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Spectator effect on team performance in college basketball.

A measles epidemic resulted in a quarantine that caused 11 North Atlantic Conference basketball games for two teams to be played in the absence of spectators. This allowed a rare opportunity to investigate possible crowd effects on game performance in the natural setting. Observed differences, median and effect size analyses showed improved performance for total points scored, field goal percentage and free throw percentage with no crowd present. No support for performance differences were found through t-test analyses. The t-tests results were discussed in the context of statistical power. The findings provide insight and important baseline information on data seldom available for study.

Une epidemie de rubeole a provoque a une quarantaine qui a occasionne 11 jeux de basket de la "North American Conference" de deux equipes etre jouer sans l'assistance. Cette evenement a permis l'occasion rare a examiner les effets possibles de foule sur l'epreuve du jeu en lieu naturel. Les differences observes, les analyses de median et l'effet de dimension, ils ont demontre l'epreuve ameliore pour tous les points marques, la pourcentage de buts de terrain et la pourcentage de jetes libres sans l'assistance. On n'a trouve pas d'appui pour les differences parmi les epreuves par les analyses du "t-test." Les resultats du "t-test" ils sont discutes en le contexte de pouvoir statistique. Les resultats donnent des prevues a la perspicacite et au renseignement important au ligne de fond sur les donnees peu souvent disponible pour l'etudier.

There has been much research and speculation on the effect of the settings in which athletic competition takes place. Most research has focused on the location in which an event is held and the possible advantages and disadvantages associated with being the home or visiting team. The "home team advantage" is the well known label applied to these notions.

A number of studies have documented the home advantage in professional and college sports (e.g., Schwartz & Barsky, 1977; Varca, 1980; Snyder & Purdy, 1985; Pollard, 1986; Edwards & Archambault, 1989). However, a home team disadvantage has also been found under some conditions. The disadvantage seems to occur in championship matches. This is labeled the "choke" effect and was found in World Series and National Basketball Association play-offs (Baumeister & Steinhilber, 1984) and golf (Wright, Christie, Mcguire, & Wright, 1991). Thus, while the general advantage for the home team is well documented, there is also a possible disadvantage when the contest is critical.

Basketball, of all the major sports, consistently shows an advantage for the home team (Schwartz & Barsky, 1977; Altman, 1975). Several theoretical formulations have been offered to help explain the cause of the home advantage. Varca (1980), suggested a frustration-aggression explanation. Functional aggressive play resulted in more blocked shots, rebounds, and steals by the home teams. Visiting teams committed more dysfunctional aggression as represented by the number of fouls. Another explanation is the territorial effect--the idea that there is an advantage if challenged in the home territory. For example, familiarity with the surroundings, the need to defend against intruders, and the overall dominance and control over the home territory should place the owner at an advantage.

Other explanations have focussed on audience effects on performance. Edwards and Archambault (1989) reviewed the literature on audience composition, size, intimacy and density in relation to performance. They concluded that while no one explanation dominates, a rationale can be offered that supports the home team advantage for each of the audience characteristics. Mizruchi (1985) and Schwartz and Barsky (1977) interpreted audience effects through social facilitation theory with the social support of the audience being the main determinant of the home advantage.

Experimental research on audience effects is, by necessity, limited to laboratory or contrived settings. Controlling the presence/absence of spectators or manipulating variables in the natural setting for purposes of research would likely result in no subjects. Thus, data on audience effects in natural settings are usually descriptive and collected in the context of an event or post-facto analyses of after game statistics.

A unique opportunity to study crowd effects occurred during the North Atlantic Conference (NAC) 1988-89 basketball season. A measles epidemic resulted in a quarantine that prevented having spectators at 11 Siena and Hartford basketball games. This made it possible to study game statistics for these two teams under the conditions of the presence or absence of spectators. It was expected that if spectators had an influence on performance, the effects should be revealed in differences in team statistics.

The initial thought was to test the directional hypothesis that performance would be better in the presence of spectators. However, when rethinking the hypothesis it was realized that the studies reported on audience effects in natural settings all had audiences. What varied were certain crowd characteristics or the location, and not the presence or absence of spectators. Thus, there was no empirical support for a directional hypothesis and the nondirectional hypothesis of no differences was examined.


