After a long hiatus, the preseason is finally upon us. Now come the discussion, speculation and, most importantly, the projections. Our very own SON has released his Top 100 H2H Rankings with more on the way. I am preparing the Top 155 Roto Projections just like last year, which should be ready to go by next week. In order to conquer the fantasy basketball maze, we must continue to learn, especially from our mistakes. That will be the focus of this article, as I highlight the players that greatly under or overperformed their preseason projections and focus on which statistical category had the most impact.

## The Process

Not the one that happened in Philadelphia complements of Hinkie, but I will ask you to trust it just like Sam did. I began by taking the final per game stats of the 155 players that comprised last years’ projections along with their stats from my projections. If you are unfamiliar with the rating process, you can read more here, but the short story is that the overall value of a player depends on how his stats fare against the average player. This overall value is a number that can be both positive and negative and represents the impact this player will have on your teams performance, especially in Roto where averages matter more. Going back to the method used, for each one of the 9 statistical categories (plus the overall value), I calculated the standard deviation to quantify the value of the corresponding stat. Then all the projected stats were compared with the actual 2017-2018 player stats. I decided that a ‘miss’ qualified as a difference that is greater than the standard deviation of the corresponding category.

I know I suck at explaining concepts, but I promise that the conclusions derived are worth your time. Below, I have gathered all the ‘miss’ players to give you a better idea. My projections are on each first row while the actual 2017-18 stats of the player can be seen on each second row.

