
In case you mi sed it, today I went within the mathematics nece sary to go ahead and take four Marquis Lucas Jersey factors of hitting one stage further. Now, its time to put it to good use. As Eric stated in the comments section, the way in which the math is completed, we could figure out how much a change in one of the factors changes a players wOBA if you take the derivative from the new formula for wOBA produced from the 4 factors equations. However, thats lots of work, so instead, as he suggests, Im likely to do that analysis numerically using a little program called Microsoft Excel. Lets test run this method around the simplest of targets: the league average player. In this example, well make use of the league average player from 2009 MLB. In 2009, the league average BB% was 8.9%, the league average K% (K/PA) Kevin McDermott Jersey was 17.9%, the league average POWH (XB/H) was .595, and also the league average BABIP was .299. The following chart shows how alterations in each variable changes the players Four Factors Equivalent wOBA (ffwOBA). In to easier visualize these on the same scale, I looked how changing each statistic by one standard deviation impacts ffwOBA. In this case, one standard deviation for BB% is 3.7%, for K% its 7.3%, for POWH Stephen Weatherly Jersey its .257, as well as for BABIP its .049 points. The slope of these lines tells us how sensitive wOBA is, at least as predicted through the four factors, to changes in each stat. BABIP may be the steepest, as changing BABIP by one standard deviation changes wOBA by 41 points. Next is POWH, which although it isnt perfectly linear, its close enough that we can addre s it as a result. Changing POWH by one standard deviation Cedric Thompson Jersey changes wOBA by 33 points. One standard deviation change in K rate changes wOBA by 26 points. A players wOBA is definitely least sensitive to BB%, like a one standard deviation change in BB rate only changes wOBA by 13 points. My explanation for the small changes in wOBA brought upon by BB rate changes is that increasing BBs, at least in this model, reduce all favorable outcomes (all DeMarcus Van Dyke Jersey hits) as well as lowering outs. The decrease in outs is enough to imply that a rise in BB% is a great thing. However, decreasing K% only means decreasing outs, increasing POWH means increasing 2B, 3B, and HR in the cost of 1B, which is a high net increase, and increasing BABIP means increasing all hits in the cost of outs, that is clearly the best of all results. Through all of those other week, Ill be considering some interesting players, hopefully examining how this method performs in the extremes.
