How Will AI Become a Sports Science Tool? by Sims Setser

[This is a guest blog by Sims Setser. Sims is pursuing a BS in Kinesiology, while completing a Sports Science and Performance Coaching Mentorship at Athletic Lab.] The rapid rise of artificial intelligence (AI) is transforming industries across the board, and sports science is no exception. From performance analysis to injury prevention and rehabilitation, AI holds significant potential to revolutionize the way athletes are trained and managed. By leveraging big data and machine learning algorithms, sports scientists can offer deeper insights into an athlete's performance, health, and overall well-being, leading to more personalized and effective training programs. However, AI’s rapid adoption also brings challenges, including data privacy risks, ethical concerns, and financial barriers that may limit accessibility. The Intersection of AI and Sports Science Sports science has traditionally relied on the combination of empirical research and practical experience to improve athletic performance. However, with the massive amounts of data now being generated by wearables, video analysis, and other technology, analyzing and interpreting this data can be an overwhelming task. This is where AI steps in. Machine learning algorithms can process vast quantities of information, detecting patterns and relationships that might not be immediately apparent to humans. In doing so, AI can enhance traditional sports science methods by making them more precise and efficient. Performance Monitoring and Enhancement One of the most exciting areas where AI is expected to make a significant impact is in performance monitoring and enhancement. Traditionally, sports scientists would analyze an athlete’s performance based on a combination of manual observation, video footage, and periodic testing. With AI, this can be taken to a whole new level. Advanced AI-driven systems can now analyze movement patterns in real-time, using data from wearable devices, GPS systems, and [...]

By |2025-02-19T14:45:51-05:00November 7th, 2024|Training Info|0 Comments

A Closer Look at Heart Rate Variability by Gaby Smith

[This is a guest blog by Gaby Smith. Gaby Smith completed her MS in Exercise Science at Northeastern University and is participating in the Athletic Lab Mentorship Program. Gaby is a Certified Strength and Conditioning Specialist and holds certifications with U.S. Soccer, USAW, and USTFCCCA.] Resting, exercise, and recovery heart rates are common measures used to monitor fatigue, fitness, and performance responses and are often used to adjust training load. Recently, heart rate variability (HRV) has become a measure more commonly used to assess an athlete's readiness to perform and ensure the appropriate dose of training (ie. preventing overtraining or detraining). Generally, training load is quantified by external and internal indicators of intensity along with training time. External indicators may include distance, power output, or number of repetitions, while internal indicators include oxygen uptake, heart rate, blood lactate, or RPE (Buchheit, 2014). When it comes to monitoring and measuring athletes? fatigue, heart rate (HR) measures are commonly used because they are inexpensive, time efficient, and can be applied routinely and simultaneously with many athletes. Resting HR, exercise HR, and HRV are all related to autonomic nervous system (ANS) activity and their use in combination may improve the monitoring of the training status of athletes. While there are a number of HR measures that can be used to assess an athlete's training status, it is of utmost importance that whatever measure is used, it is standardized in order to isolate the training-induced effects (Buccheit, 2014). Physiological determinants of resting HR include cardiac muscle morphology, ANS activity, body position, and plasma volume (Buccheit, 2014). Best practice recommends the athlete measure resting HR upon waking up in the morning from a supine, seated, or standing position - but [...]

