# BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual
Analytics

Shiyi Liu<EMAIL_ADDRESS>School of Information Science and
Technology, ShanghaiTech UniversityShanghaiChina ,  Ruofei Ma
<EMAIL_ADDRESS>School of Information Science and Technology,
ShanghaiTech UniversityShanghaiChina ,  Chuyi Zhao<EMAIL_ADDRESS>Department of Computer Science, Tufts UniversityMedfordMassachusettsUSA ,
Zhenbang Li<EMAIL_ADDRESS>School of Information Science and
Technology, ShanghaiTech UniversityShanghaiChina ,  Jianpeng Xiao
<EMAIL_ADDRESS>Guangzhou Qucheng Culture Media Co.GuangzhouChina and
Quan Li<EMAIL_ADDRESS>School of Information Science and
Technology, ShanghaiTech University, and Shanghai Engineering Research Center
of Intelligent Vision and ImagingShanghaiChina

(2024)

###### Abstract.

Hero drafting for multiplayer online arena (MOBA) games is crucial because
drafting directly affects the outcome of a match. Both sides take turns to
“ban”/“pick” a hero from a roster of approximately $100$ heroes to assemble
their drafting. In professional tournaments, the process becomes more complex
as teams are not allowed to pick heroes used in the previous rounds with the
“best-of-$N$” rule. Additionally, human factors including the team’s
familiarity with drafting and play styles are overlooked by previous studies.
Meanwhile, the huge impact of patch iteration on drafting strengths in the
professional tournament is of concern. To this end, we propose a visual
analytics system, BPCoach, to facilitate hero drafting planning by comparing
various drafting through recommendations and predictions and distilling
relevant human and in-game factors. Two case studies, expert feedback, and a
user study suggest that BPCoach helps determine hero drafting in a rounded and
efficient manner.

hero drafting, Monte-Carlo tree search, MOBA, visualization

††copyright: acmcopyright††journalyear: 2024††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation emai; November 9–13, 2024; San Jose, Costa
Rica††isbn: 978-1-4503-XXXX-X/18/06††ccs: Human-centered computing Human
computer interaction (HCI)††ccs: Human-centered computing Visualization††ccs:
Human-centered computing Visual analytics††ccs: Applied computing Computer
games

##  1\. Introduction

Multiplayer Online Battle Arenas (MOBAs) have emerged as one of the most
popular branches of esports. Prominent titles such as Defense of the Ancients
2 (DotA2), League of Legends (LoL), and Honor of King (HoK) have successfully
organized professional tournaments on an international scale, reaching
millions of global viewers through live streaming (Li et al., 2018; Kokkinakis
et al., 2020).

In a standard match, two teams consisting of five players each engage in a
battle to destroy the opponent’s base and emerge victorious. Each player
assumes control of a character within the game, commonly referred to as a
hero, and collaborates with their teammates to launch attacks against the
opposing team. The drafting phase is a strategic stage at the beginning of the
game where players select their respective heroes, taking into account their
abilities and how they synergize with the team’s overall strategy. Previous
studies have demonstrated that the drafting phase significantly influences the
outcome of a game (Semenov et al., 2017; Kim et al., 2016). Furthermore, these
games offer a vast selection of over 100 heroes, resulting in an astonishing
number of approximately $1.56\times 10^{16}$ possible combinations (Hanke and
Chaimowicz, 2017).

An advanced drafting tool has the potential to increase the strategic
complexity of the game, leading to improved tactical decision-making by
coaches and players and fostering better team cooperation. The sequence order
of drafting carries great significance as it determines priority picks and
facilitates effective counterpicking, allowing teams to leverage hidden
information. Similar to the sequential drafting problems in other domains,
such as the drafting process in the National Football League (Berri and
Simmons, 2011) or the National Basketball Association (Staw and Hoang, 1995),
team formation (Freeman and Wohn, 2019), and fantasy football game (Lee and
Liu, 2022), it influences which team gains access to highly coveted heroes,
enables strategic responses to the opponent’s selections, and introduces an
element of uncertainty for the opposing team. These enhancements can lead to a
deeper understanding of the game’s complexities, ultimately leading to
increased player satisfaction and engagement.

Drafting in professional MOBA games plays a pivotal role, not only in terms of
its strategic complexity, countering the enemy, and fostering team synergy,
but also due to its direct impact on audience engagement and the broader game
community. From the perspective of the audience, a more intricate drafting
phase can generate thrilling and unpredictable matches, enhancing viewers’
comprehension and appreciation of the game’s strategic aspects, thereby
enriching their overall viewing experience (Tang et al., 2022). Moreover,
within the gaming community, esports transcends mere entertainment to become a
global cultural phenomenon. Innovations like improved drafting tools can drive
growth and maturity within the esports industry, leading to economic and
employment opportunities (Li et al., 2018). These enhancements can also shape
social dynamics and cultural values among gamers and fans, emphasizing the
profound impact of these games within the community.

Despite the considerable research efforts dedicated to drafting
recommendations or predictions in MOBA games (Hanke and Chaimowicz, 2017;
Summerville et al., 2016; Chen et al., 2018a; Yu et al., 2019; Chen et al.,
2021), there are still several challenges when it comes to supporting
professional matches, as the hero drafting in a professional tournament
differs from regular games (Gourdeau and Archambault, 2021). The first
challenge arises from the “Ban/Pick” phase within the game itself. In standard
tournaments, the best-of-$N$ rule is typically followed, where up to $N$
rounds are played, and the team that wins $(N+1)/2$ rounds is declared the
winner (Chen et al., 2021). In each round, the ten players collectively choose
heroes from a pool of over 100 candidates, each with distinct abilities,
roles, and tactical styles (Chen et al., 2018a). Furthermore, there are
complex synergies and antagonistic relationships between these heroes (Kim et
al., 2016; Pobiedina et al., 2013b). The ten heroes cannot be duplicated and
are selected in a fixed sequence. Additionally, teams are not allowed to pick
heroes that have been chosen in previous rounds due to the global “Ban/Pick”
rule. Traditional recommendation systems are often built based on the single-
round ban/pick rule (Chen et al., 2018a; Hanke and Chaimowicz, 2017) or
consider only the “pick” phase without the “ban” phase (Chen et al., 2021).
The second challenge lies in the specificities of individual opponents.
Existing studies treat all scenarios equally, disregarding the human factors
present in matches (OpenAI et al., 2019; Mohammed et al., 2022; Gourdeau and
Archambault, 2021; Porokhnenko et al., 2019; Hanke and Chaimowicz, 2017; Chen
et al., 2018a, 2021). However, in professional matches, the particularities of
a team or player, including their familiarity with drafting, tactical style,
and individual abilities, heavily influence their decision-making and the
match outcome. For instance, if an opponent excels with a particular hero, a
team should prioritize preventing them from selecting that hero, even if it
may not be necessary in most cases. Familiarity with heroes is crucial in MOBA
games (Chen et al., 2018b; Yang et al., 2021). Players tend to pick heroes
they are confident with while banning heroes their opponents excel at. Lastly,
the frequent in-game updates and patch iterations continue to impact drafting
strategies (Zhong and Xu, 2021). Minor updates may include slight changes to
hero skills or health caps, while patch iterations can introduce significant
alterations to game mechanics. Consequently, these changes have varying
impacts on hero drafting (Wang et al., 2020). If a hero becomes weaker or less
favorable due to updates, the necessity of selecting that hero diminishes.
Since previous approaches utilize all available historical data during
training, they cannot effectively support real-world applications due to
dataset bias.

To address these research gaps, we have formulated and explored five research
questions (RQ1 – RQ5). Firstly, in order to investigate the actual
requirements of coaches when making drafting decisions in professional games,
we propose two questions: RQ1: What is the information needed for a coach’s
decision on hero drafting? and RQ2: What should be an acceptable approach for
information processing? To answer RQ1, we conducted expert interviews with
three collaborating experts. Through these interviews, we gained a valuable
understanding of the coaches’ needs. We considered hero drafting as a two-
player zero-sum game, where the advantage gained by one player is equal to the
disadvantage incurred by the other player. Based on the feedback received, we
identified seven factors and derived five design requirements for the system
(Section 4.3) to address RQ2. We organized a design workshop to discuss
potential interface designs for coaches. As a result, we developed a
visualization system called BPCoach, which provides recommendations and
predictions for coaches in professional tournaments to aid in match
preparation and hero drafting decisions. On the backend of the system, we
employed Monte Carlo Tree Search (MCTS) to calculate the optimal next pick for
our side. On the front end, we provided visualizations and interactions that
present optimal and alternative drafting paths, specific team or player data,
hero data, and in-game change data. Furthermore, we incorporated carefully
designed interactions and auxiliary views to facilitate users in exploring
information and making favorable decisions.

Based on the designed system prototype, we further investigated three research
questions: RQ3: What scenarios and when will this system be used? RQ4: What is
the usability and effectiveness of the adaptive system in hero drafting? and
RQ5: How will coaches interact and collaborate with such an AI-enabled system?
To address these questions, we explored two distinct usage scenarios,
conducted expert interviews, and performed a user study. The two usage
scenarios exemplify the practical application of the system. Experts utilized
the system to iteratively interact and refine their decisions. During the
expert interviews, we engaged in discussions with experts, covering topics
such as system performance, visual design, interaction, generalizability, and
scalability. Additionally, we conducted a within-subjects user study involving
12 participants, including three professional gamers and nine students with
extensive gaming experience from a local university. The results of the study
revealed that the proposed system demonstrated superiority in terms of
informativeness, decision-making support, and system usability. In summary,
the contributions of this work are as follows:

  * •

We have proposed a hero drafting approach that is based on the combination of
MCTS, Markov chains, and Random Forest (RF). This approach is specifically
designed to support the global “ban”/“pick” rules observed in best-of-$N$
tournaments.

  * •

We proposed an interactive visual analytics system that aids users in
developing a comprehensive understanding of the game and making well-
considered drafting decisions. This system visualizes the human factors of
specific opponents and the impact of in-game changes, enabling users to make
informed decisions during the drafting process.

