Timeline for Models that can rank relative performance among teams?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
|
|
| Feb 12, 2016 at 5:27 | comment | added | dcl | Ahh rightio. I think that could easily be done by clustering on the derived ratings/rankings. | |
| Feb 11, 2016 at 17:45 | vote | accept | Kevin Pei | ||
| Feb 11, 2016 at 17:45 | comment | added | Kevin Pei | I've phrased my last comment pretty terribly. My primary goal is in-fact to model w/l and calculate elo, this goal then feeds into my next goal in finding relevant skilled teams that can be clustered in groups based on skill: e.g Tier 1,2,3, etc. | |
| Feb 11, 2016 at 0:42 | comment | added | dcl | From your first post it sounded like your primary goal WAS to model W/L rates. Would be be accurate to say what you want to do is look at what independent variables can be used to predict ranking/ability? | |
| Feb 10, 2016 at 23:30 | history | edited | dcl | CC BY-SA 3.0 |
more detail
|
| Feb 10, 2016 at 23:28 | comment | added | Kevin Pei | Would you be able to give me a quick description of the features and methodologies of this package along with glicko, etc? The work i'm doing is very similar to what you have here. My primary goal is not to model win/lose rates but rather calculate a simple metric through cherry picking the MOST relevant subset of data. Looking at the rankings will help in terms of clustering similar skilled teams. Why would I do this? Two teams facing off is not enough data but if its Team A versus a cluster of similar skilled teams then thats what I am after. | |
| Feb 10, 2016 at 23:11 | history | answered | dcl | CC BY-SA 3.0 |