“Sometimes when I talk to journalism students and they will ask how I get people to open up to me, and the answer is that I’m genuinely curious about what those people are saying. I honestly care about the stories they are telling. There’s a force that talks to the deepest part of us. There is something that happens during therapy when the therapy session is going well: If someone is talking to a therapist about something unresolved – something they don’t understand- and they suddenly start talking about it, it just flows out in this highly narrative,highly detailed form. Most people are not articulate about everything in their life, but they are articulate about the things they’re still figuring out” – Ira Glass, host of “This American Life” being interviewed by Chuck Klosterman for his book “Eating the dinosaur”
I was on a plane to South Beach this morning when I read this passage and it struck a deep chord within me. Why do I write this blog? I write because I have something I don’t understand that I’m trying to work out. There is an intrinsic need for me to externalize these questions that are in my head. I want to lay my theories out there right or wrong and seek knowledgeable criticism that will guide on the path to truth.
When I first started reading Wages of Wins, It, like all good books, lead to a lot of questions within me. I love sports (and basketball in particular) and I love statistics (enough to make a career of it) so any exercise that tries to marry the two and create quantitative measurements (specially with a high degree of correlation is right up my alley). Wages of Wins did an excellent job at answering the question of value in the NBA but as with everything that answer a question new ones come. This is the genesis of this blog and this is why we are talking about replacement value.
Refining the Replacement Value Algorithm or the Problem with tweeners
It is a mistake to suppose that people succeed through success; they often succeed through failures. ~Author Unknown
In the previous post on finding the Replacement Level for NBA Players I used the following algorithm to identify replacement level players:
Step #1 : Sort player by years and minutes played by position.
Step#2: Calculate the productivity of the players that account for just over 20% of the minutes played at each position (or just about 10 minutes per game)
Step #3: Set that as the replacement level for each position.
There is a problem with that algorithm. Let me illustrate it. Here’s the list of replacement level centers for 2010:
Dwayne Jones,Paul Davis,Ryan Anderson,Kevin Garnett,Alexis Ajinca,Patrick O’Bryant,Byron Mullens,Chris Richard,Eddy Curry,Francisco Elson,Primoz Brezec,Jason Collins,Amare Stoudemire,Pops Mensah-Bonsu,Elton Brand,Tony Battie,Steven Hunter,Ian Mahinmi,Kosta Koufos,Zach Randolph,Earl Barron,Hamed Haddadi,Jeff Foster,Jarron Collins,DeSagana Diop,Ersan Ilyasova,Serge Ibaka,Aaron Gray,Dan Gadzuric,Etan Thomas,Hilton Armstrong,Didier Ilunga-Mbenga,Jamaal Magloire,Earl Clark,Mikki Moore,Kyrylo Fesenko,Rasho Nesterovic,Johan Petro,Chris Wilcox,Oleksiy Pecherov,Chris Hunter,Greg Oden,Al Harrington,Fabricio Oberto,Jon Brockman,Jason Smith,Kwame Brown
You’ll note that there are a couple of names that do not belong. What’s happening is that tweeners (multiple position players) who play a small percentage of their minutes at the Center are sneaking in and some are driving up the value of the replacement level pool.
