This paper studies sports injury risks and prevention, specifically focusing on figure skating. USFSA STARS science and injury biomechanics are also illustrated. Modern 3D-motion techniques were introduced to help develop sports strength training curriculum -- sports injury failure modes and injury prevention stretching techniques were analyzed and used to develop a cohesive curriculum. Clustering Principal Component Analysis was utilized to understand the different clustering methods that can help select the appropriate clustering algorithms to discover more injury insights. Statistical data visualization tools were used to provide more correlation and causation patterns on understanding the injury mechanisms. Using results from our statistical analysis, the appropriate injury prevention program was developed for figure skaters.
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This paper adopts the STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology. The objective of this paper is to introduce the benefits of integrating all 6 “STEAMS” elements, especially in the era of Big Data. There are three core visions of “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math,” and (3) integrate all six “STEAMS” elements. Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of sports science and mathematical algorithms. SPARQ science and injury biomechanics are also illustrated. Modern 3D-motion techniques were introduced to help develop sports strength training curriculum. Sports injury failure modes and injury prevention stretching techniques are common problem-solving methods in the engineering field. The popular Artificial Intelligence tools used were clustering Principal Component Analysis. Understanding the different clustering methods can help select the appropriate clustering algorithms to discover more injury insights. Statistical data visualization tools provide more correlation and causation patterns on understanding the injury mechanisms. This interdisciplinary STEAMS approach is a very powerful technique to help prevent injuries.
This paper adopts STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology. The objectives of this paper are to introduce the benefits of integrating all 6 “STEAMS” elements, especially living in the Big Data World. NBA Draft Position case study was demonstrated to present this novel “STEAMS” concept as compared to current “STEM” or” STEAM” approach. There are three core visions of this “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math”, and (3) integrate all six “STEAMS” elements. Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of “Sports” Science Research and “Math” algorithms. Separating the “Statistics” element can conduct more effective risk management and draw practical conclusions. Due to the previous two benefits, integrating all 6 “STEAMS” elements is becoming a natural critical thinking way for most scientists and engineers striving in the modern Big Data era. Several techniques are used to help determine the NBA Player position and identify the similar NBA players for benchmarking objective. It’s critical and urgent for educators and teachers to migrate from their traditional STEM approach to the new “STEAMS” approach to educate our next generations in their early school learning and career development.
This paper adopts STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology. The objectives of this paper are to introduce the benefits of integrating all 6 “STEAMS” elements, especially living in the Big Data World. There are three core visions of this “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math”, and (3) integrate all six “STEAMS” elements. Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of “Sports” Science Research and “Math” algorithms. Basketball Sport case study was demonstrated to present this novel “STEAMS” concept as compared to current “STEM”/” STEAM” approach. Basketball SPARQ science and injury biomechanics are illustrated. Modern 3D-Motion techniques were introduced to help develop Sports Strength training curriculum. Sports injury failure modes and injury-preventive stretching techniques are common engineering problem solving thinking. Clustering and Principal Component Analysis are popular Artificial Intelligence analytics. Understanding the clustering distance math could help select the appropriate clustering algorithms to discover the injury insights. Statistical data visualization tools could provide more correlation and causation patterns on understanding the Sports Injury mechanisms. This interdisciplinary STEAMS approach is very powerful on the Sports Science and Injury research.
Sports analytics tools are becoming more frequently used to help athletes enhance their skills and body strength to perform better and prevent injury. ACL tearing is one of the most common and dangerous injuries in basketball history. This injury occurs most frequently in jumping, landing, and pivoting due to the rapid change of direction and/or sudden deceleration in basketball. Recovering from an ACL injury is a brutal process, can take months – even years – to recover, and significantly decrease the player’s performance after recovery. In most glute exercises such as squatting and lunging, it is hypothesized that the more fatigued a person is, the more they droop their shoulders, they apply more pressure to the ground, and the more they internally rotate their knees which increases ACL injury risk. The goal of this project is to find the relationship between fatigue and different angle measurements in the hips, knees, and back as well as the force applied to the ground to minimize the ACL injury risk. 9 different sensors were attached to a test subject while he conducted 3 glute exercises on a force plate – running side squats, running pivot, and high jumps for 10 trials on each leg before and after an hour of vigorous exercise. Simple Linear Regression, Multivariate Clustering, and Time Series tools were used to compare the data before and after fatigue.
Each summer’s NBA draft pick is a significant event for all NBA stakeholders. An underdog team may immediately be hailed the champion contender in the next 10 years if they can draft a future superstar (such as Michal Jordan, Lebron James, and Stephen Curry). Not many people know clearly how the NBA general manager or coach evaluate the players in the NBA draft pick. The STEAMS methodology can be applied to the NBA draft potential on choosing the appropriate player position by comparing a star Stanford basketball player to the current top 50 NBA players for the Swing Guard and Small Forward positions. Based on the 2019 NBA Draft, this player’s position is still not well defined. JMP multivariate correlation analysis has concluded that this player has greater potential if playing as a Small Forward. Clustering Analyses grouped the college player with 3 similar NBA players. When comparing their statistics, this college player may identify differences which could help the NBA’s draft potential prediction. Current sports organizations are increasingly driven by data analyses, and STEAMS, a powerful and holistic approach, can be applied to other sports such as soccer and football on predicting awards and team performance.
Each summer’s NBA Draft event is a Big Thing for Basketball Sports Stakeholders. One bottom team may become Champion Contender in next 10 years if they could have drafted a superstar like Michal Jordan, Lebron James, Stephen Curry… There is always a puzzle on how the NBA GM or Coach have evaluated the players in NBA Draft. The STEAMS methodology can also be applied to the NBA Draft Potential on choosing the appropriate player position by comparing one college basketball super star (Stanford Sophomore Player) to the current top 50 NBA players on both Swing Guard and Small Forward Positions. Based on the 2019 NBA Draft Sites, this player’s position is still not well defined. JMP multivariate correlation analysis has concluded that this player has a greater NBA potential if playing at Small Forward position. Further Clustering Analysis could also group the college players with similar NBA players. The most three similar NBA stars are identified. When comparing their statistics, this college player may identify the gaps and which may help NBA Coach or GM on draft potential prediction. The STEAMS concept and methodology can be applied to other Sports such as Soccer, Football on predicting Individual Award, and Team Performance. Modern sports business is increasingly driven by Data and STEAMS holistic approach can be very powerful.
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