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Effects of No Rest between Games on NBA Players

14 Nov

There may not be NBA season but with a possibility of more packed schedule than usual due to the lockout
I wondered what are the effects of no rest between games and how important are those days providing a break. There are studies available about this issue on a team level but I haven’t seen one on a individual level.

So I’ve collected Yahoo’s split statistics for 953 player-seasons [those who played at least 41 total games in the last three years] and divided them into 5 groups based on a different situation:
“Total for a season”, “0 Days Rest”, “1 Day Rest”, “2 Days Rest”, and “3+ Days Rest”.

Here are basic averages of those groups:
[where WS/36 = Win Score per 36 minutes and GS/36 = Game Score per 36. Also please note that shooting percentages where calculated on a league level so FG% = “sum of all avg fgm” / “sum of all avg fga”]:

Situation GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
Season 68,7 24,73 46,04 36,19 76,79 4,22 2,20 0,75 1,39 0,49 2,11 10,33 6,32 10,81
0 days
of rest
15,7 25,18 45,86 36,58 76,91 4,24 2,17 0,73 1,40 0,49 2,17 10,43 6,11 10,58
1 day
of rest
34,0 25,12 46,02 35,94 76,88 4,30 2,25 0,78 1,41 0,50 2,13 10,47 6,37 10,85
2 days
of rest
10,8 24,67 46,37 36,54 76,81 4,21 2,23 0,75 1,39 0,49 2,10 10,38 6,34 10,85
3 days
of rest
8,2 23,01 46,08 36,26 75,68 3,99 2,08 0,73 1,32 0,49 2,02 9,73 6,31 10,82

4-5% loss maybe isn’t much of a difference but back-to-backs are indeed the worst and 1-to-2 days of rest seems the best time to play. On average that’s because of fouls, steals, rebounds, blocks, assists and FG%.

It pretty much confirms basic principles from a team level to an individual level so I’d like to dig in a little deeper.

For example, which group of players is affected the most by lack of rest? Those old veterans, right?
(Data about player’s age during each season was taken from http://www.basketball-reference.com,
“#” means “number of players in this range”)

Table 2 – Player’s average statistics for a full season
Age # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
>=31 167 67,5 25,01 45,55 36,88 78,46 4,27 2,42 0,75 1,31 0,47 2,04 9,90 6,69 10,62
29-30 122 69,4 26,09 45,13 36,13 78,50 4,24 2,25 0,75 1,37 0,45 2,13 10,64 6,02 10,42
27-28 138 67,9 25,59 46,90 36,38 76,67 4,45 2,26 0,75 1,44 0,48 2,18 10,60 6,43 10,76
25-26 161 69,4 25,28 46,90 36,35 76,56 4,34 2,24 0,76 1,45 0,52 2,19 11,22 6,48 11,34
23-24 192 68,7 23,78 45,91 35,92 75,23 3,96 2,08 0,78 1,32 0,51 2,08 9,82 6,09 10,70
<=22 173 69,4 23,34 45,74 35,36 76,25 4,15 2,02 0,72 1,46 0,51 2,09 10,07 6,17 10,94
Table 3 – Player’s average statistics in games with zero days for rest (back-to-backs)
Age # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
>=31 167 15,1 25,27 45,32 36,61 78,43 4,32 2,34 0,72 1,30 0,49 2,05 9,95 6,59 10,46
29-30 122 15,7 26,36 44,60 36,50 78,04 4,30 2,19 0,73 1,39 0,40 2,19 10,64 5,66 10,01
27-28 138 15,6 25,83 46,70 36,08 77,44 4,44 2,25 0,74 1,47 0,48 2,24 10,61 6,29 10,64
25-26 161 15,9 25,83 47,02 37,06 77,10 4,33 2,25 0,74 1,46 0,52 2,26 11,43 6,39 11,30
23-24 192 15,7 24,39 45,92 36,40 74,83 3,98 2,04 0,75 1,35 0,48 2,14 9,96 5,84 10,40
<=22 173 15,9 24,05 45,41 36,79 76,44 4,16 2,00 0,71 1,47 0,51 2,16 10,18 5,85 10,56
Table 4 – Difference between those two aforementioned tables [Table 3 - Table 2]
Age # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
>=31 167 0,26 -0,24 -0,27 -0,03 0,05 -0,08 -0,03 -0,01 0,02 0,01 0,05 -0,10 -0,16
29-30 122 0,27 -0,53 0,37 -0,46 0,06 -0,06 -0,02 0,02 -0,04 0,06 0,00 -0,36 -0,42
27-28 138 0,24 -0,20 -0,30 0,77 -0,02 -0,01 0,00 0,03 0,00 0,06 0,01 -0,14 -0,12
25-26 161 0,55 0,12 0,71 0,54 -0,02 0,00 -0,02 0,00 0,00 0,08 0,21 -0,09 -0,04
23-24 192 0,60 0,01 0,49 -0,41 0,01 -0,04 -0,03 0,03 -0,03 0,05 0,14 -0,25 -0,31
<=22 173 0,71 -0,33 1,43 0,19 0,00 -0,01 -0,02 0,02 0,00 0,06 0,11 -0,33 -0,38

