ACC Hoops: Inside the Numbers

Best (and Worst) WORP / 35 Seasons: BC, Clemson, and Duke

2006-10-12T12:55:00.000-07:00

Here are the 20 best WORP / 35 seasons for Clemson and Duke from 1982-2006. The 10 worst seasons are also included for players that played at least 15 MPG. Since Boston College has only been in the ACC for one season, its top 2 WORP / 35 seasons are listed.

Boston College's Best (2006)

1. Smith, Craig (2006) ................2.83
2. Dudley, Jared (2006)..............2.60

Clemson's Best (1982-2006)

1. Grant, Horace (1987)...............5.76
2. Davis, Dale (1991)....................5.13
3. Campbell, Elden (1988)..........4.83
4. Wright, Sharone (1993)..........4.63
5. Davis, Dale (1990)...................4.59
6. Campbell, Elden (1989)...........4.58
7. Campbell, Elden (1990)...........4.45
8. Grant, Horace (1986)..............4.42
9. Whitney, Chris (1993).............4.03
10. McIntyre, Terrell (1998)......3.74
11. Davis, Dale (1989)..................3.68
12. Wright, Sharone (1994)........3.45
13. McIntyre, Terrell (1999)......3.28
14. Buckner, Greg (1998)............3.27
15. McIntyre, Terrell (1997).......3.10
16. Gray, Devin (1993)................3.08
17. Ford, Sharrod (2005)............3.01
18. Marshall, Grayson (1986).....2.90
19. Buckner, Greg (1997)............2.82
20. Buckner, Greg (1995)...........2.75

Three seasons each from Dale Davis, Elden Campbell, Terrell McIntrye, and Greg Buckner, and two each from Horace Grant and Sharone Wright-- that seems to be about right. Grant's ACC POY season in 1986-87 is pretty clearly the greatest Clemson season of the past 25 years. In the past 25 years, Clemson has had two seasons above 5 WORP / 35 and nine seasons above 4 WORP/ 35. Ten different players were represented in the 20 best seasons.

Clemson's Worst (1982-2006)

1. Gilmore, Chucky (2000).........-0.81
2. Scott, Edward (2000)..............-0.59
3. Bains, Pasha (2000).................-0.33
4. Solomon, Will (1999)................-0.29
5. Howling, Kirkland (1989)........-0.21
6. Woni, Mohamed (1998)...........-0.20
7. Powell, Julius (2006)................-0.19
8. Rice, Lamar (2004)..................-0.15
9. Christie, Chey (2002)...............-0.13
10. Braddick, Dustin (2000)........-0.07

Four of Clemson's worst 10 seasons of the past 25 years came in 1999-2000, including the top 3. Not surprisingly, that was one of the worst teams in recent ACC history (10-20 overall, 4-12 in conference against a relatively weak ACC). Solomon and Scott became above-average players later in their careers after horrendous freshman campaigns. Gilmore, Bains, Christie, and Braddick all transferred after realizing that they weren't ACC-caliber players. The jury is still out of Powell, I guess.

Duke's Best (1982-2006)

1. Ferry, Danny (1989).................6.04
2. Laettner, Christian (1991).......6.02
3. Laettner, Christian (1992).......5.73
4. Hill, Grant (1993)......................5.54
5. Hurley, Bobby (1993)...............5.52
6. Battier, Shane (2001)...............5.32
7. Williams, Jason (2001).............5.23
8. Williams, Shelden (2005).........5.17
9. Brand, Elton (1999)...................4.92
10. Williams, Shelden (2004).......4.88
11. Boozer, Carlos (2002).............4.77
12. Williams, Shelden (2006).......4.43
13. Williams, Jason (2002)...........4.43
14. Laettner, Christian (1990).....4.36
15. Hill, Grant (1994)....................4.26
16. Ferry, Danny (1988)...............4.17
17. Dawkins, Johnny (1986).........4.15
18. Dawkins, Johnny (1985).........3.96
19. Parks, Cherokee (1994)..........3.88
20. Abdelnaby, Alaa (1990)..........3.86

Three seasons each from Laettner and Shelden Williams, and two apiece from Grant Hill, Ferry, Dawkins, and Jason Williams. Not a single season in Duke's top 20 from the one-dimensional J.J. Redick (he did have Duke's 21st best season according to WORP / 35), much to the surprise and consternation of Dick Vitale and Mike Patrick. Despite all of Redick's hype and scoring records, Shelden Williams was the most valuable Dukie in that recruiting class. In the past 25 years, Duke has had nine seasons above 5 WORP / 35 and seventeen seasons above 4 WORP / 35. Twelve different players were represented in the 20 best players.

