scholarly journals O032 The influence of travel and recovery inequality on game outcome in the National Basketball Association

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A14-A14
Author(s):  
J Leota ◽  
D Hoffman ◽  
L Mascaro ◽  
E Facer-Childs
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Elite Athletes ◽  
National Basketball Association ◽  
Circadian Misalignment ◽  
Home Team ◽  
Team Success ◽  
Game Outcome ◽  
Time Zones

Abstract Introduction Elite athletes are often required to travel for National and International competitions. However, the direction (westwards or eastwards), time zones crossed, and recovery days relative to their opponents may influence team success. The aim of this study was to determine whether differences in jetlag-induced circadian misalignment and number of recovery days between National Basketball Association (NBA) teams influenced the subsequent game result. Methods A total of 11,598 games from the 2011/2012 to the 2020/2021 seasons were analysed using mixed models with two fixed effects (travel, recovery) and three random effects (team, opponent, game time). Mediation modelling was also performed to determine if any influence of the fixed effects were mediated by another variable. The data is presented from the home team’s perspective. Results Teams with more recovery days between games, won by larger margins (F = 5.0, p < 0.001). Compared to one fewer recovery day (1.45 ± 13.92), one more recovery day (3.53 ± 13.51) advantaged the home team by 2.08 points (d = 0.15). The effect of travel on greater home team margins was completely mediated via recovery day differences (95% CI -0.11 to -0.03, p = 0.002). Discussion Using 10 seasons of data, our findings show that regardless of travel, recovery days between games significantly impact game margins. An advantage in recovery days should be considered for teams who travel more time zones westwards relative to their opponent. This suggests inequalities of the NBA schedule may be minimised for future seasons.

scholarly journals P075 Travel or cheers? Examining the drivers and mechanisms of home court advantage in the 2020/2021 NBA regular season

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A45-A45
Author(s):  
J Leota ◽  
D Hoffman ◽  
L Mascaro ◽  
M Czeisler ◽  
K Nash ◽  
...  
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Natural Experiment ◽  
National Basketball Association ◽  
Home Team ◽  
The Road ◽  
On The Road ◽  
The Impact ◽  
The Relationship

Abstract Introduction Home court advantage (HCA) in the National Basketball Association (NBA) is well-documented, yet the co-occurring drivers responsible for this advantage have proven difficult to examine in isolation. The Coronavirus disease (COVID-19) pandemic resulted in the elimination of crowds in ~50% of games during the 2020/2021 NBA season, whereas travel remained unchanged. Using this ‘natural experiment’, we investigated the impact of crowds and travel-related sleep and circadian disruption on NBA HCA. Methods 1080 games from the 2020/2021 NBA regular season were analyzed using mixed models (fixed effects: crowds, travel; random effects: team, opponent). Results In games with crowds, home teams won 58.65% of the time and outrebounded (M=2.28) and outscored (M=2.18) their opponents. In games without crowds, home teams won significantly less (50.60%, p = .01) and were outrebounded (M=-0.41, p < .001) and outscored (M=-0.13, p < .05) by their opponents. Further, the increase in home rebound margin fully mediated the relationship between crowds and home points margin (p < .001). No significant sleep or circadian effects were observed. Discussion Taken together, these results suggest that HCA in the 2020/2021 NBA season was predominately driven by the presence of crowds and their influence on the effort exerted by the home team to rebound the ball. Moreover, we speculate that the strict NBA COVID-19 policies may have mitigated the travel-related sleep and circadian effects on the road team. These findings are of considerable significance to a domain wherein marginal gains can have immense competitive, financial, and even historical consequences.

scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

scholarly journals Within- and Between-cluster Effects in Generalized Linear Mixed Models: A Discussion of Approaches and the Xthybrid command

2017 ◽  
Vol 17 (1) ◽  
pp. 89-115 ◽  
Author(s):  
Reinhard Schunck ◽  
Francisco Perales
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Negative Binomial ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Consistent Estimation ◽  
Random Effects Models ◽  
Fixed Effects Models ◽  
Correlated Random Effects

One typically analyzes clustered data using random- or fixed-effects models. Fixed-effects models allow consistent estimation of the effects of level-one variables, even if there is unobserved heterogeneity at level two. However, these models cannot estimate the effects of level-two variables. Hybrid and correlated random-effects models are flexible modeling specifications that separate within-and between-cluster effects and allow for both consistent estimation of level-one effects and inclusion of level-two variables. In this article, we elaborate on the separation of within- and between-cluster effects in generalized linear mixed models. These models present a unifying framework for an entire class of models whose response variables follow a distribution from the exponential family (for example, linear, logit, probit, ordered probit and logit, Poisson, and negative binomial models). We introduce the user-written command xthybrid, a shell for the meglm command. xthybrid can fit a variety of hybrid and correlated random-effects models.

