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Built a prize predictor app using machine learning to forecast NFL daily fantasy sports players' performance. This video outlines the model's construction compared to OddsJam's fantasy optimizer, which leverages extensive real-time data to maximize user betting advantages. The process involves data scraping, developing predictive models, deploying them via FastAPI, and scheduling tasks with Apache Airflow to ensure continuous learning. At the end, a bet is placed to evaluate the model's prediction accuracy against OddsJam's optimized selections, highlighting the ongoing competition between custom models and established tools in fantasy sports betting.
Introduction of a prize predictor app built with machine learning for fantasy sports.
Overview of scraping sports data and building models for predicting player performance.
Details the deployment of the model using FastAPI on Google Cloud with Airflow for scheduling.
Final predictions comparing internal model picks and OddsJam selections.
The integration of machine learning into sports betting highlights an evolving frontier in predictive analytics, especially within the competitive realm of daily fantasy sports. Utilizing both historical player data and real-time statistics augments model accuracy, a crucial factor in performance forecasting. Continuous updates via Airflow ensure models stay relevant, capitalizing on the dynamic nature of sports, which is vital for maintaining an edge against established tools like OddsJam.
As AI models increasingly influence gambling outcomes, understanding the ethical implications and responsible gambling practices becomes essential. Machine learning's role in optimizing player selections raises questions about fairness and transparency in betting. Regulatory frameworks should evolve to ensure that tools like OddsJam and user-created models operate within legal and ethical boundaries, safeguarding against potential exploitation by informed users and promoting a level playing field.
This application is used to forecast daily fantasy sports player performance based on historical data.
Data scraping is employed here to gather real-time sports statistics for model training and predictions.
FastAPI is utilized in this project to deploy the predictive model for public access on the web.
Airflow is used to automate the scheduling and execution of tasks related to data scraping and model updating.
Its optimizer tool utilizes machine learning and data analysis to identify betting opportunities, making it a direct competitor to custom-built models in the fantasy sports space.
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Google Cloud is leveraged here for hosting the predictive model and managing backend services.
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DGFantasy - Prizepicks & Sports Betting 10month
DGFantasy - Prizepicks & Sports Betting 12month
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