Duke's Worst (1982-2006)

1. Tissaw, Mike (1982)...................-0.65
2. McNeely, Doug (1982)...............-0.59
3. Collins, Chris (1995)...................-0.31
4. Meagher, Dan (1982).................-0.12
5. Anderson, Todd (1982)...............0.03
6. Melchionni, Lee (2006)...............0.04
7. Henderson, David (1983)............0.05
8. Nelson, DeMarcus (2006)..........0.09
9. Williams, Alan (1982)..................0.15
10. Meagher, Dan (1984)................0.23

Virtually the entire frontcourt from the 1981-82 team (10-17 overall, 4-10 in conference) is found on Duke's bottom 10 list. Statistically, that was the worst frontcourt in the past 25 years of ACC basketball. While Dawkins was the biggest name and best player from Duke's 1982-83 freshman class, big men Mark Alarie and Jay Bilas may have been just as important to that class. Sure, Bilas wasn't a great college player-- but he was a lot better than Tissaw/McNeely/Williams/Anderson. David Henderson, another member of the class that debuted in 1982-83, went on to have a solid career after a bad freshman campaign.

Collins recovered from a ridiculously bad 1994-95 season to have an all-ACC caliber senior season the next year. I'm not sure if he petitioned the NCAA to have the 1994-95 stats removed from his record (and assigned to Pete Gaudet's).

More on WORP with an Example

2006-10-11T10:58:00.000-07:00

Using Len Bias's 1986 ACC POY season (although Brad Daugherty had a better season according to the WORP / 35 metric), I'll walk through an example of how the WORP / 35 is calculated. This same technique is used for every player in my 1982-2006 ACC database. WORP's for years before 1982 are not available because of incomplete data (specifically, turnover data were not available for some ACC teams). For my ACC database, all statistics were pulled from Charlie Board's ACC Stats Archive-- a fantastic site and must-bookmark for any fan of the ACC.

Calculating WORP / 35:

1.) The first step is calculating Bias's raw PER from 1986. Please see Hollinger's Pro Basketball Prospectus series for a detailed description of his PER formula. Bias's PER per-minute was 0.435 in 1986 (without using Hollinger's adjustment that makes 15 the average PER value).

2.) Next, a series of adjustments must be made to the raw PER of 0.435. The adjustments are as follows:

a.) seasonal pace adjustment: multiply by 1.073 in the Bias example (pace of 64.16 for Maryland '86 vs. 68.84 ACC average pace in 1986)

b.) historical pace adjustment: multiply by 1.015 in the Bias example (pace of 68.84 for the ACC in 1986 vs. 69.90 ACC average pace in 1982-2006)

c.) team defense adjustment: multiply by 0.946 in the Bias example (defensive efficiency of 100.8 for Maryland '86 vs. 95.4 ACC average defensive efficiency in 1986)

d.) historical strength of conference adjustment: multiply by 1.112 in the Bias example-- the historical strength of conference adjustment is calculated using non-conference winning percentage, percentage of ACC teams making the NCAA Tournament, and winning percentage in the NCAA Tournament; it ranges from 0.840 (2006, the weakest year in the database) to 1.118 (1991, the strongest year in the database-- 1986 is the second strongest year)

Bias's per-minute PER goes from 0.435 to 0.498 after the above adjustments are made.

3.) Compare Bias to a replacement-level power forward (even though Bias played some small forward in 1986, he was primarily a power forward; players were compared to the replacement-level player at their primary position-- which was a judgment call in some cases).