 Value m/g p/g 3/g r/g a/g s/g b/g fg% fga ft% fta to/g Anthony Davis 0.814 35 27.81 0.6 11.5 2.1 1.3 2 0.5 20.3 0.804 8.6 2.1 1.123 36.4 28.15 0.7 11.1 2.3 1.5 2.6 0.534 19.5 0.828 8 2.2 Diff 0.308 0.33 0.1 -0.4 0.2 0.2 0.6 0.034 -0.8 0.024 -0.6 0.1 SD% 11.35 -93.1 -88.7 -84.8 -90.2 -50.9 20.9 -40.1 -77.7 -72.9 -66.9 -87.6 Kawhi Leonard 0.675 34 25.92 2 5.9 3.5 1.7 0.8 0.485 18.1 0.86 7.4 2.2 0.261 23.3 16.14 1.2 4.7 2.3 2 1 0.468 12.3 0.816 4.2 1.8 Diff -0.413 -9.78 -0.8 -1.2 -1.2 0.3 0.2 -0.017 -5.8 -0.044 -3.2 -0.4 SD% 48.98 98.3 -9.7 -54.4 -41.5 -26.4 -59.6 -70 61.3 -50.4 76.4 -50.5 Russell Westbrook 0.577 34 26.45 2 8.6 10.7 1.7 0.4 0.435 19.8 0.84 8.6 4.4 0.286 36.4 25.38 1.2 10.1 10.2 1.9 0.3 0.449 21.1 0.737 7.1 4.8 Diff -0.291 -1.06 -0.8 1.5 -0.5 0.2 -0.1 0.014 1.3 -0.103 -1.5 0.4 SD% 5.05 -78.3 -9.7 -43.1 -75.6 -50.9 -79.8 -75.3 -63.8 16 -17.3 -50.5 Rudy Gobert 0.496 35 17.51 0 12.8 1.8 0.7 2.7 0.63 10.6 0.64 6.5 1.9 0.216 32.2 13.45 0 10.7 1.4 0.8 2.3 0.623 7.9 0.682 5.3 1.9 Diff -0.279 -4.05 0 -2.1 -0.4 0.1 -0.4 -0.007 -2.7 0.042 -1.2 0 SD % 0.93 -17.7 -100 -20.3 -80.5 -75.4 -19.3 -87.6 -24.9 -52.6 -33.8 -100 Myles Turner 0.390 34 18.16 0.9 7.8 1.6 1 2.2 0.48 13.4 0.8 5.5 1.7 0.004 28.3 12.77 0.9 6.4 1.3 0.6 1.8 0.479 9.8 0.777 3.2 1.5 Diff -0.386 -5.38 0 -1.4 -0.3 -0.4 -0.4 -0.001 -3.6 -0.023 -2.3 -0.2 SD % 39.29 9.2 -100 -46.9 -85.3 -1.9 -19.3 -98.2 0.1 -74 26.7 -75.2 John Wall 0.317 36 23.18 1.2 4.3 10.5 1.9 0.6 0.45 18.2 0.79 7.1 4.1 0.013 34.4 19.42 1.5 3.6 9.6 1.4 1.1 0.421 16.2 0.726 5.9 3.9 Diff -0.304 -3.76 0.3 -0.7 -0.9 -0.5 0.5 -0.029 -2 -0.064 -1.2 -0.2 SD% 9.78 -23.6 -66.1 -73.4 -56.1 22.6 0.8 -48.9 -44.3 -27.8 -33.8 -75.2 Mike Conley 0.229 33 19.04 2.5 3.5 6.4 1.3 0.2 0.445 14.2 0.85 4.6 2.2 -0.1966 31.1 17.08 2 2.3 4.1 1 0.3 0.381 14 0.803 5.5 1.5 Diff -0.4264 -1.96 -0.5 -1.2 -2.3 -0.3 0.1 -0.064 -0.2 -0.047 0.9 -0.7 SD% 53.78 -60.1 -43.6 -54.4 12 -26.4 -79.8 12.6 -94.4 -47 -50.3 -13.4 Nerlens Noel 0.201 26 10.79 0 7.6 1 1.5 1.4 0.6 7.3 0.7 2.9 1.3 -0.301 16.3 4.52 0 5.8 0.7 1.1 0.8 0.524 3.6 0.75 1 0.9 Diff -0.503 -6.26 0 -1.8 -0.3 -0.4 -0.6 -0.076 -3.7 0.05 -1.9 -0.4 SD% 81.49 27 -100 -31.7 -85.3 -1.9 20.9 33.8 2.9 -43.6 4.7 -50.5 Brook Lopez 0.179 30 19.57 