Overall Offensive Load Carried (SportVU Aided)
More here
I'm pretty happy with the way the "isolated rim protection" analysis I've been noodling with works as a broad measure of that particular area of defensive achievement. It also translates NBA.com's SportVU numbers into a more easily digestible data point. Most of the numbers are interesting, but without context don't really explain whether the actions tracked are good, or indifferent as far as positive contributions on the floor. For that reason, I've been on the lookout for more ways to try and interpret the publicly available data, perhaps in combination with other, more traditional stats to provide that context. One area where I think this is possible is "offensive load carried."
(Update 1/11/14 Further thoughtshere)
(Update 2/1/14 Revised numbers and thoughts here, google doc with leaguewide stats here)
(Update 2/17/14 Revised again here, google doc with leaguewide stats through ASB here)
Typically, we simply refer to the size of a player's role in an offense by noting their usage statistic. And as far as it goes, this is great - we know that on a "perfectly balanced" team, all 5 players will have a USG of 20%, so we can tell some things about how much or little a player shoots by comparing their usage to this average. Under 20% usage players tend to be either limited spot up shooters or bangers with little game outside of putbacks. Above 25% represents primary options and above 30% are the true ball-dominant chuckers.
However, usage presents a slightly distorted picture of how offense (especially a high functioning offense) tends to work. If a point guard splits a double team on a pick and roll, euro-steps past the help defender, draws the rim protector into the air and drops a nifty pass off to a grinder big man who happened to be cutting to the basket for an offensive rebound, that possession was "used" and that shot "created" by the big man. As with many basketball statistical questions this gets us into the difficult area of parsing individual credit from events which are undoubtedly positive on the team level.
I'm not going to untie that whole knot here. In fact, I'm going to basically skipping the question of whom to credit by saying "why not both?" I think it meshes with simple observation that there is no reason a basketball possession has to be a one man show, though it certainly can be.
(Update 2/1/14 Revised numbers and thoughts here, google doc with leaguewide stats here)
(Update 2/17/14 Revised again here, google doc with leaguewide stats through ASB here)
Typically, we simply refer to the size of a player's role in an offense by noting their usage statistic. And as far as it goes, this is great - we know that on a "perfectly balanced" team, all 5 players will have a USG of 20%, so we can tell some things about how much or little a player shoots by comparing their usage to this average. Under 20% usage players tend to be either limited spot up shooters or bangers with little game outside of putbacks. Above 25% represents primary options and above 30% are the true ball-dominant chuckers.
However, usage presents a slightly distorted picture of how offense (especially a high functioning offense) tends to work. If a point guard splits a double team on a pick and roll, euro-steps past the help defender, draws the rim protector into the air and drops a nifty pass off to a grinder big man who happened to be cutting to the basket for an offensive rebound, that possession was "used" and that shot "created" by the big man. As with many basketball statistical questions this gets us into the difficult area of parsing individual credit from events which are undoubtedly positive on the team level.
I'm not going to untie that whole knot here. In fact, I'm going to basically skipping the question of whom to credit by saying "why not both?" I think it meshes with simple observation that there is no reason a basketball possession has to be a one man show, though it certainly can be.
So to that end, I've been messing around with a series of metrics to measure overall offensive load carried by each player. Basically, I'm combining traditional usage with TOs and SportVU defined assist chances, and expressing it as a percentage of a team's offensive possessions a player is "directly involved in" while on the floor.
This has also allowed me to express turnovers in a rate stat which encompasses both shooting and play making - traditional TOV% tends to overcredit shoot first players as the only "positive" outcome in terms of reducing TOV% is shooting the ball. Merely not turning the ball over (while for example passing to a shooter) is a neutral event which has no effect on TOV%, which has the counter-intuitive effect of making the players who presumably take the best care of the ball (pass-first PGs) look like the most profligate with it. To put it another way, a measure of taking care of the ball that makes prime Steve Nash look bad in terms of handling the rock has serious issues.
