Basketball is predominantly an anaerobic sport (6,16), where strength, power, agility, and speed can differentiate between different levels of competition and predict playing time (3,11,13). Hence, the goal for conditioning programs is to attain peak physical condition at the onset of the season and then to maintain performance throughout the competitive season. Previous researches on male basketball players have indicated that preseason physical performance abilities can be maintained throughout a competitive season in a collegiate (7,10), elite youth (8), and professional players (4,15).
Differences in the ability to maintain performance between players who start compared with those who are reserves have been previously reported in men over a season. Caterisano et al. (2) reported that nonstarters (NSs) experienced decreases in aerobic capacity during a competitive basketball season, whereas starters were unable to maintain lower-body strength. Recently, we examined the effects of a National Basketball Association (NBA) season on physiological performance measures and subjective feelings of energy, fatigue, alertness, and focus (4). Results indicated that starters were not only able to maintain their physical performance levels throughout the season, but the greater playing time appeared to have provided a greater stimulus for enhancing vertical jump (VJ) power. In addition, starters appeared to maintain their body composition and lower-body reaction time better than NSs. Greater playing time also appeared to have positive effects on feelings of fatigue and alertness, although the only detriment associated with starters was a decrease in energy as the season progressed.
Studies on the effects of a competitive basketball season in women are limited. In 1 study, Häkkinen (5) reported an increase in VJ power output and VJ height throughout a 22-week professional European women’s basketball season. However, it is not known whether similar responses are seen in collegiate female basketball players or whether playing time effects performance measures in women. Therefore, it is the purpose of this study to compare starters with NSs on their ability to maintain strength, power, and quickness over the course of a competitive women’s National Collegiate Athletic Association (NCAA) Division I basketball season.
Experimental Approach to the Problem
As part of a sport science investigation with the university’s women basketball team, NCAA Division I women basketball players were assessed for strength, power, agility, reaction time, and subjective feelings of energy, focus, alertness, and fatigue at the onset of the regular season and 1 week before the conference tournament at the end of the regular season. We retrospectively examined deidentified data and determined the effect of playing time on performance measures between starters and NSs.
Fourteen NCAA Division I women basketball players were assessed at the beginning of the season; however, because of the injuries of 2 NSs, the deidentified data from 12 (5 starters, 7 NSs) players (age = 20.6 ± 1.5 years; height = 178.0 ± 8.2 cm; weight = 74.1 ± 8.1 kg) were analyzed. Starters were distinguished as players on the starting roster and had the most playing time throughout the season. These players accounted for 132 of 145 starting opportunities (5 starters per game, 29 games). All starters were upperclassmen (3 seniors and 2 juniors), whereas NSs included 3 freshmen, 2 sophomores, a junior, and a senior. All players were scholarship athletes playing for the 2010–2011 university’s women’s basketball team. All performance assessments were part of the athletes’ regular in season assessment protocol that was designed to provide feedback to the coaching staff regarding player progress or fatigue. All players had passed the team’s mandatory preparticipation physicalexam before the onset of the season. Posttesting occurred before the conference tournament. At the time of posttesting (end of regular season), the team was 19-10. The team went on to win the conference championship (Conference USA) and lost in the first round of the NCAA tournament. The team’s final record was 22-11. The players gave their informed consent as part of their sport requirements, which is consistent with our institution’s policies for use of human subject research. The NCAA Division I women’s basketball season is just over 3 months long and consists of approximately 2-3 games per week.
During the season, all players participated in a 2-day per week resistance training program (Table 1). All exercises were performed at a repetition maximum range, which required the athletes to select a resistance that they can achieve the minimal number of repetitions required but not exceed the upper limit. If the athletes were successful in completing all required sets with the optimal number of repetitions, then they were instructed to increase the load in the subsequent training session. There was 72 hours of rest between each resistance training session.
Before each testing session, all players performed a 10-minute dynamic warm-up. This warm-up was the same warm-up performed before every practice and game during the competitive season. After the warm-up, players performed strength, power, agility, and reaction time assessments. All testing sessions were supervised by certified strength and conditioning specialists. Each testing session also included anthropometric measurements (height and body mass) and a record of subjective feelings of energy, focus, alertness, and fatigue.
