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Cover Me Gently

One thing I also will post a bunch, besides talk about football or solar, is music.  Particularly electronic music.  I used to post over at loudmusicallday.com with some friends but that kind of died off and I discover too much good music to not share.


Below are some good covers I've found recently:


Thief - Cry Me a River


Cassie Steele - Sex and Candy


Animal-Music - Mr. Brightside

Reviewing the Top 5 Mocked Picks

Digging a little deeper into aggregated mock drafts, the great resource Walter Football has a very comprehensive mock draft database.  They have gone through the trouble of tabulating the top 5 picks of each mock in one big table.  That table looked something like this:



So I quickly tabulated the top 5 results from the mock draft database, only looked at mocks in 2014 and graphed their evolution over the last 4 months of the 2014 NFL Draft process.  This ended up being 428 mocks for each pick in the top 5 from what is likely the largest mock draft database on the web.  The graph is "Month" on the X axis, January through May and "Average pick" on the Y axis for some of the top prospects.  So Jadeveon Clowney having an average close to 1 in May means, wait for it, that the "consensus" pick he would be selected was #1.



With this graph it is easy to visualize the consensus order of each prospect over time.  Some interesting insights I'd love to know more about:

  • Did mock drafters become more comfortable with Clowney to the Texans over time or is that a reflection of just what they were hearing?
  • What is responsible for the separation between Blake Bortles and Clowney starting in March?  They're pretty close and then Clowney clearly separates himself from Bortles and the pack to be the clear #1 pick.  Combine or pro day?
  • Did the Jake Matthews get worse in March or did people only realize then that Glen Robinson was a better OT?
  • Who was ultimately responsible for leading the Johnny Manziel hype train? The media, the fans or was it all smokescreens from teams?


Thoughts on the Solar Ambassador program by SolarCity

"Luck is what happens when preparation meets opportunity" - Seneca



Cliche, I know.  But even behind overused, corny phrases lies some truth.  What might look like luck after the fact was likely only made possible because of many hours spent trying to better yourself in the past.  It's good to keep that in perspective because only by taking advantage of those opportunities can you create your own luck.

Full disclosure: I've worked for a solar energy provider called SolarCity for the past 4 years.  I've seen it grow from a small Silicon Valley startup to what is now a publicly traded behemoth.  I love the company and it has been a great ride so far.  I recognize this opportunity to be a part of a potentially special company from its roots is a once in a lifetime event and I don't want to take it for granted.  It has been great for me professionally too as I've had the chance to work with a lot of truly great people and feel like I have more impact than I would at a different company.

Anyways I have spent a lot of time at work and outside of it thinking about ways to help spread solar adoption, for SolarCity's benefit as well as the environment's benefit too.  One of the themes that consistently stuck out to me was enabling solar adoption to spread virally by giving more power to the public in the process.  Our old referral process involved a referrer giving someone's name and contact information for one of our salespeople to follow up with.  This was beneficial because it allowed just about anyone to get paid for recommending solar energy systems for others but I felt it didn't go far enough  to really become a monster and spread by itself without additional SolarCity resources spent on it.  

About a year ago I presented an idea to a lunchtime gathering of coworkers that was intended to be a forum to share thoughts.  This idea was to make the referral process itself more viral by making the act of sharing quicker by cutting out the salesperson from the process and allow anybody to create very basic sales proposals for anyone else themselves.  I believed that this would encourage sharing because the referrer would assume personal responsibility over spreading the message, and that our current process was too passive.  My thoughts as to the medium to use to spread it was a social media or mobile game that would simplify sharing by making the referral process more fun.  People could set up networks of additional referrers and be rewarded when they referred others.  Who wouldn't want to kill a couple minutes in line or waiting on something as well as potentially make a couple hundred bucks by promoting the spread of clean energy?

Slide explains why a distributed network of referrers is better

Slide that explains why decreasing the amount of time through the viral loop (time to share a proposal) is important

After vetting the idea with Operations leaders, I then met with the heads of our Marketing and Sales departments and presented it to them.  By this time I had done some more prep and had found viral coefficient metrics for our referral program that previously weren't known.  Basically we would improve on the virality of the program by really encouraging not only people to refer others to get a solar energy system installed but also to get those who you refer to   get more people to refer.  We would ride the inherent viral aspects of multi-level marketing to help grow the program.  They were initially intrigued but my communication with them fell off when I moved away from our HQ to work regionally.  

