2,551 research outputs found

    Analisis Pola Defense Satria Muda Pada Babak Final Di IBL 2021

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    Penelitian ini bertujuan untuk menganalisis pola defense apa saja yang digunakan oleh Satria Muda pada quarter 1, 2, 3, 4, keberhasilan dan kegagalan pola defense yang di lakukan Satria Muda pada babak final IBL. Jenis penelitian yang digunakan adalah analisis deskriptif kuantitatif. Proses menganalis penelitian ini dengan cara mendownload hasil video pertandingan babak final IBL 2021.   Hasil penelitian ini menujukkan bahwa: 1) pada pertandingan final IBL 2021 Satria Muda lebih sering menggunakan man to man defense di area 3 point. 2) hasil keseluruhan keberhasilan pola defense pada quarter pertama berhasil 16 kali dengan presentase 30,19%, quarter kedua berhasil 12 kali dengan presentase 22,64%, quarter ketiga berhasil sebanyak 14 kali dengan presentase 26,42%, dan quarter keempat berhasil 11 kali dengan presentase 20,75%, dengan total keseluruhan 53 kali dengan presentase 51,46%. 3) keseluruhan hasil kegagalan pola defense pada quarter pertama gagal 7 kali dengan presentase 14%, quarter kedua gagal 17 kali dengan presentase 34%, quarter ketiga gagal 10 kali dengan presentase 20%, dan quarter keempat gagal 16 kali dengan presentase 32%, dengan total keseluruhan 50 kali dengan presentase 48,54%. Maka dapat disimpulkan bahwa hasil keseluruhan dari keberhasilan pola defense lebih besar daripada hasil keseluruhan dari kegagalan pola defense.   Kata Kunci: IBL 2021, Bolabasket, Defense

    Proceedings of Mathsport international 2017 conference

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    Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

    Exploiting Opponent Modeling For Learning In Multi-agent Adversarial Games

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    An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. iii In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics

    Data-driven action-value functions for evaluating players in professional team sports

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    As more and larger event stream datasets for professional sports become available, there is growing interest in modeling the complex play dynamics to evaluate player performance. Among these models, a common player evaluation method is assigning values to player actions. Traditional action-values metrics, however, consider very limited game context and player information. Furthermore, they provide directly related to goals (e.g., shots), not all actions. Recent work has shown that reinforcement learning provided powerful methods for addressing quantifying the value of player actions in sports. This dissertation develops deep reinforcement learning (DRL) methods for estimating action values in sports. We make several contributions to DRL for sports. First, we develop neural network architectures that learn an action-value Q-function from sports events logs to estimate each team\u27s expected success given the current match context. Specifically, our architecture models the game history with a recurrent network and predicts the probability that a team scores the next goal. From the learned Q-values, we derive a Goal Impact Metric (GIM) for evaluating a player\u27s performance over a game season. We show that the resulting player rankings are consistent with standard player metrics and temporally consistent within and across seasons. Second, we address the interpretability of the learned Q-values. While neural networks provided accurate estimates, the black-box structure prohibits understanding the influence of different game features on the action values. To interpret the Q-function and understand the influence of game features on action values, we design an interpretable mimic learning framework for the DRL. The framework is based on a Linear Model U-Tree (LMUT) as a transparent mimic model, which facilitates extracting the function rules and computing the feature importance for action values. Third, we incorporate information about specific players into the action values, by introducing a deep player representation framework. In this framework, each player is assigned a latent feature vector called an embedding, with the property that statistically similar players are mapped to nearby embeddings. To compute embeddings that summarize the statistical information about players, we implement a Variational Recurrent Ladder Agent Encoder (VaRLAE) to learn a contextualized representation for when and how players are likely to act. We learn and evaluate deep Q-functions from event data for both ice hockey and soccer. These are challenging continuous-flow games where game context and medium-term consequences are crucial for properly assessing the impact of a player\u27s actions

    A visual analytics approach for passing strateggies analysis in soccer using geometric features

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    Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol
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