ROTISSERIE: SGP Denominators for 2016

Standings Gain Points.

If you're new to the idea, SGPs estimate the amount of a given statistic you would need to move up one point in the standings, based on the distribution of each category. For example, depending on the league and the season, 31 RBI might, on average, move you up one place in the final standings. A player who hits 93 RBI above replacement would be worth a theoretical three SGP points in RBI.

To calculate SGP you need to know the distribution (or "lumpiness") of the standings in order to calculate the correct denominators. The best source of SGP data is from your league's history, especially if you've had the same scoring system and number of teams for a while. You can take the standings for each category and use the SLOPE function in Excel (or LINEST function in Excel or OpenOffice) to calculate the best fit between the individual stats totals and the points for each rank (use points as the X value and the stat totals as the Y value; this may be counter-intuitive to those who speak math, but it works*). Check your numbers against the tables below for reaonability.

If you don't have good data for your league, are in a new league, or just don't trust your math skills, you can use these denominators here. Ay, though, there's a rub. For there's good and bad in using "generic" numbers like these. The bad part is that you may see significant changes in value with small changes in the denominators, especially in the average categories (BA, ERA, and WHIP). However, that may dispel the illusion of precision that dollar values sometimes give us. You're much better off thinking of a player in terms of dollar ranges ($25-$30) rather than a specific number ($27). Better yet, think in terms of value ("second tier") rather than as a priceable quantity.

The Denominators

SGP denominators fluctuate from year to year and league to league. In order to mitigate the outliers, we're taking the average SGP across many leagues, and using a weighted average of the past three seasons. By averaging across a large sample, we hope to produce the most likely (note: not the "correct") distribution. Note that the dollar values you generate from SGP are not likely to match the projections on or in the Baseball ForecasterSince standings gain points are specific to a particular format, they require historical data to estimate accurately., therefore, uses PVM (percentage value method) for its player values.

So here are the values for the three most common league types: 12-team AL- and NL-only, and 15-team mixed. The "formula" shows you how to calculate the SGP for each player. Note that the ideal is to calculate performance above replacement level. This is usually found by running a preliminary valuation and taking the average of the first 5 or 10 players who fall below the draftable line.

12-team NL-only 5x5 (14 hitters/9 pitchers)

Category    SGP     Formula
========  =======  ==========
Runs        26.5    Runs/26.5
HR           8.3    HR/8.3
RBI         26.5    RBI/26.5
SB           8.6    SB/8.6
AVG       0.0022    (((1425 + Hits) / (5500 + AB)) - 0.259) / 0.0022

W            3.5    W/3.5
Sv           7.0    Sv/7.0
K           41.3    K/41.3
ERA       0.0802    (3.76 - ((459 + ER) / ((1100 + IP) / 9))) / 0.0802
WHIP      0.0151    (1.260 - ((1386 + H + BB) / (1100 + IP))) / 0.0151

12-team AL-only 5x5 (14 hitters/9 pitchers)

Category    SGP     Formula
========  =======  ==========
Runs        24.7    Runs/24.7
HR           8.5    HR/8.5
RBI         25.4    RBI/25.4
SB           6.9    SB/6.9
AVG       0.0022    (((1404 + Hits) / (5500 + AB)) - 0.255) / 0.0022

W            3.2    W/3.2
Sv           6.2    Sv/6.2
K           36.9    K/36.9
ERA       0.0788    (3.94 - ((482 + ER) / ((1100 + IP) / 9))) / 0.0788
WHIP      0.0140    (1.263 - ((1389 + H + BB) / (1100 + IP))) / 0.0140

15-team Mixed 5x5 (14 hitters/9 pitchers)

Category    SGP     Formula
========  =======  ==========
Runs        17.7    Runs/17.7
HR           6.7    HR/6.7
RBI         17.5    RBI/17.5
SB           6.9    SB/6.9
AVG       0.0016    (((1853 + Hits) / (7000 + AB)) - 0.265) / 0.0016

W            2.5    W/2.5
Sv           6.3    Sv/6.3
K           26.6    K/26.6
ERA       0.0601    (3.74 - ((582 + ER) / ((1400 + IP) / 9))) / 0.0601
WHIP      0.0109    (1.236 - ((1731 + H + BB) / (1400 + IP))) / 0.0109

4x4 Leagues and other mythical creatures

The denominators shouldn't change in standard 4x4 leagues; they would be the same within each category. But if your league doesn't exactly match the parameters of one of the above choices, you can make some adjustments. Ed DeCaria explained how in a previous column:

For single-league formats with fewer than 12 teams, the SGPs should be adjusted UPWARD for each statistic. The smaller the league, the bigger the adjustment. As a general rule, the SGPs for an 11-team league should be about 5% higher than what is shown above, the SGPs for a 10-team league should be 20-25% higher, and the SGPs for an even smaller league could approach 50% higher. The relatively scarce categories (e.g., SB, W, Sv) should be adjusted more so than the evenly-distributed categories. The main reason for these adjustments is that, with fewer teams, the battleground in the middle of the standings is often less compact, meaning it requires more of a statistic to reach the owner immediately ahead.

The same principle can be applied to mixed leagues with fewer than 15 teams, although the percentage changes would likely not be the same. You can experiment with different settings until a handful of test players look "right" to you.

If you use categories other than the canonical 5x5, DeCaria has some suggestions for that, too:

For those that use categories beyond the typical 5x5, the best solution is to use your own league's historical data (to the degree possible), or match up your non-standard category to the 5x5 category that it most closely resembles in terms of statistical distribution (e.g., OBP should behave similarly to batting average, triples should behave similarly to stolen bases, holds might fall somewhere between wins and saves).

Here's one last tip: don't be bound by the numbers. Make adjustments until they look reasonable too you overall (don't worry if a few players seem out of line). And remember that the dollar values are just a guideline. Other factors, including potential for changes in playing time, injury risk, and regression should play large roles in your buying decisions. And most of all, have fun.

A thousand thanks to the folks at OnRoto for providing the league data that made this analysis possible.

Click here to subscribe

  For more information about the terms used in this article, see our Glossary Primer.