VIRD: Immersive Match Video Analysis forHigh-Performance Badminton Coaching
Tica Lin1,2 , Alexandre Aouididi1,3 , Zhutian Chen1 , Johanna Beyer1 , Hanspeter Pfister1 , Jui-Hsien Wang2

Fig. 1: VIRD is an immersive VR platform for top-down badminton match analysis. Left: Users start with a high-level Match
Summary (a), then refine their analysis using the Shot Filter (b). Detailed rally and shot information is available through the
Rally Menu (c) and Situated Visualizations (d) on a virtual court. Right: Users can link to a Game View (e) of a selected shot
(Summary Mode) or an entire rally (Game Mode), featuring synchronized video and 3D dynamic player and shot representations.
Abstract— Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain acompetitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop
game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due
to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches
and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis
pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present
VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment
based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly
move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations andvideo. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Throughimmersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) withflexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of
VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective
badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence.
Index Terms—Sports Analytics, Immersive Analytics, Data Visualization

1

I NTRODUCTION

In the highly competitive world of professional badminton, coaches
and players constantly seek ways to gain an edge over their opponents.
Detailed match analysis has become indispensable for identifying key
patterns, developing winning strategies, and creating tailored training
plans. Traditional methods of match analysis require coaches to review
video footage of matches, take notes, and identify trends and potential
areas for improvement.
However, these traditional methods have significant drawbacks. First,
traditional methods require coaches to manually annotate videos while
analyzing the match. In order to gather insights from match footage,
coaches have to watch videos several times to collect data, such as• 1 Harvard John A. Paulson School of Engineering and Applied Sciences• 2 Adobe Research• 3 The École polytechnique fédérale de Lausanne (EPFL)
Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of Publication
xx xxx. 201x; date of current version xx xxx. 201x. For information on
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Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx

players’ statistical summaries, shuttle locations and types, and playing
styles. After that, coaches have to compare the summary data with theirobservations to iterate on their hypotheses, verify insights, and draw
conclusions. This data collection and analysis process can be highly
time-consuming and mentally demanding, as coaches have to watch
countless rallies multiple times and maintain a high level of attention todetail. While the analysis efforts vary widely, a badminton match cancontain 50 to over 100 rallies and the coaches typically spend 3 to 5
hours analyzing a full match video according to our formative study. As
a result, this can increase their cognitive load and limit the time coaches
spend working with their players. Furthermore, traditional methods
struggle to capture and communicate badminton’s inherently spatial
nature effectively. Badminton players use height- and speed-varying
tactics to control the pace and aggression of their games, which is harderto perceive in projected 2D videos. In turn, coaches rely on physicaldemonstrations to help players conceptualize the insights. The limited
coaching time available for high-performance athletes can restrict theamount of information conveyed during coaching sessions. Finally,
traditional methods separate the in-game data from its physical context.
Consequently, coaches often need to switch back and forth between thevideos and the collected data, inevitably increasing the mental effort ofthe analysis process. As shown in our formative studies (Sec. 3), there
is a demand for more efficient match analysis and coaching tools.
In this work, we closely collaborated with Olympic badminton professionals to design VR Bird (VIRD)1 , a novel interactive coachingtool for badminton match analysis in VR. To address the limitationsof traditional methods, VIRD adopts a top-down analysis approachto support an efficient iterative analysis process. It leverages computer vision (CV) techniques to extract game statistics and 3D shot andplayer data, and enables situated analysis of spatial and dynamic gamedata in immersive 3D space with an interactive approach. Immersive
analytics were found beneficial in supporting 3D data analysis withspatial understanding and immersion [50]. With its high portability andaffordability, we chose VR HMD as our targeted platform to design
immersive analysis solutions for badminton video match analysis.
To design VIRD, we performed a design study to answer the following three research questions. First, we conduct a formative study
with badminton professionals to understand “What data are required
for analyzing matches and developing coaching insights?” Second, we
identified gaps and iterated solutions with coaches to answer “Whatis the ideal coaching workflow for badminton video analysis and communication?”. Finally, we conducted a multi-staged user-centered
design to address “How to design an integrated video analysis tool
to support badminton coaching for professional coaches and players?”
We conducted case studies with high-performance badminton experts,
including Olympic and national team coaches and players, on matchanalysis using VIRD. Both coaches and the player were able to perform
effective match analyses, and verify and present insights using VIRD
with high satisfaction. They leveraged immersive 3D visualizations
to generate new insights with concrete evidence, such as observing
shot distributions or pinpointing specific game moments, and used VR
interaction to accelerate their iteration from hypotheses to insights.
Our research has four main contributions: 1) a formative study with
Olympic badminton professionals to identify gaps in current matchanalysis workflow for coaching, 2) a characterization of goals andtasks for badminton match analysis in coaching, 3) an end-to-end
immersive video analysis tool, VIRD, with state-of-the-art CV-based
data collection and a VR analytic interface for badminton coaching, and4) case studies with high-performance badminton experts to evaluate
the usefulness of VIRD. Our results suggest that applying immersive
analytics to sports videos, with CV-based data collection and humanin-the-loop analysis, can be highly effective for sports professionals.
2

R ELATED W ORK

Visual Analytics of Sports Games. Sports visualization research often
aims to visualize spatial and dynamic data in context, e.g., on a court
diagram (basketball [41], tennis [56], baseball [12]), embedded in thegame videos (soccer [48], basketball [9, 28]) or projected on a virtual
court in immersive environments (baseball [63], badminton [10]). Thisis largely because contextual understanding is crucial for deriving
meaningful and actionable insights from sports games [17, 39, 51].
For racket sports in particular, spatial and temporal data, such as shot
locations and trajectories, are crucial for game analysis and regulations.
LucentVision [40] is a commercial tennis visualization system based onreal-time ball/player tracking. It offers virtual replays of ball trajectories
and presents a color heatmap to show the coverage of player movements.
Similarly, the Hawk-eye system [38] provides 3D views of tracked
tennis balls using multiple cameras, and extends to other sports likebaseball and soccer for enhanced game viewing and officiating.
Two prior studies used immersive analytics to analyze spatial badminton stroke data. ShuttleSpace [61] visualizes badminton shot trajectories in a VR environment to support coaches in analyzing shot
data from a first-person perspective. They provide an integrated visualdesign that augments 3D trajectory data with 2D statistical information using a first-person perspective visualization and peripheral vision.
ShuttleSpace also enables natural and efficient trajectory selection in
VR with a stroke metaphor, allowing analysts to select trajectories byimitating badminton strokes. The system has been evaluated through1 A badminton shuttlecock is also informally called a bird.

