# Beyond the Meta: Leveraging Game Design
Parameters for Patch-Agnostic Esport Analitics

Alan Pedrassoli Chitayat  Florian Block  James Walker  Anders Drachen
University of York York UK
{alan.pchitayat; florian.block; james.walker<EMAIL_ADDRESS>
###### Abstract

Esport games comprise a sizeable fraction of the global games market, and is
the fastest growing segment in games. This has given rise to the domain of
esports analytics, which uses telemetry data from games to inform players,
coaches, broadcasters and other stakeholders. Compared to traditional sports,
esport titles change rapidly, in terms of mechanics as well as rules. Due to
these frequent changes to the parameters of the game, esport analytics models
can have a short life-spam, a problem which is largely ignored within the
literature. This paper extracts information from game design (i.e. patch
notes) and utilises clustering techniques to propose a new form of character
representation. As a case study, a neural network model is trained to predict
the number of kills in a Dota 2 match utilising this novel character
representation technique. The performance of this model is then evaluated
against two distinct baselines, including conventional techniques. Not only
did the model significantly outperform the baselines in terms of accuracy (85%
AUC), but the model also maintains the accuracy in two newer iterations of the
game that introduced one new character and a brand new character type. These
changes introduced to the design of the game would typically break
conventional techniques that are commonly used within the literature.
Therefore, the proposed methodology for representing characters can increase
the life-spam of machine learning models as well as contribute to a higher
performance when compared to traditional techniques typically employed within
the literature.

##  1 Introduction

Esport titles, such as _League of Legends_ and _Dota 2_ , have amassed both
large audiences and player-bases ([9]; [11]). Due to the competitive nature of
the genre, the player community often develop so called _“metas”_ as explained
by [7]. According to the author, _metas_ are naturally discovered and
developed strategies for optimum ways of playing the game that are focused in
determining competitive advantage available within the current parameters of
the game design. As a result, game developers are constantly updating and
changing the rules of the game in order to balance the game, prevent game
stagnation as well as to maintain player interest. This is done through
releasing _patches_ to the game, which can alter the rules and the parameters
of the game [14]. Those changes can be manifested by introducing new content
(such as new characters), changing existing content (such as altering the
duration or cost of an in-game ability), removing old content (such as
discarding excising mechanics or rules) or a combination of them (such as
replacing an ability for an existing character to a new ability with unique
traits).

As the patch changes are generally made with the intent to re-balance the
game, they often incur changes to the meta, forcing competitive players to
play differently and creating new game mechanics interactions. As outlines by
[2], the way in which a game is played, and the current rules set could impact
machine learning models and other forms of data analysis. This could be caused
both by differences in player decision making, as well as by the new
environmental state (i.e. the changes to the game’s parameters themselves). As
a consequence, much of the esport literature is done focusing on a limited
periods of time, to reduce the impact of those changes ([3]; [6]; [10]; [15]).
However, as the game continues to change, the performance of such models may
suffer, and models may need to be retrained to support a new architecture.

One example that can be observed in the esport literature comes from models
that include the characters present in a match. Typically, characters are
represented by their unique Character IDs (which are arbitrarily assigned
numerical identifiers), which most commonly undergo a one-hot encoding or a
similar variation [14]; [8]; [6]; [16]; [2]; [12]). In this encoding, the
unique identifier of a character is represented as a vector class. This means
that if a game only has four characters, with IDs 1 through 4 respectively,
character 1 can be presented as the vector (1,0,0,0), while character 2 would
be (0,1,0,0), etc… This can lead to an architectural problem where, as the
number of Character IDs increases so does the number of dimensions needed to
encode them. Using the same example, if a new character is then introduced
with Character ID 5, the vector needed to represent all previous character
would need an additional dimension. Thus character 1 would then be represented
as the vector (1,0,0,0,0). A machine learning model developed prior to the
introduction of the 5th character would be architectured to support a
4-dimensional input vector for each character, and thus not support a
5-dimensional input needed given the new game design parameters. This could
lead the model to be unusable due to the technical constraints of how it was
trained and applied.

Additionally, characters within esport games usually have unique abilities and
traits which allows them to be played in different ways ([3]; [2]). If a
character is re-designed in a way in which several of their original abilities
or traits are altered, the way in which they are played could significantly
change from patch to patch. Those changes would not be encompassed by the
Character IDs. Using the same example as above, if character 1 is designed to
poses several supportive traits (such as healing or otherwise improving the
in-game performance of allies) but then is re-designed to have several
offensive abilities, the way in which the character is played could be changed
significantly. However, as the character ID would remain the same, a model
that uses the ID could have its performance impacted, as it had been trained
in a different state that does not account for the new changes to the
character. In these hypothetical scenario, while the model would still be able
to produce results, it would be unclear if the results are reliable due to the
uncertainty of the impact of the change to the game’s environment.

Thus, this paper conjectures that changes to game design parameters may be
interpreted in three ways:

  * •

Breaking changes - Those are fundamental changes to the design of the game
which would require a changes in model architecture in order to produce any
results.

  * •

Impactful changes - Those do not incur changes in architecture, however have a
substantial impact in model performance.

  * •

Unimpactful changes - Those do not alter the state of the game significantly
enough to affect the performance of previously trained models.

While breaking changes are usually trivial to identify, due to their severe
disruption to the application of models, differentiating between impactful and
unimpactful changes may require analysis. Furthermore, if either breaking or
impactful changes are identified, a previously trained model may need to
undergo the training process again to account for the new parameters. This can
be a cumbersome process, especially as esport titles typically change rapidly
and abruptly ([14]; [7]).

