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Some of P3\u2019s clients include NBA stars Luka Doncic<\/a> and Zach LaVine<\/a> plus athletes from the NFL, Major League Baseball, international soccer, track and field and more.<\/p>\n Many of those NBA clients, like Philadelphia 76ers guard Josh Richardson<\/a>, return to P3 each summer for re-testing to pinpoint whether their movement patterns have gained asymmetries that could cause injury, or to reconfirm the health of physical systems they use to leap, land, stop and start, fueling their on-court edge.<\/p>\n \u201cThis is my fifth off-season now at P3,\u201d Richardson says. \u201cWhen I started with them during my NBA draft preparation, I immediately saw that their approach was different and that it could help me have the best chance to improve my athleticism. Every off-season I get to see exactly where I am physically compared to where I was before \u2013 and compared to other NBA players.<\/p>\n \u201cThey are able to help me identify where I might be at risk of injury and where I can improve physically. It\u2019s important for me to know that the training I am doing is specific to my unique needs,\u201d Richardson says.<\/p>\n To collect all that granular data, P3 outfitted its lab with a high-speed camera system manufactured by Simi Reality Motion Systems GmbH<\/a>, a German company from the ZF Group and a Microsoft partner.<\/p>\n Simi offers markerless, motion-capture software that removes the need for athletes to wear tracking sensors while they play or train. Simi also works with seven Major League Baseball clubs, deploying high-speed camera systems to those stadiums to record every pitch during every game since the 2017 season.<\/p>\n Simi\u2019s software digitizes the pitchers\u2019 arm angles and related body movements, spanning 42 different joint centers across 24,000 pitches thrown per team per season. That produces hundreds of billions of data points that are uploaded and processed on Microsoft Azure<\/a>, enabling teams to create in-depth biomechanical analyses for the players, says Pascal Russ, Simi\u2019s CEO.<\/p>\n \u201cThe first team that deploys this effectively on the field to pick lineups or to see which pitch angles worked well against which batters is going to see a huge separation between them and the other teams not using this,\u201d Russ says.<\/p>\n \u201cIt\u2019s freakishly accurate.\u201d<\/p>\n While Russ foresees this technology eventually remaking baseball, such seismic shifts already are occurring in the NBA through P3\u2019s player assessments, says Benedikt Jocham, Simi\u2019s U.S. chief operations officer.<\/p>\n \u201cWe provide the software solution that can quantify the movement and analyze, for example, how much pressure and torque a person is putting on various body parts,\u201d Jocham says. \u201cP3 adds the magic sauce. They are wizards at figuring out what it all means and making sense out of it for athletes.\u201d<\/p>\n After the cameras record a player\u2019s movements in the P3 lab, those datasets are loaded into Azure where machine-learning<\/a> algorithms reveal how that player\u2019s physical systems are most related to other NBA players who were similarly assessed. The algorithm then assigns that player into one of several clusters or branches that predict how their basketball career may unfold, Elliott says.<\/p>\n One branch, for example, contains athletes who had a brief NBA experience and never became significant players. Another branch encompasses players who were impactful during their first three or four seasons then sustained serious injuries that depleted their skills. In still another branch, players share rare combinations of length, power and force that fed elite careers \u2013 and they remained healthy.<\/p>\n \u201cThe human eye is good at measuring size and maybe estimating weight, and very bad at comparing athletes\u2019 physical systems and movement symmetries to one another,\u201d Elliot says. \u201cBut we can measure those things in the lab and the machine tells us how young athletes are most alike.<\/p>\n \u201cIt\u2019s a solid foothold into an area of sports science that has been out of sight until now,\u201d he says.<\/p>\n The data is also helping to shatter long-held theories that successful NBA players who, at first glance, lack the size, jumping ability or quickness of traditional stars are merely compensating by tapping unmeasurable intangibles such as \u201cintuition\u201d or \u201cIQ\u201d or \u201cheart.\u201d<\/p>\n \u201cThat\u2019s how people once would have defined (2017-18 NBA most valuable player) James Harden<\/a>, as somebody who just has this super-high basketball IQ,\u201d Elliott says. \u201cMaybe he does. But he also has a better stopping or braking system than anybody we\u2019ve ever assessed in the NBA.<\/p>\n \u201cThat creates competitive advantages,\u201d he adds. \u201cThere\u2019s Newtonian physics behind these advantages.\u201d<\/p>\n Case in point: Dallas Mavericks rookie Luka Doncic. In its pre-draft assessment of Doncic one year ago, P3 identified that same hidden performance metric \u2013 the elite ability to stop quickly. P3 knew, before his NBA Draft, that Doncic and Harden were in the same player branch. Doncic posted a stunning<\/a> first pro season.<\/p>\n![\"A](\"http:\/\/www.sickgaming.net\/blog\/wp-content\/uploads\/2019\/07\/from-predicting-performance-to-preventing-injuries-how-machine-learning-is-unlocking-the-secrets-of-human-movement-1.jpg\")
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