The ability to produce power during competition is essential. Football requires explosive power in order to get off the ball faster, increase speed of the first step, and increase the height of the vertical jump. Most strength training professionals use the back squat to increase power of the lower extremities; however, as large forces are placed on the back, athletes are at greater risk for injury. The trap bar dead lift is similar to the back squat in movement and form, but the trap bar dead lift reduces the force on the lower back. After testing for maximum vertical jump,athletes at Timpview High School participated in a strength program using either back squats or trap bar dead lift for seven weeks. A pre-test, mid-test and post-test were performed to calculate the increase in maximum vertical jump, if any. It was found that neither lift was significantly different than the other when testing for maximum vertical jump. These results will allow strength training professionals to use the trap bar dead lift instead of the back squats in subjects similar to those participating in the study.

Purpose: To examine the effect of three muscle potentiation protocols on changes in muscle architecture and the subsequent effect on jump power performance. Methods: Maximal (1RM) squat strength (Mean SD=178.3 ± 36.6kg), vertical jump power, and muscle architecture were obtained in 12 resistance trained men (25.2±3.6y; 90.67±12.7kg). Participants randomly completed three squatting protocols at 75% (3 x 10 reps), 90% (3 x 3 reps) or 100% (1 x 1) of their 1RM, or no workout (CON), with each protocol being separated by one week. During each testing session ultrasound and vertical jump testing were assessed at baseline (BL), 8min post (8P) and 20min post (20P) workout. Ultrasound measures of the rectus femoris (RF) and vastus lateralis (VL) muscles included; cross sectional area (CSA) and pennation angle (PNG). Following each ultrasound, peak (PVJP) and mean (MVJP) vertical jump power (using hands for maximum jump height) were measured using an accelerometer. Results: Magnitude based inferences analysis indicated that in comparison to CON, 75% resulted in a likely greater change in RF-CSA and VL-CSA (BL-8P and BL–20P), 90% resulted in a likely greater RF-CSA and VL-CSA (BL–20P), and 100% resulted in a very likely or likely decrease in VL-PNG at BL-8P and BL–20P, respectively). Meanwhile, changes in PVJP and MVJP for the 75% trial was likely decreased at BL-8P and BL–20P; and for the 90% trial MVJP was likely decreased at BL-8P and BL–20P. Analysis of the magnitude of the relationships indicated a likely negative relationship between VL-PNG and MVJP (r = -0.35; p < 0.018) at BL-8P, while at BL–20P, a negative relationship was observed between PVJP and RF-CSA (r = - 0.37; p < 0.014). iii Conclusion: Acute increases in muscle size and acute decreases in pennation angle did not result in any potentiation in vertical jump power measures. Although the inverse relationships observed between muscle architecture variables and power suggests a potential effect, the change in position (i.e. movement from standing to supine for ultrasound measures) may negate, as a result of potential fluid shifts or muscle relaxation, the potentiating effects of the exercise. It is also possible that the fatiguing nature of the squat protocols in trained but not competitive participants may have also contributed to the results.

We explore the use of machine learning to detect under-rotation in figure skating jumps. Under- rotation in jumps is difficult for the skater to sense but learning to recognize under-rotation is an impor- tant part of learning proper jump technique. To address this difficulty, we present the Under-rotation Monitor, or UR Monitor, a system for detecting under-rotated figure skating jumps in real-time. UR Monitor uses a single inertial measurement unit (IMU) attached to the skater's waist that sends a stream of accelerometer and gyroscope data to a mobile phone via Bluetooth. The mobile phone creates and sends an input vector of each jump to a web-hosted API that returns a response from our trained classifier indicating whether it considered that jump as 'under-rotated', or 'completed rotation'. The classifier is trained and tested on a collection of 444 jumps, of which only 121 are under-rotated. We also present a process for addressing an imbalanced dataset on which the classifier trains. Our classifier achieves an F1-score of only 0.66, suggesting that noise and imbalance in the data set are significant issues.

Monitoring external workloads and countermovement jump performance may be useful for coaches. PURPOSE: The purpose of this study was to determine the effects of external load on player performance as measured by a CMJ and specific blood biomarkers throughout the preseason. METHODS:10 female division 1 basketball athletes had PlayerLoadTM (PL) monitored for all mandatory basketball training during six weeks of the preseason and CMJs were performed weekly. Blood biomarkers were collected before preseason and at the end of preseason. Data were analyzed via the Catapult Sport software (Openfield, Catapult, Innovations, Melbourne, VIC, Australia) to quantify all participant movement. Data from CMJs were analyzed via Sparta Science technology (SpartaTrac; SPARTA Performance Science, v1.2.4). Cumulative effect of physical activity (CTPL) was estimated as a sum of total PL up to each jump testing session divided by the number of days. Linear mixed-effects models were used to model data related to the efficacy of PL and CTPL. Athletes (id) and their positions were examined as potential random effects. RESULTS: The best fit model suggested a high-order polynomial pattern between PL and the number of days since the first jump testing session with a random effect for the intercept (marginal R2 = 0.290; conditional R2 = 0.471). The fixed effect for the slope of the first order term was found to be positive. There was a significant negative effect of CTPL on JH (p = 0.0037). The boot strapped model showed a marginal R2 of 0.0183 (95% CI [0.000952, 0.0744]) and a conditional R2 of 0.884 (95% CI [0.762, 0.956]). For RSImod, a significant negative association between RSImod and CTPL (p = 0.0039, 95% CI [-0.0002214, -4.597081e-05]). CONCLUSION: Workloads increase during preseason. CMJ height and RSImod may have limited utility in displaying the effects cumulative workloads. Position played did not impact workload or the impact of that workload on the player. PRACTICAL APPLICATION: Cumulative effect of physical activity may be tracked using CTPL derived from PL. Practitioners may be encouraged to monitor alternative countermovement variables to better understand performance response to the cumulative effect of physical activity.

This project tries to understand how we alter existing architecture. The research leads to a proposal that attempts to answer the question: Should a death of a building be a part of its envisaged timeline? It is difficult to preempt a building being dead, but it might be possible to preempt a building being refurbished, renovated and especially replicated. I will try to incorporate the dimension of time in the new buildings and embrace the inevitable change, as well as explore the friction between originality and copying that the process of alteration generates. The site chosen for this architectural experiment is a ski jump erected at Norra Djurgården, and the proposal is its morphed copy hosting a visitors centre and an urban sports centre. The ambition is to establish guidelines or principles of mimicking and copying the elements of an existing edifice in the new one.

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