Slide that explains why the referral process in the early market of solar adoption doesn't work in the mainstream market of adoption

Slide that explains how we could "cross the chasm" to become an adopted technology

Recently though we rolled out a program called "Solar Ambassadors" that will allow anyone to build a referral network of up to three levels (someone you tell, someone that person tells and then someone else that THAT person tells) and be compensated if any of those referrers lead to solar energy customers.  I'm happy to see that principles of the idea are still alive and well and that it will be given a chance to prove its worth.  Some very smart people have been working on this for a long time it looks like and I think it has the makings of being a successful way to grow and acquire customers.  Even if it's not exactly the way I would've done it, I think the basic tenants of improving the viral coefficient of the referral program are still there.  This program allows for you to help promote the adoption of a technology that will change the world for the better while saving people money immediately. 

I'm sharing this story because I am proud to see something I worked hard on actually fleshed out and live, if it does turn out to be a big success I'll be able to take some small bit of credit for doing my part in the beginning, whether directly or indirectly.  

: )

//Edit, I guess employees aren't eligible for the Ambassadors program, but I encourage everyone else to join

Reviewing the Draft Order Prediction

So I quickly compared how my simple prediction fared against the 2014 mocks I collected and I will add the comparison numbers and rankings onto this post later today.  But I think it turned out pretty well, I believe by comparing the predicted and mocked picks to the actual selection order, I had an R-square value of 0.57 and the highest mock I looked at was Mayock's who had a R-square of 0.55.

What I find particularly impressive about this prediction is how simple it was -- all it looked at was a Top 100 composite ranking, height, weight, arm length and average mock draft position.  It  outperformed all the expert's mock accuracy and I didn't spend months working on it and I had no inside knowledge, I just aggregated what others were hearing and thought.


To review, my selection prediction for the 2013 Draft had a R-square value of 0.61 and the next highest was Todd McShay at 0.5.  My selection prediction for the 2014 Draft had a R-square value of 0.57 and the next highest of the ones I looked at was Mike Mayock at 0.55.  I could have looked at more mocks to be more comprehensive, some mocks might have performed higher than those that were looked at but if they were included in the dataset, the prediction would have improved as well.

Predicted 2014 Draft selection order

Below is the predicted order based off of the following variables:


Top 100 consensus ranking
Height
Weight
Arm Length
Avg Mock Draft Points


Order Player Pos Top100 Height Weight Arms Avg Mock Draft points Pred Mock Draft Points
1 Jadeveon Clowney DE 1 77 266 35 2911.111111 2656.180654
2 Greg Robinson OT 3 77 332 35 2600 2414.823449
3 Khalil Mack OLB 2 75 251 33 2188.888889 1990.70493
4 Sammy Watkins WR 4 73 211 32 1833.333333 1646.896388
5 Taylor Lewan OT 10 79 309 34 1494.444444 1581.072755
6 Mike Evans WR 6 77 231 35 1450 1566.586301
7 Jake Matthews OT 5 77 308 33 1572.222222 1558.98738
8 Johnny Manziel QB 11 72 207 31 1572.222222 1386.016481
9 Anthony Barr OLB 13 77 255 34 1094.444444 1250.329576
10 Eric Ebron TE 12 76 250 33 1175 1247.941459
11 Aaron Donald DT 8 73 285 33 1215.555556 1219.553732
12 Zack Martin OT 15 76 308 33 1144.444444 1216.86755
13 Justin Gilbert CB 16 72 202 33 1188.888889 1188.137736
14 Blake Bortles QB 14 77 232 33 1044.444444 1170.458139
15 Cyrus Kouandjio OT 34 79 322 36 797 1139.066329
16 C.J. Mosley ILB 9 74 234 33 1058.333333 1126.560729
17 Odell Beckham Jr. WR 18 71 198 33 1022.222222 1044.672079
18 Ha Ha Clinton-Dix FS 18 73 208 32 990 1011.699527
19 Kyle Fuller CB 27 72 190 33 950 1006.921183
20 Morgan Moses OT 36 78 314 35 695.8333333 1000.100097
21 Timmy Jernigan DT 30 74 299 32 925 966.0854087
22 Ja'Wuan James OT 57 78 311 35 660 965.7691372
23 Ra'Shede Hageman DT 24 78 310 34 640 918.4423979
24 Jimmie Ward SS 35 71 193 31 950 895.0107152
25 Calvin Pryor FS 22 71 207 31 927.7777778 881.5884295
26 Jace Amaro TE 39 77 265 34 606.6666667 874.7488037
27 Darqueze Dennard CB 19 71 199 30 975.5555556 874.4638941
28 Kony Ealy DE 26 76 273 34 620 869.9957469
29 Joel Bitonio OT 46 76 302 34 597.5 842.0860553
30 Ryan Shazier OLB 21 73 237 32 766.1111111 839.6863546
31 Derek Carr QB 33 74 214 32 743.3333333 839.5957556
32 Dee Ford DE 31 74 252 33 680 833.3038955

This was published at 1:30 am MST on 5/8/14