scholarly journals Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games

2021 ◽  
Vol 3 ◽  
Author(s):  
Sigrid B. H. Olthof ◽  
Tahmeed Tureen ◽  
Lam Tran ◽  
Benjamin Brennan ◽  
Blair Winograd ◽  
...  
Keyword(s):  
Movement Analysis ◽  
Individual Performance ◽  
Loss Probabilities ◽  
Point Field ◽  
Individual Player ◽  
Measurement Units ◽  
Player Performance ◽  
Game Performance ◽  
Level Training ◽  
And Training

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

scholarly journals Opening up the court: analyzing player performance across tennis Grand Slams

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shannon K. Gallagher ◽  
Kayla Frisoli ◽  
Amanda Luby
Keyword(s):  
Individual Performance ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Surface Type ◽  
Individual Player ◽  
Player Performance ◽  
Opening Up

Abstract In tennis, the Australian Open, French Open, Wimbledon, and US Open are the four most prestigious events (Grand Slams). These four Grand Slams differ in the composition of the court surfaces, when they are played in the year, and which city hosts the players. Individual Grand Slams come with different expectations, and it is often thought that some players achieve better results at some Grand Slams than others. It is also thought that differences in results may be attributed, at least partially, to surface type of the courts. For example, Rafael Nadal, Roger Federer, and Serena Williams have achieved their best results on clay, grass, and hard courts, respectively. This paper explores differences among Grand Slams, while adjusting for confounders such as tour, competitor strength, and player attributes. More specifically, we examine the effect of the Grand Slam on player performance for matches from 2013 to 2019. We take two approaches to modeling these data: (1) a mixed-effects model accounting for both player and tournament features and (2) models that emphasize individual performance. We identify differences across the Grand Slams at both the tournament and individual player level.

scholarly journals Can Elite Australian Football Player’s Game Performance Be Predicted?

2021 ◽  
Vol 20 (1) ◽  
pp. 55-78
Author(s):  
J. Fahey-Gilmour ◽  
J. Heasman ◽  
B. Rogalski ◽  
B. Dawson ◽  
P. Peeling
Keyword(s):  
Model Performance ◽  
Test Set ◽  
Australian Football League ◽  
Team Selection ◽  
Individual Player ◽  
Australian Football ◽  
Team Characteristics ◽  
Using Data ◽  
Player Performance ◽  
Game Performance

Abstract In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.

LAX-Score

2021 ◽  
Vol 5 (3) ◽  
pp. 1-28
Author(s):  
Woosub Jung ◽  
Amanda Watson ◽  
Scott Kuehn ◽  
Erik Korem ◽  
Ken Koltermann ◽  
...  
Keyword(s):  
Ncaa Division I ◽  
Athletic Performance ◽  
High Performance ◽  
Performance Enhancement ◽  
Ground Truth ◽  
Division I ◽  
Sports Science ◽  
Player Performance ◽  
The Relationship ◽  
Game Performance

For the past several decades, machine learning has played an important role in sports science with regard to player performance and result prediction. However, it is still challenging to quantify team-level game performance because there is no strong ground truth. Thus, a team cannot receive feedback in a standardized way. The aim of this study was twofold. First, we designed a metric called LAX-Score to quantify a collegiate lacrosse team's athletic performance. Next, we explored the relationship between our proposed metric and practice sensing features for performance enhancement. To derive the metric, we utilized feature selection and weighted regression. Then, the proposed metric was statistically validated on over 700 games from the last three seasons of NCAA Division I women's lacrosse. We also explored our biometric sensing dataset obtained from a collegiate team's athletes over the course of a season. We then identified the practice features that are most correlated with high-performance games. Our results indicate that LAX-Score provides insight into athletic performance beyond wins and losses. Moreover, though COVID-19 has stalled implementation, the collegiate team studied applied our feature outcomes to their practices, and the initial results look promising with regard to better performance.