case studies conducted by domain experts, demonstrating its potential
for facilitating badminton data analysis. TIVEE [10] designed an immersive VR system for experts to analyze sequential stroke trajectories
in badminton. It allows experts to explore different tactics from a thirdperson perspective, and provides a detailed court view for inspecting
and explaining tactics that lead to wins and losses. Case studies withprofessional badminton experts demonstrate the system’s effectivenessin identifying patterns of commonly used tactics.
In contrast to tennis, official badminton games do not use tracking
systems. Thus, most badminton professionals lack access to manually
collected shot datasets [10, 61] and rely on video-based match analysis (Sec. 3.1). Our study fills this gap by providing an end-to-end
immersive video analytic tool, VIRD, that integrates computer visionbased data collection directly from match videos. Compared to priorwork [10, 61], our study provides comprehensive match analysis forcoaching, emphasizing both analysis and communication of insights.
Game Reconstruction of Sports Videos. Reconstructing 3D sports
games from videos offers opportunities to improve game understanding
and analytics. Computer vision research has focused on reconstructing
game scenes and player or ball movement for various sports, such as
basketball [8, 62], tennis [38, 40], soccer [45], volley ball [7], or humanmotion in sports [45]. Badminton games present unique challenges,
such as single-camera recordings, fast shuttle speed (the fastest shuttle
speed can be over 250 mph), and complex player movement.
Prior studies have addressed shuttlecock detection and tracking, including an instant review system to determine whether a shot was inor out [22]. Other studies focused on tracking the speed, rotation angle, and athlete’s body transformation using sensors and path-tracking
algorithms [34]. Recently, MonoTrack [31] improved state-of-the-art
models [14, 19, 54] on court recognition and 2D trajectory estimation
based on badminton domain knowledge. We use MonoTrack [31] toaccurately extract and segment 3D shuttle trajectories in match videos.
While there has been some research on the detection and tracking
of badminton players [16, 43], there has been relatively little focus on
estimating their poses and shapes. Fortunately, SMPL [32], a learned
model of human body shape, allows accurate human shape representation from basic human 3D models. In addition, recent state-of-the-art
algorithms are capable of performing 3D human pose and shape estimation [36, 64]. Among them, CLIFF [27] can estimate SMPL parametersfrom a 2D video, which we use to reconstruct player poses.
Immersive Analytics for Spatial and Dynamic Data. Immersive analytics (IA) has gained significant attention among visualization researchers due to its ability to facilitate analytical reasoning
and collaboration for analyzing high-dimensional and multivariate
data [13, 50]. IA offers several advantages over traditional desktop
visualizations [23] due to its large screen spaces and embodied interaction offered by AR/VR technologies: immersion [18, 35], multi-modal
interaction [6, 33, 44], and presenting data in-situ [5, 15, 37], which improves spatial understanding and user experience throughout the visual
analysis process. Specifically, prior studies found several benefits ofanalyzing spatial and dynamic data in immersive environments, i.e.,
identifying spatial attributes such as distance [24, 60], transitioning
between 2D and 3D views [59], and the visceral experience of viewing
dynamic, physical data in VR [26].
With these identified benefits of IA, recent works investigate applying immersive analytics to sports [30]. Besides ShuttleSpace [61] andTIVEE [10] for badminton shuttle trajectory analysis in VR, Lin etal. [29] present real-time basketball shot arcs for situated analysis in
AR during free-throw training. Sumiya et al. [49] applied a similar
approach for basketball shooting training in VR. Rezzil [46] provides acommercial soccer training system in VR that allows players to evaluate
their performance with instant visual feedback. Zou et al. [63] presents
real-time bat swing spatial data for baseball batting training in VR.
While most IA work in sports focuses on providing real-time feedback for training, our study focuses on immersive video analytics for
sports coaching with spatial and dynamic data extracted from sports
videos. To the best of our knowledge, we are the first study to propose
an immersive analysis system for sports video analysis.

3 F ORMATIVE S TUDY WITH O LYMPIAN C OACHES & P LAYERS
We applied a user-centered design process to develop VIRD and involved target users at every design stage. All experts involved in ourstudy are Olympic or national team coaches and players. Section 3
presents the gaps in match analysis based on expert interviews withcoaches and players. Section 4 presents our goal and task analysis,
which informed the design of VIRD. Section 5 presents VIRD’s design
based on three rounds of user testing with coaches. Section 6 presents
the evaluation of VIRD with both coaches and players on match analysis for developing game strategy and communicating insights.
3.1 Procedures
To understand current practice and identify gaps in badminton coaching,
we interviewed five professional badminton players and coaches (I1-I5;
M = 2, F = 3; Age: 30-45). All of them are former Olympic players
representing Canada, Taiwan, and the US. All of them have at least 10
years of player experience and four became professional coaches after
their playing careers with 2 to 15 years of coaching experience.
We conducted 1-hour semi-structured interviews online to elicit theinterviewee’s background, overall coaching workflow, video and data
usage in their coaching, and how they evaluate the player’s performancein the video analysis. Finally, we asked interviewees to analyze a shortmatch video to demonstrate their typical analysis workflow.
All interviews were transcribed and analyzed using affinity mapping.
Our analysis focused on understanding the current badminton coachingpractice and identifying gaps in their match analysis workflow.
3.2 Findings and Gaps
Overall, we observed that coaching practices varied widely among theinterviewees due to varying resource levels, player skill levels, andcoaching styles. The coaching process typically involves a significant
amount of video analysis for both coaches and players. For players,
videos are crucial as they help players become aware of their playing
technique and style and allow them to create a mental model of otherplayers. Players are often told to record their own match and watch
the videos multiple times, e.g., “Most coaches recommend we watch
it several times and break it down to focus on one thing at a time”(I3). Coaches also rely on videos to direct coaching by analyzing
the root cause of player performance and communicating insights toplayers, e.g., “They won’t understand what I am saying unless they see
it physically” (I2).
All interviewees agreed that video analysis is time-consuming. I4
noted that “if the match is 30-40 minutes, it took 3 to 5 hours to rewatch
and discuss with your coach”. When analyzing the videos, coaches
and players watch matches, take notes, analyze them for insights, anddiscuss their findings. With more resources, coaches can proactively
share their insights with players, but this requires a significant time
investment (I2). Alternatively, in cases where access to a coach islimited, players may seek coaching by requesting feedback on areas
to improve (I3, I5) or by coaching each other (I1, I3, I4). Therefore,
analyzing matches for coaching is crucial for both coaches and players.
For clarity on badminton terminology, note that a match comprises
the best-of-three games. Each game has multiple rallies, with each rally
awarding a point. Within every rally, players execute a series of shots.
Below, we summarize four gaps in the current match video analysis.
3.2.1 The Current Bottom-Up Workflow is Inefficient
We observed that the experts we interviewed followed a bottom-up
approach to generate their insights. They began by scanning videos
to detect insights into a player’s playing style and weaknesses. For
example, during video scanning, I3 promptly observed, “She’s a lefty ...
[so] she tends to lean more to her left side for a big forehand.” Upon
forming an insight hypothesis, they scrutinized additional videos toidentify similar patterns and validate their observations, e.g., “Is thisjust an outlier or some of these random matches where we didn’t do well,or concrete things that we need to work on?” (I2). Coaches repeatedly
watched games until insights emerged and revisited the games to gather
further evidence. Because of the limited time and resources to review
footage, coaches and players might choose to only look at the mostimportant parts, which leads to incomplete analysis and potentially lesseffective communication. In summary, the current bottom-up analysis
workflow lacks support for efficient iteration and analytic reasoning.
3.2.2 Manual Data Collection from Videos is Time-Consuming
During the current workflow, experts manually collect summary statistics from watching the videos to reveal patterns quantitatively. This
allows them to compare the player performance (I1, I2, I4) and communicate better with the players (I1, I2). For example, having these data
benefit their coaching greatly, e.g., “If you had like 20 matches that you
played for an entire year, then you have a better idea where you candictate training” (I1). Further, concrete evidence like “70% of time
when you do this, you win the point” (I2) allows players to immediately
grasp the concept. However, when watching a video, paying attention
to multiple metrics and patterns simultaneously is difficult. As a result,coaches and players have to watch the videos multiple times and focus
on different aspects one at a time, such as opponent versus their player,
and winner shots versus unforced errors. Such a manual process “takes
up a lot of time” (I2). In summary, manually collecting data fromvideos hinders experts to perform match analysis efficiently.
3.2.3

