European Journal of Sport Science Effects of starting ...

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European Journal of Sport Science

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Effects of starting score-line, game location, and quality of opposition in basketball quarter score

Jaime Sampaioab; Carlos Lagoc; Luis Casaisc; Nuno Leiteab a Department of Sports Sciences, Health and Exercise, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal b Research Centre for Sports Sciences, Health and Human Development, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal c Faculty of Education and Sport, University of Vigo, Pontevedra, Spain Online publication date: 05 November 2010 To cite this Article Sampaio, Jaime , Lago, Carlos , Casais, Luis and Leite, Nuno(2010) 'Effects of starting score-line, game

location, and quality of opposition in basketball quarter score', European Journal of Sport Science, 10: 6, 391 — 396 To link to this Article: DOI: 10.1080/17461391003699104 URL: http://dx.doi.org/10.1080/17461391003699104

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European Journal of Sport Science, November 2010; 10(6): 391396

ORIGINAL ARTICLE

Effects of starting score-line, game location, and quality of opposition in basketball quarter score

JAIME SAMPAIO1,3, CARLOS LAGO2, LUIS CASAIS2, & NUNO LEITE1,3 1

Department of Sports Sciences, Health and Exercise, University of Tra´s-os-Montes e Alto Douro, Vila Real, Portugal, Faculty of Education and Sport, University of Vigo, Pontevedra, Spain and 3Research Centre for Sports Sciences, Health and Human Development, University of Tra´s-os-Montes e Alto Douro, Vila Real, Portugal

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Abstract In several team sports, the game starting score-line may be understood as a measure of performance accomplishment and hence might have an effect on players’ subsequent efforts. The aim of this study was to identify the effect of the starting score-line, game location, and quality of opposition on basketball game quarter final score. The sample comprised 504 game quarters from the Spanish Basketball Professional League and these were classified as balanced (difference in scores equal of 8 points or less, n 194) and unbalanced (difference in scores of more than 8 points, n 310) using k-means cluster procedures. The effects of the predictor variables on game quarter outcome (difference between points scored and points received) in the whole game and in the second, third, and fourth game quarters were analysed using linear regression analysis. The starting game quarter score-line was only significant in unbalanced situations with very similar effects among different game quarters. The greater the difference in accumulated score at the beginning of each quarter, the more points recovered by the teams who were losing. A small effect from the quality of the opponent was found in the second and third quarters, whereas game location only had an effect when analysing the whole game and second quarter using balanced and unbalanced game quarters combined.

Keywords: Match analysis, situational variables, team sports

Introduction Identifying the determinants of success in team sports is a major topic in the scientific community and the available research has grown intensively in the last few years. A substantial contribution to the understanding of this complex phenomenon is research into the situational variables that may influence team performance at a behavioural level, such as game location or quality of opposition (Carling, Williams, & Reilly, 2005; Jones, James, & Mellalieu, 2004; Kormelink & Seeverens, 1999; Lago, 2009; Lago & Martin, 2007; Taylor, Mellalieu, James, & Shearer, 2008; Tucker, Mellalieu, James, & Taylor, 2005). Several other factors, such as the score-line (i.e. whether the team is winning, losing or drawing), may have important effects on team performance and should be investigated to

provide a more complete framework when modelling team sports performance. In several team sports, the game starting score-line may be understood as a measure of performance accomplishment and hence might have an effect on players’ subsequent efforts. Performance accomplishments are viewed as a powerful source of efficacy expectations and it is suggested that such expectations probably determine the task-related effort that needs to be expended (Bandura, 1997). Available research suggests that sports teams with greater belief of success are more likely to put in greater effort and persevere through intense challenges (Feltz & Lirgg, 2001). Based on this idea, it was shown that football players performed less lowintensity activity when losing, trying to recover from the unfavourable score, than they did when they were winning (Bloomfield, Polman, & O’Donoghue,

Correspondence: J. Sampaio, Department of Sports Sciences, Health and Exercise, University of Tra´s-os-Montes e Alto Douro, Quinta de Prados, Apartado 1013, 5000-911 Vila Real, Portugal. E-mail: [email protected] ISSN 1746-1391 print/ISSN 1536-7290 online # 2010 European College of Sport Science DOI: 10.1080/17461391003699104


