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.

Artificial Intelligence has catapulted in recent years, and the advancements being made in this field make me feel as if it won't be long before we have robots walking amongst us all the time! There was a point in time when the only forms of robots that we could see were toys or vacuum cleaners, or if we were lucky an AI-enabled lawnmower in some tech-trendy individual's backyard! But we have come a long long way since then. From a basketball-playing Japanese robot at the Tokyo 2021 Olympics to a Doctor Octopus-inspired robot that harvests fruits – the potential and scope of robots grow exponentially day by day. The world at large is slowly moving away from the perception of robots as evil beings who want to take over the Earth, and accepting that they may have vast and undeniable utility in our day-to-day lives. Whether programmed for fun or functionality, robots are always intriguing to watch and examine!

After being delayed due to the coronavirus pandemic, the 2020 Olympics schedule is in full swing, albeit without spectators in the stands. Aside from featuring top athletic competition from nations around the globe, the quadrennial event is also showcasing several cutting-edge innovations ranging from robotics and artificial intelligence to virtual reality training solutions, carrying on a tradition of Olympic tech innovation history. "The Olympic Games have always been a catalyst and showcase for innovation, and when Tokyo last hosted the event, in 1964, it saw satellites used to relay live pictures to a global audience for the first time, as well as the debuts of close-pickup microphones and slow-motion replays," reads a portion of an Olympic blog post. Historically, human beings have traditionally located, chased down and seized game balls during Olympic competitions. But at the 2020 games, spectators may catch a glimpse of a few bots retrieving these spheres and other equipment during gameplay.

Data analysis and machine learning are the backbones of the current era. Human society has entered machine learning and data science that increases the data capacity. It has been widely acknowledged that not only does the number of information increase exponentially, but also the way of human information management and processing is completed to be changed from manual to computer, mainly depending on the transformation of information technology including a computer, network, and communication. This paper is aimed at a solution to the lag of the methods and means of volleyball technique prediction in China. Through field visits, it is found that the way of analysis and research of techniques and tactics in Chinese volleyball practice is relatively backward, which to a certain extent affected the rapid development of Chinese volleyball. Therefore, it is a necessary and urgent task to realize the reform of the methods and means of volleyball technical and tactical analysis in China. The data analysis and prediction are based on the machine learning and data mining algorithm applied to volleyball in this paper is an inevitable trend. The proposed model is applied to the data produced at the edges of the systems and thoroughly analyzed. The Apriori algorithm of the machine learning algorithm is utilized to process the data and provide a prediction about the strategies of a volleyball match. The Apriori algorithm of machine learning is also optimized to perform better data analysis. The effectiveness of the proposed model is also highlighted.

Steve Nash, who has met me at a court in Manhattan Beach on a cloudy Monday afternoon to shoot free throws, glances over and chuckles at his miss. "It's been a while," he says. When he retired from the NBA in 2015, Nash, a two-time MVP, left with a career average 90.43 percent from the line--the highest in league history. But he hasn't worked on his foul shot since. For an instant, I feel anxious for him.

You won't have seen it on the podium, but the human brain's mirror neuron system could have medaled at this year's Olympic Games, or basically any sporting event with an audience. The mirror neuron system is a network of neurons that activates both when you watch someone do something and when you do it yourself, and it turns out to be an important part of the subjective experience of being a fan. But watching a sport doesn't just flip your mirror neuron system on like a switch. There are degrees of activation. While you and the person sitting beside you probably both have your mirror neuron systems firing, your neighbor's neurons might have different levels of activation than yours.

Robotic advancements are being seen as a threat to the human aspect of things. Many experts think machines, which are built to be perfect, could one day replace humans in every field and take away all our jobs, but another optimistic group believes they would complement our lives. Though the future is unpredictable, one thing is pretty much clear -- machines are taking some giant leaps in all fields. We have already seen robots performing tasks like heavy weight lifting and high-precision surgeries and now, a new video shows a robot can even defeat professional basketball pros. Inspired by the lead character of Suramu Danku, a basketball-based Japanese comic book, the engineers at the company developed "Cue" to execute perfect free throws.

Are robots going to take over the world? Sure, we like to make jokes about a robot takeover, and things can sometimes get a bit too real when our favorite virtual assistant seems to be gaining sentience or a robot makes a sudden, bold move for freedom, but it seems we've been worrying about the wrong thing all of these years. Robots don't want to take over the world–they just want to dominate humans on the basketball court. As The Verge reports, a group of engineers from Toyota were inspired by the manga Suramu Danku (or Slam Dunk) that tells the story of a high school boy who finds he has an uncanny, natural ability for basketball. So naturally, they jumped from loving a manga about a human with a hidden talent to making a robot who is basically perfect at shooting free throws.

Businesspeople need to demand more from machine learning so they can connect data scientists' work to relevant action. This requires basic machine learning literacy -- what kinds of problems can machine learning solve, and how to talk about those problems with data scientists. Linear regression and feature selection are two such foundational topics. Linear regression is a powerful technique for predicting numbers from other data. Imagine you have an imperative to predict basketball scores from game statistics, and you miraculously know absolutely nothing about basketball. The fact that a hoop is involved is news to you.