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I just ran across this article over at The Baseball Analysts titled “BABIP: Slicing and Dicing Groundball Out Rates“. It’s well written and anyone who uses Batting Average on Balls In Play as a statistical tool to find trends in hitter and/or pitchers should really take a look at this piece. A few comments of mine from a fantasy point of view:

“Most pitchers regress toward the league average BABIP of around .300 or .305. Very few pitchers can repeatedly do better or worse than this, so we say that pitchers have very little control over BABIP. Hitters, on the other hand, can have a substantial amount of control over BABIP. Ichiro Suzuki, for example, has a .356 career BABIP. Hitters do not regress toward league average, rather, they each regress toward their own, unique number.”

It can make a huge difference if you make the mistake of calculating BABIP for hitters using league average instead of their own unique number. This is an amazing piece of advice from Derek Carty who is mentioned in this piece at The Baseball Analysts becasue of an article he wrote on BABIP estimators.

According to THT, the MLB average groundball out rate was 74 percent in 2007 and 2008. By comparison, the MLB average flyball out rate was 83 percent in 2007 and 84 percent in 2008. Another way of looking at those percentages is to say that batters hit about .260 on groundballs and .160-.170 on outfield flyballs (excluding home runs).

The line drive out rate was 29 percent in 2008, meaning batters hit roughly .710 on these batted balls. The hit rate on infield flies is nearly non-existent as pop-ups are converted into outs 99 percent of the time.

When it comes to batting average, line drives are king, followed by groundballs, outfield flyballs, and infield flies. Put it all together and National and American League teams hit .298 and .302, respectively, on balls in play in 2008. NL and AL clubs had BABIP of .301 and .305 in 2007.

When you’re looking for help with the batting average category, look at line drive percentages. Seven of the top ten hitters in line drive percentage batted above .290 in 2008. The other three batted .287, .271, and .271.

Roy Oswalt has been a dissapointment, whether you’re an Astros fan or frustrated fantasy owner. His current 5-6 record, 5.06 ERA, 1.40 WHIP have vastly underperformed even the most conservative projections. The obvious difference in Roy’s performance this year has been his unprecedented (in terms of his career) 17 HR allowed through 14 starts. Per Derek Carty’s article at the Hardball Times, Roy Oswalt has been the unluckiest pitcher, to date, it terms of HR/FB.

Since, April 16th, Evan, my co-writer, and I have been utilizing Roy Oswalt’s pitch/fx data in order to grasp at what it is that has made him so crushable. Both of us were convinced that stat-geeks latest tool, pitch/fx would shed light on Roy’s struggles. Our first stab at the data lead us to believe it was an ineffective curve ball that was ailing him — it has since returned to it’s 2007 level in terms of break (see full article for fancy charts that prove this). The next stab lead us to conclude that it was his constricted release point — stemming, most likely, from a desire not to tip his pitches — that was causing him to lose a little bit of life on all his pitches, but especially his slider. I went so far as to credit this for his HR spike.

I thought I’d discovered the problem,I was wrong. Another Astros blogger mentioned that she felt it was fastballs that were getting crushed, not “hanging-sliders.” So I enlisted the help of the incredible Josh Kalk for better pitch/fx data. With his help, I was able to isolate Roy’s 14 HR pitches (all that he’d given up at the time of my solicitation) with just about every possible piece of information about them. I narrowed it down to the information I felt was pertinent or possible explanatory. Here are the results:

The only thing that most of the pitches had in common, besides being fastballs, where that men were on base. The full article has a more detailed analysis, but there really isn’t anything about these pitches that screams “SOMETHING’S WRONG”. I was a little disapointed to not have been the genius who uncovered what was wrong with Roy Oswalt, but there was even better news: Roy Oswalts struggles, again per Derek Carty’s article can, seemingly, only be attributed to luck at this point. Therefore, given what statistics tells us about the law of averages, from here on out, we can only expect Roy Oswalt’s HR/FB numbers to return to his career level; which have never exceed 12% and average around 9%. Carty corrected Roy’s ERA to reflect a league average HR/FB rate of 11% (i.e. higher than what we should expect of Oswalt) and got a 4.08 ERA. Therefore, discerning fantasy owners could venture a gamble at a low buy Roy Oswalt and expect a return of a sub 4.00 ERA — this seems especially likely in light of his 6.99 K/9 and a 3.29 K/BB.

Dave Cameron over at FanGraphs posted a few days back about the heroes of April; the top offensive guys for the first month of MLB. He used WPA/LI to determine offensive production. Very cool!

Let me just go ahead and define WPA/LI right now. [from FanGraphs]

WPA/LI (context neutral wins / game state linear weights): How many wins a player contributes to his team with the Leverage Index aspect removed, invented by Tom Tango.

Calculating WPA/LI: WPA is divided by LI for each individual play attributed to a specific player and then the WPA/LI for the individual plays is then added up to create WPA/LI for an entire season. This is considerably different then taking a player’s WPA and dividing it by pLI.

Why you should care: Unlike standard linear weights, WPA/LI does take into account the situation. So at times when a walk would be just as valuable as a home run, WPA/LI accurately weights the walk and the home run, where linear weights would still give .13 wins to the home run and the walk .03 wins.

There is a fantastic post over at Roto Authority that looks at Contact rate in comparison to current batting average to determine which hitters will see upward swings in their batting averages and which batting averages could fall. You really should look this over each week and use the data (especially when there is more data to look through) for the free agent market and for trading.

I just read an interesting post over at The Waiver Wire speaking about xFIP as a tool for ERA forecasting.

“If you had to bet your life on the accuracy of an [tag]ERA forecast[/tag] made by one stat, what would it be? Mine would be xFIP…”

We all know that Johan Santana is far and away the #1 SP that should be taken off the board. I decided to put xFIP to the test and rank the five 2nd tier pitchers available in upcoming drafts. Here they are with their [tag]xFIP[/tag] value, in order of how I have ranked them :

Chris Carpenter - 3.53
Carlos Zambrano - 4.43
Roy Oswalt - 3.75
Brandon Webb - 3.13
Roy Halladay - 3.59