Abstract
Study Objectives:
Travel fatigue and circadian disruptions are known factors that can hinder performance in professional athletes. The present study focused on travel distance and direction on back-to-back games over the 2013–2020 seasons in the National Basketball Association (NBA).
Methods:
The outcomes were based on winning percentage with additional covariates including the direction of travel (eastward or westward), the distance traveled (0–500 km; 501–1,000 km; 1,001–1,500 km; 1,501 km and more), team quality, and season. If a team played both games of a back-to-back sequence on the road, they were considered Away-Away; if a team played the first game of a back-to-back sequence at home they were considered Home-Away; if a team played the first game of a back-to-back sequence on the road they were considered Away-Home.
Results:
The sequence Away-Home significantly increases the likelihood of winning compared with the Away-Away and Home-Away sequences: 54.4% (95% confidence interval [CI], 54.4%–54.5%), 39.2% (95% CI, 37.2%–41.2%), and 36.8% (95% CI, 36.7%–36.8%), respectively. When teams travel back home, every additional 500 km reduces the likelihood of winning by approximately 4% (P = .038). Finally, after withdrawing the Away-Home sequence, traveling eastward significantly increases the chance of winning (P = .024) compared with westward travel but has no significant impact on the probability of winning compared with neutral time zone travel (P = .091).
Conclusions:
The accumulation of travel fatigue and the chronic circadian desynchronization that occurs over the NBA season can acutely disturb sleep and recovery. It appears that tailored sleep and recovery strategies need to be dynamically developed throughout the season to overcome the different challenges of the NBA schedule.
Citation:
Charest J, Samuels CH, Bastien CH, Lawson D, Grandner MA. Impacts of travel distance and travel direction on back-to-back games in the National Basketball Association. J Clin Sleep Med. 2021;17(11):2269–2274.
Keywords: sleep, recovery, circadian rhythm, performance
BRIEF SUMMARY
Current Knowledge/Study Rationale: Sleep, recovery, and the inherent impacts of extensive travel on performance have received a lot of attention over the past years. Inevitably, professional athletes are required to travel and are expected to perform at the highest level. This article focuses on a precise aspect of the professional athlete, which is the impact of travel on back-to-back games.
Study Impact: Among the National Basketball Association, teams face different challenges and among them are the 2 games in 2 nights in 2 different cities. These scheduled challenges warrant a detailed investigation as sleep and recovery play a fundamental role in performance.
INTRODUCTION
Over the years, the style of play of National Basketball Association (NBA) players has evolved, resulting in much higher physical and physiological demand compared with the players of previous generations.1,2 Like other sports, the intensity fostered by improved training regimens has led to an increase in the duration of time that individual players are on the court.3 However, the extended minutes of play, combined with the increased training load and longer calendar season, have resulted in additional fatigue-related injuries.4 Thus, the impact of sleep and travel on fatigue has received scrutiny by performance researchers.5–8 In this regard, within the last 2 years, 3 different consensus statements have been written that address the importance of sleep health on athlete health and wellness, including fatigue.9–11
Over the past 2 years, the NBA has witnessed a phenomenon of load management by several star players.12 Hence, there has been increased media attention on the impact of fatigue, sleep deprivation, and the behavior of NBA athletes specifically.13,14
The NBA had already begun to address the issue by reducing the number of back-to-back games in 2015, as well as other adjustments to the schedule to reduce fatigue.15 Moreover, in 2017, additional changes were implemented to help NBA athletes with fatigue and recovery, including the elimination of schedules where the team plays 4 games in 5 days or 18 games in 30 days; also, other changes include further reduction in back-to-back games and single road games over 2,000 miles away.16
However, back-to-back games in an 82-game season still represent approximately 15% of all games, even though teams playing 2 days in a row were known to be at a systematic disadvantage due to fatigue. The NBA season includes 82 games in 169 days; therefore, fatigue is an inherent consequence, which becomes even more detrimental when circadian rhythms are also disturbed.7
Travel fatigue accumulates throughout the season and could be characterized by the presence of fatigue, recurrent illness, and noticeable change in behavior and mood accompanied by loss of motivation.17 In addition, the desynchronization of circadian rhythms inflicted by repeated travel across time zones will impact the central circadian clock, which alters the ability of athletes to sleep in their new environment.18 The accumulation of travel fatigue and the chronic desynchronization that occurs over the course of the NBA season might acutely disturb sleep and recovery.
