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Fusen-Gachi

Based on fight data from 2016 onward.

All scores
647
High-impact
100.0%
Fight win
99.8%
Avg ELO
1128
Ippon
647
Waza-ari
0
Yuko
0

Common setup techniques

Earlier tagged techniques by the same athlete in fights where this technique scored.

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Men vs women

Where this technique is credited — high-impact % and win % by sex.

Sex Fights High-impact Fight win
Men 381 100.0% 100.0%
Women 266 100.0% 99.6%

Weight divisions

Which divisions use this technique most; compare effectiveness by weight.

Division Fights High-impact Fight win
Men -66 kg
69 100.0% 100.0%
Men -81 kg
65 100.0% 100.0%
Men -73 kg
60 100.0% 100.0%
Men -60 kg
54 100.0% 100.0%
Women -70 kg
51 100.0% 100.0%
Men -90 kg
49 100.0% 100.0%
Men -100 kg
46 100.0% 100.0%
Women +78 kg
44 100.0% 100.0%
Women -57 kg
42 100.0% 100.0%
Women -48 kg
38 100.0% 97.4%
Women -78 kg
38 100.0% 100.0%
Men +100 kg
36 100.0% 100.0%

Average pre-fight ELO when credited with this technique (min 3 fights).

Judoka Avg ELO Fights
IGOLNIKOV Mikhail RUS
1516 3
SHAVDATUASHVILI Lasha GEO
1292 3
CVJETKO Lara CRO
1279 3
TAIMAZOVA Madina RUS
1254 3
PUPP Reka HUN
1247 3
TRIPPEL Eduard GER
1236 4
BASILE Fabio ITA
1235 3
GONCALVES Leonardo BRA
1228 3
KOCHMAN Li ISR
1219 4
KOC Muhammed TUR
1219 3
DE WIT Frank NED
1190 3
CIRJENICS Miklos HUN
1185 5
KHUBETSOV Alan RUS
1140 3
FLICKER Tal ISR
1134 3
FATIYEV Murad AZE
1120 3

Judoka with the most credited fights using this technique.

Judoka Fights Fight win
CIRJENICS Miklos HUN
5 100.0%
MISHINER Yuli Alma ISR
5 100.0%
TRIPPEL Eduard GER
4 100.0%
KOCHMAN Li ISR
4 100.0%
IGOLNIKOV Mikhail RUS
3 100.0%
SHAVDATUASHVILI Lasha GEO
3 100.0%
CVJETKO Lara CRO
3 100.0%
TAIMAZOVA Madina RUS
3 100.0%
PUPP Reka HUN
3 100.0%
BASILE Fabio ITA
3 100.0%
GONCALVES Leonardo BRA
3 100.0%
KOC Muhammed TUR
3 100.0%
DE WIT Frank NED
3 100.0%
KHUBETSOV Alan RUS
3 100.0%
FLICKER Tal ISR
3 100.0%

Countries

Where credited scorers are from (one count per fight).

Country Fights Share
RUS 37
5.7%
ISR 32
4.9%
AZE 30
4.6%
MGL 30
4.6%
FRA 29
4.5%
JPN 28
4.3%
ITA 26
4.0%
GEO 25
3.9%
NED 24
3.7%
GER 23
3.6%
UZB 23
3.6%
KAZ 21
3.2%

Statistics count IJF-tagged scoring only. Archive grows as more competitions are scraped from 2016 onward.