Best AI tools for< Sports Scientist >

Avais is a cutting-edge volleyball training application that revolutionizes the way athletes improve their game. It utilizes real-time analytics and AI technology to track and analyze volleyball matches, providing personalized coaching feedback to help users enhance their skills. With features like automatic highlight clip creation, competitive gameplay with friends and family, and easy scouting opportunities, Avais aims to empower athletes to reach their full potential conveniently from their mobile devices. The app focuses on growth and progress, offering professional coaching insights after every play to guide users towards success.

ONVY is an AI Health Coach application designed for business professionals and athletes to optimize health and performance. It offers personalized insights and actionable feedback by analyzing data from fitness trackers. Users can manage recovery, sleep, activity, and mental fitness, aiming to achieve peak performance in sports, career, and life. The app merges the latest health and performance science with AI technology to provide users with clear steps to enhance their overall well-being.

GaitAnalyzer.health is a web application designed to analyze and assess human gait patterns for healthcare professionals and researchers. The platform utilizes advanced algorithms to process gait data captured through various sensors and devices, providing valuable insights into gait abnormalities and biomechanical characteristics. Users can upload gait data, visualize results, and generate detailed reports to aid in diagnosis and treatment planning. GaitAnalyzer.health aims to improve the understanding of human movement patterns and enhance the quality of care in the field of biomechanics and rehabilitation.

Julius is an AI data analyst tool developed by the team behind HoopsGPT. It simplifies sports insights by providing users with comprehensive information about the game. With features like Stats Lookup, Shot Charts visualization, Parlay Analyzer, and Props/Game Comparisons, Julius offers a user-friendly platform for analyzing sports data and making informed decisions. Whether you're interested in checking complex stats, visualizing shot charts, or comparing odds, Julius has you covered. It's a valuable tool for sports enthusiasts and bettors looking to enhance their understanding of the game.

Sports AI is an AI-powered platform that provides the most accurate and profitable sports predictions in the market. It utilizes machine learning algorithms and advanced sports data to offer free daily predictions for a wide range of events. Users can revolutionize their betting strategy with AI-powered value bets across various sports like soccer/football, tennis, basketball, hockey, handball, and volleyball. The platform helps users make better betting decisions by analyzing data, learning from past mistakes, and accurately determining the profitability of certain outcomes.

Sports AI is an AI-powered platform that provides the most accurate and profitable sports predictions on the market. It utilizes machine learning algorithms and advanced sports data to offer daily predictions for various events across sports like soccer/football, tennis, basketball, hockey, handball, and volleyball. The platform helps users make better betting decisions by analyzing data, learning from past mistakes, and improving over time to accurately determine the profitability of certain outcomes. Sports AI also offers a blog with valuable information on bet tips, value betting, and technical details of their algorithms.

Rithmm is an AI sports betting application that offers custom models for AI picks and props. It provides AI sports picks for various sports leagues like NFL, NBA, NCAAF, NCAAB, Golf, and WNBA. Users can access player props and game picks backed by AI predictive models to make informed betting decisions. Rithmm simplifies complex data into user-friendly insights, making it suitable for both seasoned bettors and beginners. The application aims to empower users to make smarter bets effortlessly with the use of AI technology.

The website is an AI sports betting prediction platform called COPA. It offers high-quality sports predictions using Artificial Intelligence (AI) for various football events. Users can access match predictions, statistics, and betting insights for top global leagues. The platform aims to provide informed betting choices and predictive tools for European football leagues, with plans to expand to other sports in the future. COPA is designed to empower sports fans with accurate forecasts at an affordable cost.

ATS Wins is a dynamic sports analysis platform that offers expert betting advice and predictions across various sports. The platform utilizes advanced AI algorithms and expert insights to help sports enthusiasts make well-informed betting decisions. ATS Wins stands out by providing data-driven predictions based on factors like injuries, team form, standings, and more. Additionally, the platform offers sportsbook average odds, public betting splits, and transparent winning percentages. Users can access real-time updates and a range of subscription plans for comprehensive sports coverage.