  * •

Our system’s efficacy has been validated through two usage scenarios, expert
feedback, and a user study. Furthermore, we conducted an evaluation comparing
our new model with three baseline models to showcase the superiority and
effectiveness of our approach.

##  2\. Hero Drafting in King Pro Leagues

This work was developed and evaluated based on professional tournaments such
as Hok, King Pro Leagues (KPL) (Tencent-CP, 2023c). These tournaments adhere
to the best-of-$N$ rule and follow the global ‘ban”/“pick” rule. The drafting
process involves two teams taking turns to pick or ban heroes in the order of
“b1-b2-b1-b2-p1-p2-p2-p1-p1-p2-b2-b1-b2-b1-p2-p1-p1-p2”, where “b”/“p”
represents “ban”/“pick” respectively, and “$1$”/“$2$” represents the team
side. Furthermore, the ten heroes picked must be unique, and teams cannot
select heroes that have already been picked in previous rounds (Tencent-CP,
2023e). This necessitates a trade-off between the average strength over the
$N$ rounds and the immediate strength in a single round.

The KPL consists of approximately $20$ teams, with relatively stable player
lineups. Each team possesses its own unique style and understanding of
drafting strategies. Typically, teams attempt to anticipate their opponents’
tendencies in order to limit their options and assert control over the game.
Simultaneously, teams strive to strike a balance between hero relationships
and their familiarity with those heroes. It’s worth noting that HoK
experiences regular updates, ranging from minor tweaks to significant changes
(Tencent-CP, 2023f). The KPL promptly incorporates these updates, as they can
have a substantial impact on hero drafting. For instance, the hero “Wang
Zhaojun” was a popular pick with a win rate of over $50\%$ in spring 2022
(Tencent-CP, 2023a). However, after a patch iteration that shifted the game’s
focus towards combat, “Wang Zhaojun” became less effective, resulting in a win
rate drop to $34.9\%$ in the fall of 2022 (Tencent-CP, 2023b). Consequently,
the number of times “Wang Zhaojun” was picked also decreased by more than
half.

An exemplary instance of a drafting victory in KPL took place during a highly
anticipated match between two renowned teams, GK and eStarPro. Playing on the
red side, GK skillfully set up a drafting trap. In the first “ban” stage,
instead of opting for conventional bans like eStarPro did, GK made a bold move
by banning the highly skilled heroes of their opponents, namely “Zhang Fei”
and “Zhuge Liang”.  This put eStarPro in a difficult situation as the three
top-tier heroes at that time, namely “Jiang Ziya”, “Pei Qinhu”, and “Cheng
Yaojin”, were left open for selection. If eStarPro chose one of these heroes,
GK would have the opportunity to pick the remaining two, resulting in a
disastrous scenario for eStarPro. After careful deliberation, eStarPro
reluctantly picked “Jiang Ziya”. Capitalizing on this, GK seized the
opportunity and selected “Pei Qinhu” and “Cheng Yaojin”, forming a formidable
combination. In the second stage of the ban-pick phase, playing on the blue
side, eStarPro finalized their lineup first. GK had the final pick and
strategically chose the counter hero, “Nezha”, who posed a significant threat.
This proved to be troublesome for “Marco Polo”, the hero chosen by eStarPro.
Additionally, their “Jiang Ziya” in the mid lane is also an easy target for
“Nezha”. Moreover, “Nezha” exhibited ideal early game damage, which
complemented the overall transition of GK in the early game. This final
selection of “Nezha” laid a solid foundation for the performance of GK in the
match. The strategic planning of GK was meticulous and interconnected, with
each move seamlessly leading to the next, showcasing their flawless
calculations and execution.

##  3\. Related Work

###  3.1. Game Data Analysis and Visualization

The rich data collected in games have boomed the field of game data analysis,
including game analytics (Kokkinakis et al., 2020; Freeman and Wohn, 2019; Kow
and Young, 2013; Grandprey-Shores et al., 2014; Ducheneaut et al., 2007) and
machine learning (OpenAI et al., 2019; Mohammed et al., 2022; Gourdeau and
Archambault, 2021; Porokhnenko et al., 2019; Hanke and Chaimowicz, 2017; Chen
et al., 2018a, 2021). The main process of these efforts is to extract specific
patterns in the game and then make some evaluations or predictions. Machine
learning work includes win rate prediction (Kim et al., 2020; Hodge et al.,
2021; Lee et al., 2020), highlight detection (Schubert et al., 2016), factors
and strategies affecting the outcome (Ahmad et al., 2019; Xia et al., 2019;
Pobiedina et al., 2013a), and recommendation systems (Najafabadi et al., 2019;
Bertens et al., 2018).

Visualization of game data benefits multiple target groups, such as game
developers (Li et al., 2017, 2021; Wallner et al., 2021) and non-professional
players (Charleer et al., 2018; Wallner and Kriglstein, 2016). Game developers
use visual analytics to detect imbalances in-game mechanics (Li et al., 2017)
and improve the player experience (Wallner et al., 2021). For example,
Ducheneaut and Moore (Ducheneaut and Moore, 2004) analyzed player-to-player
interactions and concluded with some recommendations for the design and
support of social activities within multiplayer games. Wallner et al. (Wallner
et al., 2021) focused on post-game data visualization to explore information
needs raised by players. Javvaji et al. (Javvaji et al., 2020) presented an
interactive visualization based on game telemetry data for game designers to
study player patterns. Player-centered visualizations can support and
facilitate gameplay (Medler and Magerko, 2011). Wallner et al. (Wallner et
al., 2021) built a training visualization that allows players to review their
gameplay and thus learn from experience and from each other. Charleer et al.
(Charleer et al., 2018) designed a dashboard view that effectively facilitates
user insight and experience. Afonso et al. (Afonso et al., 2019) employed
visualization techniques on spatiotemporal data to allow players to better
understand their strategies.

Previous work on data analysis and visualization of games has focused on in-
game (Li et al., 2017; Wallner et al., 2021; Afonso et al., 2019) and post-
game data (Wallner et al., 2021), facing both non-professional players and
game designers. Numerous popular commercial tools (Cook, 2022) such as OP.GG
(Riot Games, Inc., 2023), senpai (FalconAI Technologies, Inc., 2023), and
others offer gamers access to historical data for reference during the
drafting and provide various indicators related to heroes and players. These
tools summarize information such as hero win rates, hero relationships, and
individual player win rates. For instance, senpai provides in-game assistance
by generating hero selection suggestions for the next step and allows players
to access information such as hero win rates, counter relationships, and
player game status. However, most of these existing tools primarily cater to
non-professional players, and as a result, they lack statistics on team-
specific hero combinations and do not support the global “ban”/“pick” rules
observed in professional games. Additionally, none of these tools evaluate how
in-game changes affect heroes. Players often seek information on whether a
hero’s win rate has increased or decreased, or whether changes have made a
hero more powerful. This study focuses on pre-game data analysis and
visualization for professional teams. We designed an interactive visualization
system that displays pre-game opponent team statistics in terms of heroes and
players, to help teams visualize the situation and make thoughtful drafting
decisions.

###  3.2. Hero Drafting Recommendation Techniques

Many academic works revolve around hero drafting recommendations, which fall
into three main types: algorithms based on (1) prediction, (2) win rate, and
(3) zero-sum game theory (Rapoport, 2013).

The prediction-based works (Summerville et al., 2016, 2016; Yu et al., 2019)
treat the hero drafting problem as a sequential prediction problem. The output
is the most likely choice, rather than the best choice. Win rate-based work
focuses on picking heroes that bring higher win rates to the drafting. For
example, Hanke et al. (Hanke and Chaimowicz, 2017) developed a mechanism based
on association rules that suggests more suitable heroes. Other works have
explored different methods for win prediction, including natural language
processing (NLP)-based methods (Mohammed et al., 2022), regression-based
methods (Porokhnenko et al., 2019) and neural network (NN)-based methods
(Porokhnenko et al., 2019; Gourdeau and Archambault, 2021) to recommend the
best drafting. These works output locally optimal recommendations rather than
globally optimal ones. In the third category, the researchers treat the
drafting problem as a two-player game and apply the game theory. For example,
OpenAI Five uses the Minimax algorithm (Fan, 1953) to select two 5-player
teams from a total of 17 heroes. When about 100 heroes are involved, the
computational complexity increases dramatically, which makes the method
infeasible. Chen et al. (Chen et al., 2021) first applied Monte Carlo Tree
Search (MCTS) to the global “ban”/“pick” rule. In this work, the global
evaluation of the reward in MCTS is less appropriate because it is the sum of
$N$ round win rates. In this case, a very high win rate in one round and a low
win rate in the rest of the rounds ($<50\%$) can be used as output, while the
win condition is to win $(N+1)/2$ rounds.

Nevertheless, none of the previous works considered the different tendencies
of different human teams. Moreover, most of these works ignored “ban” games
and focused more on “pick” (Hanke and Chaimowicz, 2017; Porokhnenko et al.,
2019; Chen et al., 2018a, 2021). To address these issues, we propose a model
built on MCTS, Markov chains, and RF. We design a new evaluation principle in
MCTS to satisfy the $N$ optimal winning condition. We also predict the
possible choices using the Markov chain to reduce the computational
complexity.

###  3.3. Visualization of Sequence and Alternatives

The visualization of drafting paths can be considered the problem of
visualizing prediction sequences and the alternatives. Traditional sequence
visualization can be divided into three categories, including timeline-based
methods (Karam, 1994), tree-based methods (Monroe et al., 2013), and Sankey-
based methods (Riehmann et al., 2005). Timeline-based approaches are the most
intuitive methods, which organize events directly in chronological order,
where some work aggregate events in various time granularities (Xie et al.,
2020). In tree-based approaches, the sequences are arranged in a tree
structure to show the hierarchy (Wongsuphasawat et al., 2011; Arazy et al.,
2015). Each node in the tree structure represents an event and is connected to
adjacent events (Wongsuphasawat and Lin, 2014; Monroe et al., 2013; Du et al.,
2017). Sankey-based approaches can better represent the transition
relationships of events (Guo et al., 2021). Events are encoded as nodes of
different widths according to their proportion in the timestamp
(Wongsuphasawat and Gotz, 2012; Perer and Gotz, 2013; Riehmann et al., 2005).

