What Does This Team Need Most?

[TheMobb]

Actually, I’m going to say the Warriors keyed on sagging the defense to the paint and not necessarily on Domas first. And why wouldn’t a team do that with Domas stuck in the paint to be effective at the time, Fox who was a paint monster and the Kings up and down 3 point shooting.

I was commenting on what the Warriors assistant coach said, that their priority was stopping Domas as a hub and distributor and blowing up the DHOs. Watching the games you could see that and also that they were conceding the 3 to Fox. I’m sure that had an effect in bending the defense and opening things up for the Kings guards.

To compare it to the Warriors-Rockets series, the numbers paint that Jalen Green had a terrible series. However you could see the Warriors constantly blitzing him and that he was the priority to stop, that leaves the others on a 4 on 3 but it’s up to the others to make the right reads and knock down shots.

 

[Insomniacal Fan]

I assure you that I am not purposefully ignoring it. My position, which I have stated, is that we are looking at a very small sample size (7 playoff games since he has been "Domas") and we should not believe that those 7 games have remotely the same predictive power as the 246 regular season games he has played as a King.

I recognize that he is not a good defensive player (but a GREAT rebounder, which is usually considered a component of defense statistically), but he remains wildly successful in advanced stats that include both offense and defense. He has a huge +++ impact on winning, even if he is a weak paint defender.

And, to address the remaining point, I believe that "easy to scheme against" is just a narrative to explain the difference between his large sample size stats and his small sample size stats. The idea that NBA coaches don't actually do any coaching until the playoffs start is not credible to me. And, of course, if he were statistically better in the playoffs than the regular season, the other side of the argument would call him "uniquely built for playoff basketball". I would endorse neither of those views.

The small sample sizes criticism is interesting to me. There are statistical tests that compensate for differing sample sizes between groups, so it should be possible to determine if the playoff effect is real or not

I plugged Domas' game logs into a one way ANOVA analysis. Comparing 246 regular season games as a King, to 7 in the playoffs, there were notable p-values for TS%, DRB%, and AST (There were also a few other stats that are derivative of those)

ANOVA [TS%] results: F=7.3666, p=0.0071
ANOVA [DRB%] results: F=9.8929, p=0.0019
ANOVA [AST] results: F=4.4996, p=0.0349

I think we can say that despite the small sample size, there was a statistically significant effect between Sabonis' regular season efficiency, and his playoff efficiency. I'm not a statistician, and I would appreciate any criticism of this approach; but I believe this debunks the small sample size objection to the extent I could publish these results in a scientific journal.

Now, looking at Sabonis' broken hand stats, I don't see evidence that he was hampered by his avulsion fracture; in fact his best statistical run came in the months after the break. (As an aside, this jives with my personal experience. I had an avulsion fracture on my toe once, and aside from occasionally feeling sudden-random-numbing pain, I was pretty functional after a couple of weeks.) Domas compensated effectively.

I have considered the idea GSW was a bad matchup. The stats between the regular season games against GSW and the playoffs still show a significant effect on DRB%, but there isn't enough evidence to prove that Domas' TS% was affected by playoff basketball relative to an unfavorable matchup vs GSW.

I haven't disproven the stomp hypothesis (yet).

 

[Turgenev]

The small sample sizes criticism is interesting to me. There are statistical tests that compensate for differing sample sizes between groups, so it should be possible to determine if the playoff effect is real or not

I plugged Domas' game logs into a one way ANOVA analysis. Comparing 246 regular season games as a King, to 7 in the playoffs, there were notable p-values for TS%, DRB%, and AST (There were also a few other stats that are derivative of those)

ANOVA [TS%] results: F=7.3666, p=0.0071
ANOVA [DRB%] results: F=9.8929, p=0.0019
ANOVA [AST] results: F=4.4996, p=0.0349

I think we can say that despite the small sample size, there was a statistically significant effect between Sabonis' regular season efficiency, and his playoff efficiency. I'm not a statistician, and I would appreciate any criticism of this approach; but I believe this debunks the small sample size objection to the extent I could publish these results in a scientific journal.

