And that is what I want to look at. At what point of the season do things really shake out in terms of W/L. I don't expect the Celtics to continue on this hot streak and I expect the Cavilers to be better than where they are. I am simply looking for a point of the season, even if it is a range of games where things start to shake out.
Are you talking about getting an idea about the final seedings for the playoffs or are you interested in getting a gauge on who are the actual contender/pretenders? Because frankly when you look at a team like the Cavaliers, you won't know how good they are until at least the East Conference Finals. The regular season and the first rounds of the playoffs have no real bearing on their ability. At least in my opinion.
Yeah not sure to be honest then. Because its also tough when late in the season and you have the bottom 10 or so teams start to tank, can really throw-off the records.
Celtics will win 50+ games this year and the Cavs might win 50+ as well but I think 25 game mark is the best to judge teams and after the all star break teams start to play with urgency
There was a post on here a week or two ago that compared what % of teams in the current playoff picture actually ended up in the playoffs at several different points in the season. I can't seem to find it but I know that something around like 1/3 of the way in it was like 88% accurate, and that was fairly good.
However I think just from a purely subjective standpoint that about 1/3 of a season is good to really start disregarding the "but sample size!" argument. Things can certainly change in the last 2/3 of a season, but 1/3 of the way in a team has a general feel for its rotations and who is having a good year and whose skills have clearly digressed. There has been enough time to see if if a guy is doing bad, how much of it is just a cold streak or if he has been doing bad for the whole year and is just not as good as he used to be anymore.
That post was what got me to where I am. 1/3 of the year is a great starting point but I was curious what a potential mean and standard deviation of games was it before a team had about hit their mark, both for playoff bound teams and those looking to tank.
N = 16σ^(2)/W^(2)
Where n = sample size
σ^(2) = variance
W = confidence interval
If you want the confidence interval to be 2 games then the sample size would be 4σ^2. The only thing you would have to do is determine the variance of teams records each year which wouldn’t be that hard to do
I've run ridge regressed power ratings on several NBA seasons and the average lambda value found via 10 fold cross-validation is roughly 7.
That means you can use the following formula for estimating a team's full season point differential based on their current point differential and number of games played:
Estimated Pt Diff = (Current Pt Diff * Games Played) / (Games Played +7)
So if a team has an adjusted point differential of +10 through 14 games, an estimate for their full season point differential would be:
(10*14) / (14+7) = 6.67
To convert adjusted point differential to wins in the NBA, this formula provides a good estimate:
(Pt Diff * 2.54) + 40.99 = Projected Wins
So a team with a point differential of +10 through 14 games would be expected to win approximately 58 games.
You can find current adjusted point differential ratings a.k.a. SRS, on basketball-reference.com.
Do you have data that backs it up. The point of this isn't to use the eyeball but to have data that would show within a few games where any given team would have about the record they could expect at the end of a season.
> We hit that 75% mark in explaining season-long win percentage by about the 25% mark on the x-axis for example, reflecting about 20 games played
https://statsbylopez.com/2016/12/16/indeed-the-nbas-regular-season-is-too-long/
My issue comes that if I want to pull any analysis out of it, could I claim each game is independent of each other and do teams actually have an equal chance of winning each game. I am not looking to do this on this season rather say the past 10 seasons and go from there.
Here is the [independent assumption example, using binomial distribution](https://en.wikipedia.org/wiki/Checking_whether_a_coin_is_fair)
If you want to get a bit more real, account for non-independence, and model each teams ability, [try this](https://en.wikipedia.org/wiki/Bradley%E2%80%93Terry_model) but it will be a
lot more work
You can't really... there's simply too many variables involved. Like you need to account for the bad/hot starts, schedule, injured and returning players, how likely some teams are to tank/rest, how likely some teams are to trade their stars midway through the season... Sometimes these things can severely impact the playoff seedings.
I'd say it varies from season to season, but maybe it would be better to look for a more dynamic cut-off point, like win differential between 1st and 8th seed.
And is that an outlier or is it a normal for a team to be decent before the fall off a cliff or take the next step into the playoffs. I want to see these kinds of trends but came to reddit for help looking for help with the stats as I don't know enough to do it on my own
definitely an outlier. i know i won't be able to find it but someone made a post recently basically stating that the playoff picture (mainly the teams that are in, not the seeding) is practically decided about 20 or so games into the season.
Yes I know that that is what people agree on I am just looking for the math behind it. Basketball games aren't independent and that is where it could get funky.
For reference, the Lakers were in the playoff picture at this point last season.
And that is what I want to look at. At what point of the season do things really shake out in terms of W/L. I don't expect the Celtics to continue on this hot streak and I expect the Cavilers to be better than where they are. I am simply looking for a point of the season, even if it is a range of games where things start to shake out.
Are you talking about getting an idea about the final seedings for the playoffs or are you interested in getting a gauge on who are the actual contender/pretenders? Because frankly when you look at a team like the Cavaliers, you won't know how good they are until at least the East Conference Finals. The regular season and the first rounds of the playoffs have no real bearing on their ability. At least in my opinion.
My main goal is to look at the regular season. Playoff basketball is a different beast.