Games and Locations

The archive data available consisted of the basketball performances of the ten teams that made up the 1988-89 NAC. Data for games played by Siena and Hartford were included in the study. They were the only teams that played more than one game in the absence of spectators. For Siena, nine away games were used. Five were played with spectators present and four with no spectators. For Hartford, 11 home games were used. Four had no spectators, while seven had spectators. Both teams played one other game without spectators--Siena, one home game and Hartford one away game.


Data were the official NCAA basketball box score sheets provided by the sports information personnel from each institution. The score sheet included overall offensive and defensive team performance by half and for the total game. Individual player data was also provided on the sheets.


The independent variable was the presence or absence of spectators. The unit of analysis was game performance based on the combined performance of the individual players. The dependent variables were three game statistics: (1) Total points scored, (2) Field goal percentage, and (3) Free throw percentage.

Data Coding and Analyses

The score sheets were coded by spectators (yes/no). The dependent variables were entered as raw scores or proportions. Separate analyses were done for each team. This was because all but one of the games played by Siena without spectators were away games, whereas those for Hartford were all home games but one. Also, Siena dominated the league that season by losing only one conference game, while Hartford lost six. In addition, Siena's offense averaged 88 points per game to Hartford's 67. Given these differences it was decided that it would be more informative to look at the teams individually. Descriptive statistics, t-tests and effect size analyses were employed.


Table 1 presents the results for both teams. The Siena analysis was done on the away games, while the Hartford analysis was on the home games. It was in these two locations that the teams played a number of games with and without spectators.


It can be observed (Diff column) that the performances of both teams was improved under the no spectator condition. Siena scored an average of 76.25 points when playing in front of spectators and 86.20 in the absence of spectators. Likewise, Hartford scored an average of 64.29 in front of spectators and 71.25 without spectators. The pattern was similar for field goal and free throw percentages, except that Siena's field goal percentage was about the same under each condition.

No statistically significant differences were found for any of the three variables for either team (alpha = .05). Thus, the null hypotheses associated with the three variables were not rejected and no support for crowd effects was found based on the statistical tests. The "p" column provides the actual probabilities.

It is recommended (e.g., Lipsey, 1990; Kraemer & Thieman, 1987) that statistical tests be accompanied by power and effect size analyses regardless of statistical significance. This recommendation is particularly important when an observed difference is considered large but not statistically significant. For both teams, some of the differences shown in Table 1 can be considered large, especially the differences in total points scored under the two spectator conditions.

A power analysis revealed the power of the t-test to be less than .50 for any of the hypotheses. That is, the probability of rejecting any of the hypotheses was less than 50%. This was due primarily to the small number of games available to study. For example, for the difference of nearly ten points between the two spectator conditions for Siena to have been statistically significant (alpha = .05 and power = .80), there would have to have been 20 games under each condition to have been statistically significant. Thus, the small number of games played under the two conditions made it unlikely that any of the t-ratios would be significant even if they represented true differences.

An effect size disregards statistical significance and sample size. The procedure used for determining effect sizes was simply the difference between two means divided by a standard deviation (Glass, McGaw & Smith, 1981). The result is a value that indicates how large the difference is in standard deviation units and is called an effect size. The effect sizes (ES) are presented in Table 1. The standard deviation (sd) used to compute the effect sizes were the standard deviations for the games played with spectators.

The effect size can be interpreted similar to a z-score by using a normal curve table. For example, the effect size of .93 for Siena TP is at about the 83 percentile. This suggests that when playing in the absence of spectators, the total points scored by Siena would average about the 83 percentile in the distribution of overall scores. This assumes that the mean associated with the games played in front of spectators would be the normal expectation, i.e., similar to a control group in experimental studies. The effect sizes and percentiles for both Siena and Hartford suggest the possibility of a spectator effect. That is, the performance of both teams was better with no spectators present.

Due to the small number of games under both spectator conditions, any highly divergent score or percentage might have had enough influence to effect the means, standard deviations and subsequent effect sizes. Table 2 presents the raw data used in the analyses. The values are ranked by the presence and absence of spectators for each variable.

