This week's column charts fantasy values across the top-216 players last season, as gauged by end-of-season rankings. Although there is no 'correct' way to draft a team, the patterns of value which emerge are part of the puzzle when developing informed draft strategies and assembling a winning team.
In each of the charts below, the horizontal axis marks the progression from the most valuable nine-cat player for 2013-14 (Kevin Durant) to the least valuable (Tony Wroten), while the vertical axis displays the 'standardized scores' for that category. If you're uncertain what that means, you can read my imperfect overview and/or this Wikipedia entry before wading into the stats below. As usual, these are per-game values for the regular season and my statistical population of 216 players excludes anyone who played under 30 games or averaged under 20 minutes per game. For my full 2013-14 fantasy rankings, click here.
The range for the vertical axis (standardized scores) is the same for most charts, from +4.0 to -3.0, though I had to expand the range for FT percentage and blocks. This allows for accurate comparison from one chart to another, which wouldn't be the case if I'd used the raw totals for points, rebounds, etc. I've also included polynomial 'trendlines' to give a rough idea of how values fluctuate as we progress from the most to least valuable players. Keep in mind that a league with 12 teams and 14 players per team will only include 168 players, whereas the population size of these charts is 216. I included extra players so that deep-league owners can make better use of this information, and everyone else should simply ignore the tail ends of the charts.
Points Mean: 12.88
Points Standard Deviation: 4.88
3-pointers Mean: 0.97
3-pointers Standard Deviation: 0.77
(these FG values are weighted by attempts, but I forgot to label the chart as such)
FG% Mean: 46%
FG% Standard Deviation: 5%
FGA Mean: 10.46
FGA Standard Deviation: 3.63
(these FT values are weighted by attempts, but I forgot to label the chart as such)
FT% Mean: 76.0%
FT% Standard Deviation: 10.0%
FTA Mean: 3.06
FTA Standard Deviation: 1.79
Rebounds Mean: 5.03
Rebounds Standard Deviation: 2.59
Assists Mean: 2.91
Assists Standard Deviation: 2.04
Steals Mean: 0.94
Steals Standard Deviation: 0.42
Blocks Mean: 0.52
Blocks Standard Deviation: 0.49
Turnovers Mean: 1.75
Turnovers Standard Deviation: 0.73
The idea here is to have an easy and objective 'cheat sheet' for the concentrations and fluctuations of value in a given category. Combined with last week's examination of where most value-picks and busts fall in a typical 12-team draft, and future columns in a similar vein, this should give attentive Rotoworld readers an edge in next season's fantasy drafts.
You can see, for instance, that points have a relatively uniform downward slope across the top-216 players, confirming the logic of targeting a scorer in the early rounds. The same can't be said for categories such as blocks (with a few dominant outliers scattered across the top-216) and 3-pointers (with near constant peaks and valleys). Free Throw Percentage begins with a handful of strong options, but after the top 40 players it becomes harder to find a positive contributor than someone who will drag you down in that category. Field Goal Percentage, on the other hand, reveals only a mild advantage in the early rounds, with plenty of solid options available as the draft progresses -- one implication is that you could take a volume shooter like James Harden early in the draft and feel comfortable about raising your FG Percentage in later rounds.
You can draw plenty more conclusions by comparing the above charts and thinking about them in the context of a draft-day strategy. Next week I will dig deeper into these categories by revisiting the idea of 'statistical scarcity' -- you can see it at work in the blocks chart, for instance, where a few player values spike far above their peers, thus raising that category's mean and standard deviation, while the vast majority of players hover below the league average. If you notice any interesting trends in the data above, or if you have any questions, write me an email or send me a Direct Message on Twitter. I appreciate the enthusiastic response to my recent columns, and if you’re looking for more information or just want to dig around the numbers/charts used here, you can view the data here.