Sportify: Question Answering with Embedded Visualizations andPersonified Narratives for Sports Video

arXiv:2408.05123v1 [cs.HC] 9 Aug 2024

Chunggi Lee, Tica Lin, Hanspeter Pfister, and Chen Zhu-Tian

Fig. 1: Sportify explains tactic questions in each clip for everyone, aiming to engage users and foster a love for sports. We integrate
embedded visualization and personified narratives generated by large language model (LLM) to elucidate a complex series of actions
through action detection, tactic classifier, and LLM pipelines.
Abstract—As basketball’s popularity surges, fans often find themselves confused and overwhelmed by the rapid game pace andcomplexity. Basketball tactics, involving a complex series of actions, require substantial knowledge to be fully understood. This
complexity leads to a need for additional information and explanation, which can distract fans from the game. To tackle these challenges,
we present Sportify, a Visual Question Answering system that integrates narratives and embedded visualization for demystifying
basketball tactical questions, aiding fans in understanding various game aspects. We propose three novel action visualizations (i.e.,
Pass, Cut, and Screen) to demonstrate critical action sequences. To explain the reasoning and logic behind players’ actions, weleverage a large-language model (LLM) to generate narratives. We adopt a storytelling approach for complex scenarios from both first
and third-person perspectives, integrating action visualizations. We evaluated Sportify with basketball fans to investigate its impact
on understanding of tactics, and how different personal perspectives of narratives impact the understanding of complex tactic withaction visualizations. Our evaluation with basketball fans demonstrates Sportify’s capability to deepen tactical insights and amplify
the viewing experience. Furthermore, third-person narration assists people in getting in-depth game explanations while first-person
narration enhances fans’ game engagement.
Index Terms—Embedded Visualization, Narrative and storytelling, Basketball tactic, Question-answering (QA) system

1

I NTRODUCTION

Basketball attracts 400 millions fans worldwide [5], with the NBA
Finals alone drawing a peak audience of 17 million [1]. Despitewidespread interest in basketball, the rapid pace and intricate dynamics
of the basketball plays often leave fans confused and eager for a deeper
understanding of the game [31, 66]. Commentary, while helpful, often lacks in providing the abundance of information fans desire, fromplayer performance metrics to complex tactical decisions. Particularly,
understanding the tactics represents a significant but challenging task.
It involves a series of actions—screening, passing, cutting, and shooting—that requires significant knowledge to be fully understood [47].
These tactics are crucial in maximizing scoring opportunities, fromsingle plays to team-wide tactics [26, 35, 48]. Players constantly makesplit-second decisions to either take the shot or distribute the ball, optimizing their team’s offensive tactics [49]. A deeper comprehensionof these tactics not only enhances the watching experience but also

• Chunggi Lee, Tica Lin, and Hanspeter Pfister are with John A. Paulson
School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA. E-mail: chunggi_lee, mlin, pfister@g.harvard.edu• Chen Zhu-Tian is with CSE department, University of Minnesota,
Minneapolis, MN. E-mail: ztchen@umn.edu. The work was partially done
when he was with Harvard.
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
obtaining reprints of this article, please send e-mail to: reprints@ieee.org.
Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx

deepens fans’ engagement of the game.
Recent efforts in bridging the knowledge gap for fans have embraced
the concept of embedded visualizations [58]. These visualizations enrich the watching experience by seamlessly integrating data insights
within the physical context of the game. Therefore, embedded visualizations have been widely used by both commercial [4, 9, 12] andresearch systems [31, 66–68] to create augmented sports videos. Current augmented sports video products and systems are innovative but
limited to deploying predefined visualizations, offering a fixed set of
insights for the game’s inherently dynamic character [31, 66]. Notably,
these tools lack interactive features that would allow fans to investigate
the game’s tactical dimensions, a limitation that restricts personalized
engagement and understanding of complex tactics [31]. This reveals
the requirement for advanced game watching systems that enable fansto dynamically explore and query game tactics.
In this paper, we develop a novel Visual Question Answering (VQA)
system, Sportify (Figure 1), which integrates embedded visualizationand personified narratives to explain basketball tactics. As a VQA
system, Sportify focuses on understanding and answering questions
about input images, rather than generating visual answers [20]. The
design of Sportify is guided by three design considerations thereby
enhancing the explanations it provides: 1) video-based tactic explanations, 2) narrative tactic explanations, and 3) embedded visual tactic
explanations. Together, these design considerations are crafted to ensure that the generated tactic explanations are reliable, understandable,
and engaging for the users.
To answer questions about tactics, Sportify leverages a machinelearning pipeline to identify four actions (i.e., PASS, C UT, S CREEN,

and S HOOT), which are critical to understanding basketball tactics, byleveraging the on-court positions of the players. A K-Nearest Neighbors(KNN) algorithm classifies the tactics based on the players’ coordinates.
The results are subsequently used to generate answers about the logic
behind players’ actions by using a large language model (LLM). To
present the answers, we propose three new action visualizations forthree key actions (i.e., PASS, C UT, and S CREEN) and two personified
narratives (i.e., first-person and third-person perspectives), explaining
the tactics in a storytelling format. By this, Sportify transforms thepassive viewing into an active exploration experiences, offering a deeper
understanding and engagement with the game.
We conducted two user studies (i.e., a comparative study and a
exploratory study) with basketball fans to investigate the impact onunderstanding of tactics and in-game decisions. We compare three different conditions and two narrative perspective to explore their impacts
on the comprehension of basketball tactics. The results demonstrate thatSportify helps users understand tactics and in-game decisions better,and enhances user engagement and experience compared to existing tactic explanation videos. Furthermore, third-person perspective narration
assists people in getting in-depth game explanations while first-person
narration enhances fans’ game fan and engagement.
In summary, our contributions are as follow: 1) the implementationand design of a novel VQA system for answering tactics and in-gamedecisions, 2) three novel visualizations such as Cut, Screen, and Pass,
integrating the personified narratives (i.e., first or third-person perspective) to provide a storytelling experience that enhances the engagement
for fans, and 3) two user studies that evaluate three different conditions
with the two personified perspectives texts, and the usability of Sportify
comparing to existing tactic explanation videos.
2 R ELATED W ORK
2.1 Visual Analytics in Sports
Sports data are intrinsically spatial and dynamic. To support analyzing complex sports data for enhancing game understanding andperformance evaluation, visualization researchers have developed novel
visualization techniques for different sports data and target users.
In particular, basketball has inspired a plethora of novel visualization designs with its intricate interplay among team members and thedetailed, organized spatiotemporal data. Targeting sports analysts,
BKViz [33] designed an interactive visual analytic system for analyzing individual player performance and team dynamics in a basketballgame. Users can analyze heterogeneous data with novel visualizations
to support finding patterns and correlations between player actions andperformance, such as play-by-play data on a court diagram and playerinteraction in an arc diagram. OBTracker [59] focused on evaluating thecontribution of the player’s off-ball movement and presented the player
action type and performance in a novel glyph and Voronoi diagram
design. HoopInSight [22] compared players’ shooting performances using side-by-side shot heat maps and aggregated spatial data presented as
location-based glyphs. Other popular sports also attract much attention
in the visualization community, including soccer [40, 50], baseball [21],table tennis [56, 57], and badminton [17, 30]. These studies contributenovel visualization approaches to enhance spatiotemporal data analysis
and communications in their respective sports.
Targeting non-data experts, some work focused on novel interaction
and visualization techniques to support seamless analytic workflow.
To support analyzing data during live game viewing, GameViews [64]presents box score views and a game flow chart with key events, alongwith a chat feature for basketball fans to analyze and discuss gameinsights live. GameBot [65] proposed using the conversational interface for fans to retrieve game-related data during live basketballgames instantly and designed mobile visualizations to enhance data
understanding. Lin et al. [31] proposed an embedded visualization
framework for analyzing game data within the game context in thebasketball game view without the need to switch contexts. More recently, immersive technologies were used to enhance interactive data
analysis for coaches and players in racket sports. TIVEE [17] designed
an interactive VR interface with embodied interaction to allow analyzing badminton trajectory data in an overview of small multiples

and a live-sized badminton court view. VIRD [30] further developed
a 3D reconstructed game view based on 2D badminton game videos
to support deeper insight analysis for high-performance coaching. Our
study builds upon the rich work in sports visualizations and develops novel action-based visualizations for game tactics and in-gamedecision-making in sports videos.
2.2

