A study of forecasting tennis matches via the Glicko model.

Tennis is a popular sport, and professional tennis matches are probably the most watched games globally. Many studies consider statistical or machine learning models to predict the results of professional tennis matches. In this study, we propose a statistical approach for predicting the match outcomes of Grand Slam tournaments, in addition to applying exploratory data analysis (EDA) to explore variables related to match results. The proposed approach introduces new variables via the Glicko rating model, a Bayesian method commonly used in professional chess. We use EDA tools to determine important variables and apply classification models (e.g., logistic regression, support vector machine, neural network and light gradient boosting machine) to evaluate the classification results through cross-validation. The empirical study is based on men's and women's single matches of Grand Slam tournaments (2000-2019). Our analysis results show that professional tennis ranking is the most important variable and that the accuracy of the proposed Glicko model is slightly higher than that of other models.

A modification to geographically weighted regression.

BACKGROUND: Geographically weighted regression (GWR) is a modelling technique designed to deal with spatial non-stationarity, e.g., the mean values vary by locations. It has been widely used as a visualization tool to explore the patterns of spatial data. However, the GWR tends to produce unsmooth surfaces when the mean parameters have considerable variations, partly due to that all parameter estimates are derived from a fixed- range (bandwidth) of observations. In order to deal with the varying bandwidth problem, this paper proposes an alternative approach, namely Conditional geographically weighted regression (CGWR). METHODS: The estimation of CGWR is based on an iterative procedure, analogy to the numerical optimization problem. Computer simulation, under realistic settings, is used to compare the performance between the traditional GWR, CGWR, and a local linear modification of GWR. Furthermore, this study also applies the CGWR to two empirical datasets for evaluating the model performance. The first dataset consists of disability status of Taiwan's elderly, along with some social-economic variables and the other is Ohio's crime dataset. RESULTS: Under the positively correlated scenario, we found that the CGWR produces a better fit for the response surface. Both the computer simulation and empirical analysis support the proposed approach since it significantly reduces the bias and variance of data fitting. In addition, the response surface from the CGWR reviews local spatial characteristics according to the corresponded variables. CONCLUSIONS: As an explanatory tool for spatial data, producing accurate surface is essential in order to provide a first look at the data. Any distorted outcomes would likely mislead the following analysis. Since the CGWR can generate more accurate surface, it is more appropriate to use it exploring data that contain suspicious variables with varying characteristics.

Predicting anesthesia times for diagnostic and interventional radiological procedures.

We studied anesthesia times for diagnostic and interventional radiology using anesthesia billing data and paper radiology logbooks. For computerized tomography and magnetic resonance imaging procedures, we tried to predict future anesthesia times by using historical anesthesia times classified by Current Procedural Terminology (CPT) codes. By this method, anesthesia times were estimated even less accurately than operating room cases. Computerized tomography and magnetic resonance imaging had many different CPT codes, most rare, and CPT codes reflected organs imaged, not scanning times. However, when, anesthesia times were estimated by expert judgment, face validity and accuracy were good. Lower and upper prediction bounds were also estimated from the expert estimates. For interventional radiology, predicting anesthesia times was challenging because few CPT codes accounted for most cases. Because interventional radiologists scheduled their elective cases into allocated time, the necessary goal was not to estimate the time to complete each case but rather the time to complete each day's entire series of elective cases including turnover times. We determined the time of day (e.g., 4 pm) up to when interventional radiology could schedule so that on 80% of days the anesthesia team finishes no later than a specified time (e.g., 6 pm). Both diagnostic and interventional radiology results were similarly less accurate when Version 9 of the International Classifications of Diseases' procedure codes was used instead of CPT.

Use of discharge abstract databases to differentiate among pediatric hospitals based on operative procedures: surgery in infants and young children in the state of Iowa.

INTRODUCTION: A pediatric hospital may aim to show governmental agencies, charitable organizations, and philanthropic individuals how its clinical services differ from those of nonpediatric surgical facilities and of other pediatric hospitals. Yet, it is unknown how to use existing databases to quantify where infants and young children undergo surgery, and to use that information to differentiate among facilities. METHODS: Discharge abstracts were used to study inpatient and outpatient operative procedures performed between January and June 2001 in children 0-2 yr old at hospitals or hospital-affiliated outpatient surgery centers in Iowa. RESULTS: Of the 93 facilities performing at least one procedure, the 90 performing 15 or fewer different types of procedures provided surgical care for 80% of procedures. Among procedures performed at these 90 facilities, less than 0.15% were physiologically complex (more than seven American Society of Anesthesiologists' basic units). In contrast, at the larger and smaller pediatric hospitals, the percentages were 26% and 7%, respectively. These pediatric hospitals performed 181 and 73 different types of procedures, respectively; 64% of the physiologically complex procedures performed statewide were performed at the larger pediatric hospital. The smaller pediatric hospital was no more similar to the larger pediatric hospital in its relative volumes of each type of procedure than it was to the other 91 facilities. CONCLUSIONS: Statewide discharge abstract data can be used by a hospital to quantify how its surgical practice differs from that of other hospitals (e.g., to show that it provides a more diverse, comprehensive, and physiologically complex selection of procedures in younger patients).