Virtual Fans Want to Buy Tickets
When the general public universally accepted smartphones and 4G between 2010 and 2015, the sports industry was the first to take advantage of it in order to grow its fanbase.
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