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2005; Lago, Casais, Dominguez, & Sampaio, 2010; O’Donoghue & Tenga, 2001; Shaw & O’Donoghue, 2004). In addition, Jones et al. (2004) reported variations in the duration of team ball possessions according to the score-line. Successful teams had significantly longer possessions than unsuccessful teams irrespective of game status (winning, losing or drawing) and both teams had longer possessions when they were losing matches than when winning. The authors reasoned that a greater effort was made to regain possession by a team when losing (needing a goal to avoid defeat) than a team when winning (Jones et al., 2004). Basketball may be the one of the most appropriate team sports to investigate this starting score-line effect, since there are several occasions when a game is stopped. The whole game is divided into four quarters, each 10 min long. A 15-min half time separates the first from the second half and a brief 120-s break separates the first and second, and the third and fourth, game quarters. In contrast with other team sports, coaches and players can modify their strategy and tactical behaviour several times during the game in response to changes in game situational variables. It has been suggested that game score-line has a considerable effect on players’ subsequent psychological responses (Burke, 2005; Dosil, 2005). For example, losing a game can have detrimental effects on self-control and arousal levels (Dosil, 2005; Martens, 1987), whereas winning most likely decreases concentration (Dosil, 2005). Timemotion research has identified a decrease in teams’ work rate when winning, which helps to confirm this hypothesis (O?Donoghue & Tenga, 2001). Using match analysis, Sampaio and colleagues (Sampaio, Lago, & Drinkwater, 2010b) tried to explain the dominance of the United States basketball team in the 2008 Beijing Olympic Games. Their results showed that, when the USA team played at a faster game pace, the players were able to recover more balls from their opponents, which resulted in effective field-goal shooting. However, their dominance in the first half (average lead of 25 points) decreased substantially in the second half and the measures of assertive play lost their importance, suggesting that players may have felt that no effort was needed to win the game. Thus, the aim of this study was to identify the effect of game quarter starting score on game quarter final score in basketball. We hypothesized that the greater the difference in the accumulated score at the beginning of each quarter, the higher the number of points recovered by the team that was losing. The effects of game location and quality of opposition were also analysed. Furthermore, because the current literature suggests analysing basketball games according to the game final differences (Go´ mez,

Lorenzo, Sampaio & Iban˜ ez, 2006; Sampaio & Janeira, 2003), these effects will be identified both for balanced and unbalanced game quarters. Methods Archival data were obtained from the Spanish Basketball Professional League (ACB) records for the 2008 2009 regular season. All data were gathered on-court by the league’s professional technicians. Altogether, 126 randomly chosen games were analysed using the play-by-play game-related statistics to record the score difference at the beginning of each quarter and the quarter final score (n 504 cases). The game location (home or away) and quality of opposition (difference between end-of-season rankings of the opposing teams) were also registered. Data analysis Linear regression models were used to explore the effects of the predictor variables on game quarter outcome (difference between points scored and points received) in the whole game and in the second, third, and fourth game quarters. When estimating the models, neither evidence of heteroscedasticity in residuals nor of multicollinearity among regressors was found. Moreover, the RESET test of Ramsey (1969) did not reveal specification problems. When interpreting the statistical results, positive or negative coefficients indicate a greater or lower propensity to increase/decrease game quarter outcome, respectively. Three predictor variables were included in the model to explain game quarter outcome (QO): (i) score difference at the beginning of each quarter (Score Difference: SC); (ii) game location was recorded as ‘‘home’’ or ‘‘away’’ (Game Location: GL); and (iii) quality of opposition was registered as the difference in the end-of-season ranking between opposing teams (Opposition: OP). b0 is the intercept and b1, b2, and b3 are the impacts of each one of the independent variables. Finally, oi is the disturbance term. The model is as follows: QOi b0 b1 SCb2 GLb3 OPo i A k-means cluster analysis was performed to identify a cut-off value in final score differences and classify game quarters (Rost, 1995). The results identified one cluster (balanced game quarters) with a difference in score of less than 8 points, averaging 3.792.5 points (range 08, n 194), and another cluster (unbalanced game quarters) with a difference in score of more than 8 points, averaging 12.393.8 points (range 927, n 310). The linear regression models were performed for all game quarters, for balanced and for unbalanced game quarters.

Basketball quarter score

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Table I. Final point differences between teams for balanced (difference of 8 points or less), unbalanced (difference of 8 points or more), and all game quarters Game quarter First Second Third Fourth

All quarters (n 504) 5.293.6 4.693.7 5.494.1 7.095.8

12.191.9 11.292.8 12.193.6 13.094.7

Statistical analyses was performed using SPSS software release 16.0 (SPSS Inc., Chicago, IL, USA) and STATA for Windows version 10.0 (Stata Corp. LP, College Station, TX, USA). Statistical significance was set at P B0.05.