This paper builds on some of the methodology suggested in the literature ([3];
[16]), to generate a novel character representation. This form of representing
characters utilises patch specific game design data, which is then clustered
to allow for a fixed and reusable notation that can be readily applied to
future models. The proposed method holds meaningful information about the
character’s capabilities and it is sensitive to changes introduced in patches.
This has been done using _Dota 2_ \- a popular Multiplayer Online Battle Arena
(MOBA) esport title - which contains over 100 unique characters. Each playable
character has a range of unique abilities - which are active skills that can
be used by players during play with in-game effects, such as causing damage or
healing allies - and stats, including “Intelligence”, “Agility”, “Strength”,
etc… _Dota 2_ is a team based game, in which two teams of 5 players each
attempt to destroy the main building in the opponent’s team base. This game
has been chosen as a focus in this study due to its popularity within academia
([6]; [10]; [15]; [13]; [4]; [1]; [12]; [8]), large complexity and abundant
access to data. While the resources made available within this paper are
designed for _Dota 2_ , a similar methodology could readily be applied to
other MOBA titles - such as _League of Legends_ \- with minimal alterations,
as well as advising the development of similar techniques for other esport
genres.

The character representation proposed in this paper is then tested and
evaluated in a case-study, which simulates a hypothetical future work within
the esport literature. Three versions of a Neural Network (NN) are trained
using professional games of _Dota 2_ from patches 7.27 to 7.33. These models
attempts to predict the number of kills (also referred to as the score) for
each team at the end of the match. It is important to note that these NNs bear
no direct contribution within the paper other than as a evaluation metric.
They serve as an illustration for possible use case within future research,
and outline the capabilities of this methodology. (NN1) was used as a simple
base-line, where only the match duration was used as an input. No additional
features were included in this baseline, therefore this network would have no
way of determining or modeling the characters present in the game. (NN2) was
utilised as an additional control, where Character IDs (one-hot encoded) were
used to represent the characters present in each team, as well as the duration
of the match. This network controls for the standard encoding typically used
in the literature, which holds information about the characters that have been
selected in the match. Lastly, in order to evaluate the framework proposed,
(NN3) was trained using the characters selected represented through the
clustered approach proposed in this paper, as well as the match duration.

Towards addressing the impact of game changes in machine learning models and
other forms of data analytics within esports, which poses a problem of short
life-spam, this study provides 3 major contributions:

  * •

A feature set of character traits is compiled through the literature and game-
design data made available by the game’s publisher.

  * •

A novel way of representing characters is proposed and validated through
performance, outlying how it is sensitive to patch specific context as well as
reliant to fundamental changes to the core game environment.

  * •

Access to standardized format, centroids, clustered abilities and characters
are made freely available for use for future research in the
field111https://github.com/ChitaAPC/Dota2_BeyondTheMeta_CharacterRepresentation.

##  2 Related work

Within the esport literature many studies which aims to apply data analysis
and machine learning techniques to esport data can be found. Those studies
investigate a range of problems and applications, from classifying complex
patterns [3], performing micro/macro predictions about the game state ([6];
[4]) or provide advisory tools to tournament organisers [15], esport audiences
[10], and players [14]. The application of machine learning and other forms of
analytics is vast within the literature. In this section, this paper will
summarize and evaluate different examples of applications, with a focus on how
characters were utilised and the potential impact of game changes to the work
proposed, including any steps taken to mitigate this impact.

[14] provides an early example of the application of machine learning
techniques. In this paper, [14] utilises both Bayes Nets (BNs) and Long Short-
Term Memory Recurrent Neural Networks (LSTM RNNs) to investigate the character
selection part of the game, also known as the “Draft Phase”. Using those
techniques, the paper attempts to predict the characters which professional
players choose to play for a given matches. In this work, the authors outline
the significant impact of patches to machine learning models, due to changes
to the core game mechanics that alter the environment severely and abruptly.
In this example, the characters were represented by One-Hot encoding as
described previously. This means that, as identified by [14], their model
validity is no longer guaranteed as the parameters of the game change in
patches. Additionally the model would be unable to support the addition of new
characters.

[3] introduces clustering techniques to automatically identify player roles
(which are play styles adopted by players within a team strategy) within _Dota
2_. These roles are community determined and are not labelled within the game,
but instead relate to how a player may play (for example) in a “support” role,
aiming to enhance their teammates rather than themselves, while other players
may play in a “carry” role, which aims at accumulating in-game resources to
improve themselves while being supported by their teammates. Therefore, the
study focused on player performance - i.e. player decision making and how it
impacts the game. Later the authors extended this work to measure the player
performance (i.e. how well a player is performing in a particular match) based
on their detected roles [2]. In this extended work, the authors utilised
archetype analysis - a form of unsupervised learning similar to clustering -
in the Key Performance Indicators (KPIs) available in the game to provide
insights into how well players are performing within their detected role. In
both cases the authors utilised the order in which abilities were upgraded by
the players as a key indicator of role detection (in addition to other metrics
such as spatio-temporal data). This outlines a clear importance on the
abilities of the characters, and how they may dictate play-style, however, it
is important to note that as patches change abilities, their effect could also
change. This could lead to a limiting the life-spam of such model, as
abilities are only represented by their ordering within the character. To
mitigate that impact, the clustering model would likely need to be re-trained
for each character when impactful changes are present as identified by the
authors.

Similarly, [8] proposed a model for _Dota 2_ (as well as _Counter Strike_)
that utilises roles to generate a probabilistic prediction of the winner of
the match. Within the _Dota 2_ model, [8] utilised the knowledge of
professional player role (as opposed to automatically detecting it through
performance) in addition to other features, such as aggregate metrics of
damage and resources to formulate its prediction. This model achieved
predictions ranging from 73% to 90% AUC depending on the input features used
in the function. Amongst the input features, the authors used the characters
present in the match as one of the function’s parameters. While the exact form
in which characters are used is not stated, it is conjectured that those
undergo One-Hot encoded or similar. This is conjectured as this categorical
value would need to be encoded into a numerical representation in order to be
utilised in the function. For this reason, this function may also be subject
to impact of changes in the game. However, it is unclear how much weight
characters have within the function, and the use of other metrics may
obfuscate or otherwise help minimize the impact of such data.

