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  • Project NBA Hiatus

    In my previous post, I mentioned pushing the 2007 NBA Tournament results live last Friday. Well, I sorta did …

    Ok, so I started anyway. I didn’t finish (in fact I conservatively estimate I have 2% of all the content live). So what else is new? But, really, I made significant progress! The hardest part was solving the problem of having each dynamically driven page (players, teams, etc.) contain Wordpress’ commenting system. It took me two solid days (being a 1-man blogger/programmer can take time) but I overcame this obstacle … however it still remains to be seen how well this will work out.

    Unfortunately, this means that every entry in my database, whether it be a player, a team, a series, a game, everything needs a separate Wordpress page. Thus, you can imagine how long it will take (there are 300 players, alone). I’m thinking I can get this done by the time I finish the next NBA Sim milestone: September 20 I should be done with the 2008 Legend Counterparting process. I’m actually just a shade over half-way done with that task so I’m right on schedule with it.

    So, sorry about the delay but having the ability to comment on every page was well worth the 2 extra weeks in my mind. Later!

    Friday, September 5th, 2008 at 11:51
  • Get Ready …

    I apologize for being away for so long but, believe it or not, I’ve been extremely busy. I finally moved into a Manhattan apartment (I was out in dirty Jersey) and I’ve been feverishly working on NBA Sim junk. Well, I have some crazy news for you all (are any of you out there?)

    I don’t know why I’ve been sleeping on this information (it’s been about a week) but the official team rosters for the 2008 NBA Sim Tournament have been set! I’m currently in the process of setting up the players’ attributes and tendencies. This will be so much hotter than the 2007 Tournament as I learned a lot and have made some significant upgrades to the whole process.

    Here’s the tentative schedule going forward from here:

    » Sep 06 - Push 2007 NBA Sim Tournament results live
    » Sep 20 - Finish 2008 NBA Sim Players Counterparting
    » Sep 27 - Finish 2008 NBA Sim Players Attributes and Tendencies
    » Oct 01 - BEGIN 2008 NBA SIM TOURNAMENT

     

    Tuesday, September 2nd, 2008 at 16:32
  • Estimating Blocks and Steals Pre-1974

    The old adage is “defense wins championships” yet most of the NBA Sim teams are full of offensive Legends. This is due to the fact that blocks and steals (the only defensive stats recorded) weren’t recorded until the 1974 season. Unfortunately for NBA historians and NBA Sim fans (are there any?) this means we’ll never truly know how great defensive specialists (like Bill Russell) really were between 1947 (the NBA’s inception) and 1973.

    Well, just because professional basketball ignored these stats for almost three decades doesn’t mean I can have the same cavalier attitude! For the 2007 NBA Sim Tournament I decided to fake steals and blocks the easiest way possible. First, I would set a shape for each player. NBA 2k7 had three shapes for each position, each with different starting steal and block values:

    Position Shape Default Steal Value Default Block Value
    Point Guard Distributor 70 50
    Point Guard Shoot First 70 50
    Point Guard Defense 80 50
    Shooting Guard Slasher 60 60
    Shooting Guard Pure Shooter 60 60
    Shooting Guard Defense 75 70
    Small Forward Slasher 60 60
    Small Forward Pure Shooter 60 60
    Small Forward Defense 75 70
    Power Forward Finesse 55 75
    Power Forward Banger 50 75
    Power Forward Defense 70 85
    Center Finesse 55 75
    Center Banger 50 75
    Center Defense 70 85

    Once I had a default steals and block value based on a player’s shape, I next tweaked this value using a points pool based on the Legend’s TENDEX value (discussed in detail in my old, Air Craft: Simming the Silver Legend post). Essentially, players with higher TENDEX values had more points with which to tweak their attributes higher. This system felt so artificial however. I wanted something that seemed more organic and based on the Legend’s actual season stats rather than how many TENDEX points they had left in their pool.

