This paper represents an analysis on the momentum of tennis match. And due to Generalization performance of it, it can be helpful in constructing a system to predict the result of sports game and analyze the performance of player based on the Technical statistics. 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. Finally we use LightGBM to evaluate the performance of our model and use SHAP feature importance ranking and weight analysis to find the key points that affect the performance of players.

This paper presents the first few-shot LLM-based chatbot that almost never hallucinates and has high conversationality and low latency. WikiChat is grounded on the English Wikipedia, the largest curated free-text corpus. WikiChat generates a response from an LLM, retains only the grounded facts, and combines them with additional information it retrieves from the corpus to form factual and engaging responses. We distill WikiChat based on GPT-4 into a 7B-parameter LLaMA model with minimal loss of quality, to significantly improve its latency, cost and privacy, and facilitate research and deployment. Using a novel hybrid human-and-LLM evaluation methodology, we show that our best system achieves 97.3% factual accuracy in simulated conversations. It significantly outperforms all retrieval-based and LLM-based baselines, and by 3.9%, 38.6% and 51.0% on head, tail and recent knowledge compared to GPT-4. Compared to previous state-of-the-art retrieval-based chatbots, WikiChat is also significantly more informative and engaging, just like an LLM. WikiChat achieves 97.9% factual accuracy in conversations with human users about recent topics, 55.0% better than GPT-4, while receiving significantly higher user ratings and more favorable comments.

Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to integrate and consolidate information from different parts of the text. To address this shortcoming, we propose a hierarchy-aware graph neural network (HierGNN) which captures such dependencies through three main steps: 1) learning a hierarchical document structure through a latent structure tree learned by a sparse matrix-tree computation; 2) propagating sentence information over this structure using a novel message-passing node propagation mechanism to identify salient information; 3) using graph-level attention to concentrate the decoder on salient information. Experiments confirm HierGNN improves strong sequence models such as BART, with a 0.55 and 0.75 margin in average ROUGE-1/2/L for CNN/DM and XSum. Further human evaluation demonstrates that summaries produced by our model are more relevant and less redundant than the baselines, into which HierGNN is incorporated. We also find HierGNN synthesizes summaries by fusing multiple source sentences more, rather than compressing a single source sentence, and that it processes long inputs more effectively.

Big data is changing how tennis stars train and play; but the key to success is taking all that information and turning it into something players can use to win. Craig O'Shannessy, official strategy analyst for both the ATP Tour and all-time great Novak Djokovic, says that the smart use of data when preparing can have a significant impact on a match. O'Shannessy explains to ZDNet at the ATP Tour Finals in London how he uses a range of tools to give Djokovic that data-led advantage. These tools include the Infosys Tennis Platform, which is being used for the first time in 2019 across the ATP Tour, which is the worldwide top-tier tennis tour for men organised by the Association of Tennis Professionals. The platform includes a portal that gives players and coaches access to advanced analytics and match video.

Just watching was a feat of endurance. The 2019 men's final at Wimbledon lasted four hours and 57 minutes, making it the longest on record at the All England Club. Roger Federer and Novak Djokovic seemed to be perfectly matched, until they weren't. In the end, Djokovic prevailed, and fans were left to debate what allowed the Serbian great to finally notch the win. They would probably be surprised to learn that some of Djokovic's advantage could have come from artificial intelligence, which he incorporated in his game for the first time during this year's Wimbledon.

QUT researchers have developed an algorithm that can predict where a tennis player will hit the next ball by analysing Australian Open data of thousands of shots by the top male tennis players. Dr Simon Denman, a Senior Research Fellow with the Speech, Audio, Image and Video Technology Laboratory, said the research into the match play of Novak Djokovic, Rafael Nadal and Roger Federer could lead to new ways for professional tennis players to predict their opponent's moves or virtual reality games offering the chance to go head-to-head with world's best players in an accurate but artificial grand slam. Dr Denman is part of a team of QUT researchers, including PhD student Tharindu Fernando, Professor Sridha Sridharan and Professor Clinton Fookes, all from the Vision and Signal Processing Discipline at QUT, who created the algorithm for predicting the next shot in tennis using Hawk-Eye data from the 2012 Australian Tennis Open, provided by Tennis Australia. The researchers narrowed their focus to study just the shot selection of Djokovic, Nadal and Federer because they had the complete data to input into the system on how the players' shot selection changed as the tournament progressed. The researchers analysed more than 3400 shots for Djokovic, nearly 3500 shots for Nadal and almost 1900 shots by Federer, adding context for each shot such as whether it was a return, a winner or an error.

You might have heard about AI that teaches four-legged robots to walk and autonomous systems that generate photorealistic images of butterflies, but what about models that forecast the shot location of tennis balls? In a newly published preprint paper on Arxiv.org "Inspired by recent neuroscience discoveries we incorporate neural memory modules to model the episodic and semantic memory components of a tennis player," the researchers wrote. Tracking a tennis ball is no easy feat at the professional level, given that they whiz by at speeds exceeding 130 miles per hour. Some studies suggest that expert players are more adept generally at detecting events in advance, in fact, and have better knowledge of situational probabilities.

Considering the fact that present day ball speeds exceed 130mph, the time required by the receiver to make a decision regarding the opponents' intention, and initiate a response could exceed the flight time for the ball [1], [2], [3], [4]. Several studies have shown that this reactive ability is the product of pattern recognition skills that are obtained through a "biological probabilistic engine", that derives theories regardingopponents intentions with the partial information available[1], [5], [6]. For instance, it has been shown that expert tennis players are better at detecting events in advance [1], [7] and posses better knowledge/ expertise of situational probabilities [3]. Further investigation of human neurological structures have revealed that those capabilities occur due to a bottom-up computational process [1] within the human brain, from sensory memory to the experiences stored in episodic memory [8], [9] and knowledge derived in semantic memory [9], [10]. Despite the growing interest among researchers in the machine learning domain in better understanding factors influencing decision making in fastball sports, there have been very few studies transferring the observations of the underlying neural mechanisms to neural modelling in machine learning.Current state-of-the-art methodologies try to capture the underlying semantics through a handful of handcrafted features, without paying attention to essential mechanisms in the human brain, where the expertise and observations are stored and knowledge is derived.

Infosys, a global leader in technology services and consulting, is aiming to reinvent the way people consume sport using extensive player data. The Indian firm, which had revenues of $9.5 billion in its last financial year, demonstrated its'Infosys Information Platform (IIP)' during the recent ATP Tennis tournament in London, of which it was a headline sponsor. Speaking to Access AI, the firm's head of energy and services for Europe Mohamed Anis, who joined in 2000, said Infosys uses machine learning to analyse historical data on player performance, which in turn is able to predict behaviour, shot selection, and even a probabilistic outcome of the match itself. Anis (pictured) said the data is delivered in real time and can be used to help spectators view the game/match on an entirely different level – comparable to that of the coach. "Tennis has been around for a very long time," explained Anis.