If Not Now, Then When (track)
by King Gizzard & The Lizard Wizard
Year: 2021
From the album L.W. (track #1)
Listen on Spotify
If Not Now, Then When appears on the following album(s) by King Gizzard & The Lizard Wizard:
- L.W. (track #1) (this album) (2021)
Upcoming concerts
Listen to If Not Now, Then When on YouTube
If Not Now, Then When ratings
Average Rating = (n ÷ (n + m)) × av + (m ÷ (n + m)) × AVwhere:
av = trimmed mean average rating an item has currently received.
n = number of ratings an item has currently received.
m = minimum number of ratings required for an item to appear in a 'top-rated' chart (currently 10).
AV = the site mean average rating.
| Rating | Date updated | Member | Track ratings | Avg. track rating |
|---|---|---|---|---|
| ! | 04/14/2025 07:10 | Mafedere | 4,618 | 76/100 |
| ! | 07/01/2024 17:42 | sunnydhamm | 18,323 | 56/100 |
| ! | 08/20/2023 05:16 | brent13 | 10,909 | 85/100 |
| ! | 08/11/2023 16:11 | Fevernova | 34,263 | 82/100 |
| ! | 03/01/2023 00:42 |  Mat9797 | 248 | 84/100 |
Rating metrics:
Outliers can be removed when calculating a mean average to dampen the effects of ratings outside the normal distribution. This figure is provided as the trimmed mean. A high standard deviation can be legitimate, but can sometimes indicate 'gaming' is occurring. Consider a simplified example* of an item receiving ratings of 100, 50, & 0. The mean average rating would be 50. However, ratings of 55, 50 & 45 could also result in the same average. The second average might be more trusted because there is more consensus around a particular rating (a lower deviation).
(*In practice, some tracks can have several thousand ratings)
This track has a Bayesian average rating of 77.3/100, a mean average of 76.2/100, and a trimmed mean (excluding outliers) of 77.7/100. The standard deviation for this track is 11.6.
If Not Now, Then When favourites
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If Not Now, Then When comments
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If not now, then when will I like this. I'm not sure it never really gets started for me but don't let this put you off the album. What follows is really good.
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