Since a hero drafting path is a sequence of events in a fixed order, a
straightforward approach is to place the events on a timeline, as in Lifelines
(Plaisant et al., 2003) and CloudLines (Krstajic et al., 2011). However, the
timeline-based approach has difficulties in representing alternative paths.
The traditional tree-like structure (Wongsuphasawat and Lin, 2014; Qi et al.,
2020) does not take into account the weights of nodes with different scores or
probabilities in the hero drafting sequence. Du et al. (Du et al., 2017) used
the interleaved heights of nodes to represent the weights. However, the game
boasts a roster of around 100 heroes, which consequently creates roughly 100
branches to consider. Unfortunately, previous efforts have struggled to
effectively address the resulting scalability issues. Guo et al. (Guo et al.,
2019) used a circular glyph with colored ring layers to visualize uncertainty
and alternatives. The user gets the path by exploring from the inner ring,
which makes the full picture of the path unintuitive. Therefore, we propose a
new interactive visualization method that combines a modified tree structure
with glyphs to distinguish top predictions from alternatives and to satisfy
scalability and intuitiveness requirements.

##  4\. Formative Study

###  4.1. Need-Finding Interview

The objective of this work is to provide professional teams with comprehensive
advice and insights regarding pre-game hero drafting. To ensure the
effectiveness and relevance of our work, we collaborated with three experts in
hero drafting who helped us extract detailed requirements. E1, a training
manager (male, age: $28$), has extensive experience and has been closely
following the esports scene for several seasons. E2, an experienced coach
(male, age: $30$), possesses two years of coaching experience and assumes the
responsibility of directing hero drafting and game strategy. E2 is the key
decision-maker when it comes to hero drafting for official matches. E3, a
professional player (male, age: $19$), brings valuable insights from his vast
experience of playing in hundreds of matches. In the team hierarchy, E1 is
responsible for managing all aspects of the team, E2 leads hero drafting and
game strategy, and E3 takes charge of the game while also providing his own
ideas during hero drafting.

When discussing the significance of hero drafting in professional tournaments,
E2, the coach responsible for drafting decisions, emphasized that the
“ban”/“pick” stage directly determines the strategic path that the team must
rely on to secure victory. Once the drafting is completed, it is primarily a
matter of executing the chosen strategy. According to E2, a correct strategic
path is the foundation for successful tactical execution. From their
perspective, drafting serves as the cornerstone for all subsequent strategies.
Furthermore, E3, a player, highlighted that in high-end professional leagues
where players possess similar levels of skill, gaining an advantage in hero
drafting can make it easier to secure a victory. According to E3, when the
coach provides an advantageous drafting scenario, it increases the team’s
chances of winning. E2 also mentioned a notable incident where a well-known
coach utilized the global “ban”/“pick” rule to defeat a stronger team. This
achievement instantly propelled the coach to celebrity status among game fans.
This highlights that high-quality drafting is highly desired and appreciated
by game enthusiasts, as it can greatly influence the outcome of a match and
create an exciting experience for fans.

To ascertain the requirements for high-quality drafting, we conducted a
40-minute online interview with the experts mentioned earlier. During the
interview, we initially inquired about the conventional approach and the
factors that should be taken into account when making “ban” or “pick”
decisions (RQ1). Generally, the current prevalent method of hero drafting
relies on prior experience and gathering data on opponents beforehand. Experts
used to consider the following factors: (F1) drafting sequence, (F2) player’s
performance with a specific hero, (F3) player’s overall status, (F4) team
performance, (F5) play style, (F6) hero’s adaptability to in-game changes, and
(F7) relationships among heroes. Meanwhile, experts suggest that only the most
recent year or so of relevant data is meaningful, as there will be fewer
significant changes in the game during this period.

Next, we inquired about the challenges associated with the traditional
approach. When confronted with unexpected changes, E2 mentioned that
“considering the global ’ban’/’pick’ rule, there are instances where I am
unable to swiftly provide the optimal global solution, and every decision in
the game is time-sensitive.” In the traditional method of gathering opponent
information, they manually input the required data into Excel and utilize its
functions, such as averaging and sorting. E1 expressed, “this intricate
process lacks intuitiveness and interactivity.” Moreover, the organized data,
primarily utilized for pre-match planning, prove to be of limited assistance
to coaches during the game due to the time-consuming nature of acquiring it.
Additionally, in-game adjustments often become apparent only after a hero has
failed multiple times.

###  4.2. Survey on Existing Pre-game Statistics Platforms

Table 1. Comparisons with top commercial platforms, the official KPL data
website, and BPCoach in terms of seven factors.

|  mobalytics (Mobalytics, 2023) |  senpai (FalconAI Technologies, Inc., 2023) |  porofessor (gg., 2023) |  blitz (Blitz, 2023) |  op.gg (Riot Games, Inc., 2023) |  facecheck (Facecheck, 2019) |  u.gg (Gg, 2023) |  KPL (Tencent-CP, 2023c) | BPCoach
---|---|---|---|---|---|---|---|---|---
F1 drafting sequence | ✓ | ✓ | · | · | · | ✓ | · | · | ✓
F2 player’s performance with a specific hero | · | ✓ | ✓ | · | ✓ | ✓ | ✓ | ✓ | ✓
F3 player’s overall status | · | ✓ | ✓ | · | ✓ | ✓ | ✓ | ✓ | ✓
F4 team performance | · | · | · | · | · | · | · | · | ✓
F5 play style | · | ✓ | ✓ | · | · | ✓ | ✓ | · | ✓
F6 hero’s adaptability to in-game changes | · | · | · | · | · | · | · | · | ✓
F7 relationships among heroes | ✓ | ✓ | · | ✓ | ✓ | ✓ | ✓ | · | ✓

With the advent of the digital age, gamers have become increasingly focused on
data-driven approaches (Kokkinakis et al., 2020). Consequently, numerous
platforms that provide performance statistics have emerged in the market.
Since HoK has limited commercial platforms available, we selected platforms
that support LOL, which shares a similar drafting mechanism with HoK. In Table
1, we conducted a comprehensive comparison between these leading commercial
platforms, and the official KPL data website, based on the seven factors
suggested by our experts. Platforms like mobalytic, senpai, and facecheck, are
capable of generating drafting recommendations based on the current drafting
sequence (F1). However, these commercial platforms cater to non-professional
players and lack compatibility with the global “ban”/“pick” rule. Only the
official KPL data website records this specific statistic. Some platforms,
such as Senpai and Porofessor, offer hero recommendations during drafting, but
they solely focus on suggesting heroes based on synergistic relationships
without considering the overall strength of the complete lineup that both
teams will eventually have. Most platforms provide the player’s performance
with a specific hero (F2), the player’s overall status (F3), and the
relationships among heroes (F7). However, no platform consolidates the usage
of specific hero combinations by particular teams (F4), even though non-
professional players often prefer fixed combinations of heroes who rank
together. This is likely due to the complexity involved in quantifying team
collaboration indicators, which encompass multiple dimensions such as the
players themselves, the chosen heroes, and the combinations of heroes selected
by the players. Regarding play styles (F5), many platforms such as senpai and
Porofessor, facecheck, and u.gg, assign labels to players. These labels are
typically determined by algorithms based on in-game data, selecting a suitable
label from a limited pool. While these labels provide some information, they
lack personalization. Lastly, all platforms offer real-time statistics that
are regularly updated. However, users often find it challenging to remember
every statistic at any given time, making it difficult to identify trends
through clear comparisons (F6). In the subsequent section (Section 7.2), we
will discuss how our proposed system BPCoach extracts these factors and
contributes to the improvement of pre-game data platforms.

###  4.3. Design Requirements

In order to ensure that our approach aligns well with the tasks and
requirements of the domain, we further consulted all experts (E1-E3) regarding
their main concerns and design requirements for the demo system (RQ2). Based
on their feedback, we have summarized a list of design requirements as
follows:

  * •

DR1. Recommend and predict the “ban”/“pick” option. Both E1 and E2 expressed
their need for recommended “ban”/“pan” choices for their own team. While the
general hero drafting is typically determined by the coach’s experience, they
wanted a limited set of heroes recommended based on objective winning data
from a pool of over $100$ choices. Additionally, these recommendations should
align with the global “ban”/“pick” rules in a best-of-$N$ tournament.
Furthermore, all three experts agreed that the ability to predict the
opponent’s choices would be valuable in a hero drafting game. For instance, if
teams notice a trend of opponents favoring powerful heroes, they can
strategically “ban” or “pick” those heroes or make early choices to prevent
their opponents from gaining a strong drafting advantage. Therefore,
predictive capabilities can assist in countering opponent strategies.

  * •

DR2. Display hero drafting paths and alternatives. E2 and E3 emphasized the
importance of visualizing the hero drafting path, which includes iterative
recommendations and predictions. Such visual representation would provide a
comprehensive overview of the entire drafting process, facilitating better
decision-making. Additionally, alternative paths should be presented to offer
a range of references and enable specific decisions based on other relevant
factors.

  * •

DR3. Support “what-if” assumptions and predict win rates. In addition to
recommended or predicted choices, E2 and E3 expressed a desire for a “what-if”
assumption feature. This functionality would enable them to make assumptions
about different hero drafting scenarios and observe the corresponding win
rates, thereby enhancing their understanding of the drafting process.

  * •

DR4. Quantify the synergistic and antagonistic relationships between heroes.
E2 and E3 noted that while professional coaches and players generally have a
sense of the synergistic and antagonistic relationships between heroes, these
concepts remain abstract and unquantified. E2 mentioned, “When we compare
heroes, we can have a rough idea of their rating.” However, experts’
subjective perceptions of these relationships vary. In cases where multiple
heroes may be favorable choices but only one can be selected, quantifying and
displaying these relationships would be valuable.

