bucksdiarysplainer

2008-06-18T10:55:00.000-07:00

2008-06-18T10:41:00.000-07:00

2008-06-18T10:01:00.000-07:00

2008-06-18T08:44:00.000-07:00

2008-06-18T08:41:00.001-07:00

2008-06-16T11:42:00.000-07:00

2008-06-16T06:54:00.000-07:00

2008-06-14T02:23:00.000-07:00

2008-06-14T01:11:00.000-07:00

How do you read the columns in each "Win Profile"?

2008-06-10T17:48:00.000-07:00

If you are not familiar with the basic stats used on Bucks Diary, such as Win Score and Win Contribution, please first browse the quick statitstical FAQ.

After that, its simple.

"Win Score Pos Average"= the average Win Score calculated according to the number of minutes 82games.com says the player played at each of the 5 traditional NBA positions.

"Opp Win Score per position": This breaks down the "Win Score Above Average" achieved by player's opponents at each of the position's the player played. Its meant to tell you if the player had defensive success at one position and not another. FOR THIS STAT, NEGATIVE NUMBERS ARE GOOD, POSITIVE NUMBERS ARE BAD.

"Opp Win Score Overall": Takes the player's opponents Win Scores Above Average and gives you the cumulative Win Score Above Average. FOR THIS STAT, NEGATIVE NUMBERS ARE GOOD, POSITIVE NUMBERS ARE BAD.

"Defensive Half Wins": This takes the opposite of the player's opponents cumulative Win Score Above Average and uses the Win Score Wins Produced formula to translate that number into how many wins the player produced on the defensive end by reducing the "Win Production" effectiveness of his cumulative opponents. Since you have to play both ends of the floor, these "wins" are only given 1/2 value and thus refered to as "half wins".

"Defensive Win Contribution": Takes the opposite of the player's opponents "Opp Win Score Overall", ie the player's opponents Win Score Above Average, and multiplies it by: (the player's minutes played/the cumulative player minutes for the team). The cumulative player minutes, if a team played a regulation season without over time would equal 82 * 48 * 5 or 19680. That is rarely the actual season total since most teams play overtime minutes.

"Offensive Win Score Overall": This is the player's Win Score Above Average.

"Offensive Half Wins": This is the same as "Defensive Half Wins" except that you use the player's Offensive Win Score instead of his Opponent Counterpart's Offensive Win Score.

"Offensive Win Contribution": The same as "Defensive Win Contribution" except you use the player's Offensvie Win Score overall instead of his Opponent Counterpart's Win Score.

"Total Wins (Win Contribution)": Here you add the player's Defensive and Offensive Half Wins, and divide by 2, and you add the player's Defensive and Offensive Win Contributions and divide by 2. The former is meant to show exactly how many games the player won when you consider both his offense and his defense and the later is meant to show how positive or negative an impact the player had on making the team a winning team, compared with the impact you would get from a merely average player playing the same position and taking up the same amount of playing time, with the average player represented by (+0.000)

FAQ about statistics used on Bucks Diary

2008-06-10T16:44:00.001-07:00

SEE NUMBER #11 FOR "HOW TO READ WIN CONTRIBUTION"

NOTE 1: Most every statistic on Bucks Diary is somehow based upon the Win Score metric invented by Professor David Berri based upon the research he did for his book, The Wages of Wins. His work is to basketball as Bill James work is to baseball and the Football Outsiders work is to football. Win Score, I believe, it is THE one performance metric that most closely explains, through statistics, how a player performed.

NOTE 2: Win Score is a compilation of statistics, weighted according to how each statistic correlates with victory. The Win Score weighting system has a 96% correlation with wins. In other words its pretty accurate.

