Streaming for Stolen Bases by Catcher
This week I am doing Part 2 of my ground-breaking, innovative, revolutionary, completely original piece from two weeks ago that no one has ever thought of before, but before I do I want to mention that it was pointed out to me that our very own Alex Chamberlain did some fantastic articles covering the exact same premise for DFS last season:
Streaming SBs by Opposing Pitcher
So, maybe I am not as innovative as I thought I was. We shall push on either way. Once again, this is a work in progress and I have no idea if this will work and unfortunately I am still unable to get catcher POP times and pitcher delivery times. I assume, if there is a practical application for this, it will work best in leagues with daily transactions or DFS like as Alex wrote in his articles.
So, let’s look at the catcher’s who have allowed the most stolen bases this season. One slight change is I have added a column the table with stolen bases allowed per inning which was a suggestion given to me when talking with Alex.
# | Name | Team | Pos | G | GS | Inn | SB | CS | CS % | SB/Inn |
1 | Jonathan Lucroy | MIL | C | 54 | 52 | 462.1 | 34 | 24 | 41.38% | 0.0736 |
2 | Chris Iannetta | SEA | C | 50 | 47 | 423 | 31 | 12 | 27.91% | 0.0733 |
3 | Russell Martin | TOR | C | 53 | 48 | 430 | 31 | 5 | 13.89% | 0.0721 |
4 | Tucker Barnhart | CIN | C | 41 | 41 | 361 | 29 | 16 | 35.56% | 0.0803 |
5 | Kevin Plawecki | NYM | C | 37 | 34 | 305.2 | 28 | 10 | 26.32% | 0.0917 |
6 | Miguel Montero | CHC | C | 32 | 29 | 257 | 27 | 2 | 6.90% | 0.1051 |
7 | Brian McCann | NYY | C | 48 | 44 | 389 | 25 | 6 | 19.35% | 0.0643 |
8 | Tyler Flowers | ATL | C | 36 | 31 | 284.2 | 23 | 2 | 8.00% | 0.0809 |
9 | Stephen Vogt | OAK | C | 46 | 43 | 381 | 23 | 10 | 30.30% | 0.0604 |
10 | Derek Norris | SDP | C | 50 | 50 | 430.1 | 23 | 12 | 34.29% | 0.0535 |
11 | Dioner Navarro | CHW | C | 39 | 36 | 323 | 22 | 7 | 24.14% | 0.0681 |
12 | Hank Conger | TBR | C | 31 | 24 | 206 | 21 | 6 | 22.22% | 0.1019 |
13 | Jarrod Saltalamacchia | DET | C | 32 | 29 | 261.2 | 21 | 7 | 25.00% | 0.0804 |
14 | A.J. Pierzynski | ATL | C | 34 | 32 | 282.1 | 21 | 7 | 25.00% | 0.0744 |
15 | Francisco Cervelli | PIT | C | 46 | 46 | 394.2 | 21 | 9 | 30.00% | 0.0533 |
16 | Yadier Molina | STL | C | 61 | 57 | 494.1 | 20 | 7 | 25.93% | 0.0405 |
17 | David Ross | CHC | C | 32 | 27 | 246.1 | 19 | 9 | 32.14% | 0.0772 |
18 | Yasmani Grandal | LAD | C | 39 | 35 | 313.1 | 19 | 8 | 29.63% | 0.0607 |
19 | Yan Gomes | CLE | C | 48 | 45 | 398 | 18 | 9 | 33.33% | 0.0452 |
20 | Jason Castro | HOU | C | 49 | 45 | 406.2 | 18 | 4 | 18.18% | 0.0443 |
21 | Cameron Rupp | PHI | C | 38 | 38 | 333 | 17 | 5 | 22.73% | 0.0511 |
22 | Carlos Perez | LAA | C | 44 | 42 | 372.2 | 17 | 12 | 41.38% | 0.0457 |
23 | J.T. Realmuto | MIA | C | 53 | 50 | 449.2 | 17 | 14 | 45.16% | 0.0378 |
24 | Kurt Suzuki | MIN | C | 41 | 39 | 333.1 | 16 | 6 | 27.27% | 0.0480 |
25 | Geovany Soto | LAA | C | 18 | 17 | 149 | 15 | 6 | 28.57% | 0.1007 |
