scholarly journals Investigating the most important aspect of elite grass court tennis: Short points

2021 ◽  
pp. 174795412199959
Author(s):  
Anna Fitzpatrick ◽  
Joseph A Stone ◽  
Simon Choppin ◽  
John Kelley
Keyword(s):  
Level Data ◽  
Method Point ◽  
Point Level ◽  
Court Tennis

Research has shown that short points (points of 0–4 shots) are crucial in determining the outcome of elite men’s and women’s grass court tennis matches. However, research has not explored the importance of short points in more detail to inform practice design. This study aimed to establish the prevalence and importance of individual rally lengths within short points (i.e. points of 0, 1, 2, 3 and 4 shots) in terms of winning elite grass court tennis matches. Using the recently-validated PWOL ( Percentage of matches in which the Winner Outscored the Loser) method, point-level data from 211 men’s and 209 women’s Wimbledon singles matches between 2015 and 2017 were analysed, with short points stratified into individual rally lengths. Results revealed that 1 shot (aces and missed serve-returns) was the most common rally length, with 0 shots (double faults) the least common. Points won of 1 shot, 2 shots and 4 shots were associated with winning matches and can therefore be considered important, but points won of 0 shots and 3 shots were not associated with match outcome. These results highlight the importance of serving and returning strategies at Wimbledon, and indicate that serves and serve-returns should be afforded focus during grass court training. However, the findings appear to contravene anecdotal assertions that ‘serve plus one’ strategies ( points won of 3 shots) are crucial in elite tennis, as they did not differentiate winning and losing players; so coaches should consider the associated practice designs and amount of time afforded to such strategies.

Community-driven disorder reduction: Crime prevention through a clean and green initiative in a legacy city

Urban Studies ◽  
2020 ◽  
Vol 57 (14) ◽  
pp. 2956-2972
Author(s):  
Jesenia M. Pizarro ◽  
Richard C. Sadler ◽  
Jason Goldstick ◽  
Brandon Turchan ◽  
Edmund F. McGarrell ◽  
...  
Keyword(s):  
Crime Prevention ◽  
Prevention And Control ◽  
Temporal Trends ◽  
Additive Models ◽  
Level Data ◽  
Spatial Dependencies ◽  
Temporal And Spatial ◽  
And Control ◽  
Point Level ◽  
Over Time

This study examines the effects of a neighbourhood greening and beautification strategy called Clean & Green on crime prevention and reduction. Point level data for all Part I index crimes and Clean & Green efforts in the study area from 2005 to 2014 are analysed using spatial and linear regression with two key modifications: (1) controlling for temporal and spatial dependencies between points; and (2) allowing for potentially non-linear temporal trends in the effect of cumulative greening. To accommodate those modifications, generalised additive models (GAMs) were employed. The analyses of violent and property crimes suggest that greening efforts are increasingly protective over time. The findings demonstrate that the elimination of blight and disorder via neighbourhood greening and beautification efforts can be an effective tool for crime prevention and control in communities.

A Bayesian regression approach to handicapping tennis players based on a rating system

2018 ◽  
Vol 14 (3) ◽  
pp. 131-141
Author(s):  
Timothy C.Y. Chan ◽  
Raghav Singal
Keyword(s):  
Bayesian Regression ◽  
Rating Systems ◽  
Independent Point ◽  
Tennis Players ◽  
Level Data ◽  
Regression Approach ◽  
Markov Decision ◽  
The Difference ◽  
Point Level

Abstract This paper builds on a recently developed Markov Decision Process-based (MDP) handicap system for tennis, which aims to make amateur matches more competitive. The system gives points to the weaker player based on skill difference, which is measured by the point-win probability. However, estimating point-win probabilities at the amateur level is challenging since point-level data is generally only available at the professional level. On the other hand, tennis rating systems are widely used and provide an estimate of the difference in ability between players, but a rigorous determination of handicap using rating systems is lacking. Therefore, our goal is to develop a mapping between the Universal Tennis Rating (UTR) system and the MDP-based handicaps, so that two amateur players can determine an appropriate handicap for their match based only on their UTRs. We first develop and validate an approach to extract server-independent point-win probabilities from match scores. Then, we show how to map server-independent point-win probabilities to server-specific point-win probabilities. Finally, we use the estimated probabilities to produce handicaps via the MDP model, which are regressed against UTR differences between pairs of players. We conclude with thoughts on how a handicap system could be implemented in practice.