Another example is the work by [4], in which the authors developed a Random
Forest model to predicting the winner of a _Dota 2_ match. In [4], the use of
match-state is used to predict the outcome of the game. The model achieved
accuracy ranges varying from 70% to 90% depending on the in-game time of the
prediction. This high level accuracy was achieved without including any
character selection information, instead utilising only aggregate values from
the match of features such as resource distribution (i.e. gold and
experience), score (kills), and other in-game metrics. The use of these
aggregate features and lack of information about the characters can mitigate
the impact of patches, for the potential cost of contextual information. For
example, a match which contains several characters that benefit from fighting
in both teams could be interpreted differently from a match which contains
several characters that benefit from avoiding fights and instead focusing on
gathering/denying resources. Furthermore, it is important to note that even
though characters are not included in the model directly, the current meta
could still have consequential impacts in the model. This means that, if the
meta when the model was trained revolved around team-fights and encounters,
then more kills are to be expected. A change in the meta that pivots away from
confrontations (or vise-versa) could then have an impact in the performance of
the model.

By contrast, [13] outlines a comparison of performance of different techniques
for predicting the winner of a match using data from character selection
alone. Across the many methodology evaluated by the authors, it is clear that
accuracy decreases as skill proficiency increases (i.e. the higher the player
rank, the harder it is to predict the winner based on character selection
alone). The performance of the different methodology ranged from approximately
71% (Factorization Machines classifier for normal skill level) to
approximately 64% (Naive Bayes for very high skill matches). In this
evaluation of models, the authors encoded the character present through a
variation of One-Hot encoding, where all characters present for the Radiant
team where encoded as 1, while the characters present in the Dire team were
encoded as -1 and 0 otherwise. Thus this approach is subjected to breaking
changes when new characters are released (as previously explained in this
paper) and would also be subjected to changes in the meta, as explained in the
literature ([7]; [14]).

[6] introduced a different approach to handling esport characters. In this
study [6] utilised a predictive deep neural network, which utilised shared
weights in the inputs which pertain to a single player/character within the
dataset to predict the chances of a character being killed within five
seconds. This approached allowed for greater flexibility than separating the
characters, as the same weights of a network could be trained multiple times
across all players for a given match. Later this work was extended by [12], in
which a similar approach was taken to handle the characters and their
associated data. Comparably to previously mentioned works, both [6] and [12]
represent the character selection through a One-Hot encoding of the Character
IDs, similarly arising the risk of breaking changes as new content is
released. Additionally, information related to the abilities of the characters
were also utilised, indicating another example were key importance to the
abilities can be found within the literature.

Older work in the literature by [1] has attempted to utilise character
selection data to identify strategies in order to predict the outcome. In the
work by [1], the use of aggregate performance metrics for the characters
themselves (across all players) were clustered, in order to determine
similarities. This work differs from other works in the literature, as it
focuses on the characters overall performance across the whole population, as
opposed to individual player performance, such as ([3]; [8]), or simply in the
presence of the character in the match as a binary value such as [13].
According to [1], using aggregate metrics of the population was insufficient
to meaningfully represent the state of the game and detect strategies in order
to produce accurate predictions. However, should more refined data be used,
similar approaches could be beneficial to addressing the problem of changes to
the game.

Later, [16] has taken the idea suggested by [1] further. By focusing on
individual player past performance with a character (i.e. wins/losses), in
addition to some game design parameter about the character for a given patch
(e.g. max health, attribute, attribute gain, etc..), as well as several other
features, [16] proposed a XGBoost model to predict the winner of the match
given the character selection. By performing feature analysis, the authors
concluded that, in addition to individual player’s previous experience with
their chosen character, the character’s “raw attribute” (i.e. the game design
parameters of the character excluding any player decision making) are the most
significant features in predicting the outcome of a game within their model.
In this work, the authors collected over 55 thousand professional games from
the years of 2012 to 2020. This data expanded many patches, thus, was
subjected to many core game design changes. The authors addressed this by
parsing through the game’s change logs (patch notes), which contains detailed
information about the current game state at any given date. Thus the current
iteration of game for any given match could be contextualised. However, the
model was limited on character attributes without including any information
about character abilities, which other points of literature suggest bear
significant importance to how a character is played ([3]; [2]; [8]; [6];
[12]). Furthermore, [16] suggest that more data and a further breakdown of
additional features may improve the performance further. Additionally, the
author also included the characters present in a match using through a One-Hot
encoding, making it similarly susceptible to breaking changes as new
characters are introduced.

Overall, several points can be extracted from the literature. It is clear that
changes in game design can impact the performance of models ([14]; [16]). It
is also clear that high accuracy and proficiency can be achieved in models
within the game iteration used during training, be it to predict the overall
outcome [4] or to perform micro prediction about in-game events [12]. High
emphasis in the importance of character attributes and stats can also be
observed [16], as well as the importance of abilities, which can dictate how
characters are played ([3]; [2]; [8]; [6]; [12]). It is also clear that
character selection is a very common feature utilised in the literature ([14];
[8]; [6]; [3]; [16]; [2]; [12]). However, the typical approach to including
character selection subjects models to breaking changes when new characters
are introduced, or to impactful changes through updates to existing game
design parameters. This could lead to the need of frequent retraining models,
which can be a cumbersome process. Despite its wide spread utilisation, very
little work has been done to formulate a consistent form of character
representation that is patch-aware, to allow for changes in the game design
parameters to be included in the analysis while supporting the addition of new
characters. This paper aims to address this gap, by building from [3] and [16]
to introduce a methodology that allows researchers to contextualise game
design parameters in a robust way that can support new characters without
compromising the flow of data. Through analysing patch notes, it is possible
to support a more rich environment for representing characters that includes
data from both, their attributes and their abilities.

##  3 Methodology

This section describes the steps taken to collect and process the data used.
As explained above, the methodology proposed focuses on the _Dota 2_ title, as
a popular esport game with a wealth of academic research, and large player-
base/audience.

