Lectures, tutorials, and presentations on SPORTS BIOMECHANICS, research methods, and academia.
Including the Sports Biomechanics Lecture Series #SportsBiomLS - a series of expert guest lectures on topics relating to the biomechanics of various sports and biomechanical data collection and analysis.
Also including the #ScienceofCricket Lecture Series
Please subscribe to keep updated, and follow @biomechstu on Twitter. For more information, content, and resources: stuartmcnaylor.com
In computer science, we used to refer to "pose estimation" as "articulated object tracking," where "pose estimation" referred to the problem of determining the camera pose and the object pose in 3D space. However, the term has become ubiquitous and is now commonly used to mean the posture of a human being in an image or video.
Hi. I'm currently involved in editing a biomechanics book that will have a chapter on cricket, which will be available as a standalone document as well. Not published yet, though.
15:13 can’t wrap my head around this. Is the line coming perpendicular to both thorax and pelvis?. For practical purposes, how is this angle created? By moving your hips and shoulders in opposite direction?. For example, if a batter before ball is delivered stands in a way that his hips are somewhat horizontal (ie in the direction of the cricket stumps, while his shoulders are in one line facing the bowler, ie side on. This would result a slight twist in back but is this what you are referring to the X factor?” Theoretically, would changing the batting stance in a way that hips are facing the leg side boundary and shoulders facing the bowler before ball is delivered generate more power?. Thank you for the informative content you produce.
Hello Stuart. How about a 3 groups non directional test. We have 25 participants in each group, total of 75 and we would like to run the power analysis. Thanks.
I am Physio from Pakistan Cricket Board, I agree with the difference of IO muscle thickness in fast bowler and also in spinner who have more rotatory component in their action. This difference is obvious in all active bowlers and might diminish in non active bowlers, this difference is perhaps due to inactivity of injured bowler during injury, not a primary cause of that injury.
Could you please tell estimated sample size assuming allele frequency 0.1 (10%), additive model, effect size is 2, and disease prevalence 7% with 0.05 % signig=ficance level
It looks like you have an effect size and an alpha level in mind. Just need a desired power and the statistical test that you plan to use. Then the approach in the video can be followed, if that is the desired approach for justifying sample size. Alternatively, if not a standard test then it may require simulation of data sets in R or similar, rather than G*Power
I'm not sure exactly what you mean, but this approach is purely for a sample size justification based on statistical power, not based on any alternative approach around generalisability or representative sampling, etc.
Hi. I'm not sure if any specific software was used for simulations or if bespoke code in python, etc. I believe I asked about this a long time ago but can't remember the answer. You'd be better off contacting Todd if you want a correct answer to that. Best wishes
The SPM approach (and others) can be used for other types of statistical model. For example, you can do an SPM ANOVA with multiple groups/conditions. I have some short tutorials here: ru-vid.com/group/PLI7VLEjUJidBBtyWFcZYF7f8t8IgMT9cg
How can I get a small portion of respondents from a specific amount of population? I have 351 for total population. I just want to get a portion of that population. How do I do that?
Hi. What statistical test would this be for? If your sample size justification is based onstatistical power, then out could follow the steps in the video. If the justification is based on generalisability or something else then it may be separate
If it's just for a statistical justification based on alpha, effect size, and statistical power, then you can follow the steps regardless of total population. If it is for some other reason then this may not be the required software/approach
Hi Stuart, this is really helpful. Hopefully you still read comments on here as I have a question please! Is it possible to obtain the specific percentages that the significant differences occur at? For example, for the four shaded areas these would seen to occur at ~0-30%, 35-65%, 75-85% and 95-100%. However, I was wondering if these can be calculated (perhaps with confidence interval too) and reported from MATLAB? Best, Ben
Hi Ben. Thanks for the comment. You can definitely export the percentages - i.e., you can print out or export the test statistic used to create the plots and can see where it exceeds the critical test statistic. Or you can output only where it exceeds the threshold. This just needs an extra line or two of code adding. They would be single values. Effect sizes (and confidence intervals around those effect sizes) are tricky for a number of reasons. You can consider the mean difference or maximum difference, etc. as effect sizes if reported appropriately. The forum mentions CIs: spm1d.org/NewFeatures.html#label-confidenceintervals but I would recommend asking Todd / on the forum if interested as I'm not confident to give a correct answer!
I would argue that technique is more important than strength alone, but yes the same 'technique' performed with greater force-production capabilities is likely to result in grdater bat speed and so grdater ball carry distance if it can be timed effectively.
@@biomechstu sir one last question..do strong muscles help in achieving quicker bat speed? And if 2 persons have same bat speed and technique but different body strength will it effect six distance anyways?
If technique, timing, etc. are appropriate then strength is likely to positively effect bat speed. But if bat speed is the same then strength wouldn't effect the ball outcome, as strength can only have an influence through the bat
Ball distance is a result of bat speed, bat angle, and bat-ball impact location (e.g. sweet spot). Better values for one (e.g., bat speed due to strength) can compensate for suboptimal values in the others as long as the others still remain acceptable and not completely the wrong bat angle or missing the ball, etc.
Hi Mario. Unless I've misunderstood, you're fine to follow the same guidance. i.e. figure out what statistical test is required, what your smallest effect size of interest is, your required alpha anf statistical power, and then follow the same steps. How you decide upon each of those parameters for the specific study is obviously the tricky part. Likewise, you may go for a completely different statistical and/or sample size justification approach.