Therefore, with the known detrimental impact of fatigue and frequent travel on performance among NBA athletes19 and the parity in the league, it is important to investigate the impact of travel on NBA back-to-back game results. The present study specifically examines when teams play 2 games in a row, with the hypothesis that the second game will be played under fatigue conditions. All of these back-to-back sequences from the 2013 to 2020 seasons were analyzed in the context of travel and time zone differential, in order to determine if travel fatigue and circadian disruption contributed to the outcome of the competition.
METHODS
Data from away and home games of back-to-back sequences that occurred in 2 different cities, from the 2013 to 2020 seasons of the NBA, were included in this study. Information from every game was retrieved from the official website of the NBA (https://www.nba.com). The outcomes were based on percentage of games won, with additional covariates including the direction of travel (eastward or westward) and the distance traveled (0–500 km; 501–1,000 km; 1,001–1,500 km; 1,501 km and more). Additional covariates and possible confounding covariates were also included: team quality (estimated as the winning percentage for each season played) and season (from 1 to 7).
Additionally, the order in which the back-to-back games occurred was considered. If a team played both games of a back-to-back sequence on the road, they were considered Away-Away; if a team played the first game of a back-to-back sequence at home they were considered Home-Away; if a team played the first game of a back-to-back sequence on the road they were considered Away-Home. This classification was used to account for the known phenomenon of home court advantage.
In total, 1,956 games were played without any time zone difference, 1,046 games were played with 1 time zone difference (491 eastward; 555 westward), and 3 games were played with 2 time zone differences (1 eastward; 2 westward) for a total of 3,005 back-to-back games occurring in two different cities.
Statistical analyses were performed using R software version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria).20 We used a mixed-effects logistic regression to evaluate the impact of home court advantage, direction of travel, distance traveled, and the sequence of the back-to-back games. The strength/quality of each team was used as a covariate based on their winning percentage. For the linear mixed effects (points scored for and against), the lme4 package for R was used.21 For the logistical mixed-effects model the same package was used. Type III analysis of variance was estimated using the CAR package for R.22
RESULTS
Figure 1 shows that the Away-Home sequence had a higher winning percentage compared with the Away-Away and Home-Away sequences (P < .001). The Home-Away and Away-Away sequences were not significantly different (P = .291). The winning percentages of the Away-Home, Away-Away, and Home-Away sequences are 54.4% (95% confidence interval [CI], 54.4%–54.5%), 39.2% (95% CI, 37.2%–41.2%), and 36.8% (95% CI, 36.7%–36.8%), respectively. The quality of the team explained approximately 11.8% of the variance.
Figure 1. Winning percentage based on the sequence of 2 consecutive games (*P < .001, r2 = .11).
Figure 2 illustrates the impact of distance traveled within every sequence. The Away-Away (P = .373) and Home-Away (P = .308) sequences showed no impact with regard to distance traveled on winning percentage. However, there was a linear association within the Away-Home (P = .039) sequence suggesting that teams traveling back home are significantly impacted by traveled distance. The quality of the team explained approximately 4.4% of the variance.
Figure 2. Winning percentage based on the sequence and traveled distance of 2 consecutive games (*P = .038, r2 = .044).
Figure 3 illustrates the impact of the travel direction on the performance of teams in the second in a sequence of back-to-back games. For the sequence ending at home, travel produces a significant impact compared with the sequence ending away, regardless of the direction (P < .001). However, when the sequence ending at home is withdrawn from the statistical model, traveling eastward increases the chance of winning compared with westward travel (P = .024) or travel without changing time zone (P = .094). The quality of the team explained approximately 13.8% of the variance.
Figure 3. Winning percentage based on travel direction (r2 = .139).
