[Preface]
Tomorrow evening, Connecticut and Marquette will kick off the 2006 Big East Conference season when they tussle in Milwaukee. In honor of this special occasion, I thought it appropriate to take a brief look at some tempo-free statistics and how each conference member has measured up at this, the pseudo-halfway point of the season.
If you're not familiar with tempo-free statistics, they are essentially an alternative method for analyzing production and value. Tempo-free statistics are free from inflation or deflation by measuring a team's production through a possession-based analysis, rather than of raw, aggregate data.
For more information on tempo-free statistics, I highly recommend this introduction written by Big Ten guru John Gasaway and this primer written by blogging superhero Ken Pomeroy. Additionally, for each statistical category, I've provided a link to some background information on that particular method of measurement so that each value can be put into its appropriate context.
[Data]
Note: All the data produced below represents games played through 12/31/2005.
Efficiency
Pace: Possessions Per Game
Tempo/Pace represents the number of possessions a team generates per 40 minutes of basketball. Even though possessions are not charted officially as an NCAA statistics, it can be adequately estimated by using the follow formula developed by Ken Pomeroy and Dean Oliver:
Possessions = FGA-OR+TO+.42*FTA
On average, a team will generate about 70 offensive possessions per game.
Offensive Efficiency
This value represents the number of points scored by a particular team per 100 offensive possessions. Given the fact that an average college basketball team will only generate about 70 possessions per contest, these numbers are higher than the points-per-game statistics that you may be accustomed to.
Furthermore, in order to accurately represent a team's true offensive efficiency, these values have been adjusted in order to account for a team's competition. Ken Pomeroy describes this adjustment as follows:
Defensive Efficiency
The method used for calculating defensive efficiency is the same as that used for calculating offensive efficiency. So, if you're curious as to how these values have been determined, simply used the links and information listed above.
Offensive Factors
Effective Field Goal Percentage
Using a traditional method for measuring field goal percentage is an adequate way to illustrate a team's ability to strike consistently from the field. However, with the advent of the three-point shot and its pervasive role in contemporary offensive style, using a straight-forward methodology does not illustrates the full value of a team's attempts from the field.
Thus, Dean Oliver has developed a method that accounts for both the two-point and three-point shot. This formula is a simple, yet informative evaluation of a team's shooting ability and looks as follows:
Eff FG% = (FGM + 0.5*FG3M)/FGA
Consequently, an effective field goal percentage recognizes that a made three-pointer is worth more (approximately 0.5 more) than a made two-pointer. As John Gasaway has noted, using a straight field goal percentage in the presence of the three-point shot is roughly analogous to calculating a batting average using plate appearances instead of official at-bats.
Turnover Percentage
Turnover Percentage serves as a pace-independent measure of basketball security. To measure a team's turnover percentage (whether offensive or defensive), by dividing the number of turnovers a team commits by the team's total possessions.
Offensive Rebounding Percentage
Measuring rebounding as a function of opportunities seized or squandered is much more enlightening than simply using boards per game. As described by John Gasaway:
OR% = OR / (OR + Opponents Def Reb)
Getting to the Foul Line
As noted by Ken Pomeroy:
Defensive Factors
Effective Field Goal Percentage
The principles used to derive a team's defensive effective field goal percentage are the same as those used to derive a team's offensive effective field goal percentage.
Turnover Percentage
The principles used to derive a team's defensive turnover percentage are the same as those used to derive a team's offensive turnover percentage.
Offensive Rebounding Percentage
The principles used to derive a team's defensive offensive rebounding percentage are the same as those used to derive a team's offensive rebounding percentage.
Keeping Opponents Off the Foul Line
The only difference between a team's defensive free throw rate and its offensive free throw rate is that defensive free throw rate employs free throws attempted in the numerator since the defense has little control on the percentage of free throw attempts made by the opposition.
Thus, the formula looks as follows:
FTRatedef = FTA / FGA
Tomorrow evening, Connecticut and Marquette will kick off the 2006 Big East Conference season when they tussle in Milwaukee. In honor of this special occasion, I thought it appropriate to take a brief look at some tempo-free statistics and how each conference member has measured up at this, the pseudo-halfway point of the season.