But that’s ok, to paraphrase Herm Edwards, we can build on this. I’m going to work out take 2 of the algorithm and show my work:
Step #1 : Sort player by years and minutes played total for that season. This is the ultimate assertion of the perceived value of a player to a team. For example here’s the top 20 in minutes played for 2010:
| Y2010PJason Kidd Born 1974 |
| Y2010PJoe Johnson Born 1982 |
| Y2010PStephen Curry Born 1989 |
| Y2010PBoris Diaw Born 1983 |
| Y2010PAaron Brooks Born 1985 |
| Y2010PLaMarcus Aldridge Born 1986 |
| Y2010PDavid West Born 1981 |
| Y2010PRajon Rondo Born 1987 |
| Y2010PLeBron James Born 1985 |
| Y2010PDavid Lee Born 1984 |
| Y2010PBrook Lopez Born 1989 |
| Y2010PDirk Nowitzki Born 1979 |
| Y2010PJeff Green Born 1987 |
| Y2010PZach Randolph Born 1982 |
| Y2010PO.J. Mayo Born 1988 |
| Y2010PGerald Wallace Born 1983 |
| Y2010PStephen Jackson Born 1979 |
| Y2010PRudy Gay Born 1987 |
| Y2010PAndre Iguodala Born 1984 |
| Y2010PKevin Durant Born 1989 |
Step#2: For each position, find the players with the least perceived value (i.e total minutes played and add them up until you reach just over 20% of the minutes played at each position (or just about 10 minutes per game). These are our Replacement Level players (By the way this is a hateful build which I have not found how to automate as of yet but hey I got to work out those issues 
)
For 2010 at center the list of Replacement players now looks like this:
| Player | Sum of Minutes C | MP Rank | Percent of Minutes |
| Dwayne Jones Born 1984 | 7 | 2 | 0.0% |
| Paul Davis Born 1985 | 8 | 4 | 0.0% |
| Alexis Ajinca Born 1989 | 30 | 9 | 0.0% |
| Patrick O’Bryant Born 1987 | 51 | 19 | 0.0% |
| Byron Mullens Born 1990 | 54 | 21 | 0.0% |
| Eddy Curry Born 1983 | 62 | 24 | 0.0% |
| Francisco Elson Born 1977 | 66 | 25 | 0.0% |
| Primoz Brezec Born 1980 | 95 | 31 | 0.0% |
| Jason Collins Born 1979 | 115 | 39 | 0.0% |
| Pops Mensah-Bonsu Born 1984 | 120 | 41 | 0.0% |
| Tony Battie Born 1977 | 134 | 48 | 0.0% |
| Steven Hunter Born 1982 | 158 | 51 | 0.0% |
| Ian Mahinmi Born 1987 | 165 | 52 | 0.0% |
| Kosta Koufos Born 1990 | 172 | 53 | 1.0% |
| Chris Richard Born 1985 | 57 | 58 | 1.0% |
| Earl Barron Born 1982 | 232 | 61 | 1.0% |
| Hamed Haddadi Born 1986 | 240 | 65 | 1.0% |
| Jeff Foster Born 1977 | 255 | 68 | 1.0% |
| Jarron Collins Born 1979 | 260 | 69 | 1.0% |
| DeSagana Diop Born 1982 | 262 | 70 | 2.0% |
| Aaron Gray Born 1985 | 311 | 78 | 2.0% |
| Dan Gadzuric Born 1979 | 314 | 81 | 2.0% |
| Etan Thomas Born 1979 | 321 | 83 | 2.0% |
| Hilton Armstrong Born 1985 | 335 | 87 | 3.0% |
| Didier Ilunga-Mbenga Born 1981 | 355 | 89 | 3.0% |
| Jamaal Magloire Born 1979 | 359 | 90 | 3.0% |
| Earl Clark Born 1988 | 383 | 92 | 4.0% |
| Mikki Moore Born 1976 | 406 | 97 | 4.0% |
