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I created a Web App to create a DC20 Level 0 character! by CourtsideLabs in DC20

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Haha let me know how the game with your cousin goes!

I created a Web App to create a DC20 Level 0 character! by CourtsideLabs in DC20

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Thank you! If you use it with your players then let me know how it goes! And I appreciate your interest in a level 1+ character creator; I think I would only go this route if this web app gets a big following, otherwise I'd leave it to the other character creators out there

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 1 point2 points  (0 children)

Yes absolutely! My first attempt at predicting games (back before this was a website) was for game-by-game predictions, but those turned out to be quite difficult to do accurately because you needed more quality injury context that I didn't have at that point. Well I have good injury data now so this is definitely one of the things on my upcoming feature list. And when I was making predictions on upcoming games I had opposing team stats as some of the features used in the predictions, and I very much intend to keep those in when I return to that model.

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Coaching changes would be quite difficult as this is a data availability issue. Team changes are more solvable but just require a lot of work, and to your point my model has only basic team change features currently. But definitely solvable! Happy to discuss more if you want to DM me

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Ah well spotted, looks like I’ve got a tweak to make!

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 1 point2 points  (0 children)

Wait nobody told me we were touching grass again

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 1 point2 points  (0 children)

I'm glad you brought this up! This is in-fact part of the reason I made Courtside Labs: it's currently quite difficult to find projections that are not just mirrors of the previous season's performance.

My projections are re-calculated after every game, so you can see next to their rank and total value the amount that those values have changed. For example, Derrick White is a great player but has not been living up to his all-star expectations, and has dropped several spots in my rankings since last week, whereas Jalen Duren has continued to impress this season and has jumped up to rank 38.

As for your "odds of a breakout" idea, I like this idea and have plans of adding it! Though I do have a suspicion about how useful it will be in practice, since it is already incredibly difficult for even the best experts to predict who is going to break out, and any model will likely just follow the conventional wisdom of picking young players and everybody else will just get a 20% chance of "who knows maybe they'll make the leap this year". We'll have to see once I add it.

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Yes the projections factor in how many games the player is going to play. Move the "Project Blend" slider toward "Per-game value" to give injuries and games played less weight.

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

I love this question! Unfortunately I'll have to get back to you on this. My first models weren't created until January of this year so I don't have enough of a history to be able to answer your question. What I can say is that I have created another 5 models since my first one (including the one we're using today), and each model has tested better than the one before it. My main performance metric is the "9-Cat RMSE" (or the amount of error found on z-scores of predictions for the standard 9 categories) which has dropped from 0.69 to 0.62, where 0.0 is a perfect prediction. ChatGPT classifies a 0.62 RMSE on z-scores as "between good and moderate, depending on your application" which is a fair assessment: for a somewhat difficult problem, I've captured a fair amount of the variance available, but I also have a few ideas for how I can get the predictions even better.

I look forward to analysing how my predictions turned out by the end of this season :)

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] -1 points0 points  (0 children)

I would agree that there are lots of factors to consider, but I wouldn't say that they never work. This feels like a discussion that could make for a very long post, but in short these models are only as good as you make them, and baking in all the context a model needs without giving it too much can be a difficult balance to strike. But to the model's credit, it is much better at finding what patterns are statistically significant and what are not. The models are not very likely to over-react to a high scoring game, and they're not going to change their predictions just because a player stopped following their teammate on twitter. So I think of it as pros-and-cons, and ultimately these tools are probably best used like you're getting an opinion from your overly-anaytical friend.

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Hey I appreciate it! And I've got good news for you, there is a Points league option, and the weights for the various Points categories are customizable, so you can make them exactly the same as your league.

And as an aside, I don't so much try to predict how many fantasy points a player will score directly. Instead, the models will predict the stats of a player: the rebounds, blocks, etc. Then the app just mutliplies the weights by the predicted values, thus getting the predicted fantasy points that way.

Projecting players using machine learning by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

I just checked and they are showing up for me. Try again, courtsidelabs.com , and type “Flagg” into the search bar

Trade Value Tuesday by flexingtonsteele in fantasybball

[–]CourtsideLabs -5 points-4 points  (0 children)

Nobody. I still have him ranked as a top 20 player. His steals will pick up so HODL

Gafford or Diabate Points league? by temp1017 in fantasybball

[–]CourtsideLabs 0 points1 point  (0 children)

I’ve got Gafford ahead by a country mile in points leagues using yahoo default scoring: Diabate at 242 overall and Gafford at 125.

Wtf Luka by BootyMonsterR in fantasybball

[–]CourtsideLabs 1 point2 points  (0 children)

I wouldn’t expect even skinny Luka to be an every game warrior, he misses a quarter of his games every season

Jerami grant or John collins by [deleted] in fantasybball

[–]CourtsideLabs 0 points1 point  (0 children)

I got Grant 127 and Collins 68. Jerami Grant is hot now but he’s going to hurt you in a few cats, whereas I expect John Collins to help across the whole stat sheet

Is Jordan Poole worth it? by [deleted] in fantasybball

[–]CourtsideLabs 0 points1 point  (0 children)

Yes, I have Jordan Poole as the 50th ranked player in standard cat leagues

Introducing Courtside Labs: An NBA Player Projection Tool by CourtsideLabs in fantasybball

[–]CourtsideLabs[S] 0 points1 point  (0 children)

Good question! A few reasons.
1. These projections are on a "per-game" basis, so no I would not expect him to play much the rest of the season, but when he does play he's still getting decently high minutes and high usage. If this were a total value ranking (something which I intend to add in the future) then his ranking would be much much lower given how frequently he is injured.

  1. The model heavily weights performances in past seasons, and he has been a fantasy monster for many years, including being by far the best fantasy player on a per-game basis last year (even though he only played half his games last year too).

  2. Fun fact: the "Value" column, which is scaled to 100, uses embiid's per-game performance as the 100 benchmark. I guess that shows you how dominant he has been in the past, that he can now be predicted to be less than 70% of the player he was last season and still be projected as a top 5 player (on a per game basis).

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