Situational Underlying Value for Baseball, Football and Basketball - Gallucci, Raymond
32,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
payback
16 °P sammeln
  • Broschiertes Buch

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…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
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.
Autorenporträt
Dr. Ray Gallucci, P.E., has over 35 years experience in reliability and risk analysis, probabilistic and statistical modeling, for the commercial and regulatory nuclear industry in the U.S. Since his doctoral work, he has specialized in probabilistic safety analysis for fire hazards within nuclear facilities, developing unique modeling methods.