    So I turned to my other stats-obsessed basketball nutcases at APBR. I found a gentleman by the name of Mike Goodman who said he did some experiments into estimating steals for players pre-1974. His original formulas (in Excel speak) were:

    Est. Steals = SQRT(Assists Per Game) - 0.4

    Well, I put this formula to the test. I applied the formula to all players from the 1974 season on (sample size of 15481) and came up with estimated steals and blocks. Then I compared this to the actual blocks and steals, determined the difference and then averaged the total sum of these differences. Using Goodman’s formula, the average estimate was off by 16.9 steals (not great … but not terrible either, considering). Tweaking the formula a bit I came up with something slightly more accurate (only off by an average of 15.7 steals).

    Est. Steals = SQRT(Assists Per Game) - 0.53

    Goodman’s formula for blocks (which came out to be off by an average 20.7 blocks) was:

    Est. Blocks = SQRT(Rebounds Per Game) - 1.6

    The best I could tweak this to (off by an average of 19.6 blocks) was:

    Est. Blocks = SQRT(Rebounds Per Game) - 1.46

    Just for good measure I did some regression testing on steals (using assists) and blocks (using rebounds). The steals regression came out to be less accurate an estimation device (it was off by an average of 16.9 steals). The blocks regression, however, was more accurate a predictor than the tweaked Goodman’s formula (off by 14.5 blocks).

    Est. Blocks = (Total Rebounds * 0.134) - 3.741

    I am eagerly looking forward to using this new tool in the 2008 NBA Sim Tournament (tentatively scheduled to begin mid- to late-August).

    Monday, July 21st, 2008 at 15:13
  • Popularity Contest

    Earlier in the week my girlfriend made the claim that “no one really likes” baseball and that it is lame. Now, of the three most popular American sports, I would agree that baseball is my least favorite, but is it true that no one else likes it? Of course not … but it got my stats-obsessed juices flowing.

    In fact, after a little research I discovered that baseball has the highest total attendance between it, basketball and football by a factor of four!

      Football Baseball Basketball
    Latest Complete Season Attendance 17,506,509 79,493,687 21,394,757

    Now, these attendance figures are based upon countless, different variables - variables that are completely different between the three sports. First, I isolated what I thought were the most important factors, and ones that could easily be qunatifiable: number of stadiums, number of games in a season, total seats in all stadiums, and ticket price. Here are the figures from the 2007 NFL, 2007 MLB and 2007 NBA seasons:

      Football Baseball Basketball
    Number of Teams 32 30 30
    Number of Unique Games 256 2,430 1,230
    Total Number of Stadium Seats 2,343,763 1,337,862 582,144
    Average Ticket Price $67.11 $22.77 $48.83

    In order to fairly compare the attendance figures I next had to make sure they were all translated into the same language. Essentially, what I was trying to do with my math was answer the question “with the same amount of games, the same amount of teams, the same size of stadiums, and for the same price, which of the three sports would be more popular?” Then, for each variable I decided whether a lower or higher number was more impressive and added that to the end of the row (+ = higher number more impressive, - = lower number more impressive). I decided to translate all of the attendance figures into “football numbers” since it is undoubtedly the most popular American sport and it produced the smallest figures for the next step.

    Formula: (Sport X’s Figure / Football’s Figure)
    Example: (Baseball Teams / Football Teams) = (30/32) = 0.938

      Football Baseball Basketball  
    Teams (Football base) 1 0.938 0.938 -
    Unique Games (Football base) 1 9.492 4.805 -
    Stadium Seats (Football base) 1 0.571 0.248 -
    Ticket Price (Football base) 1 0.339 0.728 +

    It didn’t matter, ultimately, which sport I chose to use for the base. Sure, this would affect the figures calculated above, but the end results would still be the same (I checked to make sure). Now, I multiplied the original attendance figures by these newly formulated coefficients. Thus, here are the attendance figures of Football, Baseball and Basketball if all three had the same amount of teams, games, seats, and prices that Football has in a regular season (obviously, Football’s attendance remains the same):

      Football Baseball Basketball
    Attendance in Similar Settings (Football is base) 17,506,509 5,309,740 13,914,054
    Popularity Strength 100% 30.33% 79.48%

    And, finally, to answer my question, I calculated the Popularity Strength. This was just basically comparing all of the attendance figures to the highest one.