If you're not familiar with tempo-free statistics, they are essentially an alternative method for analyzing production and value. Tempo-free statistics are free from inflation or deflation by measuring a team's production through a possession-based analysis, rather than of raw, aggregate data.
For more information on tempo-free statistics, I highly recommend this introduction written by Big Ten guru John Gasaway and this primer written by blogging superhero Ken Pomeroy. Additionally, for each statistical category, I've provided a link to some background information on that particular method of measurement so that each value can be put into its appropriate context.
[Data]
Note: All the data produced below represents games played through 12/31/2005.
Efficiency
Pace: Possessions Per Game
Tempo/Pace represents the number of possessions a team generates per 40 minutes of basketball. Even though possessions are not charted officially as an NCAA statistics, it can be adequately estimated by using the follow formula developed by Ken Pomeroy and Dean Oliver:
Possessions = FGA-OR+TO+.42*FTA
On average, a team will generate about 70 offensive possessions per game.
| Tempo/Pace | |
| Team | T/P |
| Cincinnati | 75.5 |
| Connecticut | 75.4 |
| Providence | 73.5 |
| Syracuse | 72.0 |
| Marquette | 70.7 |
| Villanova | 70.5 |
| Rutgers | 68.0 |
| Seton Hall | 67.9 |
| Louisville | 67.8 |
| Notre Dame | 67.4 |
| Pittsburgh | 67.3 |
| West Virginia | 67.1 |
| DePaul | 65.5 |
| St. John's | 64.7 |
| South Florida | 64.5 |
| Georgetown | 61.3 |
Offensive Efficiency
This value represents the number of points scored by a particular team per 100 offensive possessions. Given the fact that an average college basketball team will only generate about 70 possessions per contest, these numbers are higher than the points-per-game statistics that you may be accustomed to.
Furthermore, in order to accurately represent a team's true offensive efficiency, these values have been adjusted in order to account for a team's competition. Ken Pomeroy describes this adjustment as follows:
Say Team A averages a pace of 62 possessions per game and Team B averages 68 possessions per game. And for the sake of this example, let's say the average college game has 70 possessions, a nice round number. For the model I use, the expected possessions in a game involving Teams A and B would be 60. This results from the fact that Team A averages eight possessions slower than normal and Team B averages two possessions slower than normal. The sum is ten possessions slower than normal, or 60.If you are still confused and would like to read more about how Pomeroy illustrates offensive (and defensive) efficiency, this link would be helpful.
Why would the game end up being played at a slower pace than either team's average? A team's average pace is a product of how they like to play and how their opponents like to play. A team playing much slower than average, like Team A, is more than likely playing opponents that prefer to play faster than them. So Team A's average pace on the season is faster that they would really like if they were totally in control.
| Offensive Efficiency | |
| Team | OE |
| Villanova | 121.2 |
| Cincinnati | 116.7 |
| Connecticut | 116.6 |
| West Virginia | 116.3 |
| Providence | 110.9 |
| Pittsburgh | 110.8 |
| Georgetown | 109.9 |
| Notre Dame | 108.8 |
| Louisville | 107.8 |
| DePaul | 107.7 |
| Syracuse | 107.5 |
| Marquette | 106.4 |
| Rutgers | 103.6 |
| Seton Hall | 96.8 |
| St. John's | 93.3 |
| South Florida | 90.4 |
Defensive Efficiency
The method used for calculating defensive efficiency is the same as that used for calculating offensive efficiency. So, if you're curious as to how these values have been determined, simply used the links and information listed above.
| Defensive Efficiency | |
| Team | DE |
| Pittsburgh | 82.4 |
| Villanova | 83.5 |
| Connecticut | 84.8 |
| St. John's | 85.5 |
| Syracuse | 85.5 |
| Cincinnati | 89.0 |
| Seton Hall | 89.2 |
| Marquette | 91.5 |
| Louisville | 91.6 |
| Rutgers | 92.2 |
| South Florida | 93.5 |
| Notre Dame | 93.6 |
| Georgetown | 95.3 |
| West Virginia | 96.5 |
| Providence | 98.9 |
| DePaul | 100.7 |
Offensive Factors
Effective Field Goal Percentage
Using a traditional method for measuring field goal percentage is an adequate way to illustrate a team's ability to strike consistently from the field. However, with the advent of the three-point shot and its pervasive role in contemporary offensive style, using a straight-forward methodology does not illustrates the full value of a team's attempts from the field.