3. What is the Win Score Formula?
Points + Rebounds + Steals + 1/2 Assists + 1/2 Blocks - Turnovers - Field Goals Attempted - 1/2 Free Throws Attempted - 1/2 Personal Fouls = The Player's Win Score Points

4. How do you find "Win Score Above Average"?
You take the player's Win Score and divide it by the minutes he played, and then multiply that by 48. Then you adjust that number according by the average Win Score compiled at the position the played. If he played multiple positions, you have to adjust your average accordingly.

5. Can you do an example?
Sure. Let's say I look at the box score and figure out Kobe Bryant's Win Score added up to 8.5 Win Score points. Then I look at his minutes played. It was 32. Okay, so that comes out to .265 Win Score Points per minute, for a Win Score per 48 minutes of 12.3. Now I must adjust for position. Usually I do this by using the player's positions as recorded on 82games.com. According to 82games.com, Bryant plays 75% of the time at SG, and 25% at SF. The Win Score positional average for SG is 6.4 and SF is 7.8, which works out to an average of 6.8 for the posititions Kobe played. So, for the game Kobe had a Win Score Above Average of +5.5.

6. What is Opposition Win Score?
Now this one is my invention, a defensive metric I conjured based on Win Score, but not yet quite endorsed by Professor Berri, but I think its supported by math and logic. What I do is I take the "Opponent Counterpart Statistics" on 82games.com, which are the average statistics compiled by the players the particular player has guarded, and figure out the "Win Score Above Average" in reverse, essentially. I've found that if you take any given team's cumlulative Win Score and its Oppositition's Cumulative Win Score, adjust those for the NBA cumulative average, add the two numbers together, and then divide by 5, and then punch the result into the Win Score Win Prediction Formula, you will be able to predict the team's wins with 93% accuracy. So I think its a legitimate statistic.

7. What is Win Contibution?
This is my own invention based on Win Score and on baseball statistics invented by Baseball Prospectus, Hardball Times, Bill James, and others. As I have said, I believe it is the very best statistic available for evaluating a player's performance in any single game. Its meant to be the basketball equivalent of the baseball statistic "Replacement Value over the Average Player". Essentially all it does is it takes the player's "Win Score Above Average" and multiplies it by the player's percentage of total playing time. Thus, if I'm doing Win Contribution for a regulation NBA game, the formula : Player's Win Score Above Average * (player's minutes/240). Therefore, the average player (meaning a player whose Win Score per 48 is right at his positional average) would have a +0.000 Win Contribution. A team of player's who record +0.000 would be expected to have as many wins as they have losses. Thus any "Contribution" over +0.000 is a contribution to a winning team.

8. What is Offensive and Defensive Win Contribution?
This I use only when evaluating player's over a course of games. If I'm doing it that way, I'll seperate it into offensive and defensive Win Contributions. Offensive Win Contribution is the Win Contribution described in #7. Defensive Win Contribution is simply the negative value of the player's opponents Win Contribution. In other words, the formula is - Opposition Win Score Above Average * (the player's minutes/total playing minutes available over the relevant time period). To get to what I refer to as "Total Win Contribution", you add offensive and defensive Win Contributions and divide by 2.

9. How do we know which Win Contribution you are using?
Unless I indicate "Offensive" and "Defensive" Win Contribution, I'm using the straight Win Contribution described in #7. I always use #7 Win Contribution when evaluating individual games, because I will always provide both team's Win Contribution. That way, you can look at each and sort of tell who was guarding whom and do your "defensive" Win Contributions mentally. The basic reason for the two usages is over the long haul, I have access to 82games.com's "Counterpart" statistics, but they don't provide them for single games.

10. How do you translate Win Score or Win Contribution into wins?
Here's the formula: Win Score Above Average/48 * 1.621 + 0.120/48 * minutes played = wins. To translate "Wins Contribution" numbers into wins, you do the following, you add up the team's offensive and defensive Win Contributions, add those numbers together, divide that by two, and then you treat that result as if it were "Win Score Above Average" and punch it into the formula above.