    Now, of course, I realize this conclusion is only based on attendance. Many fans of these sports participate by watching them on tv. I considered getting the nielsen ratings for each, but I found that too cumbersome and just plain difficult to track down. With that said, however, for all you baseball fans out there, I hate to say it but: my girlfriend may be right.

    Total Stadium Seats thanks to Wikipedia
    Ticket prices thanks to Team Marketing Report

    Wednesday, June 18th, 2008 at 13:40
  • Oops!

    Well, I goofed.

    I don’t know how I let it slide past me but I actually managed to completely switch winners in a sim series from my 2007 NBA Sim. In the second round, the All-Time Sacramento Kings played the All-Time San Antonio Spurs. According to my detailed records, the Spurs clearly won the series 4-1 and yet I advanced the Kings to the next round. Whoops!

    I am re-simming that next series (instead of the Golden State Warriors versus the Kings I’m now calculating the Warriors versus the Spurs) and, hopefully, this won’t screw up any further series. However, if it does, I shall dutifully re-sim these as well. I’ll keep you all posted. Also, look for the 2007 NBA Sim results to finally be presented very soon. And it will be very hot!

    UPDATE: I just finished simming this series and the Spurs succumbed to the powerful Warriors, ensuring that all the rest of the series I originally simmed would remain the same. A close one, I know!

    Thursday, May 22nd, 2008 at 22:48
  • Defensively Speaking

    I really am a bit embarrassed about this one, folks. I haven’t come up with any formula that I’m even sort of fond of to encapsulate defensive performance. So, in the end, I half-assed something called DefeNDEX and used that to determine who should be the defensive player of the year. But don’t put too much weight on it. Yes, once again, Marcus Camby of the Denver Nuggets should win based on his stellar performance, but he won’t. Voters don’t like to echo themselves year after year and KG won’t be winning MVP for his historical turnaround in Boston.

    Real-world prediction: Kevin Garnett
    Real-world winner: Kevin Garnett

    Top 10 Players (Order by DefeNDEX)
    # Player Team dRPG BLK STL PF Pace DefeNDEX
    1 Marcus Camby DEN 10.2 3.6 1.1 2.7 99.7 13.83
    2 Dwight Howard ORL 10.8 2.1 0.9 3.3 93.4 13.23
    3 Chris Kaman LAC 9.6 2.8 0.6 3.2 92.1 12.50
    4 Shawn Marion^ PHX/MIA 8.0 1.3 2.0 2.4 95.1 11.24
    5 Tim Duncan SAS 8.3 1.9 0.7 2.4 88.8 11.17
    6 Josh Smith ATL 6.2 2.8 1.5 3.3 91.1 10.19
    7 Kevin Garnett BOS 7.3 1.3 1.4 2.3 90.9 10.11
    8 Yao Ming HOU 7.7 2.0 0.5 3.1 90.4 9.63
    9 Emeka Okafor CHA 7.6 1.7 0.8 2.9 91.8 9.51
    10 Al Jefferson MIN 7.4 1.5 0.9 2.7 91.9 9.34

    And here’s the All-Defensive Team selections based on my lame DefeNDEX values.

    All-Defensive First Team (by DefeNDEX)
    Player Team Position Pace DefeNDEX
    Marcus Camby DEN C 99.7 13.83
    Shawn Marion^ PHX/MIA F 95.1 11.24
    Tim Duncan SAS F 88.8 11.17
    Josh Smith ATL G 91.1 10.19
    Jason Kidd^ NJN/DAL G 91.0 8.51

     

    All-Defensive Second Team (by DefeNDEX)
    Player Team Position Pace DefeNDEX
    Dwight Howard ORL C 93.4 13.23
    Kevin Garnett BOS F 90.9 10.11
    Emeka Okafor CHA F 91.8 9.51
    Andre Iguodala PHI G 90.4 7.15
    Kobe Bryant LAL G 95.6 6.87

