StrategyMarch 30, 2004


Post to Twitter

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:

PlayerIPERAEImpWHIPWImpK
Pedro Martinez – Bos, SP186.72.22-0.1691.039-0.024206
Jason Schmidt – SF, SP207.72.34-0.1670.953-0.039208
Kevin Brown – NYA, SP2112.39-0.1621.137-0.012185
Mark Prior – ChN, SP211.32.43-0.1561.103-0.017245
Eric Gagne – LA, RP82.31.20-0.1450.692-0.033137
Tim Hudson – Oak, SP2402.70-0.1301.075-0.024162
Guillermo Mota – LA, RP1051.97-0.1200.990-0.01899
John Smoltz – Atl, RP64.31.12-0.1190.870-0.01873
Damaso Marte – ChA, RP79.71.58-0.1171.054-0.01087
Rheal Cormier – Phi, RP84.71.70-0.1160.933-0.01967
Shigetoshi Hasegawa – Sea, RP731.48-0.1131.096-0.00732
Billy Wagner – Phi, RP861.78-0.1130.872-0.023105
Brendan Donnelly – Ana, RP741.58-0.1091.068-0.00979
Mariano Rivera – NYA, RP70.71.66-0.1001.005-0.01263
LaTroy Hawkins – ChN, RP77.31.86-0.0971.086-0.00875
Keith Foulke – Bos, RP86.72.08-0.0940.888-0.02288
Esteban Loaiza – ChA, SP226.32.90-0.0921.113-0.017207
Brandon Webb – Ari, SP180.72.84-0.0841.151-0.009172
Wilson Alvarez – LA, RP952.37-0.0811.084-0.01082
Scot Shields – Ana, SP/RP148.32.85-0.0691.187-0.004111

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:

PlayerIPERAEImpWHIPWImpK
Pedro Martinez – Bos, SP186.72.22-0.1691.039-0.024206
Jason Schmidt – SF, SP207.72.34-0.1670.953-0.039208
Kevin Brown – NYA, SP2112.39-0.1621.137-0.012185
Mark Prior – ChN, SP211.32.43-0.1561.103-0.017245
Eric Gagne – LA, RP82.31.20-0.1450.692-0.033137
Tim Hudson – Oak, SP2402.70-0.1301.075-0.024162
Guillermo Mota – LA, RP1051.97-0.1200.990-0.01899
John Smoltz – Atl, RP64.31.12-0.1190.870-0.01873
Damaso Marte – ChA, RP79.71.58-0.1171.054-0.01087
Rheal Cormier – Phi, RP84.71.70-0.1160.933-0.01967
Shigetoshi Hasegawa – Sea, RP731.48-0.1131.096-0.00732
Billy Wagner – Phi, RP861.78-0.1130.872-0.023105
Brendan Donnelly – Ana, RP741.58-0.1091.068-0.00979
Mariano Rivera – NYA, RP70.71.66-0.1001.005-0.01263
LaTroy Hawkins – ChN, RP77.31.86-0.0971.086-0.00875
Keith Foulke – Bos, RP86.72.08-0.0940.888-0.02288
Esteban Loaiza – ChA, SP226.32.90-0.0921.113-0.017207
Brandon Webb – Ari, SP180.72.84-0.0841.151-0.009172
Wilson Alvarez – LA, RP952.37-0.0811.084-0.01082
Scot Shields – Ana, SP/RP148.32.85-0.0691.187-0.004111
David Riske – Cle, RP74.72.29-0.0690.964-0.01582
Oscar Villarreal – Ari, RP982.57-0.0681.2860.00580
Octavio Dotel – Hou, RP872.48-0.0670.966-0.01797
Curt Schilling – Bos, SP1682.95-0.0651.048-0.021194
Hideo Nomo – LA, SP218.33.09-0.0611.2500.005177
Carlos Zambrano – ChN, SP2143.11-0.0571.3180.015168
Jose Valverde – Ari, RP50.32.15-0.0530.993-0.00971
Danny Kolb – Mil, RP41.31.96-0.0501.2820.00239
Roy Oswalt – Hou, SP127.32.97-0.0491.139-0.008108
Johan Santana – Min, SP/RP158.33.07-0.0481.099-0.014169
Rod Beck – SD, RP35.31.78-0.0481.019-0.00632
Mark Mulder – Oak, SP186.73.13-0.0481.179-0.005128
Josh Beckett – Fla, SP1423.04-0.0471.3240.011152
Livan Hernandez – Mon, SP233.33.20-0.0471.209-0.002178
Joe Borowski – ChN, RP68.32.63-0.0461.054-0.00966
Roy Halladay – Tor, SP2663.25-0.0431.071-0.027204
Kerry Wood – ChN, SP2113.20-0.0431.194-0.004266
Ugueth Urbina – Det, RP772.81-0.0401.130-0.00578
Javier Vazquez – NYA, SP230.73.24-0.0401.105-0.018241
Jason Isringhausen – StL, RP422.36-0.0381.167-0.00241
Matt Mantei – Ari, RP552.62-0.0381.000-0.00968
Tim Worrell – Phi, RP78.32.87-0.0371.3020.00565
Francisco Cordero – Tex, RP82.72.94-0.0351.3060.00590
Jamie Moyer – Sea, SP2153.27-0.0331.2330.002129
Joe Nathan – Min, RP792.96-0.0321.063-0.01083
Luis Ayala – Mon, RP712.92-0.0311.099-0.00746
Eddie Guardado – Sea, RP65.32.89-0.0300.980-0.01260
Barry Zito – Oak, SP231.73.30-0.0301.183-0.006146
Francisco Rodriguez – Ana, RP863.03-0.0300.988-0.01595
Armando Benitez – Fla, RP732.96-0.0301.3700.00875
Kip Wells – Pit, SP197.33.28-0.0291.2520.004147
Dontrelle Willis – Fla, SP160.73.30-0.0221.2820.007142
Tom Gordon – NYA, RP743.16-0.0191.189-0.00291
Byung-Hyun Kim – Bos, SP/RP122.33.31-0.0161.120-0.009102
Mike Mussina – NYA, SP214.73.40-0.0131.081-0.021195
Troy Percival – Ana, RP49.33.47-0.0011.135-0.00348
Mike Timlin – Bos, RP83.73.550.0041.028-0.01265
Miguel Batista – Tor, SP193.33.540.0071.3290.015142
Braden Looper – NYN, RP80.73.680.0121.3760.01056
Ryan Franklin – Sea, SP2123.570.0121.2260.00199
Cal Eldred – StL, RP67.33.740.0131.3810.00867
Mark Redman – Oak, SP190.73.590.0141.2220.000151
CC Sabathia – Cle, SP197.73.600.0151.2950.010141
Danys Baez – TB, RP75.73.810.0191.163-0.00366
Vicente Padilla – Phi, SP208.73.620.0191.2360.002133
Mike MacDougal – KC, RP644.080.0291.5000.01457
Scott Williamson – Bos, RP62.74.160.0331.4040.00974
Matt Morris – StL, SP172.33.760.0331.178-0.005120
Sidney Ponson – Bal, SP2163.750.0391.2590.006134
Brian Anderson – KC, SP197.73.780.0401.2900.01087
Darrell May – KC, SP2103.770.0411.190-0.004115
Steve Trachsel – NYN, SP204.73.780.0421.3140.014111
Jorge Julio – Bal, RP61.74.380.0431.5240.01552
Joel Pineiro – Sea, SP211.73.780.0431.2660.007151
Mike Hampton – Atl, SP1903.840.0471.3890.023110
Russ Ortiz – Atl, SP212.33.810.0471.3140.014149
Lance Carter – TB, RP794.330.0511.152-0.00447
Chris Reitsma – Atl, RP844.290.0521.3210.00753
Woody Williams – StL, SP220.73.870.0581.2460.004153
Roger Clemens – Hou, SP211.73.910.0621.214-0.001190
Bartolo Colon – Ana, SP2423.870.0631.198-0.004173
Rocky Biddle – Mon, RP71.74.650.0641.5490.01854
Al Leiter – NYN, SP180.73.990.0651.4940.035139
Greg Maddux – ChN, SP218.33.960.0711.182-0.006124
Mike DeJean – Bal, RP82.74.680.0761.5120.01971
Adam Eaton – SD, SP1834.080.0771.3170.013146
Andy Pettitte – Hou, SP208.34.020.0771.3300.016180
Kevin Millwood – Phi, SP2224.010.0801.2520.005169
Wade Miller – Hou, SP187.34.130.0851.3080.012161
Jake Peavy – SD, SP194.74.110.0851.3100.012156
Tim Wakefield – Bos, SP202.34.090.0851.3050.012169
Matt Clement – ChN, SP201.74.110.0881.2300.001171
Brad Penny – Fla, SP196.34.130.0891.2780.008138
David Wells – SD, SP2134.140.0971.2300.001101
Jeff Suppan – StL, SP2044.190.1001.3140.013110
Kelvim Escobar – Ana, SP180.34.290.1031.4810.034159
Mark Buehrle – ChA, SP230.34.140.1031.3500.021119
Randy Wolf – Phi, SP2004.230.1041.2700.007177
Ted Lilly – Tor, SP178.34.340.1081.3290.014147
Carl Pavano – Fla, SP2014.300.1151.2590.006133
Brett Myers – Phi, SP1934.430.1281.4560.032143
Odalis Perez – LA, SP185.34.520.1361.2790.008141
Derek Lowe – Bos, SP203.34.470.1401.4160.028110
Freddy Garcia – Sea, SP201.34.510.1441.3260.015144
Gil Meche – Sea, SP186.34.590.1461.3420.016130
Ben Sheets – Mil, SP220.74.450.1471.2460.004157
Brad Radke – Min, SP212.34.490.1481.2720.008120
Kyle Lohse – Min, SP2014.610.1581.2740.008130

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!

Post to Twitter

Related Cafe Articles

• Other articles by Chris Berger

No related articles.