Data Insights and Contexts are Presented Separately

Currently, data insights and videos are often presented separately.
Coaches typically provide players with a summary of key insights,
such as “you are pushing the tempo and make too many mistakes (6errors in 11 points you lost)”, without directly connecting to specificmoments in the video. This disjointed presentation hinders a comprehensive understanding of the game, as players may struggle to visualize
the context behind the numbers. To bridge this gap, coaches might
manually note timestamps of critical game moments with the help ofsome tools (e.g., YouTube, Hudl [21], Clutch [11]). However, collecting the video moments is still very tedious, as noted by I2 that “I haveto spend 30 minutes per player writing things down, and another 1hour to review notes with them to show them here’s what happened”.
Moreover, without ample video evidence, sometimes it can be hard toconvince the players of a particular finding. For example, I1 mentionedthat “The kids don’t realize that they make a lot of unforced errors. Ifthey watched the video clips, they’d understand it better”. Therefore,
the current way of presenting data and video separately may impede aholistic understanding of the game due to an inefficient workflow.
3.2.4

2D Game Representation is Insufficient

When asked about the limitation of analyzing matches with videos,multiple coaches expressed that single-camera recordings might fail tocapture essential game aspects, such as environmental conditions (e.g.,wind), shot timing (“Speed seems slower in the video”), and player
reactions. Although official badminton match are limited by monocular
videos, some coaches use 360-degree videos in training for comparing
players and their opposition, as “you can see both sides and like how
the player reacts to the opponent in real-time” (I1). Slow-motion or
zoomed views also assist in breaking down techniques and offering
objective perspectives (I1, I3). I3 noted “when you’re hitting the shot,
you only know how it feels, but you can’t see how it looks”. These
remarks indicate that traditional 2D videos fall short in providing spatial
comprehension and flexible viewing angles necessary for analyzing
specific shot attributes. This finding aligns with previous work [10, 61].
3.3

Summary

Based on the formative study, we identified that high-performancebadminton coaches and players perform match analysis to reveal thestrengths and weaknesses of players and to develop playing or training
strategies. However, data collection (i.e., bottom-up workflow and manual note-taking) and presentation (i.e., separation of data and videos and
2D game representations) gaps exist in their current analysis workflow,
leading to inefficient match analysis and communication for coaching.

4
4.1

G OAL & TASK A NALYSIS
Design Goals

To support coaches and players in analyzing matches and communicating insights effectively, we characterized four design goals with respect
to the four identified gaps in Sec. 3.2.
G1. Providing a top-down analysis workflow. Our tool needs topresent summary data to enable an immediate overview of the match,and support effective data exploration to discover regions of interestfor detailed analysis. Unlike the traditional bottom-up approach that
requires users to watch the videos sequentially to observe insights,
the top-down approach supports users to analyze game details on demand, driven by observed patterns from summary data, as captured in“Overview first, zoom and filter, details on demand” [47].
G2. Collecting data from videos automatically. To avoid tedious
manual data collection from users, our tool must provide the necessary data, including summary statistics and annotation of critical shots(i.e., winners and errors). This data should be automatically collectedwithout user input during the match analysis.
G3. Integrating abstract data with game contexts. Even thoughsummary data can help coaches identify patterns in the game, it isimportant to investigate the actual game moments in the video to verify
observations and analyze root causes, as well as to present the insights
to players. Similar to the concept of ”Search, show context, expand on
demand” on large graphs [53], the user goal is to search for a meaningfulcontext. Our tool should provide an easy transition between statistics
and videos to support iterative analytical reasoning and communication.
G4. Visualizing spatial data in 3D space. 3D data should be visualized within a 3D space to support an accurate interpretation of their
spatial attributes, e.g., shot speed and trajectory. In addition, our visualizations should support an analysis from flexible viewpoints to enhanceusers’ spatial perception and support objective perspectives.
4.2

Task Abstraction

We abstract six analytic tasks users perform when analyzing matches.
Currently, users have to manually gather summary data to identify
rallies of interest and manually navigate to each rally to extract insights.
T1. Identify the rallies of interest based on game summary data.
Users first obtain an overview of the match performance at the gamelevel (e.g., game length, scores). Based on the game metadata, they canfocus on a subset of interesting rallies, such as the winning rallies by
their player in the first game.
T2. Identify the rallies of interest based on rally summary data. After filtering rallies based on the game summary, users observe patternsof the game (e.g., playing style, competition) at the rally level, such as
the ratio of winner versus error rallies. Such statistics allow them toidentify specific patterns and further filter rallies for deeper analysis.
T3. Gain a statistical overview of rallies of interest. Focusing on aset of filtered rallies, users compare the statistics of individual rallies
to obtain high-level insights, such as assessing the pace of a rally fromshot counts and stress levels from score differences.
T4. Gain a spatial overview of rallies of interest. Additionally, users
examine spatial and temporal aspects of individual shots among theserallies (e.g., shot trajectory and speed) and compare them against rally
summary data (e.g., shot location distributions). The spatial information
can help users gain an in-depth understanding of the shot-level data andform insights on specific rallies.
T5. Investigate game details of specific shots. After gaining an
overview, users can dive into rallies to examine game details, suchas player movement and shot sequence, to get deeper insights into aplayer’s performance. This often requires users to watch the game moment multiple times from different angles (e.g., player vs. opponent).
T6. Verify insights across rallies. Before concluding their analysis,users need to cross-validate other rallies with similar or contrasting patterns to update and verify their insights. Thus, users need to efficiently
navigate to other game moments based on observed patterns.