    ^ - Player was on multiple teams for season. I averaged the PaNDEX values for each team, with each value weighed by how many games they played for the team. For example, if he played 1 game with a 99.9 Pace team (A) and 4 games with a 90.1 Pace team (B) the formula would be: ((PaNDEXteamA * 1) + (PaNDEXteamB * 4)) / (1+4)
    Pace - estimate of the number of possessions per 48 minutes by a team (thanks to Basketball-reference.com)
    PaNDEX - a formula of my own design (TENDEX / Pace) to remove team play-style bias in stats
    DefeNDEX - a half-assed formula of my own design (((Def.REB + (1.25*STL) + BLK - (FLS*0.5)/ Games Played) / Pace) * 100

    Stats thanks to Basketball-reference.com

    Thursday, April 24th, 2008 at 16:47
  • Most Valuable

    I think I’m officially putting myself into the camp that believes the NBA Most Valuable Player award should go to the best player in the game, regardless of their team’s situation. If a team is really good, they’ll win the championship, but this is an individual player’s award. Actually, with an entire site dedicated to simulating NBA games based solely on historical stats, maybe my decision about this is less finally being made and more finally being admitted to myself.

    Yes, Chris Paul surprised everyone and brought his Hornets to the (near) top of the Western Conference. Yes, Kobe is the deadliest scorer in the game. But no one does it like LeBron. I remember him turning down a dunk contest invitation by saying he didn’t want to define himself as just a dunker, but rather as a complete player. 30 points per game. 7.9 rebounds per game. 7.2 assists per game. Almost 2 steals and over 1 block per game. Nearly 50% of his shots go down and his 3-point shot must be at least respected (32%). Yeah, I’d say he’s pretty complete. Complete and very, very valuable.

    Real-world prediction: Kobe Bryant
    Real-world winner: Kobe Bryant

    Top 10 Players (Order by PaNDEX)
    # Player Team PPG RPG APG SPG BPG FG% FT% 3P% TENDEX Pace PaNDEX
    1 LeBron James CLE 30.0 7.9 7.2 1.8 1.1 .484 .712 .315 32.12 90.2 35.61
    2 Chris Paul NOR 21.1 4.0 11.6 2.7 0.1 .488 .851 .369 30.03 89.9 33.41
    3 Dwight Howard ORL 20.7 14.2 1.3 0.9 2.1 .599 .590 .000 27.09 93.4 29.00
    4 Amare Stoudemire PHX 25.2 9.1 1.5 0.8 2.1 .590 .805 .161 27.46 96.7 28.40
    5 Kobe Bryant LAL 28.3 6.3 5.4 1.8 0.5 .459 .840 .361 26.96 95.6 28.20
    6 Dirk Nowitzki DAL 23.6 8.6 3.5 0.7 0.9 .479 .879 .359 25.03 90.2 27.75
    7 Tim Duncan SAS 19.3 11.3 2.8 0.7 1.9 .497 .730 .000 24.53 88.8 27.62
    8 Kevin Garnett BOS 18.8 9.2 3.4 1.4 1.3 .539 .801 .000 24.89 90.9 27.39
    9 Yao Ming HOU 22.0 10.8 2.3 0.5 2.0 .507 .850 .000 24.40 90.4 26.99
    10 Chris Bosh TOR 22.3 8.7 2.6 0.9 1.0 .494 .844 .400 23.96 90.2 26.56

    Here are the All-NBA Teams (based on PaNDEX). The only rule is they must have competed in at least 50 games (or 60% of the season).

