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2013 in Baseball: Without the Luck

DISCLAIMER: I know certain players are more likely to outperform/underperform the league-average BABIP based on their specific player profiles. This is just a fun exercise to consider if everyone’s “luck” was the same.

With that disclaimer out of the way, I began wondering who the best/worst hitters are in baseball if batted-ball luck didn’t figure into the equation. We hear analysis frequently about Player X who’s having a breakout year, and the refrain is consistently that he is having better luck on batted balls than he had been having in the past. For example, BABIP was one of the main reasons cited for how Chris Johnson batted .321 in 2013 after hitting .268 over the previous two seasons. Many people look for fantasy sleepers based on a much lower than normal BABIP. The effects of BABIP are undeniably real and have been well documented. If we take BABIP out of the equation though, who rises to the top?

Before we get to the results, let me go over my methodology. It’s extremely simple, and you can probably guess how this is done. If you don’t want the boring details, please skip ahead. The first step to these calculations was to keep all factors not included in the BABIP formula constant. Each player still hits the same number of home runs. Each player still walks at the same rate. Each player still strikes out the same amount. The only component that changes is hits that don’t leave the yard (1B, 2B, 3B). I took the denominator of the BABIP equation for each player (AB-K-HR+SF) and multiplied it by the league-average BABIP (.297). This gives us the number of non-HR hits a player would have tallied if luck was removed. To get the number of singles, doubles, and triples each player hit, I took the ratio of Actual Hit Type/Actual Total non-HR and multiplied by expected non-HR. For example, Mike Trout hit 115 singles, 39 doubles, and 9 triples in 2013. That means that 70.6% of his non-HRs were singles, 23.9% were doubles, and 5.5% were triples. When adjusted for BABIP, we would expect Trout to hit roughly 129 non-HRs this past season. Multiplying 129 by the component percentages gives us roughly 91 singles, 31 doubles, and 7 triples. Everything else remains the same.

To answer the question posed in the introduction, we can look at many different stats. We already discussed how much an effect BABIP can have on a batting average, so maybe we should start there. For what it’s worth, the MLB leader in BABIP in 2013 was Chris Johnson at .394, and the worst BABIP belonged to Darwin Barney at .222.

 AL Adjusted Batting Average Leaders – 2013 (min. 500 PA) Player 2013 AVG (Adjusted) 2013 AVG (Actual) Difference Edwin Encarnacion .313 .272 +.041 Miguel Cabrera .304 .348 -.044 Adrian Beltre .295 .315 -.020 Coco Crisp .294 .261 +.033 J.J. Hardy .291 .263 +.028

 NL Adjusted Batting Average Leaders – 2013 (min. 500 PA) Player 2013 AVG (Adjusted) 2013 AVG (Actual) Difference Andrelton Simmons .291 .248 +.044 Martin Prado .290 .282 +.008 Norichika Aoki .288 .286 +.002 Jonathan Lucroy .287 .280 +.007 Yadier Molina .283 .319 -.035

Looking at those tables, the first thing that jumps out to me is that only two players (Edwin Encarnacion and Miguel Cabrera) in all of Major League Baseball would have hit .300 last year if luck is removed. The American League seems to possess better luck-independent hitters as the NL “batting champ” would have finished tied for fifth in the AL. Also, if Andrelton Simmons could actually hit .291 each season, he’d be an MVP candidate. I also find it interesting to look at which players benefited and suffered the most from their respective BABIPs.

 Most Positive Batting Average Changes – 2013 (min. 500 PA) Player 2013 AVG (Adjusted) 2013 Average (Actual) Difference Darwin Barney .273 .208 +.065 Andrelton Simmons .292 .248 +.044 Dan Uggla .220 .179 +.042 Edwin Encarnacion .313 .272 +.041 Matt Wieters .275 .235 +.040

 Most Negative Batting Average Changes – 2013 (min. 500 PA) Player 2013 AVG (Adjusted) 2013 Average (Actual) Difference Chris Johnson .248 .321 -.073 Joe Mauer .257 .324 -.067 Michael Cuddyer .267 .331 -.064 Mike Trout .265 .323 -.058 Freddie Freeman .264 .319 -.055

Perhaps we shouldn’t limit ourselves to just simply batting average. Isn’t it more important to avoid outs that it is to just get hits? Let’s look at the OBP results.

 AL Adjusted On-Base Percentage Leaders – 2013 (min. 500 PA) Player 2013 OBP (Adjusted) 2013 OBP (Actual) Difference Edwin Encarnacion .406 .370 +.035 Miguel Cabrera .404 .442 -.037 Mike Trout .384 .432 -.047 Jose Bautista .383 .358 +.025 David Ortiz .379 .395 -.016

 NL Adjusted On-Base Percentage Leaders – 2013 (min. 500 PA) Player 2013 OBP (Adjusted) 2013 OBP (Actual) Difference Shin-Soo Choo .399 .423 -.024 Joey Votto .399 .435 -.037 Paul Goldschmidt .373 .401 -.027 Matt Holliday .372 .389 -.017 Troy Tulowitzki .366 .391 -.025

Once again, only two hitters (Encarnacion and Cabrera) would have reached based at a .400 clip. A trend is definitely starting to emerge. The gap between the AL and the NL is much less pronounced here though. As for the biggest changes in the MLB, consider the following tables.

