I've put together some Wonder Bread charts that provide information on the first 16 games - approximately 20% of the season thus far.
As a refresher, I've created two different kinds of Wonder Bread charts that attempt to show player value and effectiveness. Ideally, a player should provide good value and be effective in providing that value.
A player can have good value in a game, but value should also be viewed with how effective that player is. Conversely, a player may be effective over a short span (i.e. a player might have a 100 PER, but it might only be for one minute), but if the player only played one or two minutes in the game, he most-likely didn't have a significant impact on the overall outcome of the game.
I quantify a player's value based on his offensive effort (y-axis), defensive effort (x-axis), and calculated approximate value (size of dot). It's preferred for players to be in the upper right hand corner as that shows good defense and good offense; further, a large dot shows a greater overall stat contribution to the game than a small dot does (i.e. approximate value is a counting type number to show how a player is affecting the game stat wise).
Offensive effort is measured by an estimated offensive +/-. Offensive adjusted +/- is calculated on a 40 minute basis and uses the following weights:
-.33918 TSA (where TSA is .44*FTA + FGA)
Intercept = -8.57647 (basically all players start out negative 8.6)
Defensive effort is measured by an estimated defensive +/-. Defensive adjusted +/- is calculated on a 40 minute basis and use the following weights:
Intercept = -4.69887 (basically all players start out negative 4.7)
Approximate Value attempts to measure how much an individual player is contributing stat wise to the game; it is equal to (((((Points + Assists + Rebounds + Steals + Blocks – Turnovers – Missed Shots – Missed Free Throws) times 2 if for a half) times 82) raised to the 3/4 power) divided by 21)); and average players are in the 6 to 7 range.
Here are my charts for median player value over the first 16 games (I chose to use median instead of average as it is less affected by really good games and really bad games):
I measure effectiveness in three different ways, an estimate of the wins produced per 48 minutes (y-axis), the Larry H Miller average (x-axis), and estimated PER (size of dot). The bigger the dot the more effective a player is and vice versa for the smaller the dot. Also, the way this chart is structured, a player in the upper right hand corner played more effectively than a player in the lower left hand corner.
The Larry H Miller average is a simple efficiency measure that is equal to ((Points + Assists + Rebounds + Steals + Blocks – Fouls – Turnovers – Shots) divided by minutes). The average player falls in the .100 to .200 range. It's named after Larry Miller as he would use it as a quick means to assess how a player was doing in a game. Some say that this stat is position biased since big men can rack up a lot of points, rebounds, and blocks, but not have the fouls, missed shots, and turnovers that guards/small forwards have.
Estimated wins produced per 48 takes out position bias; it does this by comparing shooting guards to shooting guards, power forwards to power forwards, etc, and then makes position specific adjustments in the formula. The average player is .100; players producing higher than .100 are producing at a higher level than the average and those below .100 are producing at a lower level than the average.
PER was created by John Hollinger and attempts to measure a player's per minute performance (like the measures above) but then also makes an adjustment for game pace. The average PER rating is 15.
Here are my charts for median player effectiveness over the first 16 games:
Next are the four factors (team shooting, team free throws, team rebounds, and team turnovers) for the first 16 games of the year. Dean Oliver identified these factors as the factors that go into winning a basketball game (the factors are also known as the "Four Factors of Basketball Success"). Dean recognized that each of these factors did not contribute equally towards winning a basketball game, and weighted the factors.
Dean attributed 40% of a basketball win to effective shooting, 15% to getting to the line and making free throws, 20% to rebounding the ball well, and 25% to taking care of the basketball.
This first chart is team shooting. You want to have lots of purple (made 2s) and red (made 3s) and little blue (missed shots) in this chart.
This next chart covers free throw shooting. You want to have lots of orange (made free throws) and little blue (missed free throws) in this chart. As an FYI we haven't done very well in this category to date.
This chart covers rebounding. You want to have a good amount of purple and not much green on this chart. The green represents offensive rebounds that your are allowing to your opponent and the purple are the defensive rebounds you grab.
The final factor shows turnovers (red) to total possessions (blue plus red). This is another category we have struggled with.
My final chart is an update of how years within the Jazz system equates to winning. I added the years 1999 to 2013 (pink dots and trend line) to my original graph (blue dots and trend line). As you can see, there's been more variability from 1999 to 2013 than there was from 1980 to 1998. This has the effect of flattening out the original trend line (green line).