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?
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Aiheet: jalkapallo analyysi taktiikka joukkue kilpailu ennuste ohjelmisto
Aihealueet: urheilukilpailut tekniset ja luonnontieteet
Tagging: deep learning
Julkaisussa: MIT Sloan Sports Analytics Conference 2019
Julkaistu: 2019
Julkaisutyypit: kongressin muistiinpanot
Kieli: englanti (kieli)
Taso: kehittynyt