This has also allowed me to express turnovers in a rate stat which encompasses both shooting and play making - traditional TOV% tends to overcredit shoot first players as the only "positive" outcome in terms of reducing TOV% is shooting the ball. Merely not turning the ball over (while for example passing to a shooter) is a neutral event which has no effect on TOV%, which has the counter-intuitive effect of making the players who presumably take the best care of the ball (pass-first PGs) look like the most profligate with it. To put it another way, a measure of taking care of the ball that makes prime Steve Nash look bad in terms of handling the rock has serious issues.
Since I'm translating basic USG% into another metric, I wanted to also include a TS% equivalent number, so I included a "Equivalent FG%" of each individuals "plays involved". Alternatively, double the number listed to get a PPP.
Since the data seemed to naturally break down this way anyway, I've separated players into "PG's, Wings, and Bigs". Their is no position adjustment involved, it's simply a grouping of players. There are 43 PGs, 93 wings and 72 bigs included.
For the data, I used all players who through the games of Monday the 6th had played at least 16 games (more or less half the games played) and at least 20 minutes per game. This left 208 players. I think it's probable that this biased the overall league averages are probably slightly lower than the sample I used as I think minute distribution is roughly rational - better players play more. Better players handle the ball more, so the guys not selected in the sample probably handle the ball/shoot less on a per possession basis, but that's neither here nor there as the numbers aren't scaled to any average.
The three columns are
First the PG's sorted from highest load to lowest
Since the data seemed to naturally break down this way anyway, I've separated players into "PG's, Wings, and Bigs". Their is no position adjustment involved, it's simply a grouping of players. There are 43 PGs, 93 wings and 72 bigs included.
For the data, I used all players who through the games of Monday the 6th had played at least 16 games (more or less half the games played) and at least 20 minutes per game. This left 208 players. I think it's probable that this biased the overall league averages are probably slightly lower than the sample I used as I think minute distribution is roughly rational - better players play more. Better players handle the ball more, so the guys not selected in the sample probably handle the ball/shoot less on a per possession basis, but that's neither here nor there as the numbers aren't scaled to any average.
The three columns are
- OffLoad - the percentage of plays the player is "directly involved in" either as shooter, passer or "hockey assister" while on the floor. Average across the sample was just under 35%, which aligns with about 1.73 players "involved" per play on a team level which feels slightly high, but not ridiculous. Would DEFINITELY accept suggestions for a better name for this one...
- TO/Load - Reconfigured TO% representing percentage of turnovers in total plays including shots AND assist attempts. Average for the sample was 8.76%
- PlayEFG - Expressing the efficiency of possessions in which the player is involved in a shooting percentage-style notation. Alternatively, double the number for a PPP/ORTG style number. More on this below, but I want to strongly caveat the tables below that I don't think this number should carry much weight in evaluating individual players.
