Metrica Sports and BIHub, empowering football from the grassroots through technology
Tactical analysis in football has undergone major developments in recent decades, largely powered by technology.
The tech revolution has modified our behaviour and now it is also revolutionizing sports. More and more tools are capable of collecting a great amount of data, which can help us make better decisions on the pitch. That is why it is interesting to capture all this data because it can help win matches. Although a great effort is done to improve the collection of data for real-time information, we still lack a good accurate interpretation of this information to help improve the coaches decisions.
This limitation may be related to statistical data analysis. Typically, sports scientists have focused their reports and propositions on the P-value application, where value is significant “yes or no”. However, it seems that statistical models that allow informing quantitatively as well as qualitatively about the possible changes for a variable to be real can be much more useful.
In a recent article, Martin Buchheit1 suggests researchers should abandon the conventional null hypothesis significance testing (NHST) and complement it with magnitude-based inferences (MBI) for different reasons:
As a conclusion, a better data interpretation and communication to coaches about the findings through reports or charts can be complemented by adding the MBI to the conventional NSHT. Keep in mind that it is about quantifying better each time the effect of an exercise or treatment on players performance, and helping coaches make better decision to enhance the chances to win.
Carlos Lago Peñas
1 Buchheit, M. (2016). 2016, 11, 551 – 554 3 4 Title: The numbers will love you back in return – I promise, International Journal of Sports Physiology and Performance, 11, 551-554.
2 Hopkins, W.G. y A.M. Batterham (2016), Error Rates, Decisive Outcomes and Publication Bias with 200 Several Inferential Methods. Sports Medicine, 46(10): 1563-1573.
3 McCormack, J., B. Vandermeer, and G.M. Allan. (2013). How confidence intervals become confusion intervals. BMC Medical Research Methodology, 13:134.
4 Cohen, J (1994). Things I have learned (so far). American Psychologist, 45:1304-1312.
5 Hopkins, W.G. A spreadsheet for deriving a confidence interval, mechanistic inference and 207 clinical inference from a P-value. Sportscience, 2007. 11, 16-20. DOI: 208 http://newstats.org/xcl.xls
Although there are several studies on this topic, many of them have analyzed these demands by looking at just a few variables or using very broad timeframes. A new study completed by physical trainers from F.C. Barcelona has analyzed several of these details more closely.
The understanding of the modifying variables of the game, based on the degrees of freedom.
Sports Analytics has grown exponentially thanks to IT sciences and it also encompasses other subareas (e.g. sports sciences, behavior sciences, medicine or data visualization) in addition to statistics with a focus that is more tactical and sports performance related.