Embedded Visualization in Sports Videos

With advanced computer vision techniques, recent research focused on
designing visualizations that are directly embedded into sports videos
to enhance game analysis of dynamic sports movement.
Stein et al. [50] developed a visual analytic system that combines
soccer game videos with trajectory visualizations, applying computer
vision methods to derive trajectory measures from the video inputs.
Their results show that this embedded visualization method enables
expert analysts to perform effective contextualized analysis on teamperformance. Zhu-Tian et al. [68] proposed a direct manipulation
user interface to allow a direct link of the game data to a selected
player and contributed a design framework for embedding visual elements with video effects into sports videos, which supports presenting
data insights in the sports videos effectively. Lin et al. [31] further
tackled the problem of dynamic data requirements throughout sports
games by proposing a context-driven embedded visualization framework for live game analysis of sports fans. Yao et al. [60] examined thechallenges and design considerations for embedded visualizations inswimming videos from the designer’s perspective and found motion
context has an impact on the visualization design choices. Zhu-Tian etal. [66] designed gaze interaction to moderate the visualizations shownin basketball videos to avoid visual clutter and enhance fans’ gameunderstanding with adaptive visualizations.
Based on the prior research, we identified two gaps in utilizing
embedded visualizations within sports videos to enhance fans’ gamecomprehension. First, existing studies predominantly focus on presenting metadata and game statistics (such as athlete names and speedsin [60]) and spatial information (like trajectories and zones in [50]).
Aspects that involve more complex data, like game tactics and ingame decision-making, were only preliminary explored by Zhu-Tianet al. [68], which did not target sports fans. Second, there is a lackof customized interactions to support users in retrieving data and analyzing game context during the game. Present approaches tend tooffer limited engagement options, such as passive viewing (e.g., gazein [66]) or basic voice commands [31], which fall short of meeting fans’
expectations and allowing more complex data to be explored. Our study
addresses this gap by proposing a visual question-answering systemthat allows fans to explore complex game tactics within sports videos
through active conversation and embedded visualizations tailored foraction-rich game contexts.
2.3

Visual Storytelling for Spatiotemporal Data

Visual storytelling, especially in the context of spatiotemporal data, has
proven to be an effective tactic for communicating complex data [45].
Research grounded in cognitive science has demonstrated that integrating visual and verbal elements enables the construction of a cohesive
mental model of a narrative, thereby enhancing the comprehension ofcomplex data [43]. Mayr et al. [34] investigated the organization of
temporal and spatial information in supporting narrative comprehension
and identified five hybrid visualization techniques, including multiple
coordinated views [41], animations [28], layer superimposition [25],layer juxtaposition (or data comics [63]), and space-time cube [27]. As
each technique has strengths and limitations in conveying narrative data,
it is important to make careful design choices to assist users’ internal
representation in linking this multimodal information.
Drawing upon this visual storytelling framework to convey complex tactics and decisions in sports, two areas of research are of interest: visual representation of spatiotemporal data and verbal narrativetechniques. Several hybrid visualization techniques were adopted for
dynamic sports data, including multiple coordinated views [64], layer
superimposition [40], and animations [68]. SoccerStories [40] visualizes the sequence of actions in a soccer play with trajectories and linked

Fig. 2: The pipeline efficiently addresses both tactical and performance-based questions. It begins with data processing (A-1), where videos undergo
a computer vision (CV) pipeline to identify players’ coordinates, bounding boxes, and the ball’s location. This information feeds into action detection(A-2) and tactic classification (A-3), generating tactical textual information for the narrative agent (B-1). Player coordinates and LLM responses
are visually embedded (C) and displayed in the video (D). Performance-related queries are handled by the LLM, which retrieves data to provide
text-based answers (D).

faceted views on a court diagram. They further proposed a small multiple technique that embeds these diagrams in sports articles to support
journalists in communicating data insights in stories. VisCommentator [68] proposed a framework for embedding animated visualizationsin sports videos, supporting the creation of data videos with narrative
structures like linear and flashback.
In addition to organizing spatial and temporal data views, it is important to explain causality in the sequence of actions for sports gametactics. As shown by Choudhry et al. [16], natural language narratives
can complement visualizations when explaining complex causality in
network data. Despite different data types, the causal sequences in
sports actions share similarities. Zhu-Tian et al. [67] integrated natural
language commentary with visual animation in racket sports videos,
allowing coupling narratives with animated embedded visualizations
to explain game actions in more detail. Building upon the existing
work, our work explores using a natural language approach to construct
data storytelling for complex sports tactics in basketball. Our novelty
lies in adopting LLM-based question-answering techniques to create
textual narratives, coupled with animated embedded visualizations in
sports videos to create personalized visual storytelling. We also explore
how different narrative perspectives, including first- and third-person,
impact data comprehension and engagement. To the best of our knowledge, this is the first initiative to blend LLM-generated text narratives
with question-answering for sports visual storytelling.
3 D ESIGNING S PORTIFY
This section first describes the design considerations behind Sportify
and then overviews its three major components.
3.1 Design Considerations
Previous works [31, 66] identified fans are curious and eager to understand the tactics and in-game decisions (e.g., “the usage of this particular play” and “understand the offensive and defensive strategies”).
Thus, our QA system specifically focuses on answering questions related to basketball tactics. Yet, this presents significant challenges, as
the answers should explain a series of complex actions and presenting
the logic behind player movements in an reliable, understandable, andengaging manner. We conceived the design considerations as follow:
R1. Reliable – Explaining Tactics with Grounded Video Data.
Ensuring accuracy and reliability in explanations is a critical requirement for QA systems, particularly those analyzing video content. The
alignment between the video content and the provided explanations isessential, as the mismatches between the video content and the provided
explanations can lead to significant user confusion. This necessitatesa mechanism to understand the video and extract data from the video,

such as tactic types and the actions involved. This data then serves as
the foundation for the QA system to generate explanations. By ensuring that our explanations are directly tied to the observable tactics andactions in the video, we provide users with insights that are not only
precise but also verifiable, enhancing the reliability of the information
presented by Sportify.
R2. Understandable - Explaining Tactics with Narratives. Storytelling is fundamental in organizing and conveying human experiences,
playing a crucial role in how we understand and interpret events [34,44].
To help users easily understand the tactics employed by teams, we
propose the use of well-structured narratives that adhere to a logicalsequence. This approach not only clarifies the sequence of actions but
also reveals the underlying reasons and objectives guiding the players’
movements [16]. Moreover, the choice of narrative perspective (i.e.,
first, second, or third person) also demands careful consideration, as it
significantly impacts a viewer’s engagement and immersion [14]. In
basketball, the third-person perspective aligns with a commentator’sview, offering a broad overview of the game, while the first-person
perspective resonates with the individual player’s decision-making process. Each perspective offers potential benefits in game understanding.
Finally, it is essential that these narratives are not only presented asplain text but organized in a structural format that can be effectively
mapped or linked with visualizations embedded within the video.
R3. Engaging – Explaining Tactics using Embedded Visualizations.
To enrich the explanation of tactics, it is essential to complement thenarratives with visual representations, creating an experience akin to
watching a film [23]. This requires the careful design of embedded
visualizations for the key actions within the narratives. Each action demands specific animated embedded visualizations to capture its unique
objectives and to dynamic nature. Furthermore, the visual explanationmust also reflect the chosen narrative perspective. For instance, in a
first-person narrative, the narration should directly connect to specificsubjects within the visualization. In contrast, a third-person narrative
allows for a more generalized correlation between the visualizations
and the narration. The ultimate challenge for Sportify is to seamlessly
integrate these visual explanations within the video content, ensuring acohesive and engaging presentation of tactical explanation.
3.2

