearthstone ai by combining
mcts and supervised learning algorithms. In: 2018 IEEE Conference on Computa-
tional Intelligence and Games (CIG). pp. 1–8. IEEE (2018)
18. Thurau, C., Bauckhage, C.: Analyzing the evolution of social groups in world
of warcraft R . In: Proceedings of the 2010 IEEE Conference on Computational
Intelligence and Games. pp. 170–177. IEEE (2010)
19. Weber, B.G., Mateas, M.: A data mining approach to strategy prediction. In: 2009
IEEE Symposium on Computational Intelligence and Games. pp. 140–147. IEEE
(2009)
20. Weber, B.G., Mateas, M., Jhala, A.: Using data mining to model player experience.
In: FDG Workshop on Evaluating Player Experience in Games. ACM Press (2011)
21. Weber, B.G., John, M., Mateas, M., Jhala, A.: Modeling player retention in madden
nfl 11. In: Twenty-Third IAAI Conference (2011)
22. Yannakakis, G.N., Togelius, J.: Modeling Players, pp. 203–255. Springer Interna-
tional Publishing, Cham (2018)