Sports Prediction

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• Case Studies
  • Sports
• Reinforcement Learning (RL)
• AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget

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.

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.

Sport

Horse Racing

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

Betting system prediction using Deep Learning RIS AI

Predict Horse Races with BigQuery Learn how to use BigQuery ML on Google Cloud Platform to predict the outcome of horse races - Big Query https://cloud.google.com/bigquery/ - DataLab https://cloud.google.com/datalab/

Predicting Horse Race Winners Using Advanced Statistical Methods Conditional Logistic Regression with Frailty applied to predicting horse race winners in Hong Kong. http://www.helios.ai Since first proposed by Bill Benter in 1994, the Conditional Logistic Regression has been an extremely popular tool for estimating the probability of horses winning a race. I propose a new prediction process that is composed of two innovations to the common CLR model and a unique goal for parameter tuning . First, I modify the likelihood function to include a "frailty" parameter borrowed from epidemiological use of the Cox Proportional Hazards model. Secondly, I use a LASSO penalty on the likelihood, where profit is the target to be maximized. (As opposed to the much more common goal of maximizing likelihood.) Finally, I implemented a Cyclical Coordinate Descent algorithm to fit the model in high-speed parallelized code that runs on a Graphics Processing Unit (GPU), allowing me to rapidly test many tuning parameter settings. Historical data from 3681 races in Hong Kong were collected and a 10-fold cross validation was used to find the optimal outcome. Simulated betting on a hold out set of 20% of races yielded a return on investment of 36.73%.

Basketball

Predicting sports winners using data analytics with Pandas and scikit-learn by Robert Layton The Pandas and scikit-learn packages combine together to produce a powerful toolkit for data analytics. In this talk, we will be using them together to analyse the outcome of NBA games, trying to predict the winner of a match. There is plenty of data out there to allow us to create good predictions – the key is getting it in the right format and building the right model. In this talk we will go through importing data from the net, cleaning it up, creating new features, and building a predictive model. We then evaluate how well we did, using recent NBA data. The model we use will be a decision tree ensemble called a random forest. PyCon Australia is the national conference for users of the Python Programming Language. In 2015, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World. July 31-August 4, Brisbane, Queensland, Australia

How to win in NBA betting - Sports betting tips, powered by Artificial Intelligence Here comes our introduction video for #NBA betting. Why did we start publishing NBA betting predictions? How should you read and use our predictions? What is our accuracy so far? What will be the next thing? We aim to help people being interested in sports betting to develop consistent and strong betting strategies when betting on sports competitions. We only ask machines when making predictions. No one can guarantee the result of a match before it is played. We at Wise Prediction work to maximize the accuracy of our algorithms but it is a fact that it is impossible to reach to 100% accuracy and we do not guarantee that any of predictions will always hold. Wise Prediction shares football ( soccer ) and NBA match result predictions and betting tips by using advanced techniques of Artificial Intelligence. Beside (pretty much) daily NBA predictions, WisePrediction.com publishes weekly predictions for 150+ matches from 20 different football leagues in 11 countries in Europe. Our models help our members to identify matches that are comparably safer to bet on and the ones that are riskier and thereby should be avoided to bet on. Making sensible betting decisions require long analysis and it is just impractical to do everything manually on 100+ matches from 20 different leagues in a consistent manner. We have automated this process at Wise Prediction and share our predictions based on the same approach which is consistent in all predictions that is done. All betting tips shared at Wise Prediction are generated based on our AI powered algorithms and there is no human interaction involved in decision making. There is no guarantee that our predictions will always hold because sports competitions are always subject to end with unexpected results. If anyone bets on sports competitions by using our predictions, s/he does it at his/her own risk.

American Football

• Using Machine Learning for Predicting NFL Games | Amit Bhattacharyya - Berkeley School of Information

Using Machine Learning for Predicting NFL Games | Data Dialogs 2016 Presented by Amit Bhattacharyya You are a HUGE football fan. Every week you pick winners in an NFL pick-em' league. Somehow, all that fan experience doesn't translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and "knowledge" from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool.