Anthropometric assessments included height and body mass. Height was measured to the nearest 0.1 cm, and body mass was measured to the nearest 0.1 kg.
Power output during 2 sets of 8 repetitions at a load approximating 80% of the athlete’s 1 repetition maximum (1RM) in the squat exercise was measured for each repetition with a Tendo Power Output Unit (Tendo Sports Machines, Trencin, Slovak Republic). The load was adjusted for strength changes for follow-up testing to ensure 80% 1RM load. The Tendo unit consists of a transducer attached to the end of the barbell that measured linear displacement and time. Power was determined as a product of load and bar velocity by the system’s microcomputer. The mean squat power output (SQT power) (SQT power) was recorded for each repetition and used for subsequent analysis. Test-retest reliability for the Tendo unit in our laboratory has consistently shown R > 0.90.
Vertical Jump Power
To quantify VJ power, players performed 5 consecutive countermovement jumps. During each jump, players stood with their hands on their waist at all times. They were instructed to maximize the height of each jump while minimizing the contact time with the ground between jumps. During each jump, the player wore a belt connected to the Tendo Power Output Unit. The average peak and mean power outputs for all 5 jumps were recorded.
Lower-body reaction time was measured with a 20-second reaction test on the Quick Board (The Quick Board, LLC, Memphis, TN, USA) reaction timer. Players stood on a board of 5 circles, in a 2 × 1 × 2 pattern. Players straddled the middle circle and reacted to a visual stimulus located on a display box that depicted 1 of 5 potential lights that corresponded with the circles on the board. On activation of the light, the player attempted to move the foot closest to the circle that corresponded to the visual stimulus. On a successful connection, the next stimulus would appear. The total number of successful attempts for the 20-second test and the average time between the activation of the light and the response to the corresponding circle were recorded. Test-retest reliability from our laboratory for the Quick Board has been calculated as R = 0.95.
Line Drill and Blood Lactate
To assess anaerobic conditioning level, all players performed 3 line drills. The line drill was performed on the basketball court. On command, players sprinted from the baseline to the near foul line (5.8 m) and back, then sprinted to the half court line (14.3 m) and back, sprinted to the far foul line (22.9 m) and back, and then sprinted to the far baseline (28.7 m) and back. All sprints were performed consecutively (total of 143.4 m). Athletes performed a total of 3 line drills with 2 minutes of rest between each sprint. Peak time of the 3 sprints, the average time of the 3 sprints, and a fatigue rate (best time/worst time) was determined. Blood lactate concentrations were measured by fingerprick using a portable lactate analyzer (Accusport; Boehringer Mannheim, Germany). The fingerprick was taken 2 minutes after the final sprint. Test-retest reliability for the lactate analyzer has shown R = 0.94.
Subjective Measures of Energy, Focus, Alertness, and Fatigue
Players were instructed to assess their subjective feelings of energy, focus, fatigue, and alertness using a 15-cm visual analog scale (VAS). The scale was anchored by the words “low” and “high” to represent extreme ratings where the greater measured value represented the greater feeling. Questions were structured as "My level of energy is,” “My level of focus is,” “My level of alertness is,” and “My level of fatigue is.” The VAS was assessed at each test date, and players were asked to rate their feelings at that time by marking on the corresponding line. The validity and reliability of VAS in assessing fatigue and energy have been previously established (14).
The effects of the basketball season were calculated as the change from preseason to postseason measurements among starters and NSs. To account for the small and unequal sample size, magnitude-based inferences were used to identify practical differences in the performance changes between starters and NSs. Several studies have supported magnitude-based inferences as a complementary statistical tool for null hypothesis testing to reduce interpretation errors (1,12). The precision of the magnitude inference was set at 90% confidence limits, using a p value derived from an unpaired t-test, and the threshold values remained constant at ±0.2 for the small sample size. Inferences on true differences between starters and NSs were determined using the unequal variances t-statistic on a published spreadsheet (1). Inferences were calculated on whether the true population effect was substantially positive, negative, or unclear based on the range of the confidence interval relative to the value for the smallest clinical worthwhile effect. If the likely range substantially overlaps both positive and negative values, it is inferred that the outcome is unclear. The chance that the effect was positive or negative was evaluated by the following scale: <1%, almost certainly not; 1–5%, very unlikely; 5–25%, unlikely; 25–75%, possible; 75–95%, likely; 95–99% very likely; and >99% almost certain (12). Pearson’s product-moment correlations were also used to examine bivariate correlations between playing time and each performance measure. All data are reported as mean ± SD.
