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Found 6 papers, 2 papers with code
Biases in Expected Goals Models Confound Finishing Ability
We found that sustained overperformance of cumulative xG requires both high shot volumes and exceptional finishing, including all shot types can obscure the finishing ability of proficient strikers, and that there is a persistent bias that makes the actual and expected goals closer for excellent finishers than it really is.
Elastic Product Quantization for Time Series
Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements.
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations.
Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach
Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running.
A Bayesian Approach to In-Game Win Probability in Soccer
First, we demonstrate that in-game win probability models for other sports struggle to provide accurate estimates for soccer, especially towards the end of a game.
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Most positive and unlabeled data is subject to selection biases.