11. What's a good Win Contribution Score?
If you notice, over the course of a season, most of the player's Win Contributions regress to the mean, which is +0.000, which is what you would expect. For one game, though, you can read Win Contribution as such:

below -1.000: awful game, can be blamed if we lose

below -0.500: very bad game

below -0.300: bad game

-0.100 to -0.300: below average game

-0.100 to +0.200: average game

0.200 to 0.400: good game

0.400 to 1.000: outstanding game

0.1000 and above: OUT OF HIS MIND, if we win, he gets the game ball

2008-02-17T23:21:00.001-08:00

2008-02-17T22:55:00.000-08:00

What is "Win Contribution"?

2008-02-15T22:22:00.000-08:00

"Win Contribution" is my own invention, derived from the work done by those who created the "Win Score" metric.

Win Contribution is, in simple terms, a multiplication of each player's "Position Adjusted Win Score" by his percentage of overall playing time.

Here's how it works. If you add up every player's Win Contribution, and plug it into the formula below, you can very accurately calculate the team's expected win total. As a result, each player's "Win Contribution" is a precise numerical expression of the impact that player has had on making his team either a winning or a losing team.

That's because if the sum of every player's Win Contribution adds up to ".000", the expected win total for the team would be dead even, i.e. you would have a .500 team. Thus, any Win Contribution number that is positive indicates the player has contributed to making the team a winning team. And the opposite is true for negative Win Contribution numbers.

Here's a rough guide to how to read the number:
A Win Contribution of "+ .500" means that if you surrounded that player with a roster of average players (meaning players who all have Win Contributions of .000), you could expect a winning percentage of roughly .603, or a seasonal record of about 50-32. On the other hand, if a player's Win Contribution is "-.500" that means if you surrounded that player with a roster of average players you could expect a winning percentage of roughly .433, or a seasonal record of about 35-47.

Example:
The Bucks Total Win Contribution from every player adds up to -1.096. The Bucks current Total Player Minutes (53 games * 48 minutes/game * 5 players) = 12820 minutes

MILWAUKEE BUCKS ESTIMATED WINS CALCULATION

Step 1: -1.096/48 = -.0228
Step 2: -.0228 * 1.621= -.0370
Step 3: -.0370 + .104= .0669
Step 4: .0669/48 = .0013
Step 5: .0013 * 12820 = 17.89

ESTIMATED WINS: 18
ACTUAL WINS: 19

2008-02-14T22:59:00.000-08:00

2008-02-14T00:57:00.000-08:00

buckscore

2008-02-10T07:10:00.000-08:00

2008-02-08T21:11:00.000-08:00

2008-02-07T15:11:00.000-08:00

Griz

2008-02-05T22:52:00.000-08:00

Ugly Index

2008-02-05T13:50:00.000-08:00

bucksstats

2008-02-04T10:43:00.000-08:00

Translating Win Contribution into Projected Wins

2007-12-31T19:14:00.000-08:00

Here's how Win Contribution translates into wins.

You take a team's cumulative Win Contribution (which is the sum of each player's Position Adjusted Win Score per 48 minutes x each player's percentage of overall playing time).

For example, at the end of the 2006-07 season, the Milwaukee Bucks cumulative WC was
-0.959. Here's how it translates:

1. You take that number, -0.959, and divide it by 48.

2. Then you take the resulting number and multiply it by 1.621.

3. Then you take that number and add 0.104.

4. Then you take that number and divide it again by 48.

5. Then you multiply that number by the total number of player minutes (games played x 48 x 5). At the end of the season, that is around 19680.

If you do that calculation for the 2006-07 Bucks, you come up with 29.63, which is very close to their actual victory total: 28.

What do the numbers mean?

