A Look at 2015 AL-Only Standings Data

As a standings gain point disciple, one of the questions I get asked most is if I know where to get final standings data (and SGP denominators) for AL- and NL-only leagues. You can find reliable mixed-league data if you search hard enough on the web. But you won’t find much, if anything, about the only-league format.

Thanks to a tip from Mike Gianella, I finally got my hands on a nice set of AL- and NL-only standings. Mike suggested I check with the guys that run 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 historically hosted the various Tout Wars competitions. So this is legit data from a trusted fantasy resource. In this post we’ll be looking only at the AL data (NL coming soon). I was able to obtain the standings for 76 different 12-team AL-only leagues and here’s an analysis of the data…

One Important Caveat

There are keeper leagues among these 76 leagues. I don’t know exactly how many were straight re-draft leagues and how many were keeper.

That does muddy the water some. If you play in a re-draft league, you’d probably only want standings data from those same league-types. Likewise if you are in a keeper.

But this is also a larger sample of better information than I’ve found anywhere else on the web. So we’re better off than we were yesterday! I just want the keeper thing to be known.

What effect would keeper leagues have on this data? I can only theorize that this would serve to stretch the standings further apart than you’d see in a typical redraft league. I presume that years of repeated strong or poor decision making has a compounding effect. We’ve all seen those stacked keeper-league teams. And we’ve also been asked to take over that dead team that had David Dejesus as its star. So outliers may be more of an issue here.

American League Only Standings Averages

Here are the average statistics for each place within a category:

Average Statistics to Place Within a Category

RANK	POINTS	AVG	R	HR	RBI	SB	ERA	WHIP	W	K	SV
1	12	0.2701	942	255	907	128	3.47	1.18	97	1,318	83
2	11	0.2667	899	239	868	116	3.61	1.21	91	1,249	74
3	10	0.2644	872	229	843	107	3.70	1.22	87	1,202	66
4	9	0.2619	846	219	818	101	3.77	1.23	84	1,165	60
5	8	0.2608	825	210	795	96	3.84	1.25	82	1,135	55
6	7	0.2593	806	203	773	90	3.91	1.26	79	1,111	51
7	6	0.2574	785	194	748	85	3.98	1.27	77	1,071	46
8	5	0.2559	764	187	729	80	4.04	1.28	74	1,038	41
9	4	0.2540	740	180	707	75	4.10	1.29	71	1,000	36
10	3	0.2518	710	171	678	69	4.17	1.30	69	959	30
11	2	0.2489	677	162	653	63	4.27	1.32	65	916	22
12	1	0.2442	641	148	606	53	4.43	1.35	57	842	12

To be clear, these are NOT the average stats accumulated for each finisher. Achieving 239 home runs was the average number of home runs required to finish second IN THAT CATEGORY. Those 239 home runs may have come from the 7^th place overall team.

Setting Your Targets

The process of using your own projections (that likely contain bias) and setting standings targets based on those projections can be dangerous. But we all do it, so allow me to perpetuate the mistake for a moment.

You could set targets to finish first in each category. Obviously finishing first in each category of a 12-team league results in 120 points and a first place finish. But we often hear that a good goal is to aim for finishing third in each category and that doing so should be good enough for a first place finish. Does that really hold true?

Let’s look at the average final standings for these same leagues:

The third place objective seems to hold water. Finishing third in a category yields 10 points, or a 100 point total. That’s good enough to beat the average first place point total and is quite significantly more than the average second place total (beating second place wins).

In fact, of the 76 leagues looked at, a total of 100 was good enough to win (beat second place) in 67 of them. For comparison, finishing second in each category would yield 110 points in the standings and would have won all 76 of the leagues.

What Does this Mean for Each Hitter?

Again, OnRoto is home to hardcore league set ups. So if we assume traditional 14 hitter lineups, one would need this average stat line from each player:

What Does this Mean for Each Pitcher?

I’m not sure an “average pitcher” exists, but here are the stats anyway:

Per Pitcher Stats

Place	W	K	SV	ERA	WHIP
1st Place	10.8	146.4	9.2	3.47	1.18
2nd Place	10.7	138.7	8.2	3.61	1.21
3rd Place	9.7	133.5	7.4	3.70	1.22

Standings Gain Points Considerations

For those that use standings gain points (SGP) as a means of valuing players, here are the raw and relative calculations:

SGP Data

Type	AVG	R	HR	RBI	SB	ERA	WHIP	W	K	SV
SGP	0.00206	25.13	8.89	25.14	6.14	(0.07695)	(0.01328)	3.12	38.72	5.83
RELATIVE SGP	0.00008	1.000	0.354	1.000	0.244	(0.00199)	(0.00034)	0.080	1.000	0.151

The relative calculation seeks to put all the different SGP denominators onto the same scale. It’s helpful for comparing SGP denominators from different leagues (it can be misleading to just compare your own denominators to those above without converting them first). I won’t go down the rabbit hole now, but you can read more about relative standings gain points here.

NOTE: I did not remove any outliers from the data sets when calculating these. I’m planning some future analysis on how much of an effect the outliers can have. Jeff Zimmerman’s written about how he removes outliers here.

What Else Do You Want to Know?

The raw data is provided below. You are free to comb through and do your own research. But also feel free to ask questions in the comments below. What do want to know (but keep in mind I don’t have player, roster construction, or transaction info)? What questions do you have about the data?

The Raw Data

Here’s a link to download the data in an Excel file. Or here it is as a Google Sheet. Here’s a preview of the various tabs and data, if you want to know what you’re in for.

NOTE: If you find yourself looking in the detailed list of all leagues and all teams, you might find that some of those leagues or teams are then missing when you go to the specific category worksheet (e.g “R” or “RBI”). This is due to some of the leagues having what seemed to be bad data in the source file I was working with. For example, if I noticed that a team was listed as having only 1 R or an AVG of 866, I just threw the whole league out for that scoring category.

How Did I Do This?

Besides being obsessed with fantasy baseball, I have an Excel fetish. I was given the raw standings data from OnRoto.com and then had to translate that into league standings information for each category. Once I had that, I went through this process to determine averages for each category place (that link will take you to various mixed-league NFBC data).