First the PG's sorted from highest load to lowest
| Player | OffLoad | To/Load | PlayEFG |
|---|---|---|---|
| Russell Westbrook (OKC) | 65.58% | 8.80% | 0.623 |
| John Wall (WAS) | 63.83% | 7.57% | 0.583 |
| Chris Paul (LAC) | 61.32% | 5.71% | 0.594 |
| Deron Williams (BKN) | 60.58% | 7.67% | 0.672 |
| Stephen Curry (GSW) | 58.80% | 9.14% | 0.612 |
| Michael Carter-Williams (PHI) | 57.74% | 7.97% | 0.668 |
| Ty Lawson (DEN) | 57.46% | 7.60% | 0.561 |
| Brandon Jennings (DET) | 56.76% | 7.89% | 0.546 |
| Kyrie Irving (CLE) | 55.65% | 7.61% | 0.545 |
| Steve Blake (LAL) | 55.06% | 6.86% | 0.661 |
| Raymond Felton (NYK) | 54.93% | 6.11% | 0.664 |
| Jeff Teague (ATL) | 54.90% | 9.31% | 0.514 |
| Eric Bledsoe (PHX) | 53.99% | 8.84% | 0.652 |
| Brandon Knight (MIL) | 53.43% | 8.73% | 0.553 |
| Tony Parker (SAS) | 53.26% | 6.90% | 0.547 |
| Mike Conley (MEM) | 53.03% | 5.75% | 0.575 |
| Trey Burke (UTA) | 52.53% | 5.85% | 0.567 |
| Isaiah Thomas (SAC) | 52.25% | 7.88% | 0.543 |
| Jrue Holiday (NOP) | 50.96% | 8.94% | 0.554 |
| Jordan Farmar (LAL) | 50.58% | 10.12% | 0.504 |
| Goran Dragic (PHX) | 50.34% | 7.61% | 0.632 |
| Kyle Lowry (TOR) | 47.82% | 6.28% | 0.612 |
| Damian Lillard (POR) | 47.51% | 6.87% | 0.599 |
| Kemba Walker (CHA) | 46.98% | 7.87% | 0.509 |
| Jameer Nelson (ORL) | 45.26% | 8.27% | 0.529 |
| Ricky Rubio (MIN) | 44.93% | 9.15% | 0.534 |
| Jordan Crawford (BOS) | 43.43% | 8.16% | 0.512 |
| Kirk Hinrich (CHI) | 41.67% | 7.64% | 0.518 |
| Greivis Vasquez (TOTAL) | 40.97% | 8.97% | 0.427 |
| Jeremy Lin (HOU) | 40.58% | 11.23% | 0.645 |
| Reggie Jackson (OKC) | 39.36% | 9.75% | 0.440 |
| Ramon Sessions (CHA) | 38.56% | 9.01% | 0.361 |
| Mo Williams (POR) | 37.81% | 11.65% | 0.444 |
| Luke Ridnour (MIL) | 37.70% | 7.75% | 0.493 |
| Jarrett Jack (CLE) | 36.34% | 8.88% | 0.450 |
| Nate Wolters (MIL) | 35.16% | 7.37% | 0.471 |
| Beno Udrih (NYK) | 34.95% | 10.57% | 0.450 |
| Mario Chalmers (MIA) | 33.91% | 11.26% | 0.554 |
| Jose Calderon (DAL) | 33.67% | 5.55% | 0.601 |
| George Hill (IND) | 33.26% | 6.31% | 0.566 |
| Shaun Livingston (BKN) | 30.97% | 9.40% | 0.438 |
| Patrick Beverley (HOU) | 28.30% | 6.57% | 0.604 |
| Norris Cole (MIA) | 28.07% | 11.85% | 0.435 |
The wings:
| Player | OffLoad | To/Load | PlayEFG |
|---|---|---|---|
| James Harden (HOU) | 51.94% | 9.48% | 0.642 |
| LeBron James (MIA) | 51.52% | 9.35% | 0.655 |
| Dwyane Wade (MIA) | 48.64% | 8.92% | 0.663 |
| Monta Ellis (DAL) | 48.27% | 9.40% | 0.563 |
| Kevin Durant (OKC) | 47.62% | 8.31% | 0.630 |
| Carmelo Anthony (NYK) | 47.09% | 6.19% | 0.643 |
| Luol Deng (CHI) | 46.24% | 7.71% | 0.717 |
