It took me way more time than I expected but ironically it could have been a very short post.
Frankly, the main point you can sum up in one sentence:
“fantasy NBA stars are valuable in snake draft not only because they offer the most stats and an unfair advantage but also they are… the most predictable group of players“.
In a longer version I’ll throw-in a couple of nuggets, I’ll explain how I’ve came up with this conclusion [be advised! This post contains some math ;-)] and how does it translate into different scoring systems.
Let’s start with the easier one, fantasy points and a typical 12-team 10-slots per team league. Again as an example I’ll use Yahoo’s Default Points Scoring [FGA (-0.45), FGM (1.0), FTA (-0.75), FTM (1.0), 3-pt Made (3.0), Point Scored (0.5), Rebound (1.5), Assist (2.0), Steal (3.0), Turnover (-2.0), Blocked Shot (3.0)].
I collected players’ statistics from dougstats.com, for every season in the last 10 years I sorted them from best to worst according to aforementioned formula [average per game not total].
Then I checked what players from every draft slot in Top120 did during next season and finally calculated average and standard deviation for a change in rating for each pick.
OK, if it sounds too complicated, here’s an example, according to Yahoo’s Default Points Scoring…
in 2009/10 3-rd best player was Jason Kidd who finished 20-th in 2010/11.
in 2008/09 3-rd best player was Dwyane Wade who finished 5-th in 2009/10.
in 2007/08 3-rd best player was Marcus Camby who finished 27-th in 2008/09.
in 2006/07 3-rd best player was Gilbert Arenas who finished 61-st in 2007/08.
in 2005/06 3-rd best player was Kevin Garnett who finished 1-st in 2006/07.
So for pick #3 in the last 5 years we have data points of -17 [because 20-3], -2, -24, -58, +2. Got it? In short it means “how would you do at each draft slot if you pick only by ranking from previous season?”.
I’ll spare you a whole table because it’s too long but I did the same thing as above for each pick from Top 120 for the last 10 years [you can find results in this file].
For a better view, here’s a graph for standard deviation of such change in ranking:
[FYI: outlier at #21 is thanks to Troy Murphy who was kidnapped by Nets last year and finished at staggering 336-th place!]
… and average…
What’s the point of those graphs and what does all this data tell us? IMHO…
1) Top5-8 picks are the easiest to predict!
So not only they offer more statistics [as I’ve mentioned in my post “why snake draft is unfair“] but there’s little surprise where guys like LeBron or Durant will finish. They may have a worse season than usual but even their worse seasons are still very good by league’s standards!
2) After 80-th pick it’s pretty much a crapshoot.
Which means you can draft there someone who will finish in the Top50 or outside of Top200 ;-)
Please note that “island of predictability” around 90-th pick. Those are usually “boring” guys – veterans like Battier or Ben Wallace who are drafted to fill up rosters!
3) Few draft slots regularly outperform their position from previous season.
I’m not gonna lie, I was very surprised by a scale of this phenomenon. Only 10 out 120 slots on average were better than in previous season? And most of them were “boring” veterans while none of them in the Top25? Isn’t it an argument NOT to base your ratings on previous year’s numbers?
OK, all above was about fantasy points… how does it change when we switch to rotisserie?
I used standard 9 categories for a 12-team 10 players-per-team league and values from basketballmonster.com.
Unfortunately they have only data for last 4 seasons…
Because of a smaller sample size there’s more noise here… but all conclusions are pretty much the same as above with one interesting twist: it’s easier to predict players in fantasy points than in rotisserie’s values!
On average for 120 picks standard deviation was higher by 5 picks.
How is it possible? I have one theory…
in rotisserie value has many dimensions while in fantasy points it has only one. So if a player adds one turnover and one assist per game in fantasy points his value will stay the same while in rotisserie it will change.
Weak Side Awareness 
karl
December 3, 2011 at 02:44
Interesting stuff!
Only 10 out 120 slots on average were better than in previous season?
Not really. You looked at the wrong number here. You should not rely on AVERAGE CHANGE but on amount of minuses during one season. If so, last year 39 players outperformed higher-ranked players relative to their original position. Two years it was 40 players, three years ago 47 players and so on.
As you might see it is approximately 35% of players who will play relatively better than last year.
If you were right, it would mean that there be AT LEAST two teams having ALL players performing WORSE than last year. Terrible, isn’t it? :)
And also one thing. At the beginning of your analysis you should point out your assumptions. Here, I would start from the info that all managers pick their players according to the last year ranking in fantasy points. I think it is important.
wiLQ
December 3, 2011 at 12:27
1) it’s nice to know that someone reads this [somewhat hardcore] stuff, thanks ;-)
2) I don’t understand your point about my assumptions – I haven’t covered them all before first graph? I even wrote about “picking according to the last year ranking” using bold font!
3) You misinterpreted my intentions.
With amount of minuses during one season you would look at it from individual point of view of each player and that’s not what I had in mind [although I’ll try to explore it later]. I tried to start with a bigger picture. I tried to analyze mechanics of draft itself, so not “which players usually outperform their draft positions” but “where in a draft you usually could find them”. Those approximately 35% of players you’ve mentioned just are very inconsistent in terms of where they were ranked in previous season.
karl
December 3, 2011 at 16:02
2)Right, I missed that.
3) Ok, but still if your question is: “where in a draft you usually could find them” your way of analysing is not quite right. You cannot look at the average change as it doesn’t answer your question. Why? Because it can be very easily biased by one exceptional year for a particular pick even if other 9 years players picked with this number played worse, let’s say, about 10 picks. Average change is not proper for neither qualitative nor quantitative analysis if I can use these words here.
wiLQ
December 3, 2011 at 16:43
What in your opinion is the right way to answer this question?
karl
December 4, 2011 at 05:58
Well, after much consideration I would say this question is not feasible to be answered. Cos here you are asking not about picks but players. You really don’t want to know which picks are performing better but players. As you know, picks are just numbers allocated to players. Thus, the question: “where in a draft you usually could find them” can only be answered by looking at players, not picks. Picks don’t really matter here.
However, in quantitative analysis, picks play their role properly as here we have picks=players. This 35% is still valid if we look at picks.
Summarizing, I would edit this part:
“3) Few draft slots regularly outperform their position from previous season.
I’m not gonna lie, I was very surprised by a scale of this phenomenon. Only 10 out 120 slots on average were better than in previous season? And most of them were “boring” veterans while none of them in the Top25? Isn’t it an argument NOT to base your ratings on previous year’s numbers?”
Stay tuned
wiLQ
December 5, 2011 at 21:02
“You really don’t want to know which picks are performing better but players”
Why not both? Just to be clear: I’m going to look at it also from players’ point of view ;-)
karl
December 3, 2011 at 16:37
“3) Few draft slots regularly outperform their position from previous season.
I’m not gonna lie, I was very surprised by a scale of this phenomenon. Only 10 out 120 slots on average were better than in previous season? And most of them were “boring” veterans while none of them in the Top25? Isn’t it an argument NOT to base your ratings on previous year’s numbers?”
Well, after all I am not :)
It is mainly because of a) restricted number of players you have analyzed (only 120) and b) TOP25 players don’t have “room” to perform better next year. It is because they are limited and everyone has an incentive to be on TOP so it is a really crowded place.
wiLQ
December 3, 2011 at 16:54
“TOP25 players don’t have “room” to perform better next year.”
I would agree with this argument for Top5, maybe Top10 but not for Top25 – they clearly have room to improve ;-) Obviously their upside is lower than downside but that in itself is an interesting point.
Earnest Hamberry
December 10, 2011 at 05:31
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