  * •

DR5. Display data for specific teams and players. All three experts
highlighted the significance of making decisions based on specific opponents.
For example, if an opponent’s mid-laner consistently performs exceptionally
well, the drafting strategy may involve targeting that player and banning
their preferred heroes. Currently, teams manually extract data for specific
opponents using tools like Excel, which is cumbersome and unintuitive.
Displaying team-specific and player-specific data would assist teams in
effectively evaluating their opponents’ status.

  * •

DR6. Offer in-game updates and effects on heroes. Both E2 and E3 mentioned the
frequent updates in the game that have an impact on hero drafting.
Understanding and adapting to these changes quickly can be challenging. By
providing visual comparisons, they felt that they would be alerted to these
updates and be able to swiftly comprehend the situation, enabling them to make
informed decisions and trade-offs.

##  5\. BPCoach

###  5.1. Data Description

Based on the identified factors (F1-F7), we extract the following data in the
system: 1) drafting sequence (F1). The “ban”/“pick” heroes and their orders
for both sides. 2) Player performance (F2). Five player performance
indicators, including KDA ratio ($(Kill+Assist)/Death$), average damage per
minute, average damage taken per minute, average cash gained per minute, and
participation; 3) Player status (F3). The number of victories or defeats
overall or in some specific heroes. 4) Team performance (F4). The performance
of the team, including: win rate, team KDA ratio, average number of tyrants,
average number of dragons, average number of towers destroyed, and average
game duration; 5) Keywords about the team or player (F5). Keywords extracted
from news around the player or team, reflecting common heroes and playstyles;
6) Hero data (F6). Data reflecting how heroes are drafted, such as win rate,
picked rate, banned rate, etc. 7) Relationships between heroes (F7). The
winning yields the most synergistic heroes and the most antagonistic heroes.

The dataset used in our study is publicly available on the official KPL data
website (Tencent-CP, 2023c). It comprises a total of $1,182$ professional
battles that took place in the year 2022. This dataset includes information on
hero drafting, drafting sequences, as well as player and team performance
indicators. In addition to the battle data, we also collected a set of $6258$
KPL-related news articles from the official news site (Tencent-CP, 2023d).
These news articles primarily cover the content and outcomes of the games,
providing insights into various aspects of teams and players. For instance, if
a player delivers an outstanding performance with a specific hero, there will
likely be news articles highlighting their achievements. By analyzing the
frequency of appearances of certain heroes in the news articles associated
with a particular player, we can infer that the player has been performing
well with that hero.

###  5.2. System Overview

Figure 1. BPCoach Pipeline: (1) Back-end engine to generate the
recommendations and predictions; (2) Front-end visualization facilitates
exploratory data analysis.

Based on the given requirements, we have developed a visual analytics system
called BPCoach (RQ2). Its purpose is to present the recommended hero drafting
path and associated human factors, supporting professional teams during the
drafting phase of games. The system architecture and pipeline are illustrated
in Fig. 1. To begin, we collect and process battle logs to extract drafting
information, including hero drafting sequences, as well as specific data
related to heroes, players, and teams for both sides. The extracted drafting
information is then utilized to train the win rate predictor (DR4).
Simultaneously, the drafting sequences are used to construct the drafting
predictor model (DR2). The recommendations for hero selection are generated
based on the win rate predictor and the drafting predictor (DR1). The
iterative process of recommendations and predictions forms the drafting paths
(DR3). To facilitate the exploration and comprehension of the data, BPCoach
incorporates interactive visualizations. The user interface encompasses five
primary views: bp view (DR5), drafting sequence view (DR1–3), player view
(DR5), hero view (DR4), team view (DR5), in-game change view (DR6).

###  5.3. Back-end Engine

Figure 2. Recommendation or prediction iterations in hero drafting path and
the corresponding state array and win rate predictor input.

The back-end engine plays a crucial role in the BPCoach system. Its primary
responsibilities include processing the raw data, executing the model, and
transmitting the output to the front-end interface.

####  5.3.1. Drafting Prediction

We achieve drafting prediction utilizing the Markov chain, which offers both
statistical significance and ease of comprehension (Norris, 1998). We define
the terms as follows:

  * •

Team:  the two teams, $T$, in a best-of-$N$ match. We define $1$ as our side
and $2$ as the opponent’s side.

  * •

Team side: blue or red in each round. We define $1$ as the blue side and $2$
as the red side.

  * •

State: An array, $s$, with $110$ dimensions, indicating the current drafting
state. As shown in Fig. 2, if a hero is banned, $s_{hero\\_ID}=-1$. If a hero
is picked by team $T$, $s_{hero\\_ID}=T$.

  * •

Previous selection: two arrays with $110$ dimensions, $Pr_{team}$ indicating
heroes that have been used by this team or the opposing team in previous
rounds. If $Pr_{1}[hero\\_ID]=1$, the hero cannot be selected by team 1.

To generate the probability of a state change from $s$ to $s^{\prime}$, i.e.,
$P_{ss^{\prime}}$, we calculate the number of transitions $N_{ss^{\prime}}$
from $s$ to $s^{\prime}$ within the given drafting sequence. By calculating
the ratio of $N_{ss^{\prime}}$ to $N_{s}$, we can obtain $P_{ss^{\prime}}$.
Thus, the transition probability matrix $P$ is generated to predict the
drafting sequences. After possible heroes for team $T$ are generated, if the
hero is picked in previous rounds, $Pr_{T}[hero\\_ID]=1$, the possibility to
select this hero in the matrix $P$ is assigned as $0$. Based on this $P$, we
provide the top three predictions to the user, and the most probable
“ban”/“pick” choice is identified as top prediction.

Table 2. Performance of team win rate predictor. | Accuracy | AUC
---|---|---
LR | 0.528 | 0.532
RF | 0.564 | 0.572
GBDT | 0.523 | 0.523
NN | 0.507 | 0.513

####  5.3.2. Win Rate Predictor and Comparison with Baseline Models

We tested four models (RF (Pal, 2005), NN (Bishop, 2006), Logistic Regression
(LR) (Wright, 1995), Gradient Boosted Decision Tree (GBDT) (Ye et al., 2009))
in win rate prediction and compared the accuracy and area under ROC curve
(AUC). The dataset was randomly divided into training and testing sets
($8:2$). Blue and red drafting are encoded as two feature vectors of length
$110$ and the input is the concatenation of the two vectors as shown in Fig.
2. The results are listed in Table 2. LR models the probabilities as linear
combinations of individual features without awareness of the interactions
between features and in turn performs relatively poorly. Both RF and GBDT are
decision tree-based models. RF performs better as GBDT is more sensitive to
outliers (Yang et al., 2016), and there is a good chance that a player’s
performance in a tournament will lead to unexpected results. Considering the
small number of professional battles and the fact that NN is a complex model,
it leads to over-fitting problems. Due to game balance and in-game
uncertainty, in previous work, most winning predictors were accurate around
$0.6$ at the cost of $100,000$ orders of magnitude data (Chen et al., 2021,
2018a). Therefore, the performance of RF trained with a limited number of
professional games is acceptable. We thus choose the RF model as our winning
predictor.

####  5.3.3. Drafting Recommendation Model and Comparison with Baseline Models

To satisfy the global “ban”/“pick” rule, we consider this process as a zero-
sum two-player game. MCTS has been applied to many classic zero-sum games such
as Go (Silver et al., 2017) and other real-time strategy games (Balla and
Fern, 2009). Particularly, MCTS (Kocsis and Szepesvári, 2006; Coulom, 2007) is
a class of heuristic search algorithms to make decisions without building a
full search tree. It expands the branches towards the most promising actions.
The search tree is continuously expanded until the allocated time or memory
resources are exhausted.

Figure 3. Four stages in MCTS.

Drafting with a global “ban”/“pick” rule can be identified as a search tree
with the maximum depth of $10\times N$. Recommending a hero to our side is to
find out the hero that leads to a global win while deciding which hero to
“ban” is actually to find out which hero the opponent is most likely to win.
Based on this logic, the MCTS in the draft game is built with the following
four stages, as shown in Fig. 3:

1) Selection. The algorithm starts at the root node $R$ and successively
selects child nodes until it reaches the leaf node $L$. The selection follows
the Upper Confidence Bound (UCT) criterion (Auer et al., 2002):

(1)
$A_{t}=\arg\max_{a}\bigg{(}Q_{t}(a)+c\sqrt{\frac{\ln(t)}{N_{t}(a)}}\bigg{)},$

where $Q_{t}(a)$ represents the current estimate of action $a$ at $t$, and $t$
represents the timesteps, $N_{t}(a)$ represents the number of times action $a$
is taken, and $c$ is the exploration term. The tree tends to explore nodes
with higher estimates.

2) Expanding. Before reaching a leaf node, one of the potential actions is
applied randomly to that node. To reduce the search time, we list all possible
actions using the previous Markov chain prediction model. These actions are
considered potential actions for the node to expand.

3) Simulation. Upon expanding a new node, the algorithm swiftly conducts
random samplings to simulate the remainder of the game, adhering to the
fundamental rules throughout the process.

4) Back-propagation. The reward of each node is backpropagated from the child
nodes to the root node.

Table 3. Performance of MCTS hero drafting. | win rate in Bo3 | win rate in Bo5
---|---|---
RD | 0.499$\pm$0.058 | 0.478$\pm$0.043
HWR | 0.732$\pm$0.053 | 0.658$\pm$0.034
MCTS | 0.875$\pm$0.068 | 0.830$\pm$0.075

We define the reward as the number of wins in the remaining rounds. We use a
win rate predictor to predict the outcome. The node’s information is updated
after each exploration.

After the number of iterations in these four steps above, the child node with
the highest reward is returned as the output. Performance is evaluated as the
win rate of model-selected drafting versus randomly-selected drafting in the
“best-of-3” and “best-of-5” matches. We compare our MCTS with the random (RD)
method and the highest win rate (HWR) method, where heroes are greedily
selected based on the win rate of the round. Results with $95\%$ confidence
intervals are listed in Tabel 3.

