Lab 3 Pitchers and Catchers

The third lab utilizes Voyant to examine word trends and popular words used in the tweets that we find. At this time, we are preparing for spring training as well as the World Baseball Classic when players get to play for their home country and represent their nation. Popular words I examined were SpringTraining, Yankees, Red Sox, Mexico, and happy. Yankees and Red Sox are two of the most popular teams in the sport and generate fan support all around the country. Mexico is seeing a heavy representation as they prepare for the WBC. This is also just a happy time; warm weather, the beginning of baseball, and fans coming together to support their favorite teams! Lastly, Spring Training kicks off the MLB season and the first players to report are pitchers and catchers, which were also popular words that I saw. A good MLB team separates itself from the pack with good pitching and catching, making those positions highly important to a team. My next study will examine catcher production and also their draft stock as well as top pitchers that appear out of the bullpen in late innings to hold a close lead.

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Catchers are not only intended to be defensive stalwarts, but they also must have a positive impact on the team’s offense to be a great all around player. For the most part, it is difficult to evaluate a catcher defensively through statistics. Even with new sabermetric measurements used to analyze catchers, they do a poor job representing actual data because the pitchers play a large role in many facets of a catcher’s statistics. In some cases a pitcher’s ERA is more representative of a catcher than the actual catcher’s statistics. This can be seen with pitcher Francisco Liriano posting his best seasons while pitching to Russell Martin, in his seasons without Martin his ERA and overall statistics are much worse. When the battery was reunited during the second half of the 2016 season, Liriano returned to his all-star caliber with Martin as his catcher. Another example is the utilization of David Ross, one of the oldest catchers in the MLB played for multiple World Series championship teams because of how his presence and ability affects the pitching staff. Stats we can examine are the offensive production of these players, as great as they may be defensively; the MLB is a heavily offensive oriented league.