5

VIRD - VR B IRD V IDEO A NALYSIS TOOL

We designed VIRD, our immersive video analysis platform for highperformance badminton coaching, targeting professional badmintoncoaches and players. VIRD features a top-down analysis approachand supports an integrated data and video analysis workflow based on
CV-based data collection and a 3D interactive environment in VR. We
iterated the designs based on expert feedback from three coaches.
5.1

Top-Down Analysis Workflow

To address G1, we designed a top-down analysis approach and verified
it with two coaches (I1 and I2) in a follow-up interview. The topdown user flow contains four steps: First, users find rallies of interest
based on summary statistics (T1, T2). Second, they compare andanalyze the filtered rallies to gain initial insights (T3, T4). Third, they
investigate game details to extract more specific insights (T5). Finally,
they examine similar patterns across rallies to verify their insights (T6).
We tested the proposed workflow by gathering coaches’ feedback ona hypothetical top-down analysis workflow for examining a lost game:
Initially, the user reviews the game summary to form an impression of the match. Observing a tight score of 17 (Player A) to
21 (Player B), the user chooses to analyze the 21 rallies where
Player A lost points (T1). They find 10 errors among the 21
lost points and focus their analysis on those 10 rallies ending inerror shots (T2). On the rally level, the user finds that 6 out of
10 error rallies are short and towards the end of the game (T3).
Examining the heat map and shot trajectories, the user notesthat 70% are from the middle, and identifies them as defensive
shots (T4). The user selects a short rally (T5) to study player
movement and the sequence leading to the error shot. Afterwatching multiple error rallies (T6), the user concludes that
Player A needs to improve defensive shots on the backhandside and work on physical fitness.
Both coaches agreed that this workflow is precisely what they need.
“We’re manually doing that because currently I won’t know where exactly
to go back in the video to see all the unforced errors” (I2).
5.2

Computer Vision Data Preprocessing

To address G2, we applied state-of-the-art computer vision (CV) models
to automate the data collection from videos. We developed a semiautomatic data processing pipeline for monocular match videos, which
includes a manual game breakdown, shot classification algorithms,
and automatic 3D shot and player reconstructions. Based on our task
abstraction, three types of data must be extracted from a match video:
1) Game and Rally Summary are automatically computed based on
manual annotation and output from CV models:• Rally Breakdown: To obtain a game summary, player scores andaggregated rally statistics are required. Therefore, each game needs
to be split into rallies. We manually annotated the time ranges (startand end), player who serves, and the winning side of all rallies
from the match video. Our algorithm then derives score and gameinformation based on the rally breakdown.
• Shot Breakdown: To obtain a rally summary, the duration of eachrally and shot count are required. We obtain the timestamps and eachshot’s hitter running MonoTrack [31] with minor manual clean-up.
2) 3D Spatial Data are automatically reconstructed from CV models.
• 3D Shot Trajectory: We automatically reconstruct 3D trajectories
and velocities for all shots with MonoTrack [31].
• 3D Player Model: To reconstruct 3D player models, we use MonoTrack [31] to estimate court and player positions. We use CLIFF [27]to predict smooth 3D player poses from videos.
3) Shot Statistics are automatically derived from the 3D spatial data
based on experts’ analysis requirements gathered in the formative study.
• Shot Tendency: To detect whether a shot leading to a point is awinner or an unforced error, we first classify the shot tendency byapproximating the shuttle’s velocity vector when it passes the net:
the tendency is defensive when the vector is going upward (awayfrom the ground), and offensive if opposite.

Fig. 2: VIRD Visual Design. (a) and (b) provide a match overview andallow users to examine and filter rallies listed in (c) based on game andshot details. Static immersive visualizations of 3D shot data are shownin (d), whereas dynamic game views are displayed in (e).
• Shot Outcome: Our algorithm categorizes shots as winners, errors, or
normal shots to calculate the winner and error shot counts. A rally
ends with a winner by the scorer or an error by the point loser. If thelast shot is offensive by the scorer, it’s a winner; if defensive by the
point loser, the penultimate shot is the winner. Conversely, if the lastshot is offensive by the point loser, it’s an error; if defensive by thescorer, the penultimate shot is an error. All others are normal shots.
• Shot Distribution: From the formative study, coaches use shot
locations to classify shots into six areas on the court, including
front/middle/back on the left and right sides. To compute the shot distribution, our algorithm projects each shot’s start (from) and endpoint(to) onto the court to decide shot locations.
Overall, our data preprocessing pipeline is largely automated. Except
for rally breakdown and winner/server annotation, all other data are
obtained through automatic algorithms. The manual annotation takes
up roughly half of the video duration (e.g., 30 minutes for labeling a 1hour video). While our method is not entirely automatic, we anticipate
that CV techniques may be able to address these manual annotations in
the future, though they are beyond the scope of our current study.
5.3

Visual Designs

We designed five visual components to support the users’ analytic tasks
and fulfill design goals (G1, G3 and G4). On a high level, users analyze
data across rallies in Summary Mode (Fig. 1-1) and dive into a specificrally in Game Mode (Fig. 1-2). We describe components with examples
based on the 2022 BWF World Championship match between Marinand Yamaguchi [3] (M2) used in our case studies (Sec. 6).
(a) Match Summary (Fig. 2a), supporting T1, enables users to identify
rallies of interest by showing essential statistics, including the match’s
duration, rally count, average shot count per rally, winner, and game
scores. Users can select a game and view the rallies won by a playerfrom the game selector (e.g., 17 rallies won by Marin in G1). Similar
statistics for the selected game are displayed below. Game 3 is split
into two halves by default due to the switch of sides at the midpoint,
which prevents spatial data from being displayed on the same side.
These statistics help experts identify more challenging or outstanding
games for deeper analysis. Furthermore, based on user feedback, we
added the option to split games into first and second halves for finer

granularity. This was based on the need to analyze each half separately
due to coaching advice provided during the midpoint break.
(b) Shot Filter (Fig. 2b), supporting T2, enables users to analyze
specific shots based on player and shot attributes. Finding specific game
moments is important to analyze strengths and weaknesses. Therefore,
our filter design includes key metrics to support immediate access tothe necessary details, including players, shot outcomes, and locations.
Coaches in our user testing found it extremely valuable to analyze shots
filtered by players. For instance, out of Marin’s 17 points, 9 were scoredthrough Marin’s winners, while 8 were scored due to Yamaguchi’serrors. This provides a different perspective than if Marin had 17
winners. Users can also analyze shot distribution by location filtering.
For instance, users can select the purple 56% grid to filter Marin’s
winner shots from the back right. Darker colors on the heatmap indicate
more shots are from (purple) or to (orange) the area. We picked thecolor scheme to avoid visual clutter and provide an easier comparisonof hot spots and shot tendencies between players and games.
(c) Rally Menu (Fig. 2c), supporting T2 and T6, provides an overview
of shot count and scoring cadence for the rallies of interest, with directaccess to the specific rally upon selection. Each rally is displayed ina scrollable list with score and length information. Short rallies (lessthan 10 shots) are highlighted in red to draw special attention basedon coaches’ requirements. For instance, users can discern the game’s
tempo from the number of short rallies (4 out of 9) and the variationof shot counts among the rallies won by Marin. This overview of rally
statistics helps identify patterns across rallies of interest (T4). Further,users can investigate game details in Game Mode by selecting a rally(T6), which allows easy transition to the game context.
(d) Situated 3D Visualizations (Fig. 2d), supporting T4, display the
3D shot arcs and heatmap of filtered shots (e.g., all winners by Marinin Game 1) on a 1-to-1 virtual court. Shots are color-coded basedon their outcome, with red for errors, green for winners, and white
for all other shots. Interacting with individual shot arcs displays the
shuttle’s dynamic trajectory in real-time (details in Sec. 5.4). The
situated visualizations enable users to glean insights into shots’ spatial
attributes, such as arc shapes and distributions. Experts use this design
to quickly observe insights from shots across multiple rallies.
(e) Game View (Fig. 2e), supporting T5, shows a 3D reconstructed
game view along with the video to facilitate a more comprehensive
analysis of game details. The 3D game view displays the dynamic
movement of shots and players, enabling accurate spatial perception andflexible viewing angles. By examining the exact moment of the selected
shot, users can quickly compare multiple shots in Summary Mode andexpand their analysis to the selected rally in Game Mode.
5.4

