Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.
In this research article, the author has detailed a Novel Scheme of Clustering Based on Natural Metric.
Authors: Raymond H Gallucci
Comments: 90 Pages. Includes inadvertent omissions of SUV football tables for second and third downs with more than 10 yards to go.
Situational Underlying Value (SUV) arose from an attempt to develop an all-encompassing statistic for measuring “clutchiness” for individual baseball players. It was to be based on the “run expectancy” concept, whereby each base with a certain number of outs is “worth” some fraction of a run. Hitters/runners reaching these bases would acquire the “worth” of that base, with the “worth” being earned by the hitter if he reached a base or advanced a runner, or the runner himself if he advanced “on his own” (e.g., stolen base, wild pitch). After several iterations, the version for SUV Baseball presented herein evolved, and it is demonstrated via two games. Subsequently, the concept was extended to professional football and NCAA Men’s Basketball, both with two example games highlighting selected individual players. As with Major League Baseball, these are team games where individual performance may be hard to gauge with a single statistic. This is the goal of SUV, which can be used as a measure both for the team and individual players.
Authors: Glenn Healey
Comments: 24 Pages.
The deployment of sensors that characterize the trajectory of pitches and batted balls in three dimensions provides the opportunity to assign an intrinsic value to a pitch that depends on its physical properties and not on its observed outcome. We exploit this opportunity by utilizing a Bayesian framework to map five-dimensional PITCHf/x
velocity, movement, and location vectors to pitch intrinsic values. HITf/x data is used by
the model to obtain intrinsic quality-of-contact values for batted balls that are invariant to the defense, ballpark, and atmospheric conditions. Separate mappings are built to accommodate the effects of count and batter/pitcher handedness. A kernel method is used to generate nonparametric estimates for the component probability density functions in Bayes theorem while cross-validation enables the model to adapt to the size and structure of the data.
Authors: Malte Braband
Comments: 16 pages, 4 figures, 2 tables, in German. Jugend Forscht project 130423.
This paper analyses the question how to systematically reach the top flight of soccer prediction leagues. In a first step several forecast models are compared and it is shown how most models can be related to the Poisson model. Some of the relations are new. Additionally a method has been developed which allows to numerically evaluate the outcome probabilities of soccer championships instead of simulation. The main practical result for the example of the 2014 soccer World Championship was that the forecast models were significantly better than the human participants of a large public prediction league. However the results between the forecast models were small, both qualitatively and quantitatively. But it is quite unlikely that a large prediction league will be won by a forecast model although the forecast models almost all belonged to the top flight of the prediction league.
Authors: Raymond H.V. Gallucci
Comments: 48 Pages.
SUV – Situational Underlying Value – for professional baseball (MLB) is a concept based on the more traditional one of “run expectancy.” This is a statistical estimate of the number of runs expected to result from a base runner or multiple runners given his/their presence at a particular base, or bases, and the number of outs in an inning. Numerous baseball websites discuss this concept; one can find dozens more with a simple internet search on “run expectancy.”
SUV for professional football (NFL) is not as readily conceived as that for baseball, although the concept of each position on the field with down and yards to go has been examined for the possibility of assigning point values (from here on referred to as SUVs). Quantification of this concept is taken from “Expected Points and Expected Points Added Explained,” by Brian Burke, December 7, 2014.
Example applications to a pair of professional baseball games (MLB) and pair of professional football games (NFL) are included that illustrate how the SUV is used.