Difference between revisions of "Sports Prediction"
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− | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
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[http://www.youtube.com/results?search_query=sports+gambling+predict+_prediction+artificial+intelligence+deep+learning Youtube search...] | [http://www.youtube.com/results?search_query=sports+gambling+predict+_prediction+artificial+intelligence+deep+learning Youtube search...] | ||
[http://www.google.com/search?q=sports+gambling+V+prediction+deep+machine+learning+ML ...Google search] | [http://www.google.com/search?q=sports+gambling+V+prediction+deep+machine+learning+ML ...Google search] | ||
− | * [[ | + | * [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] |
− | + | * [[Sports]] | |
− | * [[Reinforcement Learning (RL)]] | + | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
+ | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]] | ||
* [http://www.engadget.com/ai-predicts-a-dodgers-world-series-win-after-a-covi-dshortened-season-130014351.html AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget] | * [http://www.engadget.com/ai-predicts-a-dodgers-world-series-win-after-a-covi-dshortened-season-130014351.html AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget] | ||
− | + | AI can be applied to various sports for wagering, predicting and betting. AI for sports betting provides the ability to synthesize large volumes of data and generate predictions based on advanced algorithms and statistical models. This means that sports bettors can make informed decisions and place winning bets with confidence | |
− | + | = Betting, Wagering & Prediction = | |
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<youtube>yeh0ar2kB_Q</youtube> | <youtube>yeh0ar2kB_Q</youtube> | ||
<b>A Modern Love Story: Machine Learning & The Global Sports Betting Industry | ICED(AI) | <b>A Modern Love Story: Machine Learning & The Global Sports Betting Industry | ICED(AI) | ||
− | </b><br> | + | </b><br>[[Development]]s 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 [[development.\]]s 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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+ | = Sports = | ||
+ | Horse Racing, Basketball, American Football, Soccer, Cricket, Tennis, Poker and Esports are all popular sports and games that people bet on. | ||
== Horse Racing == | == Horse Racing == | ||
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== Basketball == | == Basketball == | ||
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<youtube>k7hSD_-gWMw</youtube> | <youtube>k7hSD_-gWMw</youtube> | ||
<b>Predicting sports winners using data analytics with Pandas and scikit-learn by Robert Layton | <b>Predicting sports winners using data analytics with Pandas and scikit-learn by Robert Layton | ||
− | </b><br>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 | + | </b><br>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 Analytics|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 |
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− | <youtube> | + | <youtube>egTylm6C2is</youtube> |
− | <b> | + | <b>Predict NBA Games With Python And Machine Learning |
− | </b><br> | + | </b><br> We'll predict the winners of basketball games in the NBA using python. We'll start by reading in box score data that we scraped in the last video. If you didn't watch the last video, you can still download the file (link below) and follow along. We'll do feature selection to identify good predictors, and train a machine learning model to make predictions. We'll end by computing rolling predictors and improving the model. We'll discuss how you can keep improving the model and predict future games. |
+ | * [https://github.com/dataquestio/project-walkthroughs/tree/master/nba_games Full code and description of the project] | ||
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== Soccer == | == Soccer == | ||
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<youtube>6tQhoUuQrOw</youtube> | <youtube>6tQhoUuQrOw</youtube> | ||
<b>Predicting the Winning Team with Machine Learning | <b>Predicting the Winning Team with Machine Learning | ||
− | </b><br>[[Creatives#Siraj Raval|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 [[Python#scikit-learn|scikit-learn]] machine learning library as our predictive tool. [http://github.com/llSourcell/Predicting_Winning_Teams Code for this video] | + | </b><br>[[Creatives#Siraj Raval|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 [[Python#scikit-learn|scikit-learn]] machine learning library as our [[Predictive Analytics|predictive tool]]. [http://github.com/llSourcell/Predicting_Winning_Teams Code for this video] |
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+ | <youtube>vdrD7Bg5HV8</youtube> | ||
+ | <b>Innovations in Betting, Soccer & Football Analytics: Ted Knutson, StatsBomb | ||
+ | </b><br>Ted Knutson, CEO and founder of StatsBomb, joins me to talk about his journey through being a professional bettor, bookmaker and creating innovative data and insights for professional Soccer and American football. | ||
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== Cricket == | == Cricket == | ||
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− | <youtube> | + | <youtube>cbVsbQlfew0</youtube> |