Now, looking at Sabonis' broken hand stats, I don't see evidence that he was hampered by his avulsion fracture; in fact his best statistical run came in the months after the break. (As an aside, this jives with my personal experience. I had an avulsion fracture on my toe once, and aside from occasionally feeling sudden-random-numbing pain, I was pretty functional after a couple of weeks.) Domas compensated effectively.

I have considered the idea GSW was a bad matchup. The stats between the regular season games against GSW and the playoffs still show a significant effect on DRB%, but there isn't enough evidence to prove that Domas' TS% was affected by playoff basketball relative to an unfavorable matchup vs GSW.

I haven't disproven the stomp hypothesis (yet).

I do not claim to be a statistician either. But, trying to be fun:

It would be better to post your question first, define your outcomes of interest, then report the effect estimates for each outcome. Rather than performing a test on several outcomes and then reporting only the P values for outcomes that were significant and saying you have something ready to submit for peer review.

One way ANOVA is for two independent groups. Since Domas produced all the stats you have analysed, they are not independent samples. So this isn't an appropriate analysis method to use.

P values are for chumps.

When choosing which analysis method to use, you do have to consider other variables that can influence the outcome beyond just 'regular season' or 'playoffs'. These can include opponent and injury. Maybe a linear regression? But how easy is it to define 'injured' or 'stomped on' for an analysis like this?

I feel the best way is just to eyeball the stats and to acknowledge the limitations. That is, Sabonis' output seems to be less in the playoffs, but it is a small sample, he has played hurt, and Looney was pushing off all series. And maybe he wouldn't have been stomped on if he was terrible.

Then maybe say hey Sabonis might or might not be that great in the playoffs, but we are Sacramento and we don't get to go to the playoffs that often, if at all. What we really need is a good regular season team to get us to the playoffs more often.

 

[Capt. Factorial]

I think we can say that despite the small sample size, there was a statistically significant effect between Sabonis' regular season efficiency, and his playoff efficiency. I'm not a statistician, and I would appreciate any criticism of this approach; but I believe this debunks the small sample size objection to the extent I could publish these results in a scientific journal.

I think you might find that an N of 7 would not be sufficient to convince peer reviewers.

We can all agree that Domas' stats in our series against the Warriors were generally lower than his regular season stats that year: He went from 19.1/12.3/7.3 to 16.0/11.0/4.7, that's something like a 10% drop in rebounds, a 15% drop in scoring, and over a 30% drop in assists. Though let's keep in mind that assists require your teammate to hit a shot, and the Kings FG% went from .494 in the regular season to .429 in that series, so the entire burden for assists isn't on him.

The first question is whether this is a real difference, or just chance. It sounds like you pumped a lot of stats into an ANOVA and got out three "hits", but I don't know how many stats you included. Were his increases in steals and blocks also significant at a p-value of .05?

But even if we accept that these stats (remember, three fewer points, one+ fewer rebounds) are actually indicative of his true performance in that series, there are any number of causative factors (playoffs, injury, bad matchup, biased refereeing, etc.) that could be the reason. There's really no easy way to control for those, which is why I'm not convinced. Besides, if we could manage to make the playoffs again, Domas could have like two solid games and completely erase those p-values.

 

[Insomniacal Fan]

I think you might find that an N of 7 would not be sufficient to convince peer reviewers.

I am aware that peer reviewers sometimes require a minimum group size. I discussed this idea with someone who publishes papers for a living, (not ChatGPT I promise), and in their field (biology, not medicine) the minimum group size they needed to convince reviewers is 3. There might be different standards in the "journal of internet sports debates," but given that the journal is imaginary, there isn't much of a scholarly tradition to guide or restrict us here. I'm not convinced that we can simply drop the playoffs group out of hand due to size.

We can all agree that Domas' stats in our series against the Warriors were generally lower than his regular season stats that year: He went from 19.1/12.3/7.3 to 16.0/11.0/4.7, that's something like a 10% drop in rebounds, a 15% drop in scoring, and over a 30% drop in assists. Though let's keep in mind that assists require your teammate to hit a shot, and the Kings FG% went from .494 in the regular season to .429 in that series, so the entire burden for assists isn't on him.

The first question is whether this is a real difference, or just chance. It sounds like you pumped a lot of stats into an ANOVA and got out three "hits", but I don't know how many stats you included. Were his increases in steals and blocks also significant at a p-value of .05?

