Modelling of Running Performances: Comparisons of Power-Law, Hyperbolic, Logarithmic, and Exponential Models in Elite Endurance Runners

<table class="fixed-width table-group" id="tab17"><tr><td><table class="table"><colgroup><col style="width:9.01em"/><col style="width:8.69em"/></colgroup><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left"> </td><td class="align_center">RMSE</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Morton’s model</td><td class="align_center">0.00166 ± 0.00113</td></tr><tr><td class="align_left">Exponential model</td><td class="align_center">0.00321 ± 0.00131</td></tr><tr><td class="align_left">Power-law model</td><td class="align_center">0.00463 ± 0.00203</td></tr><tr><td class="align_left">Logarithmic model</td><td class="align_center">0.00464 ± 0.00302</td></tr><tr><td class="align_left"><svg height="12.0621pt" id="M269" style="vertical-align:-3.2957pt" version="1.1" viewbox="-0.0498162 -8.7664 26.1654 12.0621" width="26.1654pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M409 504C401 567 396 607 392 642C354 654 312 665 266 665C137 665 60 583 60 487C60 374 161 325 225 290C300 250 355 215 355 141C355 68 311 21 235 21C131 21 86 122 71 183L41 176C48 128 61 42 68 21C78 16 93 8 118 0C142 -7 175 -15 216 -15C349 -15 438 69 438 174C438 287 344 333 265 374C186 414 138 449 138 522C138 576 172 631 249 631C336 631 363 562 380 499L409 504Z" id="g121-81"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.162,3.132)"><path d="M618 181C565 77 513 26 413 26C264 26 151 150 151 336C151 486 240 625 407 625C516 625 573 582 600 475L634 483C627 540 622 581 613 638C585 643 518 665 435 665C211 665 46 526 46 315C46 158 157 -15 406 -15C480 -15 564 4 594 12C612 43 638 121 651 168L618 181Z" id="g58-65"></path></g><g transform="matrix(.0091,0,0,-0.0091,12.286,3.132)"><path d="M185 344V453C137 433 92 421 41 413V386C100 378 104 375 104 310V106C104 41 97 35 33 30V0H268V30C190 35 185 41 185 107V283C206 340 240 371 267 371C282 371 296 364 310 351C316 344 325 343 336 348C357 357 369 378 369 399C369 424 345 451 310 451C262 451 219 397 187 344H185Z" id="g58-112"></path></g><g transform="matrix(.0091,0,0,-0.0091,15.808,3.132)"><path d="M138 535C169 535 191 560 191 589C191 620 168 644 139 644S85 620 85 589C85 560 110 535 138 535ZM257 0V30C192 35 185 43 185 109V453C142 435 94 422 40 414V386C101 378 104 372 104 311V109C104 41 97 35 33 30V0H257Z" id="g58-103"></path></g><g transform="matrix(.0091,0,0,-0.0091,18.201,3.132)"><path d="M303 37L293 66C280 59 256 49 231 49S173 63 173 143V396H281C294 404 298 428 287 439H173V575L158 577L92 509V439H46L17 408L22 396H92V107C92 27 128 -12 192 -12C203 -12 218 -9 237 2L303 37Z" id="g58-114"></path></g><g transform="matrix(.0091,0,0,-0.0091,21.04,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z" id="g50-51"></path></g></svg> model</td><td class="align_center">0.00981 ± 0.00241</td></tr><tr><td class="align_left"><svg height="12.0621pt" id="M270" style="vertical-align:-3.2957pt" version="1.1" viewbox="-0.0498162 -8.7664 26.1654 12.0621" width="26.1654pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M409 504C401 567 396 607 392 642C354 654 312 665 266 665C137 665 60 583 60 487C60 374 161 325 225 290C300 250 355 215 355 141C355 68 311 21 235 21C131 21 86 122 71 183L41 176C48 128 61 42 68 21C78 16 93 8 118 0C142 -7 175 -15 216 -15C349 -15 438 69 438 174C438 287 344 333 265 374C186 414 138 449 138 522C138 576 172 631 249 631C336 631 363 562 380 499L409 504Z" id="g121-81"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.162,3.132)"><path d="M618 181C565 77 513 26 413 26C264 26 151 150 151 336C151 486 240 625 407 625C516 625 573 582 600 475L634 483C627 540 622 581 613 638C585 643 518 665 435 665C211 665 46 526 46 315C46 158 157 -15 406 -15C480 -15 564 4 594 12C612 43 638 121 651 168L618 181Z" id="g58-65"></path></g><g transform="matrix(.0091,0,0,-0.0091,12.286,3.132)"><path d="M185 344V453C137 433 92 421 41 413V386C100 378 104 375 104 310V106C104 41 97 35 33 30V0H268V30C190 35 185 41 185 107V283C206 340 240 371 267 371C282 371 296 364 310 351C316 344 325 343 336 348C357 357 369 378 369 399C369 424 345 451 310 451C262 451 219 397 187 344H185Z" id="g58-112"></path></g><g transform="matrix(.0091,0,0,-0.0091,15.808,3.132)"><path d="M138 535C169 535 191 560 191 589C191 620 168 644 139 644S85 620 85 589C85 560 110 535 138 535ZM257 0V30C192 35 185 43 185 109V453C142 435 94 422 40 414V386C101 378 104 372 104 311V109C104 41 97 35 33 30V0H257Z" id="g58-103"></path></g><g transform="matrix(.0091,0,0,-0.0091,18.201,3.132)"><path d="M303 37L293 66C280 59 256 49 231 49S173 63 173 143V396H281C294 404 298 428 287 439H173V575L158 577L92 509V439H46L17 408L22 396H92V107C92 27 128 -12 192 -12C203 -12 218 -9 237 2L303 37Z" id="g58-114"></path></g><g transform="matrix(.0091,0,0,-0.0091,21.04,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z" id="g50-50"></path></g></svg> model</td><td class="align_center">0.0207 ± 0.00566</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Average values of the 6 runners Roots Mean Square Errors (RMSE) for the different models.</div>

BioMed Research International

tab17

Table 17

Table 17: Modelling of Running Performances: Comparisons of Power-Law, Hyperbolic, Logarithmic, and Exponential Models in Elite Endurance Runners 