BetSense.ai is an AI sports predicting tool that leverages cutting-edge AI technology to help users make informed decisions in sports betting. The tool provides data-driven predictions based on historical and recent data inputs, covering top soccer leagues and offering daily AI predictions. With a focus on increasing productivity, BetSense.ai offers insights, machine learning capabilities, and access to an extended range of computer predictions. Users can subscribe to monthly or yearly plans for different levels of access and insights.

Mysports AI is a cutting-edge AI sports prediction tool that leverages deep learning technology to provide valuable betting insights and profitable bets. The platform offers users the opportunity to enhance the value of their betting experience by utilizing AI-generated models and strategies. With a focus on long-term profitability and high win rates, Mysports AI aims to revolutionize the sports betting industry by offering a user-friendly interface and a wide range of sportsbooks to choose from.

Wolfram|Alpha is a computational knowledge engine that answers questions using data, algorithms, and artificial intelligence. It can perform calculations, generate graphs, and provide information on a wide range of topics, including mathematics, science, history, and culture. Wolfram|Alpha is used by students, researchers, and professionals around the world to solve problems, learn new things, and make informed decisions.

Football Predictions AI is a website that provides users with accurate and reliable football match predictions. Users can access a variety of prediction types, including 1x2, BTTS, Over Under, and Correct Score predictions for matches across different leagues. The site also offers live scores, articles, and settings to customize the user experience. With a focus on user privacy and data protection, Football Predictions AI aims to enhance the football betting experience for enthusiasts and fans.

The Octagon Oracle is an AI-powered platform for UFC betting predictions and fantasy point projections. It offers highly accurate fight picks and projections based on advanced algorithms and data analysis. Users can simulate strategies, access detailed analytics, and customize their betting approach for maximum returns. The platform aims to provide sports bettors with a competitive edge in UFC betting by leveraging AI technology and historical data.

XaslarBet is an AI betting tips platform that provides advanced trained models for NBA player props and football picks. The platform offers free AI sports tools using predictive analytics and machine learning algorithms to enhance the betting experience. XaslarBet focuses on player performances and historical data to deliver accurate picks across various sports markets, including basketball, baseball, and cricket. The platform aims to revolutionize the betting landscape by offering real-time AI sports predictions for both newcomers and seasoned bettors.

AIBrain is a tech start-up in Palo Alto, California with its focus on Education and Entertainment. AIBrain was recognized as a top 5 entertainment AI company in 2023 by Datamation. This includes bestseller AI courses, Autonomous Game AI, Humanoid AI, and Soccer AI/VR Assistant. AIBrain has also been actively involved in the Stanford Computer Forum as a member company since 2013. AIBrain has been leading the technology development on the areas of entertainment and education. AIBrain provides the Game Changer Football AI x VR solutions, called SAIVA (Sports AI Virtual Assistant) and SAICA (Sports AI Coach Assistant). As a world-class football / soccer solution, it was ranked at top 3 contender in the Camera Calibration Challenge, Soccer Net Challenges 2023. AIBrain Asia has been developing robotic AI such as Tyche, Talking Robot AI and Gretchen, Humanoid AI. In addition, we provide bestseller AI training program for non-AI professionals including Udemy Online: Automated Machine Learning for Beginners (Google & Apple), Bestseller, Udemy, 60,829 students, Dec 2023 Gretchen: Open Humanoid AI Platform. Beta Launch: January.

Hive AI provides a suite of AI models and solutions for understanding, searching, and generating content. Their AI models can be integrated into applications via APIs, enabling developers to add advanced content understanding capabilities to their products. Hive AI's solutions are used by businesses in various industries, including digital platforms, sports, media, and marketing, to streamline content moderation, automate image search and authentication, measure sponsorships, and monetize ad inventory.

SAS Blogs is an AI tool that offers a platform for advanced analytics, artificial intelligence, and machine learning. It provides insights and resources on various topics such as customer intelligence, data management, risk management, and programming tips. The platform caters to a wide range of industries including banking, healthcare, manufacturing, and sports. Users can access a wealth of information, articles, and events related to SAS software and applications.

