Sage Journals: Discover world-class research

Abstract

Although current athlete performance monitoring system designs undertaken within sports engineering and technology research have a strong focus on achieving functionality requirements, considerations on usability evaluation and testing during the design process are limited. Conventional design research highlights the importance of factoring usability at the heart of performance monitoring system designs like human-computer interfaces. Consequently, based on a user-centred approach, this research aimed to design a digital dashboard to optimise a scouting information flow within a professional basketball club and improve usability issues in the design by executing formative usability assessment approaches. Thematic analysis conducted on the data obtained from an initial semi-structured interview with the Head Coach of the club clarified dashboard functionality and design requirements, importance of the scouting process and information on potential system users. Thereafter, guided by the Build-Measure-Learn cycle in The Lean Startup framework, Minimum Viable Products (MVPs) of the dashboard were designed and 25 usability issues in the initial MVP were identified from heuristic evaluations conducted with three usability experts. Following system redesign, task-based think aloud usability testing sessions conducted with five coaches (i.e. users) at the club unravelled further nine usability issues in the dashboard, which were resolved by systematically pivoting to new MVPs during the iterative design process. Overall, this paper outlines a systematic approach to conducting formative usability assessment of sports technologies and highlights the importance of resolving specific usability issues present in technological systems like dashboards designed by practitioners embedded in applied sport environments.

Keywords

Build-measure-learn dashboard design data visualisation design research formative usability testing lean startup sports informatics usability assessment

Introduction

The combination of analysis methods with a substantial increase in data due to technological enhancements have led to the formulation of data-informed decision-making processes to evaluate the value of athletes like basketball players.¹ Depending on specific decision requirements, insights generated from data as information (i.e. data in context) and evidence (i.e. information compared to standards) can be presented to practitioners using a variety of modes like reports, presentations and data stories.^2,3 Yet, owing to the need to automate corresponding information flows to support decision-making in applied sport environments, there is widespread demand to develop technological systems to monitor the performance of athletes. For instance, Mullane et al.⁴ developed a system to monitor and measure the performance of swimmers and communicate the results to coaches real-time via wireless technology. Similarly, Gorden et al.⁵ recently illustrated the development of a system to monitor and provide real-time feedback on strength and conditioning exercises. A key component of those performance monitoring systems is a human-computer interface (HCI), which acts as the key platform for the communication between end users and the system. At a time when digital transformation is gaining significant traction within the sport industry,⁶ interactive digital dashboards are used as a key HCI to communicate the insights generated from performance data to various stakeholders (e.g. coaches, players and agents) within sport environments.^7,8 Research further suggests that the specific objective of creating dashboards in performance-driven environments like sport clubs is to assemble a selection of metrics under an appropriate visual presentation to promote high quality decisions.⁹

Few¹⁰ concluded that dashboards are ‘a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance’. Design research has increasingly directed its attention on dashboard design with evidence for its application relating to sports like professional rugby union,^11,12 running¹³ and basketball.^14,15 Although detailed examination of dashboard design in sport research is limited, it has been examined extensively in other industries like healthcare and education. In public health, dashboards have been long used for internal reporting, monitoring and evaluation activities, before seeing exponential growth in popularity and use to overcome challenges caused by the COVID-19 pandemic.¹⁶ In education, the use of dashboards is centred on the need to implement online educational platforms.¹⁷ However, Matcha et al.¹⁸ warned future researchers and developers from building learning dashboards following a-priori conventions and designs, proposing instead to use a user-centred model focussing on theory, design, feedback and evaluation. This suggestion is particularly interesting as it hints at the importance of ensuring a user-centred approach when designing digital dashboards.

When implementing human-computer interfaces like dashboards, beyond achieving core functionality requirements, recent research emphasises that usability is a key component to ensuring high-quality user experiences.¹⁹ Such findings align with specific guidance from core design research,²⁰ where usability is considered at the forefront of user-interface design. Interestingly, Blessing and Chakrabarti²⁰ further highlight the importance of examining and improving the usability of user-interfaces as it can be the key factor in which a system is judged by a user. As the primary standard for working with computers, ISO 9241-210:2019²¹ defines usability as the ‘extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use’.²¹ Moreover, Nielsen²² characterised usability by five quality components: learnability, efficiency, memorability, errors and satisfaction. Additionally, ‘utility’ is also considered important, as it tries to define the functionality of a design and find an answer to whether the interface does what the user needs.²² Blessing and Chakrabarti²⁰ further suggest that the quality of user interfaces should be examined from the viewpoints of the users and system developers (e.g. engineers). From a technical perspective, usability assessments are typically approached from two angles: formative and summative.²³ The primary objective of formative assessment is to unravel potential usability issues at early stages of a design lifecycle.²³ Whereas summative assessments occur at later stages once a reasonably functioning system is developed and focusses on evaluating it against benchmarks (e.g. industry usability standards) or other systems (e.g. previous versions of the system).²³

While the functional aspects of the sport performance monitoring systems designed by Mullane et al.⁴ and Gorden et al.⁵ were validated, the design processes adopted in both system developments did not utilise specific usability assessment approaches to evaluate the usability of the user interfaces prevalent in those systems. Elsewhere, Goudsmit et al.¹³ conducted interviews with the coaches to test the practical use of the coaching dashboard designed for sports like speed skating, cycling and running based on a qualitative approach. However, the authors didn’t utilise standard usability assessment approaches in that instance.¹³ Perhaps as the most in depth illustration of usability examinations in current sport literature, Ranaweera et al.¹¹ illustrated how formative usability assessments (cognitive walkthroughs and task-based testing with think aloud technique) and summative assessments (benchmarking using the System Usability Scale (SUS)) can be conducted when designing digital dashboards in a professional rugby union environment.²⁴ However, in that study, the usability assessment process was executed by engaging only end users, without considering feedback from usability experts early in the formative assessment steps (i.e. cognitive walkthrough). There is further evidence of using the SUS in rugby union to compare the usability of a dashboard against evolving designs,¹² determining overall usability of instrumented mouthguards²⁵ and user experience evaluation of a sports arena booking website.²⁶

In terms of basketball, while there is a strong research focus on developing data science and analytics models to evaluate basketball performance,^27–29 integration of those evidence into information flows and formative usability assessment of the subsequent digital systems is limited. For instance, Juliano et al.¹⁴ developed an online dashboard to track the performances of Division I women’s basketball athletes. However, it is unclear if standardised usability assessment approaches were undertaken in that instance when assessing the usability of the designed system. Similarly, although Harlianto and Setiawan¹⁵ illustrate how the insights generated from statistical models used to predict the most valuable player in a National Basketball Association (NBA) season were visualised using a web-based dashboard, the study does not explicitly consider usability assessment of the relevant system during the design process. Overall, this context illustrates a clear gap in current sport technology research, where most HCI (e.g. dashboards) design endeavours have predominantly focussed on achieving functionality requirements, without identifying and improving specific usability issues in those system designs based on standardised formative usability assessment approaches that consider the feedback of both usability experts and system users. Such contexts enhance the risk of pushing systems to consumers rather than accommodating a pull from end users to adopt them within their core operational processes. This situation can potentially result in the suboptimal utilisation of resources (e.g. design time of engineers, hardware/software use). Consequently, there is a strong need to illustrate how the usability of user interfaces like digital dashboards designed in applied sport environments can be systematically examined by providing explicit guidance on performing formative usability assessments of sports technology systems. Therefore, to bridge those gaps and further strengthen usability literature relating to sports, beyond achieving core functionality requirements, this study aims to illustrate how standard formative usability assessments can be conducted to identify and improve usability issues prevailing in a digital dashboard designed to optimise a scouting and recruitment information flow within a professional basketball club in the United Kingdom.

