We can safely say that this has been one of the most curious seasons in NBA history, as the pandemic took over and shifted the schedule of last year’s season and subsequently, next season’s as well. On a side note, I have to admit that the Bubble was an unexpected success in my mind and all the kudos should go to Adam Silver and others who orchestrated and executed such a complicated plan that made the continuation of the rest of the season possible. Unfortunately, the schedule change inevitably delayed all fantasy content for the upcoming season, as even free agency hasn’t begun yet, and we are still waiting for the draft to happen. With that in mind, it would be foolish to try and create the usual top 155 Roto projections without having all the necessary details. Instead, I can safely review last year’s projections, as I have been doing for the last three years.

If you are curious about the math behind the process, you can check the first review I did in 2018 or last year’s relevant article. The quick and dirty answer is that the main metric is the difference between the projected and the actual overall per game value for each player.

 

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Missed Players

Each year in these review articles I take a look at those players I over/underestimated in my projections last summer. The following players had a difference in values that was greater than the standard deviation and were thus considered as “misses” for the purpose of this. Take a quick look at the below table that looks like it was taken from the deleted scenes of Matrix and we will discuss the players on a case by case basis.

NAME VAL PT 3PT REB AST STL BLK FGM FGA FTM FTA TO
Giannis

0.72

28

0.9

12.3

6.2

1.4

1.6

0.6

17.7

74

9.8

3.5

0.3

29.7

1.5

13.7

5.8

1

1

0.5

20

63.5

10

3.7

Diff

-0.42

1.69

0.6

1.4

-0.4

-0.4

-0.6

0

2.3

-0.1

0.2

0.2

SD %

66.85

-70.2

-43.5

-47.9

-80.6

-9.1

9.5

-79.1

-45.8

15.9

-89.7

-80.2

Paul George

0.53

25.1

3.5

7

3.6

2

0.4

0.4

18.7

84

6.3

2.6

0.26

21

3.2

5.7

3.9

1.3

0.5

0.4

16

88.2

4.5

2.7

Diff

-0.27

-4.11

-0.3

-1.3

0.3

-0.7

0.1

0

-2.7

0

-1.8

0.1

SD %

7.98

-27.5

-66.2

-54.2

-88.3

66

-85.7

-94.8

-37

-53.2

-4.9

-89.2

John Collins

0.19

20.1

1

10.1

2.1

0.5

0.9

0.6

14

77

4.6

1.9

0.57

21.6

1.4

10.1

1.5

0.8

1.6

0.6

14.8

80

3.7

1.8

Diff

0.38

1.5

0.4

0

-0.6

0.3

0.7

0

0.8

0

-0.9

-0.1

SD %

51.83

-73.5

-55.4

-98.3

-71.1

-36.2

33.4

-54.3

-82

-66.9

-51.5

-91.6

Malcolm Brogdon

0.16

17.4

1.8

5.1

4.7

0.9

0.3

0.5

13.2

92

2.7

1.7

-0.12

16.3

1.3

4.7

7.1

0.7

0.2

0.4

13.7

89.5

3.4

2.4

Diff

-0.28

-1.02

-0.5

-0.4

2.4

-0.2

-0.1

-0.1

0.5

0

0.7

0.7

SD %

11.9

-82

-46.3

-85.8

10.9

-47.1

-74.9

-9.4

-87.4

-72.4

-65.2

-14.7

Mike Conley

0.16

18.9

2.2

3.1

5.7

1.2

0.3

0.4

13.8

84

5.3

1.7

-0.39

13.8

2

3.2

4.3

0.8

0.1

0.4

11.9

79.5

2.9

1.9

Diff

