Latest Articles
Calculating the Acute: Chronic Workload Ratio in a Female Olympic Weightlifter: A Case Study
Authors: Jacqueline Serrano1, Ryan Belsito3, and Brian Serrano1,2
1HPI Sports Medicine
2The University of Medical Sciences Arizona
3Left Coast Weightlifting Club, Director and Head Coach
Corresponding Author:
Dr. Brian Serrano
25162 Forbes Road Unit D, Laguna Niguel, CA 92866
Brianserrano171@gmail.com
818-926-7269
Dr. Jacqueline Serrano is the Clinic Director of HPI Sports Medicine. She is a practicing Sports Chiropractor and Certified Strength and Conditioning Specialist. Her field of expertise is in Sports Medicine and Functional Medicine.
Ryan Belsito currently serves as the director and head coach for Left Coast Weightlifting Club.
Dr. Brian Serrano is the Director of Rehabilitation and Performance at HPI Sports Medicine. He serves as an Assistant Professor at The University of Medical Sciences in Arizona in the Human Movement department. His current research interest include shoulder injuries in overhead athletes.
Calculating the Acute: Chronic Workload Ratio in a Female Olympic Weightlifter: A Case Study
ABSTRACT
Purpose: The idea of workload monitoring has become popular for athletes of all levels within the last 5 years with the advent of wearable technology. The purpose of this case study was to track the workload of a female Olympic weightlifter using a commercial fitness tracker..
Methods: A competitive, female Olympic Weightlifter wore a commercial fitness tracker (WHOOP) for 1 month and specifically during training session. Metrics like strain, average heart rate (HR), max HR, and duration of session were tracked. The acute: chronic workload ratio was also calculated based off her programming. Two sample t-tests were calculated between continuous variables and an ANOVA was performed between multiple continuous variables. Statistical significance was set as a p-value of (<0.05) using a confidence interval of 95%.
Results: The WHOOP fitness tracker was able to calculate differences between strain and HR average (p<.001), between HR average and HR max (p<.001), HR average and Workload (p<.001), and HR max and Workload (p<.003). ANOVA analysis showed a p-value of (<.001) between all continuous variables. The acute: chronic workload ratio over the 4 weeks ranged from (0.85-1.10).
Conclusion: Using wearable technology has become a cost-effective and efficient technique to track athlete workload even in the recreational population. This information can then be supplemented by acute: chronic workload ratios for more information. This can lead to clinicians, coaches, and athletes having higher quality information to improve sports performance and recovery while mitigating the risk of injury.
Applications in Sport: The WHOOP fitness tracker serves as a valid way to track internal workload in Olympic Weightlifters while the ACWR serves as a valid way to track external workload in Olympic Weightlifters.
(more…)A longitudinal analysis of the differential performances of seeded male and female Grand Slam tennis players
Author: Raymond Stefani
California State University, Long Beach
Corresponding Author:
Raymond Stefani
25032 Via Del Rio
Lake Forest, CA 92630
Raystefani@aol.com
949-586-1823
Dr. Raymond Stefani is a professor emeritus at the California State University, Long Beach with 170 publications covering rating systems, individual Olympic sports, team sports, home advantage, and sports history
A longitudinal analysis of the differential performances of seeded male and female Grand Slam tennis players
ABSTRACT
Purpose: This paper evaluates Grand Slam tennis at the most fundamental level, the match-by-match competition between established players and their challengers. The competitive balance of men and women therefore is evaluated in this paper, as measured by the success of lower-seeded or un-seeded competitors at winning matches. Methods: A 14-season database was tabulated, covering 56 Grand Slams for men and 56 for women contested from 2006 through 2019, including nearly 5,000 matches for men and 5000 for women, each involving at least one seeded player. Results and Discussion: Overall, higher seeded players were upset in 25% of women’s matches and in 21% of men’s matches. As an average season progressed, women were involved in more upset matches than men by 28% at the season opening Australian open on hard court, by 15% on red clay at the French Open, by 14% on grass at Wimbledon (where the most upsets happened for both men and women) ending with 11% on hard court at the US Open. Lower-seeded or un-seeded men became consistently more competitive as each season progressed, while women remained at the same highly competitive level. On a year-by-year basis, competitive balance (upsets) have increased somewhat, that is, the predictability of higher-seeded players has decreased over time. Conclusions: The cumulative effect of the upset differential is that spectators watched the progress of the strongest men’s seeds, wondering how they would do against the three dominant men’s players, Nadal, Djokovic, and Federer, who won 48 of the men’s 56 Grand Slams over the 14-year period. In contrast, dynamic new young female players emerged, winning by upset until some became higher seeds and even Grand Slam champions themselves, only to be upset and replaced as champion by a new wave of enthusiastic and compelling competitors, exemplified by the fact that 24 women won their 56 Grand Slams. Applications to Sport: The marketing, advertising, and psychological/physical player preparation should consider the fundamental spectator’s eye views that differentially define men and women’s Grand Slam tennis.
(more…)Eras of ERA
Author: Douglas J. Jordan1
1Department of Business Administration, Sonoma State University, Rohnert Park, CA, USA
Corresponding Author:
Douglas J. Jordan
3663 Primrose Avenue
Santa Rosa, CA 95407
jordand@sonoma.edu
707-206-0563
Douglas J. Jordan, PhD, is a Professor of Business Administration at Sonoma State University in Rohnert Park, California. In addition to his professional interest in corporate finance and investments, he is a member of the Society for American Baseball Research and does research on baseball related topics.
