Hitter’s Luck as a Rate Stat

On Monday, I introduced my Luck stat for pitchers and hitters. Today, I will look into some improvements to the hitter portion of the stat.

Moving Luck to a Rate Stat

The main focus for the stat was to try to find players under- or over-performing. The Luck stat I previously released was the accumulation of the luck a player had for the entire season. The problem was that two players with similar BABIPs and HR/FB ratios would be rated differently by the stat because of their PAs. I just divided the previous Luck value by PA and then readjusted the multiplier to get near -10 to +10 scale. Here is a comparison of old and new Luck leaders and laggards (min 150 PA):

Old Luck Leaders and Laggards:

Name Old Luck New Luck
Gonzalez Adrian 8.7 8.3
Bautista Jose 8.4 8.9
Stanton Mike 7.6 8.6
Morse Michael 7.2 9.0
Kemp Matt 6.7 7.2
Escobar Alcides -8.1 -5.3
Suzuki Ichiro -8.1 -4.4
Andrus Elvis -8.2 -4.9
Bartlett Jason -8.6 -5.6
Revere Ben -9.8 -9.6

New Luck Leaders and Laggards:

Name New Luck Old Luck
Jones Andruw 14.8 4.4
Jennings Desmond 11.3 3.2
Lillibridge Brent 10.9 3.4
Downs Matt 10.0 2.5
Johnson Reed 9.0 2.9
Revere Ben -9.6 -9.8
Stewart Chris -9.7 -4.6
Counsell Craig -9.8 -4.9
Forsythe Logan -11.3 -5.3
Young Jr. Eric -13.1 -5.7

The differences between the charts can be seen fairly quickly. With the old Luck values, players that are somewhat lucky get pushed to the top of the list because of their playing time. The second table has some players with extreme values because of a smaller number of PAs.

The one name that makes both lists is Ben Revere for being unlucky. His unluckiness is from having a 0.260 BABIP with an xBABIP of 0.349. Also, he has hit no home runs on 39 fly balls. After a 0.326 average in the minors, he has only able to hit for a 0.246 average in the majors. He may be looking to improve in 2012.

Home Run Rate Regression Changed

With the old Luck, I regressed the player’s home run rate with 300 PAs of the league average value. Power hitter dominated the top of the leader board. I wanted use only this season stats for ease of computation, so I change the regression value from 300 PA to 150 PA.

The change takes a little less emphasis off HR/FB%, but still shows players that are under or over performing.

Last Year’s Results

Taking the top 10 luckiest hitters from 2010, I compared to how their wOBA did then in 2011:

Name Luck 2010 wOBA 2011 wOBA Difference
Stanton Mike 12.3 0.355 0.379 0.024
Thome Jim 12.3 0.437 0.344 -0.093
Morneau Justin 11.5 0.447 0.274 -0.173
Thames Marcus 10.5 0.365 0.254 -0.111
Alvarez Pedro 10.4 0.343 0.247 -0.096
Gonzalez Carlos 10.0 0.416 0.387 -0.029
Betemit Wilson 9.8 0.385 0.329 -0.056
Hamilton Josh 9.4 0.447 0.367 -0.080
Cust Jack 8.9 0.371 0.307 -0.064
Votto Joey 8.8 0.439 0.415 -0.024
Avg. Diff. = -0.070

Of the 10 players, only one (Mike Stanton) improved in 2011 and all the rest saw their wOBA drop. As a group, their wOBA dropped by an average of 70 points.

Final Thoughts

I like where Luck stands right now as a rate stat to find players who were helped by an over inflated BABIP and HR/FB%. Here is the complete list of players from 2010 and 2011 in a Google Doc to examine. Let me know if you have any comments and I will look at pitcher’s Luck on Monday.

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Jeff writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won three FSWA Awards including on for his MASH series. In his first season in Tout Wars, he won the H2H league. Follow him on Twitter @jeffwzimmerman.

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It’s a lot harder to stay “lucky” for a long time, because regression will inevitably cut into your “luck”. So shouldn’t players who stay “lucky” for a long time get more credit in a stat that attempts to measure “luck”?