A framework for the fine-grained evaluation of the instantaneous
expected value of soccer possessions
- URL: http://arxiv.org/abs/2011.09426v1
- Date: Wed, 18 Nov 2020 17:51:22 GMT
- Title: A framework for the fine-grained evaluation of the instantaneous
expected value of soccer possessions
- Authors: Javier Fernandez (1 and 2), Luke Bornn (3), Daniel Cervone (4) ((1)
Polytechnic University of Catalonia, (2) FC Barcelona, (3) Simon Fraser
University, (4) Zelus Analytics)
- Abstract summary: We develop a comprehensive analysis framework providing soccer practitioners with the ability to evaluate the impact of both observed and potential actions.
We show we can obtain models for all the components of EPV, including a set of yet-unexplored problems in soccer.
- Score: 1.0224234677367114
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The expected possession value (EPV) of a soccer possession represents the
likelihood of a team scoring or receiving the next goal at any time instance.
By decomposing the EPV into a series of subcomponents that are estimated
separately, we develop a comprehensive analysis framework providing soccer
practitioners with the ability to evaluate the impact of both observed and
potential actions. We show we can obtain calibrated models for all the
components of EPV, including a set of yet-unexplored problems in soccer. We
produce visually-interpretable probability surfaces for potential passes from a
series of deep neural network architectures that learn from low-level
spatiotemporal data. Additionally, we present a series of novel practical
applications providing coaches with an enriched interpretation of specific game
situations.
Related papers
- Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [56.391404083287235]
We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach.
Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations.
We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
arXiv Detail & Related papers (2024-06-24T17:59:42Z) - Foul prediction with estimated poses from soccer broadcast video [0.9002260638342727]
We introduce an innovative deep learning approach for anticipating soccer fouls.
This method integrates video data, bounding box positions, image details, and pose information by curating a novel soccer foul dataset.
Our results have important implications for a deeper understanding of foul play in soccer.
arXiv Detail & Related papers (2024-02-15T01:25:19Z) - Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models [0.7373617024876725]
This paper introduces an innovative application of Large Event Models (LEMs) in soccer analytics.
LEMs predict variables for subsequent events rather than words.
We focus on fine-tuning LEMs with the WyScout dataset for the 2017-18 Premier League season.
arXiv Detail & Related papers (2024-02-09T22:47:25Z) - ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing
Forecasting Models in Badminton [52.21869064818728]
Deep learning approaches for player tactic forecasting in badminton show promising performance partially attributed to effective reasoning about rally-player interactions.
We propose a turn-based feature attribution approach, ShuttleSHAP, for analyzing forecasting models in badminton based on variants of Shapley values.
arXiv Detail & Related papers (2023-12-18T05:37:51Z) - 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) - SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in
Soccer Videos [62.686484228479095]
We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each.
The dataset is fully annotated with bounding boxes and tracklet IDs.
Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
arXiv Detail & Related papers (2022-04-14T12:22:12Z) - Is it worth the effort? Understanding and contextualizing physical
metrics in soccer [1.2205797997133396]
This framework gives a deep insight into the link between physical and technical-tactical aspects of soccer.
It allows associating physical performance with value generation thanks to a top-down approach.
arXiv Detail & Related papers (2022-04-05T16:14:40Z) - TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
TRiPOD is a novel method for predicting body dynamics based on graph attentional networks.
To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame.
Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
arXiv Detail & Related papers (2021-04-08T20:01:00Z) - Game Plan: What AI can do for Football, and What Football can do for AI [83.79507996785838]
Predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision.
We illustrate that football analytics is a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI.
arXiv Detail & Related papers (2020-11-18T10:26:02Z) - SoccerMap: A Deep Learning Architecture for Visually-Interpretable
Analysis in Soccer [1.1377027568901037]
We present a fully convolutional neural network architecture that is capable of estimating full probability surfaces of potential passes in soccer.
We show the network can perform remarkably well in the estimation of pass success probability.
We present a set of practical applications, including the evaluation of passing risk at a player level.
arXiv Detail & Related papers (2020-10-20T11:28:48Z) - CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural
Summarization Systems [121.78477833009671]
We investigate the performance of different summarization models under a cross-dataset setting.
A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways.
arXiv Detail & Related papers (2020-10-11T02:19:15Z)
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.