# Category Archives: Statistical +/-

## D-Rose and Iverson

With Derrick Rose‘s 2011 MVP looking like a foregone conclusion, it seems only natural to compare his campaign to that of Allen Iverson in 2001, the year another popular guard won the MVP despite not being the game’s most talented player.

Here’s the numerical tale of the tape for A.I. and D-Rose, with Rose extrapolated to 82 team games: (Glossary)

Player | G | MP | ORtg | %Pos | DRtg | OSPM | DSPM | SPM |
---|---|---|---|---|---|---|---|---|

Iverson | 71 | 2979 | 106.3 | 33.8 | 99.2 | 6.79 | 0.07 | 6.86 |

Rose | 81 | 3025 | 111.5 | 32.6 | 102.2 | 6.16 | -0.96 | 5.20 |

Statistically, the two players are incredibly comparable. If you translate Iverson from the 103.0 league-ORtg environment of 2001 to the league ORtg of 107.1 in 2011, his ORtg/%Poss/DRtg becomes 110.5/33.8/103.0, production that is basically equivalent to Rose’s after adjusting for usage.

## Optimizing the Rockets II

As if everyone isn’t already tired of this debate (one which will never be satisfactorily settled, I’m sure), here’s a final note on who contributed the most to the 1995 Rockets‘ offense during the playoffs, Hakeem Olajuwon (mega-high usage, average efficiency) or Clyde Drexler (mid-to-high usage, mega-high efficiency)…

My last post attempted to create a simple model of team offensive efficiency using Dean Oliver‘s Offensive Rating, Possession %, and what Dean called “Skill Curves”, or the relationship between changes in individual usage and efficiency rates. In general, both Oliver and Eli Witus found a quantifiable inverse relationship between increases in usage and predicted offensive efficiency — in other words, there’s diminishing returns to increasing your usage, and as you add more usage you become less and less efficient (which only makes sense to anyone who’s ever played basketball).

## Who Are the “Inner-Circle” Hall of Famers? (Part I – Intro to Method)

Whenever Hall of Fame arguments come up, especially in baseball, I have a tendency to tune out from the sheer tediousness of the typical debate. On one side, there’s always an arrogant guy who saw many of Player X’s games and “knows” he’s a Hall of Famer, so he cites other, lesser players who are already in the Hall (as though that were somehow evidence Player X should be in), brings up a couple of memorable career moments, and generally fudges on borderline issues to make the player seem better than he actually was. On the opposing side, another equally narcissistic guy splits hairs about the “magic numbers” Player X failed to reach, denigrates his career because A) if he won titles, he didn’t have enough individual honors; or B) if he had a lot of individual honors, he didn’t win enough titles. Throw in a few unsubstantiated jabs at Player X’s character and/or manhood, and then start the whole process over again — how fun.

## The Top 10 Point Guards of All Time (*according to statistical +/-)

With the playoffs looming — and consequently, some postseason-related content on the horizon — I think we should finish off this series about Statistical Plus-Minus’ greatest players of all time, don’t you? Read up on the previous posts (Centers, Power Forwards, Small Forwards, Shooting Guards) if you’re curious about the method, or just skim the (alphabetically-ordered) list if you want to get right to it:

## The Top 10 Shooting Guards of All Time (*according to statistical +/-)

This has been a surprisingly popular series so far, so because of reader demand I’m going to accelerate things and go ahead with the Top 10 “statistical +/-” shooting guards in NBA history (excluding seasons prior to 1951-52, when they didn’t bother to track minutes). As a quick refresher, SPM is a linear regression formula that tries to predict the well-known adjusted plus-minus stat using just the conventional stats you’d find in the box score. Obviously some defensive value is going to be lost as a result, but so far the results haven’t been horrible, so that’s encouraging. Anyway, here are the best SGs by the method, in alphabetical order:

## The Top 10 Small Forwards of All Time (*according to statistical +/-)

This is a series we’ve been sporadically doing this month, and I figured today was as good a day as any to keep it going and check out the top 10 NBA/ABA small forwards since 1952 according to the “statistical plus-minus” method. To refresh everyone’s memory, SPM is really just a linear regression formula that tries to predict adjusted plus-minus using just the conventional stats you’d find in the box score. It has its own biases like any boxscore metric, but I kinda like it, and it’s pretty simple to use and understand — the numbers that follow are estimation of a player’s individual impact on his team’s point differential per 100 possessions. So here’s what it has to say about the top 10 small forwards ever, in alphabetical order:

## The Top 10 Power Forwards of All Time (*according to statistical +/-)

Continuing our series from last week, today we’re going to look at the top 10 power forwards ever by the “statistical plus-minus” method. If you don’t remember what it’s all about, it’s basically a linear regression formula that tries to predict adjusted plus-minus using just the conventional stats you’d find in the box score. I don’t think it’s the ideal player rating metric or anything, but at the same time it doesn’t seem to be the *worst* I’ve ever seen, either, so we’re going to keep giving it a test drive by using it to rank the all-time NBA (& ABA, forgot to make that completely clear last time) players at each position. Here’s what it has to say about the top 10 power forwards ever — again, in alphabetical order:

## The Top 10 Centers of All Time (*according to statistical +/-)

We’ve done several posts on statistical +/- here at the BBR blog over the past month, and it’s mainly because I don’t know what to make of the metric. I suppose that deep down, I very much *want* it to be a good, solid linear-weights method of player rating, because there’s not really any fudging involved in the original regression — it simply asks which stats best predict adjusted +/-, which itself is a method that feels “organic” to me (increasing your team’s point differential being literally the purpose of the game, after all). No guesswork, no worries over how to deal with assists, defensive rebounds, the value of shot creation, or any of the usual potholes we run into when developing one of these baseball-style metrics for a sport that doesn’t really lend itself to that kind of thing.