| Kyrylo Fesenko Born 1987 | 408 | 99 | 4.0% |
| Rasho Nesterovic Born 1977 | 413 | 101 | 5.0% |
| Johan Petro Born 1986 | 435 | 102 | 5.0% |
| Chris Wilcox Born 1983 | 441 | 103 | 5.0% |
| Oleksiy Pecherov Born 1986 | 447 | 104 | 6.0% |
| Greg Oden Born 1988 | 502 | 111 | 6.0% |
| Fabricio Oberto Born 1976 | 650 | 127 | 7.0% |
| Jon Brockman Born 1988 | 654 | 128 | 7.0% |
| Jason Smith Born 1987 | 658 | 129 | 8.0% |
| Kwame Brown Born 1983 | 660 | 131 | 8.0% |
| Solomon Jones Born 1985 | 675 | 133 | 9.0% |
| Joel Przybilla Born 1980 | 681 | 134 | 9.0% |
| Darko Milicic Born 1986 | 685 | 136 | 10.0% |
| Andris Biedrins Born 1987 | 763 | 149 | 11.0% |
| Chris Hunter Born 1985 | 483 | 152 | 11.0% |
| Theo Ratliff Born 1974 | 807 | 158 | 12.0% |
| Ronny Turiaf Born 1983 | 872 | 167 | 12.0% |
| Hasheem Thabeet Born 1988 | 883 | 171 | 13.0% |
| David Andersen Born 1981 | 891 | 174 | 14.0% |
| Ryan Anderson Born 1989 | 15 | 175 | 14.0% |
| JaVale McGee Born 1988 | 968 | 183 | 15.0% |
| Nazr Mohammed Born 1978 | 984 | 188 | 16.0% |
| Robin Lopez Born 1989 | 986 | 189 | 16.0% |
| Marreese Speights Born 1988 | 1016 | 190 | 17.0% |
| Josh Boone Born 1985 | 796 | 192 | 18.0% |
| Kurt Thomas Born 1973 | 1049 | 194 | 19.0% |
| Marcin Gortat Born 1985 | 1088 | 199 | 20.0% |
Here’s PF:
| Player | Sum of Minutes PF | MP Rank | Percent of Minutes |
| Othello Hunter Born 1987 | 33 | 11 | 0.0% |
| Brian Cook Born 1981 | 44 | 14 | 0.0% |
| Marcus Haislip Born 1981 | 44 | 16 | 0.0% |
| Shavlik Randolph Born 1984 | 53 | 20 | 0.0% |
| Sean Marks Born 1976 | 75 | 29 | 0.0% |
| D.J. White Born 1987 | 102 | 35 | 0.0% |
| Joey Dorsey Born 1984 | 106 | 36 | 0.0% |
| Darnell Jackson Born 1986 | 122 | 44 | 0.0% |
| Brian Skinner Born 1977 | 123 | 45 | 0.0% |
| Randolph Morris Born 1986 | 124 | 46 | 0.0% |
| Chris Richard Born 1985 | 167 | 58 | 0.0% |
| Sean Williams Born 1987 | 227 | 59 | 1.0% |
| Leon Powe Born 1984 | 236 | 63 | 1.0% |
| Brian Cardinal Born 1978 | 267 | 72 | 1.0% |
| Tim Thomas Born 1978 | 285 | 74 | 1.0% |
| Jonathan Bender Born 1981 | 292 | 75 | 1.0% |
| Reggie Evans Born 1981 | 311 | 79 | 2.0% |
| Kenny Thomas Born 1978 | 313 | 80 | 2.0% |
| Sean May Born 1985 | 331 | 85 | 2.0% |
| Steve Novak Born 1984 | 362 | 91 | 3.0% |
| Bobby Simmons Born 1981 | 395 | 94 | 3.0% |
| DaJuan Summers Born 1988 | 405 | 95 | 3.0% |
| Jeff Pendergraph Born 1988 | 405 | 96 | 4.0% |
| Nathan Jawai Born 1987 | 412 | 100 | 4.0% |
| Malik Allen Born 1979 | 456 | 105 | 4.0% |
| Darrell Arthur Born 1989 | 457 | 106 | 5.0% |
| Brian Scalabrine Born 1979 | 388 | 108 | 5.0% |
| Ricky Davis Born 1980 | 461 | 110 | 5.0% |
| Tyler Hansbrough Born 1986 | 511 | 113 | 6.0% |
| Josh McRoberts Born 1988 | 524 | 115 | 6.0% |
