Relationship of coach and player behaviors during practice to team performance in high school girls' basketball.

Curtner-Smith, Matthew D. ; Wallace, Sheila J. ; Wang, Min Qi 等

One fairly well-developed line of research in the comparatively new field of sport pedagogy is that concerned with how physical education teachers and their students spend their time during lessons (Metzler, 1989). Early research of this type involved using systematic observation instruments (see Darst, Zakrajsek, & Mancini, 1989) to describe how teachers and students spent their time in physical education (Anderson, 1980; Anderson & Barrette, 1978; Metzler, 1989, Pieron, 1986; Silverman, 1991). These instruments were designed to record time spent by teachers and students in various instructional and managerial behaviors. This descriptive-analytic research provided the foundation for a second wave of studies in which researchers attempted to discover relationships between time-dependent variables and pupil achievement (Metzler, 1989; Silverman, 1991). These process-product studies provided the basis for intervention studies in which researchers attempted to modify how teachers and students spent their time during lessons in order to enhance student learning (Metzler, 1989; Smith, 1992).

Not surprisingly, process-product research completed to-date indicates that physical education teachers who spend relatively little of their own time managing students and relatively large amounts of time instructing them are more successful in terms of enhancing learning (Curtner-Smith, 1994). Moreover, once teachers are able to spend the majority of their time in instructional behaviors, those who are most effective provide short and explicit demonstrations so as to focus students on a few relevant critical aspects of a skill, and so as not to overload them with too much information (Byra & Coulon, 1994; Rink & Werner, 1989; Werner & Rink, 1989). Once students are engaged in skill practice/game play, the most effective teachers spend relatively little time passively monitoring and relatively large amounts of time actively supervising, prompting, correcting, and encouraging students by providing feedback. This action has the effect of keeping students on-task (van der Mars, Vogler, Darst, & Cusimano, 1994) and, providing practice is successful and appropriate, its skill-related components enhance learning (Silverman, Tyson, & Krampitz, 1992). Furthermore, citing research conducted by Phillips and Carlisle (1983), Pieron (1982), and DeKnop (1986), Lee, Keh, and Magill (1993) noted that, typically, more effective physical education teachers provide more skill-related feedback than less effective teachers.

When the focus has been on student behaviors, physical education teacher effectiveness research has consistently indicated that the most successful teachers are those who are able to provide students with the highest amounts of time in which they are actively and successfully engaged in practice (Metzler, 1989, Silverman, 1991). In order to achieve this goal, effective teachers minimize the time their students spend in managerial activities and listening to instructions (Curtner-Smith, 1994; Curtner-Smith, Kerr, & Todorovich, 1996). In addition, more effective teachers organize practices, drills, small-sided game play, and full-sided game play so that the time pupils spend waiting for a turn to participate is either eradicated altogether or kept to a minimum and the time they spend actively engaged in motor activity is maximized (Siedentop, 1991).

Since this line of "effectiveness" research proved to be so fruitful in the physical education setting (see Metzler, 1989 and Silverman, 1991 for reviews of this research), a few sport pedagogists have tried to use the same behavioristic model in the athletic setting. Researchers interested in coaching effectiveness began this effort by attempting to describe the behaviors of well-known and/or successful coaches. For example, Tharp and Gallimore (1976) observed 15 practices coached by John Wooden of UCLA. They discovered that he spent most of his time instructing and that he used praising and scolding behaviors in equal numbers. Another study conducted by Langsdorf (1979) revealed that successful college football coach Frank Kush had a very similar behavioral profile to that of Wooden. By contrast, an early study of a winning high school basketball coach (Williams, 1978, in Lacy & Martin, 1994) revealed a profile that focused much more on praising than on scolding players during practice. Similarly, Segrave and Ciancio's (1990) study of a successful Pop Warner football coach revealed a profile in which instructional behaviors were dominant and the ratio of praise to scold behaviors was 2.5:1. In addition, Lacy (1989, in Lacy & Martin, 1994) found that 12 youth soccer coaches used a 3:1 praise to scold ratio. Collectively, these studies seem to suggest that successful coaches can afford to be more negative as players age.

