Basic timing from point A to point B is a mystery to some coaches, even some track coaches. My concern is no long just the huge amount of 4.4 high school football players, it’s the massive problems we are seeing with prediction tables in professional soccer. Over the last few weeks, many presentations and conferences have brought up GPS methodology. I love technology only when it’s used properly and we have a major contextual and accuracy issue with managing fatigue and breakdown with GPS and even prozone. Like I mentioned many times before, we need to understand basic timing first before we can even talk about work load algorithms.
Years ago when some professional teams (over the pond and down under) were using GPS systems I was asked about setting up a solution to handle the data being collected. The teams were very surprised and a little upset when I did a disassembly of some of the equipment to trace the actual measurement parameters and see specifically what accuracy we were dealing with. Recently I have reviewed the equipment again to look at gyros and accelerometers and of course the software. To my horror much of the workload was not even close with the accelerometers, but even the basic distance traveled was off way too much for confidence in fatigue patterns. It’s time to reboot our thoughts and get back to basics so we can see what is lost as the equipment becomes less precise and accurate.
Timing a basic 15m burst isn’t rocket science, but it’s more complicated than two timing gates and a print out. When one times we need to understand that it’s gross ability. 40 yard dashes start from 3 point positions and this may not be indicative of the abilities for soccer or even wide receivers. The gap closes tremendously when athletes start in a two point position, meaning the edge those get with starting technique and power is negated a bit. Also the closed environment of starting statically is another aspect that allows an advantage to trained speed versus field speed. I have worked with rosters and rosters of data and watch many games and wonder why some guys who test well don’t seem to take advantage of the general abilities. Much of it is technique with or without the ball and much of it is expression artistically. Many of the clinic or workshop speed athletes have the mechanics imprinted, but it’s robotic and slow. Athletes coming from the poor areas on Brazil are not using anything besides maybe a few video clips of their heroes to get agility, so we need to understand the depth of motor skill acquisition. Instead of regurgitating the Gabriele Wulf bandwagon research, let’s reengineer why guys are better than others? History is just as important as science, because with good evidence we know what happened with confidence.
Fatigue can be a factor in repeat sprint ability, but make sure the context is clear. For example one athlete I worked with was doing 8x 30m and number 4 was slow enough drop off wise to call it a day. Watching the run it was a misstep by the athlete and it was early in the season. If I was stressed about stress mentality I would have blamed the traffic or the fight with the girlfriend but realistically it was a mechanical glitch that reduced his time performance. The last 4 sprints were his best and because of experience and sucking the brains out of other coaches 8 x 30m was a reasonable volume. Much of the problem with some athletes is they tend to over think the running and are tight. After a few reps they may slow down, but then they speed up as the groove becomes an easy ride. Add in a soccer ball and all of the outside variables, I don’t know how some analysts are making conjectures on fatigue. One athlete had fatigue accumulated over a few weeks and his threshold was not adjusted, and he was injured early in the game. Another athlete was pulled because he was looking tired but it was really just a motivation issue with a lesser team. Lot’s of variables, but general volumes are still valuable.
When I see a report of GPS program I often see Blob COM that look like old atari games, box scores with gross data that looks almost estimated, and borderline load scores from 3D accelerometers that are as accurate as pocket watch hand times in 10m dashes.