2016 Catcher Stats Post All Star Break
Name Team Age Draft Round Sign Pitches zBall% oStr% Calls PerGame Runs Above Avg Career CS% 2016 CS% Career BA 2016 BA Career OPS 2016 OPS
Avila, A White Sox 29 5 Coll 2811 17.8 6 -63 -1.76 -8.4 29% 25% 0.242 0.236 0.740 0.720
Bandy, J Angels 26 31 Coll 2584 13 6.8 -6 -0.17 -0.8 42% 42% 0.284 0.281 0.861 0.832
Barnhart, T Reds 25 10 HS 6505 15.8 7.1 -54 -0.65 -7.2 34% 33% 0.250 0.261 0.669 0.723
Bethancourt, C Padres 24 Pan 2456 16.3 7.3 -22 -0.7 -2.9 36% 30% 0.230 0.250 0.586 0.698
Brown, T Giants 24 10 Coll 2483 15 6.2 -30 -0.95 -4 22% 22% 0.249 0.254 0.677 0.704
Butera, D Royals 33 5 Coll 1704 13.7 6.8 -9 -0.43 -1.3 33% 30% 0.194 0.282 0.538 0.846
Cabrera, R Reds 26 Ven 1976 21.5 6.3 -65 -2.57 -8.6 38% 33% 0.278 0.250 0.663 0.601
Casali, C Rays 27 10 Coll 4530 11.3 6.9 19 0.33 2.6 31% 37% 0.187 0.168 0.646 0.575
Castillo, W D-backs 29 DR 5818 14.2 7.7 5 0.07 0.6 30% 33% 0.252 0.254 0.731 0.736
Castro, J Astros 29 1 Coll 6044 11.3 8.5 90 1.17 12 26% 17% 0.233 0.206 0.698 0.671
Centeno, J Twins 26 32 HS (PR) 2436 16 4.4 -68 -2.18 -9 16% 18% 0.232 0.267 0.599 0.700
Cervelli, F Pirates 30 Ven 4485 16 10.1 54 0.95 7.2 22% 20% 0.281 0.263 0.734 0.671
Chirinos, R Rangers 32 Ven 2861 16.3 6 -52 -1.4 -6.9 29% 19% 0.224 0.184 0.709 0.711
Clevenger, S Mariners 30 7 Coll 1424 18.5 6.1 -36 -1.95 -4.7 17% 31% 0.227 0.221 0.608 0.611
Conger, H Rays 28 1 HS 2920 13.9 6.4 -21 -0.56 -2.8 19% 19% 0.221 0.194 0.660 0.571
Contreras, W Cubs 24 Ven 1822 14 8.8 18 0.75 2.3 38% 38% 0.265 0.265 0.795 0.795
d’Arnaud, T Mets 27 1 HS 3374 12.9 9.1 48 1.1 6.3 23% 21% 0.243 0.234 0.707 0.601
Ellis, A Dodgers 35 18 Coll 3115 14.4 7 -15 -0.38 -2 34% 29% 0.239 0.206 0.691 0.562
Flowers, T Braves 30 33 Coll 3631 11.6 9 63 1.35 8.4 25% 5% 0.227 0.253 0.677 0.769
Fryer, E Pirates 1093 19.1 8.1 -10 -0.72 -1.3
Fryer, E Cardinals 30 10 Coll 673 14.1 5.5 -9 -1.09 -1.3 21% 13% 0.257 0.280 0.667 0.670
Garneau, D Rockies 29 19 Coll 1293 17 7.1 -17 -1.05 -2.3 38% 42% 0.192 0.233 0.582 0.665
Gattis, E Astros 29 23 Coll 2503 13.1 7.9 14 0.42 1.8 28% 48% 0.250 0.246 0.772 0.748
Gimenez, C Indians 33 19 Coll 2610 13.2 8.1 15 0.45 2 22% 18% 0.217 0.210 0.625 0.555
Gomes, Y Indians 29 10 Coll 4748 14.4 7.2 -18 -0.29 -2.4 35% 37% 0.246 0.265 0.707 0.512
Gosewisch, T D-backs 32 11 Coll 1055 16.3 5.7 -18 -1.36 -2.4 36% 25% 0.205 0.154 0.513 0.421
Grandal, Y Dodgers 27 1 Coll 5677 9.4 9 132 1.82 17.6 23% 27% 0.240 0.236 0.778 0.847
Hanigan, R Red Sox 35 USA 2225 14.9 6.6 -21 -0.74 -2.8 37% 33% 0.250 0.158 0.679 0.437
Herrmann, C D-backs 28 6 Coll 2268 18.1 6.4 -45 -1.56 -6 38% 39% 0.213 0.290 0.624 0.853
Hundley, N Rockies 32 2 Coll 4308 16.7 6.3 -74 -1.33 -9.8 27% 14% 0.248 0.237 0.702 0.713
Iannetta, C Mariners 33 4 Coll 5752 15.8 6.4 -77 -1.04 -10.2 25% 29% 0.229 0.212 0.746 0.644
Joseph, C Orioles 30 7 Coll 2704 13.2 8.8 30 0.88 4 36% 33% 0.219 0.202 0.631 0.473
Kratz, E Astros 36 29 Coll 709 13.3 7.5 1 0.16 0.2
Kratz, E Pirates 1153 14.5 7.1 -5 -0.31 -0.6 34% 44% 0.200 0.094 0.610 0.258
Leon, S Red Sox 27 Ven 2609 15.4 6.5 -29 -0.86 -3.8 46% 45% 0.258 0.363 0.662 0.995
Lobaton, J Nationals 31 Ven 1700 15.8 11 29 1.34 3.9 21% 27% 0.222 0.194 0.628 0.662
Lucroy, J Rangers 30 3 Coll 6914 14.3 8.6 46 0.53 6.1 28% 40% 0.284 0.295 0.780 0.846
Maile, L Rays 25 8 Coll 1224 15.3 6.4 -12 -0.75 -1.6 27% 21% 0.169 0.167 0.429 0.428
Maldonado, M Brewers 29 27 HS (PR) 2766 13.9 9.1 27 0.75 3.5 34% 36% 0.217 0.192 0.635 0.685
Martin, R Blue Jays 33 17 Coll 6323 12.4 7.9 47 0.57 6.2 32% 15% 0.255 0.228 0.752 0.673
Mathis, J Marlins 33 1 HS 2094 15 9.4 17 0.64 2.3 27% 29% 0.196 0.240 0.563 0.622
Maxwell, B Athletics 25 2 Coll 370 8.5 6.4 4 0.8 0.5 0-2 0-2 0.100 0.100 0.150 0.150
