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- Creators: Arizona State University
- Creators: Armbruster, Dieter
Description
The purpose of this study was to examine the local residents' perceptions toward the social impacts of FIBA EuroBasket 2011, held September 7th to September 12th, 2011 in Vilnius, Lithuania. This study focuses on the social impacts of the event since most of the mega-event academia is dominated by economic impact studies and this research attempts to go beyond a narrow quantitative approach to examine the social impacts in relation to two important academic contributions: 1) the embracement-withdrawal continuum and 2) the social exchange theory. A mixed methods analysis was adopted as the best approach for this research. Both a quantitative survey and qualitative questionnaire were used which yielded a total usable sample of 128 residents of Vilnius. Exploratory Factor Analysis was conducted to examine the underlying themes of the quantitative data and coding and thematic development was used to make sense of the qualitative data. The results confirmed that the event was considered successful (97% of residents embraced the event) and they generated 6 factors, or impacts, that were deemed important for influencing resident perceptions: social costs, social benefits, sociocultural impacts, socioeconomic impacts, sociopolitical impacts, and socioenvironmental impacts. These results indicate that the use of mixed methods analyses is crucial to investigate the interdependence between each impact that affects residents' perceptions toward sport mega-events.
ContributorsAntoniou, Alexis (Author) / Phillips, Rhonda (Thesis advisor) / Mean, Lindsey (Committee member) / Hultsman, Wendy (Committee member) / Arizona State University (Publisher)
Created2011
Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
Description
This paper intends to analyze the Phoenix Suns' shooting patterns in real NBA games, and compare them to the "NBA 2k16" Suns' shooting patterns. Data was collected from the first five Suns' games of the 2015-2016 season and the same games played in "NBA 2k16". The findings of this paper indicate that "NBA 2k16" utilizes statistical findings to model their gameplay. It was also determined that "NBA 2k16" modeled the shooting patterns of the Suns in the first five games of the 2015-2016 season very closely. Both, the real Suns' games and the "NBA 2k16" Suns' games, showed a higher probability of success for shots taken in the first eight seconds of the shot clock than the last eight seconds of the shot clock. Similarly, both game types illustrated a trend that the probability of success for a shot increases as a player holds onto a ball longer. This result was not expected for either game type, however, "NBA 2k16" modeled the findings consistent with real Suns' games. The video game modeled the Suns with significantly more passes per possession than the real Suns' games, while they also showed a trend that more passes per possession has a significant effect on the outcome of the shot. This trend was not present in the real Suns' games, however literature supports this finding. Also, "NBA 2k16" did not correctly model the allocation of team shots for each player, however, the differences were found only in bench players. Lastly, "NBA 2k16" did not correctly allocate shots across the seven regions for Eric Bledsoe, however, there was no evidence indicating that the game did not correctly model the allocation of shots for the other starters, as well as the probability of success across the regions.
ContributorsHarrington, John P. (Author) / Armbruster, Dieter (Thesis director) / Kamarianakis, Ioannis (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Analytic research on basketball games is growing quickly, specifically in the National Basketball Association. This paper explored the development of this analytic research and discovered that there has been a focus on individual player metrics and a dearth of quantitative team characterizations and evaluations. Consequently, this paper continued the exploratory research of Fewell and Armbruster's "Basketball teams as strategic networks" (2012), which modeled basketball teams as networks and used metrics to characterize team strategy in the NBA's 2010 playoffs. Individual players and outcomes were nodes and passes and actions were the links. This paper used data that was recorded from playoff games of the two 2012 NBA finalists: the Miami Heat and the Oklahoma City Thunder. The same metrics that Fewell and Armbruster used were explained, then calculated using this data. The offensive networks of these two teams during the playoffs were analyzed and interpreted by using other data and qualitative characterization of the teams' strategies; the paper found that the calculated metrics largely matched with our qualitative characterizations of the teams. The validity of the metrics in this paper and Fewell and Armbruster's paper was then discussed, and modeling basketball teams as multiple-order Markov chains rather than as networks was explored.
ContributorsMohanraj, Hariharan (Co-author) / Choi, David (Co-author) / Armbruster, Dieter (Thesis director) / Fewell, Jennifer (Committee member) / Brooks, Daniel (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2013-05
Description
In sports, athletes reach new levels every day and are truly masters of their own bodies. Yet, when placed under pressure, the pin-point accuracy and elite level of performance can begin to wane. Despite plentiful literature investigating the effects of pressure on performance, the underlying mechanisms behind decreased performance in sport are not yet clear. The current research discusses possible theories for “choking under pressure”, the specific mechanisms through which pressure has its effects, and methods to prevent “choking.” Fourteen current and former basketball players shot free throws with two primary predictor variables: the presence/absence of performance pressure and the restriction
on-restriction of movement during the pre-shot routine. Results were analyzed using 2x2 Within-Subjects Analysis of Variance. For shooting performance, there was an interaction (approaching significance) such that participants were more affected by pressure when allowed to execute their pre-shot routine. For kinematic variables, significant interactions between pressure and movement restriction were found for elbow-knee cross correlations and there were significant main effects of variability of the acceleration of both the elbow and knee angles. In all kinematic measures, participants exhibited more “novice-like” patterns of movement under pressure when movement was not restricted during the pre-shot routine. Primary results indicate promising evidence that motor control may be a mediating variable between pressure and performance and bring into question the value of a pre-shot routine in basketball.
on-restriction of movement during the pre-shot routine. Results were analyzed using 2x2 Within-Subjects Analysis of Variance. For shooting performance, there was an interaction (approaching significance) such that participants were more affected by pressure when allowed to execute their pre-shot routine. For kinematic variables, significant interactions between pressure and movement restriction were found for elbow-knee cross correlations and there were significant main effects of variability of the acceleration of both the elbow and knee angles. In all kinematic measures, participants exhibited more “novice-like” patterns of movement under pressure when movement was not restricted during the pre-shot routine. Primary results indicate promising evidence that motor control may be a mediating variable between pressure and performance and bring into question the value of a pre-shot routine in basketball.
ContributorsOrn, Anders (Author) / Gray, Robert (Thesis advisor) / Branaghan, Russell (Thesis advisor) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2017
Description
This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize a decent basketball shot pattern? - by introducing a supervised learning paradigm, where the ML method takes acceleration attributes to predict the basketball shot efficiency. The solution presented in this study considers motion capture devices configuration on the right upper limb with a sole motion sensor made by BNO080 and ESP32 attached on the right wrist, right forearm, and right shoulder, respectively, By observing the rate of speed changing in the shooting movement and comparing their performance, ML models that apply K-Nearest Neighbor, and Decision Tree algorithm, conclude the best range of acceleration that different spots on the arm should implement.
ContributorsLiang, Chengxu (Author) / Ingalls, Todd (Thesis advisor) / Turaga, Pavan (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2023
Description
We attempt to analyze the effect of fatigue on free throw efficiency in the National Basketball Association (NBA) using play-by-play data from regular-season, regulation-length games in the 2016-2017, 2017-2018, and 2018-2019 seasons. Using both regression and tree-based statistical methods, we analyze the relationship between minutes played total and minutes played continuously at the time of free throw attempts on players' odds of making an attempt, while controlling for prior free throw shooting ability, longer-term fatigue, and other game factors. Our results offer strong evidence that short-term activity after periods of inactivity positively affects free throw efficiency, while longer-term fatigue has no effect.
ContributorsRisch, Oliver (Author) / Armbruster, Dieter (Thesis director) / Hahn, P. Richard (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05