Effects of No Rest between Games on NBA Players

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 ;-)