Presentation on theme: "WAR on statistics Channing Burgess, Brandon Tiong, Daniel Smith Team TATUM."— Presentation transcript:
WAR on statistics Channing Burgess, Brandon Tiong, Daniel Smith Team TATUM
Introduction War is a single complex baseball stat WAR is a type of sabermetric analysis Sabermetrics allows managers to objectively compare players. Wins above Replacement It specifically measures how a player contributes to winning.
History of WAR SABR Research group formed in the 1980’s “Search for objective knowledge about baseball” Created sabermetric methods to rate players in different areas of the games Money Ball Used statistics to analyze the stock market and predict future trends Gave statistic methods wide spread attention and peaked interest in other areas
How it works Takes a set base observable statistics to create a grade The grade combines fielding, batting, and position to form a single stat that generally falls between 0-7. Scrub0-1 WAR Role Player1-2 WAR Solid Starter2-3 WAR Good Player3-4 WAR All-Star4-5 WAR Superstar5-6 WAR MVP6+ WAR WAR = wRAA + Position + BsR + UZR + Rep Wins (R/W)
Range of Values for WAR RatingwRAA Excellent40 Great20 Above Average 10 Average0 Below Average -5 Poor-10 Awful-20 RatingUBR Excellent+6 Great+4 Above Average +1.5 Average0 Below Average -1.5 Poor-4 Awful-6 RatingPosition Catcher12.5 First Base-12.5 Second Base2.5 Short Stop2.5 Third Base7.5 Left Field-7.5 Center Field2.5 Right Field-7.5 Designated Hitter-17.5 RatingRep Wins Excellent25 Average20 Poor15
Neural Net Predictions Naive implementation 18 inputs, 1 hidden layer, 1 output Most likely will give a under fitted representation of WAR values Complex implementation 18 inputs, multiple layers, multiple outputs Gives a gradient output to represent prospective into the accuracy of the WAR value