A machine learning approach for player and position adjusted expected goals in football (soccer)

Author:

Hewitt James H.,Karakuş OktayORCID

Publisher

Elsevier BV

Reference20 articles.

1. Wenger: Man City Only Beat Us by 0.1 Expected Goals;Winterburn,2017

2. Expected goals explained: The analysis that is changing the game;Bate,2017

3. The Expected Goals Philosophy: A Game-Changing Way of Analysing Football;Tippett,2019

4. Quality Vs Quantity: Improved Shot Prediction in Soccer Using Strategic Features from Spatiotemporal Data;Lucey,2015

5. H. Ruiz, P. Lisboa, P. Neilson, W. Gregson, Measuring scoring efficiency through goal expectancy estimation, in: ESANN, 2015.

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1. Bayes-xG: player and position correction on expected goals (xG) using Bayesian hierarchical approach;Frontiers in Sports and Active Living;2024-06-14

2. Data-Driven Player Recruitment in Football;2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS);2023-12-11

3. Contextualizing Evaluation of Performance in Volleyball: Introducing Contextual Individual Contribution Coefficients to Assess Technical Actions;Perceptual and Motor Skills;2023-11-06

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