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?