Methods

This section outlines details of the case study basketball environment, sampling considerations and the overall system design and usability assessment processes considered in this study.

Case study

Guided by a pragmatic philosophical stance, this study adopted a case study design to explore the usability of a digital dashboard designed to support decision-making in a professional basketball club competing in the British Basketball League (BBL). Specifically, an organisational requirement was defined at the club to optimise the scouting and recruitment information flow by implementing a digital dashboard as a user interface to process and visualise information on the performances of basketball players from the National Collegiate Athletics Association (NCAA). The NCAA is an established intercollegiate sports organisation based in the United States of America (USA), which serves as a key talent pool for teams worldwide. Prior to data collection, formal ethical approval for this study was obtained from the affiliated institution of the first author. Accordingly, formal permission was obtained from the BBL club for the research project, and written consent was attained from the participants prior to data collection.

Participants and sample sizes

Guided by a purposive sampling process and by considering further practical and logistic constraints due to the limited number of practitioners available in applied sport environments like that considered in this study,³⁰ the initial functionality requirements of the digital dashboard were obtained from the head coach (HC) of the club, of whom, at the time of this study, possessed more than 7 years of experience in professional basketball. Although the extraction of system requirements from the viewpoint of a single user may appear to induce bias, this situation was minimised because the HC was the key stakeholder with the greatest influence/power and interest in the recruitment process at the club. Mainly because the HC was the primary subject matter expert on recruitment due to resource constraints at the considered BBL club. Thereby, from a stakeholder analysis perspective, it was important to closely manage expectations of the HC as the primary decision-maker pertaining to the considered use case. Afterwards, this study adopted three usability experts (with more than 27 years of combined technical experience in system design and usability assessment) and five coaches at the club (potential users of the dashboard) having approximately 60 years of collective experience in professional basketball for the usability assessment. Specifically, when considering the cost of testing against reward, for qualitative usability assessments, research highlights that three to five experts (e.g. User Interface (UI)/User Experience (UX) designers, usability researchers) are sufficient for a usability evaluation and that testing with at least five users can help to identify 75%–80% of the usability issues in a system.^31,32 Moreover, from a practical sense, there were only five coaches at the club, who would actually use the designed system for decision-making processes. Importantly, the selection of usability experts was guided by the following three key selection criteria: (a) experience of working as an applied practitioner in a professional sport environment with direct responsibility for digital system design or (b) evidence of conducting usability related research or (c) explicit experience of working professionally as a UI/UX Engineer/Designer.

System design and usability assessment process

To support potentially evolving user requirements in the current sport organisation,^33,34 the dashboard development needed to be agile and flexible in execution. Subsequently, due to applications in other professional sport environments,¹¹ we adopted the iterative Build-Measure-Learn cycle in The Lean Startup framework (Figure 1) to develop Minimum Viable Products (MVPs) as rapid prototypes and resolve usability issues in them.³⁵ According to Reis³⁵ this approach helps to eradicate waste (e.g. time and resources) by enabling the program developers to fail fast and learn faster from eminent failures in the design process.

Figure 1.

Iterative Build-Measure-Learn cycle used for the dashboard design and usability assessment.³⁵

Plan

While it can be perceived that the Build-Measure-Learn cycle begins with the Build phase, it practically begins with an initial Plan stage occurring in reverse to the conventional flow.¹¹ During this phase, the Build-Measure-Learn cycle required to formulate the sport innovation was planned, where key focus was given to defining clear design requirements with user engagement and appropriate usability conditions for stopping or continuing the design cycle. Aligning to user requirement extraction in performance monitoring system design research reported previously in sports engineering and technology literature,^4,5,11 a semi-structured interview (less than 30 min) with the team’s HC was initially conducted by the lead author online via Zoom (California, USA)™ to elicit system requirements. The interview questions were structured to focus on the five W’s (what, why, when, where, who) and were developed initially by the first author and face validated with author JR. Prior to data analysis, the recorded audio content of the interview was transcribed verbatim using Otter.ai (California, USA)³⁶ and grammatical errors including unclear sections due to poor audio quality were amended by author AM. Thereafter, led by author AM (validated by authors PO and JR) and guided by an inductive approach based on a semantic coding structure, the six-step reflexive thematic analysis process specified by Braun and Clarke³⁷ was used to unravel themes of system requirements and guidelines for the initial prototype development. Moreover, driven by considerations from the Critical Assessment of Usability Studies Scale (CAUSS),³⁸ it was decided to conduct at least two design iterations pertaining to the usability assessment: one each for usability evaluation with experts and usability testing with actual users.

Build

The main objective of the Build stage in The Lean Startup framework is to develop a Minimum Viable Product (MVP), which is a version of the system containing just enough features or functions to evaluate the criteria defined in the Plan stage.³⁵ Consequently, during the Build phase of the initial system, Tableau Desktop (version 2023.1.0, Salesforce, Seattle, USA) was used to create an MVP of the dashboard to meet the initial functionality requirements defined in the Plan stage. Data required to design the dashboard was imported into Tableau from Microsoft Excel (version 2402, Washington, USA), after gathering publicly available NCAA player data from multiple web sources such as RealGM³⁹ and ESPN⁴⁰ through Power Query and HTML import options.

Measure

From a theoretical perspective, the Measure stage mainly focusses on evaluating the suitability of the MVP to achieve the criteria set in the Plan stage.³⁵ Because the primary objective of this study was focussed on examining usability issues (i.e. formative) in the designed system, the Measure phase was used to examine usability of the designed dashboard with experts and users. Resultantly, the industry accepted and widely used heuristic evaluation process (based on 10 heuristics of user interface design) was conducted with the three experts to evaluate usability of the first MVP.^31,41 Importantly, independent evaluation sessions were carried out by the lead author with each expert using Zoom™ videoconferencing software. Every session lasted no more than 45-min and expert feedback on the 10 heuristics pertaining to the MVP were sequentially recorded using the template provided by Moran and Gordon³¹ Afterwards, a list of usability issues (categorised as minor or major based on the technical complexity of the expected design changes), in relation to the heuristics were listed alongside any perceived functionality changes from the expert feedback. The Measure phase of subsequent cycles implemented after designing new MVPs to overcome issues extracted from prior heuristic evaluations were used to identify further usability issues from task-based think aloud (i.e. users discussing their thoughts when interacting with a system) usability testing sessions with the five coaches (i.e. users). Crucially, the tasks (see Table 1) were created to represent real-life scenarios within the scouting process and to ensure access to the key sections of the dashboard (e.g. widgets). Specifically, the tasks represent key steps that a user would undertake to extract information from the dashboard pertaining to actual recruitment related decisions at the club. In doing so, the tasks were streamlined to consider all possible combinations of engagement with the dashboard by a user. Thus, covering all key functionalities of the dashboard.

