Department: People in Practice
Editors: Melanie Tory, m.tory@northeastern.edu and Dan Keefe, dfk@umn.edu

The Ball is in Our Court:
Conducting Visualization
Research with Sports Experts
Tica Lin
Harvard University

Zhutian Chen
Harvard University

Johanna Beyer
Harvard University

Yingcai Wu
Zhejiang University

Hanspeter Pfister
Harvard University

Yalong Yang
Virginia Tech

Abstract—Most sports visualizations rely on a combination of spatial, highly temporal, anduser-centric data, making sports a challenging target for visualization. Emerging technologies,
such as augmented and mixed reality (AR/XR), have brought exciting opportunities along withnew challenges for sports visualization. We share our experience working with sports domain
experts and present lessons learned from conducting visualization research in SportsXR. In ourprevious work, we have targeted different types of users in sports, including athletes, gameanalysts, and fans. Each user group has unique design constraints and requirements, such as
obtaining real-time visual feedback in training, automating the low-level video analysis workflow,or personalizing embedded visualizations for live game data analysis. In this paper, we
synthesize our best practices and pitfalls we identified while working on SportsXR. We highlightlessons learned in working with sports domain experts in designing and evaluating sports
visualizations and in working with emerging AR/XR technologies. We envision that sports
visualization research will benefit the larger visualization community through its uniquechallenges and opportunities for immersive and situated analytics.

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DATA analytics in sports can be a gamechanger, as it enables its stakeholders to makeclear-sighted decisions based on objective evidence [1]. One of the most successful stories isthe MLB Moneyball, where Oakland Athletics
General Manager Billy Beane, with a limited budget, used sabermetrics (i.e., a specific statisticalanalysis method in baseball) to measure player
performance. He then used discovered insights
to draft undervalued players and subsequently
won games against rich teams like the New YorkYankees. In modern sports, data-driven decisionmaking has become ubiquitous in most processes,
such as player recruitment, performance evaluation, training, and tactic analysis [2].
However, data-driven decision making andsports data analysis is still often difficult toaccess for sports professionals. Imagine a college basketball player struggling to make free
throws. The coaches tell them to improve theirshooting trajectories, but that is hard to achieve
when athletes are mostly practicing on their own.
How could athletes get immediate and data-driven
feedback directly on the court? Moreover, consider a basketball fan watching a game starring
their favorite player. It is the 2nd quarter and theplayer is not performing well. The fan is getting
worried and pulls up their phone to check theplayer’s recent performance, but it is hard to find.
Being distracted, the fan misses an all-star playof their favorite athlete, easily the best moment of
the game. How could this game-watching experience have benefited from easily accessible databased insights presented situated right next to theplayer? Lastly, consider a data analyst working
for a basketball team. After collecting and analyzing spatio-temporal data for days, they have foundsome exciting insights. However, when presenting
statistics and abstract graphs, the coaches have a
hard time understanding the point and dismiss theinsights. How could this data have been presented
closer to its spatial and temporal context, to makeinsights easier to understand?
What do these three scenarios have in common? First, they are all based on real stories
from sports practitioners we have worked with.
Second, the visualizations in these three scenarios
all come with a set of unique constraints inspace, time, and user that make their adoption

