EDIT: I fixed the strength of schedule-adjustment.

Sorry, no team analysis today. Too few games have been played for me to feel comfortable analyzing with variance or a multivariate regression.

Instead, here's a quick peek at the formula by which players will be rated (offensively) on my new site (coming soon!)

Adjusted Player Offensive Rating =

Poss% x (ORTG x LeagueAverage)/(player's team SOS of oppD) + (1-Poss%) x LeagueAvgEfficiency

This basically shows how an average team would benefit (offensively) by replacing one of their players with the player in question. However, most of the values will be very close to the league average (I assume), so we will use a Net value to better isolate the player's value.

Net Offensive Rating =

Adjusted Player Offensive Rating - LeagueAvgEfficiency

I will hopefully soon do the same with defensive rating, although Ken Pomeroy does not calculate these. I'll have to improvise.

EDIT: Fixed the system. Kyle Irving posts an 8.8 instead of a 12.3

Stay tuned.

### Quick note on Four Factors

If you look at the data from the NCAA four factors analysis (in my prior posts and in David Hess' posts) you might be thinking, correctly:

"This data explains where a team's points come from, but does not explain precisely how they could improve."

Then someone might respond:

"Now wait a second, doesn't this tell us how a team could improve? I mean, all Michigan State needs to do is take better care of the ball to win their games; the numbers say so!"

While the above answer is correct, it is important to realize that most teams don't have data points in Four-Factors ratings as striking as Michigan State's poor ball-handling.

What I will suggest is a continuation of what I have done in the past: figuring out how

Coming soon! (Gotta finish exams first...)

* By this I mean to run a linear/logistic regression to see how much an opponent's factors influence the team's factors.

"This data explains where a team's points come from, but does not explain precisely how they could improve."

Then someone might respond:

"Now wait a second, doesn't this tell us how a team could improve? I mean, all Michigan State needs to do is take better care of the ball to win their games; the numbers say so!"

While the above answer is correct, it is important to realize that most teams don't have data points in Four-Factors ratings as striking as Michigan State's poor ball-handling.

What I will suggest is a continuation of what I have done in the past: figuring out how

**variable**a team's factors are, and what causes this. For example, one might assume that a team thriving off 3-pointers (cough *Northwestern*) has much more variability in predicting offensive rating than one who thrives off 2-pointers, under the old adage, "si on vie par le trois, on mort par le trois." And I suppose it would make more sense to say that we can predict how a team's overall efficiency decreases against certain opponents via Four-Factor regression of individual games*.Coming soon! (Gotta finish exams first...)

* By this I mean to run a linear/logistic regression to see how much an opponent's factors influence the team's factors.

### Team Impacts, Part Deux

I know I haven't recently been naming any teams, any players, or any specific cases...be patient!

Per David Hess's suggestion, I am now adjusting in an 'error-free' and strength-of-schedule-adjusted environment. To do this, we plug in a team's statistical offensive efficiency (different from the regression model)

This one has a lower error than the model since it includes FT% and raw FG%. The only error involved in the equation comes from rounding , miscalculated possessions, and lack of adjustment for 'team' rebounds. So instead of comparing regressed efficiencies with actual efficiencies, I compare statistical offense minus same with the league average replaced for a certain Factor. However, the factors are labeled differently this time around per the deduced equation.

However, I adjust this for estimated strength of schedule (Adj Factor = Adj. Offense / St. Offense) to more accurately represent how the team plays.

So the end result is:

Here's the results in HTML and editable/searchable Excel format.

Per David Hess's suggestion, I am now adjusting in an 'error-free' and strength-of-schedule-adjusted environment. To do this, we plug in a team's statistical offensive efficiency (different from the regression model)

*St.Offense=(avgFGpoints + avgFTpoints)/avgPoss*This one has a lower error than the model since it includes FT% and raw FG%. The only error involved in the equation comes from rounding , miscalculated possessions, and lack of adjustment for 'team' rebounds. So instead of comparing regressed efficiencies with actual efficiencies, I compare statistical offense minus same with the league average replaced for a certain Factor. However, the factors are labeled differently this time around per the deduced equation.

**1) FG% and eFG% (eFG% does not accurately count missed vs. made shots)**

2) TO%

3) OR%

2) FT% and FTR% (FTR does not accurately count made free throws)

2) TO%

3) OR%

2) FT% and FTR% (FTR does not accurately count made free throws)

However, I adjust this for estimated strength of schedule (Adj Factor = Adj. Offense / St. Offense) to more accurately represent how the team plays.

So the end result is:

**Impact of factor(s)**=Adj.Offense - [St.Offense with factor(s) replaced with average]*Adj.FactorHere's the results in HTML and editable/searchable Excel format.

### NCAA Four-Factor Impact

Lots of credit here goes to David Hess (aka @AudacityOfHoops) for his work on a simple estimation of how turnover effect efficiency. Check out his pretty blog!