DISCUSSION
The present study investigated the impact of travel direction and travel distance among NBA teams on back-to-back games that occurred in 2 different cities between the 2013 and 2020 seasons. Results from mixed-effects logistic regression suggest a significant home court advantage regardless of the sequence of back-to-back games (Figure 1). The home court advantage has been extensively investigated and the results of this study align with prior available data.23 In fact, between the 2008–2009 and 2018–2019 seasons, the home team had a net home court advantage of 17.26%, on average.23 Our results indicate that the Away-Home sequence had a higher winning percentage than Away-Away and Home-Away sequences of 15.2% and 17.6%, respectively. Unsurprisingly, the home team has the support of their fans, which would presumably explain part of the home court advantage. However, unexpected results from Nusbaum23 showed that fan loyalty and metropolitan areas were detrimental to winning percentage. The frenetic atmosphere and the pressure on athletes to perform better in front of the home crowd may harm their ability to recover or concentrate. However, this contrasts with the general adage that players, especially role players, perform better in front of the home crowd feeding off their support.24,25 Additionally, it appears that referees’ bias toward the home team may have a significant impact on the outcome of a game. Between the 2009–2010 and 2018–2019 seasons, it has been shown that 50.69% of incorrect officiating calls benefited the home team, whereas 53.85% of incorrect calls put the visiting team at a disadvantage.26 Although the officiating factor was not explored in this study, it may partially explain the increased winning percentage at home. However, a valuable home court advantage factor is the absence of travel, as traveling across different time zones may alter and disrupt the recovery and mindset of visiting players.19 The sudden shift in individual exposure to light and darkness in a new environment can potentially desynchronize the central circadian clock, which can hinder the natural sleep phases.27 However, the 15.2%–17.6% winning percentage advantage of teams traveling back home may be more indicative of the home court advantage than the impact of traveling on performance. Last, the entire medical support team does not always travel for the road games. This decision rests with the head physician and team management. Therefore, there may be a discrepancy between the home game and the road game regarding the medical attention and care that athletes may receive, which can play a key factor in the higher number of injuries sustained on the road.3
Results illustrated in Figure 2 indicate that travel distance seems to impact teams differently depending on the back-to-back sequence. We showed a linear decrease when teams returned home following a road game of 4% for every additional 500 km traveled. These results were unexpected and could presumably be linked to the decision to fly immediately after the game instead of staying an additional night at the hotel. It could be argued that for players, having the opportunity to sleep in their own bed and reconnect with the family may pressure the support team to fly immediately after a game, regardless of the time and consequences on the team performance. Therefore, the risks of sleep and circadian disturbances may be higher for the Away-Home sequence compared with the 2 other sequences. In addition, media scrutiny is far more prevalent for home teams compared with road games. Players may have more questions from the media to answer as there are more “beat” writers at home games compared with road games, reducing the opportunity for recovery. It should be acknowledged that the 2 aforementioned attempts to explain the different impact of traveled distance on team winning percentage are highly speculative and warrant further research. Gathering information on flight departures would allow researchers to establish and compare a hotel sleeping opportunity vs an in-flight sleeping opportunity and their impact on subsequent performance. Last, as the flight lengthens, it has been shown that the oxygen stored in the blood decreases dramatically when breathing air from a pressurized cabin.28 Combined with a prolonged period of inactivity while sitting, this could potentially explain the linear decrease in winning percentage as the distance traveled increases. However, our results raise questions about the impact of traveled distance and should warrant further investigation.
Results in Figure 3 illustrate the impact of travel direction on winning percentage. Again, the sequence Away-Home significantly increased the winning percentage, highlighting the fundamental impact of home court advantage. However, when the home court advantage is removed from the statistical model, our result aligns with previous findings. In fact, the influence of travel in professional sports has long had an influence on the outcome of games but has recently received the attention it deserves. In 2013, Moore and Scott29 investigated the 1987–1995 NBA seasons, and their results showed that teams traveling eastward compared with teams traveling westward averaged 4 extra points, which may ultimately have a significant impact on winning percentage. Moreover, Roy and Forest’s30 results from the 2010–2015 NBA seasons showed that teams traveling eastward had a significantly higher winning percentage of 45.4% compared with 36.2% for teams traveling westward. In addition, it has been shown that the time of day at which a game is played and the direction of travel may give an advantage to a team over their opponent.31 Indeed, studies have demonstrated that peak performance was influenced by circadian timing.32 Typical sleepers with a circadian nadir of 3:00 am would generally have a peak performance in the late afternoon.33 Therefore, given the 3-hour time difference between the teams in the Atlantic, Southeast, and Pacific divisions, the start of the game, which, on average, occurs at 7:00 pm, may in fact be translated into a 10:00 pm start for a team traveling from the East Coast to the West Coast (westward travel), moving the athletes away from their peak performance. A West Coast team traveling to the East Coast for an identical game start of 7:00 pm, which could be translated into a 4:00 pm start time for the West Coast team, would ultimately get the athletes closer to their peak performance time. Therefore, the NBA schedule could provide an advantage to certain teams based on a circadian factor alone. Regardless of the back-to-back sequence, our results showed the team traveling eastward had a winning percentage of 44.51% compared with 40.83% when teams travel westward. Recently, findings from Jones et al13 demonstrated that players who were tweeting between 11:00 pm and 7:00 am decreased their shooting success rate by 1.7%, on average, in the following game. Two studies29,30 highlighted the impact of circadian disruption as a possible detrimental factor on performance, whereas another study13 demonstrated that fatigued and sleep-restricted players will suboptimally perform the following day. In addition, McHill and Chinoy7 demonstrated during the 2020 NBA bubble that the typical travel impact was reduced, including the home court advantage, shooting accuracy, and rebounding. The absence of travel provided the stage for a natural experiment that elegantly showed the potential impact of fatigue and circadian disruptions on performance.
