SubscribeI have mentioned that I think Standings Gain Points (SGPs) are a pretty good method of determining player values for fantasy. However, I don’t use them for the Price Guide based on some shortcomings I have found. This week I’m pointing out some of the quibbles that I have with the SGP methodology:
How many types of fantasy baseball leagues are there out there? Fantasy leagues might have 6 teams or 30 teams or anything in between. They use different stats: 4×4 and 5×5 and 6×6 and hundreds of other combinations. Different leagues start different positions: two catchers, middle infield, middle relievers, etc.
When I was developing the Price Guide to build fantasy values, I obviously wanted something that could be used by people outside of the Standard League. And requiring that flexibility ruled out using Standings Gain Points.
See, SGPs are tied to the final standings of a specific league. They are built based on a certain number of owners, a certain number of hitters, and a certain number of pitchers. The more you deviate from those specific settings, the less accurate SGP prices will be.
Consider Razzball’s Point Shares, which are essentially a SGP system. A while back I drafted a league that had 6 teams using Point Shares and 6 teams using the Price Guide, tailored specifically to the league settings that Point Shares were based on. When I checked the final results, the Price Guide teams and the Point Shares teams split the points right down the middle. Out of 550 total points, each system got 275 — exactly half. In a league customized for a SGP system, SGPs and the Price Guide can do equally well.
However, if you change any single aspect of that league, the Price Guide will very likely take the top six spots. If you add a couple of new positions or add a couple of teams, the Price Guide values will adjust for it, and SGPs won’t. (This fact became very clear when I setup the Price Guide/Point Shares league, which wasn’t even competitive when I accidentally left out CI and MI.)
I imagine most people can live with SGPs inflexibility: After all, most people aren’t trying to build an online tool that works for any league, they just want a system that works for their league.
But what happens if your league decides to expand from 10 to 12 teams this year? What if they decide to use OBP instead of AVG?
If anything in your league changes from the year before, the accuracy of your SGPs is greatly compromised; but the Price Guide will adjust for whatever settings your league chooses.
Related posts:
interesting stuff. I just noticed that a piece of software I bought (which I otherwise really like) does NOT change the SGP denominators when you change around league settings. I had thought it would, but after double checking it doesn’t appear to at all. I guess that would require that the software have data on all kinds of leagues which is unreasonable.
Upon thinking about it, rather than use real data for different league standings, why not use fake data? You could probably approximate final stat distributions by running simulations where you take a season’s final stats for a certain league type, determine the top n players at each position, and then randomly assign them among n teams and compute the final stats. Do this a whole bunch of times.
This could allow you to approximate the final averages and ranges for a league, but I’m not sure how accurate of an approximation this would be.
It’s funny because earlier today I was looking over razzball’s one and wondering what was wrong with it, using chone projections for each one they had pujols as only the 6th best player while this one correctly picked him 1st according to the stats. Seriously razzball gave an extra like 2 total points almost because arod had 4 more runs and i think 9 RBI or something it was weird.
I think that’s an average vs. replacement issue. A 1B like Pujols will have less value if compared with the average at his position rather than replacement.
Rudy and Derek Carty were sort of debating this on Razzball, and I proposed a sort of test to try and find which baseline would more accurately predict standings in a league. Proposed something similar to what Mays did earlier here, but Rudy’s not into it. Trying to think of another test to handle the “which is the superior baseline” question.
I’m not sure I agree with the assessment that SGP’s are inflexible WRT league size, categories, etc.
Either using real data or simulated data (as suggested in post #2) you can generate SGP denominators which are customized to your league’s statistical categories. One of my leagues uses RAT3 (which is BB+H-K/IP) and it was very easy to modify my calculations to factor in that difference.
As for the number of teams (and league depth) choosing the correct baseline takes care of that. IE once you figure out what the worst player is projected to do and assign them a value of $1, it’s relatively easy to figure out the marginal value of the stars.
@slackerjack: I agree that you can come up with proper SGP denominators for any category.
My contention above is that there are SGP systems that people use without considering what league particulars were used to generate values. If those settings aren’t the same as your league, your values are going to be way off.
As you point out, that’s not a problem if you are customizing your own SGPs.