McBride, M Athletics 31 2 Coll 741 16.7 5.3 -18 -1.87 -2.4 13% 13% 0.205 0.231 0.538 0.558
McCann, B Yankees 32 2 HS 5846 13.4 8.1 34 0.46 4.6 26% 23% 0.266 0.231 0.799 0.739
McCann, J Tigers 26 2 Coll 4750 14.1 6.5 -36 -0.59 -4.8 43% 51% 0.243 0.204 0.649 0.592
Mesoraco, D Reds 28 1 HS 1105 13.6 6.1 -10 -0.71 -1.3 25% 27% 0.237 0.140 0.718 0.378
Molina, Y Cardinals 34 4 HS (PR) 7496 13.2 8.1 46 0.48 6.1 43% 21% 0.282 0.281 0.733 0.735
Montero,M Cubs 33 Ven 3595 11.3 10.1 95 2.07 12.7 28% 6% 0.258 0.190 0.756 0.632
Murphy, J Twins 25 2 HS 881 15.3 7.2 -6 -0.53 -0.8 27% 20% 0.242 0.075 0.625 0.219
Narvaez, O White Sox 24 Ven 510 17 6.4 -8 -1.17 -1 0-4 0-4 0.409 0.409 1.006 1.006
Navarro, D White Sox 32 Ven 5715 18.3 6.9 -108 -1.47 -14.3 28% 22% 0.251 0.209 0.682 0.607
Nicholas, B Rangers 28 6 Coll 832 13.1 7.2 0 -0.04 -0.1 25% 25% 0.257 0.257 0.876 0.876
Norris, D Padres 27 4 HS 6785 13.2 7.9 34 0.4 4.6 27% 25% 0.237 0.189 0.698 0.600
Pena, B Cardinals 34 Cuba 130 10.9 3.6 -2 -1 -0.2 28% 0-4 0.259 0.125 0.65 0.347
Pena, F Orioles 26 DR 727 18.3 5.9 -18 -1.92 -2.4 57% 50% 0.205 0.219 0.526 0.577
Perez, C Angels 25 Ven 4703 13.6 6.4 -30 -0.5 -4 38% 38% 0.238 0.223 0.625 0.601
Perez, R Indians 27 33 Coll 1349 11.7 7.7 9 0.53 1.2 41% 50% 0.217 0.093 0.667 0.368
Perez, S Royals 26 Ven 7127 15.5 5.9 -114 -1.25 -15.2 35% 53% 0.276 0.262 0.740 0.757
Phegley, J Athletics 28 1 Coll 1830 16.5 6.6 -27 -1.13 -3.5 32% 11% 0.232 0.256 0.654 0.686
Pierzynski, A Braves 39 3 HS 4525 14.2 5.8 -56 -0.96 -7.4 24% 24% 0.280 0.215 0.739 0.530
Plawecki, K Mets 25 1 Coll 2761 10.3 7.5 30 0.84 3.9 25% 24% 0.210 0.194 0.570 0.559
Posey, B Giants 29 1 Coll 6360 10.3 9.6 165 2.02 21.9 34% 46% 0.308 0.291 0.854 0.823
Ramos, W Nationals 29 Ven 6624 13 6.5 -23 -0.27 -3 33% 35% 0.271 0.337 0.750 0.938
Realmuto, J Marlins 25 3 HS 7153 15.7 6.2 -103 -1.12 -13.7 31% 37% 0.281 0.311 0.722 0.762
Recker, A Braves 32 18 Coll 1067 17.6 5.2 -27 -1.95 -3.6 28% 29% 0.200 0.389 0.626 1.005
Rivera, R Mets 33 2 HS (PR) 2331 12.8 7.3 7 0.23 0.9 37% 29% 0.212 0.214 0.594 0.636
Romine, A Yankees 27 2 HS 2513 13.1 6.2 -15 -0.47 -2 21% 13% 0.219 0.244 0.580 0.660
Rosario, A Cardinals 29 DR 584 19 8.2 -5 -0.61 -0.6 17% 17% 0.208 0.208 0.519 0.519
Ross, D Cubs 39 7 Coll 2879 12.9 9.5 48 1.3 6.4 35% 27% 0.228 0.223 0.737 0.763
Ruiz, C Phillies 37 Pan 3449 16.4 6.3 -55 -1.24 -7.3 27% 41% 0.266 0.262 0.745 0.722
Rupp, C Phillies 27 3 Coll 5639 14.1 7.4 -9 -0.12 -1.2 32% 23% 0.248 0.275 0.722 0.820
Saltalamacchia, J Tigers 31 1 HS 3659 15.9 6 -59 -1.27 -7.9 22% 27% 0.238 0.197 0.729 0.716
Sanchez, G Yankees 23 DR 344 11.4 8.7 6 1.45 0.8 3-5 3-5 0.289 0.302 0.808 0.845
Severino, P Nationals 23 DR 300 19.4 7.2 -6 -1.47 -0.8 1-3 1-3 0.375 0.417 0.911 0.950
Soto, G Angels 33 11 HS 1534 15.2 7.5 -7 -0.36 -0.9 27% 21% 0.246 0.284 0.765 0.811
Stewart, C Pirates 34 12 Coll 1957 17.5 8 -18 -0.71 -2.4 29% 23% 0.235 0.198 0.604 0.579
Sucre, J Mariners 28 Ven 200 17.6 9.1 0 0.01 0 37% 0-4 0.176 0.125 0.436 0.425
Suzuki, K Twins 32 2 Coll 5960 14.6 7.7 -7 -0.09 -0.9 24% 19% 0.257 0.283 0.686 0.759
Swihart, B Red Sox 24 1 HS 515 14.4 7.2 -2 -0.29 -0.3 28% 25% 0.271 0.258 0.714 0.72
Thole, J Blue Jays 29 13 HS 2532 14.2 6.6 -18 -0.56 -2.4 24% 26% 0.242 0.158 0.620 0.460
Vazquez, C Red Sox 25 9 HS (PR) 3579 14.4 8.8 23 0.5 3.1 45% 36% 0.233 0.226 0.601 0.583
Vogt, S Athletics 31 12 Coll 6055 16.3 6 -110 -1.42 -14.6 33% 31% 0.261 0.272 0.743 0.749
Wieters, M Orioles 30 1 Coll 5904 15.2 6.6 -59 -0.78 -7.9 33% 39% 0.256 0.233 0.736 0.663
Wolters,T Rockies 24 3 HS 3338 11.7 9.6 70 1.64 9.3 29% 29% 0.233 0.233 0.657 0.657
Zunino, M Mariners 25 1 Coll 1333 13.8 9.5 20 1.18 2.7 28% 25% 0.196 0.271 0.630 1.108