Table 1.

The list of tasks designed for the task-based usability testing sessions.

Task	Task description
T₁	We are looking for a jump-shooting scorer of at least 198cm in his senior year. Report the name of the player(s) showingin the dashboard.
T₂	Select ‘paint bigs’ (refers to either centres or power forwards, who are most effective inside the area between thefree-throw line and the baseline) of a minimum of 210cm × 114kg. Afterwards, filter by an EFF of at least 14.9 andreport the name of the player with the lowest team ranking according to Ken Pomeroy.
T₃	How many games have been played by the only multi-level scorer with at least 1.24points per possession?
T₄	Looking at downhill scorers in their senior year of a maximum of 193cm and an EFF of at least 10. Consider players with a coefficient over 21 and report the name of the player(s) with at least 95% in free throws.

When conducting the task-based usability testing sessions, the following sequence of actions were adopted: (a) introduce main features of the interface to the participants, (b) provide a list of the four tasks, (c) perform each task sequentially and record the corresponding outcome and (d) respond to the post-task Single Ease Question (SEQ), used to indicate the difficulty of each task.⁴² Moreover, guided by similar research,¹¹ each testing session was restricted to a maximum duration of 30min and conducted in-person at the considered club by creating a lab-based testing environment, where screens of the participants were recorded to help analysing the think aloud data. In this instance, the task completion rates were used as guides to identify potential sections of the interface with usability issues. Thereafter, subsequent user perceptions extracted from the think aloud process were mapped to triangulate and identify specific user perceived usability issues in the system.

Learn

Completion of the previous Measure stage typically yields core insights (as information) pertaining to the created MVP, which can be used to Learn about the design and decide to either Persevere with the prevailing version (if criteria defined in the Plan are achieved) or Pivot to a different design if further changes are necessary.³⁵ In this research, it was decided to Preserve with an MVP version if no major usability issues were prevalent after testing with usability experts and potential system users in the considered basketball club.

Results

In this section, we report outcomes of the system requirement elicitation process, iterative dashboard design and formative usability assessment steps considered in the study.

System requirements

As specified in the later sections, outcomes from the initial semi-structured interview with the HC yielded three key themes of system requirements: (a) dashboard functionalities and design, (b) scouting process and (c) user information.

Dashboard functionalities and design

The first theme includes details regarding the dashboard functions needed to improve the scouting process and specific suggestions on its design (see Table 2).

Table 2.

Dashboard functionality requirements and design considerations.

Sub theme	Definition	Functionality requirement	Quote from head coach	Design consideration
Data filtering and management.	Criteria used for managing the data and choosing information of interest from the dataset.	Categorising players according to style of play rather than position.	‘We will try to come up with a better definition of the types of players that will fit within our style of play, […] we would categorise it in a different way than those kinds of more traditional five positions’.	Included a drop-down menu on the top left of the dashboard showing player types (not traditional player positions), previously agreed with the head coach.
		Filtering data is essential.	‘Filter in different ways is where we can gain an advantage’.	Added four different filtering boxes on the top of the dashboard.
		Should allow player profiling.	‘Basic player profile. […], name, height, weight, position, […] and then there'll be a statistical analysis’.	Included this information in the ‘Player Recap’ showing when selecting a player.
Data presentation and visualisation.	The process and the elements involved to visually represent insights generated from the dataset and conveying relevant information.	Advanced statistics would be important.	‘[…] with the ability to pull in some more advanced statistical measures, which may be created by our formulas, as those statistics may not be available in the mainstream sites’.	Included three advanced statistics in the third filter box at the top of the dashboard.
		Highlighting key KPIs with colour is important.	‘Colour is usually a good way of being able to highlight the key statistical measures’.	Other than for headings, colour was only implemented to highlight the selected player from the rest of the dataset.
		Should show basic player statistics.	‘[…] ideally, the basic […], what we call box score data: points, rebounds, assists, etc’.	Basic stats included in the ‘Player Recap’ showing after selecting a player.
		Visualisation makes working with large databases easier.	‘[…] a more visual representation of that makes it easier to get through the data and understand it quicker’.	Implemented a visualisation where each player is a data plot ranked horizontally depending on an aggregate player type score.
Talent identification and selection.	Ways in which the dashboard should aid in identifying talented players.	^♦♦Linking video to the players would be ideal.	‘A way of linking in video to that would be ideal as well’.	Not included in the project due to time and practical constraints.
		Should help identify ‘hidden gems’.	‘[…] They may be at one team where their role is limited, but they perform well in their role’.	Included a team ranking based on the work of one of the most established NCAA basketball experts (Ken Pomeroy), to better understand the relative strength of a player’s team.
		Should help shortlisting interesting players.	‘The project is successful by the number of players that we are able to identify through the modelling’.	To shortlist from the initial 5000 data points, only players in the upper quartile of each player type score were shown on the main dashboard view.
		^♦♦Should help speed up the scouting process.	‘The benefit of the dashboard is the speed of it, being able to identify potential players and dig into their profiles with more depth’.	No specific design consideration linked to this requirement. However, the dashboard allowed the gathering of data from three different sources in a single view (e.g. stats, physical attributes, class, adv. stats, etc).

Only two functionality requirements identified from the interview were not directly considered in the initial design iteration (denoted by ^♦♦).

Scouting process

The second theme focussed on providing a broad overview of the scouting process. On multiple occasions, the HC stressed that obtaining insights about a large pool of potential players (like the NCAA data considered here) helps to obtain a competitive advantage when negotiating contracts of new players. ‘The earlier we can identify players, the earlier we can start those conversations and express an interest’. With the domestic league and other national competitions in the UK ending in May, the HC indicated June as the optimal time for recruiting to start, thus providing a clear timeframe for the dashboard to be ready ahead of the 2024/2025 BBL season.

User information

The last theme provided information about the end users of the dashboard. The pool of stakeholders utilising the dashboard is quite limited, and the HC was the key stakeholder responsible for leading and coordinating the scouting process. This context further justified the decision to identify system requirements from the perspective of the HC.

‘I would say so, generally, our General Manager, myself as the head coach and two assistant coaches as well, who will be kind of loosely involved in the recruiting process… Primarily, the coaching staff and the General Manager will be in charge of recruiting for the player. So, they would be the ones who will be using the dashboard’.