1.4 6.4 2.1 0.5 1.8 0.48 14.8 0.81 4.9 2.2 -0.19 23.4 12.92 1.5 4 1.7 0.4 1.3 0.465 10.7 0.703 2.1 1.3 Diff -0.37 -6.64 0.1 -2.4 -0.4 -0.1 -0.5 -0.015 -4.1 -0.107 -2.8 -0.9 SD% 33.48 34.8 -88.7 -8.9 -80.5 -75.4 0.8 -73.5 14 20.5 54.3 11.2 Isaiah Thomas 0.137 33 21.35 2.4 2.7 5.5 0.8 0.1 0.445 14.2 0.89 7.1 2.4 -0.486 26.9 15.2 1.7 2.1 4.8 0.5 0.1 0.373 13.2 0.893 4.1 3 Diff -0.624 -6.14 -0.7 -0.6 -0.7 -0.3 0 -0.072 -1 0.003 -3 0.6 SD% 125 24.6 -21 -77.2 -65.9 -26.4 -100 26.7 -72.1 -96.6 65.3 -25.8 Victor Oladipo 0.033 33 18.32 1.5 4.5 4 1.5 0.5 0.44 15.4 0.78 4.2 2.3 0.514 34 23.12 2.1 5.2 4.3 2.3 0.8 0.478 17.9 0.799 4.9 3 Diff 0.481 4.79 0.6 0.7 0.3 0.8 0.3 0.038 2.5 0.019 0.7 0.7 SD% 73.52 -2.68 -32.3 -73.4 -85.3 96.1 -39.5 -33.1 -30.4 -78.5 -61.4 -13.4 LaMarcus Aldridge 0.025 32 16.3 0.3 7.3 1.8 0.7 1.1 0.47 14.2 0.83 3.2 1.3 0.35 33.4 23.19 0.4 8.5 2 0.6 1.2 0.51 18 0.837 5.3 1.5 Diff 0.324 6.89 0.1 1.2 0.2 -0.1 0.1 0.04 3.8 0.007 2.1 0.2 SD% 17.09 39.7 -88.7 -54.4 -90.2 -75.4 -79.8 -29.5 5.6 -92.1 15.7 -75.2 Avery Bradley 0.019 35 17.58 2 4.5 2.5 1.4 0.2 0.455 15.4 0.75 2.1 1.7 -0.432 31.2 14.24 1.6 2.5 2 1.1 0.2 0.414 13.6 0.768 1.8 2.2 Diff -0.451 -3.34 -0.4 -2 -0.5 -0.3 0 -0.041 -1.8 0.018 -0.3 0.5 SD% 62.87 -32.1 -54.8 -24.1 -75.6 -26.4 -100 -27.8 -49.9 -79.7 -83.4 -38.1 Michael Kidd-Gilchrist -0.059 29 9.12 0 6.5 1.5 1.1 0.9 0.49 7.6 0.76 2.2 0.6 -0.461 25 9.23 0 4.1 1 0.7 0.4 0.504 7.6 0.684 2.3 0.7 Diff -0.402 0.11 0 -2.4 -0.5 -0.4 -0.5 0.014 0 -0.076 0.1 0.1 SD% 45.07 -97.6 -100 -8.9 -75.6 -1.9 0.8 -75.3 -100 -14.3 -94.4 -87.6 Andre Drummond -0.09 29 13.88 0 14 1 1.5 1.2 0.53 11.5 0.36 4.7 1.9 0.284 33.7 15.04 0 16 3 1.5 1.7 0.529 11.3 0.605 5.1 2.6 Diff 0.374 1.15 0 2 2 0 0.5 -0.001 -0.2 0.245 0.4 0.7 SD% 35.21 -76.5 -100 -24.1 -2.5 -100 0.8 -98.2 -94.4 176 -77.9 -13.4 Marquese Chriss -0.099 29 13.86 1.3 7.5 1.3 1.1 1.3 0.455 10.9 0.63 4.2 2 -0.462 21.2 7.72 0.8 5.5 1.2 0.7 1 0.423 6.6 0.608 2.2 1.5 Diff -0.363 -6.14 -0.5 -2 -0.1 -0.4 -0.3 -0.032 -4.3 -0.022 -2 -0.5 SD% 30.98 24.5 -43.6 -24.1 -95.1 -1.9 -39.5 -43.6 19.5 -75.2 10.2 -38.1 Cody Zeller -0.176 24 8.83 0 6.1 1.3 0.8 0.9 0.56 6.2 0.7 2.7 0.9 -0.48 19 7.15 0.1 5.3 0.9 0.4 0.6 0.552 4.7 0.718 2.6 1 Diff -0.303 -1.67 0.1 -0.8 -0.4 -0.4 -0.3 -0.008 -1.5 0.018 -0.1 0.1 SD% 9.40 -65.9 -88.7 -69.6 -80.5 -1.9 -39.5 -85.9 -58.2 -79.7 -94.4 -87.6 Boban Marjanovic -0.188 16 9.56 0 6.4 0.6 0.5 0.5 0.6 6.1 0.8 2.8 0.6 -0.619 9.1 6.33 0 3.9 0.6 0.3 0.3 0.534 4 0.794 2.6 1 Diff -0.431 -3.22 0 -2.5 0 -0.2 -0.2 -0.066 -2.1 -0.006 -0.2 0.4 SD% 55.70 -34.6 -100 -5.1 -100 -50.9 -59.6 16.1 -41.6 -93.2 -88.9 -50.5 Tristan Thompson -0.217 30 8.61 0 8.6 1 0.5 1 0.59 5.9 0.57 2.9 0.8 -0.58 20.2 5.84 0 6.7 0.6 0.3 0.3 0.559 4.5 0.544 1.5 0.7 Diff -0.363 -2.76 0 -1.9 -0.4 -0.2 -0.7 -0.031 -1.4 -0.026 -1.4 -0.1 SD% 31.06 -43.8 -100 -27.9 -80.5 -50.9 41.1 -45.4 -61 -70.7 -22.8 -87.6 Josh Richardson -0.244 28 8.9 1 3.5 2.2 1 0.7 0.4 8.1 0.79 1.8 0.8 0.058 33.2 12.95 1.6 3.5 2.9 1.5 0.9 0.451 10.9 0.845 1.8 1.7 Diff 0.302 4.05 0.6 0 0.7 0.5 0.2 0.051 2.8 0.055 0 0.9 SD% 9.25 -17.8 -32.3 -100 -65.9 22.6 -59.6 -10.2 -22.1 -38 -100 11.2 Willy Hernangomez -0.257 24 9.52 0.1 7.8 1.8 0.8 0.4 0.5 7.8 0.74 2.2 1.5 -0.649 10.3 5.1 0.1 3.8 0.7 0.4 0.3 0.552 3.4 0.656 1.9 0.7 Diff -0.392 -4.42 0 -4 -1.1 -0.4 -0.1 0.052 -4.4 -0.084 -0.3 -0.8 SD% 41.44 -10.2 -100 51.7 -46.4 -1.9 -79.8 -8.4 22.3 -5.3 -83.4 -1 Markelle Fultz -0.262 30 14.2 1.2 4.8 5.2 0.9 0.6 0.44 11.9 0.65 3.9 2.3 -0.663 18.1 7.11 0 3.1 3.8 0.8 0.3 0.405 7.9 0.476 1.5 1.2 Diff -0.4 -7.09 -1.2 -1.7 -1.4 -0.1 -0.3 -0.035 -4 -0.174 -2.4 -1.1 SD% 44.58 43.8 35.3 -35.5 -31.8 -75.4 -39.5 -38.3 11.2 96 32.3 36 Patrick Patterson -0.264 26 7.55 1.6 4.8 1.3 0.7 0.5 0.42 6.2 0.75 1 0.7 -0.561 15.5 3.86 0.8 2.4 0.7 0.6 0.3 0.398 3.2 0.87 0.6 0.4 Diff -0.296 -3.68 -0.8 -2.4 -0.6 -0.1 -0.2 -0.022 -3 0.12 -0.4 -0.3 SD% 6.88 -25.2 -9.7 -8.9 -70.7 -75.4 -59.6 -61.2 -16.5 35.2 -77.9 -62.9 Ben Simmons -0.283 28 11.86 0 6.5 5.7 1 0.8 0.48 10.2 0.67 3.1 3.3 0.051 33.7 15.75 0 8.1 8.1 1.7 0.8 0.545 12.3 0.56 4.2 3.4 Diff 0.334 3.89 0 1.6 2.4 0.7 0 0.065 2.1 -0.11 1.1 0.1 SD% 20.45 -21.1 -100 -39.3 16.9 71.6 -100 14.4 -41.6 23.9 -39.3 -87.6 Tyreke Evans -0.287 22 10.98 1.1 4 4 1 0.2 0.42 9.5 0.76 2.5 1.6 0.092 30.9 19.44 2.2 5.1 5.2 1.1 0.3 0.452 15.6 0.785 4 2.3 Diff 0.379 8.46 1.1 1.1 1.2 0.1 0.1 0.032 6.1 0.025 1.5 0.7 SD% 36.89 71.6 24 -58.2 -41.5 -75.4 -79.8 -43.6 69.6 -71.8 -17.3 -13.4

