# Which algorithm to select in sports timetabling?

arXiv: https://arxiv.org/abs/2309.03229 · HTML: https://mlsanity.com/paper/2309.03229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 59 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [A case study of algorithm selection for the traveling thief problem](https://mlsanity.com/paper/1609.00462/) (2016)
- [Educational Timetabling: Problems, Benchmarks, and State-of-the-Art Results](https://mlsanity.com/paper/2201.07525/) (2022)
- [On Dedicated CDCL Strategies for PB Solvers](https://mlsanity.com/paper/2109.01013/) (2021)

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

- [Resolution limits for process comparison from event data](https://mlsanity.com/paper/2609.20489/) (2026)

## Abstract (arXiv)

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides an instance space analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best when given the characteristics of a sports timetabling problem instance. Furthermore, we identify which characteristics are important in making that prediction, providing insights in the performance of the algorithms, and suggestions to further improve them. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.