October 01, 2018

The Innovators is a series of stories we've crafted about the creative individuals and companies that make truly special creations for cyclists. Whether its brilliantly handmade apparel, or clever new technologies, these are the innovations that excite and inspire us, so we figured we'd share their stories with you.

Xert's Strava "segment hunter" is both thrilling and addictive. It's no wonder No. 22 co-founder Mike Smith has become a big fan of the technology, chasing KOMs out on country roads with his Drifter. Like No. 22, the idea for Xert started with new ways of thinking about solutions to age-old problems. Here's the Xert story:

Armando Mastracci likes to solve problems. Seven years ago, the Toronto-based engineer was working a sales job for IBM, and admits that he wasn’t even really that into cycling. Today, he’s cracked the algorithmic mystery behind how we can get the most out of an endurance performance, creating software-based training tools like Xert, and its new Segment Hunter tool. And it all started on an old recumbent bike.

I started doing some riding and got talked into going on longer rides,” Mastracci says. “I bought a road bike, but started training with power because I had this old recumbent bike. It had a power readings on it.” Mastracci started treating the bike, the data, and ultimately himself like a study, doing RAMP tests, writing numbers down by hand, while on the bike, taking metrics in real-time. With all these handcrafted spreadsheets, Mastracci discovered patterns in the data that hadn’t been published in the current research at the time.

“I’ve got this mathematical analytical bent to everything I approach.” says Mastracci. “When I reviewed the work of the renowned physiologist A.V. Hill who won a Nobel Prize in the 1920s and his “force-velocity curves,” or, how force and velocity work in the body, they were almost identical to the curves I found in my own testing.” Mastracci says that was his “Christmas tree moment,” when the lights flicked on. “When you see your results improve by an order of magnitude, you know you are on to something.”

Mastracci moved his theories online, on discussion boards about exercise physiology. He started using various spreadsheets and solving formulas with the aid of Wolfram Alpha, the online mathematical tool, to figure out a perfect power-duration model. “It’s all an algebraic formula,” Mastracci says of what became his suite of algorithms to figure out how to best attack a Strava segment, and also training to your maximal ability level at any given period. “It’s very simple.” As an engineer, Mastracci stresses that he didn’t invent the underlying science that drives his software, he merely discovered it. “I got a hold of some patterns in the data. I’m more the messenger. I just follow,” he says. “The math behind how the body fatigues has always been there.”

Mastracci has now figured out a way to take a cyclists’ power data from a regular power file, essentially random data, and determine fitness. He then wrote software to that could do this on a server, and the key moment was figuring out how to have the software work on a server so that any athlete around the world could get their data analyzed automatically. The results could then be used on their bike computer while attacking a segment. “Segment hunter is just a formula, not a number cruncher,” Mastracci clarifies. “The math will will keep you on track, and the math works. It can do that in real time. Our What’s My FTP? app works along the same way, in fact."

What the team at Baron Biosystems quickly realized is that there’s a mountain of data now available to the average cyclist, but the challenge remains drawing a connection between the data and how it’s influencing you to better perform, and understand what’s working and what isn’t, empirically. “In the past, when an athlete didn’t want to get into it, they'd hire a coach,” Mastracci says. “They'd ask, ‘what does this data mean?’ And a coach tells you, inferring what’s going on. We’re trying to create a way where you can avoid all of that. The data is all actionable by you, you see it directly. It’s boiled down. It’s suddenly meaningful and actionable.” Mastracci is careful to point out that a coach still can play a vital role. “We are trying to provide coaches and individuals with a direction on how they should be training and performing,” says. “Before, a coach would say, ‘this is what you’re capable of.’But coaches wouldn’t know that until the athlete performed it. Now they know you can do it.

Other researchers didn’t believe it at first, but were soon convinced by the results. Mastracci left IBM a three years ago for good, and Baron Biosystems, his company, became his all-encompassing passion project, and quickly growing business venture. Today he has a good relationship with Garmin and Strava, and is working on bringing his technologies to other vendors as well. Zwift has become a fascinating testing ground for Xert. “We have seen these videos from users while on there,” Mastracci says. “We’re seeing someone in the middle of a race, they are putting up their MPA numbers on a screen.” Another user in a video of an outdoor crit put MPA on screen to explain what was happening.

It’s not surprising to learn then that Baron Biosystems is also talking to professional teams, something that Mastracci is really excited about. He feels that this sort of technology could alter the future of pro cycling, both in terms of maximizing performances, but also improving the spectator experience during a streamed or televised broadcast. “Imagine the segment hunter for a pro during a time trial,” he suggests enthusiastically. “The numbers then become either an affirmation of what you currently feel, and answer the internal dispute of ‘should I go? Maybe I can go.’ Some people are surprised by what they are capable of, and what they can accomplish.”

This second-by-second, real-time reading of how close to 100% a pro rider is nailing their effort could also could become the most intriguing part of a live stream of, say, a Grand Tour stage. “The announcers and the viewers can have a way of comparing what they are seeing, instead of speculating,” Mastracci says. “When you have the data in front of you, you can see that someone is doing an amazing job, or struggling.” In the end, he points out, that’s the game. “We’d be able to see how they are really playing their cards; how they perform relative to their ability. And it would help an audience understand what’s really going on.”

