  Code style is an aesthetic choice exhibited in source code that reflects
programmers individual coding habits. This study is the first to investigate
whether code style can be used as an indicator to identify good programmers.
Data from Google Code Jam was chosen for conducting the study. A cluster
analysis was performed to find whether a particular coding style could be
associated with good programmers. Furthermore, supervised machine learning
models were trained using stylistic features and evaluated using recall,
macro-F1, AUC-ROC and balanced accuracy to predict good programmers. The
results demonstrate that good programmers may be identified using supervised
machine learning models, despite that no particular style groups could be
attributed as a good style.