scholarly journals Pengaruh Tingkat Pendidikan Dan Pelatihan Terhadap Kompetensi Lulusan Peserta Pada Lembaga Pelatihan Kerja (LPK) PT. Satria Tamtama Rahayu

2021 ◽  
Vol 4 (2) ◽  
pp. 612-620
Author(s):  
Ira Pebrianti Syamhadi ◽  
Nelly Martini
Keyword(s):  
Data Collection ◽  
Education And Training ◽  
Education Level ◽  
Coefficient Of Determination ◽  
Level Of Education ◽  
The Relationship ◽  
Level Training ◽  
And Training ◽  
Simultaneous Influence ◽  
Industrial Company

This research aims to know, analyse, and explain:  The extent of the relationship between education level and training on competency of participants graduates.  Partial influence between the level of education and training on the competence of graduates of participants.  Simultaneous influence between the level of education and training on the competence of graduates of participants. Data collection techniques using questionnaire and literature. The population in this research is a participant who graduated at PT Satria Tamtama Rahayu and has been placed in the industrial company with samples used as many as 135 respondent. Data obtained from respondents was processed using test aids using IBM SPSS 22. The results showed that:) There was an influence on the level of education on the competency of participant graduates. There is no training influence on the competency of participant graduates.  There are influences on the level of education and training on the competency of participant graduates. The value of coefficient of determination (R2) amounted to 0,662 or66,2% which means that 66,2% of participants graduates competencies are influenced by education and training levels, while the remaining 33,8% are influenced by other variables not examined in this study. Keywords: Competency Graduates participants, education level, training.

scholarly journals Mosquito Larvae Detection using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 804-809
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Web Application ◽  
Individual Performance ◽  
Mosquito Larvae ◽  
Accuracy Score ◽  
Performance Category ◽  
Training Time ◽  
The Cost ◽  
And Training

Dengue cases has become endemic in Malaysia. The cost of operation to exterminate mosquito habitats are also high. To do effective operation, information from community are crucial. But, without knowing the characteristic of Aedes larvae it is hard to recognize the larvae without guide from the expert. The use of deep learning in image classification and recognition is crucial to tackle this problem. The purpose of this project is to conduct a study of characteristics of Aedes larvae and determine the best convolutional neural network model in classifying the mosquito larvae. 3 performance evaluation vector which is accuracy, log-loss and AUC-ROC will be used to measure the model’s individual performance. Then performance category which consist of Accuracy Score, Loss Score, File Size Score and Training Time Score will be used to evaluate which model is the best to be implemented into web application or mobile application. From the score collected for each model, ResNet50 has proved to be the best model in classifying the mosquito larvae species.

scholarly journals The Effect of Concussive Injury on Individual Game Performance in Professional Collision-Sport Athletes

2019 ◽  
Vol 28 (7) ◽  
pp. 769-773
Author(s):  
Corey P. Ochs ◽  
Melissa C. Kay ◽  
Johna K. Register-Mihalik
Keyword(s):  
Professional Sports ◽  
Physical Nature ◽  
Time Frame ◽  
Individual Performance ◽  
Return To Play ◽  
Bottom Line ◽  
Final Study ◽  
Before And After ◽  
Game Performance ◽  
Concussive Injury

Clinical Scenario: Collision sports are often at higher risk of concussion due to the physical nature and style of play. Typically, initial clinical recovery occurs within 7 to 10 days; however, even this time frame may result in significant time lost from play. Little has been done in previous research to analyze how individual game performance may be affected upon return to play postconcussion. Focused Clinical Question: Upon return-to-play clearance, how does sport-related concussion affect game performance of professional athletes in collision sports? Summary of Key Findings: All 3 studies included found no significant change in individual performance of professional collision-sport athletes upon returning to play from concussive injury. One of the studies indicated that there was no difference in performance for NFL athletes who did not miss a single game (returned within 7 d) and those who missed at least 1 game. One study indicated that although there was no change in performance of NFL players upon returning to play from sustained concussion, there was a decline in performance in the 2 weeks before the diagnosed injury and appearing on the injury report. The final study indicated that there was no difference in performance or style of play of NHL athletes who missed time due to concussive injury when compared with athletes who missed games for a noninjury factor. Clinical Bottom Line: There was no change in performance upon return from concussive injury suggesting that players appear to be acutely recovered from the respective concussion before returning to play. This suggests that current policies and management properly evaluate and treat concussed athletes of these professional sports. Strength of Recommendation: Grade C evidence exists that there is no change in individual game performance in professional collision-sport athletes before and after suffering a concussion.