Initial system design

The dashboard design process was led by the lead author as an insider within the club (i.e. emic approach). Based on the guidelines proposed by Carroll⁴³ a single dimension scatter plot was used as the primary visual. The x-axis was specifically used to represent player rankings based on an internally generated single metric coefficient (details on generating the metric are outside the scope of this manuscript), whilst a ‘fake y-axis’ was used to separate and avoid overlapping of data plots in the visual.⁴³ To categorise players according to pre-determined types rather than by the position on the court (see Table 2), the internally derived list of 12 player types with their definitions and relevant KPIs were provided by the HC to the lead author prior to the system design. Consequently, four filter boxes (i.e. two sliders and two dropdown menus) were added at the top of the dashboard to filter players according to player type (12 categories), physical attributes (height and weight), performance statistics (efficiency, true shooting percentage and points per possession) and college class (freshman, sophomore, junior and senior). Also, to accommodate player shortlisting and reduce system process times (see Table 2), data were preliminary filtered to only consider athletes with a player-type coefficient above the upper quartile. Also, by clicking on each data plot, it would be possible to get each player’s basic performance statistics from the current season. The resulting initial dashboard version is shown in Figure 2.

Figure 2.

Evolving versions of the dashboard as the design process navigated through the Build-Measure-Learn cycles to redesign the system to overcome specific usability issues identified from the heuristic evaluations and task-based think aloud sessions. Location of each design change has been denoted by the relevant usability issue assigned in Tables 3 and 5. Individual images of each version of the evolving dashboard has been provided in the Supplemental Materials.

Usability evaluation with experts

From the three heuristic evaluation sessions, 25 usability issues were identified, including recommendations to fixing them (see Table 3). The severity of each issue was graded as minor or major by the lead author (in consultation with other authors) based on the technical complexity of the expected design changes to overcome the relevant usability issue. Thereby, it was initially decided to prioritise design changes to overcome major usability issues. However, this decision was quickly reversed due to time constraints defined for the overall project by the team management. Specifically, the dashboard design processes needed to be completed during the off-season period (i.e. in weeks and not months). Such constraints are typically prevalent when designing systems within professional sport environments. Hence, it was decided to prioritise usability issues requiring least changes to fix (thereby, minimising time spent on the design process). These rapid changes in the design approach were made possible by our mindset to fail fast and learn faster during the entire design process. This decision was also conditioned by the holistic aim of Lean Startup framework, where the focus is on engaging actual users early in the design process to swiftly turn the Build-Measure-Learn cycle to enable rapid prototyping and accelerated learning.³⁵ Hence, to engage actual users in the usability assessment process, initially, $\frac{9}{25}$ issues identified by the experts were prioritised for improvement (see Table 3). This outcome was achieved by Pivoting to a new MVP version in a fresh design cycle (see Figure 2 with a dedicated ID for each issue and design change that links back to Table 3). Importantly, the issues identified by experts that were not factored at this stage were considered again after cross referencing them with the usability issues identified later by the actual users from task-based think aloud sessions.

Table 3.

The list of usability issues identified from the heuristic evaluations and the corresponding actions taken to fix them.

Heuristic	Issue	Recommendation	Severity	Design consideration	ID
Visibility of system status	^▲▲No feedback to users during lags or page loading.	Design and implement feedback.	Minor	Feature not compatible on the software used.	A1
	^♠♠ Lack of clarity about the coefficient’s meaning.	Consider guiding the Coefficient.	Major	Add an asterisk next to the word ‘coefficient’ directing the user to an explanation at the bottom of the dashboard.	A2
	^♠♠The option to click on the data points is not explicit.	Give indications on how to get detailed stats.	Major	Add a message ‘Click on the plot to view player recap below’ when hovering on any data plot.	A3
Match between the system and the real world	^♠♠The ‘Worksheet selection parameter’ and ‘Class’ headings are unclear - Height, weight and class are truncated.	Create clearer headings.	Minor	Change ‘Worksheet selection parameter’ to ‘Click below for player type selection’; change ‘Class’ to ‘Click below to select college year’; fixed ‘Height’ and ‘Weight’ headings.	A4
	^■■The use of the dashboard is counterintuitive.	Add instructions on how to use the dashboard.	Major	Add explanation at the bottom recalling an asterisk next to the dashboard Title: ‘To use the dashboard, select the desired type of player and refine the search using the filter boxes’.	A5
	^■■The meaning of data plots is unknown.	Add info on what the data plots represent.	Minor	Add the info ‘Each plot is a player’ in the graph explanation heading.	A6
	^■■Adv. stats terminology might be obscure to non- basketball users.	Consider expanding the stats’ headings.	Minor	Expand the headings of advanced stats (EFF → Efficiency; TS% → True Shoot%; considering the available space, PtsPerPoss would be too long of a heading [Points Per Possession]).	A7
	^▲▲The selected graph might not be the best option for showing data.	Consider other types of graphs.	Minor	Although other options can be investigated in future iterations of the dashboard, it was decided to persist with the current graph to illustrate most data points at a given instance and minimise the time required to shift through individual data points. Thereby, enabling to view the stats swiftly by the user.	A8
	^■■The graph explanation seems to refer to the top section of the dashboard.	Move the graph explanation below the scatter plot.	Minor	Increase the upper padding and remove the lower so that the explanation is closer to the graph.	A9
User control and freedom	^▲▲No ‘reset filters’ option.	Add a ‘reset filters’ option.	Major	Not actioned due to software limitations.	A10
	^■■Null values shouldn’t be shown.	Fix null values from the data source.	Major	Null values fixed from data source.	A11
Consistency and standards	^▲▲The coefficient’s calculation might not be mathematically sound.	Evaluate the reliability of the coefficient’s calculation.	Major	The project focus on usability and time constraints provided limited opportunities to explore different statistical calculations to derive the coefficient. However, consistency and standards in players’ ranking were considered based on selected metrics agreed with the head coach and from the use of normalised KPIs (in this case, per 40min stats).	A12
	^♠♠The arrow next to the graph explanation looks like an actionable button.	Consider removing the arrow.	Minor	Remove the arrow next to the graph explanation.	A13
	^■■The random vertical hierarchy of the scatter plot might be obscure without explaining the graph.	Consider alternative visualisations.	Minor	Add explanation at the bottom recalling an asterisk next to the dashboard Title: ‘The vertical distribution of the plots is random and only serves to avoid overlapping’.	A14
Error prevention	^▲▲No error feedback is provided when no data is displayed.	Create an error message.	Major	Feature not available on the software used.	A15
	^▲▲No indication of how far sliders can be moved before getting to no data shown.	Consider adjusting sliders according to the available data range.	Major	Available data range on a given slider changes depending on other filtering. Thereby, not actioned due to time constraints and software limitations.	A16
Recognition rather than recall	^▲▲No option to compare players.	Consider an option to compare players after clicking on data points.	Major	Not actioned due to time constraints.	A17
	^▲▲The colour of the active section of the sliders might be more different than the inactive part.	Consider choosing different colours.	Minor	Not actioned due to software limitations.	A18
	^♠♠The colour palette might be challenging for colour- blind users.	Consider changing colour palette.	Minor	Change colours to a palette more suitable for colour-blind users.	A19
Flexibility and efficiency of use	^♠♠‘KenPom Team Ranking’ refers to a niche audience.	Provide info on KenPom Team Ranking.	Minor	Expand the team ranking heading to provide more context.	A20
	^▲▲Sliders’ controllers are small.	Create bigger controllers.	Minor	Not actioned due to software limitations.	A21
Aesthetics and minimalist design	^♠♠Logos might create an excessive cognitive load.	Consider shrinking or eliminating logos	Minor	Shrunk logos.	A22
	^♠♠Filter boxes are crammed together, no space between different blocks of information.	Consider adding spaces between the dashboard sections.	Major	Add spaces between information blocks.	A23
	^■■The stats in the player recap might be not readable enough.	Consider choosing a darker or bigger font.	Minor	Change font colour from grey to black and increased size of 2 px.	A24
Recognise and recover from errors	-	-	-	-
Help and documentation	^♠♠The sequence of the actions is unclear.	Consider if explaining the order of the actions to take could be useful.	Minor	Add numbers to the filter boxes to suggest a sequence of action.	A25