Thus, Dean Oliver has developed a method that accounts for both the two-point and three-point shot. This formula is a simple, yet informative evaluation of a team's shooting ability and looks as follows:
Eff FG% = (FGM + 0.5*FG3M)/FGA
Consequently, an effective field goal percentage recognizes that a made three-pointer is worth more (approximately 0.5 more) than a made two-pointer. As John Gasaway has noted, using a straight field goal percentage in the presence of the three-point shot is roughly analogous to calculating a batting average using plate appearances instead of official at-bats.
| Effective Field Goal Percentage | |
| Team | eFG% |
| Villanova | 57.2 |
| Georgetown | 56.7 |
| West Virginia | 56.3 |
| Connecticut | 54.1 |
| Providence | 54.0 |
| Notre Dame | 52.9 |
| Pittsburgh | 52.5 |
| Syracuse | 52.3 |
| Louisville | 52.3 |
| Marquette | 52.0 |
| Cincinnati | 50.9 |
| Rutgers | 50.3 |
| South Florida | 50.1 |
| DePaul | 49.2 |
| Seton Hall | 45.2 |
| St. John's | 44.7 |
Turnover Percentage
Turnover Percentage serves as a pace-independent measure of basketball security. To measure a team's turnover percentage (whether offensive or defensive), by dividing the number of turnovers a team commits by the team's total possessions.
| Turnover Percentage | |
| Team | TO% |
| West Virginia | 14.4 |
| Cincinnati | 16.1 |
| Villanova | 18.0 |
| Georgetown | 18.4 |
| Louisville | 18.8 |
| Connecticut | 19.5 |
| Notre Dame | 19.6 |
| Pittsburgh | 19.9 |
| Rutgers | 20.1 |
| DePaul | 20.5 |
| Syracuse | 21.6 |
| Providence | 21.7 |
| Seton Hall | 22.7 |
| Marquette | 22.7 |
| St. John's | 24.2 |
| South Florida | 30.4 |
Offensive Rebounding Percentage
Measuring rebounding as a function of opportunities seized or squandered is much more enlightening than simply using boards per game. As described by John Gasaway:
To take just one example: in the bizzaro world of per-game stats, Penn State usually “leads” the conference in offensive rebounding, as seen here on the Big Ten’s official stats page.To calculate a team's offensive rebounding percentage, the formula is quite straightforward:
How can this be? Mainly because the Nittany Lions (the poorest shooting team in the Big Ten last season) miss so many shots and thus have so very many opportunities to record an offensive rebound. So last year in conference Penn State hauled in about 11.7 offensive boards a game while Michigan State had “only” 11.1.
Does this mean PSU’s really a better offensive rebounding team than the Spartans? Of course not. In fact, Tom Izzo’s team was outstanding on the offensive glass last year, far and away the best in the Big Ten at rebounding their misses.
OR% = OR / (OR + Opponents Def Reb)
| Offensive Rebounding Percentage | |
| Team | OE |
| Connecticut | 45.2 |
| St. John's | 43.5 |
| Villanova | 39.2 |
| Pittsburgh | 38.9 |
| Syracuse | 38.7 |
| Seton Hall | 36.9 |
| Louisville | 36.9 |
| South Florida | 36.0 |
| Marquette | 35.7 |
| Providence | 35.0 |
| Notre Dame | 33.9 |
| Rutgers | 33.5 |
| Cincinnati | 32.4 |
| Georgetown | 30.9 |
| DePaul | 28.6 |
| West Virginia | 25.4 |
Getting to the Foul Line
As noted by Ken Pomeroy:
Free throw rate captures a team’s ability to score from the line:
FTRateoff = FTM / FGA
In Dean Oliver's piece, he mentions the relative importance of each factor.In the NBA, effective field goal percentage is easily the most important factor, followed by turnover percentage, offensive rebounding percentage, and free throw rate.