I have to admit I was very surprised with this table. After obvious point that players in their primes are the least affected group in back-to-backs IMHO we have two nuggets of information here:

Youngsters are as badly (if not more) affected by back-to-backs than players past their primes.
My guess would be it’s because they know how to handle those games without a rest and how to prepare for them which would be finally a tangible effect of experience ;-)

Players over 31 are less affected than those between ages of 29 and 30.
Initially I was stunned by that but I think there’s a simple explanation: minutes played.
That group 29-30 carried the heaviest load of all… even though most of those guys were past their primes.
Meanwhile players over 31 years old almost by default are more likely to play less… right?

Maybe we should focus more on how many minutes players play in the first place…

Table 5 – Player’s average statistics for a full season
Min/g # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
>=35 150 74,5 36,82 46,78 35,95 79,21 6,25 4,12 1,20 2,40 0,67 2,47 19,14 7,61 14,33
30-35 185 72,2 32,03 46,73 37,13 78,67 5,44 3,21 0,96 1,83 0,60 2,45 13,99 7,17 12,27
25-30 154 72,7 26,94 45,68 36,76 75,94 4,73 2,16 0,81 1,42 0,58 2,34 10,33 6,62 10,72
20-25 165 70,4 22,00 44,86 35,76 75,65 3,63 1,58 0,66 1,10 0,43 2,10 8,09 5,82 9,79
15-20 161 64,1 17,03 45,46 36,07 70,10 2,91 1,16 0,49 0,87 0,40 1,77 5,83 5,62 9,18
<15 138 56,5 11,57 44,18 32,24 69,18 2,07 0,76 0,33 0,62 0,27 1,43 3,81 4,82 8,26
Table 6 – Player’s average statistics in games with zero days for rest (back-to-backs)
Min/g # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
>=35 150 17,6 36,65 46,70 36,36 79,38 6,10 4,00 1,10 2,40 0,63 2,51 19,04 7,28 14,05
30-35 185 16,9 31,94 46,52 37,38 78,90 5,39 3,06 0,92 1,81 0,60 2,48 13,88 6,95 12,02
25-30 154 17,2 27,21 45,49 36,59 75,91 4,67 2,15 0,81 1,42 0,56 2,40 10,34 6,30 10,44
20-25 165 16,2 22,67 44,69 36,08 75,70 3,68 1,59 0,68 1,10 0,41 2,15 8,32 5,72 9,71
15-20 161 14,1 17,98 45,40 36,94 70,81 3,00 1,19 0,49 0,91 0,40 1,82 6,11 5,51 9,00
<15 138 11,3 12,83 43,81 34,39 69,81 2,29 0,83 0,34 0,70 0,28 1,55 4,10 4,65 7,90
Difference between those two aforementioned two [Table 6 - Table 5]
Min/g # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
>=35 150 -0,17 -0,08 0,41 0,17 -0,14 -0,12 -0,10 0,00 -0,03 0,04 -0,09 -0,33 -0,28
30-35 185 -0,10 -0,21 0,25 0,23 -0,05 -0,15 -0,04 -0,02 0,01 0,03 -0,12 -0,22 -0,25
25-30 154 0,28 -0,19 -0,17 -0,03 -0,06 -0,01 -0,01 0,01 -0,02 0,06 0,01 -0,32 -0,28
20-25 165 0,67 -0,17 0,32 0,04 0,05 0,01 0,02 0,00 -0,02 0,05 0,24 -0,10 -0,09
15-20 161 0,94 -0,07 0,86 0,71 0,09 0,03 0,00 0,03 0,00 0,05 0,29 -0,11 -0,18
<15 138 1,27 -0,37 2,15 0,64 0,22 0,07 0,01 0,07 0,01 0,12 0,29 -0,17 -0,37

That makes sense, if you play 25 or more minutes per game your production will suffer more than if you play as a reserve… but it isn’t an automatic substitution since starters are so much better than reserves.
So even with a worse-than-usual performance they could be better than bench guys.
Although it probably depend on a personnel and should be analyzed more on a team-by-team basis.