###  5.4. Front-end Visualization

Figure 4. BPCoach Interface. (a) BP view supports the users to control the
current hero drafting stage and gives overall information on the players. (b)
Drafting sequence view displays the drafting recommendation and prediction
paths (b2), and shows the player status (b1) with the interested path.
Different drafting can be compared within their scores(b3). (c) Player view
presents the player comparison between two teams with their specific hero data
highlighted. (d) Hero view shows the hero’s information and relationships with
others. (e) Team view uses a radar chart to compare the overall and specific
strengths of two teams and displays the keywords through a word cloud. (f) In-
game change view shows the timeline and the comparisons of hero status before
and after the in-game updates.

Figure 5. Design alternatives of bp view.

BPCoach enhances familiar visual metaphors to facilitate efficient
comprehension of the analysis results for domain experts. We strictly adhere
to the mantra of “overview first, zoom and filter, then details-on-demand
(Shneiderman, 2003)” to assist experts in comparing various drafting
strategies and exploring the data related to hero drafting games. Based on
these principles and the previously mentioned requirements, we have designed
and developed the front end of BPCoach, as shown in Fig. 4. The front end
comprises five main views: the bp view enables the user to control the current
drafting process in the game and displays an overview of the player’s status
(F1, F3, DR5); the drafting sequence view shows and compares possible drafting
sequence paths, their alternative and customized paths, as well as player-
specific conditions in the path of interest (F1, DR1–3); the player view shows
the comparisons between players from different teams in terms of overall and
specific strength (F2, F3, DR5); the hero view displays the basic information
of the hero and its synergic and countering relationships with others (F7,
DR4); the team view reveals the team strengths of different drafting through a
radar chart and extracts team-related keywords using a word cloud (F4, F5,
DR5); the in-game change view compares the data on the hero before and after
the in-game updates (F6, DR6).

####  5.4.1. BP View

The bp view (Fig. 4 (a)) serves two primary functions. Firstly, it allows
experts to select the blue-side and red-side teams and control the drafting
progress of the match. Once the two opposing teams are determined, experts can
choose the desired round and select the established heroes by clicking on the
gray image, following the progress of the draft.

Figure 6. Sequence node design: (a) the node with a prediction next stage; (b)
the node with a recommendation next stage; (c) design alternative for the node
with a prediction next stage; (d) design alternative for the node with a
recommendation next stage.

The design alternative of bp view is shown in Fig. 5. This design displays the
heroes banned and picked by both teams horizontally on each side, and the
“ban”/“pick” order is written on their avatars. Although this also
demonstrates the “ban”/“pick” process, collaborating experts believe that this
design is harder to adapt to. They chose the design shown in Fig. 4 (a), which
resembles the control panel used in tournaments. During the “ban” stage, a
circular icon with a “banning” symbol is displayed. The subsequent hero
selection is indicated by a blue stroke around the image. Additionally, a
comparison of the overall player status from both teams is provided. In the
middle of the interface, a bar chart presents information on the number of
times each player has been on the field, with green and red colors
representing victories and defeats, respectively. This visual representation
allows experts to quickly compare the experience and overall strength of the
players.

####  5.4.2. Drafting Sequence View

The drafting sequence view consists of four components that aim to provide
hero drafting recommendations and predictions and facilitate comparisons
between different drafting strategies. Experts can first identify and select
the paths they are interested in in the sequence path view (Fig. 4 (b2)).
Then, they can view the corresponding player status information for the heroes
on the path in the player status view (Fig. 4 (b1)). By clicking the
interesting paths, experts can pin the paths in the drafting comparison view
(Fig. 4 (b3)) so that drafting influence on the rest of the rounds can be
obtained. Experts can trade off between recommendations, player status, and
future impact.

In the sequence path view. (Fig. 4 (b2)), the sequence path is generated by
utilizing the recommendation and prediction models of the back-end engine.
This empowers experts to simulate potential future hero drafting scenarios.
Depending on whether the upcoming stage belongs to our team or the opponent’s
team, the corresponding model is employed. Experts are given the ability to
control the default branches and preview forthcoming steps through interaction
with the control panel. This interactive feature enables experts to explore
different options, make informed decisions, and strategize effectively based
on the generated sequence path.

The generated paths are displayed as shown in Fig. 4 (b2) on an infinite
canvas that supports panning operations. A stage line is pinned at the bottom,
indicating the stages of the drafting process. The sequence path and
alternatives are encoded into a modified tree diagram. Specifically, the top
path is always in a horizontal layout. The width of the paths is proportional
to the score or likelihood of the child nodes. Nodes are mainly encoded as
avatars of the output heroes because our experts suggest that avatars are more
readable. In addition, we attach a stacked chart or bars to the right side of
the avatar, as shown in Fig. 6 (a)(b). Fig. 6 (a) represents the node whose
next stage is the prediction stage. We display the three top predicted
alternative heroes with their prediction percentages stacked vertically.
Similarly, Fig. 6 (b) represents the node whose next stage is the
recommendation stage. To intuitively show the recommendation scores, we use a
horizontal bar chart. In both nodes, the expert can hover over the stack and
the bar graph to get alternative heroes and click to expand the branch if
interested. We provide experts with a customized selection to select their
desired hero.

Figure 7. An example box plot in the player view: (a) Player data on overall
strength; (b) Player data with specific hero highlighted.

During the design iteration, we proposed an alternative design using pie
charts (Fig. 6 (c)(d)). However, the pie chart is less intuitive in
representing the status of the “next” stage and the colors around the avatar
can be overwhelming. Therefore, we opted for the current design.

In the player status view. (Fig. 4 (b1)), after experts have selected the
paths of interest, the player status view allows them to assess how the player
is performing with those selected heroes in the path.

Players are connected to the heroes they have previously used. The width of
the path indicates the number of times the player has used that hero, while
the color indicates the game result associated with that hero. This allows
experts to quickly determine a player’s familiarity with a particular hero.
While a hero may have a high drafting win rate, if the player does not perform
well with that hero, the expert may consider suggesting a second choice.

The drafting comparison view (Fig. 4 (b3)) is used to compare the impact of
various drafting on the rest of the rounds.

When the user selects an interesting path, the corresponding drafting for both
sides is displayed in the comparison view as quick notes, allowing the user to
easily recall their selections for that particular path. Additionally, the
predicted win rate for the remaining rounds is represented as a bar chart. A
dashed line is used to indicate the $50\%$ win rate threshold. Bars are
colored red when the win percentage falls below $50\%$. This view provides a
more intuitive comparison between two or more interesting draftings, enabling
experts to make better decisions regarding which path to follow.

####  5.4.3. Player View

Experts can select two players from different camps who have the same role in
the team. Then the player view (Fig. 4 (c)) displays a more detailed view of
the player’s performance in the current season.

Five box plots display the following player statistics: KDA ratio, average
damage per minute, average damage taken per minute, average cash gained per
minute, and participation rate. These box plots allow for a comprehensive
comparison of the players’ overall strengths in these five aspects.
Additionally, experts have the option to assign a specific hero to a player
and evaluate their performance on that hero. The box plot dots represent games
in which the player used the assigned hero. By enabling the highlighting
option at the top, as shown in Fig. 7 (b), these dots can be highlighted.his
feature enables experts to not only assess the overall strength of a player
but also evaluate their performance on a specific hero, aiding in decisions
related to whether to “ban” or “pick” that particular hero.

####  5.4.4. Hero View

The hero view (Fig. 4 (d)) shows mainly synergies and confrontations between
heroes, as well as basic information about the heroes.

After selecting an interesting hero, the glyph of that hero is displayed and
the selection is recorded in history to select it again easily. In the glyph
(Fig. 8 (a)), we divide the space around the avatar into four parts. Three of
them are used to express the relationships with other heroes. The red section
shows the top three heroes that the hero is countered by; the green section
shows the top three heroes that the hero counters; and the blue section shows
the top three heroes that are the best teammates of the hero. The radius of
the avatar is determined by the influence of that hero. Meanwhile, the bottom
left section contains the basic information about that hero. The innermost
ring is the KDA ratio; green indicates “kills”, red indicates the “deaths”,
and blue indicates “assist”. The three outer rings represent the picked rate,
banned rate, and win rate, respectively.

Figure 8. Hero glyph design: (a) Hero glyph showing basic hero information and
synergy and antagonistic relationships between heroes; (b)(c) Design
alternatives for hero glyph.

In the design iteration, we considered both bars and circular bars. As shown
in Fig. 8 (b), while the bar chart can show the influence of other heroes by
the height of the bars, it lacks basic information about the hero, whereas our
experts said that “basic information about the hero is very helpful to
consider hero strength”. In addition, experts pointed out the importance of
direct readability of the hero’s identity. Therefore, since the circular bar
chart (Fig. 8 (c)) does not directly identify the hero, it requires the user
to spend more time hovering over the chart. Therefore, we finally decided to
use the current design as it helps experts to absorb relationships and
essential information efficiently.

####  5.4.5. Team View

In addition to individual performances, teamwork also plays a key role. Teams
possess varying proficiencies in certain strategies that necessitate close
cooperation and, as a result, pose distinct challenges to other teams.

The team view (Fig. 4 (e)) contains a radar chart and a word cloud that
reveals the teams’ strengths and play styles. The radar chart shows the team’s
status in six areas, including win rate, team KDA ratio, average game
duration, average tyrants, average dragons, and average towers destroyed
during the game. Experts can assign specific drafting to different teams with
two multi-selectors. The radar chart is updated according to the selected
drafting. If a team appears aggressive with a high KDA, win rate, and other
indicators, the expert will consider drafting against that drafting. Word
clouds are generated from one or more user-defined inputs. Experts can enter
any word related to a team, player, or hero. The word cloud displays the
relevant keywords around the inputs with different weights so that semantic
information can be extracted. For example, when the team name “AG” is entered,
the maximum output is its player name “YiNuo”, indicating that the player is
the core of the team. Therefore, he may be a breakthrough.

