Now that we’ve tackled calculating standings gain points for OBP, let’s give slugging percentage a try.
Again, special thanks are in order for reader Matt who also filled out the “What It Takes To Win Your League Calculator” with the last four years of history for the SLG category in his league. You can see the data for ten teams listed below.
How Many SLG Percentage Points Move You Up One Spot In The Standings?
Over the last four years, an average of .469 won the category and an average of .429 finished 10th.
.469 – .429 = .040 total spread between 10 teams
We have the data for 10 teams, meaning there are 9 spots you can move up in the standings by moving from .429 to .469 in team SLG.
.040 / 9 = .00444
On average, increasing your team SLG by .00444 points will result in you climbing one spot in the standings.
But I Play In A 12-Team League, Can I Use This Figure?
If you have an established league history with historic standings available to you, you can always calculate this yourself based upon your league’s specific history.
I don’t have specific evidence to support this, but I would venture a guess that limiting your SGP calculations to the top 10 out of 12 teams might be more beneficial than using all 12 teams. This would minimize or eliminate the effect of teams that lose competitiveness or that tank the season.
What Formula Do I Use To Calculate The SGP For A Given Player?
Recall from Part 5 of the “Create Your Own Rankings” series, that you determine the SGP for a ratio stat, like SLG, by removing the effect an average player has on an average team, and then inserting the player you are ranking into the equation.
- Using the table above, the average SLG for the ten teams was .450.
- If you need a refresher, the formula for SLG is:
(1B +2*2B+3*3B+4*HR) / AB
(H + 2B + 2*3B + 3*HR) / AB
- The second formula works if you do not explicitly have the number of singles, but yields the same result as the first.
- Another way to phrase this is total bases divided by at bats.
- Assuming we are trying to calculate SGP for a 12-team league drafting 14 hitters each, that means 168 players will be drafted. The top 168 hitters in my 2014 projections show an average of 518 at bats each (I had to calculate this myself by looking at the projections).
- A team with 14 hitters would then have 7,252 combined at bats (14 * 518).
- Knowing that the team SLG averages to .450, we can determine that the total bases for an average team is 3,263 (.450 * 7,252). The total bases for the average player is 233 total bases (.450 * 518).
- The effect of an average player (233 total bases, 518 at bats) on the team could be shown as:
=(233 + 3,030)/(518 + 6,734)
- Now remove the average player:
=(X + 3,030)/(Y + 6,734)
- If you’re using the “Create Your Own Rankings” series, you would first need to adjust your “Hitter Ranks” tab to pull in 2B and 3B from the projection information. Then, the specific formula that would be added to the “Hitter Ranks” tab in your rankings spreadsheet (step 6 of Part 5) would be:
The formula above has a lot going on, but it’s really just trying to find the team’s SLG after removing one average player and inserting the effect of the player you are ranking. The difference between the new team SLG and the historic league average of .450 is determined (this is the reason for the “-.450” part of the equation). The difference is then divided by .00444.
More Info About Matt’s League
The starting lineup in this example league is C, 1B, 2B, SS, 3B, OF, OF, OF, UTIL (9 hitter slots). So this is a shallower league than I use in most instructional posts. If you play in a standard league starting 14 hitters, the SLG numbers in your league will be lower than those shown here. Adding additional players, especially a second catcher and weaker middle infielders, to the lineup will drag down SLG.
If you play in a league where the historical SLG stats are not available, you could use these as a starting point. But I would recommend calculating from your league’s own history if the data is available.
In the end it is the spread from the first team to the last team that drives SGP. It is not the actual SLG numbers themselves. So while the SLG numbers in a 12-team league using larger rosters will be lower than Matt’s standings, the spread between the teams is what’s important. I imagine the spread would be affected by adding more players, but I can’t comment on how significant of an effect it would be.
The Same Concepts Still Apply
Hopefully the illustration above gives you enough usable information and you can follow the calculation process to determine the figures for your own league.
Thanks For Reading