2© 2019 IEEE

difficult. These constraints might not be present
in all sports applications, however, when they
are, conventional visualization solutions may nolonger work. This is mainly because conventional
sports analytics is often performed off-line andoff the court. As a result, users cannot access thevisualization at their preferred location and time,
forcing users to change their workflow or making
some application scenarios impossible.
On the positive side, sports visualization iscurrently reaching a pivotal moment in time.
Novel sensors and data collection techniques andrecent progress in human-computer interaction
and mixed reality offer unprecedented possibilities for modern sports visualizations. For example, state-of-the-art computer vision (CV) technologies provide real-time data collection in the
court, allowing applications that would not be
possible without access to such data; the next
generation of display and interaction devices (i.e.,
virtual and augmented reality or VR/AR) arecapable of projecting an unlimited number of interactive 2D and 3D graphics into any space, andbreaking the boundary between the physical anddigital world [3]. With these advances in technology, basketball players who want to improve theirshooting can obtain objective feedback throughCV and AR techniques in real-time [4]; fans
who want to access external information during aplay can see the data embedded in the court [5];and analysts who want to better explain theirinsights can create situated sports videos for more
effective communication [6].
The purpose of this paper is to discuss theopportunities and challenges in modern sports
visualization (or SportsXR [3]) research. We firstelaborate the spatial, temporal, and user constraints to highlight the uniqueness of modern
sports visualizations. We then share three projects
we performed with different sports user groups(see Fig. 1) and finally discuss the lessons learned
and potential future challenges we see from theseprojects.

WHAT MAKES SPORTS DIFFERENT?
Sports data, as well as sports domain users,
have some unique traits that need to be consideredwhen designing sports visualizations.
Spatial constraints. Most sports data are by
nature spatial, either containing spatial coordi-

Published by the IEEE Computer Society

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(1) Basketball AR
Target user
and workﬂow(2) VisCommentator(3) Omnioculars

Athletes

Analysts

Fans

Motor-skill training

Analytic video creation

In-game data analysis

User Goal

Evaluate and improve on
performance in training

Deliver analytic results invideos effectively

Consume game data toenhance game engagement

User Needs

Obtain immediate &precise feedback toanalyze performance

Extract and integrate data
into video authoringprocess

Access and analyze data
in-game without being
distracted from the gameUser
Research
Methods

Initial interviews
On-site ﬁeld study
Follow-up interviews

Initial Interviews
Weekly meetings for a year

Online survey
Follow-up interviews
Real game walkthrough

Data Source

Shot tracking data by our
tracking system

Data extracted from sports
videos

NBA player tracking & gamestats

Visualization

Real-time shot feedback withsituated AR visualization

Data-driven augmented
video authoring toolGame viewing system withembedded visualization

Evaluation

Performance measurement
Subjective user feedback

Case study
Expert Interview

Case study
Subjective user feedback

Figure 1. Three exemplary sports visualization projects we discuss in Sec 2. Each project targets a different
user group and workflow, including athletes’ motor-skill training, analysts’ video creation, and fans’ in-gamedata analysis. Different user goals and needs lead to tailored research and design approaches.

nates, such as the position of the basketball andplayers, or by being linked to physical objects
or spaces [7]. For example, shooting percentagesof a basketball player are linked to specific areas
on the basketball court. Moreover, sports data are
typically dynamic [8], with most entities (e.g., theplayers) moving all the time, making it challenging to design visualizations that allow viewers toeasily track these changes.
Temporal constraints. Most sports are fastpaced, creating constant streams of time-sensitive
data, such as updated player positions or velocities. Sports visualizations have to be able
to support these time-sensitive data, to allow
viewers to follow the temporal aspect of the
sport. This requires visualizations to run at realtime on high-throughput data streams. At thesame time, however, sports visualizations must
limit the mental load on viewers, to allow themto digest the presented information in a limited
time. Additionally, sports visualizations should
not distract users from their primary tasks, whichmight be coaching a team, watching a game on
TV, or practicing free shots. Instead of competingfor their attention, sports visualization needs to
be integrated into the user’s workflow to support
real-time data analysis.