 Most Positive On-Base Percentage Changes – 2013 (min. 500 PA) Player 2013 OBP (Adjusted) 2013 OBP (Actual) Difference Darwin Barney .325 .266 +.059 Andrelton Simmons .337 .296 +.041 Matt Wieters .323 .287 +.036 Edwin Encarnacion .406 .370 +.036 Dan Uggla .344 .309 +.035

 Most Negative On-Base Percentage Changes – 2013 (min. 500 PA) Player 2013 OBP (Adjusted) 2013 OBP (Actual) Difference Chris Johnson .289 .358 -.069 Joe Mauer .345 .404 -.059 Michael Cuddyer .331 .389 -.058 Allen Craig .323 .373 -.050 Freddie Freeman .347 .396 -.049

As you might expect, these tables don’t look all that much different from the batting average change tables. Other than some reordering, the only difference here sees Allen Craig replace Mike Trout on the most negative change table.

Getting on base a lot is a promising start, but you win baseball games by scoring runs. What hitters were best at driving the ball while avoiding outs? Let’s look at the OPS results.

 AL Adjusted On-Base + Slugging Leaders – 2013 (min. 500 PA) Player 2013 OPS (Adjusted) 2013 OPS (Actual) Difference Edwin Encarnacion .993 .904 +.088 Miguel Cabrera .988 1.078 -.090 Chris Davis .953 1.004 -.051 David Ortiz .918 .959 -.041 Jose Bautista .918 .856 +.062

 NL Adjusted On-Base + Slugging Leaders – 2013 (min. 500 PA) Player 2013 OPS (Adjusted) 2013 OPS (Actual) Difference Paul Goldschmidt .883 .952 -.069 Troy Tulowitzki .871 .931 -.060 Jayson Werth .839 .931 -.092 Matt Holliday .837 .879 -.042 Domonic Brown .835 .818 +.017

Once again, our Top 2 are Encarnacion and Cabrera, with a considerably gap between Cabrera and third place Chris Davis. The AL/NL split is at its most pronounced here. To see if our trend in the biggest changes tables continues, consider the following tables.

 Most Positive On-Base + Slugging Changes – 2013 (min. 500 PA) Player 2013 OPS (Adjusted) 2013 OPS (Actual) Difference Darwin Barney .712 .569 +.143 Andrelton Simmons .790 .692 +.098 Edwin Encarnacion .993 .904 +.089 Dan Uggla .759 .671 +.088 Matt Wieters .790 .704 +.086

 Most Negative On-Base + Slugging Changes – 2013 (min. 500 PA) Player 2013 OPS (Adjusted) 2013 OPS (Actual) Difference Chris Johnson .657 .816 -.159 Joe Mauer .736 .880 -.144 Michael Cuddyer .779 .919 -.140 Mike Trout .863 .988 -.125 Allen Craig .712 .830 -.118

The trend continues as expected. Also, the negative regressers are harder hit than the positive regression candidates.

This is FanGraphs though, so we can’t simply look at traditional stats. We need something that’s park-adjusted and comparative to league average. Let’s look at wRC+. (NOTE: These numbers aren’t adjusted for individual leagues as is normally done with wRC+. I’m lazy and didn’t take the time to do that extra step, so the wRC+ values won’t make up exactly with what is listed elsewhere on this site.)

 AL Adjusted wRC+ Leaders – 2013 (min. 500 PA) Player 2013 wRC+ (Adjusted) 2013 wRC+ (Actual) Difference Edwin Encarnacion 161 137 +24 Miguel Cabrera 155 180 -25 David Ortiz 155 167 -12 Coco Crisp 154 132 +22 Chris Davis 154 168 -14

 NL Adjusted wRC+ Leaders – 2013 (min. 500 PA) Player 2013 wRC+ (Adjusted) 2013 wRC+ (Actual) Difference Paul Goldschmidt 148 168 -20 Hunter Pence 142 148 -6 Andrew McCutchen 141 170 -29 Shin-Soo Choo 141 158 -17 Buster Posey 140 149 -9

As you might expect, Encarnacion and Cabrera top the charts again. Paul Goldschmidt is once again the National League leader. As for the biggest movers, they look very similar as well as you might expect.