System Overview

Based on the considerations, we have developed Sportify, a visual QA
system answering questions about videos [20] and comprising threemajor components: a Data Processor (Figure 2 a), a Narrative Agent(Figure 2 b), and a Visualizer (Figure 2 c).
At the heart of Sportify lies the Narrative Agent, which leverages

a LLM to interpret the user’s question and generate explanations in
response. For a system designed for basketball videos, the capability
to understand video content is indispensable. Although multi-modal
LLMs are capable of processing image data, they often underperform
in domain-specific tasks and require a tremendous computation costs,
such as detecting actions or tactics from a sports video. To overcome
this challenge, our methodology employs a text-only LLM, enrichedthrough the integration of a Retrieval-Augmented Generation (RAG)
framework [29] and a Reasoning-and-Actioning (ReAct) prompting
strategy [61] for different types of questions. Importantly, Sportify
leverages the data extracted from the video as the context (R1) togenerate the explanation in a narrative format (R2). These extracted
data and explanation are then presented as visualizations embedded in
the video (R3). In the subsequent sections, we delve into the specificdesign and implementation of each component.
4

DATA P ROCESSOR

To create reliable explanations (R1), extracting data from video clips ascontext for the LLM is crucial. We detail our data extraction method,
focusing on identifying tactics and actions, by leveraging the 3D coordinates from the publicly available SportsVU dataset [6] and applying
machine learning to determine players and ball positions, following
prior work [66]. The 3D coordinates is tracked through multi-camera
tracking systems [10], a technique widely adopted in professional basketball leagues, including the NBA [7]. In this study, we utilize the3D coordinates and extracted data from videos to identify tactics andactions for effectiveness and applicability.
4.1

Tactic Detection

Identifying the tactics is crucial for answering tactic-based questions.
We classify a video clip’s tactic by comparing five offensive players’
movement patterns to those in a reference dataset [53] that includes 134
annotated clips. Each of these clips contains the temporal sequencesof the five offensive players’ coordinates and the associated offensive
tactic, such as Back-Side Pick and Roll, Elevator, or Pin-Down. These
patterns are represented by the five temporal sequence of their coordinates, each denoted as {(x, y)}ti=0 , with x and y are the on-court
position, and t marking the clip’s end frame. Specifically, we leverage
the K-Nearest Neighbors (KNN) algorithm and identify the closest
match in Figure 2 (A-3). The tactic type of the best match clip in thisdataset is assigned to the current video clip in question.
Given the difference in sequence length between video clips, it
is impractical to use the traditional Euclidean distance for the KNN
algorithm’s distance metric. Consequently, we adopt FastDTW, arefined variant of Dynamic Time Warping (DTW), to overcome thischallenge. FastDTW [42] can calculate the similarity distance between
two temporal sequences of different lengths, thus offering a robust
solution for our need. We achieved an accuracy of 85.33%. For moredetails, see the supplementary material, Section A.
4.2

Action Detection and Filtering

tactic type, we also need to detect the involved actions. In this work,
we focus on four most important actions in a basketball offensive tacticin Figure 2 (A-2).:• PASS occurs when a player transfers the ball to a teammate, who
is in a more advantageous position to score. A PASS is detected bytracking ball ownership changes within the same team.
• C UT is performed by a player who doesn’t have the ball. The player
moves swiftly from one court area to another to either create space or
distance from a defender, thus enhancing offensive possibilities [18,52]. For detection, we segment the court into 10 sub-regions (e.g.,
key, post, wing), following a taxonomy [55]. Then, a C UT is detected
if an offensive players moves at a speed of 6 feet per second or fasterto a different sub-region [59].
• S CREEN refers to an offensive player’s attempt to block or slow down
a defender, thereby creating space and time for a teammate to moveinto a more advantageous offensive position or to take a shot [52].
From a technical standpoint, a screen occurs when an offensive player
blocks a defender who is closely guarding another offensive player
with possession of the ball. Thus, to detect a S CREEN, we analyze
the distances between players on the court. A S CREEN is detected if
an offensive player changes their marking or covering player.
• S HOOT results in a change of possession regardless of whether itscores. We detect a S HOOT action if the ball possession changes
between the two teams.
The outputs of action detection are a list of actions, each with its
timestamp and the associated player.
Action Filtering. Not all captured actions are relevant to the tactic of ateam. Including redundant or non-crucial actions could diminish theuser experience by cluttering the presentation with unnecessary details.
Therefore, we propose to filtering the actions and keep the important
ones. A practical approach is to prioritize key actions integral to tactic
implementation and scoring. According to Tian et al. [52], PASS playsa central role in executing tactics, while S HOOT is the end of a tactic.
Therefore, as illustrated in Figure 3, we categorize PASS and S HOOT as
primary actions, while C UT and S CREEN as secondary actions. Then,
we organize the actions chronologically and establish intervals between
consecutive PASSs (Figure 3).
Next, we filter out the ineffective secondary actions, including 1)
C UTs that are not followed by to a ball receive and 2) S CREENs thatare not positioned to benefit the ball handler or the intended receiver,based on the proximity of players and the location of the screen. For
example, if a player does not receive the ball after performing a C UT(Figure 3 1), this action is considered as ineffective and removed fromthe interval. Similarly, the S CREEN in Figure 3 (2) is excluded if it does
not impact the pass, judged by the distance between where the screen
is set and the locations of the ball pass before and after the screen.
After the filtering, we obtain a list of important actions that havedirect impact to the outcome of the tactic. Our method achieved an F1score of 73.93% (Details n the supplementary material, Section A).
4.3

Fig. 3: An action list displays the series of actions performed by offensive
players, including Pass, Cut, Screen, and Shoot. The primary actions are
identified based on ball movement or movements that enhance scoring
opportunities, such as Shoot or Pass the ball. To extract the primary
actions related to Pass and Shoot, we set criteria to filter out secondary
actions like Cuts and Screens, identified by actions 1 and 2 in red circles.

Action Detection. According to previous work [47], a tactic consistsof a series of actions, each associated with a player. Thus, besides the

Retrieving External Statistics Data

In addition to the data from the current video clip, we also aim toprovide the LLM with external meta data, such as the players basic
information, team rankings, and historical performance. To achieve this,Sportify is equipped with a suite of tools designed for both in-gamedata analysis and external information retrieval, including programming
tools like Pandas and external APIs like Google Search API, Wikipedia
search API, and Statmuse API [11]. The LLM can utilize these tools to
extract necessary external statistics data by using a LLM framework
named ReAct [61], which enhances LLMs by enabling them to performtasks and reason in a manner akin to human problem-solving.
5

N ARRATIVE AGENT

To facilitate the understanding of the tactic (R2), we aim to generate theexplanation in a form of narratives (i.e., story). This section introduce
the design of the prompt to achieve this goal.

A dynamic prompt with game Context. Rather than forwarding
a user’s query directly to the LLM, we enhance the input with extra game context from the current video clip. This includes Player
Information, detected Tactics, and Actions, all sourced fromthe Data Processor (Sec. 4). This approach aligns with the RetrievalAugmented Generation (RAG) framework, which has been proveneffective in domain-specific tasks, enabling the LLM to draw upon avast array of external knowledge without requiring retraining for specific applications. For each query, the system dynamically loads gamecontext relevant to the video clip and construct the prompt, guiding the
LLM to tailor its responses to the clip in question. Below is our prompt
template, whose details can be found in the supplementary materials.

person...). An example format is provided to ensure answer consistency,
given the challenge of maintaining uniform response formats due to thevaried nature of actions and tactics. Such a prompt technique is applied
to both the two aforementioned steps. Figure 4 shows an example of the
explanation of the same tactics in different narrative perspectives. Finally, we also prompt the LLM to output the explanations in a structureformat to facilitate their mapping to visualizations (Sec. 6).