This conclusion, however, was contradicted by studies which examined the behaviors of unsuccessful (Model, 1983) and successful (Lacy & Darst, 1985) high school football coaches. Both sets of coaches in these studies used a high percentage of instructional behaviors. However, the successful coaches used a lower praise to scold ratio. Similarly, Claxton (1988) found that less successful high school tennis coaches were more inclined to praise players while more successful coaches were more inclined to question players.

More recently, Lacy and his colleagues completed two studies aimed at describing differences between male and female coaches' behaviors and discovering what behaviors coaches used during different segments of practice. In the first of these studies, Lacy and Goldston (1990) profiled the behaviors of five male and five female high school girls' basketball coaches. They found that the praise to scold ratio was 2:1 across all 10 coaches. In addition, they found that the most frequently used behaviors were concurrent instruction, postinstruction, and management. Interestingly, females were shown to praise more and scold less than males. Moreover, females also used more postinstructional, management, and preinstructional behaviors than males.

In the second study, Lacy and Martin (1994) investigated the behaviors of collegiate women's volleyball coaches during different segments of preseason practices. Results revealed that a large proportion of practice time was spent in skill work (77.8%). Much smaller proportions of time were spent in warm-up (12.4%), scrimmage (7.7%), and conditioning activities (2.2%). Some 35% of the coaches' behaviors were coded as silence. Instructional behavior in some form was observed for close to 40% of the time. The praise to scold ratio recorded was 7:1.

Relatively few studies have been completed describing players' behaviors during practice. However, in his review of the coaching literature, Metzler (1989) observed that players appeared to spend more time engaged during practices than students did during physical education lessons. The evidence for this conclusion was provided by Rate (1981) and Tousignant, Brunelle, Pieron, and Dhillon (1983). Rate's (1981) study indicated that high school athletes were allocated 75% of their practice time for skill learning but spent only 33% of the time successfully engaged. Tousignant, Brunelle, Pieron, and Dhillon's (1983) summary of studies indicated that rates of successful motor engagement by players ranged from 10% (men's basketball) to 80% (swimming) with a median of between 30% and 40%.

Finally, Lacy and Martin (1994) discovered that the players of the eight collegiate women's' volleyball teams they studied spent some 30% of their time engaged in motor activity during practices. They also found that there was not a significant difference between starting and non-starting players in terms of motor skill engagement time across warm-up, skill work, scrimmage, and conditioning segments of practice.

Following the success of researchers in discovering effective physical education teaching behaviors and commenting on this new line of "coaching effectiveness" research, Metzler (1989) suggested that more time-based descriptions of coach and player behaviors were badly needed. He went on to argue that researchers might also discover relationships between practice behaviors and competitive outcomes or statistics. While researchers have obviously heeded Metzler's plea for more descriptions of coach and player behaviors during practice in the last 8 years, to our knowledge, none have moved to the second stage of the research model and attempted to relate practice behaviors to competitive outcomes. Therefore, the purpose of this study was to determine whether there was a relationship between time spent by girls' high school basketball coaches and their players in specific behaviors during practice and player performance in competition as measured by team free-throw percentage, point differential, and win percentage.

Method

Participants and Research Setting

Twenty coaches (15 males and 5 females) working in 20 schools in four rural North Alabama counties agreed to take part in the study. Their mean age was 39.80 years (SD = 8.72) and the mean number of years they had been coaching was 14.7 years (SD = 8.30). All possessed a Bachelor's degree while eight held a Master's degree. Seven of the coaches taught physical education for at least a portion of each school day while the remainder were involved in teaching a variety of academic subjects. Their mean number of years teaching was 15.97 (SD = 8.58).

The coaches' players ranged from 13 to 18 years of age. Many of them had been involved in organized sports for at least 3 years and had attended basketball camps. The 20 teams ranged in size from 9 to 15 players while the mean team size was I l players.

Each school had an adequate gymnasium which was used for practice as well as for competitive games. Fourteen schools had two gymnasia available for practice. In most cases the gym and practice time were shared with a boy's team. Each of the 20 schools had an adequate supply of basketball equipment.

The mean number of games played by each team during the season in which the study took place was 21.80 (SD = 4.44). Practices were generally held on Mondays, Wednesdays, and Thursdays. Games were usually played on Tuesdays and Fridays.