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User constraints. Common stakeholders in sports
include athletes, coaches, analysts, fans, and journalists, and less common roles can be managers,
sports doctors, and more. The diverse requirements of different user groups lead to completely different sports visualization designs. For
ATHLETES, visualizations should enable them toconnect the data back to their physical skills [4](e.g., to improve their technique or tactics). For
COACHES , visualizations should be deployed on
mobile devices, to be looked at while being
close to their athletes, next to the court, rather
than in front of an office computer. A NALYSTS
consider both the exploratory and explanatory
aspects of sports data and need to be able toquickly and easily create different visualizations
based on their specific purposes [6]. The wide
variety of FANS, from novices to die-hard fans,adds even more challenges to the design of visualizations [5]. Finally, JOURNALISTS leveragevisualizations to perform in-depth sports data
exploration and communicate the generated data
story to their readers [9]. These user groups
vary widely in their visualization literacy andanalytical background, but also in their motivationand the data they are interested in. Therefore, itis essential that visualization tools are customized

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People in Practiceto specific user groups, their data, and the goals
they are trying to achieve.
Summary. The spatial and temporal constraints
are often interconnected, and modern sports visualization has the potential to address them together. For example, creating visualizations situated in physical space allows viewers to consume
spatially-embedded sports data faster and withless cognitive load. Other domains, outside ofsport, share similar challenges and could benefit from SportsXR research, such as visualizing
spatio-temporal data. We also foresee that applying existing techniques from computer graphics and computer vision, like optimizing camera
placement and rendering, can advance sports visualization in certain scenarios. Some challenges
have bigger implications but have been underexplored, such as the limited amount of time forusers to perceive visual information, which has
only been discussed preliminarily [3], [8].

SPORTSXR RESEARCH
In the following, we summarize three sports
visualization projects that we have conductedwith athletes, analysts, and fans, respectively (seeFig. 1). For each project, we summarize our initialuser research, identified goals & tasks, design
considerations, and evaluation.
Basketball AR: Motor-skill training for athletes

The goal of this project was to provide realtime visual feedback for basketball players practicing free-throw shooting. Through close collaboration with college basketball teams, we prioritized a minimal visual design and fast andaccurate feedback to help improve players’ motor
skills [4].
Interviewing athletes to identify the gap inshot training. Through user interviews and inperson field studies with players and coaches, wefound players desire precise quantitative feedbackon their shots, which they typically do not get.
They also have no way to precisely specify goals
or evaluate their outcome in an actionable way,i.e., players cannot easily evaluate the shot angleof their free throws. Therefore, players werehighly interested in accessing visual feedback
during shot training.
We first characterized user goals and tasks
to form a set of visualization design require-

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Figure 2. 2D and AR free-throw shot visualizations.

ments. Basketball players wanted to have realtime quantitative feedback to increase shot consistency and refine their shot arc. We then identified four tasks a visualization system for freethrow training needs to support: T1) analyzing thetarget shooting arc before a shot, T2) analyzing
one’s shooting arc during and after each throw
immediately, T3) comparing one’s shooting arc
to the target arc, and T4) adjusting one’s target
arc to get consistently closer to an ideal arc.
Real-time visual feedback through colocated and situated visualization. To fulfill theaforementioned design requirements, the visualization needs to support the three-dimensional
reasoning of the shot, be located closely andeasily accessible to the user. We implemented ashot tracking system to detect shot metrics in realtime and designed two visualization modalities. A
co-located 2D visualization (Fig. 2a) presents theshot arc from a third-person view on a co-locatedmonitor placed next to the shooter. A situated AR
3D visualization (Fig. 2b) presents the shot arc
from a first-person view through an AR headset.
Both visualizations present information about theshooter’s target and actual shot arcs with different
presentation dimensions (2D vs. 3D) and viewing
perspectives (third-person vs. first-person).
Comparing the usefulness of 2D and AR
visualizations for free-throw training. Our evaluation focused on characterizing unique aspectsof applying our novel AR visualization to freethrow training. We conducted a comparative studyof free-throw shot training with the 2D and AR
visualizations with ten basketball players overfour days each. We collected both quantitativeand qualitative measures, such as shot metrics
and user feedback. Our results suggest that the
2D and AR visualizations were considered usefulfor free-throw training, and users significantly

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Data Visualizationd

Raw Footage

Augmented Video

Figure 3. Augmented sports videos embed visualizations into videos to present data.