Meta-Analysis

1992 ◽  
Vol 17 (4) ◽  
pp. 279-296 ◽  
Author(s):  
Larry V. Hedges
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Fixed And Random Effects ◽  
Mean And Variance ◽  
The Mean ◽  
Combining Estimates ◽  
Effect Magnitude

The use of statistical methods to combine the results of independent empirical research studies (meta-analysis) has a long history. Meta-analytic work can be divided into two traditions: tests of the statistical significance of combined results and methods for combining estimates across studies. The principal classes of combined significance tests are reviewed, and the limitations of these tests are discussed. Fixed effects approaches treat the effect magnitude parameters to be estimated as a consequence of a model involving fixed but unknown constants. Random effects approaches treat effect magnitude parameters as if they were sampled from a universe of effects and attempt to estimate the mean and variance of the hyperpopulation of effects. Mixed models incorporate both fixed and random effects. Finally, areas of current research are summarized, including methods for handling missing data, models for publication selection, models to handle studies that are not independent, and distribution-free models for random effects.

scholarly journals Maybe maximal: Good enough mixed models optimize power while controlling Type I error

2019 ◽  
Author(s):  
Michael Seedorff ◽  
Jacob Oleson ◽  
Bob McMurray
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Degrees Of Freedom ◽  
Linear Models ◽  
Type I Error ◽  
Logistic Models ◽  
Information Criteria ◽  
Type I

Mixed effects models have become a critical tool in all areas of psychology and allied fields. This is due to their ability to account for multiple random factors, and their ability to handle proportional data in repeated measures designs. While substantial research has addressed how to structure fixed effects in such models there is less understanding of appropriate random effects structures. Recent work with linear models suggests the choice of random effects structures affects Type I error in such models (Barr, Levy, Scheepers, & Tily, 2013; Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2017). This has not been examined for between subject effects, which are crucial for many areas of psychology, nor has this been examined in logistic models. Moreover, mixed models expose a number of researcher degrees of freedom: the decision to aggregate data or not, the manner in which degrees of freedom are computed, and what to do when models do not converge. However, the implications of these choices for power and Type I error are not well known. To address these issues, we conducted a series of Monte Carlo simulations which examined linear and logistic models in a mixed design with crossed random effects. These suggest that a consideration of the entire space of possible models using simple information criteria such as AIC leads to optimal power while holding Type I error constant. They also suggest data aggregation and the d.f, computation have minimal effects on Type I Error and Power, and they suggest appropriate approaches for dealing with non-convergence.

scholarly journals Benefits of Bayesian Model Selection and Averaging for Mixed-Effects Modeling

2021 ◽  
Author(s):  
Daniel W. Heck ◽  
Florence Bockting
Keyword(s):  
Model Selection ◽  
Random Effects ◽  
Mixed Models ◽  
Bayesian Model ◽  
Fixed Effects ◽  
Bayes Factor ◽  
Mixed Effects ◽  
Bayes Factors ◽  
Mixed Effects Models ◽  
Within Subjects

Bayes factors allow researchers to test the effects of experimental manipulations in within-subjects designs using mixed-effects models. van Doorn et al. (2021) showed that such hypothesis tests can be performed by comparing different pairs of models which vary in the specification of the fixed- and random-effect structure for the within-subjects factor. To discuss the question of which of these model comparisons is most appropriate, van Doorn et al. used a case study to compare the corresponding Bayes factors. We argue that researchers should not only focus on pairwise comparisons of two nested models but rather use the Bayes factor for performing model selection among a larger set of mixed models that represent different auxiliary assumptions. In a standard one-factorial, repeated-measures design, the comparison should include four mixed-effects models: fixed-effects H0, fixed-effects H1, random-effects H0, and random-effects H1. Thereby, the Bayes factor enables testing both the average effect of condition and the heterogeneity of effect sizes across individuals. Bayesian model averaging provides an inclusion Bayes factor which quantifies the evidence for or against the presence of an effect of condition while taking model-selection uncertainty about the heterogeneity of individual effects into account. We present a simulation study showing that model selection among a larger set of mixed models performs well in recovering the true, data-generating model.

scholarly journals Visualizing the Impact of Home Advantage in National Basketball Association-NBA

2021 ◽  
Vol 08 (03) ◽  
pp. 09-17
Author(s):  
Ekas Singh Abrol ◽  
Puneet Singh Lamba ◽  
Achin Jain
Keyword(s):  
National Basketball Association ◽  
Jet Lag ◽  
Cognitive Effects ◽  
Professional Basketball ◽  
Home Team ◽  
Time Zones ◽  
Home Advantage ◽  
Exploratory Data ◽  
Visualization Techniques ◽  
The Impact

In sports, home advantage describes the benefits that the home team enjoys over the away team. These benefits are manifested due to cognitive effects that the local home crowd may have over the competitors or umpires, advantages of playing in familiar situations resulting in better adaptability, specific rules favouring the home team directly or indirectly, the away teams often suffer from jet lag due to change in time zones or from the tenacity of travel, etc. In this paper, various exploratory data visualization techniques have been utilized to observe the impact of home advantage in professional basketball association league NBA- National Basketball Association. Further the factors attributing to home advantage in sports are analysed. It was observed that when the team had performed well at home games, the results reflected the same for away games; however, home advantage was still distinctively visible.

scholarly journals The Limits of Marginality

2021 ◽  
Author(s):  
Andrew Heathcote ◽  
Dora Matzke
Keyword(s):  
Model Selection ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Order Term ◽  
Test Point ◽  
Linear Regression Models ◽  
Random Effects Models ◽  
Target Article ◽  
Fixed And Random Effects

AbstractThe “marginality principle” for linear regression models states that when a higher order term is included, its constituent terms must also be included. The target article relies on this principle for the fixed-effects part of linear mixed models of ANOVA designs and considers the implication that if extended to combined fixed-and-random-effects models, model selection tests specific to some fixed-effects ANOVA terms are not possible. We review the basis for this principle for fixed-effects models and delineate its limits. We then consider its extension to combined fixed-and-random-effects models. We conclude that we have been unable to find in the literature, including the target article, and have ourselves been unable to construct any satisfactory argument against the use of incomplete ANOVA models. The only basis we could find requires one to assume that it is not possible to test point-null hypotheses, something we disagree with, and which we believe is incompatible with the Bayesian model-selection methods that are the basis of the target article.

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