###  3.1 Data Collection

In order to conduct this study, two distinct datasets were collected. Firstly
patch data was collected, which consists of data about game design parameters
for characters, including of the abilities and attributes from patches 7.27 to
7.33. Secondly, a match history dataset was compiled which consists of the
character selection data of all competitive matches for these patches, as well
as the duration of the match, the number of kills on each side and the result
of the match.

Both datasets were collected through the OpenDota
API222https://docs.opendota.com/ and its associated public table of
constants333https://github.com/odota/dotaconstants/tree/master/build. OpenDota
is a free platform that offers in-depth statistics and break down of _Dota 2_
public matches, including those in professional tournaments and events. This
platform has been commonly used by other works in the literature for data
collection ([4]; [3]; [2]).

As OpenDota’s table of constants is stored in a Git repository, it is possible
to access older versions of the tables, referring to previous game patches,
through the history of individual files or folders. The first dataset was
compiled by parsing through three JSON files retrieved from this repository
(“hero_abilities.json”, “abilities.json” and “heroes.json”).

The second dataset was compiled through OpenDota’s SQL query feature available
on their platform. The data collected included:

  * •

MatchID - a unique identifier for the match

  * •

Patch - the patch in which the match was played in (such as ’7.27’)

  * •

Duration - The duration of the match in seconds

  * •

KillsR - The number of kills obtained by the Radiant team

  * •

KillsD - The number of kills obtained by the Dire team

  * •

HeroX - The hero played by player X, where X is a number from 0 to 9. Players
0-4 correspond to the Radiant team, and 5-9 correspond to the Dire Team

  * •

RadiantWin - A binary variable containing 1 if Radiant won the game and 0 if
Radiant lost (note it is not possible for a match to end in a draw in _Dota
2_)

Only professional and premium matches were collected from patches 7.27 to
7.33, which included data deom Jun 2020 to present. Games which did not
conclude in a natural state (for example if a player has abandoned, or if
there had been a server error) were not collected. This lead to a total of
61,254 matches from the 7 different patches. (note that at the time of
writing, the latest patch (7.33) is still active, and therefore fewer matches
are available.

###  3.2 Data Processing

In order to perform clustering on the game design data, the JSON files
retrieved from the table of constants were processed. Firstly
“hero_abilities.json” contains the name of each individual character and their
associated abilities. This file was parsed to compile a list of the relevant
character abilities, which allowed abilities from non-playable entities, such
as neutral characters, to be discarded. Using the name of each individual
ability as a key, “abilities.json” was then parsed to generate a CSV file
containing the properties of every character ability for each of the relevant
patches. This was achieved by compiling a script that normalise and
standardise the way data is represented.

Several inconsistencies were detected in the way data was stored by the game
between patches and abilities. For example, an ability with the property of
“movement_speed_slow” of 30 and another ability of “movement_speed_bonus” of
-30 both have the same effect of reduce the movement speed of the target by 30
units. Through manually comparing the properties names and values and cross
referencing to the game’s wiki, a script that standardised the properties was
compiled. While this approach is subject to errors, gathering data is limited
by the way in which data is made available by the game’s publishers. However,
extracting data using this methodology allows for more in-depth analysis than
previous techniques which generally ignore what abilities do and their
associated properties ([3]; [6]). Once all of the properties for every ability
was compiled, info on the characters attributes - as described in the
literature [16] \- was extracted from the “heroes.json” file and amended to
the relevant abilities for the heroes. Thus, each entry in the newly compiled
CSV file contained data for each ability for each character as well as data
about the character itself, which contextualises its use in the game. The CSV
files have been made available in the resources listed in the Introduction
section for future use.

###  3.3 Cluster Analysis

Once all of the character abilities were processed and the CSV files were
compiled it was possible to utilise clustering algorithms on the data. As this
study aims to provide longevity to machine learning models in esports
analytics and support the addition of new unseen data, K-Means [5] was
utilised as it can be readily used without altering the labeling or number of
clusters once the centroids are found. This means that once the centroids are
found, the model would not need to be retrained with unseen patches. Instead,
the data from new patches could simply be predicted with the existing
centroids, therefore clusters do not change when adding new data. This
approach would then contribute to reducing the impact of breaking changes, as
the value of K would not need to change unless the new data leads to a new
concentration of clusters (i.e. an impactful change). In this case analysis of
the cluster distribution - such as that described in this section - would
reveal the need for new clustered. However, data from several patches are used
to generate the clusters to reduce the risk of impactful changes being
present.

In this study, K-Means was used and the “Sum of the Squared Error” (SSE) was
used to evaluate the split of the data and identify an optimum value for K
[5]. Figure 1 outlines the elbow plot generated with different values for K.
As the figure outlines, the elbow lies roughly between K=40 and K=75. The
exact value for K was then selected using the highest “Silhouette Score” [5]
within this range, which provided K=68.

Figure 1: The Elbow Plot for the K-Means clustering of the character’s
abilities and traits together

Once clustering was performed with the ability data, the labels obtained could
then be used to represent characters. As one character contains multiple
abilities, the cluster for each ability was encoding into a one-hot-encoded
format. This means that any single ability was then represented as a K-long
vectors of zeroes, except for the i’s dimension, where i is the cluster number
for that particular ability. Subsequently, one character can then be
represented by adding all of the K-long vectors of their abilities. This
allows one character to be represented as a K-long vector, containing the
modes for each of their ability clusters. Unlike the One-Hot encoding of
Character IDs, however, this approach would not be subjected to breaking
changes when new characters are introduced, as the value for K would remain
the same.

