Early Returns on the Warriors-Pacers Trade

Al Harrington is looking good in Oakland.

It’s been a little over a week since the Warriors and Pacers swapped quartets of players. Let’s take a quick look at the fantasy impact so far.

To date, the biggest beneficiary (by far) has been Al Harrington, who’s been playing some strong ball since arriving in Oakland, and is the 14th-ranked player over the past two weeks. Stephen Jackson has had one great game and two bad ones (not to mention his off-court distractions). On the Indy side of things, Troy Murphy has seen a bump up in value, playing at about a top-80 level since the trade, and Mike Dunleavy has improved a little as well, playing near a top-100 level.

Overall, from a fantasy perspective, this has been one of those trades that truly helps both sides (at least so far).

Correlating the Categories

The other day, Patrick suggested something (in response to the post comparing category distributions) I’ve been thinking about doing for a while too: Generating correlations between all the roto stat categories. This way, you could see which categories “go well” together, for the purposes of fine-tuning your team.

Without any further ado, here it is (based, of course, on the RotoPoll data):

	FG	FT	Points	Reb	Ast	Threes	Steals	Blocks	Overall
FG	–
FT	0.18	–
Points	0.35	-0.26	–
Rebounds	0.86	0.15	0.35	–
Assists	0.34	0.53	-0.34	0.20	–
Threes	0.49	-0.30	0.25	0.37	-0.29	–
Steals	0.31	-0.45	0.35	0.33	-0.42	0.27	–
Blocks	0.50	-0.26	0.15	0.30	-0.13	0.52	0.20	–
Overall	0.18	0.41	-0.43	-0.01	0.67	-0.33	-0.43	-0.18	–

For those not familiar with the concept, a correlation is a statistical measure of how well two sets of data match up. In the case of basketball stats, for example, two categories will have a high correlation if players who do well in one of them also tend to do well in the other. A negative correlation suggests that players doing well in one category tend to do poorly in the other. A correlation around zero suggests there’s no relationship at all.

Ok, so what does this mean? To help demonstrate, I’ve highlighted the best (FG and rebounds) and worst (FT and steals) correlations. If your team needs to improve in both FG% and rebounds, the good news is you’ll be able to find a lot of players who can help you in both. If you need FT% and steals, however, you’re going to have a much harder time finding someone.

This data may also be useful if you’re considering tanking (giving up in) a category. For example, if you intend to tank FG%, it’ll be hard to do it without taking a hit in rebounds as well.

Another interesting thing to observe is the correlation between cateogories and overall value. Two cateogries, FT and assists, stand out as being the most correlated with overall value. I’m not sure, but this seems to suggest that those categories are more valuable in some way. For one thing, it means that it’s harder to find low-value players who can help you in those categories, as opposed to, say, steals or points, where the correlation with overall value is actually negative. So hold on to Steve Nash.

And of course, if anyone knows stats well enough to suggest or criticize something here, by all means, go ahead.

RotoPoll’s Verdict on Today’s Trade

You’ve probably heard by now that the Pacers and Warriors have made an 8-player trade, with the Pacers sending Al Harrington, Stephen Jackson, Sarunas Jasikevicius, and Josh Powell to Golden State for Troy Murphy, Mike Dunleavy, Ike Diogu, and Keith McLeod.

In case you were wondering, RotoPoll says that Indy wins this trade, but it’s pretty close.

For more meaningful analysis, check out GMTR’s breakdown of the impact on each player in the deal.

Comparing the Categories

In fantasy, each category is a little different. For example, players who get a lot of blocks are rare, rebounds aren’t so rare, and a single free throw albatross (such as Shaq for the past 10 years) can sink your team. Unfortunately, however, most of this type of knowledge comes from intuition, observation, editorial opinion, or other informal sources. Let’s look at some hard data that will hopefully help you evaluate the differences across the categories, and make decisions to help your teams.

The following is the distribution of scores, in each category, for the top 180 players in the RotoPoll rankings.

Category	Maximum	Median	Minimum
Blocks	3.22	-0.33	-0.82
Assists	3.08	-0.62	-1.58
Points	2.60	-0.93	-2.32
Rebounds	2.28	-0.44	-1.42
Steals	2.11	-0.40	-1.69
Threes	2.02	-0.36	-1.14
FT	2.01	-0.00	-2.57
FG	1.78	-0.10	-1.40

And here’s a distribution of each category (click to see a larger version):

So what can we learn from this data? Here are some initial thoughts.

