Large data and Bayesian modeling—aging curves of NBA players
Researchers interested in changes that occur as people age are faced with a number of methodological problems, starting with the immense time scale they are trying to capture, which renders laboratory experiments useless and longitudinal studies rather rare. Fortunately, some people take part in par...
| Main Authors: | , , , |
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| 格式: | Journal article |
| 語言: | English |
| 出版: |
Springer
2019
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| _version_ | 1826302571288985600 |
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| author | Vaci, N Cocić, D Gula, B Bilalić, M |
| author_facet | Vaci, N Cocić, D Gula, B Bilalić, M |
| author_sort | Vaci, N |
| collection | OXFORD |
| description | Researchers interested in changes that occur as people age are faced with a number of methodological problems, starting with the immense time scale they are trying to capture, which renders laboratory experiments useless and longitudinal studies rather rare. Fortunately, some people take part in particular activities and pastimes throughout their lives, and often these activities are systematically recorded. In this study, we use the wealth of data collected by the National Basketball Association to describe the aging curves of elite basketball players. We have developed a new approach rooted in the Bayesian tradition in order to understand the factors behind the development and deterioration of a complex motor skill. The new model uses Bayesian structural modeling to extract two latent factors, those of development and aging. The interaction of these factors provides insight into the rates of development and deterioration of skill over the course of a player’s life. We show, for example, that elite athletes have different levels of decline in the later stages of their career, which is dependent on their skill acquisition phase. The model goes beyond description of the aging function, in that it can accommodate the aging curves of subgroups (e.g., different positions played in the game), as well as other relevant factors (e.g., the number of minutes on court per game) that might play a role in skill changes. The flexibility and general nature of the new model make it a perfect candidate for use across different domains in lifespan psychology. |
| first_indexed | 2024-03-07T05:49:36Z |
| format | Journal article |
| id | oxford-uuid:e86a19d4-c9c5-414b-bd70-a12d146a4874 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:49:36Z |
| publishDate | 2019 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:e86a19d4-c9c5-414b-bd70-a12d146a48742022-03-27T10:46:25ZLarge data and Bayesian modeling—aging curves of NBA playersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e86a19d4-c9c5-414b-bd70-a12d146a4874EnglishSymplectic Elements at OxfordSpringer2019Vaci, NCocić, DGula, BBilalić, MResearchers interested in changes that occur as people age are faced with a number of methodological problems, starting with the immense time scale they are trying to capture, which renders laboratory experiments useless and longitudinal studies rather rare. Fortunately, some people take part in particular activities and pastimes throughout their lives, and often these activities are systematically recorded. In this study, we use the wealth of data collected by the National Basketball Association to describe the aging curves of elite basketball players. We have developed a new approach rooted in the Bayesian tradition in order to understand the factors behind the development and deterioration of a complex motor skill. The new model uses Bayesian structural modeling to extract two latent factors, those of development and aging. The interaction of these factors provides insight into the rates of development and deterioration of skill over the course of a player’s life. We show, for example, that elite athletes have different levels of decline in the later stages of their career, which is dependent on their skill acquisition phase. The model goes beyond description of the aging function, in that it can accommodate the aging curves of subgroups (e.g., different positions played in the game), as well as other relevant factors (e.g., the number of minutes on court per game) that might play a role in skill changes. The flexibility and general nature of the new model make it a perfect candidate for use across different domains in lifespan psychology. |
| spellingShingle | Vaci, N Cocić, D Gula, B Bilalić, M Large data and Bayesian modeling—aging curves of NBA players |
| title | Large data and Bayesian modeling—aging curves of NBA players |
| title_full | Large data and Bayesian modeling—aging curves of NBA players |
| title_fullStr | Large data and Bayesian modeling—aging curves of NBA players |
| title_full_unstemmed | Large data and Bayesian modeling—aging curves of NBA players |
| title_short | Large data and Bayesian modeling—aging curves of NBA players |
| title_sort | large data and bayesian modeling aging curves of nba players |
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