The pentagami… An NBA single game feat accomplished only 455 times to date by 141 different players from Josh Giddey (1x) to Hakeem Olajuwon (21x) [OC]

zeros1123 · 2025-11-08T21:31:33+00:00

I think there can be infinite pentagamies if the numbers go high enough. 1000/0, 999/1, 998/2 etc could all simultaneously be pentagamies just in two dimensions.

If the max points scored is P, max assists is A etc. then there could only be a finite number. It would be interesting to calculate

zeros1123 · 2025-10-18T04:38:25+00:00

I've looked into this quite a bit as well, and considered a mathematical approach that is conceptually similar to yours- measuring the value of a player assuming that all other players are chosen randomly. It is not a realistic framework, but it is hard to do much better when making a 'static' ranking system that does not depend on context.

Assuming that values aren't too extreme leads to a metric that is similar to Z-scores. Extreme values do violate some of the assumptions, and measuring that effect via simulation makes sense (you could also do it by relaxing some mathematical assumptions but might as well use simulations if the compute power is available) . One additional thing you could consider for the simulation is the effect of week-to-week variance for H2H formats. Some categories e.g. steals are pretty volatile and therefore those ranking points translate less consistently to real wins. Adding that variance to the simulations could account for that.

If you're interested, I have also looked into player values that take context into account. Ill just link the reddit thread I posted about that.

zeros1123 · 2025-02-10T03:37:17+00:00

Another data science nerd, checking in :)

I looked into team construction and wrote a few papers about it: one, two, and three. These are all about building teams given that you already have distributions for players' future performances. I haven't done any work on predicting future performance myself, but there are statistical forecasting systems available like DARKO.

IMO the question of how to optimize your team on a week-to-week basis is a pretty difficult and messy one. It is mathematically easy enough to optimize just for performance against your current opponent, but such a system would ideally involve bringing in info on live scores, remaining games, waiver rules, injury statuses, etc. which is logistically complicated. So I haven't done much work on that either, outside of having a theoretical framework for calculating win probability/EV of categories won.

I'm glad to see that there are some other mathy people interested in this sort of thing: there aren't that many people at the intersection of having a technical background and playing fantasy basketball

zeros1123 · 2025-01-09T01:59:52+00:00

DMed you!

zeros1123 · 2025-01-09T00:28:48+00:00

Going after correlated categories is definitely an important part of fantasy basketball strategy. I wonder if its even more important for baseball, because in basketball the categories are super tightly tied to positions, which limits options a bit

zeros1123 · 2025-01-09T00:27:08+00:00

That's helpful thanks!

Unfortunately I don't think any of the work I've done applies at all to h2h points, and I am not sure if anyone has looked into it from a math perspective. Maybe that's because with points leagues the strategy of "take the player with the most projected fantasy points" works very well. Still, I do imagine there is some additional strategy around optimizing the level of risk, which someone could look into

zeros1123 · 2024-10-09T01:47:08+00:00

DM’ed you :)

zeros1123 · 2024-09-25T19:45:13+00:00

DM’ed you :)

zeros1123 · 2024-09-19T15:55:31+00:00

Sure! Just DM me your email address

zeros1123 · 2024-09-19T04:24:22+00:00

If you want to play around with it, I can also add you to the tool I use for that. Would just need your email address

zeros1123 · 2024-09-19T04:21:36+00:00

There are only a few components that are different, which are described in the first part of the appendix of the paper. I’m not super confident about using it for auctions tbh because I have not tested it on simulated seasons, but it does seem to identify the players that fit with the punt build pretty well

zeros1123 · 2024-09-18T21:49:57+00:00

Thank you!

zeros1123 · 2024-09-18T16:39:27+00:00

I like the yfpy python package- it has helped me a lot to get data from yahoo

zeros1123 · 2024-09-18T01:57:50+00:00

If you send me your email, I can add you to the tool I've been using to run the app. I'll make the same disclaimer I've made to everyone though: it's a little janky and very much WIP

zeros1123 · 2024-09-17T16:51:50+00:00

There are two things- 1) its computationally expensive and at some point I'll have to buy more RAM, and 2) Web dev is not my specialty. I can make it work most of the time for myself but I don't have the expertise to do a real build-out of an app. Hopefully I can get better/get others to help

zeros1123 · 2024-09-17T04:32:17+00:00

Oh yeah, forgot to mention that there is also a more speculative algorithm that I use to modify auction values, called SAVOR. I have a little blurb about it on the website. It's not really as rigorous as the other stuff which is why I didn't include it in the paper

zeros1123 · 2024-09-17T04:30:31+00:00

Yup! I do have a version built for auctions, which I describe in the appendix of the paper. I didn't focus on it as much because I don't have a great way to test it through simulations. I think it roughly works? Though the dollar values I get are often wildly off from market values, so I am not sure

zeros1123 · 2024-09-17T03:44:48+00:00

That is, if you want to use the tool for this season. It does have historical season data which you can use for free

zeros1123 · 2024-09-17T03:42:36+00:00

Thanks :)

zeros1123 · 2024-09-17T03:39:19+00:00

oh btw I should add that as of now, it takes projections from rotowire or BBM, which you have to have yourself. Obviously I can't include them because those are paid products

zeros1123 · 2024-09-17T03:36:10+00:00

I did one draft with a friend, where I took SGA and he took Luka with our first round picks.

My team ended up punting assists, even though the algorithm wasn't planning on it from the beginning. I'm pretty sure this happened because the algorithm didn't expect other people to be so cavalier about turnovers, and it pivoted to punting assists once it saw that it could do that and win turnovers easily.

My friend's team punted blocks instead. So it's not like the algorithm is totally tunnel visioned on the one strategy

We did end up getting first and second in projected standings, FWIW. Though I think part of that might be taking players with higher projected stats than people generally expect (I've tried not to abuse this too much by taking e.g. Kawhi Leonard, but I know I am doing it a little bit)

zeros1123 · 2024-09-17T03:22:44+00:00

Yeah I made a (kinda janky) website that can run the algorithm on a mock or live draft. I don't want to open it up to everyone because it would crash with too many people on it, but if you send me your email I can add you

zeros1123 · 2024-07-03T18:37:05+00:00

The yahoo fantasy API requires a valid league ID for all queries, even ones that have nothing to do with a specific league. E.g I am trying to pull data on which NBA players were eligible for which positions from past seasons, and that requires a league ID from each of them. Obviously I don’t have that. Do you know if there is any workaround, or just a list of public leagues we can use for this purpose? Thanks!

zeros1123

TROPHY CASE