| Derrick Brown Born 1988 | 535 | 116 | 7.0% |
| Josh Powell Born 1983 | 581 | 120 | 7.0% |
| Joe Smith Born 1976 | 592 | 122 | 8.0% |
| Shelden Williams Born 1984 | 597 | 123 | 8.0% |
| Jordan Hill Born 1988 | 624 | 124 | 9.0% |
| Brandon Bass Born 1986 | 648 | 126 | 9.0% |
| Eduardo Najera Born 1977 | 685 | 135 | 10.0% |
| Dante Cunningham Born 1988 | 707 | 139 | 10.0% |
| Travis Outlaw Born 1985 | 231 | 143 | 11.0% |
| Jawad Williams Born 1984 | 742 | 144 | 11.0% |
| Anthony Randolph Born 1990 | 749 | 145 | 12.0% |
| James Johnson Born 1988 | 399 | 146 | 12.0% |
| Devean George Born 1978 | 58 | 148 | 12.0% |
| Chris Hunter Born 1985 | 300 | 152 | 13.0% |
| Julian Wright Born 1988 | 629 | 166 | 13.0% |
| Vladimir Radmanovic Born 1981 | 891 | 173 | 14.0% |
| Ryan Anderson Born 1989 | 895 | 175 | 15.0% |
| Glen Davis Born 1986 | 933 | 179 | 15.0% |
| James Singleton Born 1982 | 766 | 187 | 16.0% |
| Josh Boone Born 1985 | 249 | 192 | 16.0% |
| Matt Bonner Born 1981 | 1161 | 207 | 17.0% |
| Louis Amundson Born 1983 | 1168 | 209 | 18.0% |
| Tyrus Thomas Born 1987 | 1220 | 213 | 19.0% |
| Kris Humphries Born 1986 | 1221 | 214 | 20.0% |
Here’s SF:
| Player | Sum of MinutesSF | MP Rank | Percent of Minutes |
| Trey Gilder Born 1985 | 5 | 1 | 0.0% |
| Ryan Bowen Born 1976 | 8 | 3 | 0.0% |
| Joe Alexander Born 1987 | 29 | 8 | 0.0% |
| Taylor Griffin Born 1987 | 32 | 10 | 0.0% |
| Yakhouba Diawara Born 1983 | 44 | 15 | 0.0% |
| Desmond Mason Born 1978 | 66 | 26 | 0.0% |
| Renaldo Balkman Born 1985 | 91 | 30 | 0.0% |
| Mike Harris Born 1984 | 96 | 32 | 0.0% |
| Alando Tucker Born 1985 | 96 | 33 | 0.0% |
| Marcus Landry Born 1986 | 111 | 37 | 0.0% |
| Danny Green Born 1988 | 115 | 40 | 0.0% |
| Matt Carroll Born 1981 | 121 | 42 | 0.0% |
| Kelenna Azubuike Born 1984 | 231 | 60 | 0.0% |
| Adam Morrison Born 1985 | 241 | 66 | 1.0% |
| Luke Walton Born 1981 | 272 | 73 | 1.0% |
| Dominic McGuire Born 1986 | 240 | 77 | 1.0% |
| Cartier Martin Born 1985 | 390 | 93 | 1.0% |
| Brian Scalabrine Born 1979 | 84 | 108 | 1.0% |
| Ricky Davis Born 1980 | 38 | 110 | 1.0% |
| James Jones Born 1981 | 503 | 112 | 2.0% |
| Quinton Ross Born 1982 | 301 | 117 | 2.0% |
| Tracy McGrady Born 1980 | 673 | 132 | 3.0% |
| Michael Finley Born 1974 | 709 | 140 | 3.0% |
| Travis Outlaw Born 1985 | 499 | 143 | 4.0% |
| James Johnson Born 1988 | 358 | 146 | 4.0% |
| Joey Graham Born 1983 | 759 | 147 | 5.0% |
| Devean George Born 1978 | 703 | 148 | 5.0% |
| Bill Walker Born 1988 | 768 | 150 | 6.0% |
| Reggie Williams Born 1987 | 782 | 151 | 7.0% |
| DeMarre Carroll Born 1987 | 795 | 154 | 7.0% |
| Rodney Carney Born 1985 | 857 | 162 | 8.0% |
| Julian Wright Born 1988 | 242 | 166 | 8.0% |
| Austin Daye Born 1989 | 915 | 176 | 9.0% |
| Josh Howard Born 1981 | 918 | 177 | 10.0% |