A "RoboScout"-type analysis of games from the 2005 season shows that the importance of each factor is similar in college, with free throw rate being slightly more important in the college game, but still taking a back seat to offensive rebounding. Each team is different though.
For instance, Gonzaga’s free throw rate was the second most important contributor to their offensive success. For Michigan State, offensive rebounding ranked second.
| FTM/FGA | |
| Team | FTM/FGA |
| Cincinnati | 31.0 |
| Providence | 30.9 |
| Notre Dame | 30.0 |
| South Florida | 29.8 |
| Connecticut | 29.3 |
| Seton Hall | 28.8 |
| Villanova | 28.7 |
| Rutgers | 28.2 |
| DePaul | 27.3 |
| Pittsburgh | 27.3 |
| Marquette | 25.7 |
| Georgetown | 25.0 |
| Syracuse | 24.7 |
| Louisville | 24.0 |
| St. John's | 22.6 |
| West Virginia | 17.7 |
Defensive Factors
Effective Field Goal Percentage
The principles used to derive a team's defensive effective field goal percentage are the same as those used to derive a team's offensive effective field goal percentage.
| Effective Field Goal Percentage | |
| Team | eFG% |
| Connecticut | 39.8 |
| St. John's | 41.1 |
| Villanova | 41.8 |
| Syracuse | 41.9 |
| Seton Hall | 43.1 |
| Notre Dame | 43.6 |
| Pittsburgh | 44.1 |
| Rutgers | 45.0 |
| Louisville | 45.2 |
| Cincinnati | 45.6 |
| Georgetown | 46.2 |
| South Florida | 46.5 |
| Marquette | 46.6 |
| Providence | 49.7 |
| DePaul | 50.2 |
| West Virginia | 51.6 |
Turnover Percentage
The principles used to derive a team's defensive turnover percentage are the same as those used to derive a team's offensive turnover percentage.
| Turnover Percentage | |
| Team | TO% |
| West Virginia | 26.7 |
| Rutgers | 25.1 |
| Syracuse | 24.7 |
| South Florida | 24.6 |
| Cincinnati | 23.9 |
| Villanova | 23.8 |
| Pittsburgh | 22.9 |
| Marquette | 22.7 |
| Connecticut | 22.6 |
| St. John's | 22.6 |
| Louisville | 22.6 |
| Providence | 21.9 |
| Georgetown | 21.8 |
| Seton Hall | 20.9 |
| DePaul | 18.5 |
| Notre Dame | 18.0 |
Offensive Rebounding Percentage
The principles used to derive a team's defensive offensive rebounding percentage are the same as those used to derive a team's offensive rebounding percentage.
| Offensive Rebounding Percentage | |
| Team | OR% |
| Pittsburgh | 21.9 |
| Seton Hall | 24.9 |
| St. John's | 26.4 |
| Connecticut | 27.0 |
| Georgetown | 27.2 |
| Rutgers | 27.3 |
| Louisville | 28.3 |
| Villanova | 29.2 |
| Notre Dame | 29.5 |
| South Florida | 30.3 |
| Syracuse | 31.0 |
| DePaul | 31.1 |
| Marquette | 33.9 |
| Cincinnati | 34.3 |
| West Virginia | 35.3 |
| Providence | 36.3 |
Keeping Opponents Off the Foul Line
The only difference between a team's defensive free throw rate and its offensive free throw rate is that defensive free throw rate employs free throws attempted in the numerator since the defense has little control on the percentage of free throw attempts made by the opposition.
Thus, the formula looks as follows:
FTRatedef = FTA / FGA
| FTA/FGA | |
| Team | FTA/FGA |
| West Virginia | 20.0 |
| Notre Dame | 22.4 |
| DePaul | 26.8 |
| Connecticut | 27.0 |
| Georgetown | 28.2 |
| Marquette | 32.4 |
| South Florida | 32.4 |
| Seton Hall | 32.6 |
| Pittsburgh | 32.8 |
| Cincinnati | 32.9 |
| Villanova | 34.3 |
| Syracuse | 34.7 |
| Louisville | 35.9 |
| St. John's | 37.5 |
| Providence | 38.8 |
| Rutgers | 43.1 |
Very nice work, Matt.