Handball is a dynamic, fast-paced, physical sport. It features lots of goals, often scored after complex set pieces. Following the example of basketball, football, volleyball or rugby, handball professionals are starting to explore the application of big data analytics and Artificial Intelligence to improve the performance of their teams and players.
Type
R&D
Sports Analytics
Role
Product Designer
Tools
Figma
Miro
Slack
Zoom
Google Suite
Contribution
Surveys
Ideation
Paper wireframes
Mid-fi wireframes
Duration
4 months
Definition
Context
Handball’s data culture revolves around gathering what basketball calls box statistics – basic information about players’ shooting percentage and discipline record. It is difficult to use such information to gain an in-depth knowledge of the game. This is typically done during the match by one of the assistant coaches, and amended during post-match video analysis.
Match sheet
The assistant coach usually annotates these, while watching the match. They are then debriefed after the match.
Shooting patterns
Also annotated by the assistants during the match. Very often hand-drawn. May require 2 people if doing them for both attack and defense.
Design challenge
The challenge is to design an app that allows handball professionals to quickly and efficiently collect data about match events. The app should enable coaches to understand their teams’ and players’ performances and ultimately take game-winning decisions.
Research
Market research
Advanced, high-quality solution.
Very expensive.
No data about the rival teams.
Generates only aggregated stats.
Not available in real-time.
No immediate action.
Good, affordable product.
Limited analysis and AI capabilities.
Interaction needs improvement.
User interviews
We conducted a series of face to face open interviews with handball coaches from teams of different categories (amateur / pro, junior / senior, male / female), coming from different handball cultures and working in different countries.
During the discussions, we gathered the information on the following:
Demographics
- Age
- Nationality
- Gender
- League Level
- Practice Level & Category
- Experience
Data Culture
- What is your opinion about the use of data and statistics in handball?
- What tools are you currently using to track team and player performance?
- What do you see as the pros/cons of using a tool like Mana?
- When would you use it? Before, during or after the match?
- What features would you want to see in such a tool?
- What is your expertise with IT/digital tools?
Analysis
User personas
Pain points
Ability to quickly log the match events, without being too distracted.
Visual metaphors to focus on important events: timeline, heatmap.
Quickly toggle between individual and team performance.
Goal statement
The Mana App will allow users to log handball match events in real time and monitor the performance of players and teams through real time and post-match analytics. This will affect handball coaches and players who want to optimise the performance of their group by switching from the tedious, error-prone pen-and-paper method to an all-digital tool that improves the data collection and allows users to focus on data centred decision making.
Design
User flow diagrams
The diagrams below describe the 2 main user flows:
- Consulting a team’s players and matches, and creating a new match.
- Logging an event during a live match.
Click on the images to enlarge. Zoom in to check the details.
Site map
Ideation
Based on the lessons learned in the Research phase, I proceeded to developing paper wireframes for some of the most important screens of the app.
These were then the basis for brainstorming sessions with the mobile developer and the data scientist of the project, in order to design mid-fi level prototypes, while keeping in sync on any issues that might impact the functionality of the app.
Mid-fi prototypes
User tests
An initial functional prototype for iPad was developed, in order to test the assumptions related to functionality and interaction (events logging, timeline). The participants were some of the users who initially interviewed. They were presented with a demo of the product and allowed to browse through it. They were then debriefed during an open discussion about their experience with the product.
