I acquired all of the Division I team data since 2002, and from that we can observe some fascinating trends and data relationships in the data. This is a multipart series exploring some of that data.

Now we'll move onto other facets of Offense, and start with Percentage Loss of Ball (%LOB). The number is computed by dividing the number of possessions by a team by the number of turnovers. This analysis uses the "Smith Method" of defining a possession which is defined as ending whenever a team loses control of the ball. A possession can end with a made basket, a trip to the free throw line (1-and-1, 2, and 3-shot situations), a turnover, or an attempted field goal. It is important to remember the latter when gaining an understanding of this data compared to data published elsewhere. Stay tuned for much more on the topic of Possessions when I address the Ken Pomeroy data.



The graph shows the Percentage Loss of Ball, by year, for the last 23 seasons, through the morning of February 21, 2024. The grey line shows the Percentage Loss of Ball for all of Division I, while the green line shows the Percentage Loss of Ball for teams that made the NCAA tournament that year. Over time there has been a noticeable decline in %LOB, about a 15% reduction in the previous couple of decades. Perhaps this is due to players staying with coaches for shorter lengths of time or changes in officiating. Regardless, this is one of the stats that has changed significantly.

What has not changed is the performance difference between tournament teams and the national average. There has been a steady 1.25% difference through the years.

UNC, shown in blue, has had significantly lower %LOB than the rest of the nation in most years, with noticeable peaks in their worst seasons. The current team is handling the ball more cleanly than any since 1996.

Percent Loss of Ball has a correlation factor with Winning Percentage of -0.47, which is exactly the figure for Rebounding. We saw from the Part 7 discussion about Possession Differential (Rebounding) that it has a bigger impact on Winning Percentage than Defense does, so we can assume that %LOB is a pretty important stat, too.

Next up: Assist/Turnover Ratio

I just have a few ideas and then I will let others express their opinions on the subject:

I would like to see Ingram posting up more defenders like he was doing earlier in the season instead of relying on so many jumpers. Maybe some passes in the paint would help him to use his bulk in close…

Similar scenario for Ryan. He is such a great free throw shooter I would like to see him drive more than once or twice and either score or get fouled…

I would like Cadeau to slow down mentally because to me most of his turnovers are because he thinks too much instead of using his natural talent and reacting to the situation.

The team needs to work on out of bounds plays when they are being pressed. And speaking of pressing if Carolina is playing at the minimum 8 players a contest why not work on some defensive pressing this week.

Finally from me the team needs to find some shooters when there are two or three subs in the game at the same time.

My bad one more except for the last game against Virginia Tech where Cadeau worked the clock and scored right before the end of the first half this time off would be a excellent opportunity for others to show they could be the man instead of Davis EVERY TIME taking the last shot…

What else in your eyes should the team work on with a full week off?

It has been too long since David and I did a pod together, so here is one. Note we have a recruiting one coming up later this week, too.

If you have Netflix, watch the documentary on the Buffalo Bills 4 Super Bowl losses. By that time, I was no longer watching much NFL, so more Ethan. bit of this is new to me, at least in the sense that it did not register then because I simply was watching little of the NFL. For example, I had forgotten that the Giants DC whose game plan won that Super Bowl vs. the Bills was Bill Belichik.

Are you encouraged by UNC's performance yesterday, or are you still just as concerned as you were after the Syracuse game. And why?

(Updated to add full table and discussion about relative impact of the variables.)

I acquired all of the Division I team data since 2002, and from that we can observe some fascinating trends and data relationships in the data. This is a multipart series exploring some of that data.

Now that we are done with shooting stats, let's take a diversion and get a little bit into Points Per Possession. After evaluating 7,788 seasons of college basketball, it appears that Dean Smith's three key performance indicators, Points Per Possession (PPP), Points Per Possession Allowed (ppp), and Possession Differential (POSDIF) go a very long way toward explaining a team's winning percentage.

If we create a multiple regression analysis using those three as explanatory variables for Winning Percentage (W%), we find an extremely high R-Squared value of 0.9855. In other words, 98.55% of the error in explaining W% is covered by this model. This certainly makes for a very interesting basis for season evaluation.

