A Look at 2015 NL-Only Standings Data
Last week we took a look at standings data, for 12-team AL-only leagues. Now I’m back to present the same findings using a sample of 51 NL-only leagues. What follows is a look at the points required to win an NL-only roto league, what it takes to finish first in each rotisserie category, standings gain points information, and a downloadable Excel file for you to analyze further.
Thanks to OnRoto
This standings information is courtesy of OnRoto.com. If you’re not familiar with OnRoto, they’re a league hosting and stat service that caters to hard-core and old-school rotisserie leagues… Meaning they host a lot of only leagues.
OnRoto has also hosts the various Tout Wars expert leagues. So this is legit data from a trusted resource in the industry.
Keeper Leagues and Roster Sizes
The data includes a total of 51 NL-only leagues (compared to 76 AL-only, interesting) and there are keeper leagues among these leagues. Additionally, we don’t have assurance that these leagues all use traditional rotisserie rosters (14 hitters, 9 pitchers), but it seems like the majority do given that they’re hosted at a site like OnRoto. You can read more about how I think this affects things in the AL-only post (you may want to check out the comments there too). But the short answer is that large quantities of only-league data are so hard to find that we’re much better off with this, even if you’re in a redraft league, than we are without it.
National League Only Standings Averages
Here are the average statistics for each place within a category:

| RANK | POINTS | AVG | R | HR | RBI | SB | ERA | WHIP | W | K | SV |
| 1 | 12 | 0.2752 | 898 | 229 | 860 | 160 | 3.20 | 1.15 | 93 | 1,344 | 91 |
| 2 | 11 | 0.2715 | 852 | 212 | 821 | 141 | 3.37 | 1.19 | 87 | 1,281 | 81 |
| 3 | 10 | 0.2690 | 826 | 200 | 790 | 128 | 3.48 | 1.21 | 83 | 1,241 | 72 |
| 4 | 9 | 0.2670 | 800 | 192 | 763 | 117 | 3.58 | 1.23 | 80 | 1,203 | 66 |
| 5 | 8 | 0.2654 | 774 | 185 | 744 | 109 | 3.65 | 1.24 | 77 | 1,164 | 59 |
| 6 | 7 | 0.2637 | 754 | 178 | 723 | 101 | 3.72 | 1.26 | 74 | 1,123 | 52 |
| 7 | 6 | 0.2616 | 735 | 169 | 700 | 95 | 3.79 | 1.27 | 71 | 1,080 | 47 |
| 8 | 5 | 0.2598 | 707 | 161 | 678 | 87 | 3.85 | 1.28 | 68 | 1,039 | 42 |
| 9 | 4 | 0.2583 | 680 | 154 | 646 | 81 | 3.93 | 1.29 | 65 | 993 | 35 |
| 10 | 3 | 0.2560 | 645 | 144 | 613 | 74 | 4.02 | 1.31 | 60 | 951 | 28 |
| 11 | 2 | 0.2526 | 598 | 134 | 576 | 67 | 4.13 | 1.33 | 56 | 897 | 18 |
| 12 | 1 | 0.2484 | 553 | 117 | 518 | 57 | 4.40 | 1.37 | 49 | 806 | 7 |
To better explain, the “Rank 3” row does NOT indicate the average statistics of third place overall finishers. That row indicates what third place was IN EACH STAT CATEGORY. The 200 home runs on the “Rank 3” row may have come from the 10th place overall team but it was good enough for third in the home run category.
Does Finishing Third in Each Category Win?
We’ve all heard the suggestion that finishing third in each category is enough to win the league. We saw that statement held true for AL-only leagues. Does it for NL? Let’s look at the average final standings for these same leagues.

