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How to Win: Batting Average

Quick Overview
Batting average is horrible. It's unpredictable and the winner of the category each year can only be described as an overly lucky person who will surely regress to the mean next year. (The loser probably drafted Adam Dunn or Carlos Pena and that's their own fault.) 

This was more or less my attitude going into the year. What's too hard to understand probably can't be understood. Well, I learned quickly enough that other people seemed non-randomly better than me at figuring out this whole batting average thing and it was my own team that sank in the BA standings. Fun times. The good news is that I'm resolved to be less intellectually lazy this year, and that I'm happy to share my newfound industriousness with you. The bad news is that you're more likely to get hit with the same number of pitches than post the same batting average two years in a row. Yeah, BA only correlates from one year to the next at a mark of 0.477--which is considered quite poor, but better than totally random.

2012's Top 12
Below is the table of the top qualified batting averages across MLB. In parentheses, I show their BABIPs. Note that this list is only twelve names long, instead of my customary 24--with the volatility of batting average, it just isn't worth reading so many players. 

1. Buster Posey        .336 (.368)
2. Miguel Cabrera    .330 (.331)
3. Andrew McCutchen    .327 (.375)
4. Mike Trout    .326 (.383)
5. Adrian Beltre    .321 (.319)
6. Ryan Braun    .319 (346) 
7. Joe Mauer    .319 (364)
8. Derek Jeter    .316 (.347)
9. Yadier Molina    .315 (.316)
10. Prince Fielder    .313 (.321)
11. Torii Hunter       .313 (.389)
12. Billy Butler    .313 (.341)

A couple things stand out--first of all, three catchers! Second, one of those catchers--Molina--posted his average with a BABIP nearly identical to his batting average, and a pretty low BABIP at that. That tells me he could actually post a better number next year with an unsurprising amount of good luck. Beltre's average exceeded his BABIP which seems pretty odd too. Like Molina, he could see a bump in his average next year through just a little more good luck.

3-Year Top 12
The more time goes on, the less volatile any stat is. Mayhaps the last three years of BA leaders will be more instructive than just one. Double points for the players on both lists.

1. Miguel Cabrera    .334 (.344)
2. Joey Votto    .321 (.367)
3. Ryan Braun    .318 (.342)
4. Buster Posey    .317 (342)
5. Victor Martinez .317 (.324)
6. Joe Mauer    .315 (.348)
7. Adrian Beltre    .314 (.310)
8. Josh Hamilton    .313 (.343)
9. Carlos Gonzalez    .313 (.355)
10. Adrian Gonzalez  .312 (.346)
11. Robinson Cano    .311 (.322)
12. Billy Butler    .307 (.333)  

Interestingly, half of the lists are the same, which isn't too far off from what a .477 correlation score would suggest. In fact, it's exactly what we should expect, so long as we have to round up to a whole Victor Martinez. The consistency of guys like Cano and Butler pays off here, but I wonder if injuries do too--look at the players who've missed time (or whole seasons) in the past three years. Maybe one of the components of having a good average is simply not playing much, to keep bad luck from catching up....

Some Discussion of Good and Evil BABIPs
Speaking of bad luck, here are some selected players whose lousy BABIPs hurt their averages and might be bouncing back a bit next year. While they might not become true helpers in BA, they might not hurt as much as last year. While their lousy 2012 averages are busy scaring people away, you might get away with drafting them and enjoying their good qualities. As above, the real average is first, the BABIP in parentheses.

Ike Davis    .227 (.246)
Eric Hosmer    .232 (.255)
Jemile Weeks    .221 (256)
Colby Rasmus    .223 (.259)
Curtis Granderson    .232 (.260)
Dustin Ackley    .226 (.265)
Edwin Encarnacion    .280 (.266)
Kevin Youkilis    .235 (.268) 
Ian Kinsler    .256 (.270)

So...odd list of names. I threw out players who'd posted lousy BABIPs for the last three years in a row, so I'm not expecting to see the likes of Adam Dunn, Mark Teixeira, J.J. Hardy, Jimmy Rollins, and Carlos Pena regressing to the happy mean of .300. Some of the names I did list are young (Hosmer, Weeks, Ackley) and I have no idea where their "true-talent" BABIP will lie--maybe it's low and they won't be regressing because they were at their own, natural, bad mean in 2012. Others, though, are veterans (Granderson, Youk, Kinsler) who might be declining and also might be feeling a little bad luck. Of those, I like Granderson best for a better average next year. Finally, there's Encarnacion, who somehow hit .280 with a bad BABIP. If I didn't like him for next year, I sure do now.

The flip side of the BABIP coin are those players who won't be repeating their good 2012 performances:

Joey Votto    .337 (.404)
Dexter Fowler    .300 (.390)
Torii Hunter    .313 (.389)
Mike Trout     .326 (.383)
Melky Cabrera    .346 (.379)
Andrew McCutchen    .327 (.375)
Austin Jackson    .300 (.371)
Buster Posey    .336 (.368)
Joe Mauer    .319 (.364)
Tyler Colvin    .290 (.364)
Miguel Montero    .286 (.362)

This list, by the way, has its PA requirement dropped down to 450, to show the red flags about a couple players who didn't qualify for the batting title (including the one who would have won it, Votto). The truly scary ones are those that didn't hit for a stratospheric average even with such a high BABIP--Fowler, Jackson, Colvin, and Montero. It's worth noting, though, that three of those guys play at high altitudes, and Jackson just barely topped his career BABIP of .370. In three Major League seasons, he hasn't been below .340, so maybe that's a skill of his. Of course, he hit just .249 with that .340 BABIP....

Park Effects
Speaking of players who hit in Coors Field, check out the hits-specific park effects around MLB here. If you'd rather stay right here, good, I've got the highlights.

Coors Field                       Rockies        1.276
Fenway Park                    Red Sox       1.173
Ballpark at Arlington    Rangers       1.117
Camden Yards                 Orioles         1.099
U.S. Cellular                    White Sox    1.081 


Tropicana Field               Rays            0.914
Angel Stadium                 Angels        0.906
AT&T Park                       Giants         0.901
PNC Park                         Pirates         0.871
Safeco Field                     Mariners    0.831 

Park effect numbers measure the difference between the given baseball stadium and the league average. The number 1.0 is exactly neutral, so Coors Field's 1.276 number means that park saw 27.6% more hits than the league average, while Safeco's 0.831 number means Seattle saw 16.9% fewer hits than average. Basically, the top five parks can really help your average and the bottom five are likely to hurt it. Conspicuously absent from this list are some parks notorious for adding to overall runs scored (or taking them away)--don't assume that Yankee Stadium will help your hitters' average or that Target Field (in Minnesota) will kill it. 

A Few Last Words
Batting average isn't an easy category to forecast, but with the tools of park effects, BABIP, and long-term trends under your belt, you can do pretty well. In fact, that's exactly what I recommend shooting for. If you write it off and load up on the B.J. Uptons and Adam Dunns of the world, you get what you pay for: power, speed, whatever else you want...and an ugly place in the BA standings. In a weekly head-to-head league, that might not be so bad. It's not as good for standard roto style, though. Instead, if you shoot to land towards the middle you can avoid overpaying for last year's best averages but still give yourself the chance to luck into some extra points--chances are that's what your league leader did last year anyway.

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