Average per-minute PER's for each position are: C--0.279, PF--0.289, SF--0.267, SG--0.279, PG--0.271

Based on 25 years of historical data (comparing back-ups/reserves to starters and replacement-level players to back-ups/reserves), the following replacement levels contribution percentages were chosen for each position: C--55%, PF--65%, SF--65%, SG--65%, PG--55%

Average replacement-level per-minute PER's for each position are (average PER * contribution percentage): C--0.154, PF--0.188, SF--0.173, SG--0.182, PG--0.149

So, Bias's per-minute adjusted PER of 0.498 is compared to a per-minute adjusted PER of 0.188 for a replacement-level power forward.

4.) Use the specified relationship between adjusted PER and winning percentage to convert Bias's value over replacement-level power forward into wins over replacement-level power forward.

Based on 25 years of historical ACC data (1982-2006), the following relationship was specified between winning percentage and adjusted PER by using a linear regression:

Winning % = -0.127 + 2.444 * (PER)

So, based on this equation, an ACC team that's average at every position (PER of 0.277) will be expected to finish with a 0.550 winning percentage. An ACC team that's replacement-level at every position (PER of 0.169) will be expected to finish with a 0.286 winning percentage. Replacement-level ACC players are predicted to win some games (based on an average ACC non-conference schedule) because a replacement-level ACC player is still better than many low-major players.

To calculate a player's WORP / 35, the following formula is used:

(Player's MPG/Team MPG) * (Player's PER - Replacement-level PER at position) * PER coefficient * # of games (35 obviously, when using the 'per 35' nomenclature)

Plugging Bias's 1986 numbers into this formula:

(37/200) * (0.498-0.188) * 2.444 * 35 = 4.92

Bias's WORP / 35 of 4.92 is the 30th best ACC season in the past 25 years.

From 1982-2006, there have been:

9 WORP / 35's greater than 6.0
17 WORP / 35's between 5.0 and 6.0
59 WORP / 35's between 4.0 and 5.0 (the range into which Bias's 1986 season falls)
139 WORP / 35's between 3.0 and 4.0
123 WORP / 35's between 2.5 and 3.0

That's 347 occurrences in the past 25 years in which a player has had a WORP / 35 of at least 2.5. That breaks down to about 14 per season, which means that a WORP / 35 of 2.5 or greater is generally enough to earn a player all-ACC accolades (1st, 2nd, or 3rd team).

Introduction and Glossary of Terms

2006-10-05T11:43:00.000-07:00

The concept of possession-based analysis will serve as the backbone for all statistics discussed in this forum. By using possession-based (or tempo-free/tempo-adjusted) stats, the effect of pace can be normalized. That is to say, the numbers put up by Paul Westhead's run-and-gun Loyola Marymount teams of the late 80's can be directly compared to the numbers of Pete Carril's methodical Princeton teams by using a common denominator (the possession).

I am certainly not the first to use possession-based stats-- they have been slowly gathering mainstream momentum over the past decade. In basketball circles, Dean Smith (and other coaching staffs across the country) have been using these concepts since at least the 1960's (see Smith's seminal book Basketball: Multiple Offense and Defense). More recently, Dean Oliver's book Basketball on Paper delves into some possession-based analysis (and is a must-read for anyone with even a passing interest in basketball statistics). John Hollinger's Pro Basketball Prospectus series also has a lot of great possession-based analysis (he's now providing Insider content at espn.com). For possession-based NBA analysis, there's nothing better than 82games.com, created by Roland Beech. Roland's site includes +/- data and possession-based stats for the entire NBA. The charting efforts at 82games.com are also the most advanced and organized that I've seen, providing breakthrough insights on individual defense and passing, among other things. Another important contributor to the tempo-free movement in basketball stats is Ken Pomeroy. Ken's site (kenpom.com) provides possession-based stats for all 334 NCAA Division I teams, as well as great insights and commentaries. If you're a college basketball fan, add his site to your bookmarks (if it's not already there). While Ken's site has a national flavor, I'm hoping mine can develop into its ACC counterpart (while adding features such as individual defensive boxscores and +/- statistics for individuals and line-up combinations).

