Tennis · M15 Bali · 09/09, 03:00

Prediction: Ryotaro Taguchi vs Azuma Visaya

Team pages: Ryotaro Taguchi · Azuma Visaya

Prediction summary
Winner
62% 38%
Ryotaro Taguchi Azuma Visaya
Estimated games
19.6
Recent form · 10 last
Ryotaro Taguchi 6-4 (W-L)
WWLLWLWWWL
Azuma Visaya 6-4 (W-L)
WLWWWLWWLL
Our model (Elo) vs Market Rating Elo · Ryotaro Taguchi 1563 · Azuma Visaya 1542
Ryotaro TaguchiAzuma Visaya
Our model53%47%
Market69%31%

The bar above blends our model with the market: where our probability diverges most from the odds is where the model sees the widest gap.

Statistics

Ryotaro TaguchiAzuma Visaya
Win %70%70%
Win % · Hard 75% (4)
Sets (G-P)1.4-0.81.6-0.7
Games (G-P)11.6-8.510.8-6.5
Aces2.32.2
Double faults1.41.7
1st serve %68.1%70.2%
Break %50.6%43.3%
Estimated total games (ours) 19.6

Last 10 matches played

Last matches of each player (sets and games). W = win, L = loss. Common opponents to both are in blue.

Ryotaro Taguchi

LWWWL

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Date Opponent Sets Games Res.
05/09 Karan Singh
M15 Bali
0-2 6-12 L
04/09 Hayden Jones
M15 Bali
2-1 18-12 W
03/09 Thomas Patton
M15 Bali
2-0 13-7 W
02/09 Nick Beamish
M15 Bali
2-0 12-2 W
30/07 Sanhui Shin
M15 Bali
0-2 6-12 L
28/07 Daisuke Sumizawa
M15 Bali
2-0 12-4 W
25/07 Ryuki Matsuda
M15 Bali
0-2 9-13 L
24/07 Isaac Becroft
M15 Bali
2-1 16-12 W
23/07 Arjun Rathi
M15 Bali
2-0 12-7 W
21/07 Yuta Kawahashi
M15 Bali
2-0 12-4 W

Azuma Visaya

LWWLW

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Date Opponent Sets Games Res.
07/09 Nick Beamish
M15 Bali
2-1 12-8 W
01/09 Digvijay Pratap Singh
M15 Bali
0-2 6-12 L
31/08 Ren Nakamura
M15 Bali
2-0 12-2 W
30/08 Kareem Abdul Hakim
M15 Bali
2-0 12-5 W
22/08 Mathieu Perchicot
ITF M25 Bali · Hard
1-2 9-15 L
20/08 Maximus Zavier Wong
ITF M25 Bali · Hard
2-0 12-3 W
18/08 Thomas Patton
ITF M25 Bali · Hard
2-0 12-6 W
17/08 Renaldi Aqila A Salim
ITF M25 Bali · Hard
2-0 13-6 W
27/07 Aniketh Venkataraman
M15 Bali
1-2 8-7 L
26/07 Jerall Yasin
M15 Bali Qual
2-0 12-1 W

Frequently asked questions

Who is the favourite in Ryotaro Taguchi vs Azuma Visaya?

Our model gives 62% to Ryotaro Taguchi, using surface Elo on hard.

What is the prediction based on?

On an Elo model by surface (hard) adjusted for opponent quality, compared with the market odds.