-0.55

-5.1

-0.2

0.1

-1.4

-0.4

-0.2

0

-1.9

0

-2.4

0.2

SD %

115.7

-10

-74.7

-94.8

-38.4

10.6

-61.9

-36.4

-53.8

-50.1

26

-81.7

Thomas Bryant

0.12

14.5

0.7

8.6

1.7

0.4

1.3

0.6

9.8

77

2.7

1.3

-0.17

12.1

0.6

6.8

1.9

0.4

0.9

0.6

8.1

73.4

2.5

1.3

Diff

-0.29

-2.46

-0.1

-1.8

0.2

0

-0.4

0

-1.7

0

-0.2

0

SD %

15.88

-56.6

-87.7

-36.3

-92.4

-92.1

-18.9

-99

-59.1

-60.3

-88.3

-95.6

Lauri Markkanen

0.12

19.3

2.2

9.7

1.5

0.7

0.6

0.4

15.5

87

4.2

1.8

-0.17

14.7

2.2

6.3

1.5

0.8

0.5

0.4

11.8

82.4

3.1

1.6

Diff

-0.28

-4.6

0

-3.4

0

0.1

-0.1

0

-3.7

0

-1.1

-0.2

SD %

10.66

-18.9

-98

24.7

-99.1

-65.1

-77.4

-83.5

-12.6

-48.7

-41.3

-78.6

Josh Richardson

0.1

15

2.4

3.4

2.9

1.4

0.7

0.4

11.9

85.5

2.8

1.3

-0.29

13.8

1.5

3.4

3.1

0.9

0.7

0.4

12

78.9

2.7

1.9

Diff

-0.39

-1.22

-0.9

0

0.2

-0.5

0

0

0.1

-0.1

-0.1

0.6

SD %

53.77

-78.6

-4.4

-99.8

-91.7

15.2

-98.4

-97.7

-98.6

-27.1

-93.1

-29.1

Trae Young

0.02

22.1

2.4

4.4

8.8

1

0.1

0.4

17.2

83

6.1

3.7

0.32

29.6

3.4

4.3

9.3

1.1

0.1

0.4

20.8

86

9.3

4.8

Diff

0.3

7.55

1

-0.2

0.5

0.1

0

0

3.6

0

3.2

1.1

SD %

16.47

33.2

3.2

-94.6

-75.8

-79.2

-93.7

-80.5

-14.1

-66.3

65.6

32.8

Dewayne Dedmon

-0.01

9,9

1,1

7

1,3

1

1

0.5

7.4

81

1.5

1.1

-0.54

5.8

0.5

5.7

0,5

0.6

0.9

0.4

5.8

83.3

0.8

1.3

Diff

-0.53

-4.06

-0.6

-1.3

-0.8

-0.4

-0.1

-0.1

-1.6

0

-0.7

0.2

SD %

107.74

-28.3

-39.1

-52.9

-63.7

-0.4

-81.2

76.4

-62

-74.6

-64

-76.2

Jonathan Isaac

-0.06

11.2

1.3

6.3

1.3

0.9

1.4

0.4

9.7

80

2

1.1

0.33

12

0.9

6.9

1.4

1.6

2.4

0.5

10.1

76.7

2.3

1.5

Diff

0.39

0.76

-0.4

0.6

0.1

0.7

1

0

0.4

0

0.3

0.4

SD %

52.94

-86.6

-63.2

-76.9

-96.6

65

95.1

-47.5

-91.4

-63.7

-85.5

-56.1

Hassan Whiteside

-0.06

14

0

11.6

0.9

0.6

2.1

0.6

10.9

50

3.6

1.5

0.5

16.3

0.1

14.2

1.2

0.4

3.1

0.6

11.1

68

3.7

1.9

Diff

0.57

2.27

0.1

2.6

0.3

-0.2

1

0.1

0.2

0.2

0.1

0.4

SD %

123.56

-60

-93.3

-4.1

-85.8

-48.6

81.5

-6.4

-95.7

98.9

-95.4

-50.3

Cody Zeller

-0.08

11.7

0.1

7.4

1.9

0.9

0.9

0.6

8.2

78

3.3

1.4

-0.39

11.1

0.3

7.1

1.5

0.7

0.4

0.5

8.3

68.2

3.1

1.3

Diff

-0.31

-0.63

0.2

-0.3

-0.4

-0.2

-0.5

0

0.1

-0.1

-0.2

-0.1

SD %

21.9

-89

-78.6

-89.2

-82.6

-47.6

-15.1

-56.6

-98.2

8.8

-89

-87.3

Enes Kanter

-0.09

14.1

0.1

10.4

1.8

0.5

0.5

0.6

10.2

80

3.1

1.9

-0.37

8.2

0

7.7

1

0.4

0.7

0.6

6.1

70.8

1.7

1

Diff

-0.28

-5.95

-0.1

-2.7

-0.8

-0.1

0.2

0

-4.1

-0.1

-1.4

-0.9

SD %

9.2

4.9

-91.8

-3

-61.9

-82.9

-53.9

-98.5

-2.6

1.8

-30.2

4.7

Kyle Anderson

-0.11

7.8

0.3

6.1

3.1

1.3

0.9

0.5

6.2

65

1.4

1.3

-0.48

5.7

0.3

4.4

2.2

0.8

0.5

0.5

4.7

65.2

1.1

0.9

Diff

-0.37

-2.08

0

-1.7

-0.9

-0.5

-0.4

0

-1.5

0

-0.3

-0.4

SD %

47.44

-63.3

-100

-38.4