| Nicolas Batum Born 1989 | 918 | 178 | 11.0% |
| Marquis Daniels Born 1981 | 937 | 180 | 11.0% |
| Ime Udoka Born 1978 | 103 | 181 | 11.0% |
| Kyle Korver Born 1982 | 952 | 182 | 12.0% |
| Morris Peterson Born 1978 | 973 | 185 | 13.0% |
| Jason Kapono Born 1982 | 976 | 186 | 14.0% |
| James Singleton Born 1982 | 211 | 187 | 14.0% |
| Jamario Moon Born 1981 | 448 | 195 | 14.0% |
| Trenton Hassell Born 1980 | 782 | 201 | 15.0% |
| Maurice Evans Born 1979 | 1317 | 222 | 16.0% |
| Sam Young Born 1986 | 710 | 223 | 17.0% |
| Antoine Wright Born 1985 | 1392 | 229 | 18.0% |
| Andres Nocioni Born 1980 | 246 | 239 | 18.0% |
| Mike Dunleavy Born 1981 | 1486 | 240 | 19.0% |
Here’s SG:
| Player | Sum of Minutes SG | MP Rank | Percent of Minutes |
| Antonio Anderson Born 1986 | 15 | 5 | 0.0% |
| Travis Diener Born 1983 | 26 | 18 | 0.0% |
| Kareem Rush Born 1981 | 58 | 22 | 0.0% |
| Othyus Jeffers Born 1986 | 72 | 27 | 0.0% |
| Cedric Jackson Born 1987 | 74 | 28 | 0.0% |
| Roko Ukic Born 1985 | 97 | 34 | 0.0% |
| Coby Karl Born 1984 | 113 | 38 | 0.0% |
| Mario West Born 1985 | 142 | 49 | 0.0% |
| Kyle Weaver Born 1987 | 144 | 50 | 0.0% |
| Raja Bell Born 1977 | 180 | 54 | 0.0% |
| Alonzo Gee Born 1988 | 182 | 55 | 0.0% |
| J.R. Giddens Born 1986 | 239 | 64 | 1.0% |
| Kevin Ollie Born 1973 | 211 | 71 | 1.0% |
| Jermaine Taylor Born 1987 | 303 | 76 | 1.0% |
| Dominic McGuire Born 1986 | 67 | 77 | 1.0% |
| Malik Hairston Born 1988 | 317 | 82 | 1.0% |
| Garrett Temple Born 1987 | 334 | 86 | 2.0% |
| Gerald Henderson Born 1988 | 355 | 88 | 2.0% |
| Anthony Johnson Born 1975 | 24 | 98 | 2.0% |
| Mardy Collins Born 1985 | 470 | 107 | 2.0% |
| Michael Redd Born 1980 | 492 | 109 | 3.0% |
| Bobby Brown Born 1985 | 200 | 114 | 3.0% |
| Quinton Ross Born 1982 | 261 | 117 | 3.0% |
| Sasha Vujacic Born 1985 | 575 | 118 | 4.0% |
| Francisco Garcia Born 1982 | 575 | 119 | 4.0% |
| Daequan Cook Born 1988 | 691 | 137 | 5.0% |
| Rodrigue Beaubois Born 1989 | 700 | 138 | 5.0% |
| Jodie Meeks Born 1988 | 719 | 142 | 6.0% |
| Leandro Barbosa Born 1983 | 786 | 153 | 7.0% |
| Stephen Graham Born 1983 | 804 | 156 | 7.0% |
| Ronnie Price Born 1984 | 237 | 157 | 7.0% |
| Luther Head Born 1983 | 813 | 159 | 8.0% |
| Jannero Pargo Born 1980 | 828 | 160 | 9.0% |
| Jerry Stackhouse Born 1975 | 855 | 161 | 10.0% |
| Anthony Carter Born 1976 | 710 | 163 | 10.0% |
| Allen Iverson Born 1976 | 820 | 165 | 11.0% |
| Marcus Williams Born 1986 | 110 | 168 | 11.0% |
| Sasha Pavlovic Born 1984 | 877 | 169 | 12.0% |
| DeShawn Stevenson Born 1982 | 883 | 170 | 12.0% |
| Tony Allen Born 1982 | 889 | 172 | 13.0% |
| Ime Udoka Born 1978 | 841 | 181 | 14.0% |
| Keyon Dooling Born 1981 | 225 | 184 | 14.0% |
| Jarvis Hayes Born 1982 | 1032 | 191 | 15.0% |
