# Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events

arXiv: https://arxiv.org/abs/2208.11191 · HTML: https://mlsanity.com/paper/2208.11191/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://mlsanity.com/paper/1904.04346/) (2019)
- [Learning To Score Olympic Events](https://mlsanity.com/paper/1611.05125/) (2016)
- [Action Quality Assessment Across Multiple Actions](https://mlsanity.com/paper/1812.06367/) (2018)
- [Learning to score the figure skating sports videos](https://mlsanity.com/paper/1802.02774/) (2018)
- [Learning Spatiotemporal Features with 3D Convolutional Networks](https://mlsanity.com/paper/1412.0767/) (2014)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [How to Catch Marathon Cheaters: New Approximation Algorithms for Tracking Paths](https://mlsanity.com/paper/2104.12337/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2502.02817/) (2025)
- [Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action](https://mlsanity.com/paper/2312.15236/) (2023)
- [An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment](https://mlsanity.com/paper/2307.12183/) (2023)
- [A Large-Scale Re-identification Analysis in Sporting Scenarios: the Betrayal of Reaching a Critical Point](https://mlsanity.com/paper/2401.00080/) (2023)
- [Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation](https://mlsanity.com/paper/2507.12292/) (2025)
- [Predicting Soccer Penalty Kick Direction Using Human Action Recognition](https://mlsanity.com/paper/2507.12617/) (2025)

## Abstract (arXiv)

Predicting an athlete's performance based on short footage is highly challenging. Performance prediction requires high domain knowledge and enough evidence to infer an appropriate quality assessment. Sports pundits can often infer this kind of information in real-time. In this paper, we propose regressing an ultra-distance runner cumulative race time (CRT), i.e., the time the runner has been in action since the race start, by using only a few seconds of footage as input. We modified the I3D ConvNet backbone slightly and trained a newly added regressor for that purpose. We use appropriate pre-processing of the visual input to enable transfer learning from a specific runner. We show that the resulting neural network can provide a remarkable performance for short input footage: 18 minutes and a half mean absolute error in estimating the CRT for runners who have been in action from 8 to 20 hours. Our methodology has several favorable properties: it does not require a human expert to provide any insight, it can be used at any moment during the race by just observing a runner, and it can inform the race staff about a runner at any given time.