-59.2

24.5

-24.8

-40.7

-64.4

-97.8

-84.6

-52.4

Nicolas Batum

-0.16

11.7

1.7

5.5

4.6

0.9

0.4

0.4

10.2

85.5

1.4

1.9

0.27

3.6

0.6

4.5

3

0.8

0.4

0.3

3.7

80

0.5

1

Diff

0.42

-8.07

-1.1

-1

-1.6

-0.1

0

-0.1

-6.5

-0.1

-0.9

-0.9

SD %

66.35

42.3

11.7

-63.7

-27.5

-75.1

-100

34.7

54.4

-39.2

-53.7

7.1

Fred VanVleet

-0.16

12.4

1.9

2.7

5.3

0.9

0.3

0.4

10.5

84

2

1.4

0.27

17.6

2.7

3.8

6.6

1.9

0.3

0.4

14.6

84.3

3.5

2.3

Diff

0.42

5.2

0.8

1.1

1.3

1

0

0

4.1

0

1.5

0.9

SD %

66.35

-8.2

-15.8

-61.2

-40.9

148

-94.5

-83

-2.5

-96.3

-24.9

6.1

Kevon Looney

-0.17

8.7

0.2

6.9

1.8

0.7

0.9

0.6

6.1

62

1.7

0.8

-0.76

3.4

0.1

3.3

1

0.6

0.3

0.4

4

75

0.6

0.7

Diff

-0.59

-5.3

-0.1

-3.6

-0.8

-0.1

-0.6

-0.2

-2.1

0.1

-1.1

-0.1

SD %

131.58

-6.6

-89.8

30.5

-63.7

-75.1

12.8

289.8

-50.1

43.6

-43.4

-88.1

Kyle Kuzma

-0.2

17.9

2

5.2

2.6

0.6

0.4

0.5

14.7

75

3.3

1.7

-0.59

12.5

1.3

4.5

1.3

0.4

0.4

0.4

10.8

73.8

2.4

1.6

Diff

-0.39

-5.35

-0.7

-0.7

-1.3

-0.2

0

0

-3.9

0

-0.9

-0.1

SD %

54.59

-5.6

-28.9

-74.6

-41.1

-50.2

-100

-63.1

-7.4

-86.7

-53.7

-88.1

Jaylen Brown

-0.3

14.2

1.5

4.5

1.6

1

0.4

0.5

11.6

66

2.9

1.4

-0.02

20.4

2.1

6.4

2.2

1.1

0.3

0.5

15.4

73.6

4.3

2.3

Diff

0.28

6.24

0.6

1.9

0.6

0.1

-0.1

0

3.8

0,1

1.4

0.9

SD %

11.36

10

-35

-32.6

-73.7

-65.1

-85

-60.5

-8.8

-15.9

-26.9

9.4

Mike Muscala

-0.31

9.4

1.7

4.7

1.4

0.4

0.7

0.4

7.2

83

1.6

1

-0.74

4.6

1

2.3

0.8

0.2

0.4

0.4

3.9

81.8

0.5

0.3

Diff

-0.43

-4.76

-0.7

-2.4

-0.6

-0.2

-0.3

0

-3.3

0

-1.1

-0.7

SD %

70.4

-16

-28.9

-13

-72.8

-50.2

-43.6

-40.7

-21.6

-86.7

-43.4

-16.7

Rodions Kurucs

-0.31

11.2

1.3

5.1

1

0.8

0.5

0.5

9.3

77

1.9

1.3

-0.83

4.2

0.6

2.5

0.8

0.4

0.1

0.4

3.6

60.7

0.7

0.9

Diff

-0.52

-7.03

-0.7

-2.6

-0.2

-0.4

-0.4

0

-5.7

-0.2

-1.2

-0.4

SD %

104.18

23.9

-28.9

-5.7

-90.9

-0.4

-24.8

-88.8

35.4

80.1

-38.2

-52.4

The percentages highlighted in red represent the stats that I missed on and were greater than the standard deviation. The closer the percentage is to 100, the greater my miss, whereas negative percentages indicate that I succeeded in the prediction. If you see a -100%, you are legally obliged to buy me a replica of the 1993 NBA Title signed by Charles Barkley himself and send it to my last known address, as this indicates a bullseye prediction with no error. Obviously, I only included in the above array the “missed” players, but I can assure you I did the exact same procedure for the remaining 124 players that were classified as “hits”. Some players that stand out from the misses are:

Giannis Antetokounmpo: A sharp decline in blocks and, more importantly, in ft% really hampered the MVP’s fantasy value this year and ranked him outside the top 20. The free throws are bound to improve, so look for a bounce back performance in this regard net year.