| Jamario Moon Born 1981 | 604 | 195 | 15.0% |
| Devin Brown Born 1979 | 1060 | 196 | 16.0% |
| Toney Douglas Born 1987 | 818 | 198 | 17.0% |
| Trenton Hassell Born 1980 | 324 | 201 | 17.0% |
| Larry Hughes Born 1979 | 1115 | 203 | 18.0% |
| Marco Belinelli Born 1987 | 1121 | 205 | 19.0% |
| Eddie House Born 1979 | 327 | 212 | 20.0% |
| Sam Young Born 1986 | 611 | 223 | 20.0% |
And here’s PG:
| Player | Sum of Minutes PG | MP Rank | Percent of Minutes |
| Oliver Lafayette Born 1985 | 22 | 6 | 0.0% |
| Jason Hart Born 1979 | 22 | 7 | 0.0% |
| Will Conroy Born 1983 | 36 | 12 | 0.0% |
| Patrick Mills Born 1989 | 38 | 13 | 0.0% |
| Mike James Born 1976 | 46 | 17 | 0.0% |
| Travis Diener Born 1983 | 25 | 18 | 0.0% |
| Mike Wilks Born 1980 | 59 | 23 | 0.0% |
| Lindsey Hunter Born 1971 | 122 | 43 | 0.0% |
| Lester Hudson Born 1985 | 131 | 47 | 0.0% |
| Sundiata Gaines Born 1987 | 217 | 56 | 0.0% |
| Chris Quinn Born 1984 | 223 | 57 | 0.0% |
| Acie Law Born 1985 | 234 | 62 | 0.0% |
| Marcus Banks Born 1982 | 244 | 67 | 1.0% |
| Kevin Ollie Born 1973 | 52 | 71 | 1.0% |
| Royal Ivey Born 1982 | 326 | 84 | 1.0% |
| Anthony Johnson Born 1975 | 382 | 98 | 1.0% |
| Bobby Brown Born 1985 | 319 | 114 | 2.0% |
| Jamaal Tinsley Born 1979 | 589 | 121 | 2.0% |
| Chucky Atkins Born 1975 | 644 | 125 | 3.0% |
| Sebastian Telfair Born 1986 | 659 | 130 | 3.0% |
| Jeff Teague Born 1989 | 719 | 141 | 4.0% |
| Shaun Livingston Born 1986 | 796 | 155 | 4.0% |
| Ronnie Price Born 1984 | 569 | 157 | 5.0% |
| Anthony Carter Born 1976 | 149 | 163 | 5.0% |
| A.J. Price Born 1987 | 865 | 164 | 6.0% |
| Allen Iverson Born 1976 | 45 | 165 | 6.0% |
| Marcus Williams Born 1986 | 762 | 168 | 6.0% |
| Keyon Dooling Born 1981 | 746 | 184 | 7.0% |
| Sergio Rodriguez Born 1987 | 1048 | 193 | 8.0% |
| Daniel Gibson Born 1987 | 1068 | 197 | 9.0% |
| Toney Douglas Born 1987 | 269 | 198 | 9.0% |
| Nate Robinson Born 1985 | 1115 | 202 | 10.0% |
| Earl Boykins Born 1977 | 1117 | 204 | 11.0% |
| Gilbert Arenas Born 1982 | 1169 | 210 | 12.0% |
| T.J. Ford Born 1984 | 1189 | 211 | 13.0% |
| Eddie House Born 1979 | 890 | 212 | 14.0% |
| Eric Maynor Born 1988 | 1269 | 218 | 15.0% |
| Jerryd Bayless Born 1989 | 1304 | 220 | 16.0% |
| Ty Lawson Born 1988 | 1316 | 221 | 17.0% |
| Rafer Alston Born 1977 | 1421 | 231 | 18.0% |
| Goran Dragic Born 1987 | 1285 | 235 | 19.0% |
| D.J. Augustin Born 1988 | 1472 | 237 | 20.0% |
You’ll note that near 20% the names start to get a little iffy. But the majority of these players could be had so for now that’s we’re the line stays.
Step#3: Calculate the productivity of the players that account for just over 20% of the minutes played at each position (or just about 10 minutes per game)
Step #4: Set that as the replacement level for each position.