I had a few hypothesis I was interested in. The primary one that I was interested in proving that there was a significant difference between Domas' stats in the playoffs and his regular season stats, per https://community.kingsfans.com/threads/what-does-this-team-need-most.101601/page-6#post-1881507 (starting with a basket of stats you picked out.). I didn't have easy access to game level VORP, PER, or WS/48, so I excluded those from the analysis

Of course, in parsing the the game logs, I picked up a bunch of other stats, advanced and basic, so I looked at the p-values for those as well. I had a secondary hypothesis that even if there wasn't a difference in the initial set of stats, that there was a significant effect in Domas' playoff performance somewhere. In total, the stats I tested were

Advanced -- ['TS%', 'eFG%', 'ORB%', 'DRB%', 'TRB%', 'AST%', 'STL%', 'BLK%', 'TOV%', 'USG%', 'ORtg', 'DRtg', 'BPM']
Basic -- ['FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'GmSc', '+/-']

(Blue stats were in the initial basket, bold stats were significant)

I found statistically significant differences (p < 0.05) in TS%, eFG%, DRB%, ORtg, BPM, DRB, AST, GmSc.

eFG% and ORtg seem to me to be duplicative of TS%, and I didn't want to double count, so I'm ignoring them. DRB is duplicative of DRB%. AST is the only other counting stat that's significant. GmSc is similar to PER, but it's derived from other stats in the basket, so I'm ignoring it. So that's how I ended up with TS%, DRB%, and AST. Steals and blocks were not significant at a p-value of 0.05
(Edit: oh, and I don't understand BPM, so I ignored that one too)

But even if we accept that these stats (remember, three fewer points, one+ fewer rebounds) are actually indicative of his true performance in that series, there are any number of causative factors (playoffs, injury, bad matchup, biased refereeing, etc.) that could be the reason. There's really no easy way to control for those, which is why I'm not convinced. Besides, if we could manage to make the playoffs again, Domas could have like two solid games and completely erase those p-values.

As a said, I did a separate analysis for the thumb injury, and Domas didn't decrease in efficiency or productivity in any significant way after the fracture. I don't have the evidence to prove the stomp at the end of game 2 had no effect, so I won't stop anyone from claiming that. It was reported that Sabonis did get a negative x-ray for what that's worth.

I also looked into Domas' performance vs the Warriors in the regular season (10 games) vs the playoffs, and the p-value for TS% does fall out of significance. So I think there could be some wiggle room to say that perhaps that's just a bad matchup. Not enough evidence to prove it either way. There is still a strong effect on DRB%.

I don't claim to have proven the specific cause of the difference, (differently biased refereeing seems to fall under the general umbrella of "playoff basketball.") I'm only claiming that the we have enough evidence to overcome the small sample sizes.

 

[Insomniacal Fan]

I do not claim to be a statistician either. But, trying to be fun:

It would be better to post your question first, define your outcomes of interest, then report the effect estimates for each outcome. Rather than performing a test on several outcomes and then reporting only the P values for outcomes that were significant and saying you have something ready to submit for peer review.

One way ANOVA is for two independent groups. Since Domas produced all the stats you have analysed, they are not independent samples. So this isn't an appropriate analysis method to use.

P values are for chumps.

When choosing which analysis method to use, you do have to consider other variables that can influence the outcome beyond just 'regular season' or 'playoffs'. These can include opponent and injury. Maybe a linear regression? But how easy is it to define 'injured' or 'stomped on' for an analysis like this?

I feel the best way is just to eyeball the stats and to acknowledge the limitations. That is, Sabonis' output seems to be less in the playoffs, but it is a small sample, he has played hurt, and Looney was pushing off all series. And maybe he wouldn't have been stomped on if he was terrible.

Then maybe say hey Sabonis might or might not be that great in the playoffs, but we are Sacramento and we don't get to go to the playoffs that often, if at all. What we really need is a good regular season team to get us to the playoffs more often.

I am glad there are people out there who share my idea of fun .

