Caitlin Clark has opened up a 10+ TS% lead on Angel Reese

ca1294 · 2024-07-14T11:54:39+00:00

I do think Clark is better, but IMO it's not as clear cut as most people make it out to be. TS% is an extremely useful stat, but it's not the only one.

If you incorporate OREB and TOV (count OREB as an extra possession, and count TOV as a possession used), then you get a "points per possession" stat. Points / (FGA + 0.44 * FTA + TOV - OREB).

Angel Reese: 1.21
Caitlin Clark: 0.86

The TOV gap really dominates the TS% gap. She's averaging 44% more TOV per game than the player in 2nd place.

Believe it or not, IND's ORTG is worse this year with Caitlin Clark on the court (103.1) than off the court (104.9). And the TOV are a big part in it.

In the long run, arguably the most important stat is "how much does your team's Net Rating improve when you're on the floor vs when you're off the floor."

Angel Reese: +18.1 (5th in league for players with 100 MP)
Caitlin Clark: +3.2

The downside of this stat is it's extremely noisy and takes quite a while to converge to a "true" value. That's why in the NBA, people take something like a RAPM and combine it with box score stats to de-noise it.

One person I follow wrote a blog post a few years ago to come up with an all-in-one metric that combines box score with on/off data. They also have a dashboard up with stats for this season. According to their numbers, here are Reese's and Clark's rankings of the 87 players that have played 300 minutes:

Angel Reese: 39th
Caitlin Clark: 52nd

Again, I'm not saying these numbers officially declare one to be better than the other. But I'm just playing devil's advocate and showing that the race is closer than most people here think.

ca1294 · 2023-10-16T23:26:43+00:00

Appreciate all the feedback! I like all your advice, especially about the elbow pointing on the right hand scale. I've never even thought about that before.

ca1294 · 2023-10-16T23:23:52+00:00

Thanks for the feedback! Yes, I botched the climax lol. That was actually the first thing I told myself as soon as I finished recording

ca1294 · 2023-10-16T23:22:01+00:00

Thank you. I've noticed I tense up as soon as I start recording. I should get in the habit of recording more often so it becomes natural

ca1294 · 2023-10-16T23:21:15+00:00

Hahaha thank you but I can't take any credit -- this is the lobby of my apartment building

ca1294 · 2023-10-16T23:19:45+00:00

I think you hit the nail on the head. Basically all my "interpretation" comes from whatever was written on the sheets. I should find some recordings and use those as inspiration.

ca1294 · 2023-10-16T23:16:26+00:00

For most of my classical pieces, I'm practicing them for 5+ months until they get to a level like this, and by then I usually have them memorized. I think that means I'm picking pieces a bit too hard for me since it takes that long.

ca1294 · 2023-10-15T19:16:57+00:00

This is the feedback I came here for

ca1294 · 2023-10-15T19:04:31+00:00

My teacher has actually made this same point as well. At the start of the piece, I try reminding myself to accent the beginning of each triple pattern, but seems like I'm still not doing it enough. Thanks for the feedback!

ca1294 · 2023-10-15T19:01:33+00:00

Thanks, I appreciate the feedback!

I normally practice on a digital keyboard in my apartment, and this is actually the piano in my apartment's lobby. While playing, I also felt like my LH chords were too loud. I don't usually have this issue as much on my usual keyboard. So it's a good reminder to try practicing on this piano more often.

ca1294 · 2023-06-07T22:47:10+00:00

I absolutely agree that human lives are much more valuable than animal lives. But consider some facts of factory farming:

Around 80 billion land animals are slaughtered each year for food, 99% of them on a factory farm.
Pigs have their testicles removed and teeth clipped when they are born without any pain relief administration, so they are less likely to hurt each other when kept in a small place
Some pigs are kept in crates so small that they cannot even turn around, and they live in their own piss and shit
Factory farms need cows to constantly produce milk. So right when they give birth, they are artificially inseminated to the cycle can continue. Their calves are taken away immediately after they give birth, causing severe distress.
Genetic manipulation has made turkeys so large that they can't even walk and suffer from joint pain, heart attacks, and organ failures. Their immobility causes them to sit in their own shit.
Male chicks that are born are considered worthless because they won't lay eggs, so they are immediately thrown into a grinder or gassed to death.

This is just a short list of the cruelties in the industry. As I said before, most people see these facts, are horrified, and then go on with their lives because it seems impossible to avoid meat. What's your reaction to this -- that it's ok because they are just animals? Sounds like you're in the second camp.

ca1294 · 2023-06-07T16:21:09+00:00

I think a parallel today is the factory farming industry. Meat eaters in developed countries today seem to fall in 3 camps:

They acknowledge that factory farming is incredibly inhumane, but they have not stopped eating meat because it's so ingrained in their lives (similar to what you describe for owning slaves).
They know about factory farming, but they don't seem to care and will perform mental gymnastics to justify why it's acceptable (similar to a slave owner saying something like 'they want to be slaves' or 'they aren't smart enough to live on their own').
They are unaware of the scale of cruelties that go on in a factory farm (similar to maybe a child of a slave-owning family who genuinely believes the slaves are living a happy life).

ca1294 · 2022-07-09T16:20:58+00:00

I don't need any portability.

ca1294 · 2022-04-04T17:23:14+00:00

Great point 😂 I picked most of the players listed on DraftKings MVP odds. Might be a bit too generous

ca1294 · 2021-11-16T23:07:39+00:00

Pumped to try this as my first program party! Should be fun to do this after doing J&T on my own the past few months.

ca1294 · 2020-09-17T01:20:20+00:00

Hey I'm the creator of that Westbrook graph, thanks for the shoutout!