Specifically, $\frac{9}{25}$ issues (denoted by ^♠♠) were fixed immediately after the heuristic evaluation sessions, further $\frac{7}{25}$ issues (denoted by ^■■) were fixed later after subsequent task-based think aloud usability tests with users, while the remaining $\frac{9}{25}$ issues (denoted by ^▲▲) were not fixed due to software limitations or project time constraints. Moreover, each issue ID has been mapped to the evolving system design changes denoted in Figure 2.

Usability testing with users

Table 4 illustrates completion rates of the four tasks used to test the usability of the redesigned dashboard (new MVP) with the five users.

Table 4.

Task completion rates (including SEQ results) of the usability testing.

Tasknumber	User										Task completionrate (%)	SEQ (geometric mean)
	U₁		U₂		U₃		U₄		U₅
	Completion	SEQ	Completion	SEQ	Completion	SEQ	Completion	SEQ	Completion	SEQ
T₁	Y	6	Y	7	Y	7	Y	6	Y	5	100	6.2
T₂	Y	6	Y	6	Y	6	N	6	N	3	60	5.4
T₃	Y	6	N	6	Y	6	Y	5	Y	2	80	5
T₄	Y	5	N	5	Y	5	N	4	Y	3	60	4.4

The first task (T₁) was completed by all participants, and they found it to be the easiest. Both U₄ and U₅ failed T₂ as they selected the player with the lowest player type coefficient rather than the one with the lowest team ranking. This result suggested there was not enough clarity about the difference between the player type coefficient and team ranking within the dashboard. Although U₄ and U₅ successfully completed T₃, both forgot to reset the sliders after completing the task. Thus resulting in no player data being displayed on the dashboard and was further evidenced through the Think Aloud feedback of U₃, where addition of ‘reset filters’ was suggested as an improvement. Most users encountered difficulties with T₄ when trying to filter data using the slider as its resolution could not support specific filtering requirements of the users (e.g. when trying to select an efficiency value of 10, the slider would skip directly from 9.8 to 10.1). Having understood that it was impossible to select certain efficiency values by moving the sliders, all users except U₄ took substantial time to realise that the desired value could be typed into the text box adjacent to the sliders to filter the data pertaining to desired resolution, highlighting the need to make it explicit to the users. There was further evidence of this issue from the think aloud data from U₁: ‘The sliders miss some values when you are trying to filter’. Moreover, U₂ stated that: ‘The sliders should be easier to use’. Furthermore, beyond usability concerns, the testing process also illustrated further functionality improvement requirements. For instance, U₄ stated: ‘The top toolbar is a bit too small for me’. This user wore corrective eyewear and mentioned it took some time to familiarise with to the size of the slider controls. Overall, completion of the task-based usability testing sessions with the users resulted in further seven usability issues and two functionality suggestions depicted in Table 5. Consequently, it was decided to continue the design process by Pivoting to a new MVP to overcome $\frac{4}{7}$ usability issues (see Figure 2 and Table 5). The remaining three issues from the usability testing sessions with users were not considered in the next design iteration due to project time constraints stated previously. Moreover, the two functionality suggestions were also not factored owing to software limitations and project time constraints.

Table 5.

List of issues identified from the task-based usability testing sessions with users.

Issue	Category	Design consideration	ID
Lack of clarity about the playertype coefficient.	Usability	Arrange a knowledge transfer session with key stakeholders (led by the Head Coach) to provide clarity on the key metrics used in the recruitment process.	B1
Lack of clarity about the ‘Ken Pomeroy Team Ranking’.	Usability		B2
Player stats must beremembered when selecting multiple players.	Usability	Not actioned due to project time constraints.	B3
No ‘reset filters’ option.	Functionality	This issue appears to have resurfaced after usability testing with users. Perhaps, this is because Tableau has no built-in 'reset filters' button and attempted alternative design changes in the previous iterations caused bugs in the system. Hence, due to tight deadlines, a different software was not considered.	B4
No error message when no datais displayed.	Usability	Not actioned due to project time constraints.	B5
No indication that desired values for filtering can be typed intothe text boxes next to the sliders.	Usability	Not actioned due to project time constraints.	B6
Slider controls should be bigger.	Functionality	This issue appears to have resurfaced after usability testing with users. Perhaps this is because Tableau lacks the functionality to adjust slider size. Due to advanced project stages and tight deadlines, switching to alternative software was not feasible.	B7
Dashboard is not straightforward.	Usability	Add dashboard information to provide guidance on usage.	B8
Acronyms in stats headingsmight be of difficult to interpret.	Usability	Extend stats heading to improve readability and provide clarity in the acronyms.	B9

Each issue ID has been mapped to the evolving system design changes denoted in Figure 2.

Thereafter, further seven residual usability issues that were identified from the earlier heuristic evaluations (that were not factored when developing the second version of the dashboard immediately after heuristic evaluations) were considered (see Table 3). Subsequently, the finalised minimum viable product of the dashboard depicted in Figure 3 was created to end the initial design process. This decision was also derived because the criteria set in the plan stage of the Lean Startup process (see section 2.3.1) to stop the cycle were achieved.

Figure 3.

Finalised version of the dashboard (minimum viable product) after overcoming key usability issues.

Discussion

This study was focussed on designing a digital dashboard to optimise a scouting and recruitment information flow operational in a British Basketball Club and improve usability of the subsequent system based on standardised formative usability assessments conducted with usability experts and system users.