Eras of ERA
ABSTRACT
This paper examines and analyzes the average ERA in major-league baseball each season between 1871 and 2019. The data shows that the maximum average ERA of 5.33 occurred in 1894 after the pitching distance was increased to 60 feet 6 inches in 1893. The lowest average ERA of 2.19 occurred in 1874 and the overall average ERA across baseball history is 3.74. From a current perspective, the overall average ERA of almost exactly 4.0 since 1920 is a more useful benchmark given the significant changes that were taking place as the game evolved over its first fifty years.
The data is used to divide baseball history into different pitching eras based on the similarity of average ERA across different time periods. For example, the overall average ERA for the years 1921-1928 was 4.05. This era is designated the Most of the Twenties Era. The lowest overall average ERA of 2.82 occurred during the appropriately named Deadball Era (1904-1919). Not surprisingly, the offensive explosion that occurred during the 1990s shows up in the average ERA data. The overall average ERA between 1994 and 2009 (designated the Camden Yards Era) was the highest for any era in baseball history, 4.46. In terms of understanding how pitching has evolved, these data driven pitching era designations are an improvement over other ways of dividing baseball history because the variation in average ERA over the time periods (measured using standard deviation) is smaller than the variation in average ERA during traditional historic eras.
(more…)Kinetics and Kinematics of Commonly Used Quarterback Throwing Approaches – A Case Study
Authors: Dimitrije Cabarkapa 1, Andrew C. Fry 1, and Eric M. Mosier 2
1Jayhawk Athletic Performance Laboratory, University of Kansas, Lawrence, KS, USA
2 Northwest Missouri State University, Maryville, MO, USA
Corresponding Author:
Dimitrije Cabarkapa, MS, CSCS, NSCA-CPT, USAW
Jayhawk Athletic Performance Laboratory
University of Kansas
1301 Sunnyside Avenue, Lawrence, KS 66047
dcabarkapa@ku.edu
785-864-5552
Kinetics and Kinematics of Commonly Used Quarterback Throwing Approaches – A Case Study
ABSTRACT
The purpose of this study was to analyze kinetic and kinematic components for six of the most commonly used quarterback drop throwing patterns and determine how further performance improvements can be made. One male right-handed quarterback athlete volunteered to perform multiple repetitions of the six most commonly used right-handed drop throwing approaches: standing still and throw (SST), one-step left-right (1SLR), one-step right-left (1SRL), three-step straight ahead (3SSA), three-step shot gun (3SSG), and five-step throw (5ST). Kinetic data was collected with a uniaxial force plate while kinematic data was captured with high definition cameras. One-way analysis of variance was used to determine the differences between the six throwing approaches for the kinetic and kinematic variables examined in this study. The statistical significance level was set a priori to p<0.05. Peak right leg force demonstrated significantly lower magnitudes for 1SRL when compared to 1SLR, 3SSG, and 5ST. Peak left leg force for the 3SSA was lower when compared to 1SRL and 1SLR. Throw arm elbow angle was greater for SST when compared to all other throwing approaches. No difference was observed for ball speed, non-throw arm elbow angle, front leg knee angle, and back leg knee angle between any of the examined throwing approaches. Our results indicate that the majority of ground reaction force production required for an optimal quarterback throwing motion comes from the rear leg, and the magnitudes may reach three times bodyweight forces. Ground reaction forces may be enhanced with a greater number of drop steps, which may ultimately increase quarterback throwing distance. Greater throwing arm elbow extension may be induced as biomechanical adjustment due to lack of force production caused by the inability of the quarterback to take a greater number of drop steps.
(more…)Testing the predictive validity of combine tests among junior elite football players: an 8-yr follow-up
Authors: Pierre-Luc Yao1, Vincent Huard Pelletier1, and Jean Lemoyne1
1 Department of Human Kinetics, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
Corresponding Author:
Pierre-Luc Yao, PhD
3351 Boulevard des Forges
Trois-Rivières, QC, Canada, G8Z 4M3
Pierre-Luc.Yao@uqtr.ca
819 376-5011, ext. 3793
Pierre-Luc Yao, PhD a lecturer and internship coordinator in the Department of Human Kinetics at Université du Québec à Trois-Rivières in Trois-Rivières, Québec. His research interests include psychometrics, sport retirement impacts and athlete development.
Vincent Huard Pelletier, MSc, PhD(c) is currently a doctoral student at Université du Québec à Trois-Rivières. Vincent research interest include athlete development, physical activity behavior amongst athletes.
Jean Lemoyne is professor of physical education in the Department of Human Kinetics at the Université du Québec à Trois-Rivières. His research interests are practice of sport amongst teens and young adults, performance evaluation in sports, advanced statistics in sports.
Testing the predictive validity of combine tests among junior elite football players: an 8-yr follow-up
ABSTRACT
Purpose: The objective of this study was to assess the relationship and contribution of physical performance test results on the final selection of an elite under-18 football selection camp. Methods: Data were drawn from 2 876 players divided into seven position groups (DB, DL, OL, LB, QB, RB, and WR) collected over an 8-year span. Players’ evaluations included performance tests (10-yd dash, 20-yd dash, 40-yd dash, 20-yd pro agility shuttle, 3-cone drill, broad jump, vertical jump, power max test) and anthropometric measures (height and weight). Student t tests were calculated for selected and non-selected groups for all positions. Results: Mean comparisons showed that for most measures, selected players obtained significantly better results than non-selected players. Linear regression models were generated for all groups, and every position was found to have its own unique prediction model. The best models were those of the DL (R2 = 0.222), OL (R2 = 0.207) and LB (R2 = 0.204), and the overall explained variance for each model was considered low (R2 = 0.173). Weight, height and 40-yd dash were the most predominant factors in all models. Conclusion: Individually, selection camp results effectively discriminate between selected and non-selected players; together, however, they explain only a limited part of the final selection for each position. Applications in sport: These results suggest that the predictive capacity of the football combine could be improved in terms of the selection of elite football players.
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