Paul George: He couldn’t repeat his breakout fantasy performance from last year playing alongside Kawhi Leonard, with his stats reduced across the board and with an uncharacteristic 0.8 decline in steals. And let’s not talk about his real-life performance in the playoffs…

John Collins: The future is his if he can use the fantasy momentum of last year and maintain his improvement in the blocks department (1.6)

Malcolm Brogdon: I was on the optimistic side with his preseason projections and while he was really good, it turns out I was too optimistic.

Mike Conley: If you were a loyal reader last year, you already know how much he burned me in multiple leagues. He showed signs of life in the playoffs but I’m not ready to forgive him. Yet.

Lauri Markkanen: I was expecting great things in his third year but the coaching situation in Chicago was a mess and that extended to the fantasy performances of most of their players. I am expecting a major improvement under Billy Donovan this year.

Hassan Whiteside: I don’t think I am the only one who didn’t see THAT fantasy season coming from Whiteside, but with Jusuf Nurkic returning and dominating in the playoffs I am not very optimistic for him this year.

Fred VanVleet: The epitome of an easy-to-root-for guy, Fred VanVleet has put in the work and is being rewarded with a starting position and plenty of usage, that I obviously wasn’t expecting during the preseason.

Conclusion

The whole process concludes with the calculation of the absolute mean difference in value between my projection and the actual stats of the 2019-20 season. As a reminder, the smaller this number, the more accurate the projections. Two years ago, the number was 0.16, while last year it was 0.162.

So all in all, almost identical accuracy in my projections from two seasons ago to last year, which can be a pleasant or unpleasant thought, depending on your optimism levels. And that is all from the torture I invoke upon myself every preseason, as I get ready to do the roto projections for the upcoming season. This year I am aiming towards releasing them after the beginning of free agency, so I can have a clearer picture of team rosters and depth charts. I genuinely thank you for reaching the end and admire your perseverance for not giving up despite the gibberish math abundant in this article.

As always, I’d be happy to reply to all your fantasy-related questions in the comments as we close in on the preseason, as well as anything related to the method used for this article.

  1. Oliver says:
    (link)

    Kostas, you’re an absolute bloody legend and don’t ever think otherwise. Keep up the good work mate!

    • Kostas

      Kostas says:
      (link)

      Thanks Oliver for the kind words, glad there is an audience for this type of articles!

  2. HardMoney Sniper says:
    (link)

    Definitely have an audience! I always look forward to your buy/sell columns. Keep it coming!

Comments are closed.