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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.

Valuem/gp/g3/gr/ga/gs/gb/gfg%fgaft%ftato/g
Anthony Davis0.8143527.810.611.52.11.320.520.30.8048.62.1
1.12336.428.150.711.12.31.52.60.53419.50.82882.2
Diff0.3080.330.1-0.40.20.20.60.034-0.80.024-0.60.1
SD%11.35-93.1-88.7-84.8-90.2-50.920.9-40.1-77.7-72.9-66.9-87.6
Kawhi Leonard0.6753425.9225.93.51.70.80.48518.10.867.42.2
0.26123.316.141.24.72.3210.46812.30.8164.21.8
Diff-0.413-9.78-0.8-1.2-1.20.30.2-0.017-5.8-0.044-3.2-0.4
SD%48.9898.3-9.7-54.4-41.5-26.4-59.6-7061.3-50.476.4-50.5
Russell Westbrook0.5773426.4528.610.71.70.40.43519.80.848.64.4
0.28636.425.381.210.110.21.90.30.44921.10.7377.14.8
Diff-0.291-1.06-0.81.5-0.50.2-0.10.0141.3-0.103-1.50.4
SD%5.05-78.3-9.7-43.1-75.6-50.9-79.8-75.3-63.816-17.3-50.5
Rudy Gobert0.4963517.51012.81.80.72.70.6310.60.646.51.9
0.21632.213.45010.71.40.82.30.6237.90.6825.31.9
Diff-0.279-4.050-2.1-0.40.1-0.4-0.007-2.70.042-1.20
SD %0.93-17.7-100-20.3-80.5-75.4-19.3-87.6-24.9-52.6-33.8-100
Myles Turner0.3903418.160.97.81.612.20.4813.40.85.51.7
0.00428.312.770.96.41.30.61.80.4799.80.7773.21.5
Diff-0.386-5.380-1.4-0.3-0.4-0.4-0.001-3.6-0.023-2.3-0.2
SD %39.299.2-100-46.9-85.3-1.9-19.3-98.20.1-7426.7-75.2
John Wall0.3173623.181.24.310.51.90.60.4518.20.797.14.1
0.01334.419.421.53.69.61.41.10.42116.20.7265.93.9
Diff-0.304-3.760.3-0.7-0.9-0.50.5-0.029-2-0.064-1.2-0.2
SD%9.78-23.6-66.1-73.4-56.122.60.8-48.9-44.3-27.8-33.8-75.2
Mike Conley0.2293319.042.53.56.41.30.20.44514.20.854.62.2
-0.196631.117.0822.34.110.30.381140.8035.51.5
Diff-0.4264-1.96-0.5-1.2-2.3-0.30.1-0.064-0.2-0.0470.9-0.7
SD%53.78-60.1-43.6-54.412-26.4-79.812.6-94.4-47-50.3-13.4
Nerlens Noel0.2012610.7907.611.51.40.67.30.72.91.3
-0.30116.34.5205.80.71.10.80.5243.60.7510.9
Diff-0.503-6.260-1.8-0.3-0.4-0.6-0.076-3.70.05-1.9-0.4
SD%81.4927-100-31.7-85.3-1.920.933.82.9-43.64.7-50.5
Brook Lopez0.1793019.571.46.42.10.51.80.4814.80.814.92.2
-0.1923.412.921.541.70.41.30.46510.70.7032.11.3
Diff-0.37-6.640.1-2.4-0.4-0.1-0.5-0.015-4.1-0.107-2.8-0.9
SD%33.4834.8-88.7-8.9-80.5-75.40.8-73.51420.554.311.2
Isaiah Thomas0.1373321.352.42.75.50.80.10.44514.20.897.12.4
-0.48626.915.21.72.14.80.50.10.37313.20.8934.13
Diff-0.624-6.14-0.7-0.6-0.7-0.30-0.072-10.003-30.6
SD%12524.6-21-77.2-65.9-26.4-10026.7-72.1-96.665.3-25.8
Victor Oladipo0.0333318.321.54.541.50.50.4415.40.784.22.3
0.5143423.122.15.24.32.30.80.47817.90.7994.93
Diff0.4814.790.60.70.30.80.30.0382.50.0190.70.7
SD%73.52-2.68-32.3-73.4-85.396.1-39.5-33.1-30.4-78.5-61.4-13.4
LaMarcus Aldridge0.0253216.30.37.31.80.71.10.4714.20.833.21.3
0.3533.423.190.48.520.61.20.51180.8375.31.5
Diff0.3246.890.11.20.2-0.10.10.043.80.0072.10.2
SD%17.0939.7-88.7-54.4-90.2-75.4-79.8-29.55.6-92.115.7-75.2
Avery Bradley0.0193517.5824.52.51.40.20.45515.40.752.11.7
