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2017 Average Standings and SGP for the 2018 Season

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In order to prepare for the upcoming season, we need some important information from last year. In this post, I’ll share with you the average standings for 12- and 15-team NFBC leagues, and the average ERA, WHIP, and batting average in those leagues. With this information, you should have everything you need to get started on your SGP rankings for the 2018 season.

Where Do I Get This Data From?

The last several years, I’ve participated in the NFBC’s Draft Champions competition. By participating in such a league, a user gets access to see the standings to all the various NFBC competitions.

As far as I can tell, it seems like you need to be a registered NFBC user to see the standings data. If you happen to be one (and you’re logged into the NFBC site), you can see standings data for the various contests at these links:

I take this data and manipulate it in Excel to calculate average standings across all the leagues using the process I describe here.

If you are not an NFBC user, you can see some of the historic analysis I’ve compiled from 2012 through 2016 here.

With that in mind, let’s take a look at the 2017 results.

12-team League Average Standings

Across the 149 Online Championship leagues hosted by NFBC, the average standings for first through twelfth are shown below. Note, the 1,156 RBI is the average of all teams that finished in first place in RBI. It is NOT the average of what league winners averaged in the RBI category. The league winner in RBI could have finished in 7th place overall, but is included in the 1,156 average figure.

RK PTS AVG R HR RBI SB ERA WHIP W K SV
1 12 .2788 1,178 365 1,156 174 3.480 1.171 107 1,522 105
2 11 .2750 1,144 349 1,121 159 3.638 1.204 102 1,469 97
3 10 .2727 1,125 340 1,098 149 3.740 1.222 98 1,435 90
4 9 .2706 1,108 332 1,080 142 3.827 1.238 96 1,403 85
5 8 .2688 1,091 324 1,061 135 3.891 1.250 93 1,372 81
6 7 .2674 1,078 317 1,045 129 3.960 1.263 90 1,341 76
7 6 .2660 1,064 310 1,027 123 4.027 1.276 88 1,313 70
8 5 .2643 1,048 303 1,011 117 4.100 1.287 85 1,282 65
9 4 .2626 1,029 296 992 111 4.171 1.300 82 1,245 59
10 3 .2609 1,010 288 972 105 4.263 1.316 78 1,209 51
11 2 .2588 983 277 948 96 4.375 1.336 73 1,150 40
12 1 .2543 937 258 896 80 4.557 1.366 66 1,057 26

12-team League SGP Factors

Using the information from the league average standings, the raw and relative SGP factors are as follows:

SGP TYPE AVG R HR RBI SB ERA WHIP W K SV
Raw 0.0019 19.060 8.526 20.635 7.405 (0.086) (0.0160) 3.288 37.244 6.461
Relative 0.00009 0.924 0.413 1.000 0.359 (0.0023) (0.0004) 0.0883 1.000 0.174

Historic Online Championship (12-team League) SGP Factors

Here are the Online Championship hitting categories:

Year Type BA R HR RBI SB
2012 Raw 0.00220 19.197 8.016 20.675 8.270
2013 Raw 0.00193 19.265 7.537 20.685 8.603
2014 Raw 0.00197 18.843 7.481 19.639 7.900
2015 Raw 0.00177 19.920 8.429 19.549 7.591
2016 Raw 0.00182 19.721 8.797 21.527 8.508
2017 Raw 0.00193 19.060 8.526 20.635 7.405
2012 Relative 0.00011 0.92848 0.38769 1.000 0.40001
2013 Relative 0.00009 0.93136 0.36435 1.000 0.41589
2014 Relative 0.00010 0.95950 0.38094 1.000 0.40224
2015 Relative 0.00009 1.01898 0.43115 1.000 0.38828
2016 Relative 0.00008 0.91607 0.40863 1.000 0.39520
2017 Relative 0.00009 0.92366 0.41320 1.000 0.35885

Here are the Online Championship pitching categories:

Year Type ERA WHIP W K SV
2012 Raw (0.07840) (0.01320) 3.253 30.968 7.184
2013 Raw (0.07623) (0.01472) 2.899 32.811 7.038
2014 Raw (0.06880) (0.01280) 2.999 31.181 6.964
2015 Raw (0.07876) (0.01464) 2.926 35.163 7.210
2016 Raw (0.08042) (0.01529) 3.184 34.212 6.842
2017 Raw (0.08587) (0.01548) 3.288 37.244 6.461
2012 Relative (0.00253) (0.00043) 0.10503 1.000 0.23197
2013 Relative (0.00232) (0.00045) 0.08837 1.000 0.21452
2014 Relative (0.00214) (0.00040) 0.09320 1.000 0.21640
2015 Relative (0.00224) (0.00042) 0.08321 1.000 0.20505
2016 Relative (0.00235) (0.00045) 0.09307 1.000 0.19998
2017 Relative (0.00231) (0.00042) 0.08827 1.000 0.17348

15-team League Average Standings

The NFBC offers two different types of 15-team leagues. The “Main Event” is a closer approximation to your typical home league, in that it allows for in season player pickups from the waiver wire. The Draft Championship does NOT allow in-season moves, but you do draft a 50-player team in order to build a deeper roster that might get you through the season without the ability to add anyone.

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Have Small Parts of Your Projecting X 2.0 Spreadsheet Stopped Working?

Have Small Parts of Your Projecting X 2.0 Spreadsheet Stopped Working?

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There were some big waves in the nerdy baseball world (I’m a proud card-carrying member) last week, when Baseball-Reference.com released a redesigned website. While the improvements are very nice, especially on a mobile device, the unfortunately broke the link to the Projecting X 2.0 spreadsheet.

Unfortunately, the newly designed site doesn’t seem to allow for the web querying function that was used to extract several of the pieces of information necessary to project pitcher stats. Because of this, we’ll have to make a few small edits to your spreadsheet that will allow you to link directly to the player you are projecting so that you’re taken right to the table containing the desired information (if we can’t pull it into the file, we’ll create a link that takes you right to where the information is located).

What follows are instructions that will help create these helpful hyperlinks and add them to your spreadsheet.

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Analyzing AL- and NL-Only Standings Data

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As much as I love the standings gain point approach to valuing players, it does have an a couple of inherent weaknesses.

First, it’s dependent upon some form of league history to work. The whole ranking and valuation process is derived from previous standings data! Those starting new leagues, or joining an existing league, don’t have this information available.

Second, assuming you have prior standings to draw from, I’ve always been bothered by the small sample sizes of that data. And I don’t know about you, but something odd always seems to happen in my leagues. One year someone runs away with it, one year it’s a tight race between five teams, one year we add two teams, the next year we contract a team.

What are we to do?!?!

Thank You OnRoto and NFBC

Thankfully, some very generous league hosting sites have made their standings information publicly available or shared it with me! With their help, I think we can put to bed the concerns over lack of league history and small sample sizes. We have MANY leagues to look at now.

The fine folks at OnRoto.com have shared their NL- and AL-only standings data. If you’re not familiar with OnRoto, their goal is to cater to sophisticated fantasy leagues, many of which play by the “old-school” rules required by “long-term players”. They also are willing to fulfill just about any customization request (more on this later!).

I’ve also written several times about NFBC standings data for mixed leagues.

What follows is a close look at the 2016 12-team “only league” data from OnRoto. If you’re curious, you can see the 2015 AL information here and the 2015 NL information here.

Now, let’s take a look at the data!

AL-Only Standings by Category

Here are the average AL statistics within each rotisserie scoring category:

RK PTS AVG R HR RBI SB ERA WHIP W K SV
1 12 0.272 987 291 964 128 3.583 1.191 94 1,311 90
2 11 0.268 945 274 926 115 3.753 1.227 88 1,271 79
3 10 0.266 917 262 894 107 3.856 1.245 85 1,229 72
4 9 0.264 889 254 867 100 3.934 1.258 82 1,194 64
5 8 0.262 867 245 846 94 4.014 1.271 80 1,159 57
6 7 0.260 844 236 823 89 4.079 1.286 77 1,133 52
7 6 0.259 824 227 793 83 4.160 1.298 74 1,108 46
8 5 0.257 804 217 773 78 4.225 1.310 72 1,083 40
9 4 0.255 777 207 747 73 4.280 1.322 70 1,048 36
10 3 0.253 743 195 714 67 4.386 1.339 66 1,005 30
11 2 0.250 711 184 681 61 4.525 1.360 61 961 21
12 1 0.246 636 162 604 49 4.728 1.392 55 901 11