If I'm eyeballing the stats, I can say it's clear that there is a playoff effect on Domas (see https://community.kingsfans.com/threads/what-does-this-team-need-most.101601/page-6#post-1881507) The criticism posted by Capt Factorial was that we have too small of a sample size to judge the effect of the playoffs games vs the regular season games. This is a "statistical-sounding" argument, one that I was interested in checking it out.

Whether or not p-values ought to be the gold standard in scientific publishing, is not a debate that I'm interested in having on a pseudonymous sports message board (I would not participate in that debate anywhere, as I am unqualified to have an opinion). For the narrow question of determining if we have enough evidence to prove an effect, then I believe it is the appropriate measure.

I'm not claiming why it happens, although I looked into a couple of options.

I'm not saying that because of this, I've proved that we should take any particular action.

 

[Joshyjosh31]

Perry needs to do the right thing here man what’s the point of gunning for the 10th seed.

Brooklyn got hosed in the draft send Sabonis and a second for the 8th pick and draft a wing player or looking at locks right now take Derrick Queen

Derozan to the clippers for Bogi and Jones

Starte Carter, ellis, and Keegan play them major minutes and let them develop there game

 

[Capt. Factorial]

Brooklyn got hosed in the draft send Sabonis and a second for the 8th pick and draft a wing player or looking at locks right now take Derrick Queen

This is basically trolling at this point.

 

[Capt. Factorial]

I am aware that peer reviewers sometimes require a minimum group size. I discussed this idea with someone who publishes papers for a living, (not ChatGPT I promise), and in their field (biology, not medicine) the minimum group size they needed to convince reviewers is 3.

Perhaps unbeknownst to you, you have discussed this with TWO people who publish papers in biology for a living! Group size is going to be a different concept than sample size; not at all sure what your friend does, but the idea is probably that they have to show a significant effect in three different animals. Certainly nobody would accept three sample points as sufficient to show an effect in any given animal. In this case games correspond to samples, not members of a group.

I had a few hypothesis I was interested in.
...
I found statistically significant differences (p < 0.05) in TS%, eFG%, DRB%, ORtg, BPM, DRB, AST, GmSc.

The list didn't copy over on the quote (folks can check above if they wish) but it looks like you tested 37 different stats (yes, some of those are interrelated, but I think we can ignore that for now). Since you checked multiple hypotheses simultaneously, the correct procedure is to adjust the p-value threshold via a method such as Bonferroni correction. The idea is that if you roll your 20-sided die enough times, it's going to come up "20" (that is, 0.05!) by chance sometimes, and you don't want to mistake that for a signal...imagine the following: "Roll with right hand...nope." "Roll with left hand...nope." "Roll from a cup...nope." "Roll from right hand with eyes closed...nope." "Roll from left hand with eyes closed...20! Clearly there is something different about rolling the die with my left hand with my eyes closed!" That Bonferroni correction would require us to adjust our "significant" p-value down to 0.0014, which is actually lower than any of the three values you reported above (one was close).

This doesn't mean that there is absolutely no difference between Domas' baseline performance in the regular season and Domas' baseline performance in the playoffs, but it does weaken the statistical case a good deal.

I don't claim to have proven the specific cause of the difference, (differently biased refereeing seems to fall under the general umbrella of "playoff basketball.") I'm only claiming that the we have enough evidence to overcome the small sample sizes.

Well, I imagine that if "differently biased refereeing" means that the refs will be more biased against us in the playoffs than they already are in the regular season (and I'm sure you'd find several here to argue that was exactly the case against GSW) then we've got bigger problems than whether Domas' production drops off a bit.

 

[Joshyjosh31]

This is basically trolling at this point.

lol not at all

 

[NewArena]

I think Jalen Brown may be on the market this summer. Hmmm

 

[sactowndog]

I think you might find that an N of 7 would not be sufficient to convince peer reviewers.

We can all agree that Domas' stats in our series against the Warriors were generally lower than his regular season stats that year: He went from 19.1/12.3/7.3 to 16.0/11.0/4.7, that's something like a 10% drop in rebounds, a 15% drop in scoring, and over a 30% drop in assists. Though let's keep in mind that assists require your teammate to hit a shot, and the Kings FG% went from .494 in the regular season to .429 in that series, so the entire burden for assists isn't on him.

