O tema do presente estudo é a motivação à prática regular de atividades físicas de atletas de Basquetebol de 13 a 16 anos. Trata-se de uma investigação com uma abordagem focada na perspectiva da Pedagogia do Treinamento Desportivo. Parte-se do pressuposto de que é importante para o treinador/professor conhecer os principais fatores que motivam os atletas para que o plano de atividades seja conduzido da melhor forma possível. O objetivo central da pesquisa foi explorar os níveis de seis dimensões motivacionais associadas à prática regular de atividades físicas (Controle de estresse, Saúde, Sociabilidade, Competitividade, Estética e Prazer), que melhor descrevem os atletas de Basquetebol da faixa etária de 13 a 16 anos. Todos participam de Competições Institucionalizadas (Campeonatos Escolares, Campeonatos Inter-Clubes, JERGS, dentre outros). Mais especificamente, o estudo procurou descrever os níveis destas seis dimensões motivacionais das seguintes variáveis: Sexo; Categorias (“até 14 anos” e “até 16 anos”); Tempo de participação em competições (“1º ano” e “mais de 1 ano”). Foi verificado, também, se houve diferenças estatisticamente significativas entre as dimensões motivacionais e as variáveis controladas. Para tanto, foi aplicado o Inventário de Motivação à Prática Regular de Atividades Físicas (IMPRAF-54; BALBINOTTI e BARBOSA, 2006). As respostas aos itens do IMPRAF-54 são dadas conforme uma escala de tipo Likert, bidirecional graduada em 5 pontos, partindo de “isto me motiva pouquíssimo” (1) a “isto me motiva muitíssimo” (5). O IMPRAF-54 foi aplicado em 441 atletas de Basquetebol infanto-juvenis de 13 e 16 anos, vinculados as Instituições esportivas dos estados do Rio Grande do Sul e Santa Catarina. Constatou-se que a dimensão que mais motiva os atletas de Basquetebol infantojuvenis é o Prazer (1º); seguida, respectivamente, pelas dimensões Competitividade e a Saúde (estatisticamente indissociáveis) (2º); Sociabilidade (3º), Estética (4º); e, por último, Controle de Estresse (5º). Com relação às variáveis controladas, percebe-se que a dimensão Sociabilidade é a que mais motiva os atletas de basquetebol que estão no seu “1º ano” de competição, quando comparados àqueles que estão a “mais de 1 ano” competindo. Outro resultado dessa pesquisa aponta que os atletas de Basquetebol da categoria “até 14 anos” são mais motivados pela Competitividade do que os atletas da categoria (“até 16 anos”). Ao compararmos as diferenças entre o sexo “masculino e feminino”, as dimensões Competitividade e Estética se destacam como aquelas que mais motivam os atletas do sexo masculino. Portanto, os resultados indicam que os atletas de Basquetebol infanto-juvenis de 13 a 16 anos praticam o esporte por motivos intrínsecos. Recomenda-se que outros estudos sejam realizados para que se possa aprofundar ainda mais os conhecimentos sobre esta temática. / The present study has as its subject the motivation regarding the regular practice of sports and physical activities in Basketball athletes from 13 to 16 years old. It is an inquiry focused in the perspective of the pedagogy of the sports training. It is an assumption that it is important for the coach or teacher to know the main factors that motivate the athletes having the plan of activities prepared in the best possible way. The main reason of this study was to explore six dimensions of motivation associated to the practice of sports and regular physical activities (Stress Control, Health, Sociability, Competitiveness, Esthetics And Pleasure), that best describe the athletes that practice Basketball with ages ranging from 13 to 16 years old. All athletes participate to institutionalized competitions (school competitions, inter-clubs competitions, JERGS, among others). More specifically, this study tries to describe the levels of these six motivational dimensions of the following variables: gender; categories (“up to 14” and “up to 16 years of age”); time of competition (“one year” and “more than one year”). It was also verified if there were any significant statistical differences between the motivational dimensions and the controllable variables. In order to obtain all this answers, it was applied an inventory called “Inventario de Motivação à Pratica Regular de Atividades Físicas” (IMPRAF-54; BALBINOTTI e BARBOSA, 2006). The IMPRAF-54 is a test that rates the answers in a Likert scale form of 5 points (1 – It’s not very motivating to 5 – It’s very motivating) verifying in nominal values, the dimensions that motivate most Basketball players to the practice of sports and physical activities on a regular basis. The IMPRAF–54, surveyed a sample of 441 young Basketball players of both genders, ages between 13 and 16 years old, tied by States of Rio Grande do Sul and Santa Catarina, Brazil. It was verified that the dimensions that most motivates young Basketball players to practice regularly is Pleasure; followed to Competitiveness and Health (indivisible statistically); Sociability; Esthetics; and, finally, Stress Control. In the relation to controlled variable it was observed that the Sociability was the dimension that most motivates the young Basketball athletes when they are in their first year as competitors. Another result that this study shows is that the athletes of the category “up to 14” are significantly more motivated by Competitiveness than the “up to 16”. When we compare the differences between “males and females”, the dimensions Competitiveness and Esthetics were prominence significantly more motivating to males athletes. Therefore, the results indicate that young Basketball athletes from 13 to 16 years old practice sports by intrinsic motives. It is recommended that further studies be conducted in order to deepen the knowledge on this topic.