####  5.4.6. In-game Change View

The in-game change view (Fig. 4 (f)) provides the changes updated by the game
operator, as well as the following hero drafting changes.

The timeline showcases the number and date of in-game changes. By clicking on
it, users can select one of the altered heroes and examine their current
status. The change details are presented in a bar chart format, where encoded
information includes a comparison of various metrics such as the hero’s win
rate, picked rate, banned rate, average kills, average deaths, and average
assists before and after the update. These variations serve as indicators of
the impact of in-game changes on hero drafting. Additionally, we provide
specific data for a particular team, represented by a dashed line, showcasing
the performance of their heroes. By analyzing these comparisons, experts can
reassess the priority of certain heroes.

####  5.4.7. Interactions Among the Views

Figure 9. Hero selector in BPCoach.

Rich interactions are integrated into BPCoach to catalyze efficient in-depth
analysis. (1)Panning and Scrolling: Users can pan the canvas of sequence paths
to address scalability issues when there are too many branches and steps. The
drafting comparison view can be scrolled to keep more interesting drafting.
(2)Linking: Users can click on a player name to change the player selection in
the player view, as this is a more intuitive way than presenting a selector.
(3)Parameter Editing: Users can customize their drafting path in the sequence
path view, select their drafting combinations in the team radar chart, and
enter any word in the team word cloud. These interactions help with “what-if”
analysis. (4)Hero Selector: The hero selector in BPCoach is built based on
user habits. According to experts, it is more difficult to read literalized
names than their avatars. Therefore, we encode the heroes as their avatars and
sort them alphabetically as shown in Fig. 9. Also, since there are up to $110$
heroes in the showcased game, categorizing and filtering the heroes based on
their roles in the game makes the selector more familiar to the user and
becomes more efficient. (5)Highlighting In the player view, users can check
the highlighting option to highlight specific dots. Also, both the team radar
chart and the player status view can be highlighted by hovering over them.
(6)Tooltips: If we hover over each chart, the exact data including ratio,
time, rate, etc. will be displayed by a tooltip, providing detailed
information.

##  6\. Evaluation

We evaluated the effectiveness of BPCoach in multiple ways from the
visualization community (Lai and Turban, 2008; Isenberg et al., 2013). First,
we described two case studies (RQ3, RQ5) with our domain experts (E1-E3) who
had participated in our user-centric design process. Second, we invited $12$
participants who had no exposure to our system and conducted a user study to
further assess the efficacy of BPCoach (RQ4). Finally, we conducted expert
interviews about their user experience and feedback (RQ4, RQ5).

###  6.1. Case I: Obtaining Hero, Player, and Team Information for Pre-match
Preparation

In the initial scenario, the experts aimed to be well-prepared for the first
round of drafting. E2 stated, “Unlike the subsequent rounds, which are fraught
with variables, drafting in the first round is planned under the best-of-$N$
rule.” In this particular setup, the experts were positioned on the blue side,
and their objective was to determine the core elements of the drafting process
during the first round.

Figure 10. Case I: (a) Observing the influence of the in-game changes. (b – c)
Exploring the team strength. (d) Checking the player’s strength.

####  6.1.1. Observing the Effects of In-game Changes

In this process, E2 first examined the impact of in-game updates in the in-
game change view (Fig. 10 (a)). He found that the hero “Ming Shiyin”, who is a
supporter, became so popular that it was rarely banned before, but now reached
a banned rate of over $50\%$. In addition, the average win rate keeps over
$50\%$. He was then wondering about how the red side performs after the in-
game change. E2 clicked the team selector and chose to display the data of
both the blue side and the red side. He admitted that both teams are good at
using “Ming Shiyin” and have a relatively high win rate compared with the
average. At the same time, the high pick rate indicated that the opponents
might have a strong tendency in picking “Ming Shiyin”. Therefore, the expert
decided to revolve around “Ming Shiyin” in the first round.

####  6.1.2. Exploring the Team Strength

To get detailed information about “Ming Shiyin”, E2 started to check the team
strength with the team view. He searched for the red side with “Ming Shiyin”
in the word cloud (Fig. 10 (b)) and found a keyword called “Sun Shangxiang”, a
marksman, indicating that the red side often chooses the combination of “Ming
Shiyin” and “Sun Shangxiang”. He then moved to the radar chart and checked the
strength of the red side with only “Ming Shiyin” (Fig. 10 (c1)), or the
combination (Fig. 10 (c2)) of “Ming Shiyin” and “Sun Shangxiang”. He compared
the six indicators in the radar chart and found that the strength of
combination is outstanding with a higher win rate. Therefore, he must act to
avoid his opponent from picking both “Ming Shiyin” and “Sun Shangxiang”.

####  6.1.3. Checking the Player’s Strength

The expert proposed two methods: “banning”, or “picking” one of the two heroes
for his side. He then selected the corresponding players and filtered their
data with “Ming Shiyin” (Fig. 10 (d1)) or “Sun Shangxiang” (Fig. 10 (d2)) in
the player view. With the data shown, he found that the red side performance
of “Ming Shiyin” is not stable, while they have chosen “Sun Shangxiang” much
more often than “Ming Shiyin” and have several outstanding performances.
Therefore, E2 judged that the key to the red side lay on “Sun Shangxiang”. As
“Sun Shangxiang” of the blue side was also outstanding, E2 decided to pick
“Sun Shangxiang” in the first “pick” stage to avoid the opponent picking her.

####  6.1.4. Predicting the Opponent’s Selections

E2 generated the predictions on the following opponent’s “pick” stages in the
sequence view (Fig. 10)(e). He found that the opponent had less tendency in
picking “Ming Shiyin” in the next two stages. The reason might be the opponent
want to leave “Ming Shiyin” to the round that they could pick “Sun
Shangxiang”. Thus, by picking “Sun Shangxiang” first, E2 could still get “Ming
Shiyin”, who is popular and strong now, in some subsequent stages.

With BPCoach, experts can extract enough information in the first round to
decide on the general direction. “I can move smoothly to the view I want
rather than clicking through many website pages to check what I want.”, said
E2.

###  6.2. Case II: Deal with Tough Situations on the Field

Figure 11. Case II: (a) Current drafting stage. (b - c) Obtaining
recommendations from sequence path view and drafting comparison view. (d)
Checking the hero information. (e) Checking the player status.

In the second case, E2 simulated real drafting to test if the system can help
solve difficult situations. In this setting, E2 is on the red side and has to
perform the last four stages, which are shown in Fig. 11 (a). The red side
still needed a juggle hero and a top-lane hero.

####  6.2.1. Obtaining Recommendations in Sequence View

E2 first generated two recommended paths in the sequence view (Fig. 11 (b)).
The results suggested that regardless of choice, the opponent would like to
pick “Shen Mengxi” and “Li Yuanfang”, and E2 agreed with this prediction. He
then checked the win rates for both paths and found himself in a tricky
situation, as shown in Fig. 11 (c). “Win rate is not everything, because
player power cannot be ignored.”, said E2. Player performance is crucial to
the output of the game. Therefore, E2 should make relatively appropriate
choices for his team so that the players can make up for the shortcomings of
the drafting with their outstanding individual abilities.

####  6.2.2. Checking the Hero Information

After glancing at the player’s overall status in Fig. 11 (a), E2 decided to
choose a better top-lane hero for the top-lane player, JiuLon, since he was
more experienced than the juggle player, Chichen. E2 checked the glyphs of the
two heroes (Fig. 11 (d1)(d2)) of “Ma Chao” and “Lian Po” (both top-lane
heroes), and found that both heroes have the best teammate appearing in the
existing drafting and “Ma Chao” has a slightly higher win rate. E2 thought
both paths are relatively promising but still felt tangled.

####  6.2.3. Checking the Player Status

E2 started checking how well the players performed with these heroes. He
clicked both two paths and generated the player status views. He found that
both players performed better with the heroes on the first path than on the
second. Specifically, JiuLon played “Ma Chao” $38$ times and won $23$ of them,
while only player “Lian Po” for $23$ times and lost more than half of them.
Therefore, he planned to draft along the first path and felt confident in that
choice.

After the simulation, the experts concluded that the system is informative and
can help make better, more confident choices. “The data is reliable, so the
help is meaningful.”, said E2.

###  6.3. User Study

In professional drafting scenarios, there is no standard dataset or process
for evaluating drafting recommendations. Therefore, we conducted a user study
focused on evaluating the informativeness, decision-making, and usability of
BPCoach (RQ5).

####  6.3.1. Participants

We recruited $12$ participants ($6$ female, $6$ male, $age_{mean}=22.7$,
$age_{sd}=3.3$) via word-of-mouth. They included $3$ professional players from
the collaborative team and $9$ students from a local university, all of whom
had extensive HoK experience (they had reached the top three tiers). $9$ of
the $12$ had been following KPL for over a year. We selected participants with
some background knowledge because the drafting process is complex and they
could provide us with more meaningful feedback. None of them had tried the
proposed system before the interview. $9$ of them used the official match data
website (Tencent-CP, 2023c) to acquire information in KPL, while the other $3$
did not care about this information. Participants were asked to think aloud
during the process. Upon completion, each participant received a $\$20$
stipend in appreciation for their contribution.

####  6.3.2. Tasks

In our study, each participant was given four tasks to complete. The objective
of Tasks 1-3 was to assess whether the system could provide sufficient
information to facilitate professional drafting preparations. In Task 1,
participants were asked to evaluate the performance of players throughout one
season. Task 2 required participants to assess how well a player performed on
a specific hero. Task 3 aimed to gauge participants’ ability to determine the
strength of a team based on their drafting choices. As for Task 4, the goal
was to investigate whether the system could assist users in making effective
drafting decisions. Participants were assigned an ongoing game scenario and
were required to make their next drafting choice. Given that, not all
participants were professional players, our primary focus was on examining the
overall functionality and usability of the system design.

