Predictive Modeling

Data-Driven Prediction of Athletes’ Performance based on their Social Media Presence

We investigated whether proxies of athlete social media activity are useful features for a machine learning model to predict athletes’ performance in subsequent competitions. We extracted millions of tweets that NBA basketball players posted themselves or were tagged in and derived features reflecting players’ mood, social media behaviour, and sleep quality before games. Using these and other non-social media-related features, we performed statistical tests to examine whether the features significantly improve the accuracy of a random forest model for predicting players’ BPM scores in upcoming games.

Classification of cardiac cohorts based on morphological and hemodynamic features derived from 4D PC-MRI data

We investigate the potential of morphological and hemodynamic features extracted from measured blood flow data in the aorta to classify hear-thealthy volunteers (HHV) and patients with bicuspid aortic valve (BAV). Furthermore, we determine features that distinguish male vs. female patients and elderly HHV vs. BAV patients. We propose a data analysis pipeline for cardiac status classification, encompassing feature selection, model training, and hyperparameter tuning.