It is important to distinguish between possible circadian disruptions and fatigue, as they are both deleterious to performance but require a different clinical approach. Essentially, circadian disruptions and fatigue may lead to daytime fatigue, sleepiness, gastrointestinal disturbance, impaired concentration, and decreased alertness.17,34,35 Travel fatigue accumulates over time, especially with the repetitive travel that is inherent in an NBA season. Circadian disruptions or jet lag will occur when an individual crosses several time zones, which consequently desynchronizes the biological master clock.27,36 Subsequently, NBA athletes are required to travel several times per year, accumulating fatigue while crossing time zones, eastward and westward, thereby disturbing their circadian rhythms, and ultimately hampering their athletic performance.8 Our results showed that both circadian disruption and travel fatigue were experienced differently according to the back-to-back sequences. As fatigue and circadian disruptions overlap, developing tailored strategies in accordance with the upcoming schedule would be warranted for NBA organizations.
With the constant pressure of performance on NBA athletes, it would be appropriate to adequately understand the impact of sleep on performance as disturbances are inevitable given the schedule. Our results showed that teams may be impacted differently by distance traveled—hence, fatigue and potential circadian disruptions. With a 4% home court winning percentage decrease for every additional 500 km traveled, future work is needed to explore and further our understanding of both travel fatigue and circadian disruption in NBA athletes. The significance of these results lies in the potential translational application to benefit the general public, as professional athletes are quite similar to shift workers. This could allow clinicians to refine and improve their medical intervention for a population larger than only professional athletes.
Limitations
Despite evident strengths (ability to have a representative pattern of the outcome of back-to-back games’ sequences, control of the impact of teams’ quality stratified per season on the outcome of games and number of traveled kilometers), we acknowledge the presence of limitations in the current study. First, given the retrospective nature of the data collection, we could not measure sleep objectively or through self-report. In addition, core body temperature or melatonin rhythms are unavailable to define the circadian phase. Therefore, assumptions were made regarding the accumulation of fatigue and disruptions in circadian phases. Second, caffeine intake and pharmacological use were not taken into consideration since such data are not accessible retrospectively and team physicians are unlikely to disclose this type of information. As such, prior to a game, regardless of the time, athletes may use stimulants such as caffeine as an attempt to maximize their physical performance during the upcoming game. Third, information on injuries was not collected, which could have impacted the likelihood of winning or losing. Fourth, as previously mentioned, the NBA is witnessing a new recovery phenomenon by starting players as they opt out of games, typically the second game of a back-to-back. We did not stratify players by status; therefore, this new phenomenon was not accounted for and may have an impact on winning percentage. Last, we did not account for some different patterns of games within elapsed times. For example, the workload of 3 games in 4 nights may impact fatigue differently than 2 games in 48 hours or even a sequence of 1 game, 1 day off, followed by 2 games in 2 nights.
CONCLUSIONS
In conclusion, additional data are required to allow a definitive conclusion on the impact of fatigue and circadian disruptions in the NBA during back-to-back games. NBA teams could potentially benefit from having a sleep clinician on their medical team to address fatigue and travel issues. Tailored travel schedules could be designed to enhance or avoid any potential detrimental impact of sleep disruption on performance in addition to an adequate sleep-screening strategy throughout the season.
DISCLOSURE STATEMENT
All authors have seen and approved the latest version of the manuscript. The authors report no conflicts of interest.
ACKNOWLEDGMENTS
Author contributions: J.C.: Concept and planning work described, interpretation of data, drafting and critical revision of the manuscript, approved final submitted version. C.H.S.: Concept and planning of work described, critical revision of the manuscript, approved final submitted version. C.H.B.: Concept and planning of work described, critical revision of the manuscript, approved final submitted version. D.L.: Critical revision of the manuscript, statistical analyses, approved final submitted version. M.A.G.: Concept and planning of work described, critical revision of the manuscript, approved final submitted version.
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