# Predicting college basketball match outcomes using machine learning techniques: some results and lessons learned

arXiv: https://arxiv.org/abs/1310.3607 · HTML: https://mlsanity.com/paper/1310.3607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2013.

## Built on by (most cited first, 1 of 1)

- [CAMP: A Context-Aware Cricket Players Performance Metric](https://mlsanity.com/paper/2307.13700/) (2023)

## Abstract (arXiv)

Most existing work on predicting NCAAB matches has been developed in a statistical context. Trusting the capabilities of ML techniques, particularly classification learners, to uncover the importance of features and learn their relationships, we evaluated a number of different paradigms on this task. In this paper, we summarize our work, pointing out that attributes seem to be more important than models, and that there seems to be an upper limit to predictive quality.