Andrew Taylor


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Andrew Taylor

  1. 1. Momentum in Australian Rules Football Andrew Taylor Email: INTRODUCTION RESULTS Momentum in team sports has long been associated with Analysis of momentum found that the teams ranked 1 to 4 [St 35 1 both good and bad performance (1, 2). Positive momentum Kilda, Geelong, Bulldogs and Carlton] were scoring on 30 Figure 3 identifies key factors that contribute to is associated with periods of success, such as winning average 16 goals and 13 behinds per game while teams ranked to momentum phases during games and ultimately streaks, while negative momentum is associated with 13 to 16 [West Coast, Fremantle, Richmond and Melbourne] 25 periods of failure, such as losing streaks (1, 2). Momentum were scoring on average 12 goals and 11 behinds per game. 20 4 performance outcomes. Increasing positive momentum, whilst limiting negative momentum, was identified as studies of team sports in the past have focused primarily on The relationship between team and amount of goals and points 15 most important to performance outcomes. Basketball and Soccer (1). scored per game was significant: χ² (15, N = 4362) = 15.89, p < .0005. The association was of a moderate strength: Φ = .39. 10 13 Current coaching resources available from the AFL fail to 5 to cover momentum (3). While most AFL coaches speak of 0 St Kilda tended to score throughout the game [24.1% 1st momentum, most fail to be able to identify key factors Quarter, 28.1 2nd Quarter, 26.7% 3rd Quarter, 21.1% 4th 16 which contribute to it and what effect it has on team 1st 2nd 3rd 4th 1st 2nd 3rd 4th performance (3). Quarter] while Geelong tended to score the majority of it’s Wins Wins Wins Wins Loss Loss Loss Loss CONCLUSION points in the first three quarters [24.7% 1st Quarter, 29.6% 2nd Quarter, 25.9% 3rd Quarter, 19.8% 4th Quarter]. The Bulldogs Figure 2: Average Quarters Won/Lost Per Grouping were found to score heavily in the 2nd Quarter [29.3%], while Performance analysis of the first 11 rounds of the 2009 Collingwood and Port Adelaide show a trend of scoring in the AFL season identified momentum as a key contributor to second half of games. The relationship between team and Figure 2 shows teams 13 to 16 were often competitive in performance outcomes. amount of goals and points scored per quarter was significant: the 1st quarter [20 wins, 22 losses] but fell away badly after Teams ranked 1 to 4 proved to be statistically the four best χ² (45, N = 4362) = 43.91, p < .0005. The association was of a that. Figure 2 also shows that 1 to 4 teams dominate the teams. These sides produced significant periods of strong strength: Φ = .518. game throughout [win 68% of quarters compared to 34%]. momentum, scored more goals and scored more often. The relationship between grouping and quarter wins was Analysis also showed that the top team [St Kilda] was significant: χ² (21, N = 174) = 14.9, p < .0005. The The bottom four teams scored less and less often. While producing more points during each quarter when compared association was of a very strong strength: Φ = .828 the bottom four teams did produce momentum periods, with the bottom side in Melbourne. St Kilda scored 73 times in these teams failed to capitalise on these periods. the first quarter compared to Melbourne’s 59, 85 times Win compared to 54 in the second, 81 compared to 57 in the third Through the plotting of opposition momentum trends, Picture 1: St Kilda players celebrate another successful and 64 compared to 57 in the last. Game Decrease Increase coaches can instruct their team to place numbers behind performance. Positive Negative the ball to stop opposition teams capitalising on positive Figure 1 shows the average positive momentum accumulated Momentum Momentum momentum. Similarly, the coach can instruct their team to METHODS by teams during rounds 1 to 11. Teams 1 to 4 clearly average attack when they have positive momentum. Key the best momentum. momentum moments during games can be identified to Performance analysis was performed on the 11 eleven rounds Limit help coaches understand at what point the game was lost. of the 2009 AFL season. 88 games were analysed with the 60 Opposition Prevent Convert Opposition Scoring focus on identifying momentum trends. Momentum was 45 Scoring Gaining Positive Frequently Momentum identified as scoring. The variables consisted of: 30 Opportunities Momentum Periods into and • Team – which team has scored 15 Points Often REFERENCES • Game – number for the season 0 • Quarter – which quarter the score occurred in -15 1. Burke KL, and Burke MM. Perceptions of Momentum in 1 3 5 7 9 11 • Time – what time during the quarter the score occurred -30 Limit Prevent College and High School Basketball: An Exploratory • Score – goal or behind -45 Opposition Score Case Study Investigation. Journal of Sport Behaviour. Opposition Score • Total accumulative score -60 to Under from Multiple 22: 303, 1999. 17 Goals, •Grouping – rank according to position on ladder as of 10 Goals, Kicking Goals in 14 Points Round 11 Round 9 Points Multiple Succession 2. Gayton WF and Very M. Psychological Momentum in Goals Team Sports. Journal of Sport Behaviour. 16: 121, 1993. Data was analysed using Chi Square and T Tests on SPSS in a Row 3. Norton KI, Craig NP, and Olds T. The Evolution of version 17 for Windows. Graphs were produced using 1 to 4 5 to 8 9 to 12 13 to 16 Australian Football, Journal of Science and Medicine in Microsoft Excel. Figure 3: Factors identified as contributing to Figure 1: Average Group Momentum Rounds 1 – 11 Sport, 2, 398 – 404, 1999. 2009 season successful performance outcomes