By |2020-09-15T23:27:48-04:00September 17th, 2020|Training Info|0 Comments

Using Vertical Jump Testing to Measure and Monitor Fatigue by Julia Zwierzynski

[Julia Zwierzynski is a graduating senior at the University of North Carolina at Chapel Hill studying Exercise and Sport Science. Julia is participating in the Athletic Lab coaching mentorship program.] Generally, fatigue is the inability of a person to perform at their maximal level of performance due to physiological or environmental factors (Halson, 2014). Fatigue is a normal component of General Adaptation Syndrome. When the body is exposed to physical stressors such as exercise, fatigue occurs. Normally, the body then adapts to the stressors in order to deal with them more efficiently in the future, and the initial fatigue is overcome. Fatigue frequently occurs in athletes, but only when it is excessive and chronic does it start to inhibit performance. Excessive fatigue leads to injury, pain, and exhaustion (Clark et. al, 2012). Fatigue can be measured in a myriad of different ways, including internally perceived measurements such as RPE as well as physiological measurements like heart rate, blood pressure, and hormone concentrations. One common method of measuring fatigue is with tests of maximal effort. For example, a maximal vertical jump test. A maximal vertical jump test is easy to administer because it requires very little experience or equipment. Ideally, athletes should experience zero fatigue and be in peak condition during their competition season. They should be able to perform to their maximal level. Therefore, a test of maximal effort during competition season is a good way to measure fatigue, because any negative differences between the maximal level of performance and the results of a test of maximal effort would indicate fatigue (Halson 2014). Fatigue should be monitored and minimized in order to keep athletes competing at as high of a level as possible. Fatigue measurements [...]

By |2020-04-25T10:04:41-04:00April 28th, 2020|Training Info|0 Comments

A Review of the High Performance Athletic Development Clinic

Athletic Lab in conjunction with UK based Proformance recently hosted the High Performance Athletic Development Clinic. The clinic brought together some of the top coaches in the world of sports performance and applied sports science. There were over 60 coaches in attendance including staff members from the Carolina Hurricanes, NC State, UNC, Duke, UNC, ECU, Clemson, Wake Forest, Ole Miss and more. The featured speaker was legendary Track & Field coach Boo Schexnayder who lectured attendees for over 7 hours over the 2 day clinic. Athletic Lab's Dr. Mike Young and John Grace were also featured speakers. The other 7 speakers were also top notch and delivered outstanding presentations. Here's a team summary from 3 attendees (Jamie Hershfang, Laurel Zimmermann, and Riley Rogers) all members of Athletic Lab's Coaching & Applied Sport Science Mentorship Program. See the bottom of the post for information about the authors. Compatible and Complementary Training Design by Boo Schexnayder reviewed by Riley Rogers Boo Schexnayder presented on Compatible and Complementary Training Design, focusing specifically on the mesocycle. By shifting the design of the training program every 28 days, the athlete is given the ideal amount of time to adapt neuromuscularly. "The difference between a good athlete and a bad athlete is the nervous system," he stated. Using this time frame, the athlete trains for three weeks and rests for one. He clarified that his concept of "rest days" are high intensity and low volume. As Schexnayder noted, "I can work hard because I rest hard," meaning that rest is of value because it gives the athletes a chance to recharge in anticipation of the coming work weeks. There are a few mesocycle schemes that Schexnayder outlined: block scheme, rotational scheme, [...]

By |2017-04-12T19:33:22-04:00June 9th, 2016|Training Info|0 Comments

Athletic Lab Player Monitoring System by John Evans

[This is a guest blog by John Evans. John is a recent Exercise Science graduate of Slippery Rock University in Pennsylvania and in his second stint as an Athletic Development Intern at Athletic Lab] Monitoring player heart rates during a fitness session. If you have ever spent a considerable amount of time studying training theory, you have likely seen a variety of fancy graphs with loading schemes. Early on in my athletic career, I would look at these graphs and find myself inquiring about how they were created. How were they measuring training load? Were they using an equation I had never seen? Was it a point system? Were they drawn to scale? In other words, how were they quantifying training load? What can be more confusing is considering how one training stimulus equates to another. How do three sets of four reps of power cleans at 85% compare to five sets of 40 meters of sprint volume? How does one stimulus affect the other? What happens if an athlete misses a day? What if they have an upcoming game and want to be sure they are fresh? How do those variables affect the graphs I am staring at in a textbook? This is where the player monitoring system comes in. It doesn't necessarily answer all of the questions stated above, but it does tell you about the athlete's fatigue and fitness state. Athletic Lab's Dr. Mike Young has developed similar monitoring systems for other teams such as the Vancouver Whitecaps of the MLS. Instead of creating a hypothetical graph with a great deal of guess work, the player monitoring system assigns values to each athlete's state of fatigue and creates a pictorial [...]

By |2017-04-13T10:54:10-04:00July 21st, 2015|Training Info|0 Comments
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