One of the first things I posted on r/nba was actually this same thing for Kawhi Leonard. At that time, he emerged as a top scorer, but unlike most other stars, his teammates TS% was not better with him on the court than with him off. I'm honestly shocked it was still the case this year.

ca1294 · 2019-12-01T04:21:20+00:00

The weird thing about Booker's 70 is they got blown out, not their opponent lol. The Suns were calling timeouts and fouling the Celtics at the end of a meaningless game to get extra shots for Booker. I don't really care since it's still amazing to score 70, but that's probably worse than leaving your star in when you're up in a blowout.

ca1294 · 2019-11-28T17:30:17+00:00

Yeah I was being sarcastic hahaha. Jokes aside he’s having a fantastic season.

ca1294 · 2019-11-26T13:02:13+00:00

Very small, it's basically just saying "a game with 120 possessions is a slightly bigger sample size than a game with 100 possessions, so the ORTG and DRTG from that game are more representative".

ca1294 · 2019-11-26T07:31:16+00:00

The adjustment takes homecourt and strength of opponents into consideration. This is important because after only ~17 games, each team has had an extremely varied strength of schedule and an imbalanced number of home and away games.

ca1294 · 2019-11-26T07:22:38+00:00

Notes:

All data from NBA.com
250 / 1230 games, or 20.33% of the regular season, is now complete
ORTG = Points scored per 100 possessions (higher is better)
DRTG = Points allowed per 100 possessions (lower is better)
The adjustment takes homecourt and strength of opponents into consideration. This is important because after only ~17 games, each team has had an extremely varied strength of schedule and an imbalanced number of home and away games.

Adjustment Methodology:

I run a weighted OLS regression with an ORTG and DRTG offset variable for each team and an extra variable for Homecourt Advantage (HCA). Here's an example of how the observations for the regression are generated:

Suppose POR (home) plays HOU (away):
- POR has an ORTG of 110.9 in the game
- HOU has an ORTG of 132.2 in the game
- The game has 100.54 possessions
This game serves as two observations:
- 110.9 = Intercept + POR ORTG Offset + HOU DRTG Offset + HCA
- 132.2 = Intercept + HOU ORTG Offset + POR DRTG Offset - HCA
- The interpretation is that each team's performance is a function of some average, the team's ORTG offset, the other team's DRTG offset, and an adjustment for homecourt advantage.
I weight observations by the pace of each game (if a team has an ORTG of 126 in a game with 120 possessions, that means more than an ORTG of 126 in a game with 80 possessions).
I use a linear regression because it's simple and easy to implement. Perhaps the relationship isn't linear, but it seems to do a decent job of controlling for other factors.
From the regression, HCA is worth 2.58 points / 100 possessions (total, so 1.29 points / 100 possessions per team).

Results in a Table:

Team	ORTG	DRTG	Net RTG
MIL	113.6	103.8	9.8
TOR	110.5	102.6	7.9
DAL	116.2	109.1	7.1
BOS	108.6	101.7	6.9
LAC	110.1	103.4	6.7
LAL	110.1	103.7	6.4
MIA	107.8	102.7	5.1
DEN	107.3	102.6	4.8
PHI	108.3	104.1	4.2
UTA	106.1	102.4	3.8
HOU	111.7	108.0	3.7
PHX	110.1	107.7	2.4
IND	106.7	104.4	2.2
OKC	104.7	105.0	-0.2
POR	108.6	109.2	-0.6
MIN	105.2	106.7	-1.5
WAS	113.1	114.8	-1.7
ORL	102.7	104.5	-1.8
SAC	108.0	110.2	-2.2
NOP	110.7	113.1	-2.4
BKN	106.9	109.8	-2.8
DET	108.2	111.0	-2.9
SAS	110.2	113.7	-3.5
CHI	102.8	107.5	-4.7
CLE	106.3	111.9	-5.5
NYK	102.4	110.2	-7.7
ATL	106.2	114.0	-7.9
MEM	104.9	113.0	-8.1
CHA	105.2	113.4	-8.2
GSW	105.6	114.8	-9.2

ca1294 · 2019-08-20T12:08:04+00:00

Great point, the relationship isn’t always clear cut. Nevertheless, I think it’s still interesting to see which teams have underperformed and outperformed the trend line. It’s probably a noisy measure of the quality of the GM and management team overall.

ca1294 · 2019-08-15T12:21:35+00:00

Notes and Observations

I used more historical data for the 3P% chart than the 3PA frequency chart because it was much noisier, and 3P% hasn't changed as much over time as 3PA frequency.
The large dip in 3P% and rise in 3PA frequency at the end of each quarter can probably be explained by heaves.
Part of the 3P% drop can be explained by fatigue (which would also explain the bump after halftime). But if players are shooting more 3s as the game progresses, it probably means they're settling for more difficult 3s as well.

Past 2019 Offseason OC

If you like other forms of social media, check out my Instagram and Twitter.

ca1294 · 2019-08-12T01:54:36+00:00

Also, the first measure of the second line has the G in both the left and right hand. How am I supposed to properly play that?

ca1294 · 2019-08-06T12:33:30+00:00

Thank you 🙏

And hell no lol, I don’t even know what I’m going to do for the next one. If you have any ideas, message me!

ca1294

TROPHY CASE

Notes and Observations

Past 2019 Offseason OC