Percent of points that come from 3 pointers by team

[drsd]

What was really significant in correlation (R squared ~ 0.6) to wins is that difference (3 point % - opponents 3 point shooting).

Bottom line is better 3 point shooting does not win more games unless defending the 3 stays constant.

This fits the general notion that FG% differential in general (and 3-pt FG% is in that) is the most decisive stat to determine a W vs. a L.

I am now hopeful to getting some maths-dept. university UGs to do a deep dive on this for all teams over thee last decades.

I looked at the data over the last 10 years.

Plot Summary:

Top grey scatter plot is a scatter plot of True Shooting (y) vs winning percentage (x)

The biggest take-away (and surprising) is that the R squared of 0.15 says that the relationship between winning percentage and true shooting is basically random.

The overlapping blue and orange points is a plot of the difference between Team and Opponent shooting % and true shooting %.
Both are reasonably predictive of winning percentage.

Most interesting is that the slope of the trend lines are almost identical, which means that winning percentage as a function of delta FG% is the same as the delta True Shooting %. I would not have guessed that.

This is useful. The question posed is not shooting or true-shooting though. It is FG% differential (or TFG%-differential or eFG%-differential).

A good shooting team that does not play defense still loses. Washington is an excellent example this year. The team is 9th in the league in FG% but 28th in opponent FG%.

Here's just FG% differential for this season compared to winning percentage:

(p.s. wow are Sacramento, Dallas, Miami and New York overachieving)
0.571 -1.3 New York Knicks
0.586 -0.9 Miami Heat
0.586 -1.7 Dallas Mavericks
0.607 -1.7 Sacramento Kings

[AaronB]

This fits the general notion that FG% differential in general (and 3-pt FG% is in that) is the most decisive stat to determine a W vs. a L.

I am now hopeful to getting some maths-dept. university UGs to do a deep dive on this for all teams over thee last decades.

I looked at the data over the last 10 years.

Plot Summary:

Top grey scatter plot is a scatter plot of True Shooting (y) vs winning percentage (x)

The biggest take-away (and surprising) is that the R squared of 0.15 says that the relationship between winning percentage and true shooting is basically random.

The overlapping blue and orange points is a plot of the difference between Team and Opponent shooting % and true shooting %.
Both are reasonably predictive of winning percentage.

Most interesting is that the slope of the trend lines are almost identical, which means that winning percentage as a function of delta FG% is the same as the delta True Shooting %. I would not have guessed that.

This is useful. The question posed is not shooting or true-shooting though. It is FG% differential (or TFG%-differential or eFG%-differential).

A good shooting team that does not play defense still loses. Washington is an excellent example this year. The team is 9th in the league in FG% but 28th in opponent FG%.

Here's just FG% differential for this season compared to winning percentage:

(p.s. wow are Sacramento, Dallas, Miami and New York overachieving)
0.571 -1.3 New York Knicks
0.586 -0.9 Miami Heat
0.586 -1.7 Dallas Mavericks
0.607 -1.7 Sacramento Kings

Yes, the differential question is answered in the Orange and Blue scatter plot below the grey scatter plot.

Ultimately the data for the last 10 years looks pretty similar to the data for this season.

Keep in mind that R squared is basically just a measure of the how reliable the data can be used as a predictive measure.

0.6 for R squared is pretty good, though it is not something I would use in my business where millions of $'s are at risk or even Billions.

For a basketball measure it is not bad.

Given the relative reliability of the predictive value, you should add the equation (with I withheld also) so you can see the impact that shooting has on winning percentage.

[drsd]

0.6 for R squared is pretty good, though it is not something I would use in my business where millions of $'s are at risk or even Billions.

For a basketball measure it is not bad.

Given the relative reliability of the predictive value, you should add the equation (with I withheld also) so you can see the impact that shooting has on winning percentage.

There is little doubt that eFG%-differential or TS-FG%-differential would have an even higher predictive value for winning than simply looking at FG%-differential. I think TS-differential is "cheating" on this stat though, as FT differential is effectively binned with eFG%-differential in such a stat (I am sure there are loads of stat if combined inflate a statistical argument).

But looking at this carefully for eFG% differential is not something to do on a laptop whilst watching Christmas movies.

Coming to your point, about "what is the predictive value", it will be interesting to see if the offensive component or defensive component of the differential matters more. At that level, this is why throwing a dozen university UGs at the problem holds merit.

[AaronB]

0.6 for R squared is pretty good, though it is not something I would use in my business where millions of $'s are at risk or even Billions.

For a basketball measure it is not bad.

Given the relative reliability of the predictive value, you should add the equation (with I withheld also) so you can see the impact that shooting has on winning percentage.

There is little doubt that eFG%-differential or TS-FG%-differential would have an even higher predictive value for winning than simply looking at FG%-differential. I think TS-differential is "cheating" on this stat though, as FT differential is effectively binned with eFG%-differential in such a stat (I am sure there are loads of stat if combined inflate a statistical argument).

But looking at this carefully for eFG% differential is not something to do on a laptop whilst watching Christmas movies.

Coming to your point, about "what is the predictive value", it will be interesting to see if the offensive component or defensive component of the differential matters more. At that level, this is why throwing a dozen university UGs at the problem holds merit.

I did the offensive piece alone, that is the grey scatter plot. It shows very little predictive value, almost random which means that is is simply not able to answer the question of whether or not it matters.

[eyriq]

Magic team sorted by EPM

Goga +3.9
Suggs +3.5
Franz +2.1
Moritz +0.7
Paolo +0.4
Isaac +0.2
Cole -0.5
Ingles -1.0
Harris -1.1
Carter -1.5
Black -2.0

Odd to have most players below our teams net rating of 2.8. Would seem to imply that the sum is greater than the value of the individual players. I would credit Mosley for this, synergy tends to be best attributed to coaching IMO.

Goga, Suggs, and Franz are the big minute defensive difference makers, and that is our core identity so it makes sense they'd be the top 3.

Moe, Cole, and Paolo are contributing on offense but that isn't our strength so they are neutral. Makes sense.

Black's a rookie and our worst player (by this measure). Reasonable. I'd suspect this is trending up though.

Edit: In all it would seem to indicate that we should start Goga over WCJ, try and find a quality guard to start alongside Suggs, and work to ramp up minutes for Isaac, while being careful not to disrupt the team chemistry that Mosley has fostered.

[basketballRob]

Black is +1 defensively.

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[JoshuaPotter]

Black is +1 defensively.

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Where does one see this?

[basketballRob]

Black is +1 defensively.

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Where does one see this? He moved up to 1.1 now. He's 3rd among rookies behind Chet and Lively.

https://dunksandthrees.com/epm

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[Mad Guru]

If I had to guess, I would say the Magic are on pace to win 46-44 games this year and finish 4th - 6th in the East

That's a pretty great year considering how young the team is.

Their ceiling is pretty capped by the shooting though. They can beat the bad teams, but the good teams are going to bury them with three point shooting over the long term.