    All-NBA First Team
    Player Team Position TENDEX Pace PaNDEX
    LeBron James CLE F 32.12 90.2 35.61
    Chris Paul NOR G 30.03 89.9 33.41
    Dwight Howard ORL C 27.09 93.4 29.00
    Amare Stoudemire PHX F 27.46 96.7 28.40
    Kobe Bryant LAL G 26.96 95.6 28.20

     

    All-NBA Second Team
    Player Team Position TENDEX Pace PaNDEX
    Dirk Nowitzki DAL F 25.03 90.2 27.75
    Tim Duncan SAS F 24.53 88.8 27.62
    Yao Ming HOU C 24.89 90.9 27.39
    Deron Williams UTA G 23.91 93.2 25.66
    Allen Iverson DEN G 25.25 99.7 25.33

     

    All-NBA Third Team
    Player Team Position TENDEX Pace PaNDEX
    Kevin Garnett BOS F 24.89 90.9 27.39
    Chris Bosh UTA F 23.96 90.2 26.56
    Dwyane Wade MIA G 22.61 90.2 25.07
    Steve Nash PHX G 23.85 96.7 24.66
    Pau Gasol^ MEM/LAL C 23.09 95.4 24.19

    ^ - Player was on multiple teams for season. I averaged the PaNDEX values for each team, with each value weighed by how many games they played for the team. For example, if he played 1 game with a 99.9 Pace team (A) and 4 games with a 90.1 Pace team (B) the formula would be: ((PaNDEXteamA * 1) + (PaNDEXteamB * 4)) / (1+4)
    Pace - estimate of the number of possessions per 48 minutes by a team (thanks to Basketball-reference.com)
    PaNDEX - a formula of my own design (TENDEX / Pace) to remove team play-style bias in stats

    Stats thanks to Basketball-reference.com

    Thursday, April 24th, 2008 at 16:20
  • Movin’ On Up

    Unlike the Sixth Man of the Year award, stats are easily applicable towards the Most Improved Player. It’s a pretty convincing argument to show that one player is more deserving of the award than another by showing a larger increase in his stats. Sure, there are small gray areas like playing time, previous injury, and trades to consider but this is probably the easiest conversation to “win” using straight up math.

    The odds on favorite to win this award is Hedo Turkoglu. He went from being a role-player to a clutch closer this year. But looking at PaNDEX change (explained below), he’s only 14th on the list. Andrew Bynum actually showed the most dramatic change this year, but he only played in 43% of the games this season. I’ve always liked a cut-off of 50 games (or 60% of the year) as this would decrease the impact of outlier games and be a stronger indication of a trend, so I’ve eliminated him from contention.

    According to straight up change in PaNDEX, Chris Kaman is clearly the Most Improved Player by over 1 point (the meaning of which I have yet to define … or understand, even) than second place, Ronnie Brewer. Kaman was the best big man on the Clippers as Elton Brand was out for most of the season. But, this really doesn’t explain his increase in numbers. He started beside Elton Brand in both the 2007 and 2006 seasons as well. One year he was decent (17.12 PaNDEX in 2006) and one year he wasn’t (13.39 in 2007). So it’s not like he was just getting some boards and shots that would normally have fallen to Brand. There was something seriously in the way of Kaman’s improvement last year and it wasn’t Brand. The following is an extract from his Wikipedia article:

    In January of 2008, Kaman revealed that he was misdiagnosed with Attention-deficit hyperactivity disorder.[3] Kaman spent much of his childhood on a farm and as a child he used to tear shingles off of neighbors’ rooftops and misbehave in school. His apparent ADHD affected his play in high school. He took Ritalin to treat the supposed condition, but the drug killed his appetite. Kaman became very skinny as a result. Kaman, diagnosed with attention-deficit hyperactivity disorder at 2½, found out the classification was wrong this summer. Instead, his brain was in overdrive, working too fast. Tim Royer, the neurosurgeon who discovered the misdiagnosis in July, worked on a daily training program with Kaman to slow down his thought process throughout the summer.