− | <b> | + | <b>Creating AI Models and Getting Predictions - Machine Learning & AI in Sports Betting |
− | </b><br> | + | </b><br>#AI #SportsBetting #MachineLearning |
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<youtube>McV4a6umbAY</youtube> | <youtube>McV4a6umbAY</youtube> | ||
<b>AI for Imperfect-Information Games: Beating Top Humans in No-Limit Poker | <b>AI for Imperfect-Information Games: Beating Top Humans in No-Limit Poker | ||
− | </b><br>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. | + | </b><br>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. |
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+ | == Esports == | ||
+ | <youtube>mGNFqV-pwu4</youtube> | ||
+ | |||
+ | = Betting with ChatGPT - Siraj Raval = | ||
+ | |||
+ | <youtube>8EFJagJkQLI</youtube> | ||
+ | <youtube>IDthta5sUGQ</youtube> | ||
+ | <youtube>BaSgUREIPkc</youtube> | ||
= Reinforcement Learning = | = Reinforcement Learning = | ||
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</b><br>[[Creatives#Siraj Raval|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! [http://github.com/llSourcell/sports_betting_with_reinforcement_learning Code for this video] | </b><br>[[Creatives#Siraj Raval|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! [http://github.com/llSourcell/sports_betting_with_reinforcement_learning Code for this video] | ||
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− | + | ||
+ | = Random Forest = | ||
+ | <youtube>XRhOZ7za9PQ</youtube> | ||
+ | |||
+ | = OddsJam = | ||
+ | * [https://bit.ly/oddsjamyt OddsJam] | ||
+ | * My Twitter: https://twitter.com/AlexMonahan100 | ||
+ | * Email: alex@oddsjam.com | ||
+ | * My author page where I write about profitable sports betting: https://oddsjam.com/author/alexmonahan | ||
+ | |||
+ | <youtube>rbqJAY0xcGY</youtube> | ||
+ | |||
+ | == Sportbooks for Line Shopping == | ||
+ | * [https://www.oddsshark.com/sports-betting/line-shopping Line Shopping | Odds Shark] | ||
+ | |||
+ | Line shopping is the practice of checking multiple sportsbooks to find the best odds and lines for a particular bet. By doing this, bettors can increase their potential profits in both the short term and the long term. With the internet being what it is today, line shopping is easier than ever before. Line shopping is an important strategy for sports bettors because it allows them to find the best value for their bets. By checking multiple sportsbooks, bettors can find the best odds and lines for their chosen bet. This can increase their potential profits and help them make more informed decisions. It’s important to note that lines and odds can vary between sportsbooks due to a variety of factors, including the book’s own analysis of the event, the amount of money being bet on each side, and the book’s own profit margin. By line shopping, bettors can take advantage of these differences to get the best value for their bets. | ||
+ | |||
+ | == Sportbook Limits == | ||
+ | * [https://www.gamblingsites.org/sports-betting/essentials/why-bookmakers-limit-accounts Why Bookmakers Limit or Close Accounts and How to Avoid It] | ||
+ | Bookmakers often set limits on the amount that can be wagered on a particular bet. These limits are put in place to help protect the bookmaker from large losses. When an account becomes limited, it means that the amount that the account holder can wager is restricted. Bookmakers may limit accounts that are winning too much or are suspected of doing something else that is against the rules. There are also some bookmakers that have exceptionally high betting and deposit limits for those looking to place larger bets. | ||
= Excel - Sports Prediction = | = Excel - Sports Prediction = | ||
+ | * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] | ||
+ | <youtube>2Bp8bytUN24</youtube> | ||
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<b>Creating a Sports Betting Model 101 - Intro to Linear Regression (The simplest model ever created!) | <b>Creating a Sports Betting Model 101 - Intro to Linear Regression (The simplest model ever created!) | ||
− | </b><br>If you have watched my previous videos, you will have seen me say the only way you stand a chance at sports betting is to have a mathematical model. The problem is most people have no idea what exactly a model is, or where to start. The purpose of this video is to illustrate a sports betting model at the very simplest level possible. This video is intentionally low level to educate the absolute novice about the basics of building a sports betting model using a simple linear regression approach in [[Microsoft]] [[ | + | </b><br>If you have watched my previous videos, you will have seen me say the only way you stand a chance at sports betting is to have a mathematical model. The problem is most people have no idea what exactly a model is, or where to start. The purpose of this video is to illustrate a sports betting model at the very simplest level possible. This video is intentionally low level to educate the absolute novice about the basics of building a sports betting model using a simple linear regression approach in [[Microsoft]] [[Excel]]. The model demoed in this video is very simple and therefore will not win you money in the long run. The purpose of the model is to illustrate the concepts and principles behind a linear regression model. The idea is to take this video and learn the basics and then advance your sports betting analytical skills to more complex methods. This channel is a tout free safe space that has zero tolerance for touting or speaking positively about touts. |
