Recent memoirs by the retired bookie Art Manteris and the storied gambler Billy Walters provide a glimpse of an industry in its fledgling form--and a preview of the DraftKings era to come. Las Vegas is no longer the seat of the sportsbook gods. In most states, it's now legal, and extremely popular, to place bets using apps or websites such as FanDuel and DraftKings. From your couch, you can wager on everything from the results of snooker championships to the color of the Gatorade poured over the victorious coach after the Super Bowl. The N.F.L., along with the other major-league American sports associations, has officially partnered with sports-betting sites, and their alliance has proved so lucrative that other industries want in on the action; last month, the Golden Globes made a deal with Polymarket, a predictions-market platform, to encourage wagering (or "trading," if you prefer) on the outcomes of its awards race.

We propose a framework which generalizes "decision making with structured observations" by allowing robust (i.e. multivalued) models. In this framework, each model associates each decision with a convex set of probability distributions over outcomes. Nature can choose distributions out of this set in an arbitrary (adversarial) manner, that can be nonoblivious and depend on past history. The resulting framework offers much greater generality than classical bandits and reinforcement learning, since the realizability assumption becomes much weaker and more realistic. We then derive a theory of regret bounds for this framework. Although our lower and upper bounds are not tight, they are sufficient to fully characterize power-law learnability. We demonstrate this theory in two special cases: robust linear bandits and tabular robust online reinforcement learning. In both cases, we derive regret bounds that improve state-of-the-art (except that we do not address computational efficiency).

We present first results from the use of XGBoost, a highly effective machine learning (ML) method, within the Bristol Betting Exchange (BBE), an open-source agent-based model (ABM) designed to simulate a contemporary sports-betting exchange with in-play betting during track-racing events such as horse races. We use the BBE ABM and its array of minimally-simple bettor-agents as a synthetic data generator which feeds into our XGBoost ML system, with the intention that XGBoost discovers profitable dynamic betting strategies by learning from the more profitable bets made by the BBE bettor-agents. After this XGBoost training, which results in one or more decision trees, a bettor-agent with a betting strategy determined by the XGBoost-learned decision tree(s) is added to the BBE ABM and made to bet on a sequence of races under various conditions and betting-market scenarios, with profitability serving as the primary metric of comparison and evaluation. Our initial findings presented here show that XGBoost trained in this way can indeed learn profitable betting strategies, and can generalise to learn strategies that outperform each of the set of strategies used for creation of the training data. To foster further research and enhancements, the complete version of our extended BBE, including the XGBoost integration, has been made freely available as an open-source release on GitHub.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. As artificial intelligence continues to completely change the way millions of fans interact with live sporting events, a platform is introducing an innovative approach to monetization. Technology company Play Anywhere has developed a proven track record of increasing fan engagement and creating new revenue streams for its partners. The technology can be seemingly integrated into mobile devices, connected televisions or various streaming devices.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Sports wagering is currently legal or poised to go legal in about 29 states across the U.S. Maine and Florida represent the two most recent states to permit sports betting. The growth of sports gambling was sparked by the Supreme Court's decision to strike down the Professional and Amateur Sports Protection Act in 2018. The ruling effectively allowed states to decide whether sports wagering would be legal within their respective borders.

Sports betting's recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker's odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that optimising the predictive model for calibration leads to greater returns than optimising for accuracy, on average (return on investment of $+34.69\%$ versus $-35.17\%$) and in the best case ($+36.93\%$ versus $+5.56\%$). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore optimise their predictive model for calibration.

We are living in a world that is constantly evolving. In general, every industry is facing new innovative changes thanks to Artificial Intelligence (AI). People are taking advantage of AI in order to find new ways that will simplify their daily life. We can notice that there have been advancements in almost every industry, however, the industry that made the biggest step forward and improved itself thanks to AI is definitely the sports betting one. From a growth perspective, we can freely say that AI already plays a significant role in sports, and this is a clear sign that it will change it even more in the future.

Artificial intelligence is no longer science fiction – it is being used everywhere you look, from e-commerce to architecture. Gambling involves a lot of luck but also preparation. Bookmakers now benefit from real-time statistics about esports players, teams and events that inform betting odds and provide context to bettors. AI can process enormous amounts of data very quickly and make predictions accordingly. PandaScore's AI platform, for example, collects 300 data points in League of Legends in half a second.

As the sports betting industry is gaining steam, I am interested in selling NBA spread picks to sports bettors via subscription to my service. I will use regression models to predict outcomes of NBA games. My goal is to make a prediction on the spreads (point differential) of each game, and use that information to bet against the Vegas spread. Because Vegas typically takes a 10% rake for each bet, I have to be able to beat Vegas 52.5% of the time in order to be profitable. My data was collected via scraping, using Beautiful Soup, basketball-reference.com and sportsbookreviewonline.com, using data from all regular season games from 2011–2020, which includes 11,656 games.

I describe the rationale for, and design of, an agent-based simulation model of a contemporary online sports-betting exchange: such exchanges, closely related to the exchange mechanisms at the heart of major financial markets, have revolutionized the gambling industry in the past 20 years, but gathering sufficiently large quantities of rich and temporally high-resolution data from real exchanges - i.e., the sort of data that is needed in large quantities for Deep Learning - is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator, which is what this simulation now provides. The simulator, named the "Bristol Betting Exchange" (BBE), is intended as a common platform, a data-source and experimental test-bed, for researchers studying the application of AI and machine learning (ML) techniques to issues arising in betting exchanges; and, as far as I have been able to determine, BBE is the first of its kind: a free open-source agent-based simulation model consisting not only of a sports-betting exchange, but also a minimal simulation model of racetrack sporting events (e.g., horse-races or car-races) about which bets may be made, and a population of simulated bettors who each form their own private evaluation of odds and place bets on the exchange before and - crucially - during the race itself (i.e., so-called "in-play" betting) and whose betting opinions change second-by-second as each race event unfolds. BBE is offered as a proof-of-concept system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques. This paper offers an extensive survey of relevant literature and explains the motivation and design of BBE, and presents brief illustrative results.