As for some of the terms and statistics that will be commonly-used on this blog, here's a glossary (yes, I realize that these aren't alphabetized-- I was attempting to start with the basics of possession-based stats and move forward from there):

Possession: the period of time that a team has control of the ball before losing control to the other team; possessions can be estimated as FGA + (0.44*FTA) + TO - OR

Play: a play is defined by Dean Oliver as "the period between when one team gains control of the ball and when they lose control of the ball, either when the opposing team gains control or when a shot goes up"; Dean Smith's definition of a possession was synonymous with this definition of a play; the critical distinction between a play and a possession involves offensive rebounding-- a team that misses a shot, gets the offensive rebound, then makes the follow-up has used two plays but only one possession

Pace: the number of possessions per 40 minutes used by a team; the league average for the ACC in 2006 was 68.0, ranging from 63.6 (Boston College) to 74.2 (Maryland)

Offensive Efficiency: points scored per 100 offensive possessions; the league average in 2006 was 107.7, ranging from 101.4 (Georgia Tech) to 114.6 (Boston College)

Defensive Efficiency: points allowed per 100 defensive possessions; the league average in 2006 was 99.6, ranging from 94.6 (Clemson) to 104.6 (Wake Forest)

Net Efficiency or Efficiency Margin: offensive efficiency minus defensive efficiency; the league average in 2006 was 8.1, ranging from -0.3 (Georgia Tech) to 18.3 (Duke)

Floor Percentage: the percentage of an individual's (or team's) possessions in which a score occurs (from Basketball on Paper)

Points Produced: the number of points generated by an individual player through field goals, free throws, assists, and offensive rebounds (see Basketball on Paper for a detailed formula)

Offensive Rating: points produced per 100 offensive possessions (from Basketball on Paper)

Defensive Rating: points allowed by an individual per 100 defensive possessions; defensive boxscores, compiled from charting games, are used to calculate defensive stops (forced missed shots, turnovers forced, and defensive rebounds) and stop percentages (defensive stops / individual defensive possessions) in order to calculate defensive ratings (see Basketball on Paper for a detailed formula)

Percentage of Team Possessions: the fraction of team offensive or defensive possessions used by an individual player; with 5 players on the court per team, the average value for this stat is 20%; percentage of defensive possessions is factored into the formula that calculates defensive rating; percentage of offensive possessions can be used to differentiate between role players and primary options and to rank players with similar offensive ratings (assuming similar above-average offensive ratings, a high usage player is more valuable than a lower usage player; conversely, assuming similar below-average offensive ratings, a high usage player is more detrimental to his team's offense than a low usage player)

PER (Player Efficiency Rating): created by John Hollinger, PER is a formula for rating players that incorporates linearly-weighted boxscore statistics into a single number; statistics are pace-adjusted and the formula includes team-dependent factors (e.g., percentage of assisted FG's and offensive rebounding percentage) which makes PER the best of the playing rating formulas that use linear weights; see Hollinger's Pro Basketball Prospectus series for details on how the linear weights are calculated/assigned and for the complete PER formula

"the four factors": Dean Oliver's four factors have been singled out as the most crucial statistics in determining wins and losses; the factors are:

1.) Effective field goal percentage (eFG%): eFG% incorporates made three-pointers into a standard FG%-- eFG% = (FG + 0.5*3Pt.FG) / FGA

2.) Offensive rebounding percentage: OR% = OR / (OR + Opponent's DR)

3.) Turnover percentage: TO% = TO / Offensive Possessions

4.) FTA / FGA: this pace-independent statistic demonstrates a team's ability to earn trips to the free throw line

The four factors be viewed from either an offensive or defensive perspective. Defensively, the corresponding stats are, of course, eFG% allowed, defensive rebounding percentage (DR/{DR + opponent's OR}), turnovers forced percentage (turnovers forced / defensive possessions), and opponent's FTA / FGA.

+/- Statistics: like the commonly-used hockey stat (for all you Hurricanes fans and Canadians), basketball's +/- stat captures the score during the minutes (or, more accurately, possessions) that an individual player, line-up combination, or player pair/trio is on the court; the +/- data is useful for determining a team's most effective offensive and defensive line-ups and frontcourt/backcourt combinations

On-Court/Off-Court: this stat is based on +/- data and shows the difference in net efficiencies during the possessions when a player is on the court versus the possessions that player is on the bench

For example, assume Reyshawn Terry plays 42 offensive and defensive possessions and UNC leads 51-36 during that time. Terry is on the bench for 34 offensive and defensive possessions, during which time UNC leads 37-33.