Data Collection Procedures

Videotaping Practices. Data collection involved videotaping three practices for each coach. One practice was videotaped during the early phase of the season, one practice was videotaped in mid-season, and one practice was videotaped at the end of the season. Practice length averaged 91.45 minutes (SD = 32.97) in the early season phase, 88.52 minutes (SD = 33.47) in mid-season, and 71.75 minutes (SD = 19.72) in the late season phase.

Practices were videotaped with a Magnavox CCD (NR977101) video camera. The camera was located off the playing area in a position that did not interfere with coach and player activity. To ensure that an accurate record of all verbal behavior was recorded, coaches wore a wireless microphone (Realistic FM wireless video camera microphone system transmitter, NO: 32-1226) which fed back to a wireless receiver (Realistic FM video camera microphone receiver NO: 32-1226) attached to the video camera.

Three target players were randomly selected to be videotaped during each practice. Players selected included both "starters" and substitutes. Videotaping of practices began when the coach and at least one of the three target players were present and continued until the coach stated that a practice was finished, until the coach dismissed the players from the gymnasium, or until the players had completed free-throw shooting practice, which was regularly designated as a concluding activity. Videotaping involved focusing on target players for 1-minute intervals throughout each practice in a repetitive rotational order.

Systematic Observation Instrument. Practices were coded for coach and player behaviors with a modified version of the Physical Education Teacher Assessment Instrument (PETAI) (Phillips, Carlisle, Steffen, & Stroot, 1986). Coding involved viewing videotaped practices, using the PETAI computer program, and depressing designated keys on an IBM compatible computer which permitted the time spent in each behavior to be recorded in minutes and seconds as well as percentage of total practice time.

The modified version of the PETAI allows an observer to record the time spent by coaches in five instructional behaviors (planned presentation, response presentation, monitoring, performance feedback, and motivational feedback) and five managerial behaviors (beginning/ending practice, organization, equipment management, behavior management, and other tasks). Definitions of these behaviors together with examples are provided in Figure 1.

The modified version of the PETAl can also be used to record the time spent by players in 13 participation behaviors (warm-up/review/fitness, allocated skill learning time, engaged skill learning time (success), engaged skill learning time (non-success), non-engaged skill learning time - listening, non-engaged skill learning time - assisting, non-engaged skill learning time - waiting, allocated game playing time, engaged game playing time (success), engaged game playing time (non-success), non-engaged game playing time - listening, non-engaged game playing time - assisting, and non-engaged game playing time - waiting) and five managerial behaviors (beginning/ending practice, equipment management, organization, behavior management, and other tasks). Definitions of these behaviors together with examples are provided in Figure 2.

Coding and Intra-Observer Reliability. All videotaped practices were coded by the second author. Observer training consisted of studying the PETAI manual, reading reports of research projects in which the PETAl had been used to generate data, observation of one university physical education methods class in which preservice physical education teachers were taught how to use the PETAI, and coding practice videotapes for approximately 12 hours.

Intra-observer reliability was checked using the methods described by van der Mars (1989). This involved the second author coding a videotaped "reliability" practice before the study commenced. This practice was recoded after 7 days. The second coding of the reliability practice was then compared to the original. Intra-observer agreement was calculated for each coach and player behavior by dividing the number of agreements by the number of agreements plus the number of disagreements and multiplying the result by 100. The unit of measurement used in these calculations was the second. The mean reliability percentage across behavior categories resulting from this check was 93.9% (range = 88-100%).

Further intra-observer reliability checks were made in order to check for "observer drift" following the coding of practices 10, 20, 30, 40, and 50. Again, this involved recoding the original reliability practice and comparing the new codings with the original. The mean reliability percentage across behavior categories resulting from these checks was 91.2% (range = 81-100%) for coach behaviors and 92.7% (range = 80-100%) for player behaviors, with an overall mean of 91.4%.

When the coding of all 60 practices had been completed, additional intra-observer reliability checks were made by recoding the reliability practice a seventh time and recoding two randomly selected practices. The mean reliability percentage across behavior categories resulting from these checks was 94.7% (range = 89-100%).