| Gordon Hayward (UTA) | 44.97% | 8.19% | 0.501 |
| Paul George (IND) | 44.25% | 8.14% | 0.586 |
| Tyreke Evans (NOP) | 44.01% | 9.17% | 0.482 |
| DeMar DeRozan (TOR) | 43.15% | 7.35% | 0.574 |
| Bradley Beal (WAS) | 42.53% | 7.38% | 0.686 |
| Tony Wroten (PHI) | 42.24% | 11.58% | 0.428 |
| Rudy Gay (TOTAL) | 42.06% | 10.86% | 0.544 |
| Evan Turner (PHI) | 41.85% | 10.01% | 0.527 |
| Manu Ginobili (SAS) | 40.62% | 10.07% | 0.492 |
| Dion Waiters (CLE) | 39.90% | 10.86% | 0.502 |
| Arron Afflalo (ORL) | 39.86% | 7.30% | 0.605 |
| Victor Oladipo (ORL) | 39.44% | 13.15% | 0.416 |
| Caron Butler (MIL) | 39.17% | 9.03% | 0.623 |
| Louis Williams (ATL) | 38.99% | 7.67% | 0.566 |
| Rodney Stuckey (DET) | 37.52% | 9.10% | 0.485 |
| Paul Pierce (BKN) | 37.14% | 11.99% | 0.514 |
| Lance Stephenson (IND) | 37.03% | 10.05% | 0.579 |
| Jamal Crawford (LAC) | 36.88% | 8.24% | 0.445 |
| Eric Gordon (NOP) | 36.56% | 8.97% | 0.533 |
| J.R. Smith (NYK) | 36.48% | 6.64% | 0.525 |
| Alec Burks (UTA) | 36.26% | 8.91% | 0.399 |
| Gary Neal (MIL) | 36.15% | 8.96% | 0.428 |
| O.J. Mayo (MIL) | 35.87% | 11.13% | 0.442 |
| JJ Redick (LAC) | 35.84% | 4.76% | 0.858 |
| Gerald Henderson (CHA) | 35.61% | 6.55% | 0.476 |
| Nicolas Batum (POR) | 35.35% | 10.12% | 0.593 |
| Kevin Martin (MIN) | 35.24% | 7.19% | 0.549 |
| Andre Iguodala (GSW) | 35.23% | 9.16% | 0.753 |
| Joe Johnson (BKN) | 34.42% | 5.67% | 0.547 |
| Nick Young (LAL) | 34.14% | 7.94% | 0.440 |
| Vince Carter (DAL) | 33.60% | 8.93% | 0.426 |
| Jimmy Butler (CHI) | 32.99% | 7.21% | 0.692 |
| Chandler Parsons (HOU) | 32.88% | 7.84% | 0.636 |
| Jerryd Bayless (MEM) | 32.30% | 5.36% | 0.406 |
| Trevor Ariza (WAS) | 32.22% | 8.56% | 0.663 |
| Klay Thompson (GSW) | 31.94% | 8.84% | 0.581 |
| Jeff Green (BOS) | 31.00% | 9.30% | 0.494 |
| Avery Bradley (BOS) | 30.60% | 8.96% | 0.439 |
| Tony Allen (MEM) | 30.57% | 11.54% | 0.507 |
| Xavier Henry (LAL) | 30.17% | 10.31% | 0.356 |
| Wesley Matthews (POR) | 28.76% | 6.00% | 0.609 |
| Gerald Green (PHX) | 28.42% | 9.61% | 0.477 |
| Mike Dunleavy (CHI) | 28.23% | 7.88% | 0.495 |
| Wilson Chandler (DEN) | 27.90% | 5.75% | 0.572 |
| Khris Middleton (MIL) | 27.70% | 8.34% | 0.481 |
| Matt Barnes (LAC) | 27.70% | 10.02% | 0.557 |
| Jeremy Lamb (OKC) | 27.07% | 5.98% | 0.398 |
| Randy Foye (DEN) | 26.88% | 8.82% | 0.436 |
| Marco Belinelli (SAS) | 26.87% | 8.90% | 0.464 |
| Marcus Morris (PHX) | 26.67% | 9.63% | 0.387 |
| Ray Allen (MIA) | 26.57% | 9.85% | 0.538 |
| Michael Kidd-Gilchrist (CHA) | 26.42% | 12.63% | 0.640 |
| Harrison Barnes (GSW) | 25.94% | 7.71% | 0.510 |
| Kawhi Leonard (SAS) | 25.93% | 7.98% | 0.483 |
