Twitter Survey – Tools used by practitioners to collect, organise, analyse and visualise data in sports

The link to the survey is still open to contribute to developing this dataset –

From the responses gathered, overall practitioners across a wide variety of sports & levels prefer to use a combination of different tools as opposed to just one tool (68%).

The figure above shows the tools used by practitioners across different sports and levels. The size of the text for certain tools indicates the most common tools used (Multiple – 68%, PowerBI – 11%, Google Sheets – 8%). The thickness of the edges (arrows) represents the number of practitioners in sport using a certain tool (or multiple tools. To illustrate, practitioners in soccer mostly favoured using multiple tools, followed by PowerBI and Tableau.

The table below demonstrates the different variations of tools used in combination with each other with many practitioners choosing to use more than 2 tools (the list of tools is not in any particular order).

Practitioners were also asked to outline the biggest benefits and negatives of the tools they are using. In terms of benefits, the following positive keywords were most associated with certain tools:

Visualisations: Tableau, Powerbi, Python

Easy-Use: Excel, R, PowerBi, Tableau

Staff-Skills/Multiple staff (Easy for multiple staff to use): Tableau, Excel, Google Sheets, R

Large-Datasets (Ability to handle): PowerBI, R, Tableau

Cost: Excel, PowerBi

Speed: Excel, Tableau, R

In terms of negatives, the following negative keywords were associated with certain tools:

Learning-Curve: Excel, R, Tableau, Powerbi, Python

Setup-Time (Time to build databases): Excel, Google Sheets, PowerBi

Speed/Basic/Capacity/Poor Customisation/Data Manipulation/Slow Development: Excel, Google Sheets

Visuals/Bugs: PowerBI

Practitioners were finally asked if they would consider using a different tool in the future and if so what would that be. The majority would switch in the future. The chart below represents how many times a tool was mentioned either on its own or in combination with another tool. R and Python were the most common combination practitioners would consider exploring. 29% of practitioners wouldn’t switch tools and 18% were unsure. Of those who wouldn’t switch or found themselves unsure, the majority were using R, Python, Excel or PowerBi.

Overall, there are many different reasons why practitioners use specific tools to collect, organise, analyse and visualise their data. A lot of these reasons seem to be based on pragmatics, personal experience and are situation dependent. There doesn’t seem to be one tool made for all tasks and situations.  

Thanks to all those who contributed to this small project.