NBA2Vec: Dense feature representations of NBA players

• URL: http://arxiv.org/abs/2302.13386v1
• Date: Sun, 26 Feb 2023 19:05:57 GMT
• Title: NBA2Vec: Dense feature representations of NBA players
• Authors: Webster Guan, Nauman Javed, Peter Lu
• Abstract summary: We present NBA2Vec, a neural network model based on Word2Vec which extracts dense feature representations of each player. NBA2Vec accurately predicts the outcomes to various 2017 NBA Playoffs series. Future applications of NBA2Vec embeddings to characterize players' style may revolutionize predictive models for player acquisition and coaching decisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding a player's performance in a basketball game requires an evaluation of the player in the context of their teammates and the opposing lineup. Here, we present NBA2Vec, a neural network model based on Word2Vec which extracts dense feature representations of each player by predicting play outcomes without the use of hand-crafted heuristics or aggregate statistical measures. Specifically, our model aimed to predict the outcome of a possession given both the offensive and defensive players on the court. By training on over 3.5 million plays involving 1551 distinct players, our model was able to achieve a 0.3 K-L divergence with respect to the empirical play-by-play distribution. The resulting embedding space is consistent with general classifications of player position and style, and the embedding dimensions correlated at a significant level with traditional box score metrics. Finally, we demonstrate that NBA2Vec accurately predicts the outcomes to various 2017 NBA Playoffs series, and shows potential in determining optimal lineup match-ups. Future applications of NBA2Vec embeddings to characterize players' style may revolutionize predictive models for player acquisition and coaching decisions that maximize team success.

Related papers

• SkillMimic: Learning Reusable Basketball Skills from Demonstrations [85.23012579911378]
  We propose SkillMimic, a data-driven approach that mimics both human and ball motions to learn a wide variety of basketball skills. SkillMimic employs a unified configuration to learn diverse skills from human-ball motion datasets. The skills acquired by SkillMimic can be easily reused by a high-level controller to accomplish complex basketball tasks.
  arXiv  Detail & Related papers  (2024-08-12T15:19:04Z)
• Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role [0.9002260638342727]
  The purpose of this study is to analyze more specifically the impact of playing style compatibility on scoring efficiency, focusing only on offense. This study employs two methods to capture the playing styles of players on offense: shooting style clustering using tracking data, and offensive role clustering based on annotated playtypes and advanced statistics. Based on the lineup information derived from these two clusterings, machine learning models Bayesian models that predict statistics representing scoring efficiency were trained and interpreted.
  arXiv  Detail & Related papers  (2024-03-04T03:09:25Z)
• 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)
• Can a face tell us anything about an NBA prospect? -- A Deep Learning approach [0.0]
  We deploy image analysis and Convolutional Neural Networks in an attempt to predict the career trajectory of newly drafted players from each draft class. We created a database consisting of about 1500 image data from players from every draft since 1990. We trained popular pre-trained image classification models in our data and conducted a series of tests in an attempt to create models that give reliable predictions of the rookie players' careers.
  arXiv  Detail & Related papers  (2022-12-13T18:36:29Z)
• Graph Neural Networks to Predict Sports Outcomes [0.0]
  We introduce a sport-agnostic graph-based representation of game states. We then use our proposed graph representation as input to graph neural networks to predict sports outcomes.
  arXiv  Detail & Related papers  (2022-07-28T14:45:02Z)
• GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports [84.55775845090542]
  We propose a semi-supervised win prediction model for esports based on graph convolutional networks. GCN-WP integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL.
  arXiv  Detail & Related papers  (2022-07-26T21:38:07Z)
• WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models [91.92346150646007]
  In this work, we introduce WinoGAViL: an online game to collect vision-and-language associations. We use the game to collect 3.5K instances, finding that they are intuitive for humans but challenging for state-of-the-art AI models. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills.
  arXiv  Detail & Related papers  (2022-07-25T23:57:44Z)
• 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)
• Features selection in NBA outcome prediction through Deep Learning [0.0]
  It is shown how models based on one feature (Elo rating or the relative victory frequency) have a quality of fit better than models using box-score predictors. Features have been ex ante calculated for a dataset containing data of 16 NBA regular seasons.
  arXiv  Detail & Related papers  (2021-11-17T16:32:11Z)
• Multi-Modal Trajectory Prediction of NBA Players [14.735704310108101]
  We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2020-08-18T11:35:44Z)
• 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.