Welcome to the second edition of the Yahoo Sports all-underrated NFL draft team. This is an exercise in analyzing the imprecise collision of NFL front-office opinions and a player’s collegiate production. We attempted to identify draft steals – and two players who went undrafted – through the prism of the college football coaches who played, studied and game-planned against them.
Yahoo Sports reached out to approximately 100 college coaches and assistants from all 10 of the FBS conferences. We asked them to give a name or two they couldn’t believe dipped in the draft considering how that player performed against them during his career. What we didn’t want is the coaches to get bogged down in testing times, injury history or the very real factors the NFL teams take into account. Just what they saw across the sideline.
Our Connecticut Sun training camp diaries are back! This week, second-year player Kaila Charles shares what it’s been like to be one of the more experienced players in Sun training camp. The former Maryland player was drafted by the Sun with their 2020 second-round draft pick. At the time, head coach/GM Curt Miller said the organization considered Charles a top-10 draft pick, so they were thrilled she fell into their hands.
arXiv, Computer Science > Computer Vision and Pattern Recognition; Aishwarya Kamath, Mannat Singh, Yann LeCun, Ishan Misra, Gabriel Synnaeve, Nicolas Carion from
Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labelled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR. The code and models are available at this https URL.
When a person’s vascular age is determined to be significantly older than their chronological age, it can be an indicator of possible cardiovascular health issues.
Teenage girls who play football run nearly twice the risk of concussion as teenage boys and take longer to recover, a study of US high school football suggests.
It found concussion in girls was less easy to spot and they were less likely to be taken off the pitch.
Females are known to be at greater risk of concussion although it’s not understood why.
It’s time to focus on girls – and not just boys, UK researchers said.
The study of 40,000 female high school footballers and a similar number of male footballers in Michigan over three years found the risk of sports-related concussion in girls was 1.88 times higher than for boys.
Background Studies on subsequent anterior cruciate ligament (ACL) ruptures and career length in male professional football players after ACL reconstruction (ACLR) are scarce.
Aim To investigate the second ACL injury rate, potential predictors of second ACL injury and the career length after ACLR.
Study design Prospective cohort study.
Setting Men’s professional football.
Methods 118 players with index ACL injury were tracked longitudinally for subsequent ACL injury and career length over 16.9 years. Multivariable Cox regression analysis with HR was carried out to study potential predictors for subsequent ACL injury.
Results Median follow-up was 4.3 (IQR 4.6) years after ACLR. The second ACL injury rate after return to training (RTT) was 17.8% (n=21), with 9.3% (n=11) to the ipsilateral and 8.5% (n=10) to the contralateral knee. Significant predictors for second ACL injury were a non-contact index ACL injury (HR 7.16, 95% CI 1.63 to 31.22) and an isolated index ACL injury (HR 2.73, 95% CI 1.06 to 7.07). In total, 11 of 26 players (42%) with a non-contact isolated index ACL injury suffered a second ACL injury. RTT time was not an independent predictor of second ACL injury, even though there was a tendency for a risk reduction with longer time to RTT. Median career length after ACLR was 4.1 (IQR 4.0) years and 60% of players were still playing at preinjury level 5 years after ACLR.
Conclusions Almost one out of five top-level professional male football players sustained a second ACL injury following ACLR and return to football, with a considerably increased risk for players with a non-contact or isolated index injury. [full text]
In today’s football, players compete more and more matches over the season. Apart from the domestic competition, the best footballers play international matches with their national team and also during preseason trips.1 Recovery time between games has also been shortened in recent years. More and more matches are played, and rest is decreasing.2 The probability of injury is increasing, and this can be a great risk for clubs.3 Injuries can negatively affect performance and prevent teams from achieving their objectives. For that reason, one of the objectives of the clubs’ coaching staff is to ensure that the athletes can be at their coach’s disposal for as much of the season as possible. Teams invest a lot of money and time in proposing preventive actions trying to reduce the risks of injury in training and competition. Do footballers get injured more now than before? Has it been possible to reduce the incidence of injuries in elite football despite playing more games?
Recent research has analysed whether the frequency of injuries in top-level football teams has increased or not in recent years.4 The study, published in the British Journal of Sports Medicine in 2021, was based on the analysis of 3,302 players belonging to 49 elite teams from 19 different European countries that participated in the Champions’ League group stage during 18 seasons played between 2000-2001 and 2018-2019. A member of each club’s coaching staff recorded each player’s injuries and participation in training sessions and games. In total, 265 seasons of the teams were evaluated. During this period, 11,820 injuries occurred after 1,784,281 hours of player exposure, accounting for an injury incidence of 6.6 per 1,000 hours. The game injury rate (both in training and competition) was of 23.8/1,000 hours of exposure, while 3.4/1,000 hours in training. Muscle (n = 4,763) and ligamentous (n = 1,971) injuries accounted for 57% of all injuries.
Like much of society, college athletics were thrown into disarray by the COVID-19 pandemic. While student athletes were suddenly prevented from competing, training or seeing as much of their teammates and coaches, those who perceived they were part of a positive sporting environment also coped better during the early days of the crisis, a new study from the University of Kansas has found.
KU researchers have long studied a caring, task-involved sporting climate, in which young athletes receive support and recognition for their efforts, while mistakes are treated as learning opportunities. But the pandemic provided a unique opportunity to see whether the approach helped collegiate athletes cope with the unique stresses and challenges that came with the disruption of their seasons. A survey of more than 700 NCAA Division I, II and III and NAIA student athletes showed those who had positive support of coaches, teammates and programs were coping with the challenges of the pandemic better than those who were involved in more ego-driven climates, where the primary focus is on performance outcomes.
arXiv, Computer Science > Artificial Intelligence; Melanie Mitchell from
Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.
… The way many people focus on the technology and development of AI programs leaves out people doing on-the-ground innovation to make them work.
“So much of the actual day-to-day work that is required to make AI function in the world is rendered invisible, and then undervalued,” [Madeline Claire] Elish said.
Even the language used to talk about launching AI systems tends to discount the importance of this work, Elish said.
“I actually try to avoid talking about ‘deploying systems’,” she said. “Deploy is a military term. It connotes a kind of contextless dropping in. And what we actually need to do with systems is to integrate them into particular context. And when you use words like ‘integrate,’ it requires you to say, ‘Integrate into what, or with whom?’
Tech entrepreneur Meghan Chayka is one of the most influential women in Canadian sports.
The co-founder of Stathletes, a sports analytics company that harnesses the power of data to provide powerful insights to athletes across the NHL, NWHL, MBA, and MLB, Meghan has spent the last decade growing her company and team to become an organization with over 50 employees.
We caught up with Meghan to discuss growing up in Ontario, the biggest challenges when scaling Stathletes in the early 2000s, and some of her best advice for aspiring entrepreneurs.