The ‘sport’ of science has long been the art of testing and comparing theories and data to understand the world.
That has become a more challenging task with the advent of data-driven analytics.
But new science and technology are helping scientists better understand and test hypotheses more comprehensively, by combining machine learning with computer science, statistics, and other disciplines.
This is the science of ‘sporting science’, and it can help us to understand our own work more accurately.
The Snowball Effect in Science There is a ‘solution’ to this problem, says Simon Ritchie of the University of Exeter in the UK, who is an expert on the ‘Snowball effect’ in science.
‘Sporting scientists’, he says, are ‘looking at their data and thinking: ‘What’s going on here?’
The answer is: it’s all about the data.
It’s all in their own heads, says Ritchie.
It is the result of the very nature of science itself, which involves collecting and processing large amounts of data.
As scientists we all have a tendency to take a lot of data, which can sometimes be quite abstract.
‘So, when you look at the data and you start to see patterns in the data, you start thinking about that data and then you start looking at the patterns that are there and then, in turn, you find that patterns are there,’ he says.
‘There are all sorts of ways that we can see patterns, but this is the best way of doing that, because you can do it systematically and it’s much easier to do that than to look at data from many different sources and get the results that you want.’
‘It’s really all about data.
If you start seeing patterns in that data, then you have a lot more to work with.’
That’s why sports science, for example, is so valuable to scientists.
It helps them understand how they can apply this knowledge in other domains, such as how to measure or analyze human performance.
The scientific method is the way scientists do that, says David Bowers, director of the Centre for Sport and Exercise at the University.
‘It means you have to understand how to look for patterns in data, how to take it as a whole, how you can combine that with other methods that have come before you, so you’re not just looking for correlations,’ he explains.
‘You have to look to see what are the underlying principles that give you the right answer, which are often not obvious from looking at other things, like if you’re measuring a horse’s heart rate and it turns out that the horse is actually doing something else, or if you can’t identify the exact mechanism of a phenomenon that you were looking for.
You have to be able to explain why that particular thing is going on.
‘And, of course, if you have all these different approaches that have been tried before, you have an understanding of how to make the most of what’s there.
If it’s an experiment, you’re using the data to make a decision about whether or not that is going to produce the result you want.
‘If it’s a study of human behaviour, you know that if you get the right information, the results will be good, because what you’re doing is taking a random sample of people and you’re then trying to explain that to them in terms of why they’re doing it.
‘In sports, the data are very useful because it allows you to make some judgements about what the outcome of a particular exercise is going as a result of what the data show.’
The ‘Snowflake Effect’ in Science Another way in which science and sport are linked is that they all rely on data that can be used to understand things that are outside the realm of science.
The concept of ‘predicting’ a behaviour is not something that scientists normally do, because it involves modelling, or modelling based on data.
The snowflake effect has been around for many years, and is based on the fact that the behaviour of an individual will fluctuate depending on a series of variables, such that, for instance, a woman’s menstrual cycle will affect how much she will lose to pregnancy, or how much exercise she is likely to do.
But it has also been used to analyse sports such as football, basketball, or cricket, which involve huge amounts of statistics that are often difficult to explain and understand.
So, for sports that involve huge numbers of players, or that involve many variables, scientists often turn to statistical models.
‘But it’s the same thing with sports.
It might be that we know how to predict certain outcomes, but we can’t explain why those outcomes are happening in the first place, so we use statistical models to explain what’s going to happen next,’ says Ricks.
‘That’s where the snowball effect comes in.
‘I think that when you talk about sports, and in particular,