To better explain what you’re looking at, a team could have finished in 10th place in the standings but still finished 1st place in the home runs category. That team’s data appears on the “Rank 1” row, not on the “Rank 10” row.

NL-Only Standings by Category

And here are the NL stats:

RK PTS AVG R HR RBI SB ERA WHIP W K SV
1 12 0.276 950 257 915 164 3.411 1.183 93 1,354 88
2 11 0.272 917 244 879 143 3.596 1.217 86 1,284 76
3 10 0.269 883 231 845 133 3.710 1.232 82 1,234 68
4 9 0.267 863 222 816 122 3.810 1.253 79 1,192 61
5 8 0.264 840 214 798 112 3.902 1.270 75 1,155 54
6 7 0.263 813 206 775 106 3.994 1.284 73 1,125 49
7 6 0.261 787 198 743 99 4.080 1.297 70 1,093 44
8 5 0.259 763 191 718 92 4.173 1.313 66 1,062 37
9 4 0.258 740 184 692 85 4.241 1.329 63 1,026 33
10 3 0.255 703 174 660 78 4.351 1.347 60 991 27
11 2 0.252 673 160 630 70 4.445 1.372 55 945 20
12 1 0.249 618 143 570 56 4.631 1.406 48 826 11

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Standings Gain Points for NFBC Leagues

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In the post that follows, I’ll share standings gain points (SGP) factors for the NFBC Main Event, NFBC Draft Championship, and NFBC Online Championship for each of the last five years (2012-2016). But I’ve got to lay some groundwork before we get there…

Raw vs. Relative

While the discussion is a bit lengthy, I think this article discussing “raw” and “relative” SGP contains one of the most significant realizations I’ve had in fantasy baseball.

The quick and dirty explanation of this realization is that it is not only the raw SGP factors (or denominators) that drive player value calculations. The relationship, or relative value, between the SGP factors is also meaningful. Not only that, but looking exclusively at raw factors can be misleading, as it is difficult to see these relationships.

To illustrate, here are two example sets of raw SGP factors for a league:

League BA R HR RBI SB
2013 15-team NFBC Main Event 0.00161 13.751 5.533 15.115 6.228
2016 15-team NFBC Main Event 0.00150 15.366 6.561 16.838 6.375

I refer to these as raw factors because they’re calculated using the standard process prescribed by SGP. A calculation is made for each scoring category and those numbers are then fed into the process that’s used to rank or assign dollar values to players.

Looking again at the table of raw data above, you might think, “Wow, what happened in the last three years that caused those significant changes in the SGP factors?”

You might even start spewing some narrative about the changing landscape of baseball, the rise in strikeouts, and the power surge MLB experienced last season.

But before you start that process, let’s take a look at those same sets of SGP factors, after they’ve been converted into relative form:

League BA R HR RBI SB
2013 15-team NFBC Main Event 0.00011 0.90976 0.36609 1.00000 0.41202
2016 15-team NFBC Main Event 0.00009 0.91256 0.38963 1.00000 0.37862

The numbers still fluctuate. And if you run the math, from 2013 to 2016 the categories changed about 10%, on average, in both the raw and relative calculations. But seeing the factors in relative form really gives me a lot more confidence in my calculations.

I was always wondering if I screwed up my calculations before making this realization. “Could RBI really have changed that much?”

To be clear, I did not develop this way of looking at the numbers. I made the realization after reading “Winning Fantasy Baseball” by Larry Schechter. Although I didn’t invent this approach, I continue to share it because I think a lot of folks are confused by the raw numbers and this confusion leads to decreased confidence in the SGP approach.

How to Calculate Relative SGP

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