These statistics represent catchers both offensively and defensively. In addition to their statistics it displays their age and their draft position or if a team instead signed them as undrafted free agents. Ideally, teams spend a lot of time trying to find a next top tier catcher, but it seems more forced than developed. Players drafted in the top two rounds are some of the best amateur athletes available and the amount of catcher drafted that early that do not pan out is astounding.

 

Catchers drafted before 3rd round 101 total since 2000

2015: 4

2014: 6

2013: 5

2012: 9

2011: 6

2010: 5

2009: 7

2008: 4

2007: 10

2006: 2

2005: 6

2004: 8

2003: 5

2002: 6

2001: 4

2000: 5

 

2000-2010: 71 total

20: did not make to pros

26: made 100 or less PA’s

37: made less than 500 PA’s

7: no longer catch: upper tier of successful draft choices

20: over 1000 PA’s (13 still at catcher)

28.2% success rate overall

20.3% success rate as catcher        

 

10 Starting Catchers in 2016 were not drafted (33%)

 

From 2000-2010 71 catchers were drafted in the first two rounds and only 13 made it as big league catchers (not all even everyday starters/impact players). This is actually a waste of a top pick when better positional players are available. 1/3 of the starting catchers this past season weren’t even drafted making catchers poor choices for a first or second round pick. Also, whether a catcher was drafted out of high school or college also tells a story about their possible future success, but I will discuss that element of the draft in a later lab. The only standout catcher drafted in the first round is Buster Posey on the San Francisco Giants. Posey is a tremendous all around catcher, and one of few that remained at the position despite how good he is offensively. It is not atypical to see a catcher switch positions to protect them from injuries that can occur from catching. Players like Wil Myers and Josh Donaldson are among superstars that did not stay at catcher once their offensive prestige was discovered. However, Posey is an interesting case because although he is a catcher, he played multiple positions in college at Florida State, including Shortstop. This tells me that he is one of the top offensive players overall and he doesn’t have to remain at catcher.

 

Switching to the bullpen, pitchers are used in high leverage situations expected to do what they do best, get outs without allowing runs. This is done most efficiently by inducing ground ball outs or getting strikeouts (swing and miss percentage). The pitchers below are some of the best relievers in the MLB.

