Predictions
Matches
Calendar
H2H
Rankings
Picks Blog
Maria Mateas vs Taylor Goetz Prediction, Head-to-Head, Odds & Pick - Matchstat.com
By Wojtek Kolan
At Matchstat.com we give you unbeatable in-depth analysis of past and current event tennis stats, to give you accurate tennis predictions, picks, odds and value bets. Let's dive in with our Mateas vs Goetz analysis and find out who is favored!
Maria Mateas vs Taylor Goetz Important H2H Prediction Stats:
Head-to-head: Mateas 0 - 0 Goetz
- Second serve performance recent form: In recent form (last 6 months), Mateas has won 48.34% of points on his second serve, while Goetz has won 42.86%. There is a high correlation between this stat and match prediction accuracy.
- Return game stats recent form: Return stats show Mateas, in recent form, has won 57.76% of his opponent's second serve points, while Goetz has won 54.55%. The same stats for first serve returns are 41.32% and 43.48% respectively and this has a high correlation to pick who is favored in this H2H matchup.
- Under pressure analysis: Mateas has saved 60.41% of breakpoints in recent form, whereas Goetz has saved 46.51% which is a useful statistic for in-game betting predictions.
- Performance overview: Over the last year Mateas has won 59.15% of matches played (W/L 42/ 29), with Goetz winning 37.5% (W/L 3/ 5) that gives us an overall head-to head prediction overview.
- Best surface: Mateas has their best career surface win % on I.hard, winning 58% (W/L 14/ 10), and worse career win % on Clay, winning 54% (W/L 94/ 79). Goetz has their best career surface win % on Clay, winning 43% (W/L 3/ 4), and worse career win % on I.hard, winning 0% (W/L 0/ 1).
- Player level: In the last year, Mateas has played most of their matches on the Challengers/ITF tournaments > $10K, winning 58.82% of matches (W/L 40/ 28), where as Goetz has played most of their matches on the Challengers/ITF tournaments > $10K, winning 40% of matches (W/L 2/ 3). When comparing stats between players to predict the favorite, it is of course all relative to the event level they have been playing at.
- Opponent quality stats: Over the last 12 months, Mateas has played against opponents with an average rank of 231.92 while Goetz has played against players with an average rank of 0.
- Deciding set performance vs all players: If you are interested in live predictions and betting, if this match goes into a deciding set, Mateas has won 60% of deciding sets over the last 12 months, while Goetz has won 0% in all matches played on tour.
- Break point conversion: In recent form, Mateas has converted 51.98% of breakpoint opportunities, and Goetz has converted 48.65% of their chances to break their opponents serve. A telling stat for in-game live betting tips when either player has a breakpoint opportunity.
260
Rank
260
High
24
Age
Plays
0
Total0
0
Hard0
0
Clay0
0
Indoor0
0
Grass0
N/A
Rank
High
18
Age
Plays
L
W
L
W
W
L
W
W
W
L
Form
W
L
W
W
L
L
L
L
56% (206-165)
Career Total W/L
38% (3-5)
59% (19-13)
YTD W/L
33% (2-4)
$13,868
Career Prize Money
$0
0
YTD Titles
0
Currently displayed stats includes matches of all levels. To exclude lower level events (as per ATP / WTA official stats) toggle button in page footer.
No H2H Matches Available
Stats Breakdown Vs All H2H Opponents
| stats | Maria Mateas | Taylor Goetz |
|---|---|---|
| YTD W/L | 59% (19/13) | 33% (2/4) |
| Sets Win/Loss | 60% (43/29) | 43% (6/8) |
| Games Win/Loss | 56% (373/297) | 48% (79/85) |
| Hard Win/Loss | 71% (10/4) | 0% (0/0) |
| Clay Win/Loss | 50% (9/9) | 40% (2/3) |
| Indoor Hard W/L | 0% (0/0) | 0% (0/1) |
| Grass Win/Loss | 0% (0/0) | 0% (0/0) |
| Aces pg | 0.14 | 0 |
| Aces Total | 46 | 0 |
| DFs per game | 0.2 | 0.16 |
| DFs Total | 65 | 13 |
| Avg Match Time | 2:4:42 | 1:26:40 |
| Avg Opp Rank | 265.47 | 0 |
| 1st Serve % | 71% (1632/2293) | 71% (192/269) |
| 1st Serve W% | 60% (985/1632) | 47% (91/192) |
| 2nd Serve W% | 49% (325/661) | 43% (33/77) |
| BPs Won% Total | 52% (145/278) | 49% (18/37) |
| Return Pts W% | 47% (1032/2184) | 46% (132/284) |
| Slam W/L | 0% (0/0) | 0% (0/0) |
| Masters W/L | 0% (0/0) | 0% (0/0) |
| Cups W/L | 0% (0/0) | 0% (0/0) |
| Main Tour W/L | 0% (0/0) | 0% (0/0) |
| Tour Finals W/L | 0% (0/0) | 0% (0/0) |
| Challenger W/L | 59% (19/13) | 40% (2/3) |