Prompt template: Please explain { user question } based on
the following context:• { Player Information }• { Detected Tactics }• { Detected Actions }Your explanation should follow the below constraints and formats:• { Constraints }• { Format }Generating Explanations with Narratives. To enhance the comprehension of explanations, we adopt a top-down approach—presenting atactic overview before delving into detailed actions. Our pilot experiments indicated that LLMs often struggle with generating comprehensive explanations in a single attempt. To address this, we’ve divided thetask into two separate steps, in line with established best practices [46].
First, we prompt the LLM to produce an overview explanation of thetactics. This overview succinctly summarizes the tactics, enabling fansto grasp the essentials of tactical unfolding. Second, we add prompts to
the LLM for generating a detailed, sequential breakdown of the tactics,
providing users with action-by-action insights into specific actions anddecision-making processes. For each step, we incorporate different
constraints to help the LLM generate different explanations alignedwith each step’s purpose.
This two-step approach not only refines the LLM’s output by incorporating detailed constraints but also safeguards against the generationof incorrect explanatory formats.

Fig. 4: The same tactics’ explanation in various narrative perspectives.

Formatting Explanations with Various Perspectives. The standard
narrative perspective of LLM is third-person. However, to generate explanations from a first-person perspective—a task LLMs don’t typically
perform automatically—we introduce specific prompts that steer themodel towards generating such responses. Specifically, we prompt themodel to frame its explanations as if part of a conversation or role-playdialogue between two players (e.g., ... answer should be a format of aconversation or a role-play dialogue between two players...), incorporating descriptions that evoke a first-person reaction to in-game actions(e.g., ... screen elicit a surprised or shocked reaction from the otherFig. 5: The iteration design process to design action visualizations (i.e.,
Pass, Cut, and Screen). From P1 to P4, we remove the occlusion andhighlight the two players who send and receive the ball. For the cut,
we indicate the exact location that a player will move with flash-forward
animation from C1 to C2, while the screen demonstrate a wall to beeasily identified a player set on screen from S1 to S2.

6

V ISUALIZING TACTIC E XPLANATIONS

Rather than conveying explanations solely through text, we aim forvisually representing them (R3). Since a tactic consists of a series ofactions, we decide to visualize these actions to illustrate the explanationof the tactic. Additionally, it’s essential to depict the narrative perspectives visually. In the following sections, we will detail the design
approach for each of these visual designs.
6.1

Visualizing Actions

Given that the S HOOT action signifies the end of a possession, we focus
on visualizing the other three actions: PASS, C UT, and S CREEN. For
clearer comprehension, we pause the video during these visualizations.
The required data for rendering these visualizations, such as players’
coordinates, bounding boxes, and specific frames, is obtained using
computer vision models (Figure 2 A1).
Visualizing a PASS. To visualize a PASS, we aim to clearly delineate
the dynamics of passing by highlighting the players involved in the
pass—the sender and receiver—along with the ball’s trajectory. As
demonstrated in Figure 5(P4), our visual design employs two rotating
circles that mark the sender and receiver, respectively, and an arrow
beneath the players to indicate the ball’s passing direction. Additionally, we incorporate a flash-forward effect [68] to preview the players’
subsequent movements.
The development of our visual design was an iterative process, detailed in Figure 5 P1 to P4. Initially, we used a basic arrow to signify
the change in ball possession and its direction (P1), but this approachproved problematic as it occludes the players. To counter this, werepositioned the arrow beneath the players on the court (Figure 5 P2).
However, the P2 design encountered visibility issues in crowded scenes.
Our subsequent iterations focused on enhancing visibility and understanding: we introduced circles beneath the players to emphasize theirroles (P3) and further refined the design (P4) by animating the circles
and arrow and adding a flash-forward effect for future movements, andemploying team-specific colors for easy identification. This iterative
approach, culminating in our final design, facilitates an intuitive andengaging visualization for fans to understand a PASS action.
Visualizing a C UT. The purpose of a C UT in basketball is for a player
without the ball to move from one part of the court to another, aiming
to create space from defenders and find better positions for shooting [18, 52]. We visualize a C UT by using an arrow to indicate theFig. 6: The figure (A) shows the third-person perspective narrative like commentaries, whereas the figure (B) demonstrates the first-person
perspective by integrating the action visualizations and narratives around the players to make people more engaged and immersive.

player’s movement direction and an area visualization to demonstrate
the specific court locations they are moving into (Figure 5 C2). Particularly, we incorporate a flash-forward effect, which previews theplayer’s trajectory before the actual movement occurs. In Figure 5 (C2),a blue dashed line represents the player’s position before the cut, and agreen line indicates the position after the cut, effectively showcasing
the movement path.
Our approach to visualizing a C UT was also iteratively refined. The
initial design (Figure 5 C1) depicted a C UT with a simple arrow, highlighting the player’s direction but failing to convey the precise future
location or the movement trajectory. To address these limitations, therefined design (Figure 5 C2) integrates the arrow with area representation and the flash-forward effect. This enhancement not only clarifies
the direction and intent behind a player’s C UT but also provides viewers
with a predictive insight into the player’s positioning, enriching theoverall understanding of the game’s dynamics.
Visualizing a S CREEN. A S CREEN in basketball is executed to impedeor slow a defender, thereby creating space and time for the offensive
players. This allows the player with the ball to either move into a moreadvantageous position, dribble past opponents, or take a shot [52]. To
visualize a S CREEN, we aim to enable fans to easily identify when andby whom a screen is set against a defender As shown in Figure 5 S2,
our design incorporates both an arrow and a depiction of a screen wall.
This combination clearly distinguishes the involved players and thelocation where the screen occurs.
The initial design (Figure 5 S1) utilized an arrow to indicate thescreen action. However, this approach proved insufficient for clear
identification, as it often blended with other actions and occluded by
the presence of multiple players on the court. We thus improved it andconcluded to the current design, which ensuring that fans can easily
recognize and understand this crucial aspect of a S CREEN
6.2 Visualizing Narratives
Alongside action visualization, we also need to present the narratives(i.e., the textual explanation of each action) together with their perspectives. We initially intended to provide audio using text-to-speechtechnology. However, due to the time required to generate the speech,
we decided to provide it in text form instead. Figure 6 demonstrates
how the scenario of Stephen Curry passing to Andre Iguodala can be
narrated from different viewpoints—illustrated through third-person(Figure 6 A) and first-person (Figure 6 B) perspectives.
The third-person perspective, akin to a commentator’s overview (e.g.,“Andre Iguodala cuts from the post to the wing to space the floor...”),
is traditionally used to explain basketball plays (Figure 6 A). Thisnarration style fits seamlessly into visualizations using a single chatbox,
streamlining integration without additional interface requirements.
Conversely, the first-person perspective, which captures players’
emotions and dialogues (e.g., “Perfect timing, Andre. Cutting towards
the high post to create a mismatch...”), can confuse fans when presented
in a traditional chatbox format. To address this, we adopt dialogue bubbles for first-person narratives, akin to comic book styles (Figure 6 B).
These bubbles move with the players on screen, enhancing engagementby allowing users to interact with the narrative—navigating throughthe conversation with ‘previous’, ‘play’, and ‘next’ controls.

