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Sports club development: data

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“Data! Data! Data!" he cried impatiently. "I can't make bricks without clay.”[1]

Introduction

Who participates in a sport?

Who is a member of that club?

How does that affect a club’s development in the short and long term?

These are all key questions that face sports clubs as they try to draw in new members and retain existing ones.

Over a series of blog posts I aim to cover a series of case studies of when population data can be used by sports clubs and governing bodies, not only to identify issues, but to develop strategies to address these issues, with real world improvements visible from proper use of good data.

We all want our clubs and sports to succeed. Anecdotally however, I have repeatedly encountered a failure to collect good population data within sports clubs and teams. The end result of this is to consistently hold back many clubs’ ability to develop both tactically and strategically, let alone develop the sport regionally or nationally. Not only that, but in some cases badly collected data can actively mask problems and paper over fundamental issues faced by clubs and sports.

What do we mean by “good” data? Good data has to be representative, accurate, repeatable and answering the question being asked.

Definitions

The principles of collecting good data are universal, so the things we should think about can be applied to most hypothetical scenarios. In this example we will use the amount of fuel used by our club, but we can collect data about any issue our club might face such as: retaining sailors, reducing the churn in our membership and removing or reducing the barriers to becoming an instructor.

In this example, in Hypothetical Sailing Club we want to work out how much fuel our RIBs use as we do not want to spend more money buying fuel than is necessary. Therefore, we need to develop a data collection method to work out how much fuel is being used, then use this to inform our fuel buying in the future. This data should answer the question, be representative, repeatable and accurate.

Answering the question: Data that is being collected which can be used to answer the question asked. Comparing the fuel efficiency of different RIBs at different speeds may tell us how much fuel they use at that specific speed, but not how much they use over the course of a day of normal use.

Representative: A sample of a population that seeks to accurately reflect the larger population. If we only measured how much fuel our RIBs used during a windy International Moth open meeting, this data is not going to be representative of normal use. Not only are International Moths faster than most boats we sail at Hypothetical Sailing Club, but because it is windy the RIBs will be using more fuel to reach sailors who need aid. Furthermore, it is an open meeting, which might involve different numbers of boats on the water than the normal activities our club undertakes.

Repeatable: The ability to repeat the study, using the same methods, which should return a very similar, or the same result. If we were to measure the amount of fuel used by filling the tanks to full at the start of the day and then measuring how much fuel was left at the end of the day, then we should use this method every time we measure. If we change the method of measuring the fuel halfway through, then we cannot compare between the two different methods, because we cannot guarantee that the changing measurement method has not affected the fuel use. One method of fuel measurement might result in more fuel evaporation for example.

Accurate: How close your sample is to the reality. If we have a measurement method which answers our question, is representative and repeatable, but is only used twice, then this is less likely to be accurate than measuring our fuel use over a month or a year. This is because the results of our short sampling time are going to be more heavily influenced by unusual results than a longer sampling time, such as an International Moth Open on the day we start our fuel measurement.

Measuring population data

If we have good data about who participates, then we can create a model of club/ organisation/ region/ nation in any given sport. If we are a coach or involved in sports club management then this model is a vital method to inform the strategic development of the team/ club and can even be used to inform the regional and national development of the sport.

The data that we measure will change depending on a number of factors: what club we are part of, what we want to focus on and what challenges that club is facing (both short and long term).

In my next article I will take the University of Highlands and Islands Wind and Wave Club as an example, and see how measuring basic population data is vital for the future of that club and brings direct benefits.

[1] Sherlock Holmes to Dr Watson, The Adventure of the Copper Beeches, The Complete Sherlock Holmes Stories, Wordsworth edition, page 641,  Arthur Conan Doyle, 2007, Ware.

 

If you enjoyed this you can find all my other ConnectedCoaches blogs here.

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