For demonstrations purpose, this approach can be applied to a fictional game,
where K=3. In this fictional game “Character X” has three abilities, of
clusters 0, 0 and 2 respectively. This means that Character X can be
represented as the addition of the vectors (1, 0, 0); (1, 0, 0) and (0, 0, 1).
Through this technique, Character X can then be represented as a single
K-dimensional vector of (2, 0, 1) - where the mode of cluster 0 is two,
cluster 1 is zero and cluster 2 is one. Conversely, a more complex character
with 5 abilities of clusters 0, 0, 1, 1 and 2 respectively could be
represented as the single vector (2, 2, 1). This simplistic, yet powerful
approach allows for complex patterns to emerge from the data while still
maintaining the meaning of each cluster as identified by the data.
Furthermore, it maintains the size of the vector needed to represent any
characters to K, even when new abilities or new characters are introduced by
future patches.

###  3.4 Predictive Neural Network

Three NN are used to help evaluate the character representation. This
simulates a potential use-case within future research, with two baselines. The
case-study of predicting the number of kills at the end of a match was
selected, as this is a under-explored area in the literature with some of the
potential highlighted by existing research ([16]; [15]). This is also a
similar use-case as to predicting the winner of a game, which contains a
wealth of knowledge ([16]; [1]; [4]; [8]; [14]).

Firstly (NN1) was trained, which used only the match duration and no
additional information. This provided a baseline for comparison, as all models
include the match duration. This was included as the literature suggests that
duration bears a significant impact in the outcome of the game, both in terms
of result and in terms of score [16]. Secondly (NN2) was trained which
contained both match duration and character selection data. The character
selection data was represented by encoding the Character IDs into a one-hot
encoding as a standard technique in the literature ([13]; [12]; [10]). Lastly,
(NN3) was trained using only the duration and the character selection data as
represented by the clustered approach. In order to train this model, whole
line-ups - rather than individual characters - were used. This was achieved by
replicating the steps for transforming an ability vector into a character
vector as depicted in the previous section. The individual character vectors
for a team’s selection is added together collapsing it into one K-long vector.
This means that vectors for abilities, characters and team compositions can
all be represented in the same number of dimensions, which expresses the modes
for each cluster available.

All matches for patches 7.32 & 7.33 were excluded from the training set to be
used as special test comparisons. Patch 7.32 introduced a brand new character,
_Muerta_ 444https://www.dota2.com/hero/muerta. Patch 7.33 had several major
changes to the game, including the way in which characters primary attributes
are handled. In this patch, a new type of primary attribute was created that
makes no attribute the primary. This means that - where before, characters
were classed as one of “Strength”, “Agility” and “Intelligence”, as of the
latest patch characters can also be
“Universal”555https://dota2.fandom.com/wiki/Universal, which means no
attribute is their primary attribute. The data collected for the two patches
serve as a illustration of breaking and impacful changes. Additionally, the
remaining match data (patch 7.27 to 7.31) were split using a (64/16/20)% split
into a training, test and validation datasets. Table 1 outlines the number of
matches per each of the different data splits.

Table 1: Number of matches per split used for training and evaluating the neural networks from the total 61,254 matches in the dataset Train | Validation | Test |  Test (7.32) |  Test (7.33)
---|---|---|---|---
27,171 | 6,793 | 8,491 | 16,564 | 2,235

All networks were trained using the same architecture with the exception of
the input layer, as described above. The networks consist of 6 hidden layers
with (1024, 512, 128, 64, 32, 8) neurons respectively, with the “Sigmoid”
activation function. These model predicts the number of kills for each team
simultaneously (i.e. two outputs neurons, one for each team). Because of the
simplistic architecture and the amount of data, batching was not used (i.e.
batch size of 1) and the model was trained for 100 epochs, which was
sufficient to outline the trends, detailed in the Results and Discussion
Sessions. The networks were trained with an Adam optimizer and learning rate
was set to 1e-4. No dropout layers or regularizer were used. Figure 2 Outlines
the train and validations loss for each of the three networks per epoch.
Further work to this architecture could produce more reliable results, however
a comprehensive study on this use-case is beyond the scope of this paper.

Figure 2: Training and validation loss and AUC graph per epoch for each neural
network.

The performance of the predictions were then compared. This includes the
training, validation and test performances for patches from 7.27 to 7.31,
which outlines the performance within representative data. Additionally, the
performance of predictions for games in patch 7.32 and 7.33, which introduced
the new character and new character types were also compared. In order to make
those predictions possible for (NN2), the maximum value used for the Character
ID encoded included the Character ID for Muerta. This was made with the sole
purpose of ensuring the architecture supports the additional dimension. It is
important to note, however, that this dimension would - necessarily - always
have zero in the input vector for all training data. Therefore this approach
would not be suitable for actually training models with real world
applications and it is limited to exactly one additional character (thus, any
future characters released would not be supported by the (NN2)’s
architecture).

##  4 Results

The results obtained for all networks can be found in Table 2. The test
dataset contains unseen matches from patches 7.27 to 7.31 - which are the same
patches used in training dataset. Conversely, all matches from Patch 7.32 and
7.33 have been entirely excluded from the training dataset. Patch 7.32
introduced a new character, while Patch 7.33 (the currently active patch as of
time of writing) introduced some significant changes to the game design, such
as a new character type, as previously discussed.

Table 2: AUC of the kills prediction for each dataset NN | Test |  Test (7.32) |  Test (7.33)
---|---|---|---
(NN1) | 0.50 | 0.50 | 0.50
(NN2) | 0.50 | 0.49 | 0.49
(NN3) | 0.85 | 0.85 | 0.86

Furthermore, both (NN1) and (NN2) typically produced the same prediction value
for both outputs. This means that for most matches, the prediction for the
number of Radiant kills was exactly equal to the prediction for the number of
Dire kills. Between the approximately 27 thousand matches in all test datasets
(Test, Patch 7.32 & 7.33), (NN1) only produced different predictions for 351
matches while (NN2) produced different prediction 403 of the matches. On those
cases (for both networks), the difference in kills only varied by one between
teams. This was not the case for (NN3), which only predicted the same number
of kills in approximately 3 thousand matches (11.1% of the dataset).