Aiternus is an AI Computer Vision and Data Analysis System that is revolutionizing industries with cutting-edge technology. It offers advanced solutions for various sectors such as manufacturing, construction, logistics, healthcare, retail, sports tech, electronics, and office spaces. Aiternus leverages AI to streamline processes, boost productivity, enhance safety and quality standards, and develop tailor-made solutions for clients' unique needs. The application provides features like work process monitoring, route optimization, AI chatbot support, demand predictions, quality control, performance analysis, and automation of tasks in office spaces.

JobtitlesAI is a machine-learning API that sorts job titles into two categories: field (sales, finance, I.T...) and position (executive, management, assistant...). It can be used in spreadsheets, Hubspot, or via API. JobtitlesAI is multilingual and GDPR compliant.

This repo contains official implementations of **Coach AI Badminton Project** from Advanced Database System Laboratory, National Yang Ming Chiao Tung University supervised by Prof. Wen-Chih Peng. The high-level concepts of each project are as follows: 1. Visualization Platform published at _Physical Education Journal 2020_ aims to construct a platform that can be used to illustrate the data from matches. 2. Shot Influence and Extension Work published at _ICDM-21_ and _ACM TIST 2022_ , respectively introduce a framework with a shot encoder, a pattern extractor, and a rally encoder to capture long short-term dependencies for evaluating players' performance of each shot. 3. Stroke Forecasting published at _AAAI-22_ proposes the first stroke forecasting task to predict the future strokes of both players based on the given strokes by ShuttleNet, a position-aware fusion of rally progress and player styles framework. 4. Strategic Environment published at _AAAI-23 Student Abstract_ designs a safe and reproducible badminton environment for turn-based sports, which simulates rallies with different angles of view and designs the states, actions, and training procedures. 5. Movement Forecasting published at _AAAI-23_ proposes the first movement forecasting task, which contains not only the goal of stroke forecasting but also the movement of players, by DyMF, a novel dynamic graphs and hierarchical fusion model based on the proposed player movements (PM) graphs. 6. CoachAI-Challenge-IJCAI2023 is a badminton challenge (CC4) hosted at _IJCAI-23_. Please find the website for more details. 7. ShuttleSet published at _KDD-23_ is the largest badminton singles dataset with stroke-level records. - An extension dataset ShuttleSet22 published at _IJCAI-24 Demo & IJCAI-23 IT4PSS Workshop_ is also released. 8. CoachAI Badminton Environment published at _AAAI-24 Student Abstract and Demo, DSAI4Sports @ KDD 2023_ is a reinforcement learning (RL) environment tailored for AI-driven sports analytics, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities.

This tool is a machine learning AI used to predict the winners and under/overs of NBA games. It takes all team data from the 2007-08 season to the current season, matched with odds of those games, and uses a neural network to predict winning bets for today's games. The tool achieves ~69% accuracy on money lines and ~55% on under/overs. It outputs expected value for teams' money lines to provide better insight and the fraction of your bankroll to bet based on the Kelly Criterion. A popular, less risky approach is to bet 50% of the stake recommended by the Kelly Criterion.

MARS5 is a novel English speech model (TTS) developed by CAMB.AI, featuring a two-stage AR-NAR pipeline with a unique NAR component. The model can generate speech for various scenarios like sports commentary and anime with just 5 seconds of audio and a text snippet. It allows steering prosody using punctuation and capitalization in the transcript. Speaker identity is specified using an audio reference file, enabling 'deep clone' for improved quality. The model can be used via torch.hub or HuggingFace, supporting both shallow and deep cloning for inference. Checkpoints are provided for AR and NAR models, with hardware requirements of 750M+450M params on GPU. Contributions to improve model stability, performance, and reference audio selection are welcome.

VideoDB Python SDK allows you to interact with the VideoDB serverless database. Manage videos as intelligent data, not files. It's scalable, cost-efficient & optimized for AI applications and LLM integration. The SDK provides functionalities for uploading videos, viewing videos, streaming specific sections of videos, searching inside a video, searching inside multiple videos in a collection, adding subtitles to a video, generating thumbnails, and more. It also offers features like indexing videos by spoken words, semantic indexing, and future indexing options for scenes, faces, and specific domains like sports. The SDK aims to simplify video management and enhance AI applications with video data.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.

Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in Video Analysis. It assesses the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. The dataset comprises 900 videos with 256 hours and 2,700 human-annotated question-answer pairs. It distinguishes itself through features like duration variety, diversity in video types, breadth in data modalities, and quality in annotations.

MME-RealWorld is a benchmark designed to address real-world applications with practical relevance, featuring 13,366 high-resolution images and 29,429 annotations across 43 tasks. It aims to provide substantial recognition challenges and overcome common barriers in existing Multimodal Large Language Model benchmarks, such as small data scale, restricted data quality, and insufficient task difficulty. The dataset offers advantages in data scale, data quality, task difficulty, and real-world utility compared to existing benchmarks. It also includes a Chinese version with additional images and QA pairs focused on Chinese scenarios.

skpro is a library for supervised probabilistic prediction in python. It provides `scikit-learn`-like, `scikit-base` compatible interfaces to: * tabular **supervised regressors for probabilistic prediction** \- interval, quantile and distribution predictions * tabular **probabilistic time-to-event and survival prediction** \- instance-individual survival distributions * **metrics to evaluate probabilistic predictions** , e.g., pinball loss, empirical coverage, CRPS, survival losses * **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal * building **pipelines and composite models** , including tuning via probabilistic performance metrics * symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

ML Contests is a platform that provides a sortable list of public machine learning/data science/AI contests, viewable on mlcontests.com. Users can submit pull requests for any changes or additions to the competitions list by editing the competitions.json file on the GitHub repository. The platform requires mandatory fields such as competition name, URL, type of ML, deadline for submissions, prize information, platform running the competition, and sponsorship details. Optional fields include conference affiliation, conference year, competition launch date, registration deadline, additional URLs, and tags relevant to the challenge type. The platform is transitioning towards assigning multiple tags to competitions for better categorization and searchability.

sktime is a Python library for time series analysis that provides a unified interface for various time series learning tasks such as classification, regression, clustering, annotation, and forecasting. It offers time series algorithms and tools compatible with scikit-learn for building, tuning, and validating time series models. sktime aims to enhance the interoperability and usability of the time series analysis ecosystem by empowering users to apply algorithms across different tasks and providing interfaces to related libraries like scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

BlendSQL is a superset of SQLite designed for problem decomposition and hybrid question-answering with Large Language Models (LLMs). It allows users to blend operations over heterogeneous data sources like tables, text, and images, combining the structured and interpretable reasoning of SQL with the generalizable reasoning of LLMs. Users can oversee all calls (LLM + SQL) within a unified query language, enabling tasks such as building LLM chatbots for travel planning and answering complex questions by injecting 'ingredients' as callable functions.

BambooAI is a lightweight library utilizing Large Language Models (LLMs) to provide natural language interaction capabilities, much like a research and data analysis assistant enabling conversation with your data. You can either provide your own data sets, or allow the library to locate and fetch data for you. It supports Internet searches and external API interactions.

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

Enhancing the sensing of Human Motion for Lifespan health Development

Your AI Sports Scientist to help you level up your game - in and off the field

Your journey to 100 consecutive push-ups starts here.

Guiding your triathlon journey, step by step.

A hardstyle old-timer with all the workout wisdom you need comrade.

Expert in cycling, triathlon, endurance sports, inspired by WEDŪ & THEMOVE

Badminton Performance Analyst

Expert ultramarathon guide offering tailored training and race strategies.

Creating the strongest of men, analyzing workouts, offering suggestions, and setting realistic goals!

Expert on altitude training research and benefits

Designs training programs for runners preparing for marathons and other races, tailored to fitness levels.

Entrenador virtual de boxeo motivador y adaptable al alumno.

Does Pool Performance of Elite Triathletes Predict Open-Water Performance?

Use me as your volleyball trainer assistant.

协助开展体育科学研究和文献工作。