APBRmetrics

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

I don't want to spend too much time on trying to rate players to a single number (since new research in this area will probably have the least impact), but I really want to understand a method for measuring a player's overall contribution while on the court. Adjusted plus/minus is the best way to do that. Problem is, I have a hard time understanding it. Therefore, I created something similar that I feel is more basic that I can more easily understand.

I call it points added, and you can find my full explanation here.

The points added offensive ratings:

http://spreadsheets.google.com/pub?key=pLJimPjd7oqu1jTSBj0LoPA&hl=en

The points added defensive ratings:

http://spreadsheets.google.com/pub?key=pLJimPjd7oquIUwMwHOHOPQ&hl=en

I don't exactly expect this to be preferred over adjusted plus/minus, but in my small world I am able to understand it better. Hopefully someone else finds this useful. Smile

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deepak · Joined: 26 Apr 2006 Posts: 665

Perhaps I missed it ... you created these ratings based on which season(s)?

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

Yeah sorry about that. It's from last year, 07-08.
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Mountain · Joined: 13 Mar 2007 Posts: 1527

How does combination of offensive and defensive ratings correlate to single season 07-08 adjusted at basketballvalue or to the splits using the multi-season version at 82games?

Does your method have smaller errors than single season adjusted or the multi-season weighted version?

As a % of value is the method better at estimating the best on offense and worst on defense or am I getting the wrong impression from the ratio of the values to the standard deviations?

If you subtracting 12 or 13 (half the difference in the top to bottom spread, setting a zero value midpoint) from all the values to put them on a positive-negative would that look like adjusted or be properly scaled? Not saying you have or want to go back to that but my mind drifts back to that as it might make it more immediately apparent to me where a player falls on the distribution and whether their contribution is above or below average.

Given position trend of who is tops is points added somehow "perimeter friendly" on offense (probably about 90 perimeter guys in top 100) and bigman friendly on defense (at least 80 bigs in top 100) or is that to be taken as the objective measure of these impacts ?

cherokee_ACB · Joined: 22 Mar 2006 Posts: 157

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

The biggest difference in the Points Added fit and the one that Adjusted +/- uses is that Adjusted +/- uses "shifts" of data in terms of points per 100 possessions, where as I use each individual possession in terms of the points scored on that possession. This is one of the reasons Adjusted +/- doesn't feel "clean" to me. I have read some articles on adjusted +/- that a weighting system is used, but thanks in large part to Eli's work I haven't been able to figure out how that works in a sensible manner. Hence my motivation for creating something I could wrap my head around, so I use each individual possession.

As for constraining the coefficients to be positive: With WinBUGS you can specify limits on the sampling distributions. In this case I limit the samples to be positive with the I(0,) piece of the model specification.

The low possession players are not included in any of the samples. I've read that Adjusted +/- uses these as "reference" players, yet another concept I don't understand. I'm not sure what the impact is of throwing these guys out instead of making them appear as one player.
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Kevin Pelton · Posted: Mon Dec 15, 2008 4:05 pm Post subject:

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

What I've done is simply removed any possessions involving players that did not make the cutoff point. So only possessions that involved the 340 players that made the cutoff were included in the fit. The rest of the possessions were thrown in the trash. Cool

For the interested, that still left a total of 165,403 possessions.
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Mountain · Joined: 13 Mar 2007 Posts: 1527

If you are willing to make this chop down would be willing to look at cutting the data again down to just when playoff teams play against playoff teams? That would interest me as a possibly improved indicator of playoff performance.

Mike G · Posted: Mon Dec 15, 2008 5:32 pm Post subject:

I put DougStats.com's position designation next to player names, sorted by position, and got these averages:

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

I'm not sure what's "weird" about that, really. I'd expect some sort of distributional difference for each position.

Do you happen to have the Doug's Stats names in the same format as mine? If so, that would be helpful. If not, I'll parse them up later this week.

What I'll do is put player's together and present the distribution by position.
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Mike G · Posted: Mon Dec 15, 2008 6:11 pm Post subject:

Heck, I already converted to First Name in one column, Last in another. E-Mail me if you wish I'd send it to you.

What's alarming about the PG deficit is that it's the one position that cannot go unfilled for more than a few seconds. Maybe, when an all-defense lineup goes in for a stop at the end of a quarter?

In football, there are just a few plays where there's no quarterback on the field: field goals, PAT's, punts, kickoffs. They might produce a stat that shows teams do better, in points per play, without a QB.

Meanwhile, how can all PG's net such a substantial negative? Even w the '08 Raptors, who seldom used Ford and Calderon together: both are majorly negative.
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Kevin Pelton · Posted: Mon Dec 15, 2008 7:21 pm Post subject:

Ryan J. Parker · Joined: 23 Mar 2007 Posts: 711 Location: Raleigh, NC

Well this is certainly something worth keeping an eye on, but there should not be a positional bias in the way the data is fit. If it turns out that there are perhaps proportionally more poor PGs than good PGs in relation to players at other positions then that does say something, but not that there is a positional bias.

That said, the size of the data means there is a lot of uncertainty. TJ Ford's net rating, for example, allows us to approximate that there is a 35% chance he has a net positive impact. Thus I would certainly want to look at other things when comparing him against other players to determine who I would want running my team.

What I do find interesting is that from this data a guy like Devin Harris has a high probability of being a net positive player (over 95%), and the probability that Devin Harris has a a higher impact than Jason Kidd is almost as high (over 93%). Hindsight being what it is, you have to wonder what the Mavs were thinking. That being said, this certainly doesn't measure the impact a coach has on these ratings, so maybe there is room for debate.
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cherokee_ACB · Joined: 22 Mar 2006 Posts: 157