The first question is whether this is a real difference, or just chance. It sounds like you pumped a lot of stats into an ANOVA and got out three "hits", but I don't know how many stats you included. Were his increases in steals and blocks also significant at a p-value of .05?

But even if we accept that these stats (remember, three fewer points, one+ fewer rebounds) are actually indicative of his true performance in that series, there are any number of causative factors (playoffs, injury, bad matchup, biased refereeing, etc.) that could be the reason. There's really no easy way to control for those, which is why I'm not convinced. Besides, if we could manage to make the playoffs again, Domas could have like two solid games and completely erase those p-values.

I think the general belief among a significant percentage of NBA eye test GM types is that Domas has a game that doesn’t translate into play-off success. You see that from the comments of people like Kevin O’Conner, Nate Duncan, etc. You see that in the coaches voting on all-star teams.

So that being the case, the burden of proof is really on proving that he can succeed in the play-offs, if you a looking for trade value.

Btw. I’m not challenging you on p-values and statistical significance. lol.

 

[gunks]

I think Jalen Brown may be on the market this summer. Hmmm

Lavine + one of the pathetically late 2032 1st rounders we got for Fox?

I'm not sure we have great retool assets, which is another reason I'm DTT (down to tank).

 

[Joshyjosh31]

I think Jalen Brown may be on the market this summer. Hmmm

Not with Tatum missing the year they need him to step up

 

[Tetsujin]

This is basically trolling at this point.

 

[sactowndog]

Lavine + one of the pathetically late 2032 1st rounders we got for Fox?

I'm not sure we have great retool assets, which is another reason I'm DTT (down to tank).

Yeah how would you feel if SA traded Fox for JB?

 

[Caccia]

A lot of virtual ink has been spilled about the Kings' defensive liabilities, especially defending the three-point line. In fact, Sacramento was dead last in opponents' three-point percentage this past season. The previous season, they were next-to-last. That has to be first priority, whether through revamped defensive schemes or changed personnel. Mike Brown, for all his reputation as a defensive savant, had his players sagging inside and leaving corner threes open, possession after possession after possession, game after game after game.

What you all may not have noticed, however, is that the Kings' scoring fell from 2022-23 to 2024-25: 120.7 to 116.6 to 115.7. There were a lot of winnable games this past season if the Kings could only have made a few more buckets. Murray, Monk, Huerter, all had terrible shooting slumps. Maybe this team needs a sport psychologist, because there is no earthly reason why those guys should be missing wide-open shots. As I have often opined, more screens and better ball movement correlate directly with better shooting. Sabonis, Valančiūnas, and Monk cannot be the only ones setting screens on a regular basis.

 

[gunks]

Yeah how would you feel if SA traded Fox for JB?

Very annoyed at Fox. I know you hate Monte but the Fox return wasn't on him.

I'm hoping SA lowballs him after landing the 2nd pick and he just bails on them for FA

 

[pdxKingsFan]

Yeah how would you feel if SA traded Fox for JB?

I would die laughing because Fox and especially Recee would be miserable living in Waltham or Newton or wherever rich people live outside the city now.

 

[Folsomkingsfan2]

Very annoyed at Fox. I know you hate Monte but the Fox return wasn't on him.

I'm hoping SA lowballs him after landing the 2nd pick and he just bails on them for FA

Yes and no. On one hand we got put in a bad position with our franchise player doing a trade demand to one team. On the other hand we didn't get one quality asset back in return for a player who has averaged 25/5/5 the last 4 years. I don't think Fox has the value of a typical all star, but I also think he was worth more than Zach Lavine, a mediocre first rounder, and a first rounder that won't convey for 6 more years. I think a more skilled GM would've done much better.

 

[gunks]

Yes and no. On one hand we got put in a bad position with our franchise player doing a trade demand to one team. On the other hand we didn't get one quality asset back in return for a player who has averaged 25/5/5 the last 4 years. I don't think Fox has the value of a typical all star, but I also think he was worth more than Zach Lavine, a mediocre first rounder, and a first rounder that won't convey for 6 more years. I think a more skilled GM would've done much better.

We also have to factor in that our idiot owner has been trying to get Zach on the team for a hot minute.

Shades of "trade Cousins but only for Buddy Hield"