This study demonstrates an effective and innovative approach to measuring the latent athletic abilities and capacities of professional golfers. I used nonlinear mixed effects growth modeling (e.g., Dynamic Measurement Modeling) to measure professional golfers’ ability levels and capacities for improvement. I accomplished this using a two-stage modeling approach. First, a crossed linear mixed effects model estimated each player’s ability level in each year. In the second stage, I used the results from the first stage to estimate several candidate nonlinear growth trajectories for players’ abilities over time. The quadratic growth trajectory was the best-fitting of these trajectories and was used to estimate each player’s individual-level capacity (maximum predicted ability). Validation results indicate that the ability estimates from stage one can outperform existing unidimensional measures of golfing ability and that the capacity estimates from the second stage are reliable and provide better forecasts of players’ future abilities than do single-timepoint estimates. This study demonstrates the applicability of latent variable statistics and longitudinal growth models to the study of sports, provides a novel statistical method to estimate player abilities and capacities in professional golf, and provides a tutorial for estimating Dynamic Measurement Models (DMM) using R.
","abstract_html":"<p>This study demonstrates an effective and innovative approach to measuring the latent athletic abilities and capacities of professional golfers. I used nonlinear mixed effects growth modeling (e.g., Dynamic Measurement Modeling) to measure professional golfers’ ability levels and capacities for improvement. I accomplished this using a two-stage modeling approach. First, a crossed linear mixed effects model estimated each player’s ability level in each year. In the second stage, I used the results from the first stage to estimate several candidate nonlinear growth trajectories for players’ abilities over time. The quadratic growth trajectory was the best-fitting of these trajectories and was used to estimate each player’s individual-level capacity (maximum predicted ability). Validation results indicate that the ability estimates from stage one can outperform existing unidimensional measures of golfing ability and that the capacity estimates from the second stage are reliable and provide better forecasts of players’ future abilities than do single-timepoint estimates. This study demonstrates the applicability of latent variable statistics and longitudinal growth models to the study of sports, provides a novel statistical method to estimate player abilities and capacities in professional golf, and provides a tutorial for estimating Dynamic Measurement Models (DMM) using <em>R</em>.</p>","abstract_has_math":false,"creators":["Wetherbee, Mac"],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Yixiao Dong","Nick Cutforth","Mei Yin","Cecilia Orphan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-06-15T07:00:00Z","date_published":"2024-06-15T07:00:00Z","updated_at":"2026-07-24T02:01:48Z","subjects":["Dynamic measurement modeling (DMM)","Golf science","Golf statistics","Growth modeling","Mixed effects modeling","Applied Mathematics","Applied Statistics","Educational Assessment, Evaluation, and Research","Other Applied Mathematics","Physical Sciences and Mathematics","Sports Studies","Statistical Models","Statistics and Probability"],"languages":["English (eng)"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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