Valuing Players Over Time

• URL: http://arxiv.org/abs/2209.03882v1
• Date: Thu, 8 Sep 2022 15:36:16 GMT
• Title: Valuing Players Over Time
• Authors: Tiago Mendes-Neves, Lu\'is Meireles, Jo\~ao Mendes-Moreira
• Abstract summary: In soccer (or association football), players quickly go from heroes to zeroes, or vice-versa. This paper introduces and explores I-VAEP and O-VAEP models to evaluate actions and rate players' intention and execution. We present who were the best players and how their performance evolved, define volatility metrics to measure a player's consistency, and build a player development curve to assist decision-making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In soccer (or association football), players quickly go from heroes to zeroes, or vice-versa. Performance is not a static measure but a somewhat volatile one. Analyzing performance as a time series rather than a stationary point in time is crucial to making better decisions. This paper introduces and explores I-VAEP and O-VAEP models to evaluate actions and rate players' intention and execution. Then, we analyze these ratings over time and propose use cases to fundament our option of treating player ratings as a continuous problem. As a result, we present who were the best players and how their performance evolved, define volatility metrics to measure a player's consistency, and build a player development curve to assist decision-making.

Related papers

• Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory [0.9449650062296823]
  This paper represents an analysis on the momentum of tennis match. We First use hidden markov models to predict the momentum which is defined as the performance of players. Then we use Xgboost to prove the significance of momentum.
  arXiv  Detail & Related papers  (2024-04-20T07:11:06Z)
• CenTime: Event-Conditional Modelling of Censoring in Survival Analysis [49.44664144472712]
  We introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance.
  arXiv  Detail & Related papers  (2023-09-07T17:07:33Z)
• Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling [70.54015529131325]
  Basketball is dictated by many variables, such as playstyle and game dynamics. It is crucial that the performance profiles can reflect the diverse playstyles. We present a tool that can visualize player performance profiles in a timely manner.
  arXiv  Detail & Related papers  (2023-03-17T01:13:18Z)
• ApproxED: Approximate exploitability descent via learned best responses [61.17702187957206]
  We study the problem of finding an approximate Nash equilibrium of games with continuous action sets. We propose two new methods that minimize an approximation of exploitability with respect to the strategy profile.
  arXiv  Detail & Related papers  (2023-01-20T23:55:30Z)
• A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification [75.93186954061943]
  Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
  arXiv  Detail & Related papers  (2022-11-22T15:23:53Z)
• Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction [7.688133652295848]
  We evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. For verification, we examined the relationship with the annual salary, the goals, and the rating in the game by experts for all games of a team in a professional soccer league in a year. Our results suggest the effectiveness of the proposed method as an indicator for a player without the ball to create a scoring chance for teammates.
  arXiv  Detail & Related papers  (2022-06-04T03:58:37Z)
• Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts [86.56462654572813]
  We focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game. We propose a novel feature pooling method based on NetVLAD, dubbed NetVLAD++, that embeds temporally-aware knowledge. We train and evaluate our methodology on the recent large-scale dataset SoccerNet-v2, reaching 53.4% Average-mAP for action spotting.
  arXiv  Detail & Related papers  (2021-04-14T11:09:03Z)
• 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)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• Using Player's Body-Orientation to Model Pass Feasibility in Soccer [7.205450793637325]
  Given a monocular video of a soccer match, this paper presents a computational model to estimate the most feasible pass at any given time. The method leverages offensive player's orientation (plus their location) and opponents' spatial configuration to compute the feasibility of pass events within players of the same team. Results show that, by including orientation as a feasibility measure, a robust computational model can be built, reaching more than 0.7 Top-3 accuracy.
  arXiv  Detail & Related papers  (2020-04-15T17:09:51Z)

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.