No, the last row of numbers for each player was neither produced by the Matrix nor by me repeatedly hitting the keyboard with my head. They refer to the difference between my projections and the actual player stats depicted on the line above and represent this difference as a percentage of the standard deviation of the corresponding stat. The smaller the number, the more accurate my projections were. If this percentage is negative, then the aforementioned difference is smaller than the standard deviation and is not considered a ‘miss’. If it is positive then it is a ‘miss’ and it is highlighted with red color. The higher this number is, the greater the ‘miss’.

## ‘Missed’ Players

I salute you for making it this far. Let’s dig deeper into each of the ‘missed’ players.

Anthony Davis: The 0.6 blocks made all the difference, as the rest of the projections were pretty close. Huge year from AD.

Kawhi Leonard: Injury. I thought the ‘minor’ preseason injury he had last year was nothing to worry about. We all know how it turned out.

Russel Westbrook: 0.8 fewer triples and a mysterious regression from the FT line destroyed his value, especially for roto. I expect the ft% percentage to improve this year, not so sure for the triples.

Rudy Gobert: I was too optimistic for Gobert with the arrival of Ricky Rubio and the potential killer P&R. No single category stands out as a ‘miss’ but collectively they all hurt his value.

Myles Turner: The same can be said for Myles, who I thought would take over with Paul George in OKC. Instead, another guy who appears later on this list, became the Alpha of the Pacers team.

Mike Conley: Injury

Nerlens Noel: He never gained Carlisle’s trust and thus his minutes disappeared. He was a bold prediction that unfortunately didn’t pan out.

Brook Lopez: His transition to the Lakers was not a smooth one as he lost a ton of the usage he had with the Nets

Isaiah Thomas: Disastrous year. At least he started the year injured so his ADP was lower than what was expected after the explosive year he had in 2016-17

Victor Oladipo: Bid farewell to Oladipo, the useful glue guy, and say hello to Oladipo the superstar. He broke out in a major way and I don’t know if anyone saw it coming, I certainly did not. He improved across the board and more than passes the eye test.

LaMarcus Aldridge: The Kawhi saga only benefitted LMA, who saw both his fg and ft attempts skyrocket, greatly surpassing preseason expectations.

Andre Drummond: From 0.36 to 0.605 in ft% ?

The standard deviation percentage is a ridiculous 176.04%, and along with an increase in blocks, helped skyrocket Drummond’s value.

Ben Simmons: I expected Simmons to be okay-to-good his ‘rookie’ year, but not thaaaaat good.

Tyreke Evans: He found the perfect opportunity in Memphis to revive his career with a one year deal. Don’t expect the same value playing for a much more talented Indiana team.

Josh Richardson: I thought a healthy Dion Waiters and the abundance of talent on the Heat backcourt would reduce his usage. I thought wrong.

Bradley, Kidd-Gilchrist, Chriss, Zeller, Marjanovic, Thompson, Hernangomez, Fultz, Patterson: Injury or bad play reduced their minutes way below the projected values.

## Conclusions

After repeating the process mentioned on all the eligible players (I excluded Hayward and Lin as they played just 1 game), I calculated the absolute mean difference in total value between my projection and the actual stats of the 2017-18 season. The result is 0.156 per player. You can stop clapping now. No really…. This number alone does not mean much, though it is encouraging that it is far smaller than the average deviation. It can be used for comparison sake, like when I repeat this excercise next year. Hopefully next year the number will be even smaller, indicating more accuracy in my projections. Or if you had your own projections you can follow the same procedure to keep a general estimation of the accuracy of your projections year-to-year or compare your accuracy with me. Furthermore, the standard deviation can be helpful to spot anomalies in stats that have a low chance of repeating so that you can draft accordingly. Two great examples are Westbrook’s ft%, which is only logical to improve and Nikola Vucevic’s percentages two years ago. He experienced a sharp decline in ft% during 2016-17 which was bound to improve and it indeed improved this past season.

I think I am approaching the word cap of my internet provider and I feel bad for SON, who has to check the whole article, so I will wrap it up for now and will see you soon again with my Top 155 Roto Projections !!