In this series, we'll look at how valuable draft picks are by round and to each manager. Who does the best job of drafting, and how likely is any given manager to find a starter in each round?
For the most part, these findings support Ben's Opportunity Cost Model, only with less depth and fewer interesting findings. Still, this descriptive account is focused on how managers themselves did, irrespective of where their draft picks landed. So instead of asking whether managers were able to maximize their assets, we're starting with the premise that where you picked doesn't matter. Let's just compare how we did against each other.
To those who are prone to losing their confidence: you might want to look at this after Sunday's draft.
For the remainder of this article, I've split up our league into its divisions. I'm sure you'll notice why very quickly - one division did very well, while another struggled. There are a number of different ways to evaluate how managers did. Below, I've shown sums and averages for just skaters (no goalies) in four areas. First, the sum of the fantasy points scored by all skaters drafted by that team. Second, that number averaged over how many skaters they selected (remembering that some people took more goalies than others). Third, I've taken just the average of the first five skaters selected, which in addition to everyone's 8 keepers, would make up the 13 man starting roster. Finally, I've looked into teams that had particularly bad injury trouble, separating out any skater who scored fewer than 20 fantasy points who could have reasonably been assumed to score more if not for injury. Players who were sent down from the NHL squad, or who scored fewer than 20 fantasy points for any reason other than injury are left in for the final average. (Sorry, the blogger editor has squeezed the formatting a bit)
Total FPTs of Skaters Drafted | AVG FPTs | AVG FPTs of 1st Five Picks | # of injured skaters with < 20FPTs | Corrected AVG FPTs | |
BATS | 766.1 | 63.84 | 58.18 | 0 | 63.84 |
BAY* | 768.6 | 54.90 | 66.18 | 2 | 69.87 |
ESCH | 932.6 | 77.72 | 76.42 | 0 | 77.72 |
ESR! | 1017.8 | 78.29 | 90.58 | 0 | 78.29 |
TODD | 972.8 | 69.49 | 79.68 | 0 | 69.49 |
UNGE | 939.1 | 72.24 | 76.22 | 0 | 72.24 |
CATS | 603.7 | 54.88 | 51.14 | 0 | 54.88 |
EM* | 733.7* | 61.14 | 76.58 | 2 | 73.37 |
GUNS | 695.7 | 53.52 | 59.88 | 1 | 57.98 |
NJR | 706 | 54.31 | 53.64 | 2 | 64.18 |
REMS | 920.3 | 65.74 | 80.52 | 0 | 65.74 |
STST | 677.5 | 56.46 | 71.18 | 1 | 61.59 |
* The Mercury’s had only 13 total draft picks as they traded one draft pick and a player for 2 players during the draft itself. Billcago had 15 through the same deal.
When we look at which teams struggled in the draft, injury likely played a role in each case. If anyone wants to build a model using number of games played (balanced for an 'expected' games played) or per game averages, I'd be excited to see it. For now, I've only been sympathetic to severe injury. The Mercury's, for example, drafted David Perron (14 FPTs), Matt Lombardi (0.1 FPTs), and Cam Barker (7.9 FPTs). Two of those three players had terrible concussions very early in the season that caused them to miss the entire year. Neither were of high risk for concussions, so the Mercury's suffered some bad luck (along with one bad choice in Cam Barker). Their average rises over 12 points when we take Perron and Lombardi out, and they join the Eschatologists and ESR! in the top 3 drafting teams last year (which were identified by Ben in his Opportunity Cost model).
Next, you can see how you did pick by pick. This is ordered by the order of your picks, not by round. So if you didn't have a fourth round pick, your fourth marker is your fifth rounder. Gaps are goalies.
You likely notice that only one team in the Wales division dips below that 20 pt barrier, while every single team in the Campbell division has at least one pick with 20 pts or fewer. What's up Campbell Div'ers? Did you guys not understand the draft? Did you think you had an affiliate team you needed to fill as well?
Finally, one last oddity in the draft. The Batarangs had to leave the draft just before their tenth pick. We decided to do the rest of his draft by consensus. Before the Batarangs left, his average was 57.77 across 7 skaters selected. After he left we picked 5 skaters for him between rounds 12 and 14. The average of those last 5 skaters was 72.34, a full 14 points higher than his first 5 selected skaters. This may be a perfect case study to show that it didn't matter what round you select in - some managers just did better than others.
What say you: Whose draft are you most surprised by? What should we look at next? Who wants to buy me another SPSS license?
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