Terry's On-Court/Off-Court for this game would be: (Off.Eff. On - Def.Eff. On) - (Off.Eff. Off - Def. Eff. Off) = {((51/42)-(36/42)) - ((37/34)-(33/34))} * 100 = +24.0

A positive On-Court/Off-Court means that the team was better with that player on the court. A negative On-Court/Off-Court means that the team was better with that player on the bench.

Defensive Boxscore: defensive boxscores (compiled while charting games) show the contributions made by individual defensive players during the course of a game; they can be interpreted as the antithesis of a traditional boxscore-- while high FG%'s and low turnover totals are good in the traditional boxscore, low FG%'s and high turnover totals represent a strong defensive boxscore; FG's and FGA's (as well as FT's and FTA's) are pretty straightforward in the defensive boxscore-- they just represent shots made and attempted against an individual defender (half credit can also be awarded at the charter's discretion for good or poor help defense); turnovers forced include steals, charges/offensive fouls drawn, forced 5-second calls, and all other turnovers that can be directly attributed to an individual defender

Some FG's and FGA's cannot be assigned to any individual defender (e.g., a fast break dunk after a steal in the open court or a wide-open three after a failed trap at midcourt). These attempts fall into the 'team' category when charting. Some forced turnovers can also fall into the team category. In general, though, 90+% of defensive possessions can be attributed to an individual defender (or pair of defenders).

WORP (Wins Over Replacement Player): this is a stat used primarily in baseball circles and pioneered by the folks at Baseball Prospectus; basically, this stat represents the number of wins that a player is responsible for, as determined by substituting that player's minutes for those of a "replacement-level" player at his position; in baseball, a replacement-level player is generally viewed as the type of player that is constantly shuttling between AAA and the majors-- the college basketball equivalent would be a walk-on who's earned some spot minutes (e.g., Will Johnson or Patrick Johnson) or a lightly-recruited player who is just filling out a roster spot (e.g., Jonathan Holmes); replacement-level varies by position, with point guards and centers being the most difficult to replace

In my version of WORP, I use Hollinger's PER and make adjustments for team defense, relative pace of the league (relative to the years 1982-2006), and relative strength of the league (relative to the years 1982-2006). After making these adjustments, a player's PER can be compared to that of a replacement-level player at his position. Using a linear regression to estimate the highly-correlated relationship between defense-adjusted PER and winning percentage, a player's adjusted PER (over replacement player) can be converted to wins (over replacement player.

WORP / 35: this stat represents WORP per 35 games; this adjustment is necessary because the college season (unlike the NBA season) is not a fixed number of games (it can range anywhere from the high 20's to the low 40's depending on post-season success, pre-season exempt tournaments, etc.); by using the 35-game denominator (along with pace and strength of conference adjustments), apples-to-apples comparisons can be made across all players from 1982-2006

The top 5 individual seasons in the ACC from 1982-2006 based on WORP / 35 were:

1. Tim Duncan (1997) 8.55
2. Tim Duncan (1996) 6.58
3. Kenny Anderson (1991) 6.40
4. Ralph Sampson (1982) 6.30
5. Tim Duncan (1995) 6.30

Duncan's WORP / 35 in 1997 can be interpreted as turning a 17-18 team into a 26-9 team by replacing the minutes of a replacement-level center (say, Ralph Kitley as a freshman) with Tim Duncan as a senior. Duncan's 1997 season is 30% better than the next best ACC season of the past 25 years. Any WORP / 35 over 5 is rarefied air-- only 26 such seasons have been recorded since 1982.

I will have a follow-up post that talks a little more about the mechanics of the WORP formula and walks through an example. That, unfortunately, will probably be just as dry as this post. Once all the terms are defined and described, though, I promise that subsequent posts will a lot more exciting and enjoyable and a lot less like your garden-variety root canal.

Let's Tap This Keg

2006-10-04T10:35:00.000-07:00

Hey! The purpose of my blog is to provide some ACC basketball statistics and commentary that goes beyond the boxscore. Specifically, I plan on charting all ACC conference games and tracking +/- stats for each team (individual players and line-up combinations). I also plan on tracking individual defensive boxscores for each conference game. I will probably be using some statistics and terminology that is unfamiliar to many fans, so I will post a glossary of often-used terms/stats soon.

Stay tuned for more updates.