scholarly journals Reliability and Agreement of 3D Trunk and Lower Extremity Movement Analysis by Means of Inertial Sensor Technology for Unipodal and Bipodal Tasks

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 141 ◽  
Author(s):  
Rob Van der Straaten ◽  
Amber K. B. D. Bruijnes ◽  
Benedicte Vanwanseele ◽  
Ilse Jonkers ◽  
Liesbet De Baets ◽  
...  
Keyword(s):  
Lower Limb ◽  
Sagittal Plane ◽  
Inertial Sensor ◽  
Movement Analysis ◽  
Sensor Technology ◽  
Inertial Measurement ◽  
Sit To Stand ◽  
Movement Strategies ◽  
Measurement Units ◽  
Healthy Participants

This study evaluates the reliability and agreement of the 3D range of motion (ROM) of trunk and lower limb joints, measured by inertial measurement units (MVN BIOMECH Awinda, Xsens Technologies), during a single leg squat (SLS) and sit to stand (STS) task. Furthermore, distinction was made between movement phases, to discuss the reliability and agreement for different phases of both movement tasks. Twenty healthy participants were measured on two testing days. On day one, measurements were conducted by two operators to determine the within-session and between-operator reliability and agreement. On day two, measurements were conducted by the same operator, to determine the between-session reliability and agreement. The SLS task had lower within-session reliability and agreement compared with between-session and between-operator reliability and agreement. The reliability and agreement of the hip, knee, and ankle ROM in the sagittal plane were good for both phases of the SLS task. For both phases of STS task, within-session reliability and agreement were good, and between-session and between-operator reliability and agreement were lower in all planes. As both tasks are physically demanding, differences may be explained by inconsistent movement strategies. These results show that inertial sensor systems show promise for use in further research to investigate (mal)adaptive movement strategies.

Measuring Process in Team Performance and Training

1992 ◽  
Vol 36 (17) ◽  
pp. 1342-1345
Author(s):  
Mary D Zalesny
Keyword(s):  
Team Performance ◽  
Individual Performance ◽  
Social Goal ◽  
Measuring Process ◽  
And Training

What if we took seriously the fact that team performance is not synonymous with individual performance? Although teams appear to be the new workhorses of economic and social goal accomplishment, the processes by which they accomplish their goals remains relatively unexplicated and not well understood. In this paper, we argue that coordination is an important unifying construct for defining, measuring, researching, and training effective team performance.

Activity Profiles in International Women’s Team Handball Using PlayerLoad

2017 ◽  
Vol 12 (7) ◽  
pp. 934-942 ◽  
Author(s):  
Eirik H. Wik ◽  
Live S. Luteberget ◽  
Matt Spencer
Keyword(s):  
Situational Factors ◽  
Activity Levels ◽  
National Team ◽  
Team Handball ◽  
Individual Player ◽  
Measurement Units ◽  
The Mean ◽  
Underlying Mechanisms ◽  
Individual Profiles ◽  
Initial Intensity