-0.43231.214.241.62.521.10.20.41413.60.7681.82.2
Diff-0.451-3.34-0.4-2-0.5-0.30-0.041-1.80.018-0.30.5
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-0.059299.1206.51.51.10.90.497.60.762.20.6
-0.461259.2304.110.70.40.5047.60.6842.30.7
Diff-0.4020.110-2.4-0.5-0.4-0.50.0140-0.0760.10.1
SD%45.07-97.6-100-8.9-75.6-1.90.8-75.3-100-14.3-94.4-87.6
Andre Drummond-0.092913.8801411.51.20.5311.50.364.71.9
0.28433.715.0401631.51.70.52911.30.6055.12.6
Diff0.3741.1502200.5-0.001-0.20.2450.40.7
SD%35.21-76.5-100-24.1-2.5-1000.8-98.2-94.4176-77.9-13.4
Marquese Chriss-0.0992913.861.37.51.31.11.30.45510.90.634.22
-0.46221.27.720.85.51.20.710.4236.60.6082.21.5
Diff-0.363-6.14-0.5-2-0.1-0.4-0.3-0.032-4.3-0.022-2-0.5
SD%30.9824.5-43.6-24.1-95.1-1.9-39.5-43.619.5-75.210.2-38.1
Cody Zeller-0.176248.8306.11.30.80.90.566.20.72.70.9
-0.48197.150.15.30.90.40.60.5524.70.7182.61
Diff-0.303-1.670.1-0.8-0.4-0.4-0.3-0.008-1.50.018-0.10.1
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-0.188169.5606.40.60.50.50.66.10.82.80.6
-0.6199.16.3303.90.60.30.30.53440.7942.61
Diff-0.431-3.220-2.50-0.2-0.2-0.066-2.1-0.006-0.20.4
SD%55.70-34.6-100-5.1-100-50.9-59.616.1-41.6-93.2-88.9-50.5
Tristan Thompson-0.217308.6108.610.510.595.90.572.90.8
-0.5820.25.8406.70.60.30.30.5594.50.5441.50.7
Diff-0.363-2.760-1.9-0.4-0.2-0.7-0.031-1.4-0.026-1.4-0.1
SD%31.06-43.8-100-27.9-80.5-50.941.1-45.4-61-70.7-22.8-87.6
Josh Richardson-0.244288.913.52.210.70.48.10.791.80.8
0.05833.212.951.63.52.91.50.90.45110.90.8451.81.7
Diff0.3024.050.600.70.50.20.0512.80.05500.9
SD%9.25-17.8-32.3-100-65.922.6-59.6-10.2-22.1-38-10011.2
Willy Hernangomez-0.257249.520.17.81.80.80.40.57.80.742.21.5
-0.64910.35.10.13.80.70.40.30.5523.40.6561.90.7
Diff-0.392-4.420-4-1.1-0.4-0.10.052-4.4-0.084-0.3-0.8
SD%41.44-10.2-10051.7-46.4-1.9-79.8-8.422.3-5.3-83.4-1
Markelle Fultz-0.2623014.21.24.85.20.90.60.4411.90.653.92.3
-0.66318.17.1103.13.80.80.30.4057.90.4761.51.2
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%44.5843.835.3-35.5-31.8-75.4-39.5-38.311.29632.336
Patrick Patterson-0.264267.551.64.81.30.70.50.426.20.7510.7
-0.56115.53.860.82.40.70.60.30.3983.20.870.60.4
Diff-0.296-3.68-0.8-2.4-0.6-0.1-0.2-0.022-30.12-0.4-0.3
SD%6.88-25.2-9.7-8.9-70.7-75.4-59.6-61.2-16.535.2-77.9-62.9
Ben Simmons-0.2832811.8606.55.710.80.4810.20.673.13.3
0.05133.715.7508.18.11.70.80.54512.30.564.23.4
Diff0.3343.8901.62.40.700.0652.1-0.111.10.1
SD%20.45-21.1-100-39.316.971.6-10014.4-41.623.9-39.3-87.6
Tyreke Evans-0.2872210.981.14410.20.429.50.762.51.6
0.09230.919.442.25.15.21.10.30.45215.60.78542.3
Diff0.3798.461.11.11.20.10.10.0326.10.0251.50.7
SD%36.8971.624-58.2-41.5-75.4-79.8-43.669.6-71.8-17.3-13.4

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 !!

As always, I’d be happy to reply to all your fantasy-related questions in the comments and anything related to the method used for this article.