Player Rating. During the second visit to each school, coaches were asked to rate their five starting players' abilities relative to other female high school basketball players in the area. The best players were rated as being in the top 20% of female high school basketball players while the remaining players were rated in decreasing order of ability on a scale that declined in 10% increments (79- 70%, 69-60%, 59-50%, 49-40%). A five-point conversion scale was used to transform the coaches' assessments of their players' abilities into a team rating score. Players rated in the top 20% were awarded a score of 5 and players rated in the lowest 10% (i.e., 49-40%) were awarded a score of 1. The sum of these player ratings was then computed. Therefore, the maximum team rating score possible was 25 points while the minimum was 5 points. In actuality, team rating scores ranged from 18 to 25 points.

Game Statistics. At the beginning of the study coaches were asked to keep a scorebook and statistic sheets on all their games. Statistics were kept on percentage of free-throws made by the team, point differentials (i.e., margin of victory/defeat), and percentage of team wins. Game statistics were collected from the coaches at the completion of the season.

Data Analysis

Percentage data generated by the PETAl coding, game statistical data, and team rating data were entered into a Statistical Analysis System (SAS) program. Partial correlation (Borg & Gall, 1989) was then used to determine the relationship between each coach and player behavior and each game statistic while taking into account coaches' ratings of their players' abilities. In addition, product-moment correlation coefficients (r) were computed to determine the relationship between coaches' ratings of their players' abilities and each game statistic. Because this study attempted to investigate an area about which little is known, it was considered exploratory in nature. Given that the study was exploratory and that when conducting exploratory research researchers are justified in selecting methods which maximize the power to detect statistically significant relationships (Toothaker, 1991), a decision was made a priori to set the alpha level at .10 for all analyses.

Results

Relationship Between Coach Behaviors in Practice and Player Performance in Competition

Table 1 shows the results of the statistical analyses examining the relationship between each coach behavior in practice and player performance in competition after controlling for the effects of player ability as determined by each coach's rating of his/her team. These analyses revealed only that there were moderate negative relationships between the percentage of time coaches spent in planned presentation during practice and players' free-throw percentage during competition (r = -0.59, p = .008) and between the percentage of time coaches spent managing equipment during practice and team point differential (r = -0.51, p = .03).

Relationship Between Player Behaviors in Practice and Player Performance in Competition

Table 2 shows the results of statistical analyses examining the relationship between player behaviors in practice and player performance in competition after controlling for the effects of player ability as determined by coaches' ratings of their players. These partial correlational tests revealed that there was a strong negative relationship between the percentage of time allocated for players' skill learning and team win percentage (r = -0.60, p = .006), a weak negative relationship between the percentage of time allocated for skill learning and free-throw percentage (r = -0.38, p = .10), and a moderate negative relationship between the percentage of time players spent successfully engaged in skill learning during practice and team win percentage (r = -0.50, p = .03). In addition, there was a moderate negative relationship between the percentage of time spent by players waiting to participate in skill drills during practice and win percentage (r = -0.41, p = .08) and a weak negative relationship between the percentage of time spent by players listening to coaches explain and describe skill drills and players' free-throw percentage in competition (r = -0.39, p = .10).

Of the managerial behaviors coded, results indicated that there was a moderate negative relationship between the percentage of time players spent organizing for skill practice or game play and team point differential (r = -0.47, p = .04). There was also a moderate negative relationship between the percentage of time players' spent in organization and win percentage (r = -0.43, p = .06). Finally, there was a moderate positive relationship between the percentage of time spent by players managing equipment and players' free-throw percentage in competition (r = 0.40, p = .09). Table 1 Correlations Between Coach Behaviors in Practice and Team Point Differential, Win Percentage, and Free-Throw Percentage During Competition After Controlling for the Effect of Player Ability Coach Behavior Point Win Free-Throw Differential Percentage Percentage Instructional Behaviors Planned Presentation -.28 -.22 -.59(*) Response Presentation .14 .32 -.18 Monitoring -.21 .13 .23 Performance Feedback .15 .26 -.06 Motivational Feedback -.13 -.23 -.17 Managerial Behaviors Beginning/Ending Practice -.09 .15 -.02 Equipment Management -.51(*) -.25 -.15 Organization -.17 -.28 .06 Behavior Management -.26 -.38 -.13 Other Tasks .14 .02 .06 Note: For all correlations N = 20 * p [less than] .10

Relationship Between Coaches' Rating of Players' Ability and Player Performance in Competition

Table 3 shows the results of statistical analyses examining the relationship between coaches' ratings of their players' abilities and player performance in competition. The table shows that there was a moderately strong positive relationship between coaches' ratings of [TABULAR DATA FOR TABLE 2 OMITTED] their players' abilities and team point differential (r = 0.43, p = .05). There was also a moderately strong positive relationship between the coaches' ratings and team win percentage (r = 0.42, p = .06).