System requirement elicitation

Selection of the HC as the primary voice for requirement elicitation was due to the high interest and power influencing the scouting and recruitment process by this stakeholder at the club. The subsequent understanding obtained regarding the scouting process from the HC was instrumental in guiding the overall dashboard design in relation to the holistic organisational objectives of the club. For instance, the importance of signing players before competitors and managing lesser-rated players to secure a contract for the following season were highlighted as key organisational philosophies.

‘The further ahead of the curve we are the more chance we potentially have of signing players […] if we were to come in and offer them a contract that guarantees their position for the following year, some players are a lot happier to do that than to wait to try and find… a more lucrative contract’.

As per the HC, it was also evident that the easiest and most common way for teams to get to know potential new recruits for an upcoming season is through agents: ‘the agents are often emailing or contacting the same teams within our league as well’, thus shrinking the chances of success in recruitment considering the team works on a low budget: ‘we are not able to offer the salary of many teams within our league’. Such feedback helped to clarify resource constraints that needed to be considered when undertaking the system design (e.g. limited finances for technology). The HC provided additional information on how the information provided by the designed dashboard would be operationalised at the club: ‘we would then work to connect with those players, either directly or through the agents who may not have proposed these players to us’. Although it is beyond the scope of this research, the overall organisational requirement to reach less popular players might indicate that ranking candidates based on traditional basketball performance statistics may not be sufficient. Considering the NCAA athletes factored in the dataset are playing in more than 350 different teams with varying competitive levels.

Usability assessment with experts and users

The usability evaluation approach examined in this research is a primer for illustrating how formative usability assessments can be conducted on systems developed for sport environments by engaging with usability experts during early stages of a design lifecycle. Mainly because, the cognitive walkthrough performed by Ranaweera et al.¹¹ when examining the usability of dashboards implemented in professional rugby union was conducted by only engaging with users. Whilst prior research justifies the applicability of using cognitive walkthroughs with users,^44,45 unlike usability testing, usability evaluation (or inspection) methods like cognitive walkthroughs, heuristic evaluations, feature inspection, etc. are explicitly conducted with usability experts and system design teams early in the design process before engaging with actual users.⁴⁶ Moreover, the three experts recruited for the usability evaluation were external to the case study organisation. While professional sport is renowned for closed operational models with limited considerations of open innovation, the design process adopted in this study illustrates how sport environments can benefit from welcoming systematic engagements with external experts when integrating technology systems to support specific segments of the overall operational process. From a technical viewpoint, beyond identifying key usability issues, the heuristic evaluation sessions enabled the experts to suggest potential design improvements to overcome those problems.

While only selected issues identified from the heuristic evaluations were remedied prior to the second stage of testing due to time constraints, most problems identified from the subsequent task-based usability tests with users overlapped with those pending issues not considered for immediate system design following the usability evaluations with experts. For instance, to overcome the issue with misinterpreting labels identified from the second task (T₂), the experts had originally suggested adding the option to select multiple players to compare them while showing the relevant statistic instead of the user remembering them when selecting different players. Also, the need to integrate a reset option identified by users when attempting the third task (T₃) was also stated by the experts. As a remedy, in addition to including reset filters, they recommended the addition of prompts to inform system status to users when no data was available due to a filtering condition. One exception was the inability to type the desired figures for filtering inside the text boxes next to the sliders by the users. It is important to note that improving the system to overcome recurring issues like ‘slider resolutions’ were not feasible due to technical limitations of the chosen data visualisation software, where options to increase the size of the slider controls or their aspect were not offered. Moreover, the software did not have an integrated functionality to implement a ‘reset filter’ button in its dashboards. Hence, considering the tight deadline of the project, the possibility of using a different data visualisation software to overcome such repeated issues was not considered in the study. While this situation may suggest that the design changes undertaken after the heuristic evaluation may not have been optimal, as per existing usability guidelines,³⁸ it nevertheless signified the importance of triangulating system usability assessment outcomes obtained from both experts and users. Moreover, aligning to recommendations of similar healthcare informatics⁴⁷ and design research,²⁰ the outcomes of this study further signified the importance of adapting user-centred approaches to system innovations in an applied sport environment like a professional basketball club. Finally, as a form of member checking, the overall system design and usability assessment process could have benefited from repeated engagements with the stakeholders. For instance, an additional early engagement with the HC after the system requirements were elicited may have helped to identify specific usability issues (e.g. lack of clarity about the coefficient’s meaning) early in the design process (i.e. even before engagements with experts and users). However, such approaches were restricted due to resource implications prevalent in the considered environments, where time commitments by a key stakeholder like the HC for research endeavours can be limited.

Usability considerations in user interface design

As stated in the introduction, prevailing performance monitoring system design research in sports engineering and technology literature primarily focusses on meeting functionality requirements of a system without factoring usability aspects during the design process. Perhaps such a situation is manifested because design research typically generates prototypes that are not considered for direct deployment.²⁰ In turn, sports engineering and technology design research have adopted this thinking by considering functionality at the forefront of a system design endeavour.^5,48 Interestingly, although Mullane et al.⁴ adopted a user-centred approach to the design of a performance monitoring system, this is only perceived from the viewpoint of user requirement elicitation and not for system usability assessment. However, outcomes of the design research presented in this study suggest the possibility of considering usability attributes as a key component pertaining to the design process of sports technology systems. Importantly, in contexts where access to real users may be challenging during the system design process, outcome of this study highlights that systematically evaluating the usability of sports engineering and technology design using an approach like heuristic evaluation with the design team and potential technology experts can still help to unravel usability issues early during the design process. Such approaches have great potential to eradicate significant time and resource wastages on eminent failure from instances of limited system adoption by users due to prevailing usability problems in a design. Furthermore, our results also align with existing design research guidelines that emphasise the importance of factoring usability at the forefront of user interface designs.²⁰ Yet, above all, as further supported by other design research that adopted similar approaches when designing user interfaces in a professional rugby union environment,¹¹ the results of this study also strengthens the applicability of the Build-Measure-Learn lifecycle in The Lean Startup thinking as an adequate framework to guide the rapid prototyping, active experimentation and validation of user interface designs like digital dashboards implemented in applied sport environments. Consequently, other sport environments or industries aiming to design digital dashboards could utilise this thinking to fail fast and learn faster during the innovation process since constant developments in artificial intelligence (AI) would further enable the rapid creation of digital system MVPs in the future. Such mindsets will strengthen the operationalisation of validated learning based on active experimentation, which is at the heart of Lean Startup approach.³⁵ In turn, helping to minimise design wastages by shifting away from conventional product development lifecycles, which may be too rigid to accommodate evolving user needs in highly dynamic decision-making environments like professional sports clubs.