If we now modeled the projected wins for a team composed entirely of our refined replacement level for both the 10% and 20 % scenarios (See graph below):
So if we use 20% of the minutes played at each position, a win total for a team composed solely of replacement level players now fluctuates between o and 15 wins. At 10% , the team looks like guys who really shouldn’t be in the NBA. Given that no team in the NBA has won less than 11 games, the results at 20% seem now even more in line with reality. So we will continue to calculate replacement level for NBA players based on the bottom tier of players based on overall minutes played and our revised algorithm per position up to the point where 20% of all player minutes at the position for the season are accounted for.
Replacement Level by position take #2
So now let’s see how everything else changes based on our re-build. The table & graph below illustrates the results for our replacement level by position:
The chart is below:
I improve the second chart significantly. You’ll note that the conclusion from the previous article hold. Tall People (Centers) and Point Guards have more value over replacement than anyone else. As before the next step is to look at win difference by position of an average player vs. a replacement level player. This is Basketball Wins over replacement by position (well call this WOR).
The gidyness still holds from before. Average Center and Point Guards have over time been much more valuable to teams than any of the other positions. Over the last 5 years the difference between an average center and a replacement level one is 4 more wins than the same at shooting guard (and 2 at Point Guard). The short supply of tall people is really not a surprise however the short supply of ball handlers is.
The next steps are still (barring any other rebuilds to the baseline 
) to start talking about WORP for individual players but you’ll have to wait to a future post for that.
Austin
08/16/2010
Good stuff. I’ve always thought that using .1 WP48 as the average, while accurate, is also misleading: it’s not nearly the median of players’ WP48s. Comparing players to replacement level (as in Kevin Pelton’s WARP) is often much more useful, since it shows you what you would lose if you had to replace that player with a D-leaguer or undrafted rookie or minimum salary vet.
I also couldn’t help but notice that Joel Przybilla and Greg Oden, due to injuries, snuck in the replacement level for center list. You definitely don’t need to fix it and it doesn’t affect the overall results, but in 2010 replacement level centers played better than almost every previous year, and I suspect the inclusion of Oden and Przybilla as the reason why.
Also, I was thinking, it might be a lot of work but it could be useful to examine WP48 as a metric overall. The APBR metric community has raised a number of valid criticisms of the metric and it would be good to have someone willing to delve deeper into those (since Prof. Berri is mainly working on other things). Then again, you might feel it’s not your place, and that’s perfectly fine, just an idea as a source of perspective.
arturogalletti
08/16/2010
Austin,
Thanks. The purpose of this is to look at possible valuation differences based on scarcity of the resource. The VORP type model is a model that’s been used previously in this kind of exercise and one I though it was appropriate to look into to reflect scarcity of resources.
I have examined WP48 as a metric previously and so has Prof Berri multiple times . A lot of the work here is meant to grow, improve and refine the model.
Muad'Dib
08/16/2010
Arturo,
What you’re doing here is terrific. I love stats-based approaches to basketball. That’s what attracted me to the WP model in the first place.
I’m a friend of Austin’s, and we’ve both found this study to be very interesting, especially in its implications for WP: http://www.countthebasket.com/blog/2008/03/06/diminishing-returns-for-scoring-usage-vs-efficiency/
You may have already seen this study before, but I’d be interested in hearing your take on it. Austin and I feel that it is well researched and shows an opportunity to “grow, improve and refine the model” as you stated. That said, I am open to the idea that I may not be thinking critically enough or that there may be a flaw in the data or interpretations of the study. I would like to hear what someone of your statistical acumen thinks about it.
As Austin said, the APBRmetrics community has raised numerous criticisms of the WP model. While it seems much of those criticisms were attempts at discrediting the model in its entirety, I believe that they simply show areas for growth in the model. In my opinion, the model is just missing a few refinements from becoming something really excellent in the realm of basketball statistics. Prof. Berri has been fairly intractable when it comes to certain aspects of the model, but research like yours gives me hope that some of the bugs can finally be hammered out.
arturogalletti
08/17/2010
Muad’dib,
Or should I call you Paul?
. It’s an interesting article with some obvious flaws in methodology (covered here in detail http://dberri.wordpress.com/2010/05/01/ted-leonsis-endorses-stumbling-on-wins/).