This study was an attempt to identify the factors that female basketball players consider important in their selection of a college to attend. A questionnaire was sent to all scholarship-granting junior colleges and Division I National Collegiate Athletic Association universities in the State of Texas during the 1991 spring semester. Students from 11 junior colleges and 11 Division I universities replied to the survey. The findings of this study are based on data collected from the 244 subjects' responses to a four-page, paired-comparison questionnaire. According to the junior college basketball players, the availability of scholarships and the opportunity to play were the two most important factors in their sleection of a college. Six other factors that were considered important to the junior college players' selection of a college were parental influence, the head coach, degrees offered, high school coaches' influence, geographical location of the university, and the style of ball played. The availability of scholarships was the most important factor in Division I female basketball players' selection of a university. Five other factors considered to be significant by Division I female players were the opportunity to play, the geographical location of the university, the degrees offered, the university facilities, and the head coach. A high positive correlation was found between the rankings of the junior college and the Division I female basketball players.

With the increasing popularity and competition in professional basketball in the past decade, data driven decision has emerged as a big competitive edge. The advent of high frequency player tracking data from SportVU has enabled a rigorous analysis of player abilities and interactions that was not possible before. The tracking data records two-dimensional x-y coordinates of 10 players on the court as well as the x-y-z coordinates of the ball at a resolution of 25 frames per second, yielding over 1 billion space-time observations over the course of a full season. This dissertation offers a collection of spatio-temporal models and player evaluation metrics that provide insight into the player interactions and their performance, hence allowing the teams to make better decisions. Conventional approaches to simulate matches have ignored that in basketball the dynamics of ball movement is very sensitive to the lineups on the court and unique identities of players on both offense and defense sides. In chapter 2, we propose the simulation infrastructure that can bridge the gap between player identity and team level network. We model the progression of a basketball match using a probabilistic graphical model. We model every touch event in a game as a sequence of transitions between discrete states. We treat the progression of a match as a graph, where each node represents the network structure of players on the court, their actions, events, etc., and edges denote possible moves in the game flow. Our results show that either changes in the team lineup or changes in the opponent team lineup significantly affects the dynamics of a match progression. Evaluation on the match data for the 2013-16 NBA season suggests that the graphical model approach is appropriate for modeling a basketball match. NBA teams value players who can ``stretch'' the floor, i.e. create space on the court by drawing their defender(s) closer to themselves. Clearly, this ability to attract defenders varies across players, and furthermore, this effect may also vary by the court location of the offensive player, and whether or not the player is the ball handler. For instance, a ball-handler near the basket attracts a defender more when compared to a non ball-handler at the 3 point line. This has a significant effect on the defensive assignment. This is particularly important because defensive assignment has become the cornerstone of all tracking data based player evaluation models. In chapter 3, we propose a new model to learn player and court location specific offensive attraction. We show that offensive players indeed have varying ability to attract the defender in different parts of the court. Using this metric, teams can evaluate players to construct a roster or lineup which maximizes spacing. We also improve upon the existing defensive matchup inference algorithm for SportVU data. While the ultimate goal of the offense is to shoot the ball, the strategy lies in creating good shot opportunities. Offensive play event detection has been a topic of research interest. Current research in this area have used a supervised learning approach to detect and classify such events. We took an unsupervised learning approach to detect these events. This has two inherent benefits: first, there is no need for pretagged data to learn identifying these events which is a lobor intensive and error prone task; second, an unsupervised approach allows us to detect events that has not been tagged yet i.e. novel events. We use a HMM based approach to detect these events at any point in the time during a possession by specifying the functional form of the prior distribution on the player movement data. We test our framework on detecting ball screen, post up, and drive. However, it can be easily extended to events like isolation or a new event that has certain distinct defensive matchup or player movement feature compared to a non event. This is the topic for chapter 4. Accurate estimation of the offensive and the defensive abilities of players in the NBA plays a crucial role in player selection and ranking. A typical approach to estimate players' defensive and offensive abilities is to learn the defensive assignment for each shot and then use a random effects model to estimate the offensive and defensive abilities for each player. The scalar estimate from the random effects model can then be used to rank player. In this approach, a shot has a binary outcome, either it is made or it is a miss. This approach is not able to take advantage of the “quality” of the shot trajectory. In chapter 5, we propose a new method for ranking players that infers the quality of a shot trajectory using a deep recurrent neural network, and then uses this quality measure in a random effects model to rank players taking defensive matchup into account. We show that the quality information significantly improves the player ranking. We also show that including the quality of shots increases the separation between the learned random effect coefficients, and thus, allows for a better differentiation of player abilities. Further, we show that we are able to infer changes in the player's ability on a game-by-game basis when using a trajectory based model. A shot based model does not have enough information to detect changes in player's ability on a game-by-game basis. A good defensive player prevents its opponent from making a shot, attempting a good shot, making an easy pass, or scoring events, eventually leading to wasted shot clock time. The salient feature here is that a good defender prevents events. Consequently, event driven metrics, such as box scores, cannot measure defensive abilities. Conventional wisdom in basketball is that ``pesky'' defenders continuously maintain a close distance to the ball handler. A closely guarded offensive player is less likely to take or make a shot, less likely to pass, and more likely to lose the ball. In chapter 6, we introduce Defensive Efficiency Rating (DER), a new statistic that measures the defensive effectiveness of a player. DER is the effective distance a defender maintains with the ball handler during an interaction where we control for the identity and wingspan of the the defender, the shot efficiency of the ball handler, and the zone on the court. DER allows us to quantify the quality of defensive interaction without being limited by the occurrence of discrete and infrequent events like shots and rebounds. We show that the ranking from this statistic naturally picks out defenders known to perform well in particular zones.

