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:

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 10^th 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.

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:

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:

Standings Gain Points Data

Here are the raw and relative SGP calculations for the NL-only information:

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.

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).