Here’s my take. Win Produced works because players (on a per minute basis) are generally are who they and remain so over time (so say countless regressions). So If a player is a low possession usage guy he’s going to remain a low possession usage guy . Players with high Rebound Rates per 48 will remain so. Inefficient volume scorers will remain so.Players have if you will a fixed basketball identity (They are who we though they were).
The Findings of the study (that when coaches tried to change the players identity by making them increase their usage they saw diminishing returns and vice versa) is what you actually would expect from Prof. Berri’s findings. The opposite effect (that players who decrease their usage have their efficiency increase) is nice but is rarely practical. Shooters are shooters, and the increase to their efficiency isn’t enough to increase their Net Productivity. So the usage discussion vs. efficiency discussion doesn’t have much practical real world value.
If we could get Iverson to shoot the ball less, would he be a better player? Probably not and he wouldn’t be Allen Iverson. Same thing applies to trying to get Ariza to take more shots that’s not who he is and this may explain some of his struggles in Houston.
Actually the only real world application is something that does not bode well for teams that are not the Miami Heat. Andres Alvarez did a post here were he detailed the fact than when Boston’s big three plus one united in 2008 much teeth gnashing occurred (similar to now with Mia) about how they needed to have the ball to be effective. The numbers showed that decreased usage made then a better team (much like it improved Wade,Lebron and Bosh’s numbers in the Olympics)
nerdnumbers
08/16/2010
So Arturo,
One of your greatest points in a previous post was the year you were in affecting your performance (something I am using in my time travel series). An interesting thing to consider is that from 2000-2004 the difference in all positions was not that large, whereas last year a missing Center was huge. So some years your team may be in trouble if their top X goes down if the value of the replacement that year is down. (Portland times 2 Centers e.g.)
I also notice the year the Nuggets put Camby on a plane while he was asleep with directions to “drop him at dah Clippers place” the value of a Center over replacement was its highest. Nice work.
Guy
08/16/2010
Arturo:
Very interesting analysis. This really challenges the legitimacy of the WP position adjustments, which assumes equal productivity at all positions. Since all replacement players have the same value by definition — zero — your numbers mean that all positions don’t provide equal value.
I think it would be very interesting to produce tables comparing average and replacement players at each position by statistical category (points, assists, rebounds, etc.), to see what separates the two. For example, is the big spread at PG mainly a function of assist totals, or other factors as well?
arturogalletti
08/16/2010
Guy,
The current WP model does an excellent job at win projection and predicting success (I’m going to do a post on this in the near future). I’m trying to actually chase down the concept of scarcity. Each role requires a different set of skills and those skills are not available in the same proportion in the population so there is marginal value to be had from tall people and PG aparently. Which skill sets are the most rare is an interesting question. Taking an Avg replacement level player and comparing his totals to the average player would provide some interesting answers. Intuitively I would think possesion skills (turnovers,steals) for PG and Rebounding/Blocks for bigs would be the key differences but I will do the analysis to see.
Edmond
08/17/2010
Great stuff. It makes perfect sense that those have been traditionally the most well-defined positions, given that they are the most specialized (ball-handling, height), and that they are the hardest to fill.
Its interesting that of the five super-awesome players that the Heat have acquired, none are traditional centers or point-guards. Not that I think it will matter much, given the levels of talent. Point guard shouldn’t be a problem at all–they’ll probably have two stellar ball-handler/passers on the court at all times. I think that the only mistake they could make would be to limit Miller’s and Haslem’s minutes in favor of a more traditional-looking lineup. I’ve wandered off topic….thanks for the post.
Fred Bush
08/17/2010
Love your blog.
Now, how about connecting the “replacement player” stats to salary? Sort all NBA players by 2010 salary, then compare their stats to replacement-level players at their position, and “replacement-level” salaries. See who’s the biggest bargain/who’s the most overpaid.
arturogalletti
08/17/2010
You read the new post right? Patience is a virtue.
R
09/01/2010
This is not really replacement level player at least according to the way I am used to using replacement level as the first hired from outside if a position opens. It is really “low ranking” player but in the league. It doesn’t matter if people become aware of your definition / system but you could call it WOLRP. (LR= Low Ranking)