How to visualise your sport data on SportFin

A quick guide to data visualisation on SportFin.

Siddesh Iyer avatar
Written by Siddesh Iyer
Updated over a week ago

Visualising data about the people your sport organisation is engaging, what sport activities you are delivering and where you are delivering them can be hugely beneficial when it comes to applying for funding or crowdfunding. The visualisations help showcase to funders/supporters the impact of their funding and that they will be able to see where their funding has been used.

In fact, our market research has shown that funders are 50% more likely to fund and are willing to put in 3 times as much funding if they can track engagement and impact data in real-time.

SportFin gives your sport organisation access to powerful data visualisation tools that can be used to tell impactful stories about the social value your organisation is generating and give your organisation a real competitive edge when you need to fundraise. This article will inform you about the different kinds of visualisations you can create on SportFin and how you can use it for you fundraising journey.

Read more about the what sport funders are looking for from beneficiary sport organisations:

Selecting a data category to visualise

There are four different data categories that can be currently visualised on SportFin:

  • Social impact data: visualises the various impact outcomes people have been correlated to through engaging in your sport activities.

  • Sport engagement data: visualises data about sport participation and volunteering within your organisation.

  • Sport activities data: visualises data about the sport activities you have delivered and its characteristics.

  • Sport participants and volunteers data: visualises data about the demographics of your participants and volunteers.

Selecting a data domain

Selecting a data domain allows you to delve deeper into the data category you want to visualise and describe what characteristics of that particular data category you want to visualise.

For example, if you have chosen 'Demographics' as your data category, you can choose which demographic characteristic like their age, gender or participation rate you want to visualise.

Selecting comparative data

Selecting comparative data allows you to compare and contrast different data categories or characteristics of data against the main data category, which can help you build different narratives to support the narrative you are looking to build with your data.

For example, if you want to visualise the age of your participants compared to their gender, choose your data domain as 'Age' and comparative data as 'Gender'.

Selecting a graph type

You can visualise your data in various different forms, selecting a graph type allows you to define how you want to visualise your data. You can visualise your data through the following graph types:

  • Donut charts: Donut Charts are instrumental visualisation tools useful in expressing data and information in terms of percentages, ratios. A pie chart is most effective when dealing with a small collection of data. It is constructed by clustering all the required data into a circular shaped frame wherein the data are depicted in slices.

  • Bar graphs: Bar graphs are among the most common types of charts used for displaying comparisons between several categories of data and variations of different values.

  • Line charts: Line charts depicts trends and behaviours over time. It displays information as a series of data points also known as “markers” connected with a line.

  • Area charts: Area charts are used to represent quantitative variations. Be it examining the variation in the number of people correlated to different impact outcomes or determining the average participation rate by gender. Area charts differ from line charts because the area bounded by the plotted data points is filled with shades or colours.

  • Cohort charts: A cohort chart provides a graphical summary of information by representing a set of data through variations in colours. The visualised datasets may differ in hue, shade, or intensity so that readers can more easily understand how the values vary across time.

  • Treemap chart: A Treemap chart is a data visualisation method that displays hierarchical (tree-structured) data as a set of nested figures – rectangles. These figures are presented through variations of colouring and size, allowing you to more easily identify patterns and trends between categories or data values. Because Treemap charts are linear, they provide a quick-to-read visual summary of information, making complex data comprehensible.

Map charts

Map charts allow you to visualise data layered on geographic maps so you can have a geographic perspective of your data. For example, you can visualise people who were correlated to social impacts from engaging in your activities and get a bird's eye view of the region your organisation has impacted.

Layering external data

If you have chosen 'Map' as your graph type, you can additionally layer various open government data on it:

  • Deprivation data: You can layer data from the Indices of Multiple Deprivation 2019 on to your map visualisations on SportFin

  • Schools Data: You can overlay information about school attendances and free school meals percentages on SportFin

  • Police Data: You can overlay data about crime incidents such as 'Anti-social behaviour' incidents, broken down month-by-month

  • Demographics Data: You can overlay data about demographics in Local Authority areas on SportFin, such as Total Population by Age and Gender.

  • Public Health Data: You can overlay key Public Health indicators like 'Physical Activity Indicators' and 'Child Obesity Indicators' on SportFin.

With SportFin's analytics features you can build data-rich and interactive data visualisations and create powerful stories about the impact your organisation is helping create for your communities.

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