xiii, 89 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Participation in athletics includes an inherent risk of becoming injured that is related to the nature of the games and activities of the players. Current literature reports that approximately seven million high school students participate in sports yearly in the United States and, during the 2005-2006 sport seasons, 1.4 million injuries were reported. Considering this high number of injuries, there is little doubt that definitive research into the determination of factors that might help predict the degree of injury risk associated with sport participation is warranted. Despite common association of variables such as joint laxity, range of motion, strength and balance with injury, these traditional measures have not proven to be reliable predictors of vulnerability. Consequently, attempts have been made to identify practical methods that may better permit identification of individuals who show a high likelihood of injury during athletic competition. This study examined one such system, the Functional Movement Screen (FMS), which utilizes measures of mobility and stability to permit its developers to assert that it can be used to practically and accurately identify vulnerable athletes. Critical data on inter-rater and intra-rater performance were first obtained on a team of athletic trainers to ensure that they could reliably execute the testing methods. Following confirmation of this fact, 112 high school basketball athletes were screened with the FMS and their injuries (non-contact neuromusculoskeletal tissue damage in school-sanctioned basketball) were tracked throughout an entire season. Data analysis to determine if a commonly-used FMS cutoff score of less than 14 out of 21 could identify vulnerable athletes revealed that this value was not significantly related to the likelihood of sustaining an injury. Furthermore, logistic regression revealed that none of the individual predictors (gender, FMS movements, and movement asymmetries) were significant predictors of injury susceptibility. The results indicate that, despite the fact that multiple evaluators and trials can be practically used to evaluate FMS scores in a large group of high school basketball athletes, the test does not appear to be a valid tool in assessing injury risk in this population during an entire season. / Committee in charge: Gary Klug, Chairperson, Human Physiology; Paul van Donkelaar, Member, Human Physiology; Andrew Lovering, Member, Human Physiology; Roland Good, Outside Member, Special Education and Clinical Sciences

published_or_final_version / Education / Master / Master of Education

Female intercollegiate basketball players experience a three times greater rate of ACL injuries than their male counterparts. This investigation hypothesized that women and men perform the cutting maneuver differently with respect to ground reaction forces and knee range of motion.The subjects performed a ninety degree cut on a force platform while being video taped. The male subjects exhibited a 23.1 percent greater (p=.0167) knee flexion angle than did the female subjects. The female subjects exhibited a 25.5 percent greater (p=.0022) braking force than did the male subjects. The impact maximum was not statistically significant (p=.3290).The performance characteristics exhibited by the female subjects in this study would suggest that the cutting maneuver is more dangerous for female basketball player. Female athletes should be taught correct performance of the cutting maneuver. I addition to technique, strength training should be encouraged to develop a stronger secondary stabilization mechanism for the knee. / School of Physical Education

published_or_final_version / Sociology / Master / Master of Philosophy

No description available.

Bibliography: leaves 92-95.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 482-495). Also available on the Internet.