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If you’ve lived in an area with access to the major Turner Broadcasting networks at any point since 1997, you’re probably familiar with the popular holiday movie A Christmas Story. The plot of the film revolves around Ralphie’s desire to obtain a BB gun (or more specifically a Red Ryder Carbine Action 200-shot Range Model air rifle – but who can remember for sure) for Christmas that year. What nine-year-old boy wouldn’t want a BB gun? I know I would’ve loved one. All I usually got were a bunch of socks and sweaters and other boring stuff that I couldn’t care less about. What the hell, Mom?

But I digress. Just like Ralphie, we’ve all wanted that shiny, new BB gun at some point. Without those BBs, how would’ve young Ralphie fared against the likes of Black Bart and his crew? This fantasy season, we want those BBs instead of Aunt Clara’s homemade gift of choice. That brings us to this week’s exercise. Watch A Christmas Story tonight and then post your review in the comments. Wait, that’s not it, though feel free to discuss the movie if you’re so inclined.

Today, we’re looking to identify the players who have posted high BB-rates and, by extension, an above average ability to reach base. I added a little wrinkle into this exercise as well. Let’s take a look at the search criteria:

2013-14 MLB seasons

Minimum 500 PA

BB% of at least 8%

wOBA of at least .340

Much like the LHP masher exercise that I recently conducted in which power was the primary focus (but not the only one), I wanted to emphasize on-base skills without completely ignoring overall offensive value. That’s where weighted On-Base Average (wOBA) comes in. wOBA is similar to OPS except that it places more emphasis on getting on base relative to hitting for extra bases. So while the OPS criterion fit the slugging exercise slightly better than wOBA, the opposite is true for this one.

To provide a point of reference, as always, here are the MLB averages for all hitters across the 2013-14 seasons:

Season BB% K% BB/K ISO AVG OPS wOBA
2013 7.90% 19.90% 0.4 0.143 0.253 0.714 0.314
2014 7.60% 20.40% 0.37 0.135 0.251 0.7 0.31

I included OPS in this exercise to give you an idea of how closely that category relates to wOBA. Since there were 54 qualifiers, I split the results into two tables. The first table consists of the players who produced a .370+ wOBA (considered a “great” mark) across the 2013-14 seasons, is sorted by wOBA, and can be seen here:

Name Team PA HR R RBI BB% K% BB/K ISO AVG OPS wOBA
Troy Tulowitzki COL 887 46 143 134 12.10% 16.00% 0.75 0.243 0.323 0.974 0.419
Miguel Cabrera DET 1337 69 204 246 11.20% 15.80% 0.71 0.248 0.329 0.983 0.418
Mike Trout LAA 1421 63 224 208 13.60% 22.50% 0.6 0.254 0.305 0.964 0.413
Jose Abreu CWS 622 36 80 107 8.20% 21.10% 0.39 0.264 0.317 0.964 0.411
Paul Goldschmidt ARI 1189 55 178 194 13.70% 21.40% 0.64 0.246 0.302 0.946 0.403
Andrew McCutchen PIT 1322 46 186 167 12.30% 16.30% 0.75 0.209 0.316 0.931 0.402
Michael Cuddyer COL 745 30 106 115 8.10% 17.40% 0.46 0.212 0.331 0.929 0.401
Hanley Ramirez LAD 848 33 126 128 9.80% 16.00% 0.61 0.216 0.308 0.907 0.394
Joey Votto CIN 998 30 133 96 18.20% 18.70% 0.97 0.177 0.291 0.891 0.389
Jayson Werth WAS 1161 41 169 164 12.30% 18.40% 0.67 0.187 0.304 0.887 0.389
Steve Pearce BAL 521 25 65 62 10.60% 19.40% 0.54 0.236 0.284 0.891 0.389
Jose Bautista TOR 1201 63 183 176 14.40% 15.00% 0.96 0.239 0.274 0.896 0.388
Edwin Encarnacion TOR 1163 70 165 202 12.40% 12.40% 1 0.27 0.27 0.903 0.388
Giancarlo Stanton MIA 1142 61 151 167 14.70% 27.10% 0.54 0.251 0.271 0.904 0.387
Yasiel Puig LAD 1072 35 158 111 9.60% 20.60% 0.47 0.197 0.305 0.888 0.387
David Ortiz BOS 1202 65 143 207 12.60% 15.20% 0.83 0.255 0.286 0.916 0.384
Freddie Freeman ATL 1337 41 182 187 11.70% 19.90% 0.59 0.177 0.303 0.871 0.38
Adrian Beltre TEX 1304 49 167 169 8.20% 11.70% 0.7 0.181 0.319 0.88 0.379
Victor Martinez DET 1309 46 155 186 9.50% 7.90% 1.19 0.178 0.317 0.876 0.374
Robinson Cano – – – 1346 41 158 189 9.40% 11.40% 0.82 0.171 0.314 0.868 0.373
Carlos Gonzalez COL 717 37 107 108 8.40% 26.20% 0.32 0.25 0.276 0.864 0.372
Chris Davis BAL 1198 79 168 210 11.00% 31.10% 0.35 0.287 0.247 0.873 0.371
Adam Lind TOR 839 29 105 107 9.40% 18.00% 0.52 0.189 0.301 0.856 0.371
Matt Holliday STL 1269 42 186 184 11.30% 14.70% 0.77 0.179 0.285 0.843 0.371

