# Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals

arXiv: https://arxiv.org/abs/2508.17611 · HTML: https://mlsanity.com/paper/2508.17611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [TacticAI: an AI assistant for football tactics](https://mlsanity.com/paper/2310.10553/) (2023)
- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [Estimating counterfactual treatment outcomes over time in complex multiagent scenarios](https://mlsanity.com/paper/2206.01900/) (2022)
- [Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling](https://mlsanity.com/paper/2007.03155/) (2020)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations](https://mlsanity.com/paper/2305.13030/) (2023)
- [A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory](https://mlsanity.com/paper/2307.14732/) (2023)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [Mathematical models for off-ball scoring prediction in basketball](https://mlsanity.com/paper/2406.08749/) (2024)
- [Space evaluation based on pitch control using drone video in Ultimate](https://mlsanity.com/paper/2409.14588/) (2024)

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

- [Right Move, Right Time: Multi-Sport Space Evaluation Platform for Ultimate Frisbee, Basketball, and Soccer](https://mlsanity.com/paper/2603.06585/) (2026)

## Abstract (arXiv)

Ultimate is a sport where points are scored by passing a disc and catching it in the opposing team's end zone. In Ultimate, the player holding the disc cannot move, making field dynamics primarily driven by other players' movements. However, current literature in team sports has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations. In this paper, we propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee. First, game footage was recorded using a drone camera, and players' positional data was obtained, which will be published as UltimateTrack dataset. Next, players' movement initiations were detected, and temporal counterfactual scenarios were generated by shifting the timing of movements using rule-based approaches. These scenarios were analyzed using a space evaluation metric based on soccer's pitch control reflecting the unique rules of Ultimate. By comparing the spatial evaluation values across scenarios, the difference between actual play and the most favorable counterfactual scenario was used to quantitatively assess the impact of movement timing. We validated our method and show that sequences in which the disc was actually thrown to the receiver received higher evaluation scores than the sequences without a throw. In practical verifications, the higher-skill group displays a broader distribution of time offsets from the model's optimal initiation point. These findings demonstrate that the proposed metric provides an objective means of assessing movement initiation timing, which has been difficult to quantify in unlabeled team sport plays.