Given the limitations of that formula, I decided to take it a step further: how much does EACH four factor affect a team's offensive performance? Because every time I check out Ken Pomeroy's team four factors I want to better-quantify those green-or-red bits of data.

I've come up with a way to quantify how deviation of the league-mean by each team's four-factors affects their overall offensive efficiency.

The same can easily be done for defense, but for right now, I'm just going to focus on offense:

WARNING: BORING MATH

I took a regression (which myself and David have done before) of the four factors on offensive efficiency. For each team, I took their four factors, save for the one in question, and multiplied them by the regression estimates. I replaced the one in question with the league average. Finally, I took their raw offense and subtracted this number from it. This gives us an estimate of how a team's deviation from the mean affects their overall offense, in terms of the Four Factors.

/BORING MATH

Here's the great news:

1) I made an Excel spreadsheet so you can easily plug this in for any team without having to scour for them (just enter the team under "Team")

2) I used the same color scheme as Ken Pomeroy's numbers :)

2) I also made a PDF for those who don't want to use Excel.

Editable Excel File

PDF File

Given the limitations of that formula, I decided to take it a step further: how much does EACH four factor affect a team's offensive performance? Because every time I check out Ken Pomeroy's team four factors I want to better-quantify those green-or-red bits of data.

I've come up with a way to quantify how deviation of the league-mean by each team's four-factors affects their overall offensive efficiency.

The same can easily be done for defense, but for right now, I'm just going to focus on offense:

WARNING: BORING MATH

I took a regression (which myself and David have done before) of the four factors on offensive efficiency. For each team, I took their four factors, save for the one in question, and multiplied them by the regression estimates. I replaced the one in question with the league average. Finally, I took their raw offense and subtracted this number from it. This gives us an estimate of how a team's deviation from the mean affects their overall offense, in terms of the Four Factors.

/BORING MATH

Here's the great news:

1) I made an Excel spreadsheet so you can easily plug this in for any team without having to scour for them (just enter the team under "Team")

2) I used the same color scheme as Ken Pomeroy's numbers :)

2) I also made a PDF for those who don't want to use Excel.

Editable Excel File

PDF File

### Offensive Impacts

*EDIT/UPDATE: This old formula has some truth to it, but I have a much more accurate method of describing this, as described in the College Basketball Prospectus 2011-201 book.*

There's a very simple stat that estimates how much a player affects their team's overall offensive rating, using Dean Oliver's Individual Offensive Rating (as is posted for all teams' significant players on Kenpom.com)

**Formula for offensive impact**=

team ORTG - (team ORTG-(%poss*%min*ORTG))/(1-%poss*%min)

(Which estimates the impact a player has on his team's overall Offensive Rating)

Here it is for UNC and Duke:

NorthCarolina | player | %Min | ORtg | %Poss | offensiveimpact | |||

Tyler Zeller | 69.4 | 119.2 | 23.4 | 3.93 | ||||

Reggie Bullock | 32.2 | 106.3 | 20.7 | 0.54 | ||||

Justin Watts | 29.4 | 106.1 | 14.8 | 0.34 | ||||

Leslie McDonald | 35.3 | 97.7 | 18.7 | -0.06 | ||||

Kendall Marshall | 35.9 | 95.5 | 19.2 | -0.23 | ||||

Harrison Barnes | 69.4 | 96.8 | 22.9 | -0.34 | ||||

Justin Knox | 37.2 | 93.9 | 24 | -0.46 | ||||

Dexter Strickland | 62.8 | 93.7 | 17 | -0.58 | ||||

John Henson | 61.6 | 94.6 | 25.5 | -0.74 | ||||

Larry Drew | 63.8 | 76.8 | 14.6 | -2.21 | ||||

Duke | player | %Min | ORtg | %Poss | offensive impact | |||

Kyrie Irving | 72.2 | 128.8 | 25.2 | 3.12 | ||||

Andre Dawkins | 57.8 | 144.3 | 13.2 | 2.44 | ||||

Seth Curry | 44.1 | 117.3 | 18 | 0.22 | ||||

Ryan Kelly | 35.9 | 116 | 13.5 | 0.07 | ||||

Tyler Thornton | 12.8 | 88.7 | 13.3 | -0.45 | ||||

Nolan Smith | 76.6 | 113 | 27.5 | -0.45 | ||||

Josh Hairston | 14.4 | 90.5 | 13 | -0.46 | ||||

Kyle Singler | 78.8 | 112.1 | 21.2 | -0.52 | ||||

Miles Plumlee | 36.6 | 90.3 | 17.8 | -1.69 | ||||

Mason Plumlee | 66.3 | 101.6 | 21 | -2.11 | ||||

Now these stats don't exactly compare (a player with a +2 on a bad team is not as good as a player with +2 on a good team) - but this allows you to estimate what current substitutions do for a team, offensively (per 100 possessions).

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