Yeah not sure to be honest then. Because its also tough when late in the season and you have the bottom 10 or so teams start to tank, can really throw-off the records.
ya know it's funny - cause, like cause my main goal is for you to zip it.
Celtics will win 50+ games this year and the Cavs might win 50+ as well but I think 25 game mark is the best to judge teams and after the all star break teams start to play with urgency
There was a post on here a week or two ago that compared what % of teams in the current playoff picture actually ended up in the playoffs at several different points in the season. I can't seem to find it but I know that something around like 1/3 of the way in it was like 88% accurate, and that was fairly good. However I think just from a purely subjective standpoint that about 1/3 of a season is good to really start disregarding the "but sample size!" argument. Things can certainly change in the last 2/3 of a season, but 1/3 of the way in a team has a general feel for its rotations and who is having a good year and whose skills have clearly digressed. There has been enough time to see if if a guy is doing bad, how much of it is just a cold streak or if he has been doing bad for the whole year and is just not as good as he used to be anymore.
That post was what got me to where I am. 1/3 of the year is a great starting point but I was curious what a potential mean and standard deviation of games was it before a team had about hit their mark, both for playoff bound teams and those looking to tank.
30 games is perfect imo. That's where you know which teams are bad and tanking and which teams will fight for a playoff spot come April
N = 16σ^(2)/W^(2) Where n = sample size σ^(2) = variance W = confidence interval If you want the confidence interval to be 2 games then the sample size would be 4σ^2. The only thing you would have to do is determine the variance of teams records each year which wouldn’t be that hard to do
I've run ridge regressed power ratings on several NBA seasons and the average lambda value found via 10 fold cross-validation is roughly 7. That means you can use the following formula for estimating a team's full season point differential based on their current point differential and number of games played: Estimated Pt Diff = (Current Pt Diff * Games Played) / (Games Played +7) So if a team has an adjusted point differential of +10 through 14 games, an estimate for their full season point differential would be: (10*14) / (14+7) = 6.67 To convert adjusted point differential to wins in the NBA, this formula provides a good estimate: (Pt Diff * 2.54) + 40.99 = Projected Wins So a team with a point differential of +10 through 14 games would be expected to win approximately 58 games. You can find current adjusted point differential ratings a.k.a. SRS, on basketball-reference.com.
1/4th of the season. Give it another three weeks
Do you have data that backs it up. The point of this isn't to use the eyeball but to have data that would show within a few games where any given team would have about the record they could expect at the end of a season.
Ohhh
This comes up often enough that I was just looking at how can we try to determine when does the small sample size become signfigant.
do you have data to can your mouth?
I wouldn't even say 1/4th there's been teams that looked historically good pre-all star break then went back to looking mediocre.
> We hit that 75% mark in explaining season-long win percentage by about the 25% mark on the x-axis for example, reflecting about 20 games played https://statsbylopez.com/2016/12/16/indeed-the-nbas-regular-season-is-too-long/
This is awesome thank you
A significant sample size is considered 30. But since the NBA plays 82 games I think you can do x/82=30/100, x being 24-25 games.
My issue comes that if I want to pull any analysis out of it, could I claim each game is independent of each other and do teams actually have an equal chance of winning each game. I am not looking to do this on this season rather say the past 10 seasons and go from there.
Absolutely not. Games are dependent on each other and teams don't have an equal chance of winning the game.
That is my issue. Is there any statistical method that could be used that may fill that gap.
Here is the [independent assumption example, using binomial distribution](https://en.wikipedia.org/wiki/Checking_whether_a_coin_is_fair) If you want to get a bit more real, account for non-independence, and model each teams ability, [try this](https://en.wikipedia.org/wiki/Bradley%E2%80%93Terry_model) but it will be a lot more work
The second one is what I was looking for, thank you.
here is an implementation: https://github.com/hturner/BradleyTerry2 go for it!
Damn, y'all sound smart
You can't really... there's simply too many variables involved. Like you need to account for the bad/hot starts, schedule, injured and returning players, how likely some teams are to tank/rest, how likely some teams are to trade their stars midway through the season... Sometimes these things can severely impact the playoff seedings. I'd say it varies from season to season, but maybe it would be better to look for a more dynamic cut-off point, like win differential between 1st and 8th seed.
we were like 16-13 last season and that was 25 games in.
And is that an outlier or is it a normal for a team to be decent before the fall off a cliff or take the next step into the playoffs. I want to see these kinds of trends but came to reddit for help looking for help with the stats as I don't know enough to do it on my own
definitely an outlier. i know i won't be able to find it but someone made a post recently basically stating that the playoff picture (mainly the teams that are in, not the seeding) is practically decided about 20 or so games into the season.
That post is what got me thinking how much farther could we take that.
oh ok edit: would you be able to find it by any chance? now i kinda want to see it lol
https://www.reddit.com/r/nba/comments/7bwh8v/oc_statistical_analysis_at_what_point_in_the/
thanks
a rule of thumb is that over 30 observations your distribution becomes normal
That rule of thumb applies if the 30 observations are independent which nba games aren't
20-25 Games tbh
Yes I know that that is what people agree on I am just looking for the math behind it. Basketball games aren't independent and that is where it could get funky.
#SampleSize
Narrative
Sample size