7

U SER S TUDY

We conduct a two-phased user study with basketball fans to evaluate theunderstanding, usability, and engagement of Sportify. Two experiments
were conducted: a comparative study of narratives with first and thirdperson perspectives with and without visualizations, and an exploratory
user study on the overall experience of Sportify.
7.1

Participants & Experiment Set-Up

We recruited 13 basketball fans (P1-P13; M = 13; Age: 23 - 33) via
university mailing lists. Due to a technical issue, we were only able tocollect P4’s subjective feedback, excluding the task completion timeand accuracy. Participants reported their fandom levels, including 3
novice, 2 casual fans, and 8 engaged fans. In addition, participants
reported their frequency of watching basketball in four different levels:
4 participants watched at least 1 game per week, 5 watched 2-4 games
per month, 1 watched 11-23 games per year, and 3 watched 1-10 games
per year. We selected two famous games: one between the GoldenState Warriors and the Cleveland Cavaliers on December 25th, 2015,
and the other between the Oklahoma City Thunder and the Los Angeles
Clippers on December 21st, 2015. These games were featured as
the best by the NBA in the 2015-16 season. The two experiments
were conducted in person on the same day, using a 14-inch laptop for
setup. The study took about one hour to finish and all participants werecompensated with a $20 gift card.
7.2

Study Design & Measure

Introduction & pre-survey (10 mins). Before starting the user study,
we introduced our user study and received the consent form. We
collected information about participants’ backgrounds, such as fandomlevel and average watching frequency, through a pre-study survey.
Task 1: A comparative study (25 mins). Our first experiment investigates the understanding of strategies, the helpfulness of visualizationand personified narratives. Participants first watched a game clip withan explanation, and were then asked to order a list of actions (e.g.,
pass, cut, and screen) to match the action sequence in the game basedon their understanding. The explanation was delivered in one of thethree conditions, including explaining strategies using pure text (Text),
text with visualization in third-person narrative (Third), and text withvisualization in first-person narrative (First) conditions. The explanations were identical between Text and Third conditions. Explaining
tactics often requires an in-depth understanding and detailed explanation, making the textual narrative necessary [13, 16, 19, 54]. Therefore,
the non-text version was excluded from the baseline. We prepared
twelve clips from both games evenly and tasked participants with theirunderstanding of the strategy in the game. Each condition has four
trials, including a practice and three actual trials, in which we collectedtask completion time and accuracy. The number and types of actions
are evenly distributed among three conditions. In addition, the order ofthe conditions and the twelve clips were counterbalanced in the study.
During the study, participants were introduced to three conditions
and practiced the trials in the training session. In the study, participants
watched the assigned game clip and used a drag-and-drop interface
to move the order of the list of actions to match the sequence in the

game. After completing all 12 trials, participants ranked their preferred
explanation conditions and rated the helpfulness of the visualizations
and narratives, providing subjective feedback in the questionnaire.
Task 2: An exploratory study (25 mins). The second part of the user
study involves a free exploration of Sportify to compare participants’
game-watching experience with tactic explanation videos on YouTube
and using Sportify. The participants chose either a first- or third-person
perspective based on their preference, and analyzed a game selectedfrom two games used in Task 1 (GSW vs. CLE or OKC vs. LAC) using
Sportify. As a baseline for a typical tactic explanation approach forbasketball games, we selected three tactic explanation videos from the
popular basketball channels on YouTube [2, 3, 8], watching 2-3 minutes.
In the study, the participants first watched a YouTube video randomly
assigned from [2, 3, 8]. After that, they were introduced to all featuresof Sportify in a training video. Participants freely watched the game
video and asked any questions at any time using Sportify. To inform
users when they can ask tactical questions, our system visually indicates which parts of video clips can be questioned and which cannot.
Following previous work [31, 38, 66], we measured user experiencesusing subjective rating questions in the post-study survey, including “Itwas helpful”, “It was fun”, “I felt in control”, “I felt encouraged”, “I
am likely”, and “I felt engaged” when using Sportify, and collected
feedback from the survey and think-aloud methods during the study.
8

S TUDY R ESULTS

We present the results of two tasks in our user study and discuss how
visualizations and different narrative perspectives affect the understanding of game strategy. Besides, we discuss how Sportify differs fromexisting strategy explanation videos in enhancing game understanding.

Fig. 7: A task 1 user study results. Figure (a) reveals that the First
person perspective ranks highest in helpfulness and confidence acrossthree conditions. Figure (b) indicates positive participant ratings for each
embedded visualization’s helpfulness. Figure (c) compares usability
across two different narrative perspectives.

more enjoyable and simpler to understand than the third person perspective text (P13). First condition was found to be most helpful andenhanced their confidence, as shown in Sec. 8.1.2.
8.1.2 First-Person Narrative Enhanced Feeling of Helpfulness
and Confidence

Figure 7 (a) reveals that the First condition was rated highest in helpfulness and enhancing their confidence in understanding by 8 and 6
participants out of 13 participants, respectively, followed by the Third
condition with 5 and 6 participants, and the Text condition with noneand 1 participant. Participants highlighted the advantages and draw8.1 How Do Three Different Conditions Affect Understandbacks of the First and Third conditions. Six participants noted thating Strategy Explanations?
placing the chat box near players was helpful and not distracting, statIn Task 1, participants matched the sequence of actions in twelve clips ing that ”First person perspective chat box comes closer to players,
under three conditions: 1) pure text in a third-person perspective (Text), which helps me better understand tactics. (P1)”. They also appreciated2) text with visualization in a third-person perspective (Third), and 3) the interactive “Play” button for navigating conversations, feeling an
text with visualization in a first-person perspective (First). We presented experience similar to playing a video game, with one saying, “I amthe findings on how visualization and personified narrations affect ‘in control’ of the players, almost like a video game.” However, somestrategy comprehension, task performance, and overall experiences.
found the First condition confusing due to its abundant text and lack ofresemblance to real commentary, with feedback like, “First condition
8.1.1 Watching Time Increased Without Affecting Accuracy
felt a bit informal and not realistic, (P12)”.
Four participants preferred the Third condition for its realism andWe measured the accuracy of nine game clips in the trials from 12
participants, excluding three practice clips. The accuracy of matching detailed information, commenting, “The Third condition was more
the actions to game strategy for the Text and Third conditions were effective in conveying detailed information.” However, they reported
identical at 72.22% (26 out of 36), while the First condition was slightly losing focus due to the need to switch attention between visualizations
lower at 69.44% (25 out of 36). The average watching time for the First and text, with one mentioning, “I was distracted by moving my eyescondition (86.27s) exceeded that of the third-person perspective (Text: between players and chat box (P1)”.
Overall, participants found the First and Third narrative perspectives
69.44s and Third: 68.72s). However, the average solving time, whichmeasures the time participants took to complete ordering actions in more helpful than Text alone in enhancing their understanding of gamethe trial after watching the clip, was similar across all three conditions strategies. With these visual narratives, participants felt more confident(Text: 40.33s, Third: 39.94s, and First: 44.53s). After conducting a in grasping game strategies, with participants equally favoring the First
normality test, we found that none of the conditions followed a normal and Third perspectives. The First offered interactivity and embedded
distribution, so we performed a Kruskal-Wallis test. This test revealed placement of the visualizations, while the Third provided realism anda significant difference in watching time among the three conditions clearer comprehension.
(p= 0.02 and H= 7.765) but not in solving time (p= 0.89 and H=0.24).
Dunn’s post-hoc test further identified a significant difference between 8.1.3 Embedded Visualizations Help Identifying Player Interactions and Ball Movements
the Text and First conditions (p=0.046) in watching time.
Overall, the results show that the Third condition has a similar As shown in the Figure 7 (b), all the three visualizations received
watching time to the Text condition, with visualizations aiding users in positive ratings from the majority, including Cut (85%), Screen (77%),
understanding the main actions in the clip. Participants found visualiza- and Pass (85%), confirming the usefulness of the three visualizations
tions in Third helpful, as noted “The explanations with visualizations for understanding strategic explanation.
help more than the text in general (P9)”. In addition, participants spent
Participants, especially novice and casual fans, commented that these
25% more time watching the video with first-person narrative in First action-specific visualizations significantly aided their understanding
than the third-person narrative in Text and Third condition. This is of basketball plays. In particular, visualizations significantly aided
likely due to that the First condition involves more interactions than in identifying “Screen” and “Cut” actions, allowing participants tothe other two conditions due to the personified narratives presented comprehend complex plays like off-ball movements and interactions
between a pair of players, leading to longer time to interact and engage between offensive and defensive players more easily. This result aligns
in the video. Participants did not find this interaction impeding their with our main goal of elucidating off-ball movements by pinpointing theunderstanding, but instead felt that First person perspective text was screen’s location and direction with the Screen visualization and adding