| Richard Jefferson (UTA) | 25.90% | 10.21% | 0.437 |
| Jeff Taylor (CHA) | 25.74% | 7.99% | 0.404 |
| Shawn Marion (DAL) | 25.35% | 7.93% | 0.486 |
| Jodie Meeks (LAL) | 25.24% | 8.59% | 0.514 |
| Marcus Thornton (SAC) | 24.93% | 5.98% | 0.462 |
| John Salmons (TOTAL) | 24.83% | 6.54% | 0.454 |
| Omri Casspi (HOU) | 24.69% | 10.43% | 0.355 |
| Kyle Korver (ATL) | 24.24% | 7.59% | 0.677 |
| Tayshaun Prince (MEM) | 24.18% | 5.78% | 0.440 |
| Martell Webster (WAS) | 24.03% | 4.72% | 0.614 |
| Ben McLemore (SAC) | 23.74% | 9.79% | 0.381 |
| Terrence Ross (TOR) | 23.55% | 8.99% | 0.395 |
| Corey Brewer (MIN) | 23.47% | 9.81% | 0.500 |
| Giannis Antetokounmpo (MIL) | 23.38% | 12.63% | 0.446 |
| PJ Tucker (PHX) | 23.37% | 9.63% | 0.480 |
| James Anderson (PHI) | 23.33% | 10.54% | 0.443 |
| Iman Shumpert (NYK) | 23.29% | 8.85% | 0.457 |
| Mike Miller (MEM) | 22.06% | 9.19% | 0.438 |
| Al-Farouq Aminu (NOP) | 21.93% | 9.19% | 0.446 |
| DeMarre Carroll (ATL) | 21.61% | 7.58% | 0.517 |
| Danny Green (SAS) | 21.51% | 10.18% | 0.405 |
| Alan Anderson (BKN) | 21.20% | 5.76% | 0.424 |
| Maurice Harkless (ORL) | 20.90% | 11.33% | 0.328 |
| Wesley Johnson (LAL) | 20.07% | 10.07% | 0.429 |
| Jared Dudley (LAC) | 19.95% | 8.52% | 0.474 |
| Gerald Wallace (BOS) | 19.60% | 20.87% | 0.402 |
| Kentavious Caldwell-Pope (DET) | 19.52% | 4.30% | 0.395 |
| Francisco Garcia (HOU) | 19.39% | 8.51% | 0.363 |
| Thabo Sefolosha (OKC) | 19.27% | 9.76% | 0.448 |
| Kyle Singler (DET) | 18.58% | 9.24% | 0.397 |
| Hollis Thompson (PHI) | 16.65% | 10.43% | 0.374 |
| Shane Battier (MIA) | 13.74% | 3.65% | 0.416 |
The Bigs
| Player | OffLoad | To/Load | PlayEFG |
|---|---|---|---|
| DeMarcus Cousins (SAC) | 47.65% | 11.36% | 0.532 |
| Kevin Love (MIN) | 45.92% | 7.00% | 0.598 |
| LaMarcus Aldridge (POR) | 42.86% | 4.98% | 0.552 |
| Pau Gasol (LAL) | 41.61% | 9.15% | 0.477 |
| Zach Randolph (MEM) | 40.17% | 9.63% | 0.514 |
| Blake Griffin (LAC) | 39.67% | 9.56% | 0.529 |
| Dirk Nowitzki (DAL) | 39.02% | 5.82% | 0.566 |
| Al Horford (ATL) | 38.88% | 8.48% | 0.605 |
| Brook Lopez (BKN) | 38.82% | 6.67% | 0.966 |
| Nene (WAS) | 38.47% | 8.38% | 0.611 |
| Al Jefferson (CHA) | 36.95% | 4.62% | 0.587 |
| Josh Smith (DET) | 36.73% | 9.28% | 0.485 |
| Tim Duncan (SAS) | 35.51% | 8.14% | 0.497 |
| Paul Millsap (ATL) | 35.14% | 9.97% | 0.532 |
| David West (IND) | 34.91% | 9.10% | 0.502 |
| Carlos Boozer (CHI) | 34.67% | 11.60% | 0.463 |
| Jared Sullinger (BOS) | 34.38% | 9.38% | 0.421 |
| Glen Davis (ORL) | 33.90% | 8.08% | 0.616 |
| David Lee (GSW) | 33.56% | 9.74% | 0.539 |
| Andray Blatche (BKN) | 33.20% | 10.83% | 0.395 |