For total points (TP), it can be seen that the scores for both Siena and Hartford were higher under the no spectator condition. For Siena, only one score (76) in the TABULAR DATA OMITTED no spectator condition was lower than the highest score in the spectator condition (92). Better performance can be observed across all three variables under the no spectator condition. For the three variables, except field goal percentage for Siena, all of the values but one under the no spectator condition are at the median or above for both teams. Thus, the examination of the medians as reference points support the effect size analysis and suggest a spectator effect.


In summary, support for improved performance under the no spectator condition was derived from the observed differences, effect sizes and using the medians as reference points. No support for performance differences was found as a result of the t-test analyses.

Based on the observed differences and effect size analyses, the results might be interpreted as contradicting audience arousal, audience evaluative and social facilitation theories (Bond & Titus, 1983; Guerin, 1986; Edwards & Archambault, 1989). That is, the presence of spectators should arouse both the home and visiting team and result in enhanced performance for the home team and perhaps the visiting team as well. However, at least in the sphere of athletic events, most theories tend to assume various crowd/spectator characteristics, i.e. noisy, active, supportive, non supportive, evaluative - not the complete absence of the crowd. Thus, it is difficult to explain the findings in light of present theories, although improved performance associated with no spectators would probably not be expected.

Finally, to our knowledge these are the first data that have been summarized and shared under the spectator conditions described in this paper for any sport. Although the data are ex-post facto and scant in number, they do provide insight into possible crowd effects. It was beneficial that the two teams were studied independently. Since the results were basically the same for both teams, the generalizability of the findings is strengthened. In addition, the results held in the home setting (Hartford) as well as on the road (Siena). Thus, there was some support for replication of the findings within the context of this one set of data.

Future replications will have to wait until the next misfortunes. Data will again be ex-post facto and likely small in number. However, an accumulation of such replications, especially if there are consistencies in findings, will advance the knowledge base. It will probably never be possible to conduct experimental studies to examine the absence of crowds (unintended absence) in the natural setting. Thus, opportunities similar to the one described in this paper should be pursued. It is in this sense that we offer our findings as one basis to refer to when similar situations arise.

With this in mind we have our measles vaccinations and are eagerly awaiting the next opportunity.


Baumeister, R. F., & Steinhilber, A. (1984). Paradoxical effects of supportive audiences on performance under pressure: The home field disadvantage in sports championships. Journal of Personality and Social Psychology, 47, 85-93.

Bond, C. F., Jr., & Titus, L. J. (1983). Social facilitation: A meta-analysis of 242 studies. Psychological Bulletin, 94, 265-293.

Edwards, J., & Archambault, D. (1989). The home-field advantage. In J. H. Goldstein (Ed.), Sports, games, and play (2nd ed., pp. 333-370). Hillsdale, NJ: Lawrence Erlbaum.

Glass, G. V., McGaw, B. & Smith, M. L. (1981). Meta-analysis in social research. Newbury Park, CA: Sage.

Guerin, B. (1986). Mere presence effects in humans: A review. Journal of Experimental Social Psychology, 22, 38-77.

Kraemer, C. H., & Thiemann, S. (1987). How many subjects. Newbury Park, CA: Sage.

Lipsey, M. W. (1990). Design sensitivity. Newbury Park, CA: Sage.

Mizruchi, M. S. (1985). Local sports teams and celebration of community: A comparative analysis of the home advantage. Sociological Quarterly, 26, 507-518.

Pollard, R. (1986). Home advantage in soccer: A retrospective analysis. Journal of Sports Sciences, 4, 237-248.

Schwartz, B., & Barsky, S. F. (1977). The home advantage. Social Forces, 55, 641-661.

Snyder, E. E., & Purdy, D. A., (1985). The home advantage in collegiate basketball. Sociology of Sport Journal, 2, 352-356.

Varca, P. E. (1980). An analysis of home and away game performance of male basketball teams. Journal of Sport Psychology, 2, 245-257.

Wright, E. F., Jackson, W., Christie, S. D., Mcguire, G. R., & Wright, R. D., (1991). The home-course disadvantage in golf championships: Further evidence for the undermining effect of supportive audiences on performance under pressure. Journal of Sport Behavior, 14, 51-60.
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Article Details
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Author:Moore, James C.; Brylinsky, Jody A.
Publication:Journal of Sport Behavior
Date:Jun 1, 1993
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