26 | Caleb Joseph | BAL | C | 23 | 19 | 175 | 15 | 8 | 34.78% | 0.0857 |
27 | Tony Wolters | COL | C | 27 | 26 | 227.2 | 15 | 7 | 31.82% | 0.0660 |
28 | Travis d’Arnaud | NYM | C | 13 | 13 | 112.2 | 14 | 3 | 17.65% | 0.1248 |
29 | Rene Rivera | NYM | C | 16 | 15 | 134 | 14 | 7 | 33.33% | 0.1045 |
30 | Trevor Brown | SFG | C | 25 | 17 | 167.2 | 14 | 4 | 22.22% | 0.0837 |
Now without having done this before, I want to make sure that I am not falling prey to too small of a sample size. So let’s look at the catchers that allowed the most SBs the last two seasons:
# | Name | Team | Pos | G | GS | Inn | SB | CS | CS% | SB/Inn |
1 | Francisco Cervelli | PIT | C | 128 | 124 | 1099.2 | 101 | 29 | 22.31% | 0.0919 |
2 | Derek Norris | SDP | C | 128 | 116 | 1040.2 | 84 | 44 | 34.38% | 0.0808 |
3 | Kurt Suzuki | MIN | C | 130 | 123 | 1096 | 80 | 14 | 14.89% | 0.0730 |
4 | Miguel Montero | CHC | C | 109 | 90 | 825 | 71 | 18 | 20.22% | 0.0861 |
5 | Jonathan Lucroy | MIL | C | 86 | 86 | 745 | 70 | 27 | 27.84% | 0.0940 |
6 | A.J. Pierzynski | ATL | C | 107 | 104 | 909.2 | 67 | 21 | 23.86% | 0.0737 |
7 | Salvador Perez | KCR | C | 139 | 137 | 1192.1 | 66 | 29 | 30.53% | 0.0554 |
8 | Brayan Pena | CIN | C | 86 | 84 | 754.1 | 58 | 13 | 18.31% | 0.0769 |
9 | Yasmani Grandal | LAD | C | 107 | 100 | 884.1 | 56 | 23 | 29.11% | 0.0633 |
10 | Tyler Flowers | CHW | C | 110 | 100 | 878.1 | 53 | 18 | 25.35% | 0.0604 |
11 | Nick Hundley | COL | C | 102 | 100 | 866.1 | 50 | 26 | 34.21% | 0.0577 |
12 | Brian McCann | NYY | C | 126 | 119 | 1042.1 | 50 | 28 | 35.90% | 0.0480 |
13 | David Ross | CHC | C | 59 | 46 | 402.1 | 49 | 17 | 25.76% | 0.1219 |
14 | Carlos Ruiz | PHI | C | 83 | 81 | 716 | 46 | 11 | 19.30% | 0.0642 |
15 | Chris Iannetta | LAA | C | 85 | 80 | 718.2 | 44 | 15 | 25.42% | 0.0613 |
16 | Stephen Vogt | OAK | C | 100 | 89 | 803 | 43 | 20 | 31.75% | 0.0535 |
17 | J.T. Realmuto | MIA | C | 118 | 116 | 1025.1 | 43 | 16 | 27.12% | 0.0419 |
18 | Hank Conger | HOU | C | 69 | 56 | 514.2 | 42 | 1 | 2.33% | 0.0817 |
19 | Jason Castro | HOU | C | 103 | 102 | 883.1 | 42 | 24 | 36.36% | 0.0476 |
20 | Mike Zunino | SEA | C | 112 | 101 | 919.2 | 42 | 22 | 34.38% | 0.0457 |
21 | Chris Stewart | PIT | C | 52 | 36 | 372.2 | 41 | 13 | 24.07% | 0.1102 |
22 | Carlos Perez | LAA | C | 80 | 75 | 665 | 41 | 25 | 37.88% | 0.0617 |
23 | James McCann | DET | C | 112 | 103 | 943.1 | 41 | 28 | 40.58% | 0.0435 |
24 | Blake Swihart | BOS | C | 83 | 78 | 688 | 41 | 16 | 28.07% | 0.0596 |
25 | Rene Rivera | TBR | C | 107 | 87 | 784.2 | 40 | 23 | 36.51% | 0.0510 |
26 | Russell Martin | TOR | C | 117 | 113 | 994 | 40 | 32 | 44.44% | 0.0402 |
27 | Buster Posey | SFG | C | 106 | 103 | 901.2 | 39 | 22 | 36.07% | 0.0433 |
28 | Yan Gomes | CLE | C | 91 | 90 | 800 | 39 | 19 | 32.76% | 0.0488 |
29 | Yadier Molina | STL | C | 134 | 131 | 1149.2 | 37 | 26 | 41.27% | 0.0322 |
30 | Caleb Joseph | BAL | C | 94 | 93 | 826.1 | 37 | 18 | 32.73% | 0.0448 |