    Real-world prediction: Hedo Turkoglu
    Real-world winner: Hedo Turkoglu

    Top 10 Most Improved Players
    # Player Team PPG RPG APG SPG BPG FG% FT% 3P% ‘08 PaNDEX ‘07 PaNDEX Diff.
    - Andrew Bynum* LAL 13.1 10.2 1.7 0.3 2.1 .636 .695 - 22.09 11.50 10.59
    1 Chris Kaman LAC 15.7 12.7 1.9 0.6 2.8 .483 .762 .000 23.87 13.39 10.48
    2 Ronnie Brewer UTA 12.0 2.9 1.8 1.7 0.3 .558 .759 .220 14.51 5.08 9.43
    3 Beno Udrih SAC 12.8 3.3 4.3 0.9 0.2 .464 .850 .387 13.64 4.33 9.31
    4 LaMarcus Aldridge POR 17.8 7.6 1.6 0.7 1.2 .483 .762 .143 19.91 10.91 9.00
    5 Chris Paul NOR 21.1 4.0 11.6 2.7 0.1 .488 .851 .369 33.41 25.03 8.38
    6 Rudy Gay MEM 20.1 6.2 2.0 1.4 1.0 .462 .785 .346 18.53 10.18 8.35
    7 Jose Calderon TOR 11.2 2.9 8.3 1.1 0.1 .519 .908 .429 20.95 12.75 8.20
    8 Anthony Carter DEN 7.8 2.9 5.5 1.5 0.4 .458 .753 .349 12.66 4.75 7.91
    9 Josh Boone NJN 8.2 7.3 0.8 0.5 0.9 .548 .456 - 13.13 5.26 7.87
    10 Rashad McCants MIN 14.9 2.7 2.2 0.9 0.2 .453 .748 .407 10.96 3.23 7.73

    So where do all the other, popular Most Improved Player nominees stack up?

    Popular Most Improved Player Nominees
    # Player Team PPG RPG APG SPG BPG FG% FT% 3P% ‘08 PaNDEX ‘07 PaNDEX Diff.
    14 Hedo Turkoglu ORL 19.5 5.7 5.0 0.9 0.3 .456 .829 .400 20.57 13.20 7.37
    22 Monta Ellis GSW 20.2 5.0 3.9 1.5 0.3 .531 .767 .231 20.97 15.02 5.95
    27 Rajon Rondo BOS 10.6 4.2 5.1 1.7 0.2 .492 .611 .263 16.12 10.81 5.31
    33 Mike Dunleavy, Jr. IND 19.1 5.2 3.5 1.0 0.4 .476 .834 .424 19.07 14.20^ 4.87
    64 Manu Ginobili SAS 19.5 4.8 4.5 1.5 0.4 .461 .860 .401 22.56 19.32 3.24

    * - Didn’t play enough games to qualify (I like at least 50 games played)
    Pace - estimate of the number of possessions per 48 minutes by a team (thanks to Basketball-reference.com)
    PaNDEX - a formula of my own design (TENDEX / Pace) to remove team play-style bias in stats

    Stats thanks to Basketball-reference.com

    Monday, April 21st, 2008 at 14:04
  • Freshmen with Honors

    Greg Oden and Kevin Durant went 1-2 in last year’s draft and they were also expected to go 1-2 (in one order or another) for this year’s Rookie of the Year honor. As we all know, however, Oden was out of the contest even before the season started and Durant was only decent, if inconsistent. Lucky #3 pick, Al Horford, took advantage of the opportunity, playing his way into Atlanta’s starting lineup and narrowly ousting Durant for my ROY vote (if I had one).

    Real-world prediction: Kevin Durant
    Real-world winner: Kevin Durant

    ROY Nominees
    Player Team PPG RPG APG SPG BPG FG% FT% 3P% TENDEX Pace PaNDEX
    Al Horford ATL 10.1 9.7 1.5 0.7 0.9 .499 .731 .000 15.37 91.1 16.86
    Kevin Durant SEA 20.3 4.4 2.4 1.0 0.9 .430 .873 .288 15.31 96.3 15.90
    Jamario Moon TOR 8.5 6.2 1.2 1.0 1.4 .485 .741 .328 13.06 90.2 14.48
    Luis Scola HOU 10.3 6.4 1.3 0.7 0.2 .515 .668 .000 12.02 90.4 13.30
    Al Thornton LAC 12.7 4.5 1.2 0.6 0.5 .430 .743 .331 10.07 92.1 10.93
    Joakim Noah CHI 6.6 5.6 1.1 0.9 0.9 .482 .691 .000 10.00 93.0 10.76
    Thaddeus Young PHI 8.2 4.2 0.8 1.0 0.1 .539 .738 .316 9.61 90.4 10.63
    Juan Carlos Navarro MEM 10.9 2.6 2.2 0.6 0.0 .402 .849 .361 8.58 95.3 9.01

