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How Rest Days in the NBA affect DFS performance

Updated on February 2, 2016

Introduction

The NBA season is a grueling 82 game stretch that consists of back-to-backs, home games, extended road trips, 1 day rest, or 2 or more days of rest sprinkled in between. Wouldn't it be common sense to believe that team X, playing its last game of a 5 game road trip against a well rested team Y, will have a disadvantage? According to Rotogrinders, teams play its worst during a road-road B2B, winning only 32% of the time, than a home-home B2B that wins slightly more than 50% of the time. However, how does this information translate into fantasy terms?

Methods

Box score data was gathered from the top 4-5 players from each team for the entire 82 game, 2014-2015 season. Fantasy points were calculated using the Fan-Duel scoring structure for each game. Fantasy points were only included in the statistical analysis if a player played greater than or equal to 25 minutes and played more than 30 games. This would prevent other confounding variables, such as minutes played from interfering with the results. Multiple paired sample t-tests were ran to compare levels of significance between groups:

  • Average (Condition 1)
  • Back to Back (Condition 2)
  • 2 or More Days of Rest (Condition 3)
  • Road-Road Back to Back (Condition 4)
  • Last Two Games of Road Trip (Condition 5)
  • Last Game of Road Trip (Condition 6)

A road trip was defined as 3 or more consecutive away games. The Back-to-Back group does not differentiate between various B2B conditions. i.e. away-away, away-home, home-away, or home-home. 2 or more rest days was defined if a player rested 2 or more days between games. Lastly, the average group did not discriminate against any particular game and took the average of all available games.

Paired t-tests were ran under the following format:


Table 1: Condition x vs. Condition y paired sample t-test

Team
Condition x
Condition y
A
Average from Players under condition x
Average from Players under condition y
B
Average from Players under condition x
Average from Players under condition y
C
Average from Players under condition x
Average from Players under condition y
D
Average from Players under condition x
Average from Players under condition y
....
Average from Players under condition x
Average from Players under condition y

Table 2: Average Fantasy Points/Game/Player/Team

Team
Count
Mean
Std
 
Team
Count
Mean
Std
 
Team
Count
Mean
Std
GSW
291
33
11.3
 
TOR
259
29.2
9.9
 
POR
345
30
11.3
DET
181
27.1
11.3
 
NYK
133
27.2
10.5
 
UTA
231
30.1
9.4
CHO
269
28.4
9.9
 
MIL
228
28.8
9.6
 
DEN
247
28.9
9.9
NOP
237
33.6
14.2
 
MEM
271
29.5
10.7
 
IND
202
24.7
8.8
BRK
243
29.5
9.6
 
HOU
288
31.8
13.2
 
ATL
259
28.7
9.9
PHO
343
28.4
9.9
 
ORL
346
29.4
10
 
LAC
346
33.3
12.4
CLE
289
33.7
11.8
 
CHI
289
32
11.1
 
MIN
266
28
9
BOS
223
26.9
9
 
SAS
212
31.3
10.4
 
PHI
205
27.7
10
WAS
285
29.9
10.4
 
OKC
195
35.7
15.5
 
SAC
227
31.3
14.7
MIA
217
30.1
9.6
 
DAL
272
28.7
8.8
 
LAL
178
27.4
10.9

x̄=29.805
Variance= 10.8

As you can see the teams with the most fantasy studs are the Warrior, Pelicans, Miami, Houston, OKC, Clippers, and Kings.

To further simply the data, Table 3 displays the mean and standard deviation under each condition.

Table 3: Average Fantasy Points/Game/Player/Condition

 
Condition 1
Condition� 2
Condition 3
Condition 4
Condition 5
Condition 6
Mean
29.81
29.52
29.59
29.9
28.85
28.94
Variance
10.8
10.64
10.63
10.9
10.34
10.23

Statistical Analysis

Multiple pair sample t-tests were ran to determine whether the sample means from table 3 are significant. In other words, does rest days or lack thereof matter? Our hypothesis: The more rest days between games correlates with an increase in fantasy point production. To determine this, Table 4 displays the two-tail p-value between multiple two-related groups.

Table 4. Level of Significance

Condition x/Condition y
P-value
Mean Difference
Condition 1/Condition 2
0.84
-2.09
Condition 1/Condition 5
0.004
-28.6
Condition 1/Condition 6
0.03
-19.7

Table 5 displays the fantasy point distribution between each condition.

%
 
 
 
 
 
 
Fantasy Points
Condition 1
Condition 2
Condition 3
Condition 4
Condition 5
Condition 6
<10
1.66
2.02
1.57
N/A
1.6
1.65
20
17.25
17.23
17.42
N/A
18.1
17.53
20-30
31.36
30.89
32.55
N/A
32.19
33.81
30-40
25.56
26.46
24.66
N/A
24.63
25.15
40-50
10.99
10.61
11.22
N/A
9.51
9.07
50>
4.79
4.5
4.96
N/A
4.7
4.12

Discussion

Results show that during the 2014-2015 season, OKC were the fantasy studs while the Celtics were the fantasy duds. The average fantasy points/team/player was 29.8 ± 10.8. However, the average includes all conditions, but we wanted to see how rest affected a players fantasy performance. The p-value between back-to-backs and 2 or more rest days was a surprising 0.83. This suggests very weak evidence for our hypothesis that the more rest days between games correlates with an increase in fantasy point production. In fact, the mean differences ranged from +2 to -2! In other words, more rest—on average—did not significantly improve a players fantasy performance.

However, to test if road-road B2B's affect performance, we formed a new hypothesis that road-road B2B's or extended road trips negatively impact fantasy points. Results show that when compared to the average, road- road B2B's (at a p<.05) the difference was significant(.035). The mean difference was -17.5 between all teams and ranged from +5 to -5 for each individual team. For example, results showed that the each of the Hornet's starting five scored on average 5 less fantasy points.

The biggest mean difference was seen in extended road trips (p<.05). The Washington Wizards saw a decrease of 16% in fantasy point production from each player during the last two games of an extended road trip. Even though this was an extreme case, the NBA on average score 3% less fantasy points under these conditions.

Once we determined that our 2nd hypothesis was significant, we ask are the results meaningful? For example, if our results are correct and the average player scores 3-5% less fantasy during extended road trips or road-road b2b's, then we can expect 1-1.5 less fan duel points/player. To further illustrate its "meaningfulness", Table 5 showed that fantasy point distribution did not differ much between any of the 6 conditions during the 2014-2015 season. The largest difference was that after 2 or more rest days players scored 40-50 fantasy points 11.21% of the time compared to 9.07 % after the last game of an extended road trip. This implies that average players, such as Patrick Beverley, do not play like Russel Westbrook when they have a few rest days between games. It also implies that our example of Patrick Beverley does not play like bench warmer during back to backs.

In summary, amount of rest between games is another variable to account for when selecting players. However, how much weight you add to this category is up to you, but our research shows that this variable is only slightly significant under certain conditions(P<.05).

Comments

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    • profile imageAUTHOR

      Fantasyscience 

      2 years ago

      Thank you. I was very surprised when I was looking at the numbers, because I expected to see dramatic difference between conditions.

    • profile image

      Shey 

      2 years ago

      Wow, very informative article! I like your perspective on the data-based angle. Most people like to use their gut to make decisions when it comes to sports, it's refreshing to see someone use an analytical approach instead.

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