Results


Balanced quarters (n 194)

Unbalanced quarters (n310)

Table I presents average final point differences between opposing teams for balanced, unbalanced, and all game quarters. The effects of the independent variables on game quarter outcome are displayed in Table II. In the whole game model, this outcome was explained by the three factors. For each point of difference in the accumulated score at the beginning of each quarter, the analysed team decreased game quarter outcome by 0.23 points. Playing away decreased game quarter outcome by 1.61 points compared with playing at home. Playing against strong opposition was associated with a decrease in the game quarter outcome. Each unit of distance in the end-of-season ranking between competing teams increased/decreased game quarter outcome by 0.29 points. The intercept was not statistically significant. The linear regression model explained about 35% of the variance in game quarter outcome. Second quarter outcome (see Table II) was explained by the three variables included in the model. For each point of difference in the accumulated score at the beginning of each quarter, the analysed team decreased game quarter outcome by

4.092.3 3.592.5 4.092.4 3.592.7

0.27 points. Playing away decreased game quarter outcome by 2.67 points compared with playing at home. Each unit of distance in the-end-of-season ranking between competing teams increased/decreased game quarter outcome by 0.34 points. The coefficient of determination was 0.41. Third quarter outcome (see Table II) was explained only by the variable quality of opposition. Each unit of distance in the-end-of-season ranking between competing teams increased/decreased game quarter outcome by 0.52 points. The coefficient of determination was 0.28. Finally, fourth quarter outcome (see Table II) was explained only by the difference in the accumulated score at the beginning of the quarter. For each point of difference in the accumulated score at the beginning of each quarter, the observed team decreased game quarter outcome by 0.23 points. The coefficient of determination was 0.29. The effects of the independent variables on game quarter outcome in unbalanced games are displayed in Table III. Game quarter outcome in the whole game, in the second, third, and fourth game quarters was explained by the difference in the accumulated score at the beginning of the quarter. For each point of difference in the accumulated score at the beginning of each quarter, the observed team decreased game quarter outcome by 0.27, 0.30, 0.21, and 0.29 points, respectively. The coefficients of determination were 0.38, 0.26, 0.44, and 0.29, respectively. Third quarter outcome (see Table III) was explained most by the variable quality of opposition. Each unit of

Table II. Effects of starting score on quarter final outcome in all games (means with standard errors in parentheses) Variables Starting quarter score Game location Quality of opposition Intercept Number of observations R2 **P B0.01, *PB0.05.

Whole game

Second quarter

Third quarter

Fourth quarter

0.23** (0.04) 1.61* (0.63) 0.29** (0.05) 0.80 (0.45)

0.27* (0.09) 2.67* (1.22) 0.34** (0.09) 1.33 (0.87)

0.08 (0.06) 0.14 (1.11) 0.52** (0.90) 0.76 (0.19)

0.28** (0.08) 0.12 (1.49) 0.21 (0.14) 0.87 (1.07)

504 0.35

126 0.41

126 0.28

126 0.29

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Table III. Effects of starting score on quarter final outcomes in unbalanced game quarters (means with standard errors in parentheses) Variables Starting quarter score Game location Quality of opposition Intercept Number of observations R2

Whole game

Second quarter

Third quarter

Fourth quarter

0.27** (0.07) 1.85 (0.95) 0.26 (0.05) 0.76 (0.49)

0.30* (0.15) 1.59 (3.59) 0.31 (0.27) 0.80 (1.44)

0.21** (0.09) 2.65 (2.20) 0.98** (0.18) 1.50 (1.41)

0.29** (0.11) 2.76 (2.49) 0.13 (0.15) 2.02 (1.65)

194 0.38

18 0.26

55 0.44

66 0.29


**P B0.01, *PB0.05.

distance in the-end-of-season ranking between competing teams increased/decreased game quarter outcome by 0.98 points. The effects of the independent variables on game quarter outcome in balanced games are displayed in Table IV. There was no effect of the accumulated score at the beginning of each quarter on game quarter outcome. Game quarter outcome in the second and third game quarters was explained by the quality of opposition. The coefficient of determination ranged from 0.06 to 0.15.

