Strategy March 30, 2004

# Closers Are Three-Dimensional

By Chris Berger

Closers. Everybody knows that they’re one-dimensional players in roto baseball, right? A top reliever should never be taken in the early rounds, because while he will help you win the saves category, his low earned run average and WHIP won’t matter much because he doesn’t pitch enough innings. At least, that’s the conventional fantasy logic, passed on by armchair sluggers and back-seat bench coaches. It’s also a guideline that is likely to cost you in the final standings if your league-mates know the truth and you don’t.

Now, there may be some debate about the value of a 1.60 ERA in “only” 80 innings of work if you’re in an innings limit league, but if your league has no IP maximum, there’s no way that Eric Gagne could help your ERA as much as Mark Prior, right? (Assuming that you have a generic pitcher slot on your team, using weekly lineups and 5×5 roto scoring.) Well, let’s do the calculations.

To understand how much a pitcher helps out your staff ratios (ERA and WHIP), I use two derived stats that I call ERA Imp (for ‘improvement’) and WHIP Imp. ERA Imp measures how much a player’s ERA will affect the average fantasy team by taking into account both ERA and IP; the lower its value, the better.

The formula for calculating these stats is highly dependent on your league settings, specifically the number of teams per league and the number of pitchers per team.

Let’s assume you’re in a 12-team mixed league. Each team starts nine pitchers, with each team having an average of 5.5 starters and 3.5 relievers. I’ve selected the top 66 starters and the top 42 relievers from RotoTimes‘ value list, ranked by year-end dollar value. These may not be the top 108 pitchers in terms of ERA or ERA Imp, but they were the 108 pitchers deemed by RotoTimes to be the best pitchers last year in a 5×5 league (or at least the top 108 based on our assumed SP/RP ratio).

To form a team, we assume that we have eight average pitchers, plus the player whom we are going to calculate ERA Imp and WHIP Imp for. We will give each team five copies of Joe Average Starter, and three copies of Joe Average Reliever (and we assume that approximately half of the teams will pick a starter for their last pitcher, while half pick a reliever). For purposes of this delineation, Scot Shields, Johan Santana, and Byung-Hyun Kim were considered starters.

There are three stats we need for both groups of pitchers in order to complete our formula. Those are the Average WHIP, Average ERA, and Average IP of all pitchers in the league. These numbers are 1.24, 3.65, and 200 for starters and 1.14, 2.78, and 73.3 for relievers. That means that an average staff of eight pitchers (before adding our ninth pitcher, who can be either a starter or a reliever) will have 1220 innings pitched (1000 by starters, 220 by relievers), and will have a WHIP of 1.22 and an ERA of 3.49. (That may look as if starters weigh a lot more heavily on your stats than relievers, but also remember that we have five starters and just three relievers so far.)

If you now take Mark Prior (211.3 IP, 2.43 ERA, 1.10 WHIP) and add him to this team, you end up with a 1.20 staff WHIP and a 3.33 ERA. If you instead add Eric Gagne (82.3 IP, 1.20 ERA, 0.69 WHIP) to the same roster, the result will be a 1.18 WHIP and a 3.34 ERA. That means that Gagne has a WHIP Imp of -.04 and an ERA Imp of -.15, while Prior’s stats are -.02 and -.16, respectively. These scores are virtually identical, even though Gagne pitched less than half as many innings. Here are the stats for the top 20 pitchers, rated by ERA Imp:

 Player IP ERA EImp WHIP WImp K Pedro Martinez – Bos, SP 186.7 2.22 -0.169 1.039 -0.024 206 Jason Schmidt – SF, SP 207.7 2.34 -0.167 0.953 -0.039 208 Kevin Brown – NYA, SP 211 2.39 -0.162 1.137 -0.012 185 Mark Prior – ChN, SP 211.3 2.43 -0.156 1.103 -0.017 245 Eric Gagne – LA, RP 82.3 1.20 -0.145 0.692 -0.033 137 Tim Hudson – Oak, SP 240 2.70 -0.130 1.075 -0.024 162 Guillermo Mota – LA, RP 105 1.97 -0.120 0.990 -0.018 99 John Smoltz – Atl, RP 64.3 1.12 -0.119 0.870 -0.018 73 Damaso Marte – ChA, RP 79.7 1.58 -0.117 1.054 -0.010 87 Rheal Cormier – Phi, RP 84.7 1.70 -0.116 0.933 -0.019 67 Shigetoshi Hasegawa – Sea, RP 73 1.48 -0.113 1.096 -0.007 32 Billy Wagner – Phi, RP 86 1.78 -0.113 0.872 -0.023 105 Brendan Donnelly – Ana, RP 74 1.58 -0.109 1.068 -0.009 79 Mariano Rivera – NYA, RP 70.7 1.66 -0.100 1.005 -0.012 63 LaTroy Hawkins – ChN, RP 77.3 1.86 -0.097 1.086 -0.008 75 Keith Foulke – Bos, RP 86.7 2.08 -0.094 0.888 -0.022 88 Esteban Loaiza – ChA, SP 226.3 2.90 -0.092 1.113 -0.017 207 Brandon Webb – Ari, SP 180.7 2.84 -0.084 1.151 -0.009 172 Wilson Alvarez – LA, RP 95 2.37 -0.081 1.084 -0.010 82 Scot Shields – Ana, SP/RP 148.3 2.85 -0.069 1.187 -0.004 111

 Legend EImp = ERA Imp WImp = WHIP Imp

Note that the first few players on the list are starters, but out of the top 20, 12 are relievers (not including Scot Shields). Gagne has the second best WHIP Imp, and is very close to the top players in ERA Imp. So we can see from this that the best relievers are definitely three-category players, particularly the top closers (five of the top 20 pitchers are closers, plus Marte, who might fill that role this year; the other top six relievers are all set-up men).

These considerations apply to leagues with no innings limits. In such leagues, starters can be four-category standouts, while the best closers are only 3.5-category contributors, since their Ks just can’t keep up with those compiled by SPs. Many leagues, however, have innings maximums.

In an innings limit league, K/9 is a much more important stat than pure Ks, and closers generally have far better K/9 ratios than SPs (Gagne has 15 K/9, while Prior comes in at only about 10.5 K/9). Therefore, a closer will eat up less of your precious innings to give you the same ERA and WHIP improvement as your starters, and for each inning they use, they will give you more Ks.

So far, this comparison has only focused on the elite starters and closers. What about average players? Well, both starters and closers with average stats obviously will not change your WHIP or ERA at all. Therefore, you have to pick those players based on wins and saves. But the below-average closers generally have very similar ERAs to lower-tier starters (as opposed to the top closers who have much better ERAs than stud starters). In fewer innings, a late-round closer will thus hurt your ERA much less than a below-average starter, while still giving you decent K/9, and they are usually more help in the saves category than poor starters are in wins. But if you have to flesh out those last few spots on your staff, consider taking a premier set-up man who will significantly help your ratios.

Here are the complete stats:

 Player IP ERA EImp WHIP WImp K Pedro Martinez – Bos, SP 186.7 2.22 -0.169 1.039 -0.024 206 Jason Schmidt – SF, SP 207.7 2.34 -0.167 0.953 -0.039 208 Kevin Brown – NYA, SP 211 2.39 -0.162 1.137 -0.012 185 Mark Prior – ChN, SP 211.3 2.43 -0.156 1.103 -0.017 245 Eric Gagne – LA, RP 82.3 1.20 -0.145 0.692 -0.033 137 Tim Hudson – Oak, SP 240 2.70 -0.130 1.075 -0.024 162 Guillermo Mota – LA, RP 105 1.97 -0.120 0.990 -0.018 99 John Smoltz – Atl, RP 64.3 1.12 -0.119 0.870 -0.018 73 Damaso Marte – ChA, RP 79.7 1.58 -0.117 1.054 -0.010 87 Rheal Cormier – Phi, RP 84.7 1.70 -0.116 0.933 -0.019 67 Shigetoshi Hasegawa – Sea, RP 73 1.48 -0.113 1.096 -0.007 32 Billy Wagner – Phi, RP 86 1.78 -0.113 0.872 -0.023 105 Brendan Donnelly – Ana, RP 74 1.58 -0.109 1.068 -0.009 79 Mariano Rivera – NYA, RP 70.7 1.66 -0.100 1.005 -0.012 63 LaTroy Hawkins – ChN, RP 77.3 1.86 -0.097 1.086 -0.008 75 Keith Foulke – Bos, RP 86.7 2.08 -0.094 0.888 -0.022 88 Esteban Loaiza – ChA, SP 226.3 2.90 -0.092 1.113 -0.017 207 Brandon Webb – Ari, SP 180.7 2.84 -0.084 1.151 -0.009 172 Wilson Alvarez – LA, RP 95 2.37 -0.081 1.084 -0.010 82 Scot Shields – Ana, SP/RP 148.3 2.85 -0.069 1.187 -0.004 111 David Riske – Cle, RP 74.7 2.29 -0.069 0.964 -0.015 82 Oscar Villarreal – Ari, RP 98 2.57 -0.068 1.286 0.005 80 Octavio Dotel – Hou, RP 87 2.48 -0.067 0.966 -0.017 97 Curt Schilling – Bos, SP 168 2.95 -0.065 1.048 -0.021 194 Hideo Nomo – LA, SP 218.3 3.09 -0.061 1.250 0.005 177 Carlos Zambrano – ChN, SP 214 3.11 -0.057 1.318 0.015 168 Jose Valverde – Ari, RP 50.3 2.15 -0.053 0.993 -0.009 71 Danny Kolb – Mil, RP 41.3 1.96 -0.050 1.282 0.002 39 Roy Oswalt – Hou, SP 127.3 2.97 -0.049 1.139 -0.008 108 Johan Santana – Min, SP/RP 158.3 3.07 -0.048 1.099 -0.014 169 Rod Beck – SD, RP 35.3 1.78 -0.048 1.019 -0.006 32 Mark Mulder – Oak, SP 186.7 3.13 -0.048 1.179 -0.005 128 Josh Beckett – Fla, SP 142 3.04 -0.047 1.324 0.011 152 Livan Hernandez – Mon, SP 233.3 3.20 -0.047 1.209 -0.002 178 Joe Borowski – ChN, RP 68.3 2.63 -0.046 1.054 -0.009 66 Roy Halladay – Tor, SP 266 3.25 -0.043 1.071 -0.027 204 Kerry Wood – ChN, SP 211 3.20 -0.043 1.194 -0.004 266 Ugueth Urbina – Det, RP 77 2.81 -0.040 1.130 -0.005 78 Javier Vazquez – NYA, SP 230.7 3.24 -0.040 1.105 -0.018 241 Jason Isringhausen – StL, RP 42 2.36 -0.038 1.167 -0.002 41 Matt Mantei – Ari, RP 55 2.62 -0.038 1.000 -0.009 68 Tim Worrell – Phi, RP 78.3 2.87 -0.037 1.302 0.005 65 Francisco Cordero – Tex, RP 82.7 2.94 -0.035 1.306 0.005 90 Jamie Moyer – Sea, SP 215 3.27 -0.033 1.233 0.002 129 Joe Nathan – Min, RP 79 2.96 -0.032 1.063 -0.010 83 Luis Ayala – Mon, RP 71 2.92 -0.031 1.099 -0.007 46 Eddie Guardado – Sea, RP 65.3 2.89 -0.030 0.980 -0.012 60 Barry Zito – Oak, SP 231.7 3.30 -0.030 1.183 -0.006 146 Francisco Rodriguez – Ana, RP 86 3.03 -0.030 0.988 -0.015 95 Armando Benitez – Fla, RP 73 2.96 -0.030 1.370 0.008 75 Kip Wells – Pit, SP 197.3 3.28 -0.029 1.252 0.004 147 Dontrelle Willis – Fla, SP 160.7 3.30 -0.022 1.282 0.007 142 Tom Gordon – NYA, RP 74 3.16 -0.019 1.189 -0.002 91 Byung-Hyun Kim – Bos, SP/RP 122.3 3.31 -0.016 1.120 -0.009 102 Mike Mussina – NYA, SP 214.7 3.40 -0.013 1.081 -0.021 195 Troy Percival – Ana, RP 49.3 3.47 -0.001 1.135 -0.003 48 Mike Timlin – Bos, RP 83.7 3.55 0.004 1.028 -0.012 65 Miguel Batista – Tor, SP 193.3 3.54 0.007 1.329 0.015 142 Braden Looper – NYN, RP 80.7 3.68 0.012 1.376 0.010 56 Ryan Franklin – Sea, SP 212 3.57 0.012 1.226 0.001 99 Cal Eldred – StL, RP 67.3 3.74 0.013 1.381 0.008 67 Mark Redman – Oak, SP 190.7 3.59 0.014 1.222 0.000 151 CC Sabathia – Cle, SP 197.7 3.60 0.015 1.295 0.010 141 Danys Baez – TB, RP 75.7 3.81 0.019 1.163 -0.003 66 Vicente Padilla – Phi, SP 208.7 3.62 0.019 1.236 0.002 133 Mike MacDougal – KC, RP 64 4.08 0.029 1.500 0.014 57 Scott Williamson – Bos, RP 62.7 4.16 0.033 1.404 0.009 74 Matt Morris – StL, SP 172.3 3.76 0.033 1.178 -0.005 120 Sidney Ponson – Bal, SP 216 3.75 0.039 1.259 0.006 134 Brian Anderson – KC, SP 197.7 3.78 0.040 1.290 0.010 87 Darrell May – KC, SP 210 3.77 0.041 1.190 -0.004 115 Steve Trachsel – NYN, SP 204.7 3.78 0.042 1.314 0.014 111 Jorge Julio – Bal, RP 61.7 4.38 0.043 1.524 0.015 52 Joel Pineiro – Sea, SP 211.7 3.78 0.043 1.266 0.007 151 Mike Hampton – Atl, SP 190 3.84 0.047 1.389 0.023 110 Russ Ortiz – Atl, SP 212.3 3.81 0.047 1.314 0.014 149 Lance Carter – TB, RP 79 4.33 0.051 1.152 -0.004 47 Chris Reitsma – Atl, RP 84 4.29 0.052 1.321 0.007 53 Woody Williams – StL, SP 220.7 3.87 0.058 1.246 0.004 153 Roger Clemens – Hou, SP 211.7 3.91 0.062 1.214 -0.001 190 Bartolo Colon – Ana, SP 242 3.87 0.063 1.198 -0.004 173 Rocky Biddle – Mon, RP 71.7 4.65 0.064 1.549 0.018 54 Al Leiter – NYN, SP 180.7 3.99 0.065 1.494 0.035 139 Greg Maddux – ChN, SP 218.3 3.96 0.071 1.182 -0.006 124 Mike DeJean – Bal, RP 82.7 4.68 0.076 1.512 0.019 71 Adam Eaton – SD, SP 183 4.08 0.077 1.317 0.013 146 Andy Pettitte – Hou, SP 208.3 4.02 0.077 1.330 0.016 180 Kevin Millwood – Phi, SP 222 4.01 0.080 1.252 0.005 169 Wade Miller – Hou, SP 187.3 4.13 0.085 1.308 0.012 161 Jake Peavy – SD, SP 194.7 4.11 0.085 1.310 0.012 156 Tim Wakefield – Bos, SP 202.3 4.09 0.085 1.305 0.012 169 Matt Clement – ChN, SP 201.7 4.11 0.088 1.230 0.001 171 Brad Penny – Fla, SP 196.3 4.13 0.089 1.278 0.008 138 David Wells – SD, SP 213 4.14 0.097 1.230 0.001 101 Jeff Suppan – StL, SP 204 4.19 0.100 1.314 0.013 110 Kelvim Escobar – Ana, SP 180.3 4.29 0.103 1.481 0.034 159 Mark Buehrle – ChA, SP 230.3 4.14 0.103 1.350 0.021 119 Randy Wolf – Phi, SP 200 4.23 0.104 1.270 0.007 177 Ted Lilly – Tor, SP 178.3 4.34 0.108 1.329 0.014 147 Carl Pavano – Fla, SP 201 4.30 0.115 1.259 0.006 133 Brett Myers – Phi, SP 193 4.43 0.128 1.456 0.032 143 Odalis Perez – LA, SP 185.3 4.52 0.136 1.279 0.008 141 Derek Lowe – Bos, SP 203.3 4.47 0.140 1.416 0.028 110 Freddy Garcia – Sea, SP 201.3 4.51 0.144 1.326 0.015 144 Gil Meche – Sea, SP 186.3 4.59 0.146 1.342 0.016 130 Ben Sheets – Mil, SP 220.7 4.45 0.147 1.246 0.004 157 Brad Radke – Min, SP 212.3 4.49 0.148 1.272 0.008 120 Kyle Lohse – Min, SP 201 4.61 0.158 1.274 0.008 130

Chris Berger, also known in the Forums as EugeneStyles, is always willing to give an opinion on topics ranging from fantasy baseball to hot fall fashion trends.

 What strategy do you use when it comes to closers? Do you use them for help in several categories, or only as a source of saves? Join the discussion in the Forums!

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