improved their shot angle consistency throughout
the study. More interestingly, the AR visualization promotes an increased focus on body form
as users prioritized “improve the shooting form”
as their top training goal, as opposed to “improve
shot accuracy” with the 2D visualization.
VisCommentator: Augmented sports video
creation for analysts

In this project, we focused on enabling analysts to create augmented sports videos in adata-driven workflow (see Fig. 3) rather than acompletely manual annotation process [6].
A Formative Study to Understand Constituents of Augmented Sports Videos. Augmented sports videos help in communicating analytical insights. However, creating augmented
sports videos is often difficult, involving complex
design decisions and video editing. To understandthe design practices of augmented sports videos,
we collected and analyzed 233 videos from TV,
teams, and leagues. Based on our analysis, we
propose a design space that characterizes augmented sports videos at element- and clip-levels
with four design dimensions (i.e., Data Type,
Visual Type, Data Level, and Narrative Order).
A Semi-Automated System to Create Augmented Sports Videos. Informed by the design
space and the close collaboration with experts,
we developed VisCommentator, a system thatallows sports analysts to augment table tennis
videos efficiently by selecting the data to visualize instead of manually drawing graphical marks.
Given a raw table tennis video, VisCommentator
first extracts sports data (e.g., players and ballpositions, ball events) using a set of machinelearning models. The extracted data are grouped
together based on their semantic levels. Next, VisCommentator uses the extracted data to decorate
the video objects and the video timeline, so that

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Video
Editing

users can directly interact with the video objects
and events to select the data to visualize. Finally,
VisCommentator suggests visual effects for theuser-selected data based on our design space andrenders the visual effects into the video.
Expert evaluation. To evaluate the usabilityof VisCommentator, we conducted a qualitative
user study with seven sports analysts. We tasked
participants with reproducing target augmented
videos and recorded the completion time and success rate for each task, and subjective feedback.
All participants could successfully reproduce theaugmented videos.
Omnioculars: In-game data analysis for fans

In this project, we focused on improving theexperience of sports fans in live game watching
scenarios by embedding visualizations into basketball games.
We investigated the design space of embedding visualizations into live game videos anddeveloped an interactive game-viewing prototype with personalized embedded visualizations
to support fans’ in-game data analysis [5].
Characterizing basketball fans’ data needs
during a live game. We first deployed an online survey to collect fans’ data needs in a live
game and interviewed six fans about their gameviewing experiences. We found that fans seek
data in various contexts during games, which
we characterized into three components, including S CENARIOS (when fans desire to look fordata), DATA (what in-game data fans look up),and TASKS (why fans look for data). Based on
this framework, we derived design requirements
under specific game contexts. All interviewees
confirmed their habits of looking up game stats
on separate screens during live games, which
introduces context switching and reduces gameengagement.
Design exploration of embedded visualizations in sports games. To present contextual
data with embedded visualization in game videos,
we designed an interactive game viewing system,
Omnioculars. Based on the design feedback fromfans, we prioritized five game contexts to drive
our embedded visualization designs, including
shooting, offense, defense, player performance,
and team performance (Fig. 4).
Using embedded visualizations for in-game

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People in Practice

Figure 4. Embedded visualizations for five selected game contexts, including (a) shooting performance, (b)offense player trajectories, (c) defenders’ movement, (d) player performance, and (e) team performance.

data analysis. To evaluate our system for in-gamedata analysis, we conducted a user study withsimulated basketball game clips in two parts. The
participants evaluated each of the five embedded
visualizations and then freely combined different
visualizations to derive game insights.
Participants considered all five embedded visualizations useful and novel. Moreover, participants used different interaction approaches basedon their game focus and preferences (i.e., configuring a fixed set of visualizations ahead oftime or altering visualizations on-the-fly based oncontext). They could derive distinct game insights
with their chosen embedded visualizations. Overall, they found Omnioculars helpful and fun touse, felt in control of their experience, and werelikely to use it in live games.