• The category with the highest average is free throws, and if you look at the chart, you can see that it drops off precipitously at the end (in fact, it has a noticeably different shape than all other categories, which tail off gradually), meaning that there are just a few guys who are really dangerous. It seems you want to avoid those guys (Ben Wallace, Tyson Chandler, etc.), and if you need to bump up your FT% a little, there are a lot of guys out there who can help you.
• By contrast, the category with the lowest line on the graph is points. This means that it’s harder to find someone who can help you in the points category than any other category. This might seem counterintuitive, since almost everyone scores some points, and it’s not that hard to find a waiver pickup who can give you 10-12 per game, but it’s harder to find someone who can actually help you than in other categories.
• Blocks is the most top-heavy category on the graph. It has the highest minimum and maximum, which suggests that production is concentrated in fewer players than in other categories. Conversely, a player who gets no blocks doesn’t hurt you as much as a player with a zero in another category (or poor percentages).

And of course, these observations apply to each category in greater or lesser degrees.

There’s certainly a lot more to take away from this data, but that’s a start.

Guy You Need to Know About: Andrea Bargnani

A stat-stuffer star on the rise.

It’s a rare player who can contribute in all the roto categories, and it’s always a very short list of guys who can get you at least a block, a steal, and a three per night. For the season, only Shawn Marion and Josh Howard (quietly having a phenomenal season) are accomplishing this feat.

But over the last two weeks, only one player in the NBA has averaged at least one block, steal, and triple: Toronto’s Andrea Bargnani.

Well ok, Chauncey Billups techincally meets this condition as well, but he only had one game two weeks ago, and has been injured since.

Bargnani is a fantasy stud in the making. His chart shows steady improvement throughout the season, and it’s exciting to think what he’ll be able to produce in the next few years.

What’s the Deal with Joe Johnson?

Joe is struggling.

I need some help on this one, because I have no idea what’s going on. If you have Joe Johnson on any of your teams, you already know this, but his value is plummeting like an Internet stock in May 2000.

The best guess I have is that he’s suffering lingering effects from the calf injury that kept out for a few games back in December. It sounds like he may also be battling the flu and turf toe (I didn’t know basketball players could get that either) at the moment, so maybe it’s just a case of a lot of things adding up at once. But whatever it is, we’ve officially reached “slump” status and his fans (and teammates) are hoping he can break out of it soon.

The Ups and Downs of Devin Brown

First we loved him.

Then we didn’t love him.

But aside from one disappointing game, Devin is playing some decent ball right now. Therefore, we’ll go ahead and repeat our first recommendation, that “you can look for good production from him for a while, as Chris Paul, Peja, B-Jax and David West are all expected to remain sidelined for weeks.”

Updated Player Pages

There were a couple bugs fixed today, but in addition, the player pages have been updated a little. The main cool thing to notice is that each player’s page now highlights his best game (fantasy-wise, of course) of the season. You can use this to get a rough idea of what the ceiling on a player’s value might be.

For example: Andrei Kirilenko (his best game is probably not as good as you thought… he made only one field goal).

Rick Kamla and RotoPoll Agree: Manu Blew Up

Manu can do a lot in only 27 minutes.

Sometimes I like to compare the RotoPoll rankings against other rankings that are out there.

Last night on NBA TV’s NBA Fantasy Hoops, host Rick Kamla proclaimed that Manu Ginobili had the best fantasy line of the night. This morning, I checked the RotoPoll top games list to see if it agrees with Rick. Good news: It does. So in case you were worried that these numbers are being conjured out of thin air, there’s one tiny piece of anecdotal evidence that they’re legit.

New Features in the Pipeline

I’m constantly updating my list of things I want to do to improve RotoPoll. These ideas from comments, emails, and stuff I just cooked up in my head. So, time permitting, here are some features I’m planning to get worked in soon:

• Adding minutes to player stats
• Including turnovers as an optional category, so RotoPoll is more useful for 9-category leagues

Somewhat less likely:

• Having the trade evaluator suggest players to add to trades
• Add data and trends from previous seasons

I’m probably forgetting some stuff, and there are also some longer-term ideas (as in, less likely to get done because they’ll be harder to do), but this is what I’m hoping to tackle right now.

If you have any ideas or suggestions, please let me know at rotopoll (at) gmail (dot) com.