The Road to the NBA Finals

Using a modification of the BoxScores formula, and applying it to single games, I’ve developed an estimate of Points Created (which accounts for both points scored by the player’s team (positive), and points scored by their opponent (negative)) for each player on both NBA finalists for each game through the end of the Conference Finals. The result is a very accurate estimate of player contributions to victory in each game–the big stars often rise to the top, but in an 82-game season, followed by a 15- or 20-game playoff run, role players often make the difference. Bryant, Gasol and Odom often lead their team’s production, but even Walton and Radmanovic have lead the Lakers to victory. The Celtics appear even more balanced in this sense–while Garnett, Pierce, and Allen often top the Points Created table, Rondo and Perkins often play huge roles–even leading their team to victory in the fifth game of the Conference Finals against Detroit.

Points Created by Game: Boston

Points Created by Game: LA

One of the more interesting things about this representation is the different types of games each player has–indicated here by coloration. Games in which a player primarily contributes with scoring are redder, those in which he does “interior” things–blocks and rebounds–are bluer, and those with mainly perimeter contributions–assists and steals–are greener. Most games fail to fall easily into a single category, and so color combinations identify the degree to which each player’s performance can be characterized in each of these three ways. Thus, a purple box indicates a scoring performance by an interior player, a yellow box shows scoring/perimeter, etc. It is interesting to note that these colors often reflect commonly held notions about playing style–Bryant is often relatively red, leading his team offensively, and occassionally green, when he passes out assists or locks down on defense. Garnett is every possible color in the graphic, revealing his multifaceted ability to do whatever his team needs.

Below is a crudely compiled comparison of the teams’ head to head matchups during the regular season (click through to get a top-to-bottom matchup of both teams’ entire seasons). Boston won both meetings decisively, though the Lakers had not yet gained Pau Gasol (though they did have Bynum, who will not play in the finals). Garnett and Pierce had huge second games, creating 37.9 and 34.5 points, respectively. Bryant’s performance in the first was below his average, and in the second was dismal–focusing mainly on scoring (orange/pink coloration), and failing to create much difference between the teams. I would be interested to hear any comments you might have, or things you notice in the graphics. There is a ton of information to glean here, and I haven’t begun yet to absorb much of it.

Both Teams’ Seasons

7 responses to “The Road to the NBA Finals

  1. Thanks for explaining / using color for the “flavor ” of a player ‘s contributions.

    “Net BoxScore” (orShare) using either the BoxScore of the counterpart or the minutes weighted average of the counterpart position or a weighting of that and overall opponent efficency could be even better in my view. That would fully capture defense side of things (including shot defense) beyond the defensive elements in the small b “boxscore”. I know you said you were ok with proceeding without it but it is possible to include it, in this way.

  2. Brilliant, brilliant post.

    I had a couple of questions:

    1) How do you make these graphics? I think the presentation is the best part.

    2) Why is Leon Powe’s box white in one of the playoff games versus cleveland?

    3) Because point differential is integer values, did you ever consider using poisson regression instead of typical OLS?

    Thanks and again great work!

  3. rapidadverbssuck

    Mountain: I may try that someday, I do think it would be more… theoretically sound, but it seems like a lot of work, given my current dataset.

    Ben: Thanks for the compliments:

    1) I use the statistical program R, which in my opinion is THE best program for creating noninteractive statistical graphics. And it’s free!

    2) Funny you should ask that: Powe’s only contribution in that game was four of six free throws. No fgas, assists, blocks, steals, or rebounds–which are the components of the coloring algorithm–so we can’t tell what kind of game he played that day, based on his stats, only that he took some foul shots. His fts did helpt the team win, though, so he has the odd colorless box.

    3) I haven’t tried Poisson regression, but it’s probably not a bad idea… although I think my weightings are pretty solid as they stand…

    Thanks so much for taking the time to comment!

  4. Hmmm… I’m a Stata man myself, but seeing these graphics makes me pine for more a sophisticated graphics package.

  5. I don’t get it.

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