A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics

Numerous statistics have been proposed for the measure of offensive ability in major league baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue with a Bayesian hierarchical model for variable selection to capture which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of 50 different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that 33/50 metrics demonstrate signal. However, these metrics are highly correlated with one another and related to traditional notions of performance (e.g., plate discipline, power, and ability to make contact)

Estimating Fielding Ability in Baseball Players Over Time

Quantitative evaluation of fielding ability in baseball has been an ongoing challenge for statisticians. Detailed recording of ball-in-play data in recent years has spurred the development of sophisticated fielding models. Foremost among these approaches, Jensen et al. (2009) used a hierarchical Bayesian model to estimate spatial fielding curves for individual players. These previous efforts have not addressed evolution in a player’s fielding ability over time. We expand the work of Jensen et al. (2009) to model the fielding ability of individual players over multiple seasons. Several different models are implemented and compared via posterior predictive validation on hold-out data. Among our choices, we find that a model which imposes shrinkage towards an age-specific average gives the best performance. Our temporal models allow us to delineate the performance of a fielder on a season-to-season basis versus their entire career

Association between depression and concurrent Type 2 diabetes outcomes varies by diabetes regimen

Aims  Although depression has weak associations with several Type 2 diabetes mellitus (DM) outcomes, it is possible that these associations are concentrated within certain patient subgroups that are more vulnerable to their effects. This study tested the hypothesis that depression is related to glycaemic control and diabetes-related quality of life (DQOL) in patients who are prescribed injected insulin, but not those on oral glucose-lowering agents alone. Methods  Participants (103 on insulin, 155 on oral glucose-lowering agents alone) with Type 2 DM were recruited from a large US healthcare system and underwent assessment of glycaemic control (glycated haemoglobin; HbA 1c ), medication adherence and diabetes self-care behaviours, DQOL and depression (none, mild, moderate/severe). Results  There was a significant regimen × depression interaction on HbA 1c ( P  = 0.002), such that depression was associated with HbA 1c in patients using insulin (β = 0.35, P  < 0.001) but not in patients using oral agents alone (β = –0.08, P  = NS). There was a similar interaction when quality of life was analysed as an outcome ( P  = 0.002). Neither effect was mediated by regimen adherence. Conclusions  The generally weak association between depression and glycaemic control is concentrated among patients who are prescribed insulin. Similarly, the association between depression and illness quality of life is strongest in patients prescribed insulin. Because this is not attributable to depression-related adherence problems, psychophysiological mechanisms unique to this group ought to be carefully investigated. Clinicians might be especially vigilant for depression in Type 2 DM patients who use insulin and consider its potential impact upon their illness course. Diabet. Med. 25, 1324–1329 (2008)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73538/1/j.1464-5491.2008.02590.x.pd

A Point-Mass Mixture Random Effects Model for Pitching Metrics

A plethora of statistics have been proposed to measure the effectiveness of pitchers in Major League Baseball. While many of these are quite traditional (e.g., ERA, wins), some have gained currency only recently (e.g., WHIP, K/BB). Some of these metrics may have predictive power, but it is unclear which are the most reliable or consistent. We address this question by constructing a Bayesian random effects model that incorporates a point mass mixture and fitting it to data on twenty metrics spanning approximately 2,500 players and 35 years. Our model identifies FIP, HR/9, ERA, and BB/9 as the highest signal metrics for starters and GB%, FB%, and K/9 as the highest signal metrics for relievers. In general, the metrics identified by our model are independent of team defense. Our procedure also provides a relative ranking of metrics separately by starters and relievers and shows that these rankings differ quite substantially between them. Our methodology is compared to a Lasso-based procedure and is internally validated by detailed case studies

A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics

Numerous statistics have been proposed to measure offensive ability in Major League Baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue by using a hierarchical Bayesian variable selection model to determine which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of fifty different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that a large number of metrics demonstrate signal. However, these metrics are (i) highly correlated with one another, (ii) can be reduced to about five without much loss of information, and (iii) these five relate to traditional notions of performance (e.g., plate discipline, power, and ability to make contact)

A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics

Numerous statistics have been proposed to measure offensive ability in Major League Baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue by using a hierarchical Bayesian variable selection model to determine which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of fifty different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that a large number of metrics demonstrate signal. However, these metrics are (i) highly correlated with one another, (ii) can be reduced to about five without much loss of information, and (iii) these five relate to traditional notions of performance (e.g., plate discipline, power, and ability to make contact)

Feasibility of an interactive voice response system for monitoring depressive symptoms in a lower-middle income Latin American country

Abstract Background Innovative, scalable solutions are needed to address the vast unmet need for mental health care in low- and middle-income countries (LMICs). Methods We conducted a feasibility study of a 14-week automated telephonic interactive voice response (IVR) depression self-care service among Bolivian primary care patients with at least moderately severe depressive symptoms. We analyzed IVR call completion rates, the reliability and validity of IVR-collected data, and participant satisfaction. Results Of the 32 participants, the majority were women (78 % or 25/32) and non-indigenous (75 % or 24/32). Participants had moderate depressive symptoms at baseline (PHQ-8 score mean 13.3, SD = 3.5) and reported good or fair general health status (88 % or 28/32). Fifty-four percent of weekly IVR calls (approximately 7 out of 13 active call-weeks) were completed. Neither PHQ-8 scores nor IVR call completion differed significantly by ethnicity, education, self-reported depression diagnosis, self-reported overall health, number of chronic conditions, or health literacy. The reliability for IVR-collected PHQ-8 scores was good (Cronbach’s alpha = 0.83). Virtually every participant (97 %) was “mostly” or “very” satisfied with the program. Many described the program as beneficial for their mood and self-care, albeit limited by some technological difficulties and the lack of human interaction. Conclusion Findings suggest that IVR could feasibly be used to provide monitoring and self-care education to depressed patients in Bolivia. An expanded stepped-care service offering contact with lay health workers for more depressed individuals and expanded mHealth content may foster greater patient engagement and enhance its therapeutic value while remaining cost-effective. Trial registration ISRCTN ISRCTN 18403214. Registered 14 September 2016. Retrospectively registeredhttp://deepblue.lib.umich.edu/bitstream/2027.42/134641/1/13033_2016_Article_93.pd

Racial Discrimination in Health Care Is Associated with Worse Glycemic Control among Black Men but Not Black Women with Type 2 Diabetes

BackgroundA growing body of research suggests that racial discrimination may affect the health of Black men and Black women differently.AimsThis study examined Black patients with diabetes mellitus (DM) in order to test gender differences in (1) levels of perceived racial discrimination in health care and (2) how perceived discrimination relates to glycemic control.MethodsA total of 163 Black patients with type 2 DM (78 women and 85 men) provided data on demographics (age and gender), socioeconomic status, perceived racial discrimination in health care, self-rated health, and hemoglobin A1c (HbA1c). Data were analyzed using linear regression.ResultsBlack men reported more racial discrimination in health care than Black women. Although racial discrimination in health care was not significantly associated with HbA1c in the pooled sample (b = 0.20, 95% CI = −0.41 −0.80), gender-stratified analysis indicated an association between perceived discrimination and higher HbA1c levels for Black men (b = 0.86, 95% confidence intervals (CI) = 0.01–1.73) but not Black women (b = −0.31, 95% CI = −1.17 to −0.54).ConclusionPerceived racial discrimination in diabetes care may be more salient for glycemic control of Black men than Black women. Scholars and clinicians should take gender into account when considering the impacts of race-related discrimination experiences on health outcomes. Policies should reduce racial discrimination in the health care