Discussion The aim of this study was to identify the effect of game quarter starting score on game quarter final score in basketball. We hypothesized that a greater difference in the accumulated score at the beginning of each quarter would correspond with a higher number of recovered points by the losing team. This hypothesis was confirmed. However, when the analysis was performed separately for unbalanced and balanced game quarters, the effect was emphasized but only in unbalanced quarters. Anecdotally, basketball coaches and players often mention a 10-point advantage as the balanced/

unbalanced game cut-off. However, the literature has consistently failed to confirm such a cut-off in male professional basketball (see Go´ mez, Lorenzo, Sampaio, Iban˜ ez, & Ortega, 2008; Sampaio & Janeira, 2003). The current results show an 8-point cut-off and the range of the adjusted predictability of the obtained models (r2) is high for unbalanced (0.260.44) and low for balanced quarters (0.06 0.15). These results confirm that balanced and unbalanced game quarters were affected much differently by the studied variables. The starting game score was only significant in unbalanced situations and surprisingly had very similar effects among different game quarters (range 0.21 to 0.30). In a more practical sense, a 9-point lead at the start of the quarter will likely be decreased with each game quarter by 1.92.7 points, and a 27-point lead will likely be decreased by 5.78.1 points. Although speculative, reasons for this game scoreline effect may be related to players’ lower concentration (Dosil, 2005) and a feeling of performance accomplishment by winning teams (Bandura, 1995), which probably result in players committing more errors (such as turnovers) or being less careful when selecting shooting opportunities. In contrast, the losing team will likely increase its work rate

Table IV. Effects of starting score on quarter final outcomes in balanced game quarters (means with standard errors in parentheses) Variables Starting quarter score Game location Quality of opposition Intercept Number of observations R2 **PB0.01, *P B0.05.

Whole game

Second quarter

Third quarter

Fourth quarter

0.13 (0.09) 1.64 (1.45) 0.25 (0.05) 0.82 (0.53)

0.11 (0.15) 2.52 (1.41) 0.34** (0.09) 1.26 (0.97)

0.19 (0.02) 1.23 (1.65) 0.32** (0.19) 0.62 (0.54)

0.17 (0.25) 2.34 (1.59) 0.17 (0.54) 1.72 (1.08)

310 0.11

108 0.12

72 0.15

60 0.06


Basketball quarter score (O?Donoghue & Tenga, 2001), which increases game pace and the opportunities to score more points. Also, an increase in losing teams’ defensive readiness may be expected due to the effects of increasing game pace (Sampaio, Drinkwater, & Leite, 2010a), which will help them to reduce the deficit. In the balanced game quarters, the results were unpredictable as already discussed in earlier literature (Sampaio & Janeira, 2003), followed by a weak effect of quality of the opponent in the second and third quarters. The quality of opposition effect has previously been identified in women’s (Madrigal & James, 1999) and in men’s basketball (Sampaio et al., in press), and in the latter study the differences between stronger and weaker teams seemed to be wider in offence-related variables (field-goal shooting and passing). Available research confirms that high-level performers typically show very high levels of consistency (Hopkins & Hewson, 2001; Stewart & Hopkins, 2000). Therefore, a quality of opposition effect in the second and third game quarters was not surprising, but failing to find the effect in the fourth game quarter was. In fact, the adjusted predictability of the fourth quarter model was the lowest (0.06), reflecting the lack of contribution from the studied variables and suggests the need to search for other variables and approaches to explain these game final moments. Surprisingly, the game location effect was only identified when analysing the whole game and second quarter when combining the balanced and unbalanced game quarters. The effect of the second quarter was particularly strong, for example, playing away decreased the second game quarter outcome by 2.67 points. The home advantage in basketball is currently stated between 60% and 64% (Courneya & Carron, 1992; Pollard & Pollard, 2005). The main causes of home advantage are believed to be crowd effects, travel and learning/familiarity (see Carron, Loughead, & Bray, 2005; Nevill & Holder, 1999; Pollard & Pollard, 2005), and these will be operating together, each interacting with the other in ways difficult to investigate, isolate, and quantify (Pollard, 2008). Therefore, our results may indicate that the effects of these causes may be stronger at the beginning of a game. Although speculative, it is possible that crowd effects are stronger at the beginning of the game and that away players are more strongly affected during this time and become more used to as the game progresses. The same may be true of familiarity, with away players starting the game unfamiliar with court facilities (e.g. lines, basket rims, and lighting) but becoming progressively familiar with them as the goes on. Further research is needed to understand the intra-game variation of home advantage in team sports.

395

In summary, our results have identified different contributions of the starting score-line, game location, and quality of opposition on basketball game quarters outcome. Being aware of the effects of situational variables should contribute to practice sessions by simulating possible competition scenarios and to competition by being proactive when analysing the situational factors and taking coaching decisions.

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