Starters (n = 5) played an average of 785.6 ± 140.7 total game time minutes (28.3 ± 5.2 minutes per game) and NSs (n = 7) played an average of 202.4 ± 161.1 total minutes (8.3 ± 5.3 minutes per game) over the competitive season. Evaluation of magnitude-based inferences indicated a most likely difference (100%) in playing time between starters and NSs. Preseason and postseason performance measures are depicted in Table 2. Magnitude-based inferences on changes in performance and anthropometric measures are depicted in Table 3. Over the course of the season, it appeared that starters were likely to have greater increases in absolute and relative VJ peak power (87.9 and 90.7%, respectively). In addition, starters were likely (81.6%) to have a greater average squat power (Figure 1).
Examination of the individual responses of each player revealed that 4 of the 5 players in starter group increased VJ peak power, whereas 5 of the 7 players in NS decreased VJ peak power during the season (Figure 2). There appeared to be no clear difference in Δ body weight, reaction time, line drill time, or blood lactate response between starters and NSs.
Preseason and postseason measures of energy, focus, fatigue, and alertness are depicted in Table 2, and changes in the subjective measures can be observed in Figure 3. Magnitude-based inferences on these changes are depicted in Table 3. It appeared that starters experienced a greater detriment in energy, focus, and alertness over the course of the season. Subjective measures of energy, focus, and alertness were possibly (72.9%), very likely (97.3%), and likely (79.2%) to decrease more in starters compared with NSs, respectively. There were unclear differences between starters and NSs in regard to feelings of fatigue. Pearson’s product-moment correlation analysis only revealed significant (p < 0.05) correlations between playing time and focus (r = −0.79) among all players.
The purpose of this study was to compare performance changes of starters and NSs during an NCAA Division I women’s basketball season. The prolonged physical and psychological demand placed on NCAA Division I women basketball players challenges their ability to maintain performance levels throughout a competitive season. To our knowledge, this is the first study comparing changes in performance between starters and NSs during an NCAA Division I women’s basketball season. The results of this study indicate that significant improvements in VJ power performance were seen in starters compared with NSs along with an increase in average squat power among starters, despite greater decreases in subjective measures of energy, focus, and alertness. No clear differences were observed in lower-body reaction time, average time for the line drill, body weight, or subjective feelings of fatigue between starters and NSs.
The findings of this investigation suggest that starters and NSs have the ability to maintain most performance measures during the competitive season. This is consistent with previous research evaluating European professional women (5), collegiate men (10), elite youth (8), and professional men (4,15). In addition to routine practice and games, the team also participated in a strength training program at an intensity of approximately 80% of the athletes’ 1RM. Previous research has shown that strength improvements can be seen in strength and power athletes during an in season resistance training program, as long as the exercise intensity is greater than or equal to 80% of the 1RM (9).
The greater playing time experienced by starters may have acted as a stimulus to increase VJ power and average squat power. This is consistent with our recent study on NBA players (4), which showed that starters were not only able to maintain baseline physical performance levels throughout the season but also the greater playing time actually provided a greater stimulus for enhancing VJ power and body composition compared with NSs. Similarly, Häkkinen (5) examining European professional women basketball players reported that improvements in VJ and squat performance may only be seen among players with greater playing time because of the constant increased intensity of gameplay. As a result of the elevated intensity of gameplay, starters seems to elicit greater gains in VJ and squat performance in comparison to NSs over the course of a season.