# | Name | Team | Pos | G | GS | Inn | SB | CS | CS% | SB/Inn |
1 | Jonathan Lucroy | MIL | C | 136 | 133 | 1182.1 | 83 | 29 | 25.89% | 0.0702 |
2 | Jason Castro | HOU | C | 114 | 110 | 971 | 81 | 23 | 22.12% | 0.0834 |
3 | Carlos Ruiz | PHI | C | 109 | 104 | 960 | 74 | 28 | 27.45% | 0.0771 |
4 | Jarrod Saltalamacchia | MIA | C | 107 | 103 | 922.2 | 72 | 17 | 19.10% | 0.0781 |
5 | Alex Avila | DET | C | 122 | 116 | 1017.2 | 71 | 36 | 33.64% | 0.0698 |
6 | Mike Zunino | SEA | C | 130 | 125 | 1121 | 71 | 28 | 28.28% | 0.0633 |
7 | Yan Gomes | CLE | C | 126 | 121 | 1082 | 66 | 31 | 31.96% | 0.0610 |
8 | Miguel Montero | ARI | C | 131 | 130 | 1152 | 64 | 26 | 28.89% | 0.0556 |
9 | Kurt Suzuki | MIN | C | 119 | 115 | 1017.2 | 64 | 21 | 24.71% | 0.0629 |
10 | Tyler Flowers | CHW | C | 124 | 120 | 1052 | 62 | 26 | 29.55% | 0.0589 |
11 | Derek Norris | OAK | C | 114 | 93 | 870.1 | 60 | 12 | 16.67% | 0.0690 |
12 | Russell Martin | PIT | C | 107 | 106 | 940.2 | 59 | 37 | 38.54% | 0.0628 |
13 | Buster Posey | SFG | C | 111 | 109 | 929.1 | 59 | 25 | 29.76% | 0.0635 |
14 | Dioner Navarro | TOR | C | 112 | 102 | 907.1 | 58 | 15 | 20.55% | 0.0639 |
15 | Rene Rivera | SDP | C | 89 | 85 | 734 | 58 | 33 | 36.26% | 0.0790 |
16 | Travis d’Arnaud | NYM | C | 105 | 103 | 909 | 58 | 14 | 19.44% | 0.0638 |
17 | Hank Conger | LAA | C | 79 | 70 | 637.1 | 57 | 18 | 24.00% | 0.0895 |
18 | Welington Castillo | CHC | C | 106 | 103 | 916.1 | 57 | 28 | 32.94% | 0.0622 |
19 | Salvador Perez | KCR | C | 146 | 143 | 1248.2 | 57 | 25 | 30.49% | 0.0457 |
20 | Evan Gattis | ATL | C | 93 | 89 | 799 | 53 | 13 | 19.70% | 0.0663 |
21 | Devin Mesoraco | CIN | C | 109 | 104 | 936.2 | 51 | 18 | 26.09% | 0.0545 |
22 | A.J. Pierzynski | – – – | C | 87 | 80 | 721 | 50 | 11 | 18.03% | 0.0693 |
23 | John Baker | CHC | C | 55 | 51 | 463 | 50 | 9 | 15.25% | 0.1080 |
24 | Brian McCann | NYY | C | 108 | 101 | 889 | 49 | 29 | 37.18% | 0.0551 |
25 | Chris Iannetta | LAA | C | 104 | 92 | 835.1 | 49 | 21 | 30.00% | 0.0587 |
26 | Yasmani Grandal | SDP | C | 76 | 67 | 607.2 | 49 | 7 | 12.50% | 0.0807 |
27 | A.J. Ellis | LAD | C | 92 | 89 | 773.2 | 48 | 16 | 25.00% | 0.0621 |
28 | Robinson Chirinos | TEX | C | 91 | 88 | 784 | 44 | 29 | 39.73% | 0.0561 |
29 | Jose Molina | TBR | C | 80 | 70 | 628.1 | 38 | 14 | 26.92% | 0.0605 |
30 | Wilin Rosario | COL | C | 96 | 94 | 824 | 37 | 7 | 15.91% | 0.0449 |
The first thing I noticed with once reviewing this data is four names that appear in the Top 12 of each list: Jonathan Lucroy led the Majors in stolen bases allowed in 2014 and is on pace to do so again. Miguel Montero appears high upon all three lists as well Derek Norris. The surprising name in the Top 12 is Tyler Flowers. This surprises me because of his lack of innings this year in the time share in Atlanta, though maybe it shouldn’t considering his teammate A.J. Pierzynski is only a few slots below him and allowed the sixth most last season in MLB on the Braves.