a flash-forward effect to demonstrate the next player move in the Cutvisualization. Furthermore, participants found the Pass visualization
very helpful for its clear depiction of ball movement directions, senders,
and receivers. For instance, P7 mentioned, “I sometimes miss who
receives the pass, but with visualization, I could easily notice it.” This
feedback confirms the primary design goal of the Pass visualization inhighlighting the players initiating and receiving passes.
However, watching the simultaneous presentation of the Screen
visualization and text presents a challenge, as noted by P11: “It would
take some time to get used to seeing the text and visualization, since
it shows a lot at once.” This indicates a learning curve for users whensimultaneously comprehending the visualization and text integration.
8.1.4 First-Person Enhances Fun and Enjoyment, While ThirdPerson Allows Feeling of Control and Formality
Figure 7 (c) shows the usability ratings for the First and Third person
perspective narrative designs. This result revealed a clear preference
for First person perspective narrations over Third person on fun andengagement by 12 and 11 participants out of 13. This preference isattributed to the enjoyment of story-based narratives and a user interface
design that simulates players engaging in conversation with one another.
P2 praised the narrative for its use of nicknames, expressing “I likethe story-based narrative where they even called each other nicknames
like Matty”. The First person perspective was also seen as creativeand interactive, with P11 finding the player communication innovativeand enjoying the conversational navigation. P11 noted that “I felt mostinteractive and creative when manipulating players’ conversations.”
Moreover, participants found this perspective helpful in understanding
player thoughts and intentions, enhancing their grasp of overall tactics,
as P5 mentioned “It is easier to understand each player’s intention...”However, P8, a former university team player, criticized the dialogue inthis condition for lacking realism and not accurately reflecting players’
in-game thought processes. As a result, our results reveal that thefirst-person perspective enhances engagement, encouragement, and fun,supporting the findings in previous research [14, 37].
On the other hand, Figure 7 (c) demonstrates that from the postsurvey feedback, 7 and 5 participants found the Third person perspective narratives make them feel in control and found it more helpful,respectively. The text in third-person perspective is viewed as moreconcise and accurate for strategy comprehension compared to the firstperson perspective. P3 stated “Third person perspective avoids unnecessary information like ‘Now, it is time to shoot’.”. Furthermore,some participants favored the third-person perspective for its conventional and formal explanatory style like game commentaries, mirroring
their typical basketball viewing experiences. As P6 mentioned, “Third
person perspective seems more standard, formal, and familiar to me.”
Additionally, P9 noted that the third-person perspective is easier tofollow and control with its predictable layout, highlighting that “It’seasy to lose attention while following the chatbox above the players,
and the third-person perspective is easier to control with the static chatbox navigation button.” According to other research [36], shifting theperspective from third-person to first-person requires additional reading
time and effort, causing discomfort for users who are accustomed tothe third-person view.
In sum, participants’ feedback distinctly indicates that first-person
narratives provide a greater sense of fun and engagement, inspiring
users to explore further, whereas third-person narratives, with theirfamiliar format, enhance the feeling of control.
8.2 How Does Sportify Compare to Traditional Basketball
Strategy Explanation Videos?
Task 2 evaluated Sportify’s usability by asking participants to watch an
existing strategy explanation video and then freely used Sportify.
Participants predominantly favored Sportify over the existing strategy explanation videos, as shown in numbers of participants on metricsof helpful (9), fun (12), feel in control (12), feel encouraged (11),likely to use (11), and feel engaged (10) in Figure 8 (a). The majorityof participants valued Sportify’s capability to generate personalized
answers to their queries and provide infinite content. Notably, two

Fig. 8: Sportify received better results compared to existing tactical explanation videos, with positive outcomes in all questionnaire of usability.

casual fans mentioned that while the existing videos left their questions
unanswered, Sportify provided the desired information on demand.
P5 highlighted “Sportify’s advantage of offering limitless, real-time
content compared to the fixed material in traditional videos.” The
tool’s explicit presentation of strategies and motivations was especiallyhelpful, with one user noting, “It helped me see the clear motivationand strategy explanation from a first-person view (P4)”, underscoring
Sportify’s effectiveness in delivering detailed play insights.
On the other hand, some participants preferred the existing explanation videos for their structured content and insights from professional
analysts. Engaged fans with an understanding of basic strategies appreciated the depth of professional analysis, as P4 noted, “More rigorous
analysis of plays with explanations from professional analysts.” Additionally, the advocates of the video highlighted their ease of use,pointing out that they demand less effort to navigate, with P9 stating,“The video is easier to watch since it requires less effort.”
Figure 8 (b) illustrates Sportify’s usability ratings on a scale from 1(strongly disagree) to 7 (strongly agree), with all aspects receiving positive feedback (above 85%). Sportify received notable 100% positive
responses in fun, encouragement, and engagement. Users particularly
enjoyed the first-person perspective for its dynamic explanation of tactics and player thought processes, as P4 highlighted: “It described thethought processes of players in a fun way, which helped me understand.”The ability to ask personalized questions at their own pace encouraged
participants to explore Sportify, exploring specific areas of interest andseeking detailed information. P11 describes “It moves at your own
pace, allowing you to probe the system with more specific questions
about any play.” The enhanced accessibility and in-depth analysis
motivated users to engage more deeply with the strategies, with P12
appreciating the “in-depth analysis that focuses on overall strategy,
especially showing the thought processes and reasoning among players.” P5 commented a strong positive feedback in terms of “likely touse”, citing its real-time capabilities as particularly appealing: “If thissystem is public, I will absolutely try it. The real-time ability is reallycool.” In summary, eleven participants found the system extremely
helpful, praising its accurate and sensible explanations and the freedom
to inquire at will.
9

D ISCUSSIONS

We summarize the design insights derived from developing our system,
along with feedback and observations from the user studies.
9.1 Sports Narratives: Various Preferences for Perspective
Previous studies [24, 37] have shown that readers familiar with theevents and emotions of a story’s protagonist are more likely to choose afirst-person perspective, while those with less experiences prefer a thirdperson perspective. Based on this, we thought that novice or casual fansless familiar with basketball would choose the third-person perspective,
while engaged fans would select the first-person perspective.
However, during free exploration of our system, participants showed
an equal preference for both perspectives: seven for first-person andsix for third-person. Among the participants, two novices and one
casual fan chose the first-person perspective, as did four out of eight
engaged fans. This trend was consistent across various levels of basketball knowledge and viewing frequency. Unlike general storytelling, acrucial factor here is the need to understand complex basketball strategies. As mentioned in [66], novice and casual fans tend to enjoy games
more for their entertainment value and likely prefer the first-person
perspective for its immersive experience. In contrast, engaged fans
focus on technical aspects and strategies, often seeking a deeper understanding, and might lean towards the third-person perspective for its
broader overview. Therefore, this mixed conclusion likely stems fromindividual familiarity with the protagonist and the need for a deeper
understanding of the narratives.
9.2

Using Text-Based LLMs Instead of Multi-Modal LLMs

In developing Sportify, we experimented with utilizing current state-ofthe-art multi-modal LLMs [15, 32, 62] that are capable of processing
images or videos to interpret visual content. However, we found that
applying multi-modal LLMs is still challenging in the sports domain,which is characterized by numerous players and complex dynamics
and interactions. Instead, we transformed visual data from the videos
into textual information using a computer vision pipeline, providing
the LLM with detailed information such as actions and tactical insights
to facilitate answer generation. In this framework, the LLM served
as a central hub or “brain” to synthesize all information and makeinferences. In the future, we anticipate that advancements in multimodal LLMs’ capabilities in understanding images and videos will
enable a more integrated approach to system development, offering aseamless, all-encompassing solution. One limitation of our study was
the inability to provide defensive tactics due to the lack of data, but webelieve that this will be possible with multi-modal LLMs in the future.
9.3