Machine Learning and (wait for it!) --- Fantasy Football!! You read that title right people! The time has come for Machine Learning and Fantasy Football! Our resident Data Scientist, Laura Edell is back and this time she shows us how she dominates her Fantasy Football league by using Microsoft Azure Machine Learning. Short on time? Just click on any of the below links and jump to that section of the video: 0:01:40 – So, let’s get straight to the point --- you’ve been using a “Deep Learning, A.I. powered” Fantasy Football model to dominate your Fantasy Football league, year-over-year? 0:02:00 – Ok --- please explain. And go slow so we can make lots of notes… 0:07:52 – DEMO: Exclusive!! Creating a Fantasy Football “Deep Learning, A.I.” model Additional Resources: http://www.microsoft.com/ai

Data Science Final Project: NFL Fantasy Predictor Darshil Patel Regression Model for fantasy football

Using Machine Learning to make Fantasy Football Projections Louis Rosenblum

Soccer

Predicting the Winning Team with Machine Learning Siraj Raval Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video

Artificial Intelligence Football Analysis or machine learning football Artificial Intelligence Football Analysis or machine learning football Most beautiful watch ever ! with Speaker, Heart Rate, GPS, NFC, and Smartphone Notifications: http://amzn.to/2JDSSGX To make sense of data, OLOCIP brings together the most advanced modeling strategies, capable of transforming temporal-space data into transparent statistical models, with a thorough insight to the differential factors of the sport. Artificial Intelligence (AI ) enables a complete and secure analysis based on a more efficient capability that can register, retain and discern the events captured. In this way, Olocip transparent IA models, not only detect and identify each of the weak points, but also provides instructions in order to maximize the achievement of marked objectives. Combining scientific rigor, technological innovation and best in class soccer expertise, This is how OLOCIP creates support applications that facilitate and optimize decision making, acting from the descriptive dimension to predictive and prescriptive dimension that optimize the management of soccer clubs and related companies.

Hockey

Sports Betting with Reinforcement Learning Siraj Raval - Sports betting is a popular past-time for many and a great use-case for an important concept known as dynamic programming that I’ll introduce in this video. We'll go over concepts like value iteration, the Markov Decision Process (MDP), and the bellman optimality principle, all to help create a system that will help US optimally bet on the winning hockey team in order to maximize profits. Code, animations, theory, and yours truly. Enjoy! Code for this video

NHL Betting model 2019 - testing March 21, 2019 Underdog Chance

Tennis

Tennis Betting: Machine Learning First Steps The nice folks at http://www.tennis-data.co.uk/ have put up all the matches played in tennis for us which we should be able to do something with? Here, it is the beginning? I have found the site. I have loaded up the data and produced some early plots. Next, I need to read up on some strategies? How do people bet on tennis? Once we know the approaches used, we can look to process the data which we have to help guide us? If you have any ideas, please comment since I know very little about tennis! I'll have a look on OLBG and other places to see what people do. I don't have a site like soccerstats to show me the kinds of things that people look at. Curious to see how tennis is different to football. There is the basic difference that you don't have draws in tennis. The better player seems to win more often in tennis than football.

Coded an A.I Betting Bot and Won _____! I use artificial intelligence to create a bot that beats the odds of betting sites. CONTACT: halldenkalle@gmail.com

Poker

How Science is Taking the Luck out of Gambling - with Adam Kucharski From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies. Adam's book "The Perfect Bet" is available now - http://geni.us/9Ao9j Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence. Adam Kucharski is a Lecturer at London School of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London. The Ri is on Twitter: http://twitter.com/ri_science and Facebook: http://www.facebook.com/royalinstitution

Superhuman AI for heads-up no-limit poker: Libratus beats top professionals This talk gives a high-level explanation of Libratus, the first AI to defeat top humans in no-limit poker. A paper on the AI was published in Science in 2017. No-limit Texas hold’em is the most popular form of poker. Despite AI successes in perfect-information games, the private information and massive game tree have made no-limit poker difficult to tackle. We present Libratus, an AI that, in a 120,000-hand competition, defeated four top human specialist professionals in heads-up no-limit Texas hold’em, the leading benchmark and long-standing challenge problem in imperfect-information game solving. Our game-theoretic approach features application-independent techniques: an algorithm for computing a blueprint for the overall strategy, an algorithm that fleshes out the details of the strategy for subgames that are reached during play, and a self-improver algorithm that fixes potential weaknesses that opponents have identified in the blueprint strategy.

Pluribus - First Look - Poker Tutorial Join Zenith Poker to get free access to advanced poker theory, cutting edge preflop ranges, postflop solutions, and group tutorials. http://www.zenithpoker.com/

Michael Bowling – "Artificial Intelligence Goes All-In: Computers Playing Poker" Artificial intelligence has seen several breakthroughs in recent years, with games such as checkers, chess, and go often serving as milestones of progress. Poker is another game entirely, with players having their own asymmetric information about what's happening in the game. In this talk, University of Alberta researcher Michael Bowling (also a principal investigator at the Alberta Machine Intelligence Institute) describes a decade long research program to build AI that can cope with the hallmarks of poker — deception, bluffing, and manipulating what other players know. This research has culminated in two landmark results: Cepheus playing a perfect game of limit poker, and most recently, DeepStack (in a collaboration with Czech researchers) beating poker pros at the game of no-limit poker. These two computer programs take very different approaches, and shed light on what is required to play a game at an expert-level and what is required to play it perfectly. Learn more about DeepStack: Website: http://www.deepstack.ai

Dr. Michael Bowling – Artificial Intelligence Goes All-In: Computers Playing Poker Lecture of prof. Michael Bowling organized by dept. of Computer Science, FEL ČVUT in cooperation with MFF UK

DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker | AISC Discussion lead: Rick Valenzano Motivation: Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated with statistical significance professional poker players in heads-up no-limit Texas hold’em. The approach is theoretically sound and is shown to produce more difficult to exploit strategies than prior approaches.