##  5 Discussion

As outlined by Figure 2, all three models had a sharp decrease on the loss
followed by a plateau within the first 25 epochs. This suggests that all three
models reached their convergent point within a few epochs. It is also
noteworthy that the train and validation losses did not deviate significantly
at any stage for any of the three neural networks. This suggests that none of
the models overfit to the training data. Table 2 also suggests that there was
no overfitting in any of the models, as the overall performance of each
network was consistent from that observed with its respective
training/validation accuracy at the 100th epoch.

Furthermore, both (NN1) and (NN2) produced very similar results throughout all
test datasets. Thus, it is conjectured that (NN2) trained in a way to put very
little significance for the lineup vectors, instead having more significant
weights around the duration feature. This brings to question the use of line-
up vectors as input features. Particularly when comparing similar performance
in literature models observed when comparing the works of [4] \- which does
not include any character selection data - and [14] \- which does - as well as
the comparatively lower accuracy obtained by [13] which only uses character
IDs to [16] which includes additional information.

Moreover, both (NN1) and (NN2) produced a relatively flat training AUC graph,
despite the drop in loss at the early epochs. This may indicate that the
models trained to produce a consistent prediction that minimizes the loss
without detecting any meaningful patterns. In the other hand, (NN3)’s AUC
graph produces a sharp rise in AUC, which is consistent with the drop in the
loss. In addition to this, it significantly outperformed both baseline models
(NN1 & NN2), pointing to the network detecting meaningful patterns in input
data.

When comparing the performance of the three networks across the Test, Patches
7.32 and 7.33 datasets, it is clear that (NN3) produced consistent results
despite the introduction of the new character and the change in character
primary attributes observed in the two new patches. No observable difference
could be detected in (NN1), which is expected, as it was not trained with any
character information. Although there was a small drop in performance for
(NN2), this could fall within the margin of error, particularly as the
predicted values for this model resembles that of (NN1). Similarly, while
there was a small increase in accuracy for (NN3) in Patch 7.33, that could
also fall within the margin of error, particularly because there are fewer
matches in that patch as the current live patch at the time of writing.
However, as (NN3) maintained a high degree of accuracy through all patches,
that is indicative that the clustered character representation approach may
hold significant underlying principles of the game that is able to contribute
the prediction.

It is important to note, however, that significant changes to the core game
design - beyond that of the patches present in this study - could impact the
cluster themselves and change the density distribution of the clusters. In
this case new clustering may be required, which would similarly cause breaking
changes to (NN3) - as the number of clusters or the meaning of a individual
cluster may change. Thus, while this approach is a step towards more robust
machine learning models, it is not completely devoid of risks of future
impacts. Nevertheless, it reduces the risk of breaking changes, which provide
improvements over the existing methodology (One-Hot encoding of Character
IDs).

Lastly, the advantages observed in this study (maintaining high performance,
supporting the addition of any number of characters) suggest that the
clustered approach for representing characters can increase the longevity of
models developed in future research. Additionally, the results presented here
suggests that this approach can more easily represent underlying patterns in
the data, as observed by the differences in results between (NN2) and (NN3).

##  6 Conclusion

In conclusion, this paper outlined how the use of character selection data is
a common feature for a range of machine learning models within the esport
analytics literature. The impact risk of changes in the game design has also
been covered in the literature, which often reduces the lifespam of models,
limiting them to a single or few iterations of the game, while iterations are
released rapidly. Despite that, very little work has been done to support
changes to the game design parameters. In the current literature that relies
on character selection data, no model could be found that decisively support
the addition of a new character without needing to change its architecture
(i.e. the introduction of new characters would incur breaking changes).
Through clustering characters abilities and attributes, as extracted from the
literature, this paper proposes a novel character representation that both
encapsulate the iteration of the game for any given patch and support the
inclusion of future characters without the need for a change in architecture.

By evaluating the character representation through a case-study, this paper
simulated a potential use case for future research. Through comparing three
predictive neural networks (NN1-3), this paper conclude that this novel
character representation can add longevity to future models, while providing
more information about the character selection than the standard methodology
currently employed within the literature. This paper has made the the
resources available to use for future research, including the unclustered
data; the centroids used; and the clustered representations of abilities and
characters for _Dota 2_.

### Ethical Statement

In esports analytics research, a considerable amount of publicly available
data is utilized. Such data is usually shared via APIs developed from the
publisher of the game in question. OpenDOTA collects a significant amount of
data from players of _Dota 2_ via querying the API system provided by the
publisher of the game, Valve. All matches of _Dota 2_ played are recorded and
available via the game’s API, which can includes recording of gamer tags. No
device information or similar is made available through the API, and neither
are private chat logs. When you install _Dota 2_ , you agree to the data
collection and sharing through the End User Licence Agreement (EULA) presented
when installing Steam, the platform through which _Dota 2_ is installed. While
performance data is always recorded as agreed by the EULA, gamer tag is not
collected unless explicitly opted in by players to agree to have this data
made publicly available.

The data used in the current study does not include personally identifiable
information (PII). Gamer tag information was not included from the dataset, as
these can sometimes be used across multiple online profile accounts and thus
potentially be used to identify people. Collectively, we consider this use of
data within reasonable ethical use under research ethics norms and
expectations.

### Acknowledgements

This work was supported by the EPSRC Centre for Doctoral Training in
Intelligent Games & Games Intelligence (IGGI) EP/S022325/1.

## References

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coach, or prior to each session. This especially becomes a concern in the case
of commercially available robots, where the informed consent of users may not
be as meticulously implemented as in academic studies.

We recommend that designers of robotic well-being coaches take user education
into consideration on a user-by-user basis, and provide user education as
applicable to their specific application keeping in mind emotional
considerations and user characteristics.