Creating a Bullpen (Post all star break)
Player Team POS IP ERA ERA- FIP DRA HR allowed OPP BA BA vs R BA v L ERA Rest 0 ERA Rest 1 ERA Rest 2+ K/9 K% SO/BB GB% FB mov FB xmov FB vel FB swing/miss% LD%
O’Day, D BAL RHP 26.0 3.46 77 4.67 3.71 5 0.214 0.215 0.212 5.40 6.14 1.32 11.1 28.80% 2.67 36.40% 8.4 -7.3 86.5 19.30% 32%
Britton, Z BAL LHP 46.2 0.58 13 1.89 2.23 1.00 0.138 0.139 0.135 1.46 0.00 0.39 10.60 31.80% 4.23 79.60% 8.60 7.4 96.30 19.20% 14%
Chapman, A NYY/CHC LHP 36.0 2.00 46 1.69 2.48 2 0.165 0.160 0.190 0.00 4.15 2.40 12.8 37.80% 6.38 42.70% 12.3 5.0 100.1 17.50% 33%
Capps, C MIA RHP (2015) 31.0 1.16 30 1.1 1.69 2 0.168 0.175 0.160 0.00 1.29 1.33 16.8 49.20% 8.29 40.80% 10.2 -6.8 98 16.40% 29%
Jansen, K LAD RHP 48.0 1.31 33 1.57 2.57 2 0.147 0.096 0.200 1.93 0.00 1.59 12 36.60% 9.14 32.70% 9.8 2.0 93.2 15.40% 17%
Wilson, J DET LHP 42.0 3.64 98 1.92 2.82 2 0.250 0.247 0.254 7.27 2.03 3.15 10.5 28.50% 8.17 59.50% 12.5 5.6 95.4 15.20% 19%
Familia, J NYM RHP 51.2 2.96 75 2.51 3.71 0 0.241 0.229 0.264 2.00 5.74 1.50 9.1 24.10% 2.26 62.50% 9.7 -8.2 95.9 15.00% 21%
Herrera, K KC RHP 49.2 1.63 50 2.05 2.37 3 0.199 0.179 0.221 1.54 0.00 2.78 11.4 32.60% 7.88 44.90% 10.7 -4.8 97.3 14.80% 22%
Oh, S STL RHP 56.2 2.06 51 2.13 2.41 3 0.172 0.167 0.177 2.20 2.60 1.57 11.4 32.60% 5.14 40.30% 10.5 -4.8 92.7 13.70% 22%
Kimbrel, C BOS RHP 35.1 3.31 79 2.6 2.69 3 0.167 0.162 0.172 2.08 3.86 3.68 14.3 38.90% 3.5 32.90% 10.3 -5.6 97.5 13.60% 19%
Allen, C CLE RHP 46.0 2.54 58 3.53 2.71 6 0.193 0.173 0.213 0.68 5.68 1.80 11.2 31.30% 3.17 43.80% 12.2 -5.0 94.1 12.30% 25%
Brach, B BAL RHP 57.2 1.25 29 2.49 2.86 3 0.174 0.081 0.265 0.00 2.65 0.98 10.1 29.30% 3.61 40.30% 9.9 -4.7 94.4 12.20% 23%
Osuna, R TOR RHP 50.1 1.79 45 2.32 3.05 3 0.180 0.149 0.221 1.13 2.08 2.11 10.9 31.30% 5.55 33.10% 11.7 -3.5 95.7 12.00% 23%
Davis, W KC RHP 33.2 1.60 38 2.75 4.12 0 0.186 0.196 0.177 0.00 0.00 2.84 8.6 23.70% 2.13 49.40% 11.1 -3.0 95.1 11.70% 15%
Betances, D NYY RHP 52.0 2.42 55 1.27 1.57 3 0.196 0.154 0.247 1.38 3.86 1.50 15.9 44.70% 6.13 53.10% 9.8 -2.1 97.4 11.70% 19%
Thornburg, T MIL RHP 44.2 2.22 51 2.87 2.56 5 0.175 0.211 0.123 4.35 1.38 1.69 12.3 34.90% 4.36 35.40% 11.7 -2.8 94.1 10.20% 25%
Gregerson, L HOU RHP 43.2 3.09 73 2.42 2.59 2 0.168 0.111 0.230 5.19 3.00 2.25 10.5 29.80% 3.92 62.70% 5.5 -5.0 89.4 9.60% 18%
Madson, R OAK RHP 45.2 3.74 88 4.54 4.67 7 0.254 0.265 0.240 8.44 2.65 2.00 7.1 19.00% 2.57 40.70% 10.9 -6.9 94.5 9.30% 26%
