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| − | </b><br>BB1 | + | </b><br>It's Skynet meets Vegas! An AI just turned a $20 bet into $11,000 this weekend, thanks to using our human intelligence against us for big winnings. Hopefully it'll share, but probably not. Linkdump: http://bit.ly/1T68zpo Written By: Eddy Rivas Hosted By: Ashely Jenkins & Meg Turney |
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Revision as of 21:17, 15 September 2020
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Creating a Sports Betting Model 101: Basics of Testing & Backtesting
Sports Betting Truth - Before you can actually deploy a model for betting purposes you need to test it to make sure it works. You can test it in real time as the season goes on, day by day, but this is a slow process and by the time you build up a large enough sample, the season could already be over. Or you can backtest. Backtesting is quicker and allows you to test against a much larger set of games in a much shorter time, but there are some drawbacks as well, most notably lines being set at what was known about the two teams at the time, instead of what we know now. This creates false positives and inflated profit margins. However, you can still use this approach to test different models to see which one performs best. You can also use it to combine models and see which approaches have synergy together. The best way to do it would be to have a set of data that is split out day by day and you backtest against what the stats were at the time the game was played. However this would be more trouble than it is worth due to the massive amount of data and effort required to run such a backtest system.
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A Modern Love Story: Machine Learning & The Global Sports Betting Industry | ICED(AI)
Developments in Artificial Intelligence, particularly those within the subfield of Machine Learning, are revolutionizing virtually every industry on the planet. The global sports betting industry, especially with the United States' repeal of PASPA in 2018, is ripe for disruption. Exponential increases in the ability to collect, distribute, and analyze sports data have led to an influx of top engineers entering the space. This presentation will focus on some of the principal ways in which Machine Learning is revolutionizing the industry, ranging from oddsmaking and risk management to fraud detection and responsible gaming implementations. It will also offer a guide to the economics of the business side of the industry and discuss relevant current topics in the tech space, such as adversarial machine learning. ABOUT THE SPEAKER Lloyd Danzig is the Chairman & Founder of the International Consortium for the Ethical Development of Artificial Intelligence, a non-profit dedicated to ensuring that rapid developments in A.I. are made with a keen eye toward the long-term interests of humanity. He is also the Founder & CEO of Sharp Alpha Advisors, a sports betting business and investment consultancy with a focus on companies deploying cutting-edge tech. He has previously managed institutional portfolios for BlackRock, data science initiatives for Samsung, and Machine Learning engines for sportsbook operators, along with a lifelong passion for entrepreneurship and innovation. He has been privileged to be featured as a guest speaker on the evolving role of Machine Learning in gaming at numerous prestigious universities including Stanford University, Columbia University, and The Wharton School of Business, in addition to private sector conferences including QConAI, Betting on Sports, The AI Summit, MathSport International, The All American Sports Betting Summit, Sport & Society, and IAGR 2019.
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Sport
Horse Racing
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Gambling AI Wins BIG Money - The Know
It's Skynet meets Vegas! An AI just turned a $20 bet into $11,000 this weekend, thanks to using our human intelligence against us for big winnings. Hopefully it'll share, but probably not. Linkdump: http://bit.ly/1T68zpo Written By: Eddy Rivas Hosted By: Ashely Jenkins & Meg Turney
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Betting system prediction using Deep Learning
RIS AI
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Excel - Sports Prediction