User Interaction

Users interact with the VIRD interface and visualizations using VR
controllers. Each shot can be hovered to select, which will link to thegame context of the shot (Fig. 3-1) while in Summary Mode, showing aGame View that contains 3D dynamic shot trajectory and player poses,
and the same shot duration in the video. This interaction allows users
to instantly review the game moment of each shot in the filtered group(e.g., all winner shots) to obtain the context of 3D data. To navigate
to the Game Mode (Fig. 3-2), the users can select “View Match” fromthe Game View of the hovered shot, or select a rally from the Rally
Menu. Furthermore, the user can directly hover over a shot arc in therally (Fig. 3-3) to play the video from the desired game moment. Thisfeature allows replaying a specific shot or shot sequence efficiently.
Meanwhile, users can flexibly navigate the virtual court, by using a
thumb stick or physically moving around, to obtain an accurate spatial
and temporal perception of 3D data (Fig. 3-3). Our VR environment
also offers flexible viewpoints to analyze the 3D game from different
perspectives, such as the first-person player view (Fig. 3-4).
5.5

Design Iterations

We conducted three rounds of user testing throughout the design processwith three active high-performance US badminton coaches (C1-C3;
M=3; Age: 40-60). All were former players on the US, Malaysia, andhance the usefulness and engagement of the 3D game view. Therefore,
we worked on player pose estimation after the third user testing.
During the user testing, we also elicited coaches’ feedback on the
VR environment. They agreed that VR provides additional benefits
in analyzing a match video, such as immediate access to all relevant
information, flexible viewpoints, and an interactive approach. C1 commented “it was very helpful to see the video and the bird going withthe trajectory at the same time.” C2 moved to the bottom left of thecourt while watching the 3D game view because “this is where I sit asa coach”. C3 shared that the interaction to select a shot and link to theactual match video is very helpful as “it’s important to know how theplayer put the pressure and create a situation [in the game]”.
5.6

Fig. 3: VIRD Interaction. Users directly point at visualizations to 1)
preview a hovered shot and 2) link to the entire rally. They can use athumb stick to 3) move flexibly in VR and 4) change viewing angle.
Nepal badminton national teams, respectively. They have coachingexperiences ranging from 15 to over 30 years. C1 had participated in ourformative study while C2 and C3 were newly introduced at the design
iteration stage. Given the challenges in accessing domain experts,
we adopted a progressive approach wherein each coach evaluated our
prototype at various design stages, focusing on distinct aspects. We
tested VIRD on the Women’s Single match of the 2020 BWF World
Tour Finals between Tai Tzu Ying and Carolina Marin [1].
Round 1. User flow and data analysis. The first round of testing
was conducted on the initial prototype with C1, where we elicited thecoach’s feedback on the overall analysis approach and the shot filteringfeatures. The coach appreciated the top-down approach and interactive
analysis process with immediate access to all match data and videos.
On top of the existing summary data and filters, he suggested showing
winner and error shots separately to support an immediate comparisonof the shot patterns. Further, we designed two interaction methods
to apply the shot location filter, 1) select buttons on the Shot Filter
panel and 2) physically move to the desired area on the virtual court.
However, the coach felt 1) is more useful as moving around the court
to filter shots during the analysis would be tedious and distracting.
Round 2. Interaction with the visualization and interface. The
second round of testing was run a month later with C2, with a focus
on the interaction of linking the static data to the dynamic trajectories
and videos. We showed the dynamic shot trajectory of a hovered shotarc, but to view the original rally video the user had to scroll throughand select from the Rally Menu. The coach suggested augmenting thepreview of the selected shot arc with the video, as “it takes time to findthe video part of it right now”. He also emphasized the importanceof pinpointing on the cause of the outcome during coaching. It is notenough to see where an error shot occurred in general, but to let theplayer see the shot sequence and player movement that lead to theoutcome. Therefore, we implemented a shot-to-rally interaction (Fig. 31 to 2), which allows coaches to look into a specific rally (and the cause
for errors/winners) more efficiently.
Round 3. Immersive 3D visualization. We conducted the third test
two months later with C3 on the usefulness of immersive visualizations.
The coach was able to preview each group of filtered shots (e.g., winners) efficiently and interpreted the shot types from the 3D shot arcs
to answer his coaching questions, like “What are the shots Tai used to
win?”- 1 cross drop [shot], 2 smash [shots], 1 block [shot]. He also
valued the color usage in the visualizations to tell the shot percentage
on the heat map and highlight winner and error shots. However, since
3D player poses were not implemented at the moment, we found thatcoaches still watched the video view to analyze the rally, as player
movement is critical in finding the root cause, e.g., “You hit the shot
and it was a winning rally, why? Because the opponent wasn’t there yet”
(C2). Both C2 and C3 mentioned the inclusion of player poses to en-

Implementation

The VIRD backend is implemented in Pytorch and leverages CV models [27, 31] to extract data from badminton videos. The processed
match data is subsequently rendered in real-time within the front-end’s
3D scene. The font-end of VIRD is built with Unity3D [52], including the user interface and the 3D scene. To be compatible with theMeta Quest 2 platform, we have implemented VR interactions using
the XR Interaction Toolkit [57], ensuring a natural, intuitive user experience. The VIRD interface can be accessed at our public website:
https://github.com/ticahere/VIRD-demo.
6
6.1

E VALUATION
Case Study Design

We conducted in-person case studies [25] with domain experts to evaluate VIRD on match analysis in four aspects: 1) data analysis method, 2)derived insights, 3) useful components, and 4) overall user experience.
Participants & Data. We invited two high-performance coaches fromthe user testing phase (C1 & C2; M=2; Age: 40-60) along with a US
national team player (P1; M; Age: 20-25) who had been mentored by
C1 for a decade. None of them had prior experience using VR outsideof our study. We selected three professional matches, including two
public matches (M1, M2) and one personal match provided by P1 (M3).
• M1: 2021 Denmark Open Final, MS, Momota vs. Axelson [2]• M2: 2022 BWF World Champ. QF, WS, Yamaguchi vs. Marin [3]• M3: 2022 Mexican International, R32, MS, Ma (P1) vs. Castillo
We processed the match data as described in Sec. 5.2.
Experiment Set-up. The user study took place in a 300 sq ft meeting
room, where participants used VIRD with a Meta Quest 2 headset.
VIRD was run on a PC equipped with an i7-11800H 2.30GHz processor
and an NVIDIA GeForce RTX 3060 graphics card. The Meta Quest
2 VR headset has a resolution of 1,920 x 1,832 per eye and a 90 Hz
refresh rate, connected to the PC via a 5m USB3 Type-C cable. The
VIRD view was also projected onto a 65” 4K TV screen connected to
the PC, allowing the instructor to observe the VR view.
Study Design. To evaluate how VIRD supports match analysis, eachcoach analyzed one public match for developing game strategy. C1 andC2 analyzed M1 and M2, respectively. In addition, to evaluate how
VIRD helps derive and communicate insights for coaching, both C1and P1 analyze M3 in the same session. During the study, C1 analyzed
M3 using VIRD and provided coaching advice directly to P1, whowatched C1’s interaction on a TV screen.
Procedures. We first introduced the study to the expert and obtained
their consent to participate and be recorded. They agreed to disclose
their identity in the paper. The experts first watched the match video on
the desktop for 10 minutes to familiarize themselves with the players in
the match, as they had not coached them before. Next, we introduced
key features of VIRD with a list of example tasks, such as “selectall winners by Momota in G1”, and asked the expert to explore thefeatures freely. This training step took around 10 minutes. The expert
then analyzed the assigned match for 10 minutes in think-aloud fashion.
After match analysis with VIRD, they were asked to conclude theircoaching advice. In addition, C1 performed another match analysis of
M3 and shared his advice with P1 in the study for 10 minutes. Finally,
we gathered feedback from the expert about their experience with VIRD