Call, Raise or Fold - Python for Simulating Poker Games - Abhijit Gadgil This talk was presented at PyCon India 2019, on Oct 12th - 13th, at the Chennai Trade Centre. Website: http://in.pycon.org/2019

PWLSF - 3/2017 - Tom Santero on DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker Talks given on March 30, 2017 at OpenDNS. Matt Adereth on the January 1965 issue of The Computer Journal. This issue contains one of the most important techniques in numerical optimization, the Nelder-Mead simplex method. An entire full-length talk could be dedicated to it, but instead we’re going to try and understand the historical context by looking at everything else in the journal, from the other papers to the letters to the editor to the advertisements. Matt's Bio Matt builds tools and infrastructure for quantitative research at Two Sigma. He previously worked at Microsoft on Visio, focusing on ways to connect data to shapes In his spare time, he builds ergonomic keyboards using Clojure.

Imperfect Information

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the Annual Computer Poker Competition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly Noam Brown, Sam Ganzfried, and Christian Kroer.

AI for Imperfect-Information Games: Beating Top Humans in No-Limit Poker Despite AI successes in perfect-information games, the hidden information and large size of no-limit poker have made the game difficult for AI to tackle. Libratus is an AI that, in a 120,000-hand competition, defeated four top pros in heads-up no-limit Texas hold’em poker, the leading benchmark in imperfect-information game solving. This talk explains why imperfect-information games are fundamentally more difficult than perfect-information games, and the advances in Libratus that overcame those challenges. In particular, this talk describes new methods for real-time planning in imperfect-information games that have theoretical guarantees. Additional research has extended these methods to deeper game trees, enabling the development of the master-level poker AI Modicum which was constructed using only a 4-core CPU and 16 GB of RAM. These algorithms are domain-independent and can be applied to a variety of strategic interactions involving hidden information.

code.talks 2019 - The Building Blocks of Superhuman Poker AI by Max Pumperla Imperfect information games deal with taking strategic decisions when facing hidden information. Poker has been a classic benchmark for these games since the early days of AI. Games like Go and Chess are perfect information games, so the techniques used to solve them don't work for imperfect information games. For instance, poker-solving algorithms need to take into account how opponents might adapt to and exploit your strategy, which makes it difficult to estimate the value of your cards during play. In this talk we introduce some of the core ideas used by the superhuman poker AI called Pluribus. This AI system uses a clever self-play algorithm based on counterfactual regret minimization (CFR) to compute a blueprint poker strategy. During actual game-play against humans it then improves its strategy by searching for better options in real-time. We give a glimpse at how to implement the basics of Pluribus in Python.

Super-Human AI for Strategic Reasoning Poker has been a challenge problem in game theory, operations research, and artificial intelligence for decades. As a game of imperfect information, it involves obstacles not present in games like chess and go, and requires totally different techniques. In 2017, our AI, Libratus, beat a team of four top specialist pros in the main benchmark for imperfect-information game solving, heads-up no-limit Texas hold'em, which has 10^161 decision points. This was the first time AI has beaten top players in a very large poker game. Libratus is powered by new algorithms in each of its three main modules: 1) computing approximate Nash equilibrium strategies before the event (i.e., computing a blueprint strategy for the entire game), 2) safe nested endgame solving during play (i.e., refining the blueprint strategy on the fly in parts of the game that are reached while preserving guarantees on exploitability), and 3) fixing its own strategy to play even closer to equilibrium based on what holes opponents have tried to identify and exploit. The algorithms are domain independent and have applicability to video games, strategic pricing, finance, negotiation, business strategy, strategic market segmentation, sports, investment banking, strategic product portfolio optimization, electricity markets, bidding, auction design, acquisition strategy (e.g., for streaming companies to acquire movies), political campaigns, cybersecurity, physical security, military, bot detection, and steering evolution and biological adaptation (such as for medical treatment planning and synthetic biology). The Libratus part of this talk is joint work with my PhD student Noam Brown.

Excel - Sports Prediction

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