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Every year, there’s usually at least one fantasy player who poses a question along these lines:

What totals should I be targeting in my fantasy league this year? Is there a sweet spot I should be trying to aim for in each category?

Scott Wilderman of the stat service On Roto was kind enough to provide BP with a sampling of league totals from 2012.  Below are the average totals for 42 American League, eight National League, and eight mixed 5×5 leagues.

Table 1: American League only 12-team, 5×5 Category Finish Averages

Finish

HR

RBI

SB

R

BA

W

SV

ERA

WHIP

K

1st

254

902

165

936

.272

92

86

3.53

1.207

1202

2nd

236

861

149

892

.268

86

75

3.66

1.235

1150

3rd

227

834

140

864

.265

82

67

3.79

1.252

1111

4th

217

810

131

840

.262

79

60

3.87

1.264

1081

5th

207

789

124

818

.261

76

55

3.96

1.278

1054

6th

199

766

118

789

.258

74

49

4.03

1.290

1026

7th

190

740

111

768

.257

71

42

4.12

1.303

995

8th

184

717

104

742

.255

68

37

4.21

1.314

963

9th

175

680

  97

714

.252

65

32

4.29

1.332

928

10th

164

647

  89

679

.250

62

25

4.39

1.346

891

11th

153

612

  79

641

.248

57

17

4.52

1.369

847

12th

138

570

  65

590

.241

51

  9

4.70

1.397

768

The bolded numbers indicate the highest and second-highest gaps between one category and the category ahead of or behind it. In every instance in Table 1, the biggest gap is between first and second place and 11th and 12th place. I found this surprising; most of the leagues I play in are keeper or carryover leagues, and there is usually a sizeable gap between sixth and seventh place. Half of the teams in my keeper leagues generally go for it, while the other half dump and play for next season. This wasn’t the case here.

Some of the larger gaps can be explained by outliers at the top or bottom of the standings, but not too many of them. There is definitely an inefficiency among owners at the top of the standings, as they fail to trade away their excess in a category. For winning teams, this doesn’t matter, but if you didn’t win and finished with a large excess in a category, a trading opportunity was missed.

At the bottom of the standings, it appears that there is an opportunity to toss a category overboard and play a nine-category game, but a deeper dive into the standings shows that this typically isn’t the case. One drawback to this approach is that success or failure is contingent on the competitiveness of your league. I found a 90-point squad (out of a possible 120 points) that won finishing dead last in ERA/WHIP, but there were not a lot of examples of winning squads with one- or two-point finishes in any category. Steals and saves—as you might expect—saw some low category finishes from winning teams, but no one- or two-point finishes. 

Table 2: National League only 13-team, 5×5 Category Finish Averages

Finish

HR

RBI

SB

R

BA

W

SV

ERA

WHIP

K

1st

227

883

183

900

.278

97

86

3.38

1.194

1265

2nd

211

849

156

853

.272

91

76

3.48

1.217

1237

3rd

201

810

149

823

.271

87

72

3.56

1.231

1211

4th

194

777

139

807

.268

83

66

3.69

1.249

1167

5th

185

757

130

793

.267

81

59

3.76

1.263

1149

6th

181

738

126

782

.265

78

52

3.79

1.268

1136

7th

171

716

122

762

.262

77

48

3.84

1.277

1117

8th

164

684

115

727

.260

75

42

3.91

1.289

1082

9th

155

665

110

708

.259

72

38

3.97

1.296

1058

10th

149

643

106

686

.257

70

31

4.01

1.307

1033

11th

143

615

  98

664

.256

66

24

4.09

1.327

1010

12th

131

567

  88

630

.254

62

19

4.20

1.343

971

13th

115

527

  71

581

.251

58

  9

4.33

1.353

875

The largest gaps in the National League weren’t quite as uniform, but with six of the 10 categories showing the largest and second-largest gaps between first and second and 12th and 13th, the National League wasn’t exactly bucking the trend. The same difficulty with locating a great category-dumping strategy applies. One league winner did a nifty category dump where he finished 11th in batting average and wins and dominated his league, winning by 12 points. I’d love to know if this was by design or merely an accident.

Table 3: Mixed League 15-team, 5×5 Category Finish Averages

Finish

HR

RBI

SB

R

BA

W

SV

ERA

WHIP

K

1st

282

1036

195

1041

.280

104

94

3.34

1.170

1352

2nd

268

  994

184

1015

.278

  99

88

3.46

1.195

1317

3rd

264

  978

176

  999

.276

  97

84

3.55

1.209

1277

4th

259

  962

165

  987

.274

  95

78

3.61

1.220

1245

5th

256

  941

161

  975

.272

  92

75

3.68

1.231

1228

6th

246

  927

157

  957

.270

  90

72

3.73

1.240

1205

7th

240

  912

153

  950

.268

  88

69

3.83

1.258

1195

8th

236

  893

149

  936

.266

  86

65

3.91

1.273

1177

9th

230

  876

143

  923

.264

  84

59

3.96

1.281

1158

10th

227

  866

132

  915

.263

  82

57

4.02

1.288

1133

11th

216

  855

128

  892

.262

  80

53

4.07

1.295

1117

12th

211

  841

124

  874

.261

  77

49

4.14

1.302

1105

13th

204

  819

115

  864

.258

  76

40

4.21

1.318

1073

14th

196

  796

104

  835

.255

  71

32

4.24

1.333

1051

15th

175

  749

  93

  797

.248

  66

19

4.40

1.362

977

The mixed-league data present a more divided picture. Half of the largest and second-largest category differences are between teams at or near the top and at the bottom. However, the other half of significant category differences is between two teams at or near the bottom.  The other thing that leaps out from Table 3 is that the category differences are much tighter than in the one-circuit leagues. This makes sense. Even in a “deep” 15-team mixer, there are still more everyday players to be had; it’s less likely that a squad is going to get decimated by injuries and be stuck carrying seven or eight everyday players and more than a few part-time fill-ins.

So are there definitive conclusions that can be drawn?

This is a problematic question. It is easy to go back and examine what happened last year, but harder to predict what will happen in 2013. Many projection systems spew optimistic totals and can’t or don’t predict the significant injuries that will inevitably dash someone’s hopes and dreams. Looking back at past results and attempting to balance this against future projections can and often does create an expectations gap.

Nevertheless, if you’re coming into your auction with limited freezes or a lopsided roster, it helps to know where the categorical soft spots are or might be. I don’t recommend category dumping as a primary option, but if your freeze list is weak, it helps to know where your league’s weak points are. If you’re going to take this approach, I recommend looking at your league data, as well, to ensure that you maximize the use of your rival owners’ trends and data points.