… “I didn’t get a chance to play high school ball,” [Benjamin] Cumbo said Saturday morning. “I wasn’t good enough. Got cut from P.G. (Prince George’s) Community College. But I just believed in myself, and said I could play pro. I always held my own with you guys; I just couldn’t make it. And one day, playing down at Barry Farms (in D.C.), somebody saw me. Next thing you know, I’m at a Division I Juco, and it started my pro career.”
Cumbo was in his native DMV over the weekend with more than 100 other ballers who were at the open tryout for the Capital City Go-Go, the expansion team that will begin play this fall as the Washington Wizards’ G League affiliate and the 27th overall, as the NBA and G League inch ever closer to the one-to-one affiliation that has been a longtime goal. (Only the Portland Trail Blazers, New Orleans Pelicans and Denver Nuggets lack their own G League teams.)
The staff of the world’s top-ranked Women’s National Team are not resting on their laurels.
As the U.S. Women’s National Team continues preparation for October’s 2018 CONCACAF Women’s Championship, which will serve as the World Cup qualifying tournament, the players will do so with the support of a newly-created, dedicated High Performance support team that consists of full-time experts in the fields of sports medicine, sports science and performance analysis to assist players and coaches on a year-round basis.
“One of the High Performance departments objectives is to link the multi-disciplinary teams together more cohesively, so there is more integration of knowledge, of data and of operation,” says James Bunce, Director the High Performance department. “My role is to bring these hugely integral technical and skilled areas together to optimize every area of player health, performance, psychology, conditioning and analysis of team tactical principles to make sure that every player – when they step on that pitch – not only is healthy and fit, but also prepared to win at the highest level.”
Favored to win their third straight championship, Steve Kerr’s Golden State Warriors face more adversity than fans realize. Kerr speaks with his former coach Phil Jackson — who led two teams to 11 NBA championships — about surviving success.
… “I think we’ve got more guys healthy past the first three games than I can remember,” junior left tackle Coy Cronk said.
Injuries limited Indiana in all three phases last season, and at every level. No position group on offense or defense went unaffected by health problems suffered by a key contributor. Players like Marcelino Ball, A’Shon Riggins, Simon Stepaniak, Peyton Ramsey, Nick Westbrook and J-Shun Harris all suffered long-term or season-ending injuries.
How do we learn what to learn? This fundamental question drives the work of Rachel Wu at the University of California, Riverside. Before we can learn anything, we need to know what to pay attention to. From infancy, people are bombarded with distractions that can make that challenging.
While there is a wealth of cognitive neuroscience research on the topic of attention and learning, that work has yet to fully translate to educational settings for youth. In a new review paper published in the Journal of Cognitive Neuroscience, Wu, along with colleagues at Cyprus Institute of Neurology and Genetics the and the University of Oxford, explore a potential path toward integrating existing research with developmental and applied work.
… All around us, algorithms provide a kind of convenient source of authority: an easy way to delegate responsibility, a short cut we take without thinking. Who is really going to click through to the second page of Google results every time and think critically about the information that has been served up? Or go to every airline to check if a comparison site is listing the cheapest deals? Or get out a ruler and a road map to confirm that their GPS is offering the shortest route?
But already in our hospitals, our schools, our shops, our courtrooms and our police stations, artificial intelligence is silently working behind the scenes, feeding on our data and making decisions on our behalf. Sure, this technology has the capacity for enormous social good – it can help us diagnose breast cancer, catch serial killers, avoid plane crashes and, as the health secretary, Matt Hancock, has proposed, potentially save lives using NHS data and genomics. Unless we know when to trust our own instincts over the output of a piece of software, however, it also brings the potential for disruption, injustice and unfairness.
VivaLNK, a company that provides connected healthcare devices, has agreed to loan its Vital Scout wearable devices to Stanford University for use in a study on teenage stress and depression.
Vital Scout is a patch worn on the chest under clothes. It utilizes ECG sensors and established heart rate variability algorithms to measure how the body responds to physiological impacts. The device can track stress, recovery levels, activity, sleep quality, heart rate and respiratory rate.
Researchers in Stanford’s Department of Psychiatry and Behavioral Sciences are studying whether there are associations between stress and depression in teenagers. Study participants wear the Vital Scout devices and are monitored over continuous 24-hour periods.
Journal of the American Medical Informatics Association from
Clinical decision support (CDS) hard-stop alerts—those in which the user is either prevented from taking an action altogether or allowed to proceed only with the external override of a third party—are increasingly common but can be problematic. To understand their appropriate application, we asked 3 key questions: (1) To what extent are hard-stop alerts effective in improving patient health and healthcare delivery outcomes? (2) What are the adverse events and unintended consequences of hard-stop alerts? (3) How do hard-stop alerts compare to soft-stop alerts?
DroneBase and Hangar partner to enable industries to scale and streamline actionable 4D Visual Insight into their business decisions.
This partnership allows enterprises to routinely digitize their projects without the need of building an internal drone program or investing in technology to manage raw imagery. Stakeholders simply request a location to monitor and then select the desired insight types and capture frequency. Hangar and DroneBase capture the data, convert the imagery to the requested specs, and deliver the insights in as little as 48 hours.
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature as the these methods become more prevalent. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.
From the beginning, StatsBomb has been about fostering an analytics community dedicated to learning more about the game of football. One of the major issues for new potential analysts is difficulty in obtaining high quality data at scale. The same problem exists for teachers who are responsible for training new data scientists. The World Cup data, along with our continuing release of women’s football data, helps address this problem directly.
Datafloq, International Institute for Analytics from
Everyone who has lived within the world of analytics has seen cases where different parts of a business have made use of slightly differing definitions of core business metrics. Sometimes these differences lead to only minor and non-material disagreement. At other times, the differences in definition can cause massive divergence of reported results and related actions taken. Organizations must ensure that where differences exist in definitions those differences are either reconciled or clearly labeled and articulated to provide the proper context.
Since the 2008-09 season, no NHL team had played with more than one former winner of the Norris Trophy, given to the league’s top defenseman, according to the Elias Sports Bureau.1 But all that changed last week when the San Jose Sharks shook the NHL by trading for Erik Karlsson, among the league’s most coveted defensemen.
Karlsson — a two-time Norris Trophy winner and the league’s highest scoring defenseman since the beginning of the 2009-10 season2 — will be joining a defensive core that already features Brent Burns, the uber-bearded, gap-toothed wonder who won the 2016-17 Norris Trophy while scoring 29 goals for San Jose. The move obviously makes the Sharks a better and more offensively dangerous team, but the extent to which it does so has the potential to be historic.
… The difference between a computational Social Scientist and a Data Scientist is that the Social Scientist is an expert in studying human behavior and finding patterns in data about population groups.
Social Scientists come from different streams but the major groups include Economists, Anthropologists, Sociologists and Psychologists.
A Data Scientist on the other hand typically leans toward a deep knowledge of both statistics and data computation. Data Scientists typically have statistics, mathematics, computer science and data engineering backgrounds.