Presenting LLM-Generated Text in Videos

Explaining the dynamics of sports is challenging. Previous research has
mainly focused on enhancing understanding through embedded visualizations, often neglecting textual descriptions. With text descriptions
generated by LLMs, we have explored different methods to present
these descriptions in the video.
Presenting Text with Visualizations. Presenting LLM-generated text
with visualizations in the video is an intuitive method. However, simply
integrating LLM-generated text and visualizations without careful design consideration could hinder the user experience. As identified in ourpaper, placing third-person perspective text with visualizations forces
viewers to switch between action visualizations around players and text
in the chat box, leading to a loss of concentration and degrading the user
experience. This highlights the need for careful visualization designs
and user interfaces. While LLMs can potentially identify positions toplace text with visualizations automatically, they are not specificallydesigned for particular tasks, making it challenging to achieve complex
output. Therefore, simplifying the problem through an intermediarystep and then subsequent stages such as visualization helps us achieve
better results [46, 51].
Presenting Text using Audio. Another alternative we considered was
the use of audio. We attempted to convert the generated text into audio
and play it together with the visualizations, but found that this process
was time-consuming. Consequently, we decided to omit this feature as
it could degrade the user experience. However, during our user surveys,
two participants inquired about the availability of audio. We believethat incorporating audio with visualizations could potentially allow fora more seamless integration.
9.4 Designing User-Centered LLMs for Domain-Specific
Tasks
Strategies for Ensuring Consistent LLM Responses. Given theunfamiliarity of first-person narratives to LLMs, ensuring a uniform
style of response presented a significant challenge. The complexity
increased when attempting to generate conversation-like formats wheremultiple players interact and address each other by name. To achieve
consistency in responses, we applied multiple strategies:
First, we utilized a template-based approach within the prompts,
integrating various constraints as suggested by previous studies [46].
Despite these efforts, the LLM still struggled to deduce the logic or

reasoning behind in-game decisions from textual input alone. To aid inthis reasoning process, we explicitly outlined the rationale for 3 to 5
actions within the prompt.
Additionally, we employed ReAct (Reason + Act) [61], which generates human-like task-solving trajectories and prevents error propagation.
We believe that these approaches help prevent issues of hallucination.
However, if hallucinations still occurred despite these methods, users
were encouraged to rephrase their questions.
Selecting Key Information for Enhanced LLM Performance. The
number of actions detected in a video is usually too many for the LLM
to reason through effectively. By filtering these actions to include only
essential information for the LLM, we streamlined the data, akin to
applying an importance score for selecting critical information [39].
These strategies resulted in a more precise system, surprising users with
its accuracy in providing explanations during the user study. Feedback
from the study highlights the critical role of fusing domain-specificknowledge to curate information for the LLM in developing LLM
applications. Our approach of guiding the LLM with key information
proved to be effective and satisfying.
Addressing Various User Needs. We observed that not every fan
seeks intricate tactical insights or understands the players’ thought processes. Several participants favored succinct explanations, indicating apreference for customizable narrative detail levels. This suggests thatallowing users to select their desired level of detail could significantly
enhance their experience.
9.5 Limitations
The sample size of our study is comparable to other similar sports
visualization papers [31, 66]. Besides, the focus of our study is on
qualitative feedback rather than quantitative results. Through qualitative feedback, we found that not only novices but also engaged
fans benefited from and were immersed in understanding the tactics.
Nonetheless, further experiments with a larger user base are beneficial.
In real-world settings, our VQA system faces challenges related to data
precision, processing speed, and user diversity. The precision of 3D
tracking data from single monocular videos is often insufficient, but thiscan be improved with 3D vision models using multiple-angle videos
or extra sensors. The system’s multiple pipelines require significant
processing time, hindering real-time performance, though this can be
mitigated with better machinery and lighter models. Additionally, customizing conversation content to suit diverse user needs and skill levels,
including adjustable explanation detail and length, is crucial. Lastly,
our initial focus was on explaining tactics through key actions in the
proposed visualizations. However, more diverse visual explanations
are needed to fully support the tool’s practical use.
10 C ONCLUSION
Our work introduces Sportify, an innovative VQA system. It significantly enriches the basketball watching experience by integratingembedded visualization with personalized narrative explanations oftactics. This novel system enables fans to investigate understanding thegame through both statistical queries and complex tactical questions.
By employing a computer vision pipeline and leveraging a LLM to generate insightful explanations of players’ logic and reasoning, Sportify
transforms the paradigm from passive watching to an interactive, engaging exploration of basketball. Our user studies evaluate three different
conditions and two narrative perspectives in a comparative study, as
well as the usability of our tool in a free exploratory study. The results
reveal that Sportify significantly enhances users’ comprehension of tactics made during games and elevates user engagement and experiencebeyond what is offered by existing tactic explanation videos. Moreover,narration from a third-person perspective aids in providing detailed
explanations of the game, while first-person perspective increases fans’enjoyment and engagement with the game.
ACKNOWLEDGMENTS
This work is supported by SEAS Graduate Fellowship, NSF grantIIS-1901030, NIH grant R01HD104969, NSF grant III-2107328.

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A. ACTION D ETECTION AND TACTIC C LASSIFICATION P ERFOR MANCE

A.1 Action Detection
We selected one quarter from each of the GSW vs. CLE and OKC
vs. LAC games. Both were 9 minutes long. Our two authors watchedthe videos and manually reviewed, and revised the results extracted
by the action detection model. The inter-coder agreement level was
75%, which was calculated using Cohen’s kappa. Any disagreements
were resolved through discussion between the two authors. Throughdiscussion, they reached a consensus to establish the ground truth. The
results are that we extracted a total of 117 actions: 59 passes, 15 screens,
14 cuts, and 29 shoots in the GSW vs. CLE game. Similarly, in the
OKC vs. LAC game, we identified 104 actions: 45 passes, 24 screens, 8
cuts, and 27 shoots. The overall F1 score was 0.7393, and the confusion
matrix is provided in Table 1. We used the manually revised actions
from the model in the actual experiments.
Cut
0.5
0.06
0.08
0.0

Cut
Pass
Screen
Shoot

Pass
0.36
0.63
0.22
0.17

Screen
0.0
0.07
0.48
0.08

Shoot
0.14
0.24
0.22
0.75

HK
0
0
1
0
0
0
0
0
0
0

PD
0
0
0
0.90
0
0
0
0
0
0

PT
0
0.10
0
0.10
0.60
0
0
0
0.18
0

RB
0
0
0
0
0
0.93
0
0
0.07
0

SP
0
0
0
0
0.23
0.07
0.80
0
0
0

WS
0.07
0
0
0
0
0
0.20
1
0.07
0

[TACTIC]{Tactic Description}

B.1.2 First-person perspectives

We built our tactic classification model based on the previous work[53], which includes 134 videos from the NBA 2013-2014 season,covering 10 different offensive tactics. The dataset consists of the
key players’ trajectory coordinates data and their corresponding labels.
Each trajectory is denoted as {(x, y)}ti=0 , with x and y are the oncourt position, and t marking the clip’s end frame. Specifically, weleverage the K-Nearest Neighbors (KNN) algorithm to identify theclosest match. We evaluated our model by following the experimental
procedure described in the previous work. The study was validated
using k-fold cross-validation, where the dataset was divided into 5
subsets. The overall accuracy was 0.8533, and the confusion matrix isprovided in Table 2. The labels represent offensive tactics: F23 (2-3Flex), EV (Elevator), HK (Hawk), PD (Pin-Down), PT (Princeton), RB(Back-Side Pick and Roll), SP (Side-Pick Slip and Pop), WS (Warrior
Single), WV (Weave), and WW (Wing-Wheel). The predicted tactics
are given in the columns, with diagonal entries representing correctclassifications. The overall accuracy of this classifier is lower than thatreported in the previous work [53]. However, we believe this level ofaccuracy is acceptable for providing users with useful explanations of
the tactics.
EV
0.07
0.80
0
0
0
0
0
0
0
0

[CONSTRAINT]
Note that you have to answer integrating tactic
description and actions within 2 sentences.