But what Mastracci is most excited about with his creation is that it helps cyclists of all ability levels to see how to best train and doll out a hard effort, and that just because others may seem stronger on a climb or are posting big wattage numbers, it is actually about nailing your own individual efficiencies. “The game becomes your own fitness and improvement,” Mastracci says. “It isn’t just about ‘can I beat this person in a race,’ or worrying that ‘my numbers are so modest.’ I’ll never beat Peter Sagan, and it’s not about that. It’s about how I make myself the best I can be; how do I encourage that. What drives that behaviour—happiness and motivation.”


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2018 Geometry Charts

Great Divide Rim Brake

Size 48cm 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 495 510 525 545 560 580 600
Seat tube, center-center (mm) 445 462 490 518 540 565 590
Head tube length (mm) 90 100 120 140 160 180 200
Seat tube angle (deg) 75 74.5 74 73.5 73 73 72.5
Head tube angle (deg) 70.5 71.0 71.2 72.2 72.5 73 73
Front center (mm) 567 574 584 584 589 607 621
Chain stay length (mm) 412 412 412 412 415 415 418
BB drop (mm) 78 78 75 75 75 73 73
Reach (mm) 357 364 370 379 383 397 406
Stack (mm) 506 517 534 559 580 597 617

 

Great Divide Disc

Size 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 510 525 545 560 580 600
Seat tube, center-center (mm) 462 490 518 540 565 590
Head tube length (mm) 95 115 135 155 175 195
Seat tube angle (deg) 74.5 74 73.5 73 73 72.5
Head tube angle (deg) 71.0 71.2 72.2 72.5 73 73
Front center (mm) 574 584 586 594 609 623
Chain stay length (mm) 412 412 412 415 415 418
BB drop (mm) 78 75 75 75 73 73
Reach (mm) 364 369 378 382 397 405
Stack (mm) 520 536 560 580 599 618

 

Drifter

Size 48cm 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 502 510 525 545 560 575 595
Seat tube, center-center (mm) 464 478 503 525 535 549 577
Head tube length (mm) 90 100 120 140 150 170 190
Seat tube angle (deg) 74.5 74 74 73.5 73 73 72.5
Head tube angle (deg) 70.5 70.5 70.8 71 71.2 71.2 71.5
Front center (mm) 571 575 589 603 611 627 639
Chain stay length (mm) 430 430 430 430 432 435 435
BB drop (mm) 78 77 75 75 75 73 73
Reach (mm) 352 353 364 373 379 389 398
Stack (mm) 530 538 556 576 586 603 623

 

Reactor

Size 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 515 530 548 565 580 595
Seat tube, center-center (mm) 456 475 496 514 542 568
Head tube length (mm) 100 120 140 160 185 205
Seat tube angle (deg) 74.5 74 74 73.2 73 73
Head tube angle (deg) 72 72 73 73.5 73.5 73.5
Front center (mm) 571 582 585 590 603 618
Chain stay length (mm) 405 408 408 410 410 410
BB drop (mm) 72 72 72 70 68 68
Reach (mm) 374 379 391 395 401 410
Stack (mm) 503 522 546 565 587 606

 

Aurora

Size 48cm 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 495 510 525 545 560 580 600
Seat tube, center-center (mm) 445 462 490 518 540 565 590
Head tube length (mm) 100 110 130 150 170 190 205
Seat tube angle (deg) 75 74.5 74 73.5 73 73 72.5
Head tube angle (deg) 70.5 71 71.2 72.2 72.5 73 73
Front center (mm) 567 574 584 586 594 609 624
Chain stay length (mm) 412 412 412 412 415 415 418
BB drop (mm) 78 78 75 75 75 73 73
Reach (mm) 355 363 369 378 382 396 406
Stack (mm) 510 521 538 562 582 601 615

 

Broken Arrow

Size 48cm 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 505 510 525 545 560 575 590
Seat tube, center-center (mm) 467 490 510 532 548 567 590
Head tube length (mm) 90 100 120 140 150 160 180
Seat tube angle (deg) 74 74 73.5 73.3 73 72.5 72.5
Head tube angle (deg) 70.8 70.8 71 71 71.3 71.5 72
Front center (mm) 565 571 580 599 609 622 627
Chain stay length (mm) 425 425 425 425 425 425 425
BB drop (mm) 68 68 68 68 66 66 64
Reach (mm) 352 355 359 372 382 393 397
Stack (mm) 524 533 553 572 580 591 610

 

Little Wing

Size 50cm 52cm 54cm 56cm 58cm 60cm
Effective top tube length (mm) 515 525 540 555 575 585
Seat tube, center-center (mm) 477 499 524 552 575 595
Head tube length (mm) 100 120 140 160 180 200
Seat tube angle (deg) 75.5 75 74.5 74.5 74 74
Head tube angle (deg) 72.5 73 73 73.5 74 74
Front center (mm) 566 569 579 590 601 611
Chain stay length (mm) 393 393 393 396 396 396
BB drop (mm) 58 58 58 58 58 58
Reach (mm) 382 383 387 397 407 411
Stack (mm) 506 527 546 567 588 607

 

Old King

Size Small Medium Large Extra Large
Effective top tube length (mm) 580 605 622 642
Seat tube, center-center (mm) 380 410 445 481
Head tube length (mm) 100 110 120 140
Seat tube angle (deg) 73.5 73.5 73.5 73.3
Head tube angle (deg) 70 70 70 70
Front center (mm) 655 680 698 719
Chain stay length (mm) 435 435 435 435
BB drop (mm) 58 58 58 58
Reach (mm) 401 423 437 451
Stack (mm) 599 609 619 637