## 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.

#### Batting Average Leaders

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) |
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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 |

#### On-Base Percentage Leaders

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.

#### On-Base + Slugging Leaders

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.

#### Weighted Runs Created Plus Leaders

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.

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Are those NL/AL numbers generated using MLB average BABIP or each individual league’s? If the former, that might account for some of the gap between the two.

They are generated using MLB average. Using individual league averages could be interesting to look at as well. Thanks for the comment.

This was some interesting stuff. I’d also be curious to see these numbers with each player normalized to their respective xBABIP. This could probably help to account for the fact that hitters probably have an adjusted normal BABIP.

Yeah I’d like to look at either xBABIP or career BABIP prior to 2013. Probably give more meaningful results. I’ll try and look into doing that and see how the results turn out.

shoot me a line eph_unit@yahoo.com

I have a spreadsheet with all of this info neatly laid out

shoot me a line eph_unit@yahoo.com

I have a spreadsheet with all of this info neatly laid out