in a post-study survey and a follow-up interview. Each study took 60 to
75 minutes and we compensated each participant with a $50 gift card.
Measure & Data Analysis. We recorded the user interaction, voices,and VR screen records for analysis. In the post-study survey, we
collected subjective ratings on a five-point Likert Scale, including learnability, usability, usefulness of each feature, and overall satisfactionof VIRD. In the follow-up interview, experts commented on the mostuseful features, pros and cons, and suggestions for using VIRD in actual coaching. To evaluate the experts’ analysis and the insights theyobtained, we performed text analysis on audio transcripts. We labeleduser comments based on knowledge type, including prior knowledge,
analysis, or insight. We also mapped user comments to VR screen
records to extract the visualizations used in the analysis.
6.2

Case Study Results

We present the results of two case studies. Case 1 examines a coachdeveloping game strategies using VIRD. Case 2 explores a pair of coachand player communicating insights for coaching. For both cases, we
describe coaches’ findings using the match player’s last name.
6.2.1

Case 1: Developing Game Strategy in a Match

Fig. 4: Visualizations used by the coach in Case 1. (a) Stats collected
in a worksheet on the Desktop. (b) and (c) show the shot heatmap and
3D locations used to generate and present insights in VIRD.
First, he referred to the shot locations on the court (Fig. 4c) to demonstrate that Marin is barely moving the opponent. Further, based on
the heatmap showing Yamaguchi has most of her winning shots fromthe front (Fig. 4b right), he suggested “Marin should try to avoid thiscorner because Yamaguchi is creating scoring opportunities from thiscorner.” Second, he replayed a short rally and pointed out the flat shot,“Marin’s backhand serve was really flat. [Yamaguchi] didn’t have to
move at all. She scored right away”. Based on examining the shortrallies, the coach suggested that Marin should “either make the serve
higher ... or don’t use that type of serve because it’s not working.” To
explain these findings, the coach used actual video clips and spatial dataand compared patterns between players and rallies. Throughout theanalysis, the coach also used the first-person view to describe findings,
such as “I want to avoid this corner and play the other ones.”
Case 1 Summary. C2 analyzed the first half of game 1 in match M2
with Desktop and VIRD. The coach effectively verified two initialobservations from Desktop using VIRD and explained his insights
with spatial data visualizations and specific rallies. He also seamlessly
iterated between summary data and detailed game views to support his
analytic reasoning.

We demonstrate C2’s analysis workflow on M2 with both desktop and
VIRD, highlighting his analysis approach, insights, and interactions.
Desktop. During the 10-minute warm-up phase, the coach went
through M2’s first half (11 points) of game 1. Using his usual video analysis approach, the coach went through the YouTube video sequentially,
pausing or fast-forwarding to the rally, and manually recorded statistics(the number of winners, errors, and short rallies) on a spreadsheet after each rally (Fig. 4a). Using the collected stats and observations in
the video, he discovered that Marin had won 8 out of 11 points very
quickly, with 4 winners versus 5 unforced errors. Further, he observedthat Marin was playing very flat and trying to push the tempo, leading
to Yamaguchi only playing from a small area on the court.
These observations led to two coaching insights. The coach stressedthat these were initial observations that he would usually first validate
in more detail. First, Marin is playing very fast and not moving the
opponent. “[Yamaguchi]’s getting more comfortable with what Marinis doing.” The advice is that Marin needs to utilize the backcourt and 6.2.2 Case 2: Verifying and Communicating Coaching Insights
open the court more. To explain this insight, the coach pointed to the We describe how C1 verified and shared coaching advice with P1 using
mid-court areas on a court diagram. Second, Marin is pressing the VIRD and how P1, as a player, obtained analysis insights.
match and only playing flat shots. “These are world-class players. You Coach. Prior to our study, C1 had already spent 2 hours analyzing M3
can’t just do the same thing the whole time.” The coach thinks she and shared his insights with P1 virtually. He had asked P1 questions
needs to change techniques, such as varying the speed and angle of about his strategy and his opinion about the match. Based on thethe shot. Lastly, the coach commented that 5 unforced errors were a discussion, the coach followed up with match insights and statisticallittle too high for the first half of the game. Yamaguchi is not moving trends to explain his advice. In this case study, the coach used VIRD tomuch, leading Marin to waste her energy, “Marin’s going to make more explore the match data in more depth, to communicate directly with P1,
and to verify previous insights and update some original hypotheses.
mistakes in the long-term if she doesn’t change the strategy.”
The match between Castillo and Ma was won by Ma (P1) with 21-11
VIRD. During the 10-minute match analysis with VIRD, the coachused the filters to focus on each player’s winners and errors separately. (G1), 19-21 (G2), and 13-21 (G3). The coach first read the score of the
He also drilled down to specific rallies or the shot video to verify match from the Match Summary, and selected G2 with a score of 19-21
observations, and focused on spatial aspects such as shot location, won by Ma because it was a close game. He focused on 18 errors madedistribution, and shot trajectory in the analysis. To continue his analysis by Ma and examined each rally. Utilizing the virtual red shot arc toof M2 from the warm-up, he selected the first half of game 1 and immediately pinpoint where the errors were coming from in each rally,
explored the shot distribution by players and shot outcomes. He found he quickly browsed three rallies of errors and identified that Ma madethat Marin’s winners came mostly from the back and she attacked the all mistakes on defensive shots. This confirmed one of his previous
bottom right corner (Fig. 4b left), while most of Yamaguchi’s winning analysis insights, where he pointed out P1’s recovery shots gave theshots were from the front (Fig. 4b right). He also found that Marin’s opponent too many opportunities, and suggested P1 working on his
errors were pretty evenly distributed while Yamaguchi had more errors defense, “this shows we’re working on the right thing because he’sin the front. After an overview, the coach continued his analysis based making mistakes here”. To demonstrate this insight, he continued toon different hypotheses, such as wanting to see how Marin did on her select another rally and used the VR pointer to point at the red shot arc,
winners because that’s an important part of her game. He went through “you can see the mistake from this mid court when his opponent attacks.”
each of the winner rallies in detail and examined the shot locations on Based on this analysis, he asked follow-up questions to drill down tothe court. He commented “Look at all the dots on the Yamaguchi’s the root cause with the player, such as footwork issues.
court, none of them pass this white line back here”, pointing at the court
The coach analyzed the 14 winners by Ma and found that 43% ofwith the VR controller (Fig. 4c). He further focused on short rallies the winners were hit to the front left area on the heatmap. He filtered(less than 10 shots) using the Rally Menu, and observed that Marin’s the shot location to focus on the 6 winners hit to the front left. He
backhand serve was really flat, giving Yamaguchi scoring opportunities. was surprised, since P1 is not confident in their net play. He pointed
The coach was able to verify his previous insights obtained on the to the heatmap to show P1 that “your front is better than what you
desktop with concrete evidence while pointing out additional details. expected. You scored from the front the most”. He ended the analysis