Finally, let’s take a look at a positional breakdown.

Table 8 – Player’s average statistics for a full season
Pos # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
PG 212 70,0 25,49 43,51 36,26 81,49 2,55 4,35 0,95 1,78 0,16 1,87 10,57 4,64 11,09
SG 195 67,8 25,11 43,95 36,62 80,62 2,91 2,14 0,82 1,29 0,27 1,81 10,85 4,70 10,24
SF 193 68,8 25,64 44,72 35,86 78,17 4,11 1,70 0,79 1,29 0,44 2,01 10,73 5,83 10,21
PF 188 68,8 24,05 48,75 36,00 74,75 5,80 1,31 0,63 1,27 0,63 2,41 10,54 8,05 11,52
C 165 67,9 23,00 51,76 33,74 67,61 6,26 1,12 0,52 1,26 1,10 2,58 8,72 8,98 11,03
Table 9 – Player’s average statistics in games with zero days for rest (back-to-backs)
Pos # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
PG 212 16,1 25,94 43,49 36,87 81,16 2,59 4,26 0,92 1,79 0,16 1,88 10,77 4,46 10,88
SG 195 15,4 25,87 43,87 36,90 80,89 2,97 2,13 0,79 1,33 0,24 1,88 11,05 4,51 9,98
SF 193 15,7 26,11 44,56 36,15 78,91 4,10 1,66 0,78 1,29 0,44 2,07 10,75 5,58 9,93
PF 188 15,6 24,31 48,54 36,66 74,46 5,82 1,30 0,60 1,28 0,60 2,49 10,61 7,96 11,41
C 165 15,4 23,31 51,28 33,26 67,68 6,20 1,11 0,50 1,26 1,10 2,63 8,69 8,63 10,70
Table 10 – Difference between those two aforementioned two [Table 9 - Table 8]
Pos # GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
PG 212 0,45 -0,01 0,61 -0,33 0,04 -0,09 -0,03 0,01 0,01 0,02 0,20 -0,18 -0,21
SG 195 0,76 -0,08 0,28 0,27 0,06 -0,01 -0,02 0,04 -0,03 0,07 0,20 -0,19 -0,26
SF 193 0,47 -0,16 0,29 0,74 -0,01 -0,05 -0,01 0,00 0,00 0,06 0,02 -0,25 -0,28
PF 188 0,27 -0,20 0,66 -0,29 0,02 -0,01 -0,03 0,01 -0,03 0,07 0,07 -0,09 -0,11
C 165 0,31 -0,48 -0,48 0,07 -0,05 0,00 -0,02 0,00 0,00 0,06 -0,04 -0,35 -0,34

So centers seem to fall of a cliff, huh? That’s because of way worse FG% so less points but also less rebounds per contest. Also it’s probably not a coincidence that it’s the oldest group…

Surprisingly other group of big guys are the least affected group… so it could just mean that samples were too small and whole this article is just a noise ;-)

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4 Comments

Posted by on November 14, 2011 in Scrutiny

 

Tags: , , , , , ,

4 responses to “Effects of No Rest between Games on NBA Players

  1. Leszczur

    November 17, 2011 at 14:27

    Regarding the analysis of age: perhaps it would be better if you expanded the upper age limit a little more. I think nowadays we see a league-wide shift in the way NBA players treat their bodies and in effect how much longer they can play on a very high level.
    I remember 15-20 years ago 32-year old players were considered old farts. Now when you have a look at last years WS/48 (for players averaging at least 15 mpg) you’ll see that in Top30 you have at least 12 guys with the age of 30 or more.

     
    • wiLQ

      November 17, 2011 at 23:57

      I’ve set it at 31 only because of sample size… obviously the higher you go the less players you will measure and I didn’t want to check only those freaks of nature ;-)
      For example, the group “over 34″ would contain only 36 players.

       
      • Leszczur

        November 18, 2011 at 10:51

        The thing is – you cannot really estimate infulence of “small” sample size on your results since you do not check whether they are statistically significant in the form you are presenting it now. 200 players or even more may still not be enough to reach stat significance.
        Right now it’s more your perception of what’s enough rather than _knowing_ that indeed it is enogugh ;-)

         
  2. emz

    January 18, 2013 at 22:28

    what about a cross reference of age/position or age/min played, would that produce more interesting findings or is it more of the same patterns?

     

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