| Zaza Pachulia (MIL) | 33.09% | 10.42% | 0.607 |
| Dwight Howard (HOU) | 32.59% | 14.68% | 0.512 |
| Thaddeus Young (PHI) | 31.68% | 7.82% | 0.571 |
| Spencer Hawes (PHI) | 31.67% | 10.62% | 0.532 |
| Joakim Noah (CHI) | 31.51% | 9.55% | 0.533 |
| Greg Monroe (DET) | 31.33% | 10.52% | 0.486 |
| Anthony Davis (NOP) | 31.23% | 6.46% | 0.725 |
| John Henson (MIL) | 31.12% | 8.63% | 0.549 |
| Andrea Bargnani (NYK) | 30.75% | 7.41% | 0.441 |
| Markieff Morris (PHX) | 30.66% | 11.21% | 0.456 |
| Ersan Ilyasova (MIL) | 30.02% | 10.83% | 0.471 |
| Ryan Anderson (NOP) | 30.01% | 4.15% | 0.860 |
| Josh McRoberts (CHA) | 29.75% | 6.38% | 0.489 |
| Taj Gibson (CHI) | 29.41% | 12.75% | 0.421 |
| Chris Bosh (MIA) | 29.24% | 7.95% | 0.519 |
| Nikola Vucevic (ORL) | 28.95% | 12.70% | 0.571 |
| Nikola Pekovic (MIN) | 28.58% | 7.93% | 0.542 |
| Enes Kanter (UTA) | 28.46% | 13.29% | 0.358 |
| Roy Hibbert (IND) | 28.42% | 12.37% | 0.456 |
| JJ Hickson (DEN) | 28.16% | 10.04% | 0.410 |
| Boris Diaw (SAS) | 28.00% | 9.77% | 0.457 |
| Derrick Favors (UTA) | 27.95% | 11.67% | 0.471 |
| Kevin Garnett (BKN) | 26.86% | 10.61% | 0.358 |
| Marcin Gortat (WAS) | 26.76% | 9.82% | 0.535 |
| Jason Smith (NOP) | 25.77% | 5.68% | 0.521 |
| Marvin Williams (UTA) | 25.32% | 7.60% | 0.545 |
| Kosta Koufos (MEM) | 25.15% | 10.35% | 0.329 |
| Serge Ibaka (OKC) | 24.88% | 9.02% | 0.487 |
| Tristan Thompson (CLE) | 24.84% | 9.86% | 0.459 |
| Jordan Hill (LAL) | 24.77% | 9.84% | 0.369 |
| Brandon Bass (BOS) | 24.76% | 8.37% | 0.464 |
| Tiago Splitter (SAS) | 24.47% | 12.41% | 0.406 |
| Channing Frye (PHX) | 24.07% | 8.41% | 0.502 |
| DeJuan Blair (DAL) | 24.02% | 13.27% | 0.360 |
| Jonas Valanciunas (TOR) | 23.63% | 13.31% | 0.461 |
| Terrence Jones (HOU) | 23.60% | 7.25% | 0.462 |
| Kenneth Faried (DEN) | 23.46% | 8.85% | 0.412 |
| Anderson Varejao (CLE) | 23.35% | 7.42% | 0.476 |
| Amir Johnson (TOR) | 22.95% | 12.48% | 0.534 |
| Miles Plumlee (PHX) | 22.93% | 11.54% | 0.418 |
| Patrick Patterson (TOTAL) | 22.33% | 9.85% | 0.402 |
| Trevor Booker (WAS) | 21.75% | 8.55% | 0.536 |
| Derrick Williams (SAC) | 21.52% | 8.67% | 0.378 |
| Andre Drummond (DET) | 21.28% | 10.12% | 0.510 |
| Jason Thompson (SAC) | 20.78% | 12.94% | 0.408 |
| Andrew Bogut (GSW) | 20.11% | 15.28% | 0.461 |
| Robin Lopez (POR) | 19.07% | 10.21% | 0.490 |
| Ekpe Udoh (MIL) | 17.43% | 14.13% | 0.382 |
| Shawne Williams (LAL) | 16.57% | 8.76% | 0.377 |
| DeAndre Jordan (LAC) | 16.53% | 13.46% | 0.547 |
| Samuel Dalembert (DAL) | 16.48% | 15.65% | 0.385 |
| Anthony Tolliver (CHA) | 15.24% | 8.06% | 0.437 |
Some analysis and conjecture about the results.