 Most Positive wRC+ Changes – 2013 (min. 500 PA) Player 2013 wRC+ (Adjusted) 2013 wRC+ (Actual) Difference Darwin Barney 79 40 +39 Andrelton Simmons 127 97 +30 Dan Uggla 122 97 +25 Matt Wieters 111 86 +25 Edwin Encarnacion 161 137 +24

 Most Negative wRC+ Changes – 2013 (min. 500 PA) Player 2013 wRC+ (Adjusted) 2013 wRC+ (Actual) Difference Chris Johnson 85 135 -50 Joe Mauer 105 147 -42 Allen Craig 113 150 -37 Michael Cuddyer 88 125 -37 Mike Trout 147 183 -36

Since we looked at the leaders in each category, let’s look at those who failed to meet such lofty standards in 2013.

Batting Average Laggards

 AL Adjusted Batting Average Laggards – 2013 (min. 500 PA) Player 2013 AVG (Adjusted) 2013 AVG (Actual) Difference Chris Carter .216 .223 -.007 Mike Napoli .219 .259 -.040 Mark Reynolds .230 .220 +.010 Michael Bourn .232 .263 -.031 Stephen Drew .237 .253 -.016

 NL Adjusted Batting Average Laggards – 2013 (min. 500 PA) Player 2013 AVG (Adjusted) 2013 AVG (Actual) Difference Dan Uggla .220 .179 +.042 Starling Marte .234 .280 -.046 Chase Headley .235 .250 -.015 Giancarlo Stanton .240 .249 -.009 Gregor Blanco .241 .265 -.024

The most startling thing I notice from these tables is that Dan Uggla gained .042 points in his batting average and still finished last in the league. Now, that’s impressive.

On-Base Percentage Laggards

 AL Adjusted On-Base Percentage Laggards – 2013 (min. 500 PA) Player 2013 OBP (Adjusted) 2013 OBP (Actual) Difference Alcides Escobar .287 .259 +.028 Michael Bourn .288 .316 -.028 Manny Machado .294 .314 -.019 Leonys Martin .297 .313 -.015 Torii Hunter .300 .334 -.035

 NL Adjusted On-Base Percentage Laggards – 2013 (min. 500 PA) Player 2013 OBP (Adjusted) 2013 OBP (Actual) Difference Adeiny Hechavarria .288 .267 +.021 Chris Johnson .289 .358 -.069 Starlin Castro .290 .284 +.006 Zack Cozart .294 .284 +.010 Marlon Byrd .300 .336 -.036

Michael Bourn is our only carryover from the batting average tables that appears on the OBP tables as well. Probably not a great sign for Cleveland.

On-Base + Slugging Laggards

 AL Adjusted On-Base + Slugging Laggards – 2013 (min. 500 PA) Player 2013 OPS (Adjusted) 2013 OPS (Actual) Difference Michael Bourn .609 .676 -.066 Alcides Escobar .621 .559 +.062 Elvis Andrus .633 .659 -.026 Jose Altuve .643 .678 -.035 Leonys Martin .661 .698 -.037

 NL Adjusted On-Base + Slugging Laggards – 2013 (min. 500 PA) Player 2013 OPS (Adjusted) 2013 OPS (Actual) Difference Adeiny Hechavarria .615 .565 +.050 Eric Young .638 .645 -.007 Gregor Blanco .639 .690 -.051 Starlin Castro .644 .631 +.013 Chris Johnson .657 .816 -.158

Uh-oh, Bourn is back again, and the only player relatively close to him is Adeiny Hechavarria. Hechavarria is a fine defensive shortstop who has noted offensive woes. Bourn was a big free agent signing for Cleveland expected to jump start the Indians offense. Those represent completely different expectations.

Weighted Runs Created Plus Laggards

 AL Adjusted wRC+ Laggards – 2013 (min. 500 PA) Player 2013 wRC+ (Adjusted) 2013 wRC+ (Actual) Difference Alcides Escobar 64 45 +19 Jose Altuve 70 80 -10 Ichiro Suzuki 75 68 +7 Michael Bourn 77 98 -21 Elvis Andrus 81 89 -8

 NL Adjusted wRC+ Laggards – 2013 (min. 500 PA) Player 2013 wRC+ (Adjusted) 2013 wRC+ (Actual) Difference Starlin Castro 62 58 +4 Adeiny Hechavarria 68 53 +15 Nolan Arenado 70 70 0 Darwin Barney 79 40 +39 Eric Young 80 82 -2

Nothing here is meant to be used to draw conclusions about any hitters. I’m not advocating for Edwin Encarnacion as the best regular in baseball or Starlin Castro as the worst. I just thought this would be an interesting simple exercise to consider. Just for fun though, let’s look at the AL MVP race one more (“luck-independent”) time.

 Statistic Miguel Cabrera Mike Trout AVG .304 .265 SLG .584 .479 OBP .404 .384 OPS .988 .863 wOBA .418 .374 wRC+ 155 147

If we take luck out of the debate, Cabrera is an 8% better hitter compared to league average than is Trout. I guess the BBWAA doesn’t think Trout is an 8% better fielder and base runner than Cabrera. Surely they know what they’re talking about though. I mean they do get to decide who belongs in the Hall of Fame after all. They’re the smartest baseball folks out there.