Big data and gambling business intelligence
News on 21 Aug 2013
News on 21 Aug 2013
The smart use of large volumes of business data is growing across a multitude of industries, with online gambling featuring prominently among them, an article on the Siliconangle blog revealed this week.
Online bookmaking companies are among the leaders in using large volumes of data to best advantage, the article notes, focusing on Paddy Power, where the rise of online gambling led to the accumulation of hundreds of terabytes of data.
The blog quotes Conor McMenamin, Paddy Power’s business intelligence manager, who explained that by putting in place a scalable platform that could be developed quickly, the company was able to make full use of data in a dynamic business constantly evolving in real time.
Standard data warehousing techniques were unsuitable, as bookies need to be able to access data instantaneously. Paddy Power got around the problem with QlikView’s business intelligence solution, which gives decision makers wider access to the company’s data in way that keeps its IT managers in control of governance and data structure, whilst affording managers more time to focus on strategic goals.
The Siliconangle piece also examines new Polish start-up Betegy, which claims high success rates in the accurate prediction of English Premier League football matches.
Betegy’s website boasts that it’s able to successfully predict the outcomes of 90 percent of all English Premier League matches via a proprietary and complex algorithm that considers every possible factor that might affect the game, from the coach’s birthday to the weather.
The company turns this capability into forecasts for 21 different soccer leagues around the world, and claims success rates between 50 percent and 90 percent accuracy – the latter figure in the English Premier League.
CEO Alex Kornilov says the company uses two layers of data to predict the outcome of soccer matches. The first layer includes basic stats such as recent performances, history between the two teams in question, form and the average number of goals. The second layer goes deeper into it, taking into account factors like the weather and other details that may affect player’s motivation.