The greater decline in feelings of energy, focus, and alertness over the course of the season in starters did not appear to have any negative effect on the physiological performance measures. Only decreases in focus were significantly correlated to increases in playing time. However, decrements in energy, focus, and alertness did not appear to affect any of the performance measures. Additionally, feelings of fatigue did not differ between starters and NSs, indicating that playing time did not appear to increase fatigue. This contrasts with previous work from our laboratory that found a greater decline in feelings of energy among NBA starters in comparison to NSs over the course of a competitive season; but starters’ feelings of alertness and fatigue showed to be better maintained than NSs (4).
In conclusion, the results of this study demonstrate that greater playing time in starters vs. NSs may not result in any negative effects on physical performance attributes in female basketball players and may possibly enhance VJ performance and average squat power, despite greater decreases in feelings of energy, focus, and alertness.
Results of this study indicated that performance levels in NCAA Division I women basketball players remained the same or improved during the competitive season. In addition, power performance in starters may become sensitized by the additional playing time. This may be an area of concern for athletes who do not get in the regular playing rotation. The ability of monitoring and potentially manipulating training intensity or volume during the practice schedule allows the coach to identify individual athletes who respond differently to the stresses of a competitive collegiate basketball season. When needed, specific adjustments to the athlete’s daily routine and recovery pattern can be performed. This approach may aid in maintaining athletic performance and also increase the success of a team.
1. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Sportscience 9: 6–13, 2005.
2. Caterisano A, Patrick BT, Edenfield WL, Batson MJ. The effects of a basketball season on aerobic and strength parameters among college men: Starters vs. reserves. J Strength Cond Res 11: 21–24, 1997.
3. Delextrat A, Cohen D. Physiological testing of basketball players: Toward a standard evaluation of anaerobic fitness. J Strength Cond Res 4: 1066–1072, 2008.
4. Gonzalez AM, Hoffman JR, Rogowski JP, Burgos W, Manalo E, Weise K, Fragala MS, Stout JR. Performance changes in NBA basketball players vary in starters vs. nonstarters over a competitive season. J Strength Cond Res 2012.
5. Häkkinen K. Changes in physical fitness profile in female basketball players during the competitive season including explosive type strength training. J Sports Med Phys Fitness 33: 19–26, 1993.
6. Hoffman JR. Physiology of basketball. In: Handbook on Basketball: Olympic Handbook of Sport Medicine. McKeag D.B., ed. Oxford, United Kingdom: Blackwell Publishing, 2003. pp. 1–11.
7. Hoffman JR, Fry AC, Howard R, Maresh CM, Kraemer WJ. Strength, speed, and endurance changes during the course of a division I basketball season. J Appl Sport Sci Res 5: 144–149, 1991.
8. Hoffman JR, Kaminsky M. Use of performance testing for monitoring overtraining in elite youth basketball players. Strength Cond J 22: 54–62, 2000.
9. Hoffman JR, Kang J. Strength changes during an in-season resistance-training program for football. J Strength Cond Res 17: 109–114, 2003.
10. Hoffman JR, Maresh CM, Armstrong LE, Kraemer WJ. The effects of off-season and inseason resistance training programs on a collegiate male basketball team. J Hum Muscle Perform 1: 48–55, 1991.
11. Hoffman JR, Tenenbaum G, Maresh C, Kraemer WJ. Relationship between athletic performance tests and playing time in elite college basketball players. J Strength Cond Res 10: 67–71, 1996.
12. Hopkins WG, Batterham AM, Marshall SW, Hanin J. Progressive statistics. Sportscience 13: 55–70, 2009.
13. Latin RW, Berg K, Baechle T. Physical and performance characteristics of NCAA division I male basketball players. J Strength Cond Res 8: 214–218, 1994.
14. Lee KA, Hicks G, Nino-Murcia G. Validity and reliability of a scale to assess fatigue. Psychiatry Res 36: 291–298, 1991.
15. Martinez AC, Seco Calvo J, Tur Mari J, Abecia Inchaurregui L, Orella E, Biescas A. Testosterone and cortisol changes in professional basketball players through a season competition. J Strength Cond Res 24: 1102–1108, 2010.
16. Ostojic SM, Mazic S, Dikic N. Profiling in basketball: Physical and physiological characteristics of elite players. J Strength Cond Res 20: 740–744, 2006.