LESSONS LEARNED
Theme 1: Working with Sports Experts

The entry barrier is high due to the required
domain knowledge, seasonality of sports leagues,
and limited data availability.
Without personal connections and a deep understanding of the sport, it is difficult to findcollaborators and keep them engaged. For ourbasketball AR project, the collaboration withthe varsity teams only started because the leadauthor had experience working with a professional basketball team and was introduced to thecoach through a mutual connection. Similarly,
the augmented video project was only initialized
because of the researcher’s prior experience withtable tennis visual analytics systems. At the very
least, coaches, analysts, or fans usually expect
the researchers to have a good understanding
about the sport, such as the rules of the game6

and the composition of the professional league
or collegiate tournament. For example, when interviewing NBA fans, they often refer to specificplayers or teams and technical terms (e.g. pickand-roll, floater) to describe their game-viewing
experiences. It is crucial that researchers show
understanding and enthusiasm to encourage theconversation and extract more insights from theinterviewees.
In addition, sports leagues are seasonal andfollow strict timelines. Researchers need to beaware of the constrained availability of experts
when planning project phases. For example, in thebasketball AR project, the varsity teams were able
to support us throughout the design phases, butthey were competing in a tournament at the later
phase. Therefore, we had to find other intramural
teams to support our user testing. We planned
our Omnioculars project to align with the NBA
season to increase our chances to find passionate
fans and keep them in the loop throughout theentire project.
Lastly, access to sports data is usually proprietary to the team or league. Since the natureof sports is competitive, teams and athletes try to
get advantages over others through data collection
and analysis. Therefore, sharing data is usually
not encouraged or even impossible. For example,
all teams we collaborated with explicitly asked us
not to publicize their data and process. Similarly,
when designing embedded visualizations for basketball game viewing, we could not obtain upto-date player tracking data from the NBA andhad to use previously collected game data. A
workaround could be to build our own camera
tracking system and train computer vision models to extract data. However, this would require

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expertise in CV and considerable resources.
Educating sports experts to use new techniques is hard since some experts are reluctant
to try alternatives while others may overestimate
the capabilities of emerging techniques.
Oftentimes, sports experts have wellestablished workflows and are reluctant toconsider alternatives. In VisCommentator, eventhough our proposed authoring system cansupport fast prototyping of augmented videos,some analysts still preferred using traditional
court diagrams. To this end, we sought tofind motivating factors in the target users. For
example, collegiate teams usually are open tocollaborating with school research labs because
innovation appeals to student athletes, which
may also enhance their recruitment process.
On the other hand, sports experts may overestimate the capabilities of emerging technologies that are still in an evolving stage but are
advertised as omnipotent (e.g., universal artificial
intelligence). In the AR free-shot project, forexample, some experts were disappointed as theyfound that the HoloLens1 was far from matureand thus provided biased negative feedback forthe visualizations in the user study. It is crucial
to properly manage experts’ expectations of theemerging techniques to obtain unbiased and useful feedback.
Theme 2: Evaluating Immersive Visualizations

Identifying the benefits of visualizationsin the analytic workflow. A key consideration
during the design process is to distinguish between the added values from the data versus
visualization designs. When presented with anovel analytic system, sports experts may findthe system useful because of the data they wereable to obtain. To identify the design values,
it is important to evaluate them explicitly. In
our free-shot project, we present the same shot
tracking data in the 2D and AR visualizations.
The direct comparison between different visual
representations allowed us to separate the benefit
of immersive visualization, leading to more generalizable design guidelines.
Using Wizard-of-Oz user testing allows us
to focus on the visualization research questions. Considering the high implementation costsof AR, we advocate for developing a subset of tar-