####  6.3.3. Procedures

We compared BPCoach with the official match data website as a baseline system
where current professional teams get the information they need. The experiment
consisted of four sessions. In the first session, we conducted a tutorial
session for approximately $20$ minutes where we briefly introduced two
systems. We then asked participants to operate these systems themselves and to
ask us questions. When participants were ready, we assigned each of the four
tasks described above using two separate systems. Participants were first
randomly assigned to one of the systems. After completing the tasks, they were
given a questionnaire containing questions on the Likert scale criteria
(O’Brien et al., 2018), with $1$ representing “strongly disagree” and $7$
representing “strongly agree”. The questions included four items on
informativeness, two items on design convenience, and two items on system
usability, which are shown in Table 4. Then, participants were then assigned
to another system and were asked to complete the same four tasks and the
questionnaires again. In addition, they were asked how they felt about the two
systems.

Table 4. The questions in the questionnaire consisted of three parts:
informativeness (Q1-Q2), decision-making (Q3-Q5), and system usability
(Q6-Q8).

Q1 | Informativeness | Accessibility | The information needed to plan a drafting path is easy to access.
---|---|---|---
Q2 | Sufficiency | The information is sufficient to plan the drafting.
Q3 | Decision-making | Comprehension | I can explore drafting process comprehensively.
Q4 | Assistance | The system is helpful for me to plan the drafting.
Q5 | Confidence | I am confident that I find a suitable drafting selection.
Q6 | System Usability | Easy to learn | It was easy to learn the system.
Q7 | Understandability | I can understand the information provided by the system.
Q8 | Intuitiveness | The visual design is intuitive and friendly.

####  6.3.4. Hypothesis

We propose the following hypotheses: H1. BPCoach is superior in accessibility
(H1a) and sufficiency (H1b) compared to the baseline system. H2. BPCoach has
advantages in comprehension (H2a), confidence (H2b), and assistance (H2c) in
helping decision-making compared to the baseline system. H3. BPCoach is easier
to learn (H3a) and easier to understand (H3b). H4. The proposed design is more
intuitive and user-friendly.

####  6.3.5. Results and Analysis

Figure 12. Means and standard errors of Baseline System and BPCoach in terms
of informativeness, decision-making, and system usability using a 7-point
Likert scale. ($*:p<.05$)

We reported quantitative ratings by the paired $t$-test. Results are shown in
Fig. 12 and Table 5.

Table 5. Paired sample t-test results. |  | $t$ |  $Sig.$ ($2$-tailed)
---|---|---|---
Informativeness | Accessibility | 8.12 | ¡0.001
Sufficiency | 14.20 | ¡0.001
Decision-making | Comprehension | 8.685 | ¡0.001
Assistance | 8.248 | ¡0.001
Confidence | 5.49 | ¡0.001
System Usability | Easy to learn | 1.646 | 0.13
Understandability | 0.944 | 0.36
Intuitiveness | 1.698 | 0.12

  * •

Informativeness. Compared to the baseline system, the proposed system scored
significantly higher in both accessibility and sufficiency. In the
accessibility rating, participants found it easier to access the information
using the proposed BPCoach ($Mean=6.08$, $SD=0.90$) than the baseline system
($Mean=2.33$, $SD=1.37$) with $p<0.05$ (H1a supported). In the sufficiency
rating, participants found that the proposed system provided more information
($Mean=6.25$, $SD=0.96$) than the baseline system ($Mean=2.17$, $SD=0.84$)
(H1b supported). One participant reported that “the distribution of players on
certain heroes is meaningful and intuitive. And this kind of information is
hard to get on other platforms.” (ID:5, male, age: $18$).

  * •

Decision-making. The proposed system scored significantly higher in all three
aspects. The comprehension of the proposed system ($Mean=6.33$, $SD=0.89$)
outperformed the baseline system ($Mean=2.33$, $SD=1.37$) within $p<0.05$ (H2a
supported). One participant said that, “the exploration process is logical and
intriguing.” (ID:6, male, age: $28$). The proposed system ($Mean=6.42$,
$SD=0.79$) was significantly more helpful for hero drafting than the baseline
system ($Mean=2.83$, $SD=1.53$), $p<0.05$ (H2b supported). Participants felt
more confident in their decisions made using the proposed system ($Mean=5.25$,
$SD=1.60$) than the baseline system ($Mean=1.92$, $SD=1.16$) with $p<0.05$
(H2c supported). Here is representative verbal feedback, “the evaluated win
rate on the rest of the rounds gives me an overview of the match. Thus, I feel
more confident in picking some heroes.” (ID:1, male, age: $24$).

  * •

System usability. Overall, these two systems received relatively high scores
in three aspects. BPCoach performs slightly better in this part. Both the
proposed system ($Mean=5.50$, $SD=1.57$) and the baseline system ($Mean=5.00$,
$SD=1.95$) were considered too easy to learn, but their differences were not
significant (H3a rejected). The comprehensibility of both the proposed system
($Mean=6.00$, $SD=1.54$) and baseline system ($Mean=5.08$, $SD=1.88$) was
highly rated with no significant difference (H3b rejected). Meanwhile, the
proposed system ($Mean=5.92$, $SD=1.73$) was rated slightly higher than the
baseline system ($Mean=4.42$, $SD=2.06$) on intuitiveness (H4 rejected). One
of the participants said that “the design in BPCoach is intuitive and helps me
acquire information at a glance.” (ID:7, male, age: $18$). The reason behind
this may be that both systems refer partly to the expressions that users are
familiar with in the game.

###  6.4. Expert Interview

We conducted online interviews with the aforementioned experts, E1 and E2, to
collect their feedback and to observe their work process (RQ4, RQ5). Neither
of them had tried the system prior to the interviews. First, we introduced the
background of the system and conducted a 20-minute tutorial session. Then, we
let them explore the system on their own and ask us questions if needed. After
that, the experts were asked to perform a “ban/pick” simulation with the help
of BPCoach. Finally, we collected their feedback on the system’s performance,
visual design and interaction, generalization, and scalability.

####  6.4.1. System Performance

We asked the experts the following questions: “Does the system meet each
requirement?”, “Is the system sufficient to support a more effective and
efficient ‘ban/pick’ decision-making process?”, and “Did you encounter any
potential problems in the exploration process?” Both experts agreed that the
system was meaningful and well-organized. “The design of the system is
consistent with the logic of our exploration.” The recommendations provided by
our system are informative and serve as a reminder for E2. E2 can iterate and
optimize his drafting plan with the help of BPCoach. In discussing the hero
view, E1 said that “new insights can be drawn from quantitatively displaying
hero relationships.” Experts had some assumptions about some hero
relationships, but they found that this is not the case.

####  6.4.2. Visual Design and Interaction

We interviewed experts in detail to get their specific comments on the visual
design and interaction of each view:

  * •

BP view: Both experts were satisfied with the usability of BP view because
“its visual design aligns well with the intuition of MOBA gamers.”

  * •

Drafting sequence view: Experts gained some new insights into drafting
selection from this perspective. “We found some unusual patterns in the
exploration. These patterns don’t fit the conventional ideas based on hero
strength and setting,” said E1.

  * •

Player view: A vivid box plot can show the difference between two players more
visually than a coach’s subjective experience and a poorly organized Excel
sheet. Both experts agreed that “this is a good solution to our subjectivity.
And box plots are actually more efficient with just a glance.”

  * •

Hero view: Experts appreciated the simplicity of glyphs and the wealth of
information encoded. “This helps us quickly understand the strength of the
heroes in the current patch.” When it comes to the design, E2 stated that
“after being explained, I agree that the glyph is easy to learn. The color
chosen at the background is intuitive.”

  * •

Team view: “The radar plot is very useful for drafting selection, and it can
provide data to support some specific tactical plans.” As for word clouds,
“news doesn’t fully describe a team or a player, but it’s a good try,” said
E1.

  * •

In-game change view: Experts praised this view. E2 stated that “patch
iteration has a great impact on the team’s style of play. However, this
dimension of data has not been previously focused on and quantified, and the
bar chart is familiar.”

Both experts agreed that the designs and interactions were friendly to green
hands and the system was easy to learn and use. Compared to the methods they
had used before, “we can efficiently extract some specific information instead
of manually check the battles one by one,” said E1. “The visualizations also
help me get and understand the data faster.”, E2 added. With BPCoach, the
experts’ exploration process becomes simple and efficient.

##  7\. Discussion

###  7.1. Findings

The findings are embodied in how the five research questions are answered and
what can we learn from them.

  * •

RQ1: What is the information needed for a coach’s decision on hero drafting?
By performing expert interviews, we successfully extracted seven factors (F1 –
F7) (Summerville et al., 2016, 2016; Yu et al., 2019), i.e., comprehensive
coverage of factors affecting hero drafting. Since previous works on win rate
prediction only considered F1 and F7, the accuracy of around $60\%$ has been
unbreakable for a long time, most likely related to the absence of other
factors. Therefore, future work can be done from this perspective.

  * •

RQ2: What should be an acceptable approach for information processing?
Handling complex but small amounts of data is always a challenge for machine
learning based methods, which leads to ignoring unavailable features or
simulating an ideal scenario (Hanke and Chaimowicz, 2017; Porokhnenko et al.,
2019; Chen et al., 2018a, 2021). To find out a practical approach for real
application, we built a visualization system. The interactive user interface
provides the opportunity for the model and humans to iterate together for
optimization. Therefore, future work could integrate this “human-in-the-loop”
approach into the proposed approach.

  * •

RQ3: What scenarios and when will this system be used? In the study, we
presented two use scenarios to verify that the system can be used before and
during a match. However, currently, it is not permissible to utilize an AI
system like BPCoach in actual matches due to rule restrictions. Experts have
proposed that the system could be employed during scrims (training matches) to
gain fresh insights through collaboration with AI. Specifically, experts
believe that in cases involving new team formations or personnel rotations
(Freeman and Wohn, 2019), BPCoach can offer a rapid and meaningful solution
for training and team cohesion. Therefore, for the time being, our focus is on
providing a strategy for obtaining and interacting with relevant data rather
than circumventing the game’s rules.

