Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities

• URL: http://arxiv.org/abs/2212.00021v1
• Date: Wed, 30 Nov 2022 12:43:11 GMT
• Title: Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities
• Authors: Rikuhei Umemoto, Kazushi Tsutsui, Keisuke Fujii
• Abstract summary: We propose a generalized valuation method of defensive teams by score-scaling the predicted probabilities of the events. Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction.
• Score: 0.6946929968559495
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Analyzing defenses in team sports is generally challenging because of the limited event data. Researchers have previously proposed methods to evaluate football team defense by predicting the events of ball gain and being attacked using locations of all players and the ball. However, they did not consider the importance of the events, assumed the perfect observation of all 22 players, and did not fully investigated the influence of the diversity (e.g., nationality and sex). Here, we propose a generalized valuation method of defensive teams by score-scaling the predicted probabilities of the events. Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction and validated our approach by analyzing the games. Results show that for the predictions of being attacked, scoring, and conceding, all players' information was not necessary, while that of ball gain required information on three to four offensive and defensive players. With game analyses we explained the excellence in defense of finalist teams in Euro 2020. Our approach might be applicable to location data from broadcast video frames in football games.

Related papers

• SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap [102.5232204867158]
  We formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration. Our experiments show that GSR is a challenging novel task, which opens the field for future research.
  arXiv  Detail & Related papers  (2024-04-17T12:53:45Z)
• Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach [55.2480439325792]
  This study investigates the influence of player or positional factors in predicting a shot resulting in a goal, measured by the expected goals (xG) metric. It uses publicly available data from StatsBomb to analyse 10,000 shots from the English Premier League. The study extends its analysis to data from Spain's La Liga and Germany's Bundesliga, yielding comparable results.
  arXiv  Detail & Related papers  (2023-11-22T21:54:02Z)
• 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)
• Betting the system: Using lineups to predict football scores [0.0]
  This paper aims to reduce randomness in football by analysing the role of lineups in final scores. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Sports betting is growing exponentially and being able to predict the future is profitable and desirable.
  arXiv  Detail & Related papers  (2022-10-12T15:47:42Z)
• 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)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data [1.290382979353427]
  We use deep learning techniques to define a novel metric that values such defensive actions. By studying the threat of passages of play that preceded them, we are able to value defensive actions. Our model is able to predict the impact of defensive defenders using event-data.
  arXiv  Detail & Related papers  (2021-06-03T12:18:26Z)
• Evaluation of soccer team defense based on prediction models of ball recovery and being attacked [0.8921166277011345]
  We propose a method to evaluate team defense based on the prediction of ball recovery and being attacked. Using data from 45 soccer matches, we examined the relationship between the proposed index and team performance.
  arXiv  Detail & Related papers  (2021-03-17T13:15:41Z)
• 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)
• Analysing Long Short Term Memory Models for Cricket Match Outcome Prediction [0.0]
  Recently, various machine learning techniques have been used to analyse the cricket match data and predict the match outcome as win or lose. Here we propose a novel Recurrent Neural Network model which can predict the win probability of a match at regular intervals given the ball-by-ball statistics.
  arXiv  Detail & Related papers  (2020-11-04T04:49:11Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)

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.