This first table contains 24 players, and is quite the impressive list. The cream of the crop mixing patience and pop. I’m a poet and I don’t even know it! Well, not really. Some thoughts and observations:

• As you can see, OPS correlates closely with wOBA throughout these results. However, you can see the slight differences between them if you look closely. For example, Adrian Beltre has a higher ISO and batting average than Freddie Freeman, but Freeman’s wOBA edges out Beltre’s due to his much higher BB% (11.7% to 8.2%), while Beltre holds a slight edge in OPS.

• Speaking of Freeman, let’s compare his seasonal averages over the past two years to another qualifier on this list – Matt Holliday:

Freeman: 91 R, 20.5 HR, 93.5 RBI, 2 SB, .303 BA

Holliday: 93 R, 21 HR, 92 RBI, 5 SB, .285 BA

As you can see in the above comparison of their respective 5×5 stat lines as well as some of the more advanced metrics that are included in the table, their numbers have been quite similar across the board. In ESPN drafts, Freeman’s current ADP is 26.3 while Holliday’s is 84.7.

• These search requirements focusing on on-base skills result in mostly balanced R/RBI production throughout the list. Some players such as Ortiz and Davis skew more toward the RBI side while Votto and Puig are heavier on the runs side. That might change for Puig if he sticks in the 3rd spot in the Dodgers lineup this season.

• How much regression in Cuddyer’s numbers can be expected after trading Coors Field for Citi Field in the offseason? Does the soon-to-be 36 year old outfielder have anything left in the tank? The good news is that it won’t cost you much to find out the answers to those questions (NFBC ADP 0f 249.68, ESPN is 211.7).

Table #2 features the players who produced a wOBA between .340 (considered to be “above average”) and .370 and can be seen here:

