17 April 2018 SportPerfSci Reports What performance-related variables best differentiate between eliminated and qualified teams for the knockout phase of UEFA Champions League? PDF file: SPSR25_Almeida C_180417_final Dataset: Click here to download Partager :Click to share on Twitter (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on WhatsApp (Opens in new window)Click to email a link to a friend (Opens in new window)Click to print (Opens in new window)Like this:Like Loading...
Your paper confirms what I published, as an Academic Paper, in England back in 2016 and re-confirms what my research has shown since 2013/2014. What you fail to capture here is that the ‘summation’ of some of these indicators (parts) actually shows even higher correlation (differentiation) than just the parts viewed individually; you also exclude penetration into the attacking final third (passes in that zone of play separate from general passes across the pitch). You should read this presentation I built awhile ago: https://chrisgluck.files.wordpress.com/2017/08/possession-with-purpose-tsi-v21.pdf it’s published as part of this article: https://possessionwithpurpose.com/2017/08/01/gluck-updated-possession-with-purpose-and-the-new-total-soccer-index/
Dear Chris Gluck,
First things first: I am sorry for the (huge) delay in my response.
I disagree with you when you state that I failed to capture the “summation” of game-related statistics (performance indicators). Although I recognise the value of using composite performance indicators to evaluate/predict match or competitive outcomes, I solely tried to compare simple performance indicators based on a methodology employed in previous research (for instance, see references 1, 3, 10 and 11 of the report). Why I did so? “(…) to provide coaches with quick profiles of (un)successful performances and objective information to optimise the design of training practices and game plan preparation”. Besides, UEFA.com presents other interesting variables, such as deliveries / solo runs into the attacking third, into the key area and into the penalty area, however I remain stick to the ones most investigated previously. Is was not a failure, but a methodological option.
Given that, I do not contest that there are more feasible performance variables/metrics to study.
Regarding your work, I must commend your efforts and reasoning. Your tool (Total Soccer Index) deserves to be validated. Despite being intuitive and logic, I think you should clarify some issues:
a) provide operational definitions of all variables/metrics, including an example of how you calculate each one of them;
b) the variable “ball possession” is calculated only considering attempted passes. Usually “ball possession” (%) refers to time in possession; how do you deal with the time an individual retains the ball or runs/dribbles with the ball?
c) applying simple correlations is not the most compelling way to ascertain the consistency and the effectiveness of the Total Soccer Index that you have proposed. Did you ever consider applying logistic regression techniques with the outcome variable being “match outcome”, i.e. 1) loss (0 points), 2) tie (1 point) and 3) win (3 points)?
Thanks for you comment!