Difference between revisions of "Sports Prediction"
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| − | <b> | + | <b>Predict Horse Races with BigQuery |
| − | </b><br> | + | </b><br>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/ | ||
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| − | <b> | + | <b>Predicting Horse Race Winners Using Advanced Statistical Methods |
| − | </b><br> | + | </b><br>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%. |
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| − | <b> | + | <b>Predicting sports winners using data analytics with Pandas and scikit-learn by Robert Layton |
| − | </b><br> | + | </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 |
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| − | <b> | + | <b>How to win in NBA betting - Sports betting tips, powered by Artificial Intelligence |
| − | </b><br> | + | </b><br>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. |
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== American Football == | == American Football == | ||
| + | * [https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games Using Machine Learning for Predicting NFL Games | Amit Bhattacharyya - Berkeley School of Information] | ||
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| − | <b> | + | <b>Using Machine Learning for Predicting NFL Games | Data Dialogs 2016 |
| − | </b><br> | + | </b><br>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. |
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| − | <b> | + | <b>Machine Learning: How to Beat Your Friends in an NFL Confidence Pool |
| − | </b><br> | + | </b><br>Presented by Amit Bhattacharyya 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 supposed knowledge from years of being a fan. 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. |
| + | This presentation is brought to you by the MS in Data Analytics online degree program at CUNY School of Professional Studies. | ||
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Revision as of 21:37, 15 September 2020
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Contents
Sport
Horse Racing
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Basketball
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American Football
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American Football - Fantasy
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Soccer
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Hockey
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Tennis
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Poker
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Imperfect Information
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Excel - Sports Prediction
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