Rosenthal, T STL RHP 33.1 5.13 127 4.12 4.6 3 0.301 0.307 0.292 2.57 0.00 8.83 13 28.70% 1.78 55.70% 11.1 -4.7 97.1 9.30% 27%
Reed, A NYM RHP 51.1 1.93 48 1.85 2.3 3 0.186 0.179 0.197 1.65 0.60 3.15 11.6 33.20% 7.33 41.00% 9.7 -2.1 92.2 9.30% 25%
Rondon, H CHC RHP 42.1 1.70 41 2.38 2.62 4 0.171 0.148 0.207 2.45 2.08 0.98 10.8 32.90% 10.2 45.80% 10 -4.6 95.9 9.20% 20%
Melancon, M PIT/WAS RHP 44.2 1.41 35 2.51 3.18 2 0.199 0.185 0.217 0.00 1.59 2.12 8.5 24.30% 4.67 49.20% 5.2 3.1 90.6 9.00% 22%
Siegrist, K STL LHP 42.0 2.79 71 4.47 3.97 7 0.185 0.170 0.217 3.12 3.55 2.89 9.2 25.90% 2.69 32.70% 11.5 5.7 93.2 8.70% 18%
Robertson, D CHW RHP 45.1 3.97 95 4.13 3.27 6 0.232 0.256 0.209 2.16 0.90 7.23 10.1 26.30% 2.22 48.70% 7.8 2.2 91.9 7.80% 15%
Watson, T PIT LHP 47.1 2.66 71 3.66 3.93 4 0.194 0.188 0.208 1.74 0.53 3.60 8 22.20% 2.47 47.20% 12.4 11.2 93.2 7.50% 19%
Hendriks, L OAK RHP 40.1 4.69 107 3.46 4.49 5 0.292 0.311 0.259 2.25 6.48 3.66 8.5 22.00% 5.43 42.90% 11.1 -2.2 94.1 7.30% 26%
Giles,K HOU RHP 42.2 3.80 89 2.76 2.91 5 0.246 0.253 0.238 5.00 6.10 2.31 13.3 34.10% 4.5 35.60% 10.8 -1.7 96.9 7.30% 32%
Colome, A TBR RHP 40.0 2.03 50 2.76 2.43 4 0.201 0.200 0.203 0.00 2.89 2.61 11.9 33.80% 4.42 54.90% 11.3 0.2 94.6 7.30% 21%
Barraclough, K MIA RHP 48.0 2.81 70 2.28 2.24 1 0.179 0.170 0.189 1.72 2.35 4.24 14.6 38.10% 2.44 55.60% 10 -3.2 95.5 7.20% 20%
Smith, J LAA/CHC RHP 39.1 3.89 97 4.96 4.03 5 0.264 0.272 0.254 2.08 4.24 4.97 5.7 14.80% 1.79 52.80% 10.3 -10.0 88 6.60% 26%
Strop, P CHC RHP 42.0 2.79 65 2.94 2.43 4 0.168 0.176 0.149 7.71 2.53 0.82 11.6 32.30% 4.15 58.90% 10.9 -8.6 95.1 6.10% 20%
Storen, D TOR/SEA RHP 37.2 6.45 144 4.89 4.43 6 0.306 0.276 0.356 8.44 7.62 5.12 7.9 18.80% 2.75 48.00% 10.5 -9.5 91.6 6.00% 22%
Grimm, J CHC RHP 35.1 4.58 111 3.78 3.79 4 0.235 0.195 0.296 6.75 7.00 2.45 10.2 26.10% 2.5 43.30% 11.1 -3.2 94.3 6.00% 27%
Ramos, A MIA RHP 44.1 3.05 74 2.93 3.17 1 0.210 0.213 0.207 0.63 3.27 4.74 11.4 29.00% 2.15 37.50% 10.3 -2.6 91.8 5.80% 29%
Wood, T CHC LHP 44.2 2.62 64 4.61 4.69 6 0.185 0.235 0.136 0.92 5.79 0.87 7.3 19.70% 1.89 37.30% 11.3 2.8 90.3 5.70% 26%
Cecil, B TOR LHP 21.1 5.06 119 3.84 4.87 3 0.318 0.308 0.333 10.38 0.00 5.73 8.9 21.90% 4.2 40.30% 10 4.6 91 5.10% 29%
Smith, W MIL/SFG LHP 24.0 4.13 119 3.93 3.83 3 0.236 0.174 0.302 7.50 3.86 1.42 9.4 24.80% 2.78 34.90% 11.6 4.5 91.7 4.60% 24%
Miller, A NYY/CLE LHP 48.0 1.50 39 1.9 1.45 6 0.175 0.163 0.208 0.00 0.96 3.00 15.4 45.10% 11.71 54.40% 10.9 6.3 94.3 3.20% 16%