Discussion

Based on the findings by researchers of physical education teacher effectiveness, in the present study we expected that there would be positive relationships between the percentages of time spent in the coach instructional behaviors of performance feedback, motivational feedback, and response presentation and all three game statistics. We also expected that there would be negative relationships between the percentages of time spent in each of the five coach managerial behaviors and these three measures of player performance. Results of the partial correlational tests, however, only supported some of these hypothesized relationships. Firstly, they indicated that player performance in competition was adversely affected by the percentage of time coaches spent setting up and distributing equipment. In addition, they suggested that time spent by coaches presenting material either detracted from their team's free-throw performance or that coaches of players who were poor free-throw shooters spent more time presenting material and demonstrating in order to rectify the problem. In congruence with Lee et al. (1993), we speculate that we did not find positive relationships between any of our three measures of teacher feedback and player performance because effective coaching involves "an orchestration of teaching behaviors." A lone behavior like feedback will infrequently be powerful enough to distinguish between more and less effective coaches.

Both classroom (Graham & Heimerer, 1989) and physical education (Metzler, 1989; Silverman, 1991) researchers have consistently found positive relationships between students' successful engagement in instructional tasks and their achievement. Therefore, we expected to find positive relationships between the percentages of time during which players were engaged successfully in skill learning and game play during practice and their performance in competition. Conversely, time spent by players in other behaviors is likely to detract from the amount of successful skill learning and game playing time they accrue. Therefore, we expected that percentages of time spent unsuccessfully engaged in skill drills and game play, in non-engaged skill learning and game playing behaviors, and in player managerial behaviors would be negatively related to player performance in competition. Table 3 Correlations Between Coaches' Ratings of Players' Abilities and Team Point Differential, Win Percentage, and Free-Throw Percentage During Competition Point Win Free-Throw Differential Percentage Percentage Coaches' Ratings of .43(*) .42(*) .06 Players' Abilities Note: For all correlations N = 20 * p [less than] .10

The study's results indicated that there were indeed negative relationships between the percentages of time players spent listening to their coaches explain skill drills, waiting for a turn to participate in skill practice, and organizing, and at least one measure of player performance. Strangely, however, results also revealed that the percentage of time allocated for skill learning and the percentage of time players were successfully engaged in skill learning activity were negatively related to at least one measure of player performance in competition. We cautiously speculate that these results indicated that coaches of weaker teams allocated more time for skill learning. Moreover, we speculate that coaches of weaker teams made sure that their players spent more time successfully engaged in learning basketball skills in order to improve their skill levels. In other words, we rejected the proposition that spending time successfully engaged in skill practice led to poor player performance in competition.

Finally, it is very difficult to explain why the percentage of time players spent managing equipment was positively related with team free-throw percentage in competition. We tentatively suggest that this may have been the result of some coaches making more basketballs available to players during free-throw practice than others.

In congruence with the arguments and findings of those who have conducted process-product research in the physical education setting (Metzler, 1989; Silverman, 1991), the results of this study suggest that the level of player performance in competition is more closely related to how players spend their time in practice than do coaches. In addition, the finding that coaches' ratings of players' abilities were positively related with team point differential and win percentage indicates that previous experience and factors which comprise "natural ability" play a significant part in determining outcomes in terms of team performance in competition.

Future studies aimed at discovering relationships between coach and player behaviors in practice and player performance in competition might prove more successful than our initial attempt if they include more participants in order to increase statistical power. In addition, researchers might have more success if they use more refined or different measures of coach and player behaviors, player performance, and player ability. For example, researchers might measure time players spend engaged working on specific skills or game play strategies and tactics during practice. In addition, they might record the time players spend engaged in different types of game play (e.g., small-sided game play, conditioned game play, full game play). Similarly, investigators might measure time coaches spend providing different types of performance feedback. For example, it might be worth using Tan's (1996) modification of Fishman and Tobey's (1978) Augmented Feedback Observation System that categorizes each feedback statement according to its content, direction, intent, focus, and type. Moreover, since, as pointed out by Lee et al. (1993), quality of feedback might be more important than quantity, studies which include a measure of feedback accuracy might be enlightening.