Connecting Point-Level and Gridded Moments in the Analysis of Climate Data*

2015 ◽  
Vol 28 (9) ◽  
pp. 3496-3510 ◽  
Author(s):  
Hannah Director ◽  
Luke Bornn
Keyword(s):  
Geographic Area ◽  
Effective Sample Size ◽  
Climate Data ◽  
Fundamental Properties ◽  
Level Data ◽  
The Mean ◽  
Mathematical Relationships ◽  
Grid Level ◽  
Point Level ◽  
Level Monitoring

Abstract The need to draw climate-related inferences from historical data makes understanding the biases and errors in these data critical. While climate data are collected at point-level monitoring sites, they are often postprocessed by averaging sites within a geographic area to align the data to a grid, easing analysis and visualization. Although this aggregation generally provides reasonable estimates of the mean, its use can be problematic for characterizing the full distribution of climate measures. Specifically, the process of averaging point-level data up to grid level can lead to inconsistencies, particularly when the grid box is heterogeneous and extremes are of interest. Point-level data are measured at individual points, while gridded data are the averaged product of many measurements within a larger spatial area. Because of this aggregation, point-level and grid-level distributions differ in many fundamental properties, such as their shape, skew, and tail behavior. This paper highlights these differences and their effects on analyses pertaining to current climatological questions. Mathematical relationships are derived to link the distributions of grid-level climate measures to the distributions of point-level climate measures using the notion of effective sample size. Then, these relationships are leveraged to propose a correction factor to use when modeling higher moments and extreme events.

scholarly journals A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE

2017 ◽  
Vol 21 ◽  
pp. 27-41 ◽  
Author(s):  
Paula Moraga ◽  
Susanna M. Cramb ◽  
Kerrie L. Mengersen ◽  
Marcello Pagano
Keyword(s):  
Geostatistical Model ◽  
Combined Analysis ◽  
Level Data ◽  
Point Level

Mapping the invisibles: Using non-conventional point-level data to analyse residential patterns of deprived people in a mid-sized city

Urban Studies ◽  
2020 ◽  
pp. 004209802091423
Author(s):  
Francesco Balducci
Keyword(s):  
Residential Segregation ◽  
Residential Patterns ◽  
Socially Disadvantaged ◽  
Level Data ◽  
Multidimensional Deprivation ◽  
Italian City ◽  
Micro Level ◽  
Official Sources ◽  
Point Level ◽  
Level Analysis

In urban geography it is a common practice to refer to censuses and other official sources of data to analyse residential segregation. However, there are limitations to those data, especially where particular groups of people are concerned. Often, official sources of data do not allow micro-level analysis of the characteristics, needs and residential patterns of socially disadvantaged residents. At the same time, measures based on income thresholds fail to fully take into account the complexity of multidimensional deprivation. This work uses the unconventional information coming from a voluntary organisation to investigate and understand the residential patterns of disadvantaged residents living in a mid-sized Italian city. The factors associated with the relative presence of deprived residents in city neighbourhoods are tested with a GLM/Poisson regression model. The results are differentiated among the sub-groups: the disadvantaged people pertaining to the Asian community are more residentially clustered than others. Their distribution, unlike that of other ethnic groups, is not significantly related to the economic characteristics at area level but to the presence of other Asian residents in an area.

New Evidence on the Construct Validity of the ASI-3 and the Dimensional Conceptualization of Trait Anxiety Sensitivity From IRT Modeling

2017 ◽  
Vol 33 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Christoph J. Kemper ◽  
Michael Hock
Keyword(s):  
Construct Validity ◽  
Anxiety Sensitivity ◽  
Latent Class ◽  
Dimensional Structure ◽  
Sensitivity Index ◽  
Response Scale ◽  
Established Risk Factor ◽  
Irt Models ◽  
Level Data ◽  
Extreme Response

Abstract. Anxiety Sensitivity (AS) denotes the tendency to fear anxiety-related sensations. Trait AS is an established risk factor for anxiety pathology. The Anxiety Sensitivity Index-3 (ASI-3) is a widely used measure of AS and its three most robust dimensions with well-established construct validity. At present, the dimensional conceptualization of AS, and thus, the construct validity of the ASI-3 is challenged. A latent class structure with two distinct and qualitatively different forms, an adaptive form (normative AS) and a maladaptive form (AS taxon, predisposing for anxiety pathology) was postulated. Item Response Theory (IRT) models were applied to item-level data of the ASI-3 in an attempt to replicate previous findings in a large nonclinical sample (N = 2,603) and to examine possible interpretations for the latent discontinuity observed. Two latent classes with a pattern of distinct responses to ASI-3 items were found. However, classes were indicative of participant’s differential use of the response scale (midpoint and extreme response style) rather than differing in AS content (adaptive and maladaptive AS forms). A dimensional structure of AS and the construct validity of the ASI-3 was supported.

Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 95-108 ◽  
Author(s):  
Steffen Nestler ◽  
Katharina Geukes ◽  
Mitja D. Back
Keyword(s):  
Multilevel Model ◽  
Multilevel Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Scale Model ◽  
Suggested Approach ◽  
Psychological Study ◽  
Level Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

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