Archive for September 6th, 2011

Earlier today, I addressed a provocative look at WAR from a philosophical point of view. However, there was one statistical element nestled within the IIATMS post that seems worthy of a closer examination.

There is, however, significant evidence that pitching staffs with extreme batted ball tendencies can dramatically affect their outfielders UZR numbers.  (These extremes I defined at upward of 40% at the high end and below 33% at the low end.)

Average OF UZR for FB% > 40.0: 10.1
Average OF UZR for FB% < 33.0: -10.6

Of the sixteen teams at the high end of the range, five finished #1 in their league in OF UZR.  Of the 21 teams at the low-end, only five finished with a UZR north of zero

If there really “significant evidence” to support such a conclusion?

Based on current data, there are actually 15 teams that have had a FB% above 40% and 23 teams with a FB% below 33%, but otherwise the general findings from the post are accurate (four high FB teams had a negative UZR, while five low FB teams had a positive UZR). So, it does seem as if there is a link between the two elements. However, when you consider that a relatively few teams have exhibited extreme fly ball tendencies (38), and of that group almost half (18) were from 2003-2005 (a period when batted ball data collection was less refined as it is now), it’s hard to draw any meaningful conclusion from such an arbitrary analysis.

Furthermore, if one compares the fly ball rate of every team since 2003 to its outfield UZR, the correlation is a very low .14. What’s more, within each “extreme” end of the entire sample, there also isn’t a compelling correlation (.18 at the high end and -.30 at the low end). So, that leaves us wondering why being at the extreme end of the defined fly ball parameters would influence the positive or negative value of outfield UZR. Finally, it’s worth noting that If UZR/150 is used instead of UZR, the mean rates at each extreme parameter are +4.4 on the high end and -3.5 on the low end, and the overall correlation is a similar .15.

One reason why there may not be much of a correlation between the two figures is because FB% is based only on balls put in play. By ignoring, among other things, strikeout rate, and therefore total chances, using batted ball percentages can be misleading. The 2010 Giants are a perfect example of this dynamic. Last year, San Francisco pitchers allowed the second highest fly ball rate at 40.7%, but also struck out a league leading 1,331 batters. As a result, the team’s outfielders only encountered 884 balls into the “outfield zone”, which ranked 19th in the major leagues.

Somewhere in the data may be a meaningful link between fly ball rates and outfield UZR, but on the surface, it seems mostly anecdotal. For a link to the data used in this quick analysis click here, and feel free to pass along any more meaningful findings derived from its use.

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Is WAR the new RBI? That was the question asked in a thought provoking post at IIATMS, which is sure to draw a new battle line in the statistical debate over the value of composite metrics.

How much of Adrian Gonzalez' success is attributable to those who get on base before him?

At the heart of author’s argument is the suggestion that WAR, like RBIs, is context-based because so many elements of performance are interconnected. To illustrate this point, Adrian Gonzalez’ higher career OPS with men on base is offered as one of the exhibits. In this case, the implication is that Gonzalez’ performance benefits from his teammates getting on base ahead of him (just like with RBIs), so it’s unfair to consider OBP and SLG as strictly individual stats.  If we look more closely at Gonzalez’ splits, however, we see that a significant portion of his 50 OPS increase with men on base stems from the 108 intentional walks he has been given (he has only received two with no men on). Although one could still argue that those intentional walks are as much attributable to the men on base as the pitcher’s fear of Gonzalez, it raises other questions as well. Specifically, one must then also consider to what degree the hitters batting ahead of Gonzalez benefit from his presence in the lineup?

It should be pretty obvious that everything that happens on a baseball field is interconnected. Not only do players interact with their teammates, but the opposition has a say as well. In particular, the pitcher influences a batter’s outcome as much as any variable present on his team (other than his individual batting skill). Using the same example, we could posit that Gonzalez has a higher OPS with men on base because stronger pitchers don’t allow them as frequently as weaker ones (and especially not when they are on top of their game). If so, we might then expect to see that all hitters have a higher OPS with men on base, and in fact, this is the case. Since Gonzalez broke into the majors, the average OPS gap in these two splits is 34 points.


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