[PyMCon Web Series 08] A Soccer-Factor-Model to Decipher a Soccer-Player's Inherent Skill (July 17, 2023) (Maximilian Goebel)

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PyMCon Web Series - A Soccer-Factor-Model to Decipher a Soccer-Player's Inherent Skill
PyMCon Web Series - A Soccer-Factor-Model to Decipher a Soccer-Player's Inherent Skill800×320 172 KB

Welcome to the 8th event of the PyMCon Web Series!

Speaker: Maximilian Goebel, a Post-Doc in Finance/Economics at Bocconi University
Event type: Live Talk
Date: 2023-07-17T14:00:00Z (subscribe here for email updates)
Time: 2 pm UTC / 7 am PT / 10 am ET / 4 pm Berlin
Register for the event: Meetup event (to get the Zoom link)
Website: PyMCon Events · PyMCon Web Series

Abstract of the Talk:

Inspired by the asset-pricing literature, the Soccer-Factor-Model (SFM) is an attempt to determine a soccer player’s “alpha”, i.e. his/her inherent skill. In this application, “skill” is defined as a player’s probability to score a goal after accounting for factors that are actually to be attributed to his team’s out- or under-performance vis-à-vis the opponent. In that sense, the SFM tries to answer the question, whether a player’s observed goals are an over- or understatement of his “true” skill/ability.

Content:

Max’s Interview:

Live Talk:

Notebook:

About the Speaker:

Maximilian Goebel

Max is a Post-Doc at Università Bocconi, specializing in finance/economics. With research interests in climate econometrics, machine learning, macroeconomic forecasting, and asset-pricing, Max combines expertise in these areas to drive innovation and uncover valuable insights.

Connect with Max:

Website:

LinkedIn: https://www.linkedin.com/in/maximilian-göbel-188b0413a/

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Connecting with PyMC

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FYI @RavinKumar, in case you see this during the talk, the Zoom chat is disabled.

edit: no longer disabled after the link was sent at 10:04.

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The motivating idea behind your features basically seems to be establishing the goal scoring with/without a given player - effectively using the time with them not on the field as a “control” and with them on the field as a “treatment”. If that’s correct, does this implicitly assumes a defensive team will respond the same way agnostic of who is on the field? Is there a good way to consider a change in the defensive behavior when a certain player is on the field? I could see this being the case for a superstar, for example. Or do you think that information is contained in the scoring difference?

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How would you control for skill over time? Basically, estimating the “aging curve” of a player?

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do the features and included rows account for when players are on the team but are not playing? For instance, a player on the bench would influence the probability of the team scoring

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Thanks for the response! Even the diff-in-diff idea is a good thought!

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seems related to the questions above with “superstars”

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In case you missed the live session, the video has been posted to YouTube.

Watch the recording here:

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