The next thing I noticed was Yadier Molina’s name appearing as high as it did on the list. After speaking with Matt Thompson, a devout Cardinals homer and fellow fantasy analyst at FWFB, we believe his surgically repaired thumb from last year may be bothering him and has led to some defensive issues which are very uncharacteristic of him. This became very noticeable in the Cardinals recent series against the Giants; where defensive his miscues were rampant.
The last thing that struck me is when sorting by SB/Inn the names that jumped up to the top of the list were Travis d’Arnaud, Miguel Montero, Rene Rivera, and Hank Conger. Two Mets catchers being up there makes a ton of sense with Noah Syndergaard having ten more stolen bases allowed than the next pitcher in the majors (22) and Steven Matz being tied in third on that list (11). The Mets may be an interesting team to stream for stolen bases against rest of the way. Conger is renowned for being a poor catcher, so it is not surprising that he vaults to the top of that list. The Cubs are another team that may be a team to stream against considering that Montero is near the top of the list no matter how you sort it and Chicago has three of the top 25 pitchers that have allowed the most stolen bases this season in Jacob Arrieta (11), Kyle Hendricks (8), and Jon Lester (7).
So how do we apply this?
If we start with DFS, looking at today’s slate, one would like to think that the Mets catchers are the easiest to attack especially with Syndergaard on the mound. Thor starting tends to force DFS sites to lower the prices on opposing batters, so Gregory Polanco and Starling Marte are both undervalued at $4,200 on Draft Kings. The Cubs draw the Nationals in an early game, so a slightly depressed Bryce Harper ($4,600) and Anthony Rendon is $4,200 or which ever CF starts the game (both Michael Taylor and Ben Revere are $3,500.) are all interesting plays that can swipe a bag against either catcher. If you are playing the afternoon slate only, it may be better to look for cheap options in the Seattle/Tampa Bay game as long as Chris Iannetta and Hank Conger are each catching. Nori Aoki struggles against left-handed pitching but does have half his stolen bases against them this year and is only $3,400 on DK. However, the better options are probably on the Rays. Logan Morrison ($3,900) is tied with Brad Miller ($3,500) for second on the team with four stolen bases this year and both are particularly cheap today. Or we could always attempt to exploit Russell Martin’s poor caught stealing rate and add some underpriced Phillies, though with how Marco Estrada has pitched and how bad the Phillies are, I might avoid anyone except Odubel Herrera ($3,700).
In season long leagues, with daily transactions, I would look for under owned stolen base threats with good upcoming matchups. Atlanta has the Mets this weekend and SS Chase d’Arnaud may be filling in for injured shortstop Erick Aybar. He has two SBs in his last 15 days and is only 1% owned. Lucroy’s Brewers face the Dodgers this weekend and Howie Kendrick could be an interesting guy to target. He has two stolen bases, is only 12.3% owned in ESPN leagues and is eligible at multiple positions. They might be worth a stream in leagues where you are fishing for SBs.
Over the course of the rest of the season I will try and use these strategies to see if it helps my teams, especially in DFS and H2H formats. After the conclusion of the season, I will do a follow up article on how the implementation worked for my teams and look at the full-season data more closely to see if I see any usable trends.
Justin is the co-host on The Sleeper and The Bust Podcast and writes for Rotographs covering the Roto Riteup as well as other periodic articles. In addition to his work at Rotographs, Justin is the lead fantasy writer/analyst and co-owner for FriendswithFantasyBenefits.com, and the owner of The Great Fantasy Baseball Invitational. He is also a certified addiction treatment counselor. Follow Justin on Twitter @JustinMasonFWFB.
Great idea in concept. These tables should be sorted by SB/Inn and all inferences should be based on that. Looking at total SB allowed is a nearly useless way to determine who to stream against.