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Yoko-otoshi

Based on fight data from 2016 onward.

All scores
273
High-impact
86.8%
Fight win
89.9%
Avg ELO
1148
Ippon
65
Waza-ari
172
Yuko
36

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 216 88.4% 90.0%
Women 57 80.7% 89.5%

Weight divisions

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

Division Fights High-impact Fight win
Men -60 kg
70 92.9% 94.2%
Men -66 kg
37 81.1% 94.3%
Men -73 kg
29 86.2% 82.8%
Men -100 kg
26 92.3% 75.0%
Men -81 kg
22 77.3% 86.4%
Men -90 kg
17 94.1% 100.0%
Women -52 kg
15 73.3% 80.0%
Men +100 kg
14 92.9% 92.9%
Women -48 kg
14 85.7% 100.0%
Women -57 kg
9 66.7% 77.8%
Women -70 kg
9 88.9% 100.0%
Women -63 kg
6 83.3% 100.0%

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

Judoka Avg ELO Fights
MEHDIYEV Mammadali AZE
1330 3
JEONG Bokyeong KOR
1281 3
SHIKHALIZADA Nijat AZE
1280 3
UROZBOEV Diyorbek UZB
1256 5
GANBAT Boldbaatar MGL
1200 6
GASIMOV Elmar AZE
1196 5
AGHAYEV Balabay AZE
1159 10
GJAKOVA Akil KOS
1130 3
POLLERES Michaela AUT
1128 4
MINASKIN Grigori EST
1098 4
NURILLAEV Kemran UZB
1067 4
FLORIMONT Jolan FRA
1063 3
MINKOU Dzmitry BLR
1017 3
VAN GANSBEKE Kenneth BEL
1001 3

Judoka with the most credited fights using this technique.

Judoka Fights Fight win
AGHAYEV Balabay AZE
10 90.0%
GANBAT Boldbaatar MGL
6 100.0%
UROZBOEV Diyorbek UZB
5 80.0%
GASIMOV Elmar AZE
5 100.0%
POLLERES Michaela AUT
4 100.0%
MINASKIN Grigori EST
4 75.0%
NURILLAEV Kemran UZB
4 100.0%
MEHDIYEV Mammadali AZE
3 100.0%
JEONG Bokyeong KOR
3 100.0%
SHIKHALIZADA Nijat AZE
3 66.7%
GJAKOVA Akil KOS
3 100.0%
FLORIMONT Jolan FRA
3 66.7%
MINKOU Dzmitry BLR
3 100.0%
VAN GANSBEKE Kenneth BEL
3 100.0%
ABUZHAKYNOVA Abiba KAZ
2 100.0%

Countries

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

Country Fights Share
AZE 29
10.8%
MGL 27
10.1%
UZB 19
7.1%
RUS 17
6.3%
KAZ 14
5.2%
BEL 12
4.5%
FRA 11
4.1%
KOR 11
4.1%
GEO 10
3.7%
HUN 8
3.0%
ITA 7
2.6%
BRA 6
2.2%

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