Team handball matches place diverse physical demands on players, which may result in fatigue and decreased activity levels. However, previous speed-based methods of quantifying player activity may not be sensitive for capturing short-lasting team-handball-specific movements.Purpose:To examine activity profiles of a women’s team handball team and individual player profiles, using inertial measurement units.Methods:Match data were obtained from 1 women’s national team in 9 international matches (N = 85 individual player samples), using the Catapult OptimEye S5. PlayerLoad/min was used as a measure of intensity in 5- and 10-min periods. Team profiles were presented as relative to the player’s match means, and individual profiles were presented as relative to the mean of the 5-min periods with >60% field time.Results:A high initial intensity was observed for team profiles and for players with ≥2 consecutive periods of play. Substantial declines in PlayerLoad/min were observed throughout matches for the team and for players with several consecutive periods of field time. These trends were found for all positional categories. Intensity increased substantially in the final 5 min of the first half for team profiles. Activity levels were substantially lower in the 5 min after a player’s most intense period and were partly restored in the subsequent 5-min period.Discussion:Possible explanations for the observed declines in activity profiles for the team and individual players include fatigue, situational factors, and pacing. However, underlying mechanisms were not accounted for, and these assumptions are therefore based on previous team-sport studies.

scholarly journals Concussions in the National Basketball Association: An Analysis from 1999-2018

2019 ◽  
Vol 7 (7_suppl5) ◽  
pp. 2325967119S0040
Author(s):  
Kelechi Okoroha ◽  
Bhavik H. Patel ◽  
Yining Lu ◽  
Alexander J. Idarraga ◽  
Brian Forsythe
Keyword(s):  
Standardized Testing ◽  
Media Coverage ◽  
Medical Knowledge ◽  
National Basketball Association ◽  
Return To Play ◽  
Before And After ◽  
Education Medical ◽  
Player Performance ◽  
First Time ◽  
Game Performance

Objectives: Several studies have examined the incidence and effects of concussions in professional football, baseball, and hockey, but there has been limited evaluation of the effects of concussions in National Basketball Association (NBA) players. This study aims to evaluate the epidemiologic trends of concussions, as well as the effects of concussions on in-game performance, in NBA players. Methods: Publicly available records were searched to include all players who sustained an in-game concussion while playing in the NBA from the beginning of the 1999 NBA season to the conclusion of the 2018 season. For each player the following variables were collected: date of injury, number of days and games missed before returning to game play, player efficiency rating (PER) in the season of injury, the season preceding the injury, and the season following the injury, position of the injured player, and the incidence of multiple concussions for a single player. Concussion trends before and after the institution of the NBA Concussion Protocol were calculated, as well as the effects on PER after return to play. Results: From the start of the 1999 season to the end of the 2018 season, 185 basketball-related concussions were incurred across 149 NBA players. All players were able to return to play following a first-time concussion after missing an average of 7.7 days and 3.5 games. The NBA Concussion Protocol was instituted ahead of the 2011-2012 season, prior to which there were 5.7 concussions recorded/season, with an average of 6.7 days and 3.0 games missed per first-time concussion. Following the institution of the concussion protocol, there were approximately 11 more concussions recorded/season (16.7 vs. 5.7, P = 0.007), with 1.7 more days missed (8.4 vs. 6.7, P = 0.27) and 0.9 more games missed (3.9 vs. 3.0, P = 0.24) per concussion, compared to prior seasons. Of the 149 players who suffered concussions, 27 were concussed multiple times (18.1%). There was no difference found in the incidence of recurrent concussions within the same season before vs. after the institution of the concussion protocol (4 vs. 5, P > 0.05). PER was almost identical for concussed players in the season prior to the injury, the season in which the injury occurred, and the season following the injury (13.93 vs. 13.94 vs. 13.91, P = 0.998). Conclusion: There has been a significant increase in the incidence of concussions in the NBA player following the institution of a league-wide concussion protocol. This likely reflects more accurate reporting secondary to advances in player education, medical knowledge, national media coverage, and standardized testing protocols. Despite this increase in reported concussions, the amount of time missed following injury has remained relatively constant. Player performance as reported by PER was not significantly affected by sustaining a concussion. [Figure: see text][Table: see text]

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