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− | <b>[[Microsoft]] [[ | + | <b>[[Microsoft]] [[Excel]] Football Predictions and Statistics Workbook v5.4 |
− | </b><br>Thank you for checking out my new demo with the current version 30/01/2020. This demo is on my Football predictions application using [[Microsoft]] [[ | + | </b><br>Thank you for checking out my new demo with the current version 30/01/2020. This demo is on my Football predictions application using [[Microsoft]] [[Excel]] . As you can see in the video there is a lot of stats and predictions to help you make informed decision. Currently listed on eBay and e-junkie. |
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<youtube>PtTgyDBK0jg</youtube> | <youtube>PtTgyDBK0jg</youtube> | ||
− | <b>How to Analyze Sports with [[ | + | <b>How to Analyze Sports with [[Excel]] - Part 5: Using Our Data to Make Predictions for Betting Purposes |
</b><br>[http://docs.google.com/spreadsheets/d/11oaW1SLveX7216FsVlQ5WHP53omkoVmgFbRHLO7TAhg/edit#gid=2084699678 Spreadsheet | Sports Betting Truth] | </b><br>[http://docs.google.com/spreadsheets/d/11oaW1SLveX7216FsVlQ5WHP53omkoVmgFbRHLO7TAhg/edit#gid=2084699678 Spreadsheet | Sports Betting Truth] | ||
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<youtube>V20rb9RL2ng</youtube> | <youtube>V20rb9RL2ng</youtube> | ||
− | <b>How to Simulate Sports Games in [[ | + | <b>How to Simulate Sports Games in [[Excel]] based on Lineups |
</b><br>Riley Wichmann | </b><br>Riley Wichmann | ||
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Latest revision as of 11:43, 16 March 2024
Youtube search... ...Google search
- Prescriptive & Predictive Analytics ... Predictive Operations ... Forecasting ... with Excel ... Market Trading ... Sports Prediction ... Marketing ... Politics
- Sports
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
- AI predicts a Dodgers World Series win after a COVID-shortened season | Andrew Tarantola - Engadget
AI can be applied to various sports for wagering, predicting and betting. AI for sports betting provides the ability to synthesize large volumes of data and generate predictions based on advanced algorithms and statistical models. This means that sports bettors can make informed decisions and place winning bets with confidence
Contents
Betting, Wagering & Prediction
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Sports
Horse Racing, Basketball, American Football, Soccer, Cricket, Tennis, Poker and Esports are all popular sports and games that people bet on.
Horse Racing
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Basketball
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American Football
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Soccer
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Cricket
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Tennis
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Poker
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Imperfect Information
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Esports
Betting with ChatGPT - Siraj Raval
Reinforcement Learning
Sports Betting with Reinforcement Learning
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Random Forest
OddsJam
- OddsJam
- My Twitter: https://twitter.com/AlexMonahan100
- Email: alex@oddsjam.com
- My author page where I write about profitable sports betting: https://oddsjam.com/author/alexmonahan
Sportbooks for Line Shopping
Line shopping is the practice of checking multiple sportsbooks to find the best odds and lines for a particular bet. By doing this, bettors can increase their potential profits in both the short term and the long term. With the internet being what it is today, line shopping is easier than ever before. Line shopping is an important strategy for sports bettors because it allows them to find the best value for their bets. By checking multiple sportsbooks, bettors can find the best odds and lines for their chosen bet. This can increase their potential profits and help them make more informed decisions. It’s important to note that lines and odds can vary between sportsbooks due to a variety of factors, including the book’s own analysis of the event, the amount of money being bet on each side, and the book’s own profit margin. By line shopping, bettors can take advantage of these differences to get the best value for their bets.
Sportbook Limits
Bookmakers often set limits on the amount that can be wagered on a particular bet. These limits are put in place to help protect the bookmaker from large losses. When an account becomes limited, it means that the amount that the account holder can wager is restricted. Bookmakers may limit accounts that are winning too much or are suspected of doing something else that is against the rules. There are also some bookmakers that have exceptionally high betting and deposit limits for those looking to place larger bets.
Excel - Sports Prediction
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
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