Name Team PA HR R RBI BB% K% BB/K ISO AVG OPS wOBA
Josh Donaldson OAK 1363 53 182 191 11.20% 17.60% 0.63 0.2 0.277 0.84 0.367
Buster Posey SF 1200 37 133 161 8.90% 11.60% 0.77 0.168 0.303 0.838 0.364
Shin-Soo Choo – – – 1241 34 165 94 13.70% 21.30% 0.64 0.157 0.266 0.811 0.362
Mike Napoli BOS 1078 40 128 147 14.00% 29.70% 0.47 0.199 0.254 0.818 0.361
Justin Upton ATL 1284 56 171 172 10.50% 25.90% 0.41 0.211 0.267 0.826 0.36
Matt Carpenter STL 1426 19 225 137 11.70% 14.70% 0.8 0.133 0.296 0.813 0.36
Carlos Santana CLE 1302 47 143 159 15.80% 18.00% 0.88 0.191 0.25 0.812 0.359
Anthony Rizzo CHC 1306 55 160 158 11.40% 18.60% 0.61 0.212 0.258 0.822 0.359
Jonathan Lucroy MIL 1235 31 132 151 9.10% 11.30% 0.8 0.169 0.291 0.817 0.357
Bryce Harper WAS 892 33 112 90 11.10% 22.20% 0.5 0.184 0.273 0.815 0.356
Brandon Belt SF 806 29 106 94 8.70% 23.40% 0.37 0.196 0.275 0.816 0.355
Brandon Moss OAK 1085 55 143 168 10.80% 27.00% 0.4 0.234 0.244 0.813 0.353
Lucas Duda NYM 980 45 116 125 12.70% 24.20% 0.52 0.214 0.242 0.806 0.353
Joe Mauer MIN 1026 15 122 102 11.80% 18.00% 0.65 0.123 0.3 0.805 0.353
Matt Kemp LAD 889 31 112 122 8.30% 24.90% 0.33 0.189 0.281 0.81 0.352
Ryan Zimmerman WAS 873 31 110 117 9.40% 19.50% 0.48 0.184 0.276 0.804 0.351
Ryan Braun MIL 833 28 98 119 8.20% 20.30% 0.4 0.191 0.275 0.805 0.349
Jhonny Peralta – – – 1076 32 111 130 8.60% 19.50% 0.44 0.169 0.28 0.794 0.349
Prince Fielder – – – 890 28 101 122 11.20% 15.80% 0.71 0.165 0.273 0.8 0.348
Dexter Fowler – – – 997 20 132 77 13.10% 21.40% 0.62 0.133 0.27 0.775 0.347
David Wright NYM 1078 26 117 121 9.00% 17.80% 0.51 0.15 0.286 0.791 0.346
Daniel Nava BOS 944 16 118 103 8.90% 18.40% 0.48 0.119 0.289 0.776 0.345
Anthony Rendon WAS 1077 28 151 118 8.30% 16.10% 0.51 0.166 0.279 0.788 0.345
Neil Walker PIT 1122 39 136 129 8.50% 15.40% 0.55 0.182 0.262 0.784 0.345
John Jaso OAK 593 12 73 61 11.10% 17.70% 0.63 0.14 0.267 0.765 0.342
Kyle Seager SEA 1349 47 150 165 8.90% 17.80% 0.5 0.176 0.264 0.776 0.341
Russell Martin PIT 966 26 96 122 12.10% 19.30% 0.63 0.146 0.256 0.764 0.341
Chris Carter HOU 1157 66 132 170 10.90% 34.10% 0.32 0.246 0.225 0.785 0.341
Christian Yelich MIA 933 13 128 70 10.80% 21.80% 0.5 0.116 0.285 0.765 0.341
Devin Mesoraco CIN 792 34 85 122 8.20% 20.70% 0.4 0.198 0.257 0.782 0.34

• Lots of potential rebound candidates appear on this second list. Choo, Napoli, Mauer, Zimmerman, Braun, Fielder, and Wright just to name a few. Were their injuries and disappointing results in ’14 a sign of things to come or just a blip on the radar?

• Several catchers qualified here as well. Posey and Lucroy have established themselves as elite producers at the position. Jaso, Martin, and Mesoraco have proven to be capable options too. Jaso in particular is looks like a useful player to utilize in the RCL format if punting or streaming the catcher position is a strategy that you plan on using.

• Upton and Rizzo get dinged slightly when sorting by wOBA instead of OPS, while Choo, Napoli, and Santana get rewarded for their high BB-rates.

OPS or wOBA – which metric do you place more emphasis on?