Space evaluation at the starting point of soccer transitions

• URL: http://arxiv.org/abs/2505.14711v1
• Date: Sat, 17 May 2025 06:28:06 GMT
• Title: Space evaluation at the starting point of soccer transitions
• Abstract summary: We propose OBPV (Off-Ball Positioning Value) to evaluate space across the pitch, including the starting points of transitions.<n>Experiments using La Liga 2023/24 season tracking and event data show that OBPV highlights effective space utilization during counter-attacks.
• Score: 0.8009842832476994
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• SoccerLens: Grounded Soccer Video Understanding Beyond Accuracy [52.09081885158698]
  Vision-language models (VLMs) have recently shown strong potential in soccer video understanding.<n>Existing evaluation protocols focus on classification accuracy and do not assess visual grounding.<n>We introduce SoccerLens, a benchmark for grounded soccer video understanding.
  arXiv  Detail & Related papers  (2026-05-10T15:18:08Z)
• Right Move, Right Time: Multi-Sport Space Evaluation Platform for Ultimate Frisbee, Basketball, and Soccer [3.4384744577889297]
  We present an open, sport-agnostic platform that turns tracking into comparable spatial measures across professional Ultimate, basketball, and soccer.<n>Our workflow standardizes inputs, provides timing-aware spatial evaluations, and makes it possible to reuse the same analysis across sports.
  arXiv  Detail & Related papers  (2026-01-26T21:18:53Z)
• ScoutGPT: Capturing Player Impact from Team Action Sequences Using GPT-Based Framework [5.36551574565267]
  We introduce EventGPT, a player-conditioned, value-aware next-event prediction model built on a GPT-style autoregressive transformer.<n>Our model treats match play as a sequence of discrete tokens, jointly learning to predict the next on-ball action's type, location, timing, and its estimated residual On-Ball Value.<n>By substituting learned player embeddings into new event sequences, we can simulate how a player's behavioral distribution and value profile would change when placed in a different team or tactical structure.
  arXiv  Detail & Related papers  (2025-12-19T06:30:11Z)
• Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals [9.570401141299756]
  In Ultimate Frisbee, points are scored by passing a disc and catching it in the opposing team's end zone.<n>Current literature has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations.<n>We propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee.
  arXiv  Detail & Related papers  (2025-08-25T02:42:08Z)
• Speaker Embeddings to Improve Tracking of Intermittent and Moving Speakers [53.12031345322412]
  We propose to perform identity reassignment post-tracking, using speaker embeddings.<n>Beamforming is used to enhance the signal towards the speakers' positions in order to compute speaker embeddings.<n>We evaluate the performance of the proposed speaker embedding-based identity reassignment method on a dataset where speakers change position during inactivity periods.
  arXiv  Detail & Related papers  (2025-06-23T13:02:20Z)
• Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics [0.9207076627649226]
  Rule-based models align with expert knowledge but have not fully considered all players' states.<n>Our approach defines interpretable state variables for both the ball-holder potential pass receivers.<n>The analysis revealed that the distance between the player and the ball, as well as the player's space score, were key factors in determining successful passes.
  arXiv  Detail & Related papers  (2025-06-20T02:37:52Z)
• Unveiling Hidden Pivotal Players with GoalNet: A GNN-Based Soccer Player Evaluation System [8.957579200590988]
  Soccer analysis tools emphasize metrics such as expected goals, leading to an overrepresentation of attacking players' contributions.<n>We introduce a GNN-based framework that assigns individual credit for changes in expected threat (xT)<n>Our pipeline encodes both spatial and temporal features in event-centric graphs, enabling fair attribution of non-scoring actions.
  arXiv  Detail & Related papers  (2025-03-12T18:36:55Z)
• TraCS: Trajectory Collection in Continuous Space under Local Differential Privacy [5.90975025491779]
  Local differential privacy (LDP) provides provable privacy guarantees for users, even when the data collector is untrusted.<n>Existing trajectory collection methods ensure LDP only for discrete location spaces.<n>This paper proposes two trajectory collection methods that ensure LDP for continuous spaces.
  arXiv  Detail & Related papers  (2024-12-01T00:07:04Z)
• Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs.<n>Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction.<n>We benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Zone Evaluation: Revealing Spatial Bias in Object Detection [69.59295428233844]
  A fundamental limitation of object detectors is that they suffer from "spatial bias" We present a new zone evaluation protocol, which measures the detection performance over zones. For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones.
  arXiv  Detail & Related papers  (2023-10-20T01:44:49Z)
• Passing Heatmap Prediction Based on Transformer Model and Tracking Data [0.0]
  This research presents a novel deep-learning network architecture which is capable to predict the potential end location of passes. Once analysed more than 28,000 pass events, a robust prediction can be achieved with more than 0.7 Top-1 accuracy. And based on the prediction, a better understanding of the pitch control and pass option could be reached to measure players' off-ball movement contribution to defensive performance.
  arXiv  Detail & Related papers  (2023-09-04T11:14:22Z)
• Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis [0.6946929968559495]
  We propose a model for football event data based on the neural temporal point processes framework. For verification, we examined the relationship with football teams' final ranking, average goal score, and average xG over season. It was observed that the average HPUS showed significant correlations regardless of not using goal and shot details.
  arXiv  Detail & Related papers  (2023-02-18T10:02:45Z)
• Point-Level Region Contrast for Object Detection Pre-Training [147.47349344401806]
  We present point-level region contrast, a self-supervised pre-training approach for the task of object detection. Our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality.
  arXiv  Detail & Related papers  (2022-02-09T18:56:41Z)
• Predicting the outcome of team movements -- Player time series analysis using fuzzy and deep methods for representation learning [0.0]
  We provide a framework for the useful encoding of short tactics and space occupations in a more extended sequence of movements or tactical plans. We discuss the effectiveness of the proposed approach for prediction and recognition tasks on the professional basketball SportVU dataset for the 2015-16 half-season.
  arXiv  Detail & Related papers  (2021-09-13T18:42:37Z)
• Unsupervised Domain Adaptation for Spatio-Temporal Action Localization [69.12982544509427]
  S-temporal action localization is an important problem in computer vision. We propose an end-to-end unsupervised domain adaptation algorithm. We show that significant performance gain can be achieved when spatial and temporal features are adapted separately or jointly.
  arXiv  Detail & Related papers  (2020-10-19T04:25:10Z)
• Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation [62.29076080124199]
  This paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains.
  arXiv  Detail & Related papers  (2020-03-23T13:40:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.