The result of this regression is an equation we can use to estimate Winning Percentage given the three explanatory variables:

W% = 2.30*PPP - 1.76*ppp + 0.0006*POSSDIF

Where:
W% = Winning Percentage (0.0-1.0)
PPP = Points Per Possession using Smith Method
ppp = Points Per Possession Allowed using Smith Method
POSDIF = Total Possession Differential on the Season (not per game)

What does this mean? For every hundredth of a point per possession on offense a team improves, it improves its winning percentage by 0.023. For every hundredth of a point per possession it allows by an opponent, its winning percentage decreases by 0.0176. In other words, offense has a 31% bigger effect on winning percentage than defense does.

If a team has a 1 possession advantage over its opponent per game, it should (by now) have a +26 POSSDIF, resulting in a 0.016 winning percentage increase.

To increase a team's winning percentage by 1%, it would need to increase its Points Per Possession on Offense by 0.04, decrease ppp allowed on defense by 0.06, or increase POSSDIF by 0.641 per game. Those figures represent 4.3%, 6.7%, and 5.6% of the national averages, respectively. Therefore, it is easiest to increase winning percentage by improving offense, and next easiest by improving rebounding. The most difficult way to improve a team's winning percentage is by improving the defense.

In the following table we see the data for the current season thru this morning (2/20/24), the three explanatory variables, True W% (WLPCT), the predicted result based on those variable using the equation (WLPRED), and the error (WLDIFF). A large WLDIFF means that a team is winning far beyond its expectations, while a very negative number means a team is not meeting expectations.

I haven't experimented with an analysis like this before, but my hypothesis is that very negative WLDIFF values mean that the team is bound to win more games in the near future, while a high WLDIFF means that the team is bound to lose some upcoming games. We'll see how that works out over the ensuing 6 weeks.

I acquired all of the Division I team data since 2002, and from that we can observe some fascinating trends and data relationships in the data. This is a multipart series exploring some of that data.

Free Throw Attempts​

The graph shows the national average in grey, the average for NCAA Tournament teams in green, and UNC's average in blue.

In the last 23 seasons of college basketball games each team has taken an average 20.06 free throw attempts per game. That number has precipitously declined, however, in the last 10 seasons for some reason. Do any of you have any theories on how this happened?

As we can see in the graph, NCAA Tournament teams take, on average, a free throw attempt more than the national average. UNC, however, has generally been much higher than either average, especially in years with strong big man play.

Is this stat relevant? We can all remember games where it felt that free throw shooting won or lost a game for our team, but the correlation between free throw attempts and winning percentage is only 0.342, just over half of what FG% is. This statistic is mildly important, and far too much time and attention is paid to it.

Billy Packer's Free Throw Theory​

While commenting on games Billy Packer repeatedly claimed that teams that make more free throws than their opponents attempt win the game. If we look at case of UNC's last 1,015 games, we see that the theory is about 91% right. When there is a difference in made free throws and the opponent's attempts, there are four possible outcomes. In the 970 games where there was a difference, here is how that scenario played out:

FT Advantage + Win: 400
FT Advantage + Loss: 42
No FT Advantage + Win: 301
No FT Advantage + Loss: 277

Therefore UNC won 91% of the time that UNC made more free throws than opponents attempted. In the cases where UNC made fewer free throws than opponents attempted, UNC won 57% of the time. UNC's overall winning percentage in that period has been 73%.

From UNC's opponents perspective, the numbers are different. Of the 127 times where UNC's opponents made more free throws than UNC attempted, the opponents only won 69% of the time. In the cases where opponents made fewer free throws than UNC attempted, the opponent only won 21% of the time.

Several years ago I tested this differential against Margin of Victory and found it to not be very useful; mainly because of the enormous discrepancy between UNC's experiences and its opponents experiences. I supposed that if we looked at several teams, we'd see a wide variance in the level of advantage this differential offers.

If this were a useful statistic, opponents should have seen a much higher rate of success when meeting the criteria being tested. This is likely a theory that holds truer for teams that play a certain style more than others.

Free Throw Percentage​

Over the past 23 seasons, free throw shooting has improved by about 2.5 percentage points. Tournament teams have followed suit, and remained a point or so higher than the national average. UNC has excelled especially during times of great guard play.

Keep in mind, though, that this graph's origin is 60%, so these differences are exaggerated on the graph. A percentage point improvement in FT% only results in about 0.2 points per game.

Free Throw Percentage has only a 0.254 correlation factor with winning percentage. So it is not a particularly useful tool for evaluating teams. Be careful not to overemphasize this stat when evaluating performance.

Next up: Points Per Possession as a Predictor