Data Science Gambling
The authors discuss the use of data science and machine learning to analyze behavioural tracking data. Studies have reported a range of behavioural markers of problem gambling. These include both monetary markers (e.g., bet size) and non-monetary markers (e.g., number of days gambled). Darina Goldin, the Director of Data Science at Bayes Esports writes for Esports Insider to discuss Riot Games’ new hot commodity, VALORANT.In the feature, Goldin breaks down the game’s key markets and need-to-know data when it comes to providing customers with betting offerings. It's fair to say that recovering and analyzing data about gamblers is a very big part of the casino business. For example, Gary Loveman, former CEO of Caesar's Entertainment, established the company's Total Rewards loyalty management system, which gathers data on casino customers.
This week, Hugo speaks with Marco Blume, Trading Director at Pinnacle Sports. Marco and Hugo will talk about the role of data science in large-scale bets and bookmaking, how Marco is training an army of data scientists and much more. At Pinnacle, Marco uses tight risk-management built on cutting-edge models to provide bets not only on sports but on questions such as who will be the next pope? Who will be the world hot dog eating champion, who will land on mars first and who will be on the iron throne at the end of game of thrones. They’ll discuss the relations between risk management and uncertainty, how great forecasters are necessarily good at updating their predictions in the light of new data and evidence, how you can model this using Bayesian inference and the future of biometric sensing in sports betting. And, as always, much, much more.This is the DataCamp podcast link and check it out for the show notes and other goodies: https://www.datacamp.com/community/podcast/data-science-gambling-bookmaking?utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_53
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Comment by Sushrut
51:03 Now we know Bran Stark is the one!!
Comment by Kareem Alaraby
did Justin record those segments about distribution's storie's ? if so can you provide a link ?
Comment by DataFramed
@crist-bal-j-correa-r: Hey, we suggest taking our intro courses depending on what language you prefer. Intro to SQL(https://www.datacamp.com/courses/intro-to-sql-for-data-science), our Tidyverse Fundamentals with R Track(https://www.datacamp.com/tracks/tidyverse-fundamentals), and finally for Python, our Introduction to Data Science in Python course(https://www.datacamp.com/courses/introduction-to-data-science-in-python).
Comment by correacristobal
Hey! Thank you for sharing this. I've been a DC member for over three years now and still haven't taken any course, but I've been working in the gambling industry for longer and this podcast inspired me to start learning. Where would you say a non-programmer should start if they're looking to learn all of this? From 0 to building a proper data lake house to pull several live datapoints and have them analyzed and weighed? :D
In a numbers game, whoever has the most information usually ends up winning. That has largely been the thinking in the gambling industry, with the house having the better odds in any game that is played. Gambling is already all about the numbers, so it should probably come as no surprise that the rise of big data has caught industry leaders’ attention. Big data has already made a huge impact in businesses of all types, from financial services to healthcare institutions to retail stores. That it would make some changes to the world of gambling is no shocking development. In fact, understanding how those changes are occurring can also help people get a clear picture of where the gambling industry will likely be in the near future.
The first people to adopt big data in the gambling world were the bookmakers -- those that live by the odds they establish. Getting as much information as possible is a crucial aspect of the business, so the moment new techniques designed to analyze and transmit that data cropped up, adopting it was a simple decision to make. The traditional data warehousing strategy was unneeded, especially since data needed to be accessed almost in the moment. Betting firms quickly utilized big data analytics as a way to manage their businesses and stay on top of the game.
At the same time, other companies saw the potential of actually placing the odds more in favor of the gamers themselves. Big data services quickly appeared that were designed to empower gamblers, giving them more information and helping them strategize more effectively. One such site that made full use of big data was SharkScope, which collects data from millions of online poker games every day. Players can track all their statistics on the site as a way to improve and increase their chances of winning. SharkScope quickly discovered that as the company gathered more data, querying would take longer, so they adopted new big data tools allowing for faster querying and use of ad hoc data to provide a much desired service for gamblers.
Sports gambling is also being transformed by big data. Sports organizations have already embraced big data as a way to study players and tactics, which means there’s a lot of data out there to collect and analyze. Using that data to predict sport outcomes has become a popular way to generate buzz. For instance, during the 2014 World Cup, Google used big data analytics to predict the winner of 14 out of 16 matches. Microsoft did even better, predicting 15 of 16 match outcomes correctly. Based on these developments, many gamers are trying to use data to get rich by betting on sports. Some gambling companies even boast a 90 percent accuracy rate, depending on the sport and the league. Most bookmakers, however, aren’t changing their traditional techniques since the most cited examples, like the World Cup, were mostly just a case of the favorites winning against the underdogs. A lot of sports outcomes, according to the bookies, can be predicted based off of just a few statistics. Whatever the case may be, many gamblers see big data as the way to swing the odds in their favor, which has lead to the growing popularity of fantasy sports betting.
Data Science Gambling Definition
But gambling companies aren’t just using big data for their games. Much like other businesses, casinos have used it to improve on their marketing efforts. Station Casinos and Harrah’s are just a couple of examples of this idea being put into practice. By finding out a wealth of information about their customers, from what kind of food they like to what games they play, the casinos can create marketing campaigns that are tailored to individuals. The more personalized approach gets a better response and has already achieved good results. Based off of the changes to their marketing strategy, Station Casinos has increased guest retention by 14 percent.
Data Science Gambling And Bookmaking
Whether used by casinos or gamblers, for oddsmakers or those playing the odds, big data has had a transformative effect on the gambling industry. With so much information now being generated and gathered, it’s clear that the full impact will likely continue to be felt well into the future. Businesses are only just starting to get used to what big data can do, and once they’re more familiar with its capabilities, big data’s full potential will be unleashed. This is true for any business, gambling or otherwise.