6.2.3Fig. 5: Subjective ratings from experts (N=3) analyzing matches with
VIRD show high learnability, usability, and usefulness of VIRD. 3D
player posture is rated less useful due to technical limitations. Experts
were satisfied, engaged, and preferred to use VIRD for match analysis.
with a comment that he would go through each rally with the player inmore depth, e.g., “Did you score because you’re always in the front, or
was it your skill?”
Player. The case study with P1 focused on how VIRD helps players
analyze their own match in an immersive environment.
After being introduced to the features of VIRD, the player analyzed
M3 for 10 minutes. He first looked at winners and focused on comparing his shot locations and trajectories across each game. He found that
his winners came majority from the back left, “the first game was 60%
from the back left”. Further, he found almost all of his winners weregoing down or flat across three games by examining the shot arcs on thevirtual court. He then confirmed his observations with the rally video.
By comparing data across games, he found that he had more winners
in the front but not in the back in G2, but upon further investigation of
a rally in G2, “for that rally it seemed like I did win it in the front but a
lot of it was set up in the back actually”. Looking at the winner shot
analysis, he was surprised that the majority of winners came from his
backhand side, “I thought for me it was a lot easier in general to attackfrom the forehand side”. He also contemplated that he might need tofind more ways to keep the shot down as it seemed beneficial if he did
not hit it up as often.
The player continued to analyze his errors in each game and compared the shot heat map. Seeing too many errors shown in a game, hechose to split each game into half. He found that most of his errors
came from the back across all games. He moved in the virtual court tomatch his position on the court from the first-person view and pointed
at the shot arcs and heat map on the virtual court to communicate his
observations. He found his errors were mostly on the backhand side in
the second half of G2 , while the majority were on the forehand side.
He contemplated that “maybe he started changing strategy...” as G2
had a tight score (19-21) where the opponent was close to winning thematch. Upon analyzing the shots on the virtual court and previewing
them in the video view, he also found that all of the errors were very
high arcing, “the arc tells me I’m losing because of defensive shots.
It’s either because I’m hitting into the back or my shots in the front are
too high”. The player commented he would use this tool to go throughevery rally, and considered VIRD very helpful to get a sense of his
overall performance and identify areas to discuss with the coach.
Case 2 Summary. In Case 2, C1 analyzed M3 and shared coachinginsights with P1, while P1 also analyzed his own performance. The
coach used VIRD to verify one previous insight (P1 needs to improve
defensive shots) and discovered a new insight (P1 has better net shots
than he thought), and used a combination of spatial visualizations andrally videos to explain his insights to the player. The player focusedon finding patterns in his performance with spatial visualizations anddrew conclusions with two new findings about his shots, including
most winners coming from his backhand side and downwards, andmost errors coming from the back across all games.

Post-study Survey & Interview

Fig. 5 shows the average subjective ratings collected in the post-study
survey, ranging from 1 (strongly disagree) to 5 (strongly agree). Overall,
experts rated VIRD with high learnability, usability and usefulness (𝜇 ≥4.0 except 3D player posture).
Learnability. Experts found it easy to learn the features in VIRD.
Particularly, the data provided in each visual element (Match Summary,
Shot Filter, Rally Menu) were clearly understandable. The overall
top-down analysis method, and spatial data visualizations in shot trajectories and heat maps were also properly learned during training.
Usability. Experts rated the ease of use of VIRD for analyzing matches
highly in each of the analysis tasks, including getting overviews, filtering shots and rallies, and navigating across rallies. The main issues
arose in operating the VR controllers, such as clicking on the trigger
button. Some suggestions included adding an onboarding tutorial totrain users on accurately interacting with each component.
Usefulness. Experts found VIRD helpful for match analysis from finding shot patterns, verifying insights, and explaining coaching advice.
The coaches found the static data panels (Match Summary & Shot
Filter) most helpful as they provide the foundation of analysis to link
to videos and spatial data visualizations, and visually showcase data
and videos to the player. Further, experts found it helpful to have a 3D
virtual court with flexible view points, use a VR controller to selectand navigate, and view dynamic 3D shot trajectories. The interactive
approach was highlighted by both the coach and player as a benefitof VIRD, which supports an iterative analysis loop as well as linking
static data to dynamic video and shots. The player found 3D visualizations (heatmap and trajectory) very useful in finding shot patterns
and generating insights on his performance, especially being able tocompare the video with 3D game from different angles. However, 3D
player posture was considered less helpful (𝜇=2.7) as coaches found theplayers off balance occasionally due to technical limitations. Experts
mentioned they found the player positions in 3D game views helpful,and did not pay much attention to the actual posture. We observed thatcoaches were mostly interested in player movement and whether they
were in good positions when hitting the shot. According to experts,
observing slightly off-balanced player postures did not significantly
impede their ability to comprehend the 3D gameplay. In cases where it
was necessary, they would refer to video views for comparison.
User Experience. Experts felt satisfied, engaged, and prefer to use
VIRD for analyzing match videos (all 𝜇=5.0). The major advantages
for coaches were getting instant access to data, using an interactive
approach, and access to 3D visualizations, which could lead to a huge
reduction on the time to perform match analysis (less than 30 minutes
vs. 3-5 hours). For the player, the pros are having spatial data tohelp dive into one’s own strengths and weaknesses. On the downside,coaches found the shot and pose detection occasionally inaccurate.
Although they could refer to the actual video to verify, it hindered theexperience of viewing the entire game in 3D. The player felt that the
VR environment made him feel like he was in a game and he would
get distracted. As a player, he considered using this system for gamewatching instead of analyzing, since “If I’m training a lot and reallytired, then going into this [VR], of course I want to have fun”.
7
7.1

D ISCUSSION
Top-down Analysis Approach for Sports Videos

We proposed a top-down data analysis approach in our study to enhance badminton coaches’ video analysis workflow. The approach
involved providing an overview of the match and then using filters andvisualizations to narrow down the area of interest and identify specificgame moments. All experts agreed that this approach was effective insupporting coaches to generate and verify insights in the case study.
While the top-down analysis workflow is a well-established visual
analytics approach [47], it is rarely used by sports domain experts foranalyzing sports videos. Instead, sports professionals, such as scoutsand coaches, typically watch individual game videos to evaluate aplayer’s performance due to the inaccessibility of high-quality data fromvideos, and limited resources that lead to experts relying on their own
interpretation and annotation of videos. However, this approach can
result in a gap between data and context, leading to less comprehensive
and communicable insights, as found in Sec. 3.2.3.
To tackle this problem, we combined two promising avenues for
sports analytics: CV-based data collection and human-in-the-loop analysis. Our study found that experts leveraged immediate access to bothsummary data and video to perform top-down analysis, and integrate
multiple data sources to develop and communicate their insights, suchas domain knowledge, static and dynamic data. Even when automation
fails, experts can use the actual video to verify the data. As computer
vision techniques continue to improve, we envision automatic datacollection to benefit more sports domains, while human-centered design empowers experts with an effective top-down analysis approachwithout losing contextual understanding of the data.
7.2