I'm not sure, sorry. I don't use mine for file storage, so I'm not sure how that aspect works. Although it does seem that greater storage is offered with the Premium plan: www.mendeley.com/reference-management/premium
The Optimum launch angle says only the ball angle w.r.t its own plane (above the ground),not the bat angle ? When bat angle is said how we measure that? If it is a straight bat hit
The bat angle is also measured relative to the pitch. In that specific case, we would have been looking at the angle of the bat face about the global medio-lateral axis (i.e. bat pointing down at the ground, pointing back behind, pointing forwards, or what angle in-between) so that this could then be related to the launch angle you mention. Full details of all methods are in the published journal articles, all of which should be free to access in some format. I believe there are links somewhere in the video description.
Type I error = Illusion (you are seeing an effect when there is not one) Type 2 error = 2 blind 2 see (you are failing to see an effect when there is one)
Yeah, unfortunately a product of a poor WiFi connection during a live stream during COVID lockdown. Paul Felton's bowling lecture was streamed on the same day and has the same issue. Let me know if anything unclear as I have the original slides still.
What statistical test do you plan to use in your study? Once you know that, you just need to choose a desired alpha (e.g. 0.05), power (e.g. 80%), and minimum effect size of interest.
Seems like you erroneously state that the effect size box is for inputting the desired effect size but it is actually where you input the effect size you think your intervention or between-condition effect may be. This is why the sample size actually decrease when you increased the value in the effect size box. This is obviously a pretty important issue and the video should be edited to correct the issue.
Hi. This is the effect size you are powering your study to be able to detect if it exists. In some cases, this may be the exact size of effect that you expect but in most cases it won't be. It is often the smallest effect size that would be considered clinically or practically meaningful. For example, I may want to power the study adequately to detect effects of 0.2 or greater if they exist (for some a priori stated reason). This is regardless of what I actually think the effect size might be. As another example, if I anticipate an effect of 0.5 and set up the study to have 80% power to detect an effect size of 0.5 or greater, then my study would be underpowered to detect effects of 0.40 or 0.45, even if these effects are still large enough to be of clinical or practical significance. There are many ways of choosing an effect size such as (probably in order of preference): smallest effect size of interest; smallest effect size arbitrarily considered 'small', 'moderate', 'large', etc.; a central effect size estimate previously reported in the literature (but be aware this is only the central estimate and is also dependent on many factors in that study); a pilot test (but be aware of limitations in sample size of pilot work and the effect that can have on certainty of effect size estimates). I thin this is a long way of saying sometimes you may input the effect size you expect and other times you may input a different effect size for another valid a priori stated reason. The main thing is to justify the values chosen (including the alpha, beta, and effect size).
Many congratulations. Great presentation. I havent found the study that you present in 39:20 to 43:00 min (flywheel and Smith machine squats - a comparison). Do you have a title or a link? Thanks
Another note, with regard to the interesting discrepancy between forehand trunk speeds of ATP pros and high performance juniors of 16 years old. The professor’s speculative explanatory comment is based once again on some unwarranted assumption like that the technique used by the pros were optimal and/or that there were a linear relation between trunk speed and racquet speed in the upper range of trunk speeds. That is hardly the case in either. It is also highly suspectible that the juniors could generate almost as fast racquet head speeds by ”using upper limbs/shoulder areas” if the pros had optimal techniques utilising efficiently the trunk rotational energy in their forehand speeds. It might rather be argued that the juniors achieve almost the same forehand speed output by much less energy spent by being more relaxed in their arm and shoulder areas than the energy wasting pros: so much work for so scant results! It all can just be due to the pros over-powering the forehand swings by too tight (and muscular and heavy) arms while grunting along! Less is more, guys. Just saying. Learn from the kids.
Important observations posing further questions. The serve seems to be the great unknown forever eluding biomechanical analysis as this analysis conventionally and quite problematically presupposes and takes as a given something that should be addressed first and foremost: What is the optimal, most efficient configuration of the body segements in the load phase/trophy positon (the role and function of different bends, tilts and rotations and the question of their possible sequential coordination and degress of emphasis: back tilt: sommersault (nor squatting!), hip intrusion and the related shoulder tilt: cartwheel, trunk and shoulder/upper arm rotation : spin)? Is it even an intelligible/valid/pedagogically optimal approach to view serve power as a net sum springing out of different power sources attributed to different muscle groups and body parts each of which then allegedly contributing with a given, more or less arbitrary rate (%) to the final product measured in serve speed? What do we even and exactly mean with this loosely used term ”leg drive”? What is its real role in the serve - it should be revisited and by the same token clarified the relation between this so called leg drive and the role of core muscles in the serve dynamics (see the first point above). And finally, related to the critized piecemeal framework allocating rations of serve power to the different body segments, what parts and motions of the body/serve are active generators and what rather passive followers in the complete and optimal service action? This comment also serves as a general (implicit) critique of all such biomechanical models and frameworks that incorporate unproblematically and ”quite pragmatically” these unwarranted putative presumption without addressing such fundamental questions as posed above and thus producing these ”elaborate” multi-phase/stage models like the serve 8-stage model by Kovacs et al. This kind of reductive analytics employed in the conceptualization of harmonized fluid atheltic motions of emergent nature are good at missing the forest for the trees which might even not be not there!