2007-12-21T16:02:00.000-08:00

WIN SCORE

The concept of the Win Score I'm using is the brainchild of some Stanford economists who spent an inordinate amount of time and resources figuring out exactly which statistics correlate with winning and losing in basketball. From that research they created this algorithm:

Win Score/48: Points + rebounds + steals + 1/2 assists + 1/2 blocks - field goal attempts - turnovers - 1/2 personal fouls - 1/2 free throw attempts / minutes played * 48

SIDENOTE: If you notice, their research determined that the two most important factors in winning and losing are: Scoring efficiency (the number of points you can make per possession of the basketball), and the ability to maintain or gain possession of the basketball. Thus points, rebounds, and steals count as a full Win Score point, while turnovers and field goal attempts count as a full negative Win Score point. The other statistics, such as blocked shots, assists, and personal fouls are more peripheral... they are important but to a lesser degree. Thus they are only counted as 1/2.

The economists then calculated the average "Win Score" points/48 minutes amassed by each player at each of the 5 positions on the basketball floor. From that they can compute each player's "position adjusted" Win Score. And once they've computed each player's "Position Adjusted Win Score", they can compute a team's "Expected Win Total" within one or two games. It works incredibly well.

Example:

Michael Redd's Win Score computes to 10.3 per 48 minutes. Shooting guards have the lowest Win Score Averages on the court at 6.1 per 48, so Redd's Position Adjusted Win Score is +4.2. That's really good. Here's how it translates into Wins:

WIN CONTRIBUTION

Lets say Michael Redd played 38 minutes a game for all 82 games, and his PAWS was the aforementioned +4.2. To figure out his Win Contribution, I take his total number of court minutes, 3116, divide that by the total number of court minutes available for the entire team for the entire season (5 players play 48 minutes each for 82 games), and that is 19680 minutes, which means Redd used up 15.83% of the Bucks available court time.

I then multiply 15.83 times his PAWS of +4.2 and I get +0.664, a very high Win Contribution. To put it in perspective, let's say you put Redd on a team comprised entirely of average players (PAWS = +0.00). The team's aggregate Win Contribution is then entirely Redd's contribution of +.664. Here's the algorithm for determining the number of wins that team would expect:

Expected Wins= (Aggregate Win Contribution/48 * 1.621 +.104) / 48 * Total Team Courttime

Thus in an entire season, a team consisting of Michael Redd and a bunch of average players would be expected to win:

.664/48 * 1.621 + .104 /48 * 19680 minutes = 51.83 games

Now let's look at Charlie Bell, who has the Win Contribution of -.366. If Bell were surrounded by average players, that would mean the team could expect to win only 37.3 games. So Bell is effectively costing the Bucks 4+ games (I like expressing it in terms of production versus the average replacement, because that better captures the negative contribution).

Bucks Diary Relative Power Ranking

2007-12-13T09:43:00.000-08:00

Here is the PVOA Relative Power Ranking updated through games played on December 12, 2007.

1. Boston Celtics...+13.23
2. San Antonio Spurs...+7.98
3. Los Angeles Lakers...+7.36
4. Detroit Pistons...+6.03
5. Orlando Magic...+5.88
6. Toronto Raptors...+5.34
7. Phoenix Suns...+4.23
8. Golden State Warriors...+4.10
9. Houston Rockets...+3.11
10. Utah Jazz...+3.01
11. Dallas Mavericks...+2.43
12. New Orleans Hornets...+2.35
13. Washington Bullets...+1.73
14. Denver Nuggets...+1.72
15. Indiana Pacers...-0.46
16. Atlanta Hawks...-0.74
17. Sacramento Kings...-1.06
18. Portland Trailblazers...-1.26
19. Philadelphia Sixers...-1.32
20. Memphis Grizzlies...-1.38
21. Miami Heat...-1.97
22. Cleveland Cavaliers...-2.52
23. Chicago Bulls...-3.04
24. Charlotte Bobcats...-5.07
25. Milwaukee Bucks...-5.87
26. New Jersey Nets...-5.99
27. Los Angeles Clippers...-6.15
28. Seattle Supersonics...-8.29
29. Minnesota Timberwolves...-8.82
30. New York Knickerbockers...-12.57