Immersive Video Analysis for Sports Coaching

Based on our case studies, we found that an end-to-end immersive
analytic pipeline like VIRD can be suitable for sports coaching in badminton. Both coaches and the player in the studies were able to achieve
their match analysis goals throughout the entire analysis pipeline in
VR, from data exploration, insight generation to communication (using
VIRD to showcase their insights to viewers on a TV screen in our study).
Unlike immersive analytics for scientific data analysis, where several
analytic steps such as analyzing abstract data are better conducted in
traditional desktop environments [4, 20], sports data are intrinsicallyspatial and dynamic, making analyzing and presenting insights using
videos and visualizations in 3D desirable. Furthermore, analyzing
sports videos for coaching relies heavily on domain knowledge without
the need for complex data manipulation. Thus, we found immersive
analytics provide several advantages for sports coaching.
1. Situated visualization reduces context-switching costs and shortens the path from hypotheses to insights. As both coaches commented, the most beneficial feature of VIRD was the immediate accessto all the required data, “when I put the headset on I already haveinformation that I may need without even having to watch the video first”(C1). In the standard workflow on the desktop, coaches spend muchtime navigating and finding the critical game moments (e.g., whenan error happens) while tallying rallies of interest on a separate note.
This process induces high context-switching costs [55] as the coaches
need to constantly re-interpret the changed views between data in thenotes and the game moments in the video, leading to a longer analysis
cycle. With large screen space and situated visualization placed incontext (e.g., shots on the badminton court), coaches are presented withall required data in multiple views with spatial-continuous movement,
which was found to reduce context-switching costs [42, 58]. Therefore,
experts can leverage their visual working memory [42] and focus exclusively on the match analysis. This was shown in the hypothesis-driven
workflow with VIRD, where experts can plan their analysis and immediately verify their insights with data, “I’d like to see where I made a
mistake on the court. Almost all of it was on the forehand” (P1).
2. Multi-modal data analysis improves visibility of critical game
moments. An essential task in video coaching is identifying criticalgame moments to reveal root causes, “when we coach a player, wehave to pinpoint the exact cause and outcome” (C2). The ability to
breakdown a rally stroke by stroke and instantly preview each shot inthe video with embodied interaction in VR allows experts to directly
access and focus on these critical moments. One coach even suggested“it would be good if there’s a way to play a loop of all the winning shots.
Just because I’ll spend a lot of time between going into different strokes.”
Beyond individual shots, experts also dive into selected rallies in further
detail. By comparing static data and 3D visualization from the game,they can develop a more comprehensive analysis from data summary
to key moments. For instance, the coach (C1) went through all error
rallies and pointed out the player’s weakness on defensive shots as hepinpointed the location of error shots on the court with the VR pointer.
3. Immersive 3D visualizations deepen game understanding andengagement. Experts expressed excitement that they could movefreely in the virtual court and watch the game from different angles.
Further, all of them were excited seeing the moving bird and the 3D
reconstructed game. Throughout the analysis, experts felt engaged andinteractive, “I like how I can move my body around and face the shot. I
find that more beneficial than just looking at a TV screen or computer”(C1). Experts also expressed their tendency to view a match in 3D
and refer to the video only when the 3D view did not make sense. A
coach also suggested adding rackets to improve the 3D view. Using
VIRD, experts obtained deeper insights on the spatial aspects in theiranalysis. P1 observed most of his winners were hit downwards fromhis backhand side from analyzing the shot locations and trajectories.
One interesting finding was the sense of presence in VIRD. A coachused the first-person view to describe his analysis for a player in thematch, saying “these are my errors”, while the other coach moved tothe coaching position by the court and the player moved to his side of
the court to view the game from a first-person viewpoint.
7.3 Limitations & Generalizability
Limitations. Our computer vision models are around 90% and 96%
accurate in detecting shots and player poses, which causes confusion
when inaccuracy occurs. While experts can still perform match analysis
by accessing the video view in VIRD, this was mentioned as an area for
improvement by all experts. As CV techniques advance, we envision
the limitation on automatic data collection can be largely improved
with better-trained models, making our approach more reliable.
Due to the limited access to high-performance badminton experts,
such as Olympian coaches and players, our study reports the feedback
from a few domain experts. We believe the identified problem issignificant and common among badminton athletes, but our solution
might not generalize to all experts due to varying analysis approaches
and resources among coaches and players. Instead, we consider our
main contribution to be a design study exploring the use of immersive
analytics in real-world sports coaching.
Generalizability. We believe our established data preprocessing
pipeline (based on MonoTrack [31] and CLIFF [27]) and the immersive
and interactive way to analyze multi-modal game data in badminton
can be applied to general match videos and benefit the broader community beyond professional coaches, such as players at all levels, andother racket sports. With the VR benefits in visualizing spatial data andrevisiting critical game moments, we also envision expanding VIRD
beyond match analysis for leisure game viewing or broadcasting, as
noted by the player that watching the game in 3D was fun.
8 C ONCLUSIONS AND F UTURE W ORK
In this study, we introduced VIRD, an immersive badminton match
video analysis tool for high-performance coaching based on a formative
study with Olympic coaches and players. VIRD employs a top-down
analytic approach in VR with 3D reconstructed game views and multimodal data analysis. Experts successfully developed game strategies
and effectively communicated insights using VIRD in case studies,
showcasing the advantages of immersive analytics in badminton coaching. These benefits include reduced context-switching costs, enhanced
visibility of critical game moments, and a deeper understanding of andengagement with the game through situated 3D visualizations.
Promising future work includes enabling remote collaboration between coaches and players in a shared immersive VR space, addressing
the limited coaching time and insufficient support for video discussions.
Additionally, VR could be employed to simulate game scenarios for enhanced athlete training. Incorporating natural language input methods,
such as GPT, may also help minimize context-switching costs duringmatch analysis, enabling more efficient analytical iteration.
S UPPLEMENTAL M ATERIALS
Our supplemental materials, including post-study survey questions forthe case study, are available on OSF at https://osf.io/92px8. The
front-end code for VIRD’s VR interface described in Sec.5 has been
open-sourced and can be found at https://github.com/ticahere/
VIRD-demo. Our other code and video data are the intellectual propertyof Adobe Research and Badminton World Federation.

ACKNOWLEDGMENTS
This work is supported by Adobe Research, NSF grants III-2107328 and
IIS-1901030. We are grateful for the invaluable support of Andy Chong,
Daphne Chang, Kai-Hsin Chang, Justin Ma, Hsiao-Ma Pai, Pashupati
Paneru, Raju Rai, Rena Wang, Paul Liu, and Toby Ng. Their dedication
to advancing badminton has significantly inspired and contributed tothis project. We thank the reviewers for their valuable comments.
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