    All-Rookie First Team
    Player Pos Team PPG RPG APG SPG BPG FG% FT% 3P% TENDEX Pace PaNDEX
    Al Horford C ATL 10.1 9.7 1.5 0.7 0.9 .499 .731 .000 15.37 91.1 16.86
    Ramon Sessions* G MIL 8.1 3.4 7.5 1.0 0.2 .436 .780 .429 14.75 91.3 16.16
    Kevin Durant G SEA 20.3 4.4 2.4 1.0 0.9 .430 .873 .288 15.31 96.3 15.90
    Jamario Moon F TOR 8.5 6.2 1.2 1.0 1.4 .485 .741 .328 13.06 90.2 14.48
    Luis Scola F HOU 10.3 6.4 1.3 0.7 0.2 .515 .668 .000 12.02 90.4 13.30
    Mike Conley G MEM 9.4 2.6 4.2 0.8 0.0 .428 .732 .330 10.34 95.3 10.85

    All-Rookie Second Team
    Player Pos Team PPG RPG APG SPG BPG FG% FT% 3P% TENDEX Pace PaNDEX
    Carl Landry F HOU 8.1 4.9 0.5 0.4 0.2 .616 .661 .000 9.99 90.4 11.05
    Al Thornton F LAC 12.7 4.5 1.2 0.6 0.5 .430 .743 .331 10.07 92.1 10.93
    Joakim Noah C CHI 6.6 5.6 1.1 0.9 0.9 .482 .691 .000 10.00 93.0 10.76
    Juan Carlos Navarro G MEM 10.9 2.6 2.2 0.6 0.0 .402 .849 .361 8.58 95.3 9.01
    Rodney Stuckey G DET 7.6 2.3 2.8 0.9 0.1 .401 .814 .188 7.63 87.3 8.74

    * - Didn’t play enough games to qualify (I like at least 50 games played)
    TENDEX - formula for TENDEX and explanation why I use it
    Pace - estimate of the number of possessions per 48 minutes by a team (thanks to Basketball-reference.com)
    PaNDEX - a formula of my own design (TENDEX / Pace) to remove team play-style bias in stats

    Stats thanks to Basketball-reference.com

    Friday, April 18th, 2008 at 12:32
  • Best of the Bench

    I always found the Sixth Man of the Year award to be a strange one. It’s usually easy to tell who the top 2 favorites are, but it’s harder to tell who even qualifies as being a sixth man on any given team. Sure, many teams announce at the beginning of the year who the first person coming off their bench is going to be - but that is rarely the same person at the end of the year. With so many trades, injuries, and jockeying for playing time, it seems more of an art than a science.

    Still, I’ve applied my science to figuring out who 1) qualifies as being a sixth man and 2) is the best sixth man in the league. To figure out each team’s nominees, I removed the first 5 people on each team based on Games Started. From those remaining on the roster I picked out the player with the highest amount of minutes played. This actually came out pretty close to what I “felt” was right for each team. Oh, and by the way, as if I needed to go into any sort of detail at all, Manu is the clear favorite and should be receiving a unanimous decision.

