Consistency and Pythagoras
Pythagorean win share has been one of the fundamental contributions to Sabermetrics. Several hundred articles, both academic and non-academic, have explored variations on Bill James' original formula and its fit to empirical data. This paper considers a variation that is previously unexplored on any systematic level, consistency. After discussing several important contributions to the line of literature, we demonstrate the strong correlation between Pythagorean residuals and several notions of run distribution consistency. Finally, we select the "correct" form of consistency and use it to construct a simple regression estimator, which improves RMSE by 11%.
Baseball Between the Numbers: Why Everything You Know About the Game Is Wrong
This is the ultimate guide to the new statistical thinking that's revolutionizing the game of baseball. In the numbers-obsessed sport of baseball, statistics don't merely record what players, managers and owners have done. Properly understood, they can tell us how the teams could employ better strategies, put more effective players on the field, and win more games. The 1970s saw a revolution in baseball statistics, and it's a controversial subject that professionals and fans alike argue over without end. Despite this fundamental change in the way the sport is watched and understood, no-one has ever written the book that reveals, across every area of strategy and management, how the best practitioners of statistical analysis in baseball think about the game. "Baseball Between the Numbers" is that book. In separate chapters covering every aspect of the game - such as hitting, pitching and fielding - the experts at Baseball Prospectus examine the subtle, hidden aspects of the game, bring them out into the open, and show us how various teams could win more games.
An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers
A method, using the concept of local means and local standard deviations of small imaging blocks and using a box counting procedure, has been developed for unsupervised estimation of the average signal to noise ratios of images in which the noise is additive. The method has been applied to simulated images with Gaussian noise, to images acquired with the Airborne Visible Infrared Imaging Spectrometer and with the Geophysical and Environmental Research Imaging Spectrometer. The method is compared with other techniques for estimating signal to noise ratios from imaging data. It is believed that the method is generally applicable to images having many small homogeneous blocks. Because the method does not take into account interband effects, it is not appropriate to use the method to estimate signal to noise ratios from images having nonnegligible interband radiometric calibration errors at spatial scales less than or equal to the size of the small imaging blocks used in the noise estimation.