Design methods adopted to overcome usability issues

Most usability issues discovered in the heuristic evaluation and the user testing phases were improved by integrating textual descriptors within the main view of the dashboard. Although this system redesign approach helped to improve usability of the interface, it may have risked enhancing cognitive load on prospective users.³ Thereby, it was important to find the ideal balance between being informative without overwhelming users with text. Consequently, asterisks were added at the bottom of the dashboard to redirect users to short explanations. These explanations provided an initial context which proved beneficial during the task completion phase. Another issue revealed during the heuristic evaluation sessions was the general organisation of sections and spaces within the interface. Enhancing spacing between the top section and the main body of the visualisation and distancing the individual filter boxes helped attain a neat overall appearance. Moreover, to further minimise cognitive overload and improve readability, logos were shrunk early in the design process. In the third version of the evolving interface (see Figure 2), the text within player recap was increased in size and darkened in colour. Finally, while some usability issues identified from the formative assessment could not be addressed due to technical limitations of the software used and project time constraints stated earlier, the overall approaches adopted to improve the dashboard highlighted that complex design changes are not always necessary to improve the usability of a system. Such findings should further encourage practitioners to integrate usability considerations at the forefront of system design endeavours operationalised in applied sport environments.

Study limitations

Although engagement of the HC as the only stakeholder for requirement elicitation helped to obtain a holistic overview of system needs and organisational philosophies, perhaps it limited collating micro level functionality requirements during dashboard operation. For instance, as evidenced in Table 5, in addition to specific usability issues, further functionality requirements were unravelled from the task-based usability testing sessions with the users. As a solution to overcome such limitations, Ranaweera et al.¹¹ integrated practitioners from the major user groups (i.e. strength and conditioning coaches and physiotherapists) when eliciting system design requirements. The relatively small pool of actual users (n = 5) that were used for the task-based think aloud usability assessment sessions can be perceived as a limitation. This situation was mainly caused due to the limited number of practitioners operating in resource constrained applied sport environments like the one considered in this study. While researchers have highlighted such implications that can exist when conducting applied research,^11,30 there was minimal impact on the overall study results due to this situation. That is because, when factoring cost of testing versus reward, research highlights that testing with five users is sufficient to identify most usability issues in a system.³² However, such resource constraints could impact the use of quantitative usability testing methods in an applied sport environment. Mainly because limitations in sample sizes caused due to resource constraints could enhance the risk of errors generated in statistical judgements (e.g. Type II) from usability testing approaches like split tests, benchmarking, etc. This context was a primary reason that the current study did not consider a summative test to benchmark the designed dashboard against industry usability standards, as only five actual users would be available to generate the relevant data. In contexts where the primary aim is on designing generalisable systems across sport organisations (e.g. injury management information system introduced by the league across all clubs), the design process could use a diverse pool of actual users from different environments during the usability assessment process. Designing a generalisable dashboard applicable to different basketball clubs was not a consideration of this research. Instead, the focus was on illustrating a systematic approach to conducting formative usability assessments pertaining to a system implemented to meet organisational objectives relevant to a specific case study environment. Therefore, we encourage sport usability researchers to consider the examined approaches in this study and report if they are applicable to assessing the usability of systems (hardware and software) operationalised in similar basketball environments and/or other sport organisations.

Conclusion

Overall, this case study illustrates how The Lean Startup framework can be used to guide the design and usability assessment of a digital dashboard within a professional basketball organisation. As supported by prior case studies in professional sport, the results reassure the capability of utilising the Build-Measure-Learn cycle in The Lean Startup framework to guide system developments in applied sport contexts. Perhaps more notably, the article illustrates a systematic approach to conducting formative usability assessments on digital systems like user interfaces implemented to support decision-making in a professional sport setting. Importantly, the study emphasises how the entire design and usability assessment process can be operationalised by practitioners embedded in a highly volatile, uncertain, complex and ambiguous (VUCA) applied sport environment like a professional basketball club in England. Hence, future design research that focusses on developing digital user interfaces like dashboards in sport environments could adopt the methodological framework presented in this study when aiming to identify specific usability issues in those systems. The results generated from such wider applications would help in validating the generalisability of the formative usability assessment approach utilised in this study across other applied sport contexts. Conversely, as stated earlier, it is important to emphasise that the dashboard itself in its current form may not be directly translated to other similar environments before examining specific operational objectives of those sport settings. Particularly, since the system developed in the current study was explicitly designed to meet the organisational goals of the considered club and because each environment contains unique operational models (e.g. case specific financial plan, game model, recruitment strategy), it is important to evaluate those contextual factors when adopting systems from other sport environments. Such endeavours could also consider the integration of the dashboard with other information systems, artificial intelligence, augmented reality, diverse data sources (e.g. data from college/youth players outside the United States of America) and metrics pertaining to scouting and talent identification during system adoption. Moreover, the findings provide evidence on the importance of considering a user-centred approach to sport related system design and further highlights how systems implemented in applied sport environments can contain usability issues that require systematic resolution prior to full scale implementation. Thereby, emphasising the need to further strengthen usability research pertaining to the sport industry.

Supplemental Material

sj-pdf-1-pip-10.1177_17543371251324816 – Supplemental material for Formative usability assessment of a digital dashboard designed in a British Basketball League club

Supplemental material, sj-pdf-1-pip-10.1177_17543371251324816 for Formative usability assessment of a digital dashboard designed in a British Basketball League club by Alessandro Mezzone, Patrick Oxenham and Jayamini Ranaweera in Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Informed consent/declaration of Helsinki/ethics committee approval

Formal ethical approval for this study was obtained in line with the ethics process of the affiliated institution of the first author, including written informed consent from the BBL club to conduct the research and from the participants prior to data collection.

ORCID iD

Jayamini Ranaweera

Data availability

Sources of the publicly available player data used to develop the dashboards have been stated in the manuscript. However, because the professional basketball club considered in this study wishes to remain unidentified, specific research data generated within the project will not be shared publicly.

Supplemental material

Supplemental material for this article is available online.

References

Sarlis

Tjortjis

Sports analytics: evaluation of basketball players and team performance. Inf Syst 2020; 93: 101562.

Dammann

Data, information, evidence, and knowledge: a proposal for health informatics and data science. Online J Public Health Inform 2018; 10: e224.

Knaflic

CN.

Storytelling with data: a data visualization guide for business professionals. Hoboken, NJ: John Wiley & Sons, 2015.

Mullane

Chakravorti

Conway

, et al. Design and implementation of a user-centric swimming performance monitoring tool. Proc IMechE, Part P: J Sports Engineering and Technology 2011; 225: 213–229.

Gordon

van Lopik

Hayward

, et al. Development of a system for monitoring performance in the strength and conditioning domain. Proc IMechE, Part P: J Sports Engineering and Technology 2021; 236: 295–312.

Magaz-González

García-Tascón

Sahelices-Pinto

, et al. Technology and digital transformation for the structural reform of the sports industry: building the roadmap. Proc IMechE, Part P: J Sports Engineering and Technology 2023; 238: 150–158.

Thornton

Delaney

Duthie

, et al. Developing athlete monitoring systems in team sports: data analysis and visualization. Int J Sports Physiol Perform 2019; 14: 698–705.

Neupert

Gupta

Holder

, et al. Athlete monitoring practices in elite sport in the United Kingdom. J Sports Sci 2022; 40: 1450–1457.