Researchers might also be more successful if they attempt to relate the actual time coaches and players spend in various behaviors during practice with player performance in competition rather than using proportions of practice time as we did in this study. Moreover, in similar vein to those who have studied physical education teacher effectiveness (e.g., Silverman, 1985; Pellett & Harrison, 1995), researchers might find that, for some sports, skills, and strategies, the number of successful trials players complete in practice will predict their success in competition whereas time spent engaged in skill learning or game play will not. Similarly, it might be that for some sports, skills, and strategies, the number of quality feedback statements players receive during practice will be positively related with their competitive performance while time spent by coaches providing feedback will not. To discover whether this is the case in the coaching setting, researchers will need to collect data during practices using both duration and event recording protocols.

In terms of refining and/or improving measures of player performance in competition, researchers might begin by collecting data on a number of outcome or product variables. For example, in the present study we could also have gathered data on turnovers, assists, and rebounding. In addition, researchers should be able to develop systematic observation instruments that measure skill and strategy execution in terms of technique rather than end product. In all cases, researchers should seek to use measures of player performance which match those skills, strategies, and tactics for which engagement and feedback data are being collected during practice. Moreover, if at all possible, player performance data should also be generated by the observation of videotaped (rather than live) competition.

Finally, researchers could explore a number of options for refining and improving their measures of players' abilities prior to beginning a study. For example, they could factor in the results of objective measures of game play performance (i.e., generated by systematic observation). They could also factor in the results of skills tests, tests of players' sport-related knowledge, and measures of various physical and mental attributes thought to influence performance of the particular sport.

Figure 1. Definitions of the coach instructional and managerial behaviors coded by the modified version of the Physical Education Teacher Assessment Instrument.

Instructional Behaviors

Planned Presentation: The time the coach utilizes to present planned instructional material to the players. Examples: (1) "During today's practice we will focus on the fast break." (2) "For the next five minutes, let's work on our lay-ups."

Response Presentation: The time the coach utilizes to restate, emphasize, or summarize information relative to the aspects of a performance. Examples: (1) "Let me explain the main points to you again. . . ." (2) "Remember everyone, we must get back on defense."

Monitoring: The time the coach utilizes to observe the learning environment. This may include some incidental talk. Examples: (1) The players are engaged in a drill and the coach watches from the side of the gym. (2) The coach watches one player shooting a free-throw.

Performance Feedback: The time the coach utilizes to provide information relative to the aspects of a performance that is specific to the immediate execution of a skill. Examples: (1) "Keep your hands up in the zone." (2) "You need more arch on your shot."

Motivational Feedback: The time the coach utilizes to provide general responses to a skill attempt. Examples: (1) "Well done." (2) "Nice job."

Managerial Behaviors

Beginning/Ending Practice: The time the coach utilizes to begin practice, record tasks, and end practice. Examples: (1) The coach checks that all members of the squad are present. (2) The coach sends groups of players to the locker room at the end of a practice.

Organization: The time the coach utilizes to organize for skill development or game play. Examples: (1) The coach organizes players into groups for a new drill. (2) "Get into pairs as quickly as you can."

Equipment Management: The time the coach utilizes to obtain, set up, distribute, or collect equipment. Examples: (1) The coach passes out basketballs to the players. (2) The coach places cones on the gym floor in order to mark out areas for the practice of different skills.

Behavior Management: The time the coach utilizes to provide feedback relative to player behavior. Examples: (1) The coach reprimands a player for off-task behavior. (2) The coach speaks to a player who arrives late for practice.

Other Tasks: The time the coach utilizes for purposes other than practice management or instruction. Examples: (1) The coach stops monitoring the players to converse with the principal. (2) The coach attends to a player who has been injured.

Figure 2 Definitions of the player participation and managerial behaviors coded by the modified version of the Physical Education Teacher Assessment Instrument.