##  9\. Conclusion, Limitations and Future Work

In this paper, we present the Qualitative Meta-Analysis of three studies
focusing on the design and evaluation of robotic mental well-being coaches.
Based on this analysis, we present design guidelines including ethical
considerations for such robots. Additionally, we discuss what aspects of the
design of such a robot should be considered in the future. We present these
analyses and recommendations in order to help future designers and roboticists
creating robotic well-being coaches, and to operationalize the knowledge we
have found from these studies.

We propose the design recommendations and guidelines, as well as the ethical
considerations presented in this paper based on the coverage of the three
studies, which examined a robotic well-being coach from different perspectives
(see Table 13) and achieved data saturation (see Section 3). However, our
studies did not examine the longitudinal use of robotic mental well-being
coaches (though the motivations to use such a robot in the long-term were
discussed in Study 3, Section 6). Future research should examine how robots
designed with these considerations in mind are experienced by participants in
the long term, over multiple interaction sessions. Such examinations of robots
could potentially bring forth the need for additional recommendations
regarding capabilities that can only be examined in the long term, such as
long-term personalization. There are several aspects that are known to impact
HRI we have not discussed in this work and could be addressed in future works.
We highlight them as follows.

Cultural Influences. In recent years, researchers have argued the importance
of considering different cultures during human-agent interactions (Joosse et
al., 2014); even so, most of the HRI works has not yet incorporated those
aspects, designing robots that are still mainly influenced by Western
cultures. The few works that focused on HRI cross-cultural research
investigated the Western and Eastern cultures represented respectively by the
American and Japanese populations (Lim et al., 2021). However, even within the
same culture (e.g., Western), many differences and nuances are observed (e.g.,
Italians vs. British people) (Conti et al., 2015). In our context, cross-
cultural research would be beneficial for creating robotic well-being coaches.
Future work could examine how the guidelines presented here are applicable to
different cultures and populations, expanding the results of Study 3, with
participants remarking that e.g., a user’s socio-economical status might
affect how they experience the robot.

Role of Context. Lately, the HRI community has highlighted the role of the
context during human-robot interactions (Cameron et al., 2015). In fact, human
behaviors, mental processes, and emotional states vary depending on the
environment in which they occur (Salam and Chetouani, 2015). In parallel, many
efforts have been made to perceive and model the social and normative context
in human-agent interactions within the affective computing field (Wang et al.,
2018; De Magistris et al., 2022). It is crucial to develop robots that are
context-aware to be able to adapt appropriately to various situations during
human-robot interactions. Particularly, in our work, designing robotic coaches
that are aware of the context could positively impact the well-being practice.
Future work could investigate further what is the role of the context in this
application scenario.

Cross-fertilization Between Affective Computing and Social Robotics. The
affective computing field is advancing in the automated analysis of emotional
and social signals. However, social robotics has not yet integrated these most
recent developments into robotic platforms (Celiktutan et al., 2018). In
addition, due to the necessity for real-time processing skills and the lack of
computational capacity of the robotic platforms on the market, deploying
social robots in real human-robot interaction scenarios is still an open
challenge (Park et al., 2020). Future work should focus on the collaboration
between the affective computing and social robotics fields by leveraging cloud
computing or external sensors as suggested in (Park et al., 2020), to design
autonomous robots that can deliver well-being exercises.

###### Acknowledgements.

We would like to thank Dr. Indu Bodala for contributing to Study 1, Nikhil
Churamani for contributing to Study 2, and Katherine Parkin for her
contributions to Study 1, Study 2 and Study 3. We would like to thank all
study participants and coaches for their valuable contributions to this
research. Funding: M. Axelsson is funded by the Osk. Huttunen foundation and
the EPSRC under grant EP/T517847/1. H. Gunes and M. Spitale are supported by
the EPSRC under grant ref. EP/R030782/1. Data Access Statement: Overall
qualitative analysis of research data underpinning this publication is
contained in the manuscript, as well as manuscripts related to the original
studies as referred to in the manuscript. Additional raw data related to this
publication cannot be openly released as the raw data contains and transcripts
of the participants’ interviews, group discussions, and interaction with the
robot, which were impossible to anonymise.

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See samples/THRI_codebook-2 See samples/THRI_codebook-2 See
samples/THRI_codebook-2 See samples/THRI_codebook-2 See
samples/THRI_codebook-2

##  Appendix B Study 1: Prospective user interview

An example prospective user interview. We modified interviews according to the
participant’s level of active well-being practice: currently doing well-being
practices, considering doing well-being practices, and previously did well-
being practices but stopped. Additionally, we tailored interview questions
based on participants’ responses to a survey prior to the interview. This
example for the semi-structured interview is for a participant who is
currently doing well-being practices.

  1. (1)

Current well-being practices

     * •

You said you are currently doing well-being practices. Could you say what
makes you motivated to do them?

     * •

Are you experiencing particular benefits? In your survey you specify reduced
anxiety, improved performance, better sleep, developed gratitude, increased
happiness, and reduced stress. Could you elaborate on this?

     * •

Are there particular challenges you are addressing with well-being practices?

  2. (2)

Questions about specific survey answers

     * •

In the survey you say you are currently doing mindfulness and meditation, what
made you decide to do these things? Had you seen or heard of them before? In
your survey you specify personal interest and research.

     * •

You said you are aiming to achieve reduced anxiety and improved performance
and better sleep and develop gratitude and increase happiness and reduce
stress with the practices. Could you elaborate?

     * •

You said that you are practicing once a week. What motivates you to keep
practicing at this frequency?

     * •

You’ve practiced since _[year omitted]_. What made you stick with it?

     * •

In your survey you specify having a routine to your practice. Could you
elaborate on that?

  3. (3)

Tools used for well-being practice

     * •

You say you are using non-digital tools: note taking by hand and in-person
practice with an instructor. Could you elaborate on how you’re using them, and
when?

     * •

What benefits are you finding with these tools?

     * •

Are these benefits motivating you to practice, and to stick with the practice?

     * •

You say you were previously using digital tools. Could you tell me a bit about
them?