May/June 2019

get scenarios and using the Wizard-of-Oz method
for evaluation. In the VisCommentator project,
we focused on a selected game video instead oftrying to implement a complete solution for all
game videos. On the other hand, in the Omnioculars project, we aimed to evaluate how people use
embedded visualizations to analyze a basketballgame. Instead of tackling CV problems beyond
our expertise, we designed a simulated game environment with 3D models and manually craftedplayer animation for selected game scenarios. We
also used a Wizard-of-Oz method to evaluate
how users interact with different visualizations
through verbal commands. In both cases, we haveobtained valuable insights from users interactingwith our visualization tools under realistic use
cases without being blocked by implementation
details.
Evaluating sports visualizations requiresconsideration for individual user differences
and contexts. It is often insufficient to evaluate
sports visualizations using general quantitative
measurement with simplified tasks. One mitigation is to use qualitative methods across usergroups, contexts, and times. For example, we
evaluated Omnioculars with novice and hardcore fans and recorded their interaction patterns.
We also evaluated visualizations under different
game contexts, such as under shooting or clutch
time scenarios. In AR basketball training, wecollected user feedback at the beginning, during,
and after training. This allowed us to evaluate theusefulness of visualizations at a finer granularity
with considerations for various factors on top of
the standard quantitative measures.
Evaluating skill transfer and long-term
adoption are still lacking in sports visualizations. As much as we are excited about the new
SportsXR solutions, it is difficult to convince
sports experts to change their workflow without
evidence for long-term improvement and adoption. Long-term evaluations of visual analysis
tools for sports are still lacking. However, to havea meaningful impact on the sports domain, it
is necessary to show evidence of skill transfer
through longitudinal evaluation. We thus envision
and advocate for sports visualization research toexpand its impact through collaborations withother research areas, such as Kinesiology andSports Sciences.

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People in Practice

CONCLUSIONS
Exploring new possibilities in sports visualization with modern computing and display technologies is extremely exciting, especially whenseeing its impact on sports practitioners. However, designing SportsXR applications is not atrivial task. Sports is a data-rich scenario for visualization that comes with many specific design
considerations and constraints. Although there
are some inspiring discussions and preliminary
studies [7], [8], [10], fundamentally, it is still
unclear how existing visualization principles canbest be applied to modern sports visualizations,and there is a lack of established guidelines.
We need to invest more in understanding human factors in sports visualizations and closelycollaborate with other communities (e.g., CV,
ML, NLP) to improve the current workflows
in sports. Furthermore, we envision that withvisualization becoming ubiquitous in space andtime, there will be more and more scenarios thatshare similar challenges as we have discussed
here for sports. Ultimately, research in different
applications can complement empirical knowledge, guidelines, and techniques from different
perspectives, forming a complete ecosystem for
future visualization applications.

5. Tica Lin, Zhutian Chen, Yalong Yang, Daniele Chiappalupi, Johanna Beyer, and Hanspeter Pfister.

The

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6. Zhutian Chen, Shuainan Ye, Xiangtong Chu, Haijun
Xia, Hui Zhang, Huamin Qu, and Yingcai Wu. Augmenting Sports Videos with VisCommentator.

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Embedded data representations.

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agenda and two evaluations.

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ACKNOWLEDGMENT
This work was partially supported by NSF
grants III-2107328 and IIS-1901030.

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Tica Lin is a Ph.D. student at the Visual Computing Group at Harvard University. Contact her at
mlin@g.harvard.edu.
Zhutian Chen is a postdoc at the Visual Computing Group at Harvard University. Contact him atztchen@seas.harvard.edu.
Johanna Beyer is a research scientist at the VisualComputing Group at Harvard University. Contact her
at jbeyer@g.harvard.edu.
Yingcai Wu is a Professor at the State Key Lab
of CAD&CG, Zhejiang University. Contact him at
ycwu@zju.edu.cn.

Pfister. SportsXR – Immersive Analytics in Sports. In
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Hanspeter Pfister is the Academic Dean of Computational Sciences and Engineering and An Wang
Professor of Computer Science at Harvard University.
Contact him at pfister@g.harvard.edu.
Yalong Yang is an Assistant Professor in the Department of Computer Science at Virginia Tech. Contact
him at yalongyang@vt.edu.

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