- OffLoad is purely descriptive, in a vacuum a higher percentage is not better or worse based on this data, it just is. Better offensive players tending to carry higher loads is a result of coaches, players and execs having eyes.
- I like the way TO/Load tracks by player category, as it stands to reason that the guys who play PG are generally better at taking care of the ball than guys who play wing than the guys who play big becuase that's part of why they play those positions.
- Raw USG substantially underrates the role of a PG or similar creator in a functioning offense.
- Best guess is that players as a whole are overcredited with involvement and more so in terms of PlayEFG by generous assist giving. TheNick Van Exel homer assist probably doesn't count as a "chance" in SportVU, so it only gets recorded if the shot goes in. So much in the same way getting fouled while shooting only helps a players FG% because it only counts as an attempt if it goes in, I think the EFG's are slightly high because of that factor.
- That said, I don't think it matters terribly because A) it's a relative stat Luke Ridnour's .493 doesn't mean much except as compared to the league average of .536 and/or Nate Wolter's .471. But more importantly B) I don't want to put much stock at all in EFG as far as giving credit to the individual players as it's to a large degree a reflection of teammate quality rather than individual player talent to the extent player's "PlayEFG" diverges from TS%
- Some interesting individual player comparisons, Russell Westbrook and Reggie Jackson jumped out at me:
| Player | OffLoad | To/Load | PlayEFG |
|---|---|---|---|
| Russell Westbrook (OKC) | 65.58% | 8.80% | 0.623 |
| Reggie Jackson (OKC) | 39.36% | 9.75% | 0.440 |
- Top and bottom 20 players in terms of turnover rate:
|
|
- All kinds of other interesting things show up here - Ryan Anderson has potential assists on just over 4% of New Orleans possessions while he is on the floor. I almost have to think this represents a failure in the design of their offense given how hard teams have to close out on him. EDIT: I actually misread my data, he actually has potential assists on only 2.91% of possessions he's on the floor. The only other players under 3% are Andre Drummond and Samuel Dalembert, so the point still holds I think.
So, what do people think?
I haven't attempted to perform any advanced statstical methodology on the data, first because my knowledge on that front gets me to linear regression, barely. But beyond that I worry at times that for all but the very few people who both require the exactitude provided by and either have the ability themselves or in their organization to translate the numbers into an understanding of what's happening on the court and vice versa, I worry that basketball analytics at times becomes too much about the numbers, and I wanted to try and come up with a measure that is both broadly understandable for fans with decent numeracy but not advanced mathematical knowledge and as first-pass accurate. Hopefully this has succeeded on at least one of those goals.
I haven't attempted to perform any advanced statstical methodology on the data, first because my knowledge on that front gets me to linear regression, barely. But beyond that I worry at times that for all but the very few people who both require the exactitude provided by and either have the ability themselves or in their organization to translate the numbers into an understanding of what's happening on the court and vice versa, I worry that basketball analytics at times becomes too much about the numbers, and I wanted to try and come up with a measure that is both broadly understandable for fans with decent numeracy but not advanced mathematical knowledge and as first-pass accurate. Hopefully this has succeeded on at least one of those goals.
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