Factors Affecting the Winning Percentages of Division III Football Teams

We study factors affecting the winning percentages of Division III football teams. Using data from the NCAA for the 2014 through 2016 seasons, we find that both offensive and defensive outcomes equally affect winning percentages. Our results suggest that when it comes to winning, there is no statistically significant difference between the impact of having a more prolific offense or having a solid defense

An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6

We document the data transfer workflow, data transfer performance, and other aspects of staging approximately 56 terabytes of climate model output data from the distributed Coupled Model Intercomparison Project (CMIP5) archive to the National Energy Research Supercomputing Center (NERSC) at the Lawrence Berkeley National Laboratory required for tracking and characterizing extratropical storms, a phenomena of importance in the mid-latitudes. We present this analysis to illustrate the current challenges in assembling multi-model data sets at major computing facilities for large-scale studies of CMIP5 data. Because of the larger archive size of the upcoming CMIP6 phase of model intercomparison, we expect such data transfers to become of increasing importance, and perhaps of routine necessity. We find that data transfer rates using the ESGF are often slower than what is typically available to US residences and that there is significant room for improvement in the data transfer capabilities of the ESGF portal and data centers both in terms of workflow mechanics and in data transfer performance. We believe performance improvements of at least an order of magnitude are within technical reach using current best practices, as illustrated by the performance we achieved in transferring the complete raw data set between two high performance computing facilities. To achieve these performance improvements, we recommend: that current best practices (such as the Science DMZ model) be applied to the data servers and networks at ESGF data centers; that sufficient financial and human resources be devoted at the ESGF data centers for systems and network engineering tasks to support high performance data movement; and that performance metrics for data transfer between ESGF data centers and major computing facilities used for climate data analysis be established, regularly tested, and published

On metrizable enveloping semigroups

When a topological group $G$ acts on a compact space $X$, its enveloping semigroup $E(X)$ is the closure of the set of $g$-translations, $g\in G$, in the compact space $X^X$. Assume that $X$ is metrizable. It has recently been shown by the first two authors that the following conditions are equivalent: (1) $X$ is hereditarily almost equicontinuous; (2) $X$ is hereditarily non-sensitive; (3) for any compatible metric $d$ on $X$ the metric $d_G(x,y):=\sup\{d(gx,gy): g\in G\}$ defines a separable topology on $X$; (4) the dynamical system $(G,X)$ admits a proper representation on an Asplund Banach space. We prove that these conditions are also equivalent to the following: the enveloping semigroup $E(X)$ is metrizable.Comment: 11 pages. Revised version 20 September 2006. Minor improvement

The Augmented Synthetic Control Method

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pre-treatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pre-treatment fit is infeasible. Analogous to bias correction for inexact matching, Augmented SCM uses an outcome model to estimate the bias due to imperfect pre-treatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pre-treatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package