| Place | Roto Points |
| 1st Place | 100.92 |
| 2nd Place | 92.03 |
| 3rd Place | 85.32 |
| 4th Place | 79.13 |
| 5th Place | 72.32 |
| 6th Place | 67.41 |
| 7th Place | 62.00 |
| 8th Place | 55.90 |
| 9th Place | 49.41 |
| 10th Place | 42.81 |
| 11th Place | 37.24 |
| 12th Place | 29.06 |
Again we see that an across-the-board third place finish is a nice goal to have. Earning 10 points in each category yields a 100 point total. Those 100 points are not quite the average winner’s score, but are enough to give you a comfortable cushion over the average second place finish.
The OnRoto 12-team NL data included 51 leagues. A 100 point finish was good enough to win 47 of those leagues. And a 110 point finish (second place across-the-board) would have won all 51 leagues.
The Average Hitter Line
Here’s the average hitter stat line for the top three positions:

| Place | BA | R | HR | RBI | SB |
| 1st Place | .275 | 64.1 | 16.3 | 61.4 | 11.4 |
| 2nd Place | .272 | 60.9 | 15.2 | 58.6 | 10.1 |
| 3rd Place | .269 | 59.0 | 14.3 | 56.4 | 9.1 |
It’s interesting to see how thin the margins are when you look at things on a per player basis.
The Average Pitcher Line
Dellin Betances, who doesn’t even play in the NL, is the only pitcher I could see approaching a line like this… But here you are:

| Place | W | K | SV | ERA | WHIP |
| 1st Place | 10.4 | 149.3 | 10.1 | 3.20 | 1.15 |
| 2nd Place | 9.7 | 142.3 | 9.0 | 3.37 | 1.19 |
| 3rd Place | 9.2 | 137.9 | 8.0 | 3.48 | 1.21 |
Standings Gain Points Data
Here are the raw and relative SGP calculations for the NL-only information:

| Type | AVG | R | HR | RBI | SB | ERA | WHIP | W | K | SV |
| SGP | 0.00216 | 28.58 | 9.07 | 28.03 | 8.50 | (0.09186) | (0.01717) | 3.60 | 45.01 | 7.00 |
| RELATIVE SGP | 0.00008 | 1.002 | 0.324 | 1.000 | 0.303 | (0.00204) | (0.00038) | 0.080 | 1.000 | 0.156 |
Relative SGP is a concept I uncovered reading Larry Schechter’s book, Winning Fantasy Baseball. It’s the practice of placing all the hitting and pitching denominators on the same scale so they become more comparable to denominators for other leagues and formats. You can see a bigger demonstration of the concept here.
As I mentioned in the AL post, I did not remove any outliers from the data, but I’ve given you the raw information below and you’re welcome to adjust as you desire.
AL and NL Comparison
How does this NL data compare to the AL? The two are very similar once you adjust the raw denominators to relative scale. After that conversion, it takes more SB to move up in the NL (speed is more plentiful there) and more HR to move up in the AL (power is more plentiful there). All other categories are comparable.
| Type | AVG | R | HR | RBI | SB | ERA | WHIP | W | K | SV |
| AL SGP | 0.00206 | 25.13 | 8.89 | 25.14 | 6.14 | (0.07695) | (0.01328) | 3.12 | 38.72 | 5.83 |
| NL SGP | 0.00216 | 28.58 | 9.07 | 28.03 | 8.50 | (0.09186) | (0.01717) | 3.60 | 45.01 | 7.00 |
| AL RELATIVE SGP | 0.00008 | 1.000 | 0.354 | 1.000 | 0.244 | (0.00199) | (0.00034) | 0.080 | 1.000 | 0.151 |
| NL RELATIVE SGP | 0.00008 | 1.002 | 0.324 | 1.000 | 0.303 | (0.00204) | (0.00038) | 0.080 | 1.000 | 0.156 |
The Raw NL Standings Data
Here’s a link to download the data in an Excel file. Or here’s a link to a Google Sheet (if you have a Google Documents account you should be able to make a copy). While I mentioned previously that I did not remove any outliers, I did remove obvious errors in the data. If a league had something in the standings that was an obvious data problem, I threw the whole league out for that rotisserie category’s calculations.
Here’s a preview of the various tabs and data:
Have Any Questions?
Do you have any questions about the data? Let me know in the comments below. I’ll do my best to answer them (keep in mind I do not have any draft, roster, or transaction data to accompany the standings data).
Tanner writes for Fangraphs as well as his own site, Smart Fantasy Baseball . He's the co-auther of The Process with Jeff Zimmerman, and has written two e-books, Using SGP to Rank and Value Fantasy Baseball Players and How to Rank and Value Players for Points Leagues, and worked with Mike Podhorzer developing a spreadsheet to accompany Projecting X 2.0. Much of his writings focus on instructional "how to" topics, Excel, and strategy. Follow him on Twitter @smartfantasybb.
Awesome post, thanks for aggregating all this information!
Thanks, RC! Hope it helps.
In my long time NL only league, if a user had finished 3rd in every category, the amount of points generated would have been good for a 3rd place finish.
That must be a keeper league with some juggernaut teams. It’s pretty hard to have teams that dominate all 10 categories
Ha. Yes, I’m going to agree with Dustin. This has to be an exception to the rule. But I can definitely see how it could happen in a keeper league.
I’m new to ‘Only’ leagues this year and I’m really intrigued by the Stats Points stuff. Is there a good/convenient way to adapt this data to a 10 team NL League, or do I need to find separate data for 10-teamers and calculate from there? Thanks!
Shallower leagues are going to have better stats across the board – higher counting and batting average and lower WHIP/ERA because the active rosters will include slightly better players. The gaps between teams shouldn’t change much though.
You may want to check out this piece I wrote recently about relative SGP. When you convert 10 or 12-team data to a relative scale it actually ends up being about the same (like Mike alludes to, the gaps between teams doesn’t change much).
In that piece I use 12 and 15-teams to illustrate. But the same should hold for adjusting from 10 to 12-teams.
There’s a link there you can’t really see. It goes to http://www.smartfantasybaseball.com/2016/01/how-to-analyze-sgp-denominators-from-different-sources/.
My guess is that most only leagues are keeper, at least the auction ones. If you are hardcore enough to want to go that deep into the player pool, you also want to incorporate the consideration of multiple time horizons in your roster construction. I’m in two NL leagues, one AL, and all are keeper hosted on Onroto. Incredibly customizable setup.
I am in one of these NL Only leagues on onroto. Our league champion had 88 points (5×5 standard roto, 14 H, 9 P.) 2nd place had 85.5, I was 3rd with 76. It is a keeper (10 max) league. Our stat category leaders beat the average listed in the article in almost every category. This article was extremely helpful to see meta data and get a better understanding of stat category targets for 2016. Saying it another way, I was 1st in one category, 2nd in one, 3rd in 5 categories, 4th in one, 5th in one, 7th in the last category. it was my first year in fantasy baseball and I think I under-rated the importance of SB; but I also I drafted Pagan projecting him for a decent amount of steals. that was a mistake. lol.
Could you be in a 10-team league? I am guessing that due to your comments about the lower overall scores and your category leaders beating all the averages above. I only included 12-team leagues in the above analysis but 10-team data is available. Reach out to me if you’d like to see it (I haven’t analyzed it but can provide the data).
Oh man, yeah I skipped right over that. Yes I am in a 10 team league. I would love to see that data, thank you. That makes so much more sense lol.
I’ll send it to the e-mail address you used when you left your comment. If you don’t get it, reach out to me on Twitter and we can coordinate.
m9vc, I’ve sent it to the e-mail address you used when you left your comment. If you don’t get it, reach out to me on Twitter and we can coordinate.
Any plans for the analysis in mixed-leagues?
I suppose that would be the logical thing to do but I didn’t even have it on my radar. I will try to work that in soon. In the meantime you can get some of the same information from here. But it admittedly does not answer all of the same questions the AL and NL pieces have addressed.