# Space evaluation at the starting point of soccer transitions

arXiv: https://arxiv.org/abs/2505.14711 · HTML: https://mlsanity.com/paper/2505.14711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 12 of 12)

- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study](https://mlsanity.com/paper/2103.09627/) (2021)
- [Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis](https://mlsanity.com/paper/2302.09276/) (2023)
- [Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities](https://mlsanity.com/paper/2212.00021/) (2022)
- [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)
- [Forecasting Events in Soccer Matches Through Language](https://mlsanity.com/paper/2402.06820/) (2024)
- [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)
- [Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models](https://mlsanity.com/paper/2402.06815/) (2024)
- [Potential Penetrative Pass (P3)](https://mlsanity.com/paper/2302.10760/) (2023)

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

- [FOOTPASS: A Multi-Modal Multi-Agent Tactical Context Dataset for Play-by-Play Action Spotting in Soccer Broadcast Videos](https://mlsanity.com/paper/2511.16183/) (2025)

## Abstract (arXiv)

Soccer is a sport played on a pitch where effective use of space is crucial. Decision-making during transitions, when possession switches between teams, has been increasingly important, but research on space evaluation in these moments has been limited. Recent space evaluation methods such as OBSO (Off-Ball Scoring Opportunity) use scoring probability, so it is not well-suited for assessing areas far from the goal, where transitions typically occur. In this paper, we propose OBPV (Off-Ball Positioning Value) to evaluate space across the pitch, including the starting points of transitions. OBPV extends OBSO by introducing the field value model, which evaluates the entire pitch, and by employing the transition kernel model, which reflects positional specificity through kernel density estimation of pass distributions. Experiments using La Liga 2023/24 season tracking and event data show that OBPV highlights effective space utilization during counter-attacks and reveals team-specific characteristics in how the teams utilize space after positive and negative transitions.