Tokyo Tech News
The graduate-level men of the Tokyo Tech Track & Field Club finished third at the 98th Kanto Intercollegiate Track & Field Contest, held from May 23 to 26 at Sagamihara Gion Stadium in Kanagawa Prefecture.
At this contest, undergraduate and graduate-level students compete in different categories. A victory in an event earns the team three points, second place is worth two points, while a third-place finish equals one point.
This year, Tokyo Tech's graduate-level athletes competed against 20 other universities, finishing with 12 points. Most of these came from short-distance races. Kazuki Masuda, Yuya Nagashima, Tomoya Takahashi, and Kazuyuki Sanada won the 4x100 m relay. Takahashi, Nagashima, Yusuke Otsuka, and Masaki Shibae also won the 4x400 m relay. Sanada finished second in the 200 m individual final, while Nagashima finished second in the 110 m hurdles.
Comments from graduate-level team captain
Yuya Nagashima, 2nd-year master's student in Mechanical Engineering
2nd in 110 m hurdles
1st in 4x100 m relay
1st in 4x400 m relay
At Tokyo Tech, I am focusing on vibro-acoustics research, and I am busy with seminars, academic conferences, and the like. This means I cannot find as much time as I would like for athletics. That said, I would like to thank our academic supervisors and the alumni association for their support.
At the undergraduate level, finishing on the podium at this contest seemed like a distant, unachievable dream. I am delighted to be able to compete against strong schools at the graduate level, and to produce such a great team result. While we aim to win this competition in the future, I think Tokyo Tech's undergraduate-level team can also grow into a real podium contender. Come and cheer us on!
The Tokyo Tech Track & Field Club holds daily practices to train for intercollegiate competitions, Hakone Ekiden qualifiers, and simply to achieve new personal bests and perform well as a unified team. Through discussions among each other and under the guidance of coaches and trainers, the team aims to create an environment where both individual and group performances are optimized.