    Real-world prediction: Manu Ginobili
    Real-world winner: Manu Ginobili

    Sixth Man for Each Team (Ordered by PaNDEX)
    Player Team PPG RPG APG SPG BPG FG% FT% 3P% TENDEX Pace PaNDEX
    Manu Ginobili SAS 19.5 4.8 4.5 1.5 0.4 .461 .860 .401 20.04 88.8 22.56
    Nick Collison SEA 9.8 9.4 1.4 0.6 0.8 .502 .737 .000 14.66 96.3 15.22
    Ben Gordon CHI 18.6 3.1 3.0 0.8 0.1 .434 .908 .410 14.02 93.0 15.08
    Leandro Barbosa PHX 15.6 2.8 2.6 0.9 0.2 .462 .822 .389 13.23 96.7 13.69
    John Salmons SAC 12.5 4.3 2.6 1.1 0.4 .477 .823 .325 12.74 94.7 13.46
    Luis Scola HOU 10.3 6.4 1.3 0.7 0.2 .515 .668 .000 12.02 90.4 13.30
    Cuttino Mobley LAC 12.8 3.6 2.6 1.0 0.4 .433 .819 .349 11.72 92.1 12.72
    Jarret Jack POR 9.9 2.9 3.8 0.7 0.0 .430 .867 .342 10.48 87.9 11.92
    Charlie Villanueva MIL 11.7 6.1 1.0 0.4 0.5 .435 .783 .297 10.86 91.3 11.90
    Nate Robinson NYK 12.7 3.1 2.9 0.8 0.0 .423 .786 .332 10.86 91.6 11.86
    Jason Maxiell DET 7.9 5.3 0.6 0.3 1.1 .538 .633 .000 10.08 87.3 11.55
    Louis Williams PHI 11.5 2.1 3.2 1.0 0.2 .424 .783 .359 10.40 90.4 11.50
    Anthony Johnson ATL 6.7 2.3 4.8 1.0 0.2 .431 .813 .420 10.32 91.1 11.33
    Paul Millsap UTA 8.1 5.6 1.0 0.9 0.9 .504 .677 .000 10.44 93.2 11.20
    Rashad McCants MIN 14.9 2.7 2.2 0.9 0.2 .453 .748 .407 10.07 91.9 10.96
    Linas Kleiza DEN 11.1 4.2 1.2 0.6 0.2 .472 .770 .339 10.53 99.7 10.56
    Daniel Gibson CLE 10.4 2.3 2.5 0.8 0.2 .432 .810 .440 9.35 90.2 10.37
    James Posey BOS 7.4 4.4 1.5 1.0 0.3 .418 .809 .380 9.38 90.9 10.32
    Luke Walton LAL 7.2 3.9 2.9 0.8 0.2 .450 .706 .333 9.74 95.6 10.19
    Jerry Stackhouse DAL 10.7 2.3 2.5 0.5 0.2 .405 .892 .326 8.75 90.2 9.71
    Juan Carlos Navarro MEM 10.9 2.6 2.2 0.6 0.0 .402 .849 .361 8.58 95.3 9.01
    Keith Bogans ORL 8.7 3.2 1.3 0.7 0.1 .411 .736 .363 8.32 93.4 8.90
    Kelenna Azubuike GSW 8.5 4.0 0.9 0.6 0.4 .445 .717 .364 8.67 98.8 8.78
    Bostjan Nachbar NJN 9.8 3.5 1.2 0.6 0.3 .402 .786 .359 8.02 91.5 8.77
    Roger Mason WAS 9.1 1.6 1.7 0.5 0.2 .443 .873 .398 7.43 89.5 8.31
    Matt Carroll CHA 9.0 2.8 0.9 0.6 0.2 .428 .804 .436 7.47 91.8 8.13
    Jannero Pargo NOR 8.1 1.6 2.4 0.6 0.1 .390 .877 .349 6.26 89.9 6.96
    Kareem Rush IND 8.3 2.4 1.3 0.6 0.3 .401 .714 .389 6.48 97.7 6.63
    Daequan Cook MIA 8.9 3.0 1.3 0.4 0.2 .386 .825 .336 5.95 90.2 6.59
    Jason Kapono TOR 7.2 1.5 0.8 0.4 0.0 .488 .860 .483 5.31 90.2 5.88

    TENDEX - formula for TENDEX and explanation why I use it
    Pace - estimate of the number of possessions per 48 minutes by a team (thanks to Basketball-reference.com)
    PaNDEX - a formula of my own design (TENDEX / Pace) to remove team play-style bias in stats

    Stats thanks to Basketball-reference.com

    Thursday, April 17th, 2008 at 15:37
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