Brath

Peters

Dashboard design: why design is important. DM Direct 2004; 85: 1011285–1011281.

10.

Few

Information dashboard design: the effective visual communication of data. Sebastopol: O’Reilly Media, Inc., 2006.

11.

Ranaweera

Weaving

Zanin

, et al. Digitally optimizing the information flows necessary to manage professional athletes: a case study in Rugby union. Front Sports Act Living 2022; 4: 850885.

12.

Ranaweera

Weaving

Zanin

, et al. Evaluating the impact of a digitally implemented subjective standard on professional rugby union player management decision-making. Int J Sports Sci Coach 2023; 19: 1082–1092.

13.

Goudsmit

Otter

RTA

Stoter

, et al. Co-operative design of a coach dashboard for training monitoring and feedback. Sensors 2022; 22(23): 9073.

14.

Juliano

Thakkar

Taber

, et al. A dynamic online dashboard for tracking the performance of division 1 basketball athletic performance. In: 2023 IEEE 28th Pacific Rim international symposium on dependable computing (PRDC), Singapore, Singapore, 24–27 October 2023, pp.314–318. New York, NY: IEEE.

15.

Harlianto

Setiawan

. Forecasting the NBA’s most valuable player: a regression analysis approach. In: 2024 5th international conference on big data analytics and practices (IBDAP), Bangkok, Thailand, 23–25 August 2024, pp.56–62. New York, NY: IEEE.

16.

Dasgupta

Kapadia

The future of the public health data dashboard. Am J Public Health 2022; 112: 886–888.

17.

Elias

. Learning analytics: definitions, processes and potential. Athabasca, AB: The Landing-Athabasca University, 2011.

18.

Matcha

Uzir

Gašević

, et al. A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective. IEEE Trans Learn Technol 2020; 13: 226–245.

19.

Robertson

Zendler

De Mey

, et al. Development of a sports technology quality framework. J Sports Sci 2023; 41: 1983–1993.

20.

Blessing

LTM

Chakrabarti

. DRM: a design research methodology. Cham: Springer, 2009.

21.

ISO 9241-210:2019 Ergonomics of human-system interaction.

22.

Nielsen

Usability 101: introduction to usability, https://www.nngroup.com/articles/usability-101-introduction-to-usability/ (2012, accessed 22 April 2024).

23.

Joyce

Formative vs. summative evaluations, https://www.nngroup.com/articles/formative-vs-summative-evaluations/ (2019, accessed 25 April 2024).

24.

Brooke

SUS-A quick and dirty usability scale. Usabil Eval Ind 1996; 189: 4–7.

25.

Jones

Tooby

Weaving

, et al. Ready for impact? A validity and feasibility study of instrumented mouthguards (iMGs). Br J Sports Med 2022; 56: 1171.

26.

Hutapea

Sitohang

Sihombing

, et al. User experience evaluation of the booking website using system usability scale and usability testing (study case sports arena). In: 2023 international conference on information management and technology (ICIMTech), Malang, Indonesia, 24–25 August 2023, pp.391–396. New York, NY: IEEE.

27.

Papageorgiou

Sarlis

Tjortjis

Evaluating the effectiveness of machine learning models for performance forecasting in basketball: a comparative study. Knowl Inf Syst 2024; 66: 4333–4375.

28.

Sarlis

Chatziilias

Tjortjis

, et al. A data science approach analysing the impact of injuries on basketball player and team performance. Inf Syst 2021; 99: 101750.

29.

Sarlis

Gerakas

Tjortjis

A data science and sports analytics approach to decode clutch dynamics in the last minutes of NBA games. Mach Learn Knowl Extr 2024; 6: 2074–2095.

30.

Lakens

Sample size justification. Collabra Psychol 2022; 8: 33267.

31.

Moran

Gordon

How to conduct a heuristic evaluation, https://www.nngroup.com/articles/how-to-conduct-a-heuristic-evaluation/ (2023, accessed 25 April 2024).

32.

Nielsen

Why you only need to test with 5 users, https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/ (2000, accessed 22 April 2024).

33.

Kutz

MR.

The need for contextual intelligence in athletic training. Int J Athl Ther Train 2022; 27: 261–265.

34.

Cantillon

Premiership Rugby’s ongoing crisis: why are clubs like Wasps and Worcester Warriors struggling?, https://www.skysports.com/rugby-union/news/12321/12718790/premiership-rugby-s-ongoing-crisis-why-are-clubs-like-wasps-and-worcester-warriors-struggling#:~:text=Worcester's%20plight%20came%20in%20large,saw%20them%20relocate%20to%20Coventry (2022, accessed 12 May 2024).

35.

Reis

The lean startup. New York: Crown Business, 2011, vol. 27, pp.2016–2020.

36.

Otter. Otter voice meeting notes, https://otter.ai/home (n.d., accessed 10 April 2024).

37.

Braun

Clarke

Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101.

38.

Silva

Simões

Santos

, et al. A scale to assess the methodological quality of studies assessing usability of electronic health products and services: Delphi study followed by validity and reliability testing. J Med Internet Res 2019; 21: e14829.

39.

RealGM LLC. RealGM: basketball news, rumors, scores, stats, analysis, depth charts, forums, https://basketball.realgm.com/ (2024, accessed 2 April 2024).

40.

Entertainment and Sports Programming Network (ESPN). Men’s college basketball teams, scores, stats, news, standings, rumors - ESPN, https://www.espn.co.uk/mens-college-basketball/ (n.d., accessed 2 April 2024).

41.

Nielsen

. 10 usability heuristics for user interface design, https://www.nngroup.com/articles/ten-usability-heuristics/ (1994, accessed 2 April 2024).

42.

Sauro

Lewis

. Quantifying the user experience: practical statistics for user research. Cambridge, MA: Morgan Kaufmann, 2016.

43.

Carroll

. Tableau for sport: football scouting dashboard, https://www.youtube.com/watch?v=-oAfUTqut8o (2020, accessed 3 May 2024).

44.

Mahatody

Sagar

Kolski

State of the art on the cognitive walkthrough method, its variants and evolutions. Intl J Hum Comput Interact 2010; 26: 741–785.

45.

Lira

Ferreira

de Souza

, et al. Experimenting on the cognitive walkthrough with users. In: Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services, Toronto, ON, Canada, 23–26 September 2014, pp.613–618. New York, NY: ACM.

46.

Nielsen

. Summary of usability inspection methods, https://www.nngroup.com/articles/summary-of-usability-inspection-methods/ (1994, accessed 28 April 2024).

47.

DTY

Vennemeyer

Brown

, et al. Usability testing of an interactive dashboard for surgical quality improvement in a large congenital heart center. Appl Clin Inform 2019; 10: 859–869.

48.

Jensen

Schmidt

Hennig

, et al. An IMU-based mobile system for golf putt analysis. Sports Eng 2015; 18: 123–133.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.85 MB