Doctoral Degrees (Exercise and Sport Sciences)

Permanent URI for this collection

http://hdl.handle.net/11660/264

Browse

Browsing Doctoral Degrees (Exercise and Sport Sciences) by Author "Schoeman, Riaan"

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    Positional match statistics in Currie Cup and Super Rugby competitions between winning and losing teams
    (University of the Free State, 2016-11) Schoeman, Riaan; Coetzee, F. F.
    English: Background Rugby union (here after referred to as rugby), as most other team sports, is becoming more aware of statistics as a reliable method to evaluate players and match variables during match play. This non-invasive evaluation method provides coaches and conditioning coaching with much needed information regarding player attendance to match situations and the successful execution of these match situations. Winning and losing teams from all levels of competitions use statistics to not only evaluate the team’s performance, but to determine which variables might be responsible for the outcome of the game. It is accepted that teams from a winning side might perform better in certain areas of play than losing teams, and players from higher levels of participation can execute certain skills more effectively. Previous research has been conducted on various teams from different participation levels on the physiological differences, mental toughness and match variables. The increased professionalism of rugby players may also indicate an increased ability of players from one season to the next. The ability of players will also vary from one position to the next and may be approximately exposed to certain match variables. Aims The first aim of this study was to determine the tackle and collision count for Super Rugby players during the 2013 competition. The second was to analyse the passing and kicking statistics that discriminate between winning and losing teams during the 2014 Super Rugby season. Thirdly, the study attempted to differentiate between the Super Rugby competition and the Currie Cup competition according to the occurrence of match activities and lastly to evaluate the evolution of the Super Rugby competition from 2011 to 2015 by the use of regression statistics. Method Sample The first aim consisted of conducting an analysis of 1,900 players from 30 games played during the 2013 Super Rugby competition. Two games from each of the participating franchises were used and selected in regards to number of matches available and balance of the sample. The second aim included an analysis of 1298 players from the 2013 Super Rugby season, whilst the third aim involved 1800 players with n=900 players from Super Rugby and n=900 players from the Currie Cup competition. Furthermore, aim 4 consisted of 4500 players and included n=900 from each of the Super Rugby seasons from 2011 to 2015. Measuring instruments Data was supplied by the Cheetahs Super Rugby Franchise, Bloemfontein, South Africa, using the Verusco TryMaker Pro. Verusco has provided Super Rugby teams with TryMaker Pro since the year 2000. TryMaker Pro is the most advanced analysis system custom-made for rugby, and it is the preferred system for the professional teams using Verusco. The Verusco coding centre codes all the games for registered teams and delivers high-detail, high-speed analysis within hours of the game having been played. Data analysis All data were captured in Microsoft Excel 2007 and subsequently converted into an SAS data set. For aim 1 the following analysis was done: The GLIMMIX procedure of the SAS Version 9.22 statistical software package was used for further statistical analysis (SAS, 2009). Means and standard deviations were used for numerical data. Individual tackle counts for each position, team and game were analysed using a generalised linear mixed model (GLIMM) with position and team as fixed effects, the natural logarithm of individual time played in minutes as offset, and position-by-team and game-by-team interaction terms as random effects. Regarding the fitted random effects, it seemed reasonable to allow for correlation between tackle counts for a specific individual across several games (modelled by the position-by-team random effect), and for correlation between tackle counts across players in a given team and game (modelled by the team-by-game random effect). Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function. Individual collision counts for each position, team and game were analysed in the same manner. In both cases – tackle counts and collision counts – the model fitted the data well and there was no evidence of residual over-dispersion. Based on the GLIMM, the mean rate of tackles and mean rate of collisions per 80 minutes (that is, normalised to a full-length rugby game) were estimated for each playing position, with 95% confidence intervals (CIs) of the mean rates. Similarly, in order to compare the mean rates of tackles and collisions between different playing positions, rate ratios (that is, the ratio of tackle and collision rates between playing positions) were estimated, with 95% CIs for the rate ratios. Aim 2 included the following statistical analysis: Means and standard deviations were used for numerical data. Individual tackle counts for each position, team and game were analysed using a generalised linear mixed model (GLIMM) with position and team as fixed effects, the natural logarithm of individual time played in minutes as offset, and position-by-team and game-by-team interaction terms as random effects. Regarding the fitted random effects, it seemed reasonable to allow for correlation between tackle counts for a specific individual across several games (modelled by the position-by-team random effect), and for correlation between tackle counts across players in a specific team and game (modelled by the team-by-game random effect). Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function. Team rates for passing and kicking were analysed in the same manner. In both cases, passing and kicking rates, the model fitted the data well and there was no evidence of residual over-dispersion. Based the GLIMM, the mean rate of passing and mean rate of kicking per 80 min were estimated for each team, with 95% confidence intervals (CIs) of the mean rates. Aim 3 consisted of each count variable (number of lineouts, scrums, rucks, mauls etc.) to be analysed using a generalised linear mixed model (GLIMM) with season (2011 versus 2015) as fixed effect, and both winning team and losing team as random effect. (The fitting of the variables winning team and losing team as random effects allowed for correlation between the counts in question for a given team across several games.) Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function; residual over-dispersion was allowed for in the model. Based on the GLIMM, the mean rates of lineouts, scrums, rucks, mauls etc. per game were estimated for the 2011 and 2015 seasons. Similarly, in order to compare the mean rates between the 2011 and 2015 seasons, ratios of lineout rates etc. between the 2015 and 2011 seasons were estimated, together with 95% CIs for the rate ratios. The above analyses were carried out separately for the data of the winning teams, for the data of the losing teams, and for the data of two teams involved in each game combined (that is, for the game). The analysis was carried out using SAS procedure GLIMMIX (SAS, 2013). Aim 4 used descriptive statistics for the count and percentage data calculated for the 2011 to 2015 seasons. Descriptive statistics were calculated per season for the winning teams, for the losing teams, and for the two teams involved in each game combined (that is, for the total count per game). Each count variable (number of lineouts, scrums, rucks, mauls etc.) was analysed using a generalised linear mixed model (GLIMM) with Season (2011 versus 2015) as fixed effect, and both winning team and losing team as random effect. (The fitting of the variables winning team and losing team as random effects allowed for correlation between the counts in question for a given team across several games.) Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function; residual over-dispersion was allowed for in the model. Based on the GLIMM, the mean rates of lineouts, scrums, rucks, mauls etc. per game were estimated for the 2011 and 2015 seasons. Similarly, in order to compare the mean rates between the 2011 and 2015 seasons, rate ratios, that is, ratios of lineout rates etc. between the 2015 and 2011 seasons were estimated, together with 95% CIs for the rate ratios. The above analyses were carried out separately for the data of the winning teams, for the data of the losing teams, and for the data of two teams involved in each game combined (that is, for the game). Percentage territory and percentage possession of the winning team in each game were analysed using a linear mixed model with Season as fixed effect, and both Winning Team and Losing Team as random effects. Based on the linear mixed model, the mean percentage territory (and possession) was estimated for each season, together with a 95% CI for the mean percentage. Similarly, in order to compare the mean percentage between the 2011 and 2015 seasons, mean differences, that is, differences of mean percentage territory and possession between the 2015 and 2011 seasons were estimated, together with 95% CIs for the mean differences. The analysis was carried out using SAS procedure MIXED (see SAS, 2013). Results The results from aim one underlined the importance of specific demands on the various playing positions regarding the tackles and collisions sustained by Super Rugby players. Clearly, loose forwards (6: = 16.65 tackles/80 min; 7: = 17.30 tackles/80 min; 8: = 14.68 tackles/80 min) had the highest tackling rates, followed by the locks (4: = 13.74 tackles/80 min; 5: = 14.07 tackles/80 min). Amongst the backs, the inside centre (12: = 12.89 tackles/80 min) was the player with the highest tackling rates, followed by the outside centre (13: = 9.96 tackles/80 min). The results showed that the open-side flanker (7) had the highest tackle rate of all playing positions (17.30 tackles/80 min). The open-side flank (7) was involved in the most collisions (50.91), followed by the blind-side flank (6), loosehead lock (4) and eighthman (8), with collision rates of 46.08, 44.81 and 43.03 respectively, per 80 minutes collision count per game. The results showed significant differences between positional groups for tackles, except for the front row players and the second row (1, 2, 3 vs 4, 5; p=0.0715 to p=0.6324). Within a positional group, namely the backline players, the tackling rate of the inside centre differed significantly from the tackling rate of the other backline players (9 vs 12, p=0.0029; 10 vs 12, p=0.0045; and 12 vs 13, p=0.0100). Aim two indicated that losing teams tend to pass the ball more (157.41) than winning teams (127.02). The results illustrated a significant difference between winning teams and losing teams regarding total passes, bad passes, and good passes (p=<0.05). Winning teams tend to kick the ball more (25.77) than losing teams (20.23). Results indicated a significant difference between winning teams and losing teams regarding total kicks, long kicks, short kicks, and kicking metres (p=<0.05). Winning teams kicked more long kicks (18.55) than losing teams (14.19). Winning teams also used the short kick (7.22) more effectively and more often than losing teams (6.04). Losing teams gain a mean total of 660.01m per game in comparison to winning teams who gain 901.4m per game. In the third aim it was discovered that, when the two competitions are compared, it is evident that only two variables can be distinguished. The mauls and tackles missed are the only two variables that show remarkable difference, with 3.23 mauls and 8.9 tackles missed per game more in Currie Cup competition than the Super Rugby. The results of this study underline the importance of measuring and analysing specific performance indicators on a regular basis as these performance indicators can increase or decrease as the level of competition change. The greatest increase occurred with rucking, as this variable increased from 139.63 in Currie Cup to 143.13 in Super Rugby. Super Rugby teams lose fewer lineouts, and have less missed tackles, while Currie Cup teams utilise mauls more as an offensive weapon. Aim 4 identified playing time, lineouts lost, scrums, scrums lost, tackles and penalties decreased from 2011 to 2015, while lineouts, mauls and the number of missed tackles increased. The results of this study underline the importance of measuring and analysing specific performance indicators on a regular basis as these performance indicators can increase or decrease in a short time frame. From 2011 to 2015 winning teams consistently lost fewer lineouts than losing teams, even with an overall increase in the number of lineouts per game. The study indicates a slight decrease in the number of tackles, but still supports the fact that winning teams have higher tackle rates than losing teams. Conclusions The results of the study show that there are significant differences between individual playing positions within the same positional group with regard to tackling and collision rates sustained during match play. The study confirms that losing teams pass more than winning teams and that winning teams kick more than losing teams during match play. The study also discovered a greater distance gained through kicks by winning teams. The higher or lower numbers of performance indicators performed by teams over competitions emphasise the different physiological demands for teams. The study concluded that playing time, lineouts lost, scrums, scrums lost, tackles and penalties decreased from 2011 to 2015, while lineouts, mauls and the number of missed tackles increased. The findings may be important for future research as they indicate a constant shift in statistics and outcomes of teams over seasons within a particular competition.