Validating Tackle Mechanics in American Football: Improving Safety and Performance

Maerlender, Arthur C.; Masterson, Caitlin J.; Norris, R.J.; Hinthorne, Adam

doi:10.1007/s10439-020-02625-7

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…We realize the electronic sensor for collecting the flight attitude parameters of the football training sphere. In this paper, the ARM5.0 video acquisition instrument is used to capture the time-lapse of the football training drop point [10]. The time interval of data collection is 0.12s.…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Football Offense Training Strategy Based on Fractional Differential Mathematical Modeling

Liu

Chu

2022

Applied Mathematics and Nonlinear Sciences

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This paper establishes the parameters of the ball trajectory based on the fractional differential equation model. At the same time, this paper quantifies the shooting power of direct free kicks. In this paper, the resistance effect of air on the motion of the center of mass is considered. And the dynamic equation of the football arc motion is established. In this way, we obtain the analytical solution of the differential equation of order. The research shows that the correlation between the fitting equation of the football flight trajectory and the prediction equation is 96.55%. The algorithm model in this paper can effectively predict the flight path of football. The research theory of this paper provides a theoretical basis for football offensive training strategies.

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Football Offense Training Strategy Based on Fractional Differential Mathematical Modeling

Liu

Chu

2022

Applied Mathematics and Nonlinear Sciences

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“…Another potential modifier of exposure would be differences in coaching practice and skill technique that may have contributed systematically to the amount of HAE (Campolettano et al, 2016;Gellner et al, 2020;Kelley et al, 2019;Maerlender, Masterson, et al, 2020). Further, the small sample size in season-two limits statistical power and may have reduced generalizability of findings; however, by replicating and extending findings from the original study with the same participants, confidence in the findings should be increased.…”

Section: Discussionmentioning

confidence: 99%

Weight and Age Interact to Modify Cognitive Effects of Head Acceleration Events after Two Seasons of Youth Football

Maerlender,

Smith,

Brolinson

et al. 2023

Preprint

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Weight and age interact to modify cognitive effects of head acceleration events after two seasons of youth football Abstract This is a follow-up study of youth American football players in a second season of play. Season-one findings identified a relationship between a head acceleration measure (HITsp) and cognitive score changes with weight modifying the effect in 9 to 10 year-olds. Sixty-eight youth completed a second season of play wearing helmet-mounted sensors and were assessed with neuropsychological tests pre- and post-season. Regression analysis of the full sample demonstrated a small but significant negative effect of HAE on cognition as indexed by test score changes: R2 = .06, F = 4.06, p = .024. Outcome differences between those who started playing at ages nine to 10, compared to those who started between 11 and 13 were identified: t(66) = -3.39, p<.01, d = -.84, 95th CI -2.77 to -.72. Regression models including players’ weights found that the relationship of weight to outcome was different by group: greater negative cognitive effects were found in younger-heavier players (R2 = 0.21, F =3.21, p = 0.03) and older-lighter players (R2 = 0.18, F =4.26, p <.001). These findings confirmed a negative relationship of HAE and cognitive change and point to player weight as an important developmental factor to consider in understanding concussion biomechanics.

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“…Reduce), the winner of the next point will gain an 'advantage' (marked as 'AD'). If the player with an advantage wins another point, he/she will win the game; If the opponent wins the next point, both sides return to Deuce [7] [10]. In data analysis, we need to convert 'AD' scores into numerical data, and categorical variables like this need to be converted into numerical variables.…”

Section: Data Descriptionmentioning

confidence: 99%

Football Momentum Analysis based on Logistic Regression

Wen,

Liu,

Liu

2024

FCIS

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In tennis, momentum is pivotal and can be quantified using metrics like Consecutive Win Rate (CWR), Unforced Error Rate (UER), Break Point Save Rate (BPSR), and Fatigue Factor (FF). Each metric provides insight into a player's performance and state during a match. CWR is a clear momentum indicator, reflecting a player's game dominance, while UER highlights potential lapses in concentration or physical condition. BPSR evaluates a player's clutch performance in critical situations, and FF gauges physical exertion. Utilizing logistic regression, we can predict a player's probability to win at any scoring point, incorporating these metrics as variables. The coefficients obtained from MATLAB analysis (e.g., p1_cwr at 22.73 and p2_ff at -3.26) reveal the positive or negative correlation of these factors with a player's winning chances. In the case of the "2023-wimbledon-1301" match, the logistic model's predictions showed a symmetrical distribution of win probabilities between players, suggesting a balance in momentum swings throughout the match. Initial volatility in Player 1's success rate indicated a strong start, which diminished over time, possibly due to fatigue or the opponent's improving performance. Despite the fluctuations and a period of deadlock, Player 1's consistent performance and superior win rate for most of the game secured the victory. This outcome underscores the importance of maintaining momentum and physical resilience in tennis.

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Validating Tackle Mechanics in American Football: Improving Safety and Performance

Cited by 9 publications

References 25 publications

Football Offense Training Strategy Based on Fractional Differential Mathematical Modeling

Football Offense Training Strategy Based on Fractional Differential Mathematical Modeling

Weight and Age Interact to Modify Cognitive Effects of Head Acceleration Events after Two Seasons of Youth Football

Football Momentum Analysis based on Logistic Regression

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