BetterGuards has teamed up with the NBA Training Association to outfit players with its adaptive ankle brace. The pro ballers are avoiding serious injury while evaluating the stabilizing design. Austin Reaves of the Los Angeles Lakers wears a BetterGuards ankle brace during the game against the Phoenix Suns in October, 2025. Matas Buzelis was in a situation every professional basketball player dreads. This sickening scenario often means an ankle injury is about to occur, especially for players like Buzelis with a lengthy history of them dating back to his high school years.

The topic of ag ing decline on performance of NBA players has been discussed in this study. The autoencoder with K - means clustering machine learning method was adopted to career trend classification of NBA players, and the LSTM deep learning method was adopted in performance predicti on of each NBA player . The dataset was collected from the basketball game data of veteran NBA players . The contribution of the work performed better than the other methods with generalization ability for evaluat ing various types of NBA career t rend, and can be applied in different types of s p o r ts in the field of sport analytics . In the field of sports analytics, m achine learning methods are being widely used to analyze player behavior and performance prediction for the strong capabilities in pattern recognition and career trend prediction, and the topic of aging decline on the performance of basketball game players were discussed in the study.

This article presents the Quasar system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. The system adopts a RAG-based architecture, with a pipeline of evidence retrieval followed by answer generation, with the latter powered by a moderate-sized language model. Additionally and uniquely, Quasar has components for question understanding, to derive crisper input for evidence retrieval, and for re-ranking and filtering the retrieved evidence before feeding the most informative pieces into the answer generation. Experiments with three different benchmarks demonstrate the high answering quality of our approach, being on par with or better than large GPT models, while keeping the computational cost and energy consumption orders of magnitude lower.

Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the ambiguous question (AQ) and to generate a long-form answer addressing them all, as suggested by Stelmakh et al., (2022). While it provides a comprehensive response without bothering the user for clarification, considering multiple dimensions of ambiguity and gathering corresponding knowledge remains a challenge. To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer. ToC outperforms existing baselines on ASQA in a few-shot setup across the metrics, while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at https://github.com/gankim/tree-of-clarifications.

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.

While the NBA continues to move toward restarting its season with players and other personnel isolated at Walt Disney World in Orlando, details of how it hopes to manage the people on site are leaking out. According to Shams Charania of The Athletic, the specifics were laid out in an informational memo dubbed "Life inside the Bubble," that described testing plans, quarantine protocols and more. Inside the Orlando bubble, NBA players will have the option of wearing a ring that could help with early detection of coronavirus; track temperature, respiratory and heart rate. The part that's specifically interesting to us -- other than players only lounges with NBA 2K and bracelets that beep if people are within sx feet of each other for too long -- is its proposed use of Oura's smart rings. Earlier this month, study results from West Virginia University's Rockefeller Neuroscience Institute suggested that physiological data from the rings, combined in its digital platform with information obtained from wearers via in-app surveys, can "forecast and predict the onset of COVID-19 related symptoms" three days in advance, with 90 percent accuracy.

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider as well as collecting, prepping and plotting data. A complementary Domino project is available. Collecting and prepping data are core research tasks. While the most ideal situation is to start a project with clean well-labeled data, the reality is that data scientists spend countless hours on obtaining and prepping data. As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt "Predicting Social-Media Influence in the NBA" from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift. The excerpt dives into techniques for collecting, prepping, and plotting data. Many thanks to AWP Pearson for providing the permissions to excerpt the work as well as providing the data and code for us to include in a complementary Domino project. Sports is a fascinating topic for data scientists because there is always a story behind every number. Just because an NBA player scores more points than another player, it doesn't necessarily mean [they] add more value to the team. As a result, there has been a recent explosion in individual statistics that try to measure a player's impact.

They must be doing something." Amos and Danny didn't have much doubt that a lot of people would get the questions they had dreamed up wrong--because Danny and Amos had gotten them, or versions of them, wrong. If they both committed the same mental errors, or were tempted to commit them, they assumed--rightly, as it turned out--that most other people would commit them, too. The questions they had spent the year cooking up were not so much experiments as they were little dramas: Here, look, this is what the uncertain human mind actually does. Their first paper had shown that people faced with a problem that had a statistically correct answer did not think like statisticians.

They must be doing something." Amos and Danny didn't have much doubt that a lot of people would get the questions they had dreamed up wrong--because Danny and Amos had gotten them, or versions of them, wrong. If they both committed the same mental errors, or were tempted to commit them, they assumed--rightly, as it turned out--that most other people would commit them, too. The questions they had spent the year cooking up were not so much experiments as they were little dramas: Here, look, this is what the uncertain human mind actually does. Their first paper had shown that people faced with a problem that had a statistically correct answer did not think like statisticians.