Participation Behaviors

Warm-up/Review/Fitness: The time players utilize in warm-up activities or exercises, fitness exercises, or in review of previously presented material. Examples: (1) Players engage in a fast break drill at the beginning of a practice, a skill they have previously been taught. (2) Players run three laps of the gym to warm-up.

Allocated Skill Learning Time: The time available to players to learn or practice skills. This is the sum of engaged skill learning time (success), engaged skill learning time (non-success), non-engaged skill learning time (listening), non-engaged skill learning time (assisting), and non-engaged skill learning time (waiting).

Engaged Skill Learning Time (Success): The time players experience success when practicing skills. Examples: (1) A player makes a lay-up during a drill. (2) A Player dribbles through an obstacle course of cones as directed by the coach.

Engaged Skill Learning Time (Non-Success): The time players do not experience success when practicing skills Examples: (1) A player fails to make a lay-up during a drill. (2) A player drops a pass during a passing drill.

Non-Engaged Skill Learning Time (Listening): The time players spend listening to instructions or directions or watching a demonstration of the skills to be learned. Examples: (1) Players listen to a description of a lay-up drill. (2) Players watch a demonstration of a set offensive play.

Non-Engaged Skill Learning Time (Assisting): The time players spend assisting others in practicing or reviewing skills Examples: (1) A player retrieves halls that have been shot by her partner during a three-point shooting drill. (2) A player observes her partner in a screen setting drill and provides the partner with feedback.

Non-Engaged Skill Learning Time (Waiting): The time players wait to practice or assist with learning a skill. Examples: (1) A player waits for her turn to perform a lay-up during a lay-up drill. (2) A player waits in line for her turn to participate in a passing drill.

Allocated Game Playing Time: The time available to players to learn about or play the game. This is the sum of engaged game playing time (success), engaged game playing time (non-success), non-engaged game playing time (listening), non-engaged game playing time (assisting), and non-engaged game playing time (waiting).

Engaged Game Playing Time (Success): The time players successfully perform skills directly related to playing the game. Examples: (1) A player makes a lay-up during a game. (2) A player sets a screen during a game.

Engaged Game Playing Time (Non-Success): The time players are unsuccessful when performing skills during a game. Examples: (1) A player misses a lay-up during a game. (2) A player turns the ball over during a game.

Non-Engaged Game Playing Time (Listening): The time players spend listening to instructions or directions or watching demonstrations during the time allocated for game play. Examples: (1) Players listen to the coach describing the type of offense he wants them to play during the game. (2) Players watch a demonstration of the type of defense the coach wants them to play during a game.

Non. Engaged Game Playing Time (Assisting): The time players spend assisting others in practicing or playing the game. Examples: (1) A player officiates while her peers play a 3 versus 3 conditioned game. (2) A player acts as a coach for a team of her peers engaged in a 2 versus 2 conditioned game.

Non-Engaged Game Playing Time (Waiting): The time players wait to execute a skill during the game. Examples: (1) A player sits on the bench waiting for a chance to play in a full game. (2) A player in a group of three waits for her turn to rotate into a one versus one conditioned game.

Managerial Behaviors

Beginning/Ending Practice: The time players take to begin and end practice Examples: (1) Players listen as the coach checks that all members of the team are present. (2) Players line up at the end of a practice before moving to the locker room.

Equipment Management: The time players utilize to obtain, set up, and return equipment. Examples: (1) Players go to the storeroom in order to obtain basketballs. (2) Players set up cones marking each group's practice area.

Organization: The time players utilize to organize for allocated skill learning time or allocated game playing time Examples: (1) Players gather round a teacher in order to see a demonstration. (2) Players find partners so that they can participate in a new drill.

Behavior Management: The time players are engaged in unapproved behaviors or fail to he engaged in approved or expected behaviors. Examples: (1) A player ignores instructions and modifies the drill she has been asked to complete (2) Players stand and talk when they are supposed to be playing in a small-sided game.

Other Tasks: The time players utilize in tasks other than warm-up/review/fitness, allocated skill learning time, allocated game playing time, or in specified managerial behaviors. Examples: (1) A player gets a drink of water. (2) Players pick up litter left on the gym floor.

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