Please explain {Question}.
"""

A.2 Tactic Classification

F23
0.86
0.10
0
0
0.17
0
0
0
0
0

[PLAYER INFORMATION]
Offense Players: {Offensive Players}
Defense Players: {Defensive Players}[ACTION]{Actions}

Table 1: A confusion matrix of the action detection

Accu.
F23
EV
HK
PD
PT
RB
SP
WS
WV
WW

Please briefly explain the question from casualfans.
When explaining offensive tactics, describe using
the attacking players, and when explaining
defensive tactics, describe using the defending
players.

WV
0
0
0
0
0
0
0
0
0.68
0

Table 2: A confusion matrix of the tactic classifier.

B. P ROMPTS EXAMPLES
B.1 Prompts for generating overview of tactics

WW
0
0
0
0
0
0
0
0
0
1

"""
You are the basketball coach who knows basketball
tactics.
The response should be in the format of a role-playdialogue between players, and no other text
should be added besides the players’
conversation.
I would like it to consist of only 2 to 4
conversations between players.
When explaining offensive tactics, describe using
the attacking players, and when explaining
defensive tactics, describe using the defending
players.
[ANSWER FORMAT]The Answer Format is as follows:
Stephen Curry: Alright, let’s now use the pick androll tactic. Draymond Green, set a screen forme. Then, I’ll make my move. \n\nDraymond Green: Let’s confuse the opponent with ourtactic and score. \n\n[PLAYER INFORMATION]
Offense Players: {Offensive Players}
Defense Players: {Defensive Players}[CONSTRAINT]
Note that you have to answer integrating tactic
description and actions within 2 sentences.
[TACTIC]{Tactic Description}[ACTION]{Actions}

B.1.1 Third-person perspectives

Please explain {Question}.
""""""
You are the basketball coach who knows basketball
tactics.
The tactic description and actions are provided.

B.2 Prompts for generating action-by-action explanation
B.2.1 Third-person perspectives

"""The actions represent player’s movements such as
Cut, Pass, Screen, and Shoot.
[CONSTRAINT]
1. The format of the answer must be the same as the
explanation and must be described in third
person within 1 sentence.
2. The names of all players must be accurately
written.
3. When explaining offensive tactics, describe
using the attacking players, and when
explaining defensive tactics, describe using
the defending players. Answer only one
offensive or defensive tactic for me.
4. All conversations should include the reason whythat player is taking such action.
The reasons for cut: create scoring opportunities,
disturb the defense, enhance ball movement,
space the floor, or implement offensive
strategy.
The reasons for pass: Create Better Scoring
Opportunities, Control the Pace of the Game,
Enhance Team Play, Overcome Tight Defense, orImprove Court Vision and Awareness.
The reasons for screen: Disrupt Defensive Schemes,
Force Defensive Adjustments, Diversify
Offensive Strategies, or Creating space forother players.
[PLAYER INFORMATION]
Offense Players: {Offensive Players}
Defense Players: {Defensive Players}[ANSWER FORMAT]
1. Generate the answer using only conversational
responses, without including any action titles
or additional explanations.
2. Each conversation will be divided by \n\n andensure that the Answer Format is as follows:
Action 1. Explanation 1 \n\n Action 2. Explanation
2 \n\n Action 3. Explanation 3 \n\n[ACTION]{Actions}
Please explain question "{question}" based on eachindividual action with reasoning"""

B.2.2 First-person perspectives"""
The actions represent player’s movements such as
Cut, Pass, Screen, and Shoot.
[CONSTRAINT]
1. The answer format should be conversation between
two players if the action is the interaction
between two players with first person
perspective.
2. In the "Screen", the two players are on
different teams, and one player is setting ascreen on another. It\’s important not to
inform or alert the other player about thescreen being set; instead, dialogue should becreated that disrupts or expresses confusion.

3. Please ensure that the player name is the full
name, and the "Shoot" action results in only
one answer from the Shooter.
4. The response should be in the format of a roleplay dialogue between players, and no othertext should be added besides the players\’
conversation.
5. When explaining offensive tactics, describe
using the attacking players, and when
explaining defensive tactics, describe using
the defending players. Please explain only oneof these in response to the question.
6. All conversations SHOULD include the detailed
and complex reason why that player is taking
such action within one or two sentences for one
conversation by referring to below reasons foreach action.
The reasons for cut: create scoring opportunities,
disturb the defense, enhance ball movement,
space the floor, or implement offensive
strategy.
The reasons for pass: Create Better Scoring
Opportunities, Control the Pace of the Game,
Enhance Team Play, Overcome Tight Defense, orImprove Court Vision and Awareness.
The reasons for screen: Disrupt Defensive Schemes,
Force Defensive Adjustments, Diversify
Offensive Strategies, or Creating space forother players.
[PLAYER INFORMATION]
Offense Players: {Offensive Players}
Defense Players: {Defensive Players}[ANSWER FORMAT]
1. Generate the answer using only conversational
responses with reasons, without including any
action titles or additional explanations.
2. "Pass" and "Screen" should consist of twoconversations, while "Shoot" should consist of
one conversation.
This example is an answer format of "Pass" and "Screen" actions.
Example 1) Action: Pass Player 1 -> Player 2 Answer:
Player 1: Conversation 1 \n Player 2:
Conversation 2 \n\nExample 2) Action: Screen Player 3 -> Player 4
Answer: Player 3: Conversation 3 \n Player 4:
Conversation 4 \n\nThis example is an answer format of "Shoot" action.
Example) Action: Shoot Player 2 Answer: Player
2: Conversation 5 \n
3. Each conversation will be divided by and ensurethat the Answer Format is as follows:
Player 1: Conversation 1 \n Player 2: Conversation
2 \n\n Player 2: Conversation 3 \n Player 3:
Conversation 4 \n\n Player 2: Conversation 5 \nMake sure these \n and \n\n delimiter.
[ACTION]{Actions}

PLEASE FOLLOW and MAKE SURE [Answer Format]!!!.
Please explain question "{question}" based on eachindividual action with reasoning"""B.3 Examples of variables{Offensive Players}:Andre Iguodala, Stephen Curry,
Klay Thompson, Draymond Green{Defensive Players}:Kevin Love, LeBron James\{Tactic Description}:The down screen consists of abasketball strategy that occurs when one
offensive player faces the general direction of
the baseline to set a screen on a defender who
is guarding a second offensive player.
Following that, the second offensive player canthen use the down screen to get open for a
possible scoring or playmaking opportunity. The
pin down screen is a type of down screen thatis typically set near or within the lane at anangle towards the basket. The pin down screen
is generally utilized to help an offensive
player get open, especially to take a mid-rangeor three-point jump shot near the perimeterareas of the court. The pin down screen could
also be used in conjunction with an offensive
set, generally known as floppy action or a
floppy screen. Essentially, with the floppy
action, an offensive player gets in position
near or under the basket and then that sameoffensive player can cut off a single pin down
screen set near the side of one particular laneline or a double screen, which is usually astagger screen, set near the side of the
opposite lane line.
{Actions}:
1. Cut Draymond Green Wing -> Top and Pass Stephen
Curry -> Draymond Green\n
2. Pass Draymond Green -> Stephen Curry\n3. Screen Draymond Green -> Kevin Love\n4. Cut Andre Iguodala Post -> Wing and Pass Stephen
Curry -> Andre Iguodala\n5. Screen Draymond Green -> LeBron James\n6. Cut Klay Thompson Wing -> Top and Pass Andre
Iguodala -> Klay Thompson\n7. Shoot Klay Thompson{question}:
What happened?
What is the offensive tactic here?
Why does Draymond Green pass the ball to Stephen
Curry?