Decomposing the immeasurable sport: A deep learning expected possession value framework for soccer
What is the right way to think about analytics in soccer? Is the sport about measured events such as passes and goals, possession percentages and traveled distance, or even more abstract notions such as mistakes (to quote Cruyff, "Soccer is a game of mistakes, whoever makes the fewer wins")? Analytical work to date has focused primarily on these more isolated aspects of the sport, while coaches tend to focus on the tactical interplay of all 22 players on the pitch. Soccer analytics is lacking from a comprehensive approach that can start to address performance-related questions that are closer to the language of the game. Questions such as: who adds more value? How and where is this value added? Are the teammates creating spaces of value? When and how should a backward pass be taken? How risky is a team attacking strategy? What is a player`s decisionmaking profile?
© Copyright 2019 MIT Sloan Sports Analytics Conference 2019. Kaikki oikeudet pidätetään.
Aiheet: | jalkapallo analyysi taktiikka joukkue kilpailu ennuste ohjelmisto |
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Aihealueet: | urheilukilpailut tekniset ja luonnontieteet |
Tagging: | deep learning |
Julkaisussa: | MIT Sloan Sports Analytics Conference 2019 |
Julkaistu: |
2019
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Julkaisutyypit: | kongressin muistiinpanot |
Kieli: | englanti (kieli) |
Taso: | kehittynyt |