  * •

RQ4: What is the usability and effectiveness of the adaptive system in hero
drafting? We conducted a user study to test the usability and effectiveness of
BPCoach. The results validated the merits of the proposed system. Therefore,
we can infer that this workflow can lead to an effective tool for complex
decision-making problems.

  * •

RQ5: How will coaches interact and collaborate with such an AI-enabled system?
Expert interviews and two cases provide us with an opportunity to see the
process of iterative decision-making using the system. Primary decisions are
provided by the model, and experts then start to compare the decisions and
utilize other information to optimize the decision. While we cannot guarantee
that every decision is the best one, we can see that the “human-in-the-loop”
process can give experts more confidence in their decisions than a black box
model. How to open the black box of a model is a question worth thinking
about.

###  7.2. Contribution to Pre-game Platforms

BPCoach effectively incorporates and enhances the interactions of seven
factors outlined in Table 1. The predicted and recommended drafting sequences
(F1) are based on the global “ban”/“pick” rule, and BPCoach explicitly
displays these potential sequences as paths in the drafting sequence view. The
player’s performance on a specific hero (F2) and their overall status (F3) are
presented in the player view and bp view, respectively. These views establish
links between players to ensure coherence and consistency. Moreover, the
proficiency of each player with specific heroes (F2) is displayed at the top
of the drafting sequence view, enabling a quick and intuitive visualization.
By simply clicking on a path, users can switch between heroes in different
sequences. BPCoach summarizes team performance (F4) in the team view based on
various heroes and their combinations. The player style (F5) is also depicted
in the team view. To achieve a more personalized play style assessment, we
employ a method that extracts keywords from news articles. This approach
allows us to go beyond fixed labels and identify not only a player’s style but
also the style of a team or a specific hero, among other possibilities. The
in-game change view employs a bar chart to facilitate the easy perception of
trends following game updates. The relationship among heroes is presented in
the hero view, which is linked with the drafting sequence view to alleviate
the need for repetitive clicks and streamline the user experience.

In summary, BPCoach makes significant contributions in four key areas. First,
we have identified and incorporated seven crucial factors into a comprehensive
system, ensuring that all relevant aspects are considered. Second, we have
taken a global perspective when addressing the challenge of automated
prediction and recommendation. Third, we have leveraged visualization
techniques to enhance the user experience and enable quick comprehension of
complex data. This visual approach enables users to grasp important
information at a glance. Fourth, we have implemented an interactive interface
that minimizes unnecessary interactions and streamlines the user’s workflow.
This reduces the burden on users and ensures a smoother and more efficient
experience when interacting with the system.

###  7.3. Generalizability

Our workflow and system can be applied to drafting in other games with minimal
adjustments to the algorithms and interface, due to the similarity of hero
drafting mechanics.

####  7.3.1. Recommendation and Prediction Algorithms

Table 6. Comparison of hero drafting processes in professional tournaments of
different MOBA games.

| “B/P” sequence | No. of heroes | Global “b/p”
---|---|---|---
LoL | b1-b2-b1-b2-b1-b2-p1-p2-p2-p1-p1-p2-b2-b1-b2-b1-p2-p1-p1-p2 | 163 | ·
DotA2 | b1-b2-b1-b2-p1-p2-p2-p1-b1-b2-b1-b2-b1-b2-p2-p1-p1-p2-b1-b2-b1-b2-p1-p2 | 117 | ·
HoK | b1-b2-b1-b2-p1-p2-p2-p1-p1-p2-b2-b1-b2-b1-p2-p1-p1-p2 | 113 | ✓

We compared the two other most famous MOBA tournaments - LoL and DotA2, with
KPL. As shown in Table 6, there are slight variations in the hero sequences
and numbers. These processes generally follow a specific order, consisting of
“ban” and “pick” options exclusively. The number of heroes in all three games
is also similar, with approximately $100$ heroes in each. Consequently, our
MCTS-based recommendation model and Markov-based prediction model can be
applied to all three games. Additionally, in LoL and DotA2, where the global
“ban”/“pick” rule is not enforced, the computational complexity can be
reduced. The reward rule in MCTS is modified to consider only the win rate of
the current round, without taking into account subsequent rounds (Chen et al.,
2018a). Thus, the recommendation and prediction algorithms can generally be
adapted to other MOBA games by configuring the specific heroes and the
drafting order.

####  7.3.2. Interactive Visual Analytics

The user study included three representative evaluations to verify the
effectiveness of BPCoach in drafting plans. Therefore, as long as the relevant
data contains sufficient dimensions such as player and hero indicators, the
system can be applied to the game. However, due to the inclusion of more
complex gameplay in LoL and DotA2, it is highly likely that there are
additional quantitative indicators to assess the abilities of players or
heroes. To address this issue, we suggest incorporating more data on the
charts or including additional charts to enhance the system’s capabilities.

####  7.3.3. Generalization to Non-professional Players

We also took into consideration how to generalize the system for non-
professional players. To cater to these players, we simplified the algorithm
to focus on a one-round problem. In the visualization interface, most views
can be utilized, with the exception of the team view, as non-professional
players typically do not have a fixed five-player team. However, many of them
have a consistent group of teammates, forming a sub-team of duo queues or
trios. In such cases, the five-player team in the team view can be transformed
into a sub-team to address this issue. Furthermore, while it may not be
possible to assess the compatibility of randomly matched players, it is
feasible to showcase their performance when encountering the hero that one
plays. This can aid in understanding their compatibility at least with
specific heroes. Another challenge in the team view is the lack of news data
for non-professional players. In this situation, label-based methods employed
by commercial platforms can be valuable. However, we can go beyond providing
labels solely for individual players. Similar algorithms can be used to assign
labels to teams, players using specific heroes, and more. This approach allows
us to refine the initially general labels across different dimensions,
providing more nuanced insights. Lastly, we recognize the success of many
commercial platforms, such as Senpai, in creating in-game apps. These apps
enable users to seamlessly access the platform without the need for frequent
switching between the game and the platform itself. Therefore, future work can
involve the development of in-game support to provide a more integrated and
convenient experience for users.

###  7.4. Lessons Learned

####  7.4.1. Leave Human Factors to Humans

In BPCoach, we only used three factors (F1, F6, F7) related to heroes to train
the model. We did not input all the identified factors because the remaining
involves human factors related to players and teams, which are less objective
and stable than hero data. For example, a player adept at a specific hero may
underperform when using this hero if they are not in top form. However, a
model built with historical player data may wrongly predict a high win rate on
this player-hero pair. For more accurate decision-making, it requires on-the-
spot observation and human intervention. Our collaborating experts insisted
that when humans are involved in these factors, the responsibility for making
trade-offs should remain with humans rather than being delegated to models.

####  7.4.2. Tap into Expertise

During the iterative design process, we have gained a deep appreciation for
the unique qualities that MOBA environments possess. We recognize that these
qualities require specialized knowledge and expertise to design effectively.
As a result, we have collaborated closely with domain experts to ensure that
our design incorporates their insights and experience. Specifically, we
designed the interface resembling the HoK style, such as the interactions of
the hero selector (Section 5.4.7) and the visual appearance bp view (Section
5.4.1). Additionally, experts also suggested using hero avatars instead of
showing hero names solely, harnessing the power of visual cues for quicker
character recognition and selection. This collaborative approach has been
instrumental in developing an interface that not only adheres to MOBA-specific
affordances but also enhances overall usability and user satisfaction.

####  7.4.3. Considerations of AI-enabled Systems in Tournaments

The trend of using AI in tournaments is becoming increasingly popular (Wu et
al., 2023; Wang et al., 2022), but the use of AI systems is restricted in
formal matches (Ma et al., 2022) due to fairness issues and the impact on the
audience experience (Wallace et al., 2012). We believe that a unified platform
could help ensure fairness. For example, in some recreational Go matches, both
sides use a unified AI system as a teammate. Thus, if BPCoach is used as a
unified platform by both sides in MOBA games, it will not affect the fairness
of the game. Furthermore, to ensure the entertainment value of such systems,
it is crucial for AI to be used as an assisting tool (Hern, 2017) and not as a
replacement for human decision-making (Weisz et al., 2023). Previous work has
also shown that the interaction with AI and the differences in understanding
among players can add to the excitement of the game (Munyaka et al., 2023;
Merritt et al., 2011). As BPCoach requires humans to make decisions, the
entertainment value of the game will not be affected. In conclusion, the use
of AI in tournaments has the potential to enhance the overall experience for
players and audiences alike, but it is important to consider the impact on
fairness and entertainment value. We should propose unified platforms for
formal matches and use AI as an assisting tool rather than a decision-maker.

###  7.5. Limitations

There are several limitations to this work. First, we only have access to
professional battle data, which is insufficient. As experts have suggested,
professional data is different from ordinary data. Therefore, models with only
limited professional data do not perform as well as models with millions of
data. Second, since these battles are from official public matches, experts
believed this leads to some bias because teams usually try more offensive
drafting in scrims, but are conservative in official games. Third, running the
MCTS algorithms repeatedly is time-consuming. If we limit the time, the
algorithm may not have converged to a good enough answer. Therefore, the
current output still has a certain randomness.

##  8\. Conclusion and Future Work

In this study, we propose a visual analytics approach to exploring the hero
drafting process in professional tournaments with the global “ban”/“pick”
rule. We first extract the requirements through several discussions based on
experts’ opinions and finally specify the design requirements. The proposed
BPCoach helps to get information quickly, prepare for the tournament, and plan
the drafting on the field. Two case studies, a user study, and expert
interviews validate the efficacy of our approach. Currently, BPCoach is only
applicable to professional tournaments. We expect to extend the system to fit
the non-professional players, using a more suitable model and more data, and
extracting more information from the non-professional players.

###### Acknowledgements.

We would like to express our gratitude to our domain experts and the anonymous
reviewers for their insightful comments. This work is funded by grants from
the National Natural Science Foundation of China (No. 62372298), the Shanghai
Frontiers Science Center of Human-centered Artificial Intelligence (ShangHAI),
and the Key Laboratory of Intelligent Perception and Human-Machine
Collaboration (ShanghaiTech University), Ministry of Education.

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