How sports fans use data analytics to increase their enjoyment and understanding of sport

By Sony T
8 Min Read
How sports fans use data analytics to increase their enjoyment and understanding of sport 1

Once upon a time, people thought that the Earth was flat (some still do) and that the only statistic that really mattered in sports is the final result. Some still believe that too.

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One of the most informative developments in cognitive science in recent times has been understanding the process rather than fixating on results. It doesn’t matter whether you are coaching a kid’s basketball team or running a multimillion-dollar empire, the basic idea remains the same.

Good results can be caused by luck, variance or a range of other unpredictable factors, so getting your processes down perfectly – it could be tactics in sport or your marketing plan in business – is key to ensuring that results are deserved, consistent and sustainable.

That’s where data comes in, particularly in a sporting context. Many professional sports teams have their own in-house analysts these days to crunch the numbers, and that’s to achieve one main goal – ensuring the processes are in place to deliver expected or hoped-for results.

As sports fans, we have access to a selection of publicly available stats too, and we can use these for our own ends – whether it’s to increase our knowledge and impress our friends, or enhance our abilities when it comes to picking our fantasy teams or making the most of any betting odds bonus offers we can lay our hands on.

So, let’s take a look at a few ways that the Average Joe and Jane can use freely available stats to increase their enjoyment of their favorite sports.

Using data for performance analysis

Most elite sports have had their data revolution, and now the general public are able to access the necessary numbers they need to perform data analysis of their own.

Whether it’s Field Goal % in basketball, Yards Gained in football or Expected Goals in soccer, new analytical tools are constantly being developed to understand the ‘game within the game’ – or to compare process to results, to see where marginal gains/losses are being made.

You may have heard the name Billy Beane before. He was the general manager of the Oakland Athletics, an MLB franchise that was struggling to progress due to their rather tiny budget in comparison to other teams in the competition.

So Beane set about creating his own data revolution, and rather than judging potential new recruits based upon how they look when pitching or hitting, he instead dug down into the numbers – which the MLB has a deep well of – to instigate his own scouting reports on players that other teams simply weren’t interested in.

He utilized On-Base Percentage, and a view of its variants, to find players that were a) under-appreciated by the baseball ‘market’, and who b) would add genuine value to his roster. Before long, the A’s performances out on the diamond improved significantly… Beane even had a book and Hollywood movie, Moneyball, made about his processes.

Now, that’s not to suggest that creating your own data revolution is easy, but at least you have access to all manner of data these days. The NBA and NFL websites have useful stats pages, while sites such as Basketball-Reference and FBref are used by professional sports teams in their analytics.

So why not take a look at the numbers, and see if you can identify teams/players that are undervalued?

Making it rain

It’s true that the vast majority of sports bettors lose money in the long run – for them, the focus is on enjoyment and having some skin in the game.

However, if you have designs on trying to make a second income through sports betting or daily fantasy gaming, you really do need to dig a bit deeper in your analysis.

Why? Because the sportsbooks design their odds in a way that protects their financial bottom line, so they build an overround — sometimes known as ‘juice’ or ‘vig’ — into their prices.

So how are you going to overcome that? Well, you need two things: 1) a definable edge over the sportsbooks, and 2) an ability to spot value odds on the rare occasions when the books don’t quite get it ‘right’.

To achieve 1) and 2), you need to know something that the sportsbook odds compilers don’t, and that’s where data – and your analysis of it – comes in handy.

Make no mistake, the sportsbooks are using advanced data to help them build their prices (and add in that profit margin). But it’s how you bend and shape the numbers that could give you an edge – particularly for sports or competitions in which the bookies may spend less time crunching the numbers themselves.

The same is true for daily fantasy, although this time your objective is to analyze the data better than your rivals in a contest. Either way, using data and nuanced analysis will help to create an advantage that will ensure your bets and/or fantasy teams can be considered value plays.

A career change

Given that the sports world has now fully embraced deep stats, guess what? They need to recruit employees to crunch the numbers on their behalf.

What’s interesting about data analysis as a career is that, while you do need to have some coding skills, it’s actually the application of the numbers – and the lessons these can teach – rather than their manipulation that is key.

Most people that work in sports analytics have some programming knowledge, with Python among the key coding languages used in the industry. The good news is that you can take training courses both online and in-person that will provide you with a sound understanding of its various applications, and then you will be able to start building your own models and insights in your spare time using freely available data.

This sort of DIY learning experience is how many sports analysts got their jobs in the first place, so a pathway from amateur to enthusiast to paid analyst isn’t as far-fetched as you may think.

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By Sony T
Sony is a passionate bloggers writes on Futuristic technologies ...