After the 2015 season, the Syracuse University Diamond Dollars Team concluded that the best bullpen (per player role) was:

Andrew Miller

Zach Britton

Juerys Familia

Darren O’Day

Brett Cecil

 

This is the best combination of power pitching (fastball velocity) as well as ball movement generated from spin rate and pitcher arm slot (angle). These pitchers are the best in their particular late inning roles because of their ability to get outs. The most interesting pitcher did not make this list due to suffering an injury during the season was Carter Capps.

 

Carter Capps: (Height: 6’5)

Velocity:                          Pitch                              Perceived Velocity         Spin Rate         Extension

101.3 Four-seam FB    104.4        2,483  

 

8.1

 

 

Capps has a unique pitching motion allowing him to reach farther out and cut distance between him and the plate allowing his 101mph fastball to appear to be moving at 104mph.

Pitchers and catcher play a major role in defining a team’s success throughout the year. Examining these players this in depth allows teams to gather a better understanding of the expected output these players can provide. Fans understand the importance of these players and that is why they also have more time at the beginning of the year to develop their skills before the rest of the team arrives in the spring.

 

Gennaro, Vince. Diamond dollars: the economics of winning in baseball. Purchase, NY: Diamond Analytics, 2013. Print.

SunSportsFOXSportsFL. “Marlins Mechanics- Carter Capps.” YouTube. YouTube, 13 Aug. 2015. Web. 26 Mar. 2017.

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