  4. (4)

Technology providing help

     * •

You mention that you stopped using technology during your practice because it
was inconvenient and you were not satisfied with the quality of the technology
and it felt impersonal. How did you experience these things?

     * •

Did you experience any benefits when using technology?

     * •

Did you experience or could you imagine experiencing any benefits to using
technology for your practice?

     * •

Are there any additional features that would make you want to use technology
for your practice again?

     * •

Is there something that would make you stop using technology again?

     * •

Did you have any concerns about using technology for well-being practice?

     * •

Have you seen or heard about some interesting well-being practices employing
technology in some way?

     * •

Do you have ideas about how technology could be used to create well-being
practices?

     * •

Does something else come to your mind about technology and well-being
practices?

##  Appendix C Study 1: Coach interview

An example semi-structured coach interview structure, here for the Life Coach.
All interviews shared a general structure, and were then tailored specifically
to the coach. Some questions were based on background information the coach
had given prior to the interview. Prior to questions about robots, all coaches
were shown the same five photos and videos of robots used with the prospective
users in the group discussions.

  1. (1)

Types of practices

     * •

What types of coaching practices do you instruct?

     * •

Do you do one-to-one or group instructions? If both, what are the differences?

     * •

Could you briefly expand on the concept of Life Coaching?

     * •

You mentioned you use positive psychology as part of coaching, would you
please expand on that? How do you use PS in the context of life coaching?

     * •

You mentioned that you use cognitive behaviour psychology as part of coaching,
would you please expand on that? How do you use cognitive behaviour psychology
in the context of life coaching?

  2. (2)

Benefits and goals

     * •

What do you think are the benefits of life coaching for the coachee ?

     * •

What have your coachees told you they think are the benefits?

     * •

What goals do the coachees usually have?

     * •

How are these goals identified?

  3. (3)

Coachees’ motivations

     * •

What types of needs do people have when they come for coaching?

     * •

How do the coachees find out about your services and life coaching? Do they
come through recommendations / word of mouth?

  4. (4)

Practicalities

     * •

How long are coaching sessions usually?

     * •

How often are coaching sessions usually?

     * •

How many sessions are there usually?

  5. (5)

Tools and technology

     * •

Do you use tools when you instruct / coach? What kinds of tools?

     * •

How are the tools helping the coachee?

     * •

How are the tools helping you as the coach?

     * •

Do you use technology as a tool when you coach? What kinds of technology?

     * •

How is technology helping the coachee?

     * •

How is technology helping you as the coach?

     * •

What advantages and disadvantages do you see practicing online virtually, vs.
face-to-face?

     * •

What are the differences when the sessions are mediated through technology vs.
having face-to-face sessions?

     * •

Do you think there could be benefits to using technology during COVID-19?

  6. (6)

Further technology

     * •

How do you think technology could help further with coaching?

     * •

Are you aware of technologies that have helped people with their coaching or
their goals as coachees? (e.g. Headspace)

     * •

Have you ever used such technologies?

     * •

What do you think the benefits of such technologies are?

     * •

Do you think such technologies can become the primary method of delivery for
coaching? Or rather, are they secondary and should be used in support with a
human instructor?

  7. (7)

Specific questions about practice

     * •

How do you decide which goals to work on with clients?

     * •

Who is in charge of the coaching sessions you instruct? Who leads the
interaction?

     * •

How do you decide when to talk and when to stay silent?

     * •

Do you use a script during the sessions?

     * •

How do you adapt the sessions? According to the day? According to the person?

  8. (8)

Robots

     * •

Would you be able to imagine such a robot providing or giving a life coaching
session?

     * •

Which aspects of life coaching do you think would be appropriate in this
context, and why?

     * •

Are there any other coaching practices that you think would be appropriate to
be delivered by such robots? Why?

  9. (9)

Robot advantages and disadvantages

     * •

What do you think the advantages of using a robot for these types of practices
may be? Previous participants have mentioned for example: personalization and
adaptation, analyzing previous practice patterns and progress, accessibility
(having it in your house/work), consistency, and being non-judgemental.

     * •

How about the disadvantages? Previous participants have mentioned for example:
Privacy, expensiveness, repetitive interactions, unreliable technology, and
intrusive reminders.

  10. (10)

Robot capabilities

     * •

Some participants noted they like self-disclosure done by their coach — do you
use self-disclosure (i.e. sharing your own experiences) as part of coaching
instruction?

     * •

Do you think you express emotions during sessions? How?

     * •

Do people need guidance during the coaching sessions?

     * •

Do people get sidetracked when exploring an area or a particular topic?

     * •

Do people need to be given some time to think?

##  Appendix D Study 2: Post-interaction interview

The questions asked during the semi-structured post-interaction interview:

  1. (1)

Robot

     * •

What do you think was good about the robot’s appearance? What did you like?

     * •

What do you think was bad about the robot’s appearance? What would you change?

     * •

How appropriate do you think the robot was for helping you focus on the
positive aspects in your life? How appropriate do you think the robot was for
helping you foster a positive attitude toward things that happen in your life?

     * •

How appropriate do you think the robot was for these coaching tasks?

     * •

How useful do you think the robot was for these coaching tasks?

     * •

How beneficial do you think the robot was for these coaching tasks?

  2. (2)

Behaviour

     * •

What do you think was good about the robot’s behaviour? What did you like?

     * •

What do you think was bad about the robot’s behaviour? What would you change?

     * •

How appropriate do you think the robot’s behaviour was for these coaching
tasks?

     * •

How useful do you think the robot’s behaviour was for these coaching tasks?

     * •

How beneficial do you think the robot’s behaviour was for these coaching
tasks?

     * •

Do you think the robot understood what you said?

     * •

Do you think the robot understood how you felt?

     * •

Do you think the root adapted to what you said and did?

     * •

Did you notice any differences?

See samples/bsfp_script See samples/bsfp_script See samples/bsfp_script See
samples/bsfp_script See samples/bsfp_script