If you are using the BetRegal app and you want to know the outcome of the match, you usually talk with your buddies, listen to podcasts, or watch interviews with the experts in the field. If, instead, you are looking to take all the fun out of the game and just want to boil it down to numbers, big data will be of interests to you.
In this piece, we will cover what big data is, what it is used for and its application in the world of sports. Is it just a more complex coin toss, or is there any merit to that kind of data analysis? We will also discuss the impact this might have on the sports industry.
What Is Big Data?
Big data is a term that denotes taking the data and analyzing what it means to reach a conclusion, when the amount of data itself is too large to be processed by conventional means, like with data-application software.
If you like challenges, imagine someone gave you a puzzle made out of million pieces without giving you the picture and asked you to tell them what the picture was. Or, more accurately, if you went into a big warehouse with boxes of papers as far as the eye could see, and you were asked to read every single document to assess whether a company will make a profit in five years. That should give you some idea of the scope of big data.
Big data is used in many different industries like healthcare, education, and Iot. It is something that is so massive and becoming so needed in today’s world that tech giants have their own daughter companies dedicated entirely to data analysis and interpretation.
Big data can, and is, used in sports to predict the outcome of an event, or simply to improve your team’s overall performance. If you have ever wondered why athletes earn as much as they do, a part of it comes from data analysis – there is a solid prediction of their future performance and how much money they are likely to bring to their team.
Springboks vs All Blacks
Data scientists at Principa, a South African company for processing data, have been able to accurately predict two matches of the national rugby team, the Springboks, when they faced against Wales, and then New Zealand. They predicted the team would win against Wales and lose against New Zealand – and they were right.
In order to make sure they have the right outcome, the good people at Principa created two teams to analyze the big data – Nero and Trojan. The teams used different data sources and models, but the outcome was more or less the same.
I have mentioned that a salary an athlete is paid comes from data analysis. This part of the equation will not change. However, it is possible that all of the future deals between teams and sponsors will be affected as the big data analysis becomes the industry standard.
Sponsors might become fair-weather friends when they pay teams of data scientists only to discover that their athletic protégés are not going to step up. Personally, I hope this doesn’t become a thing (though it already is), because it may eliminate the suspense that makes watching sports entertaining. However, the reality is that sports are a multibillion-dollar business. People and companies will do almost anything to get an advantage.