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June 21, 2022

15:00 UTC   Start Times Around the World

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Description

Use Case: Oracle Machine Learning in football - Expected Goals
Thanks to the partnership between the Premier League (the top tier of England's football) and Oracle we have the opportunity to experience what it is to be a football analyst and apply advanced analytics and machine learning to real match data.

In this session, you will learn about the concept of "Expected Goals" (xG) in football. Expected goals (xG) is a predictive model used to assess every goal-scoring chance and the likelihood of scoring. The xG model computes for each chance, the probability to score based on factors such as distance, the position of defenders, type and speed of pass, type of shot, shot angles, and various other aspects.

In this demonstration, you'll see how we can use Oracle Machine Learning, Autonomous Datawarehouse, and Analytics Cloud to:
- Visualize data on a football pitch
- Prepare data for machine learning
- Train the xG model
- Apply the model to recent matches to understand team and player performance.
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Your Experts

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    Jeroen Kloosterman

    Jeroen Kloosterman

    Jeroen Kloosterman is Technology Product Strategy Director for Analytics and Data Science for Oracle's SMB market in EMEA. He has over 20 years of experience in business development, product development and sales training, and is an advocate for analytics and data science.
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    Mark Hornick

    Mark Hornick   

    Mark Hornick is the Senior Director of Product Management for the Oracle Machine Learning (OML) family of products. He leads the OML PM team and works closely with Product Development on product strategy, positioning, and evangelization. Mark has over 20 years of experience with integrating and leveraging machine learning with Oracle technologies, and working with internal and external customers in applying Oracle’s machine learning technologies for scalable and deployable data science projects. Mark is Oracle’s representative to the R Consortium and an Oracle adviser and founding member of the Analytics and Data Oracle User Community. He holds a bachelor's degree from Rutgers University and a master's degree from Brown University, both in Computer Science.
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    Sherry LaMonica

    Sherry LaMonica

    Sherry is a member of the Oracle Machine Learning Product Management team. She has 20 years of software experience focused on enabling the commercial use of the open-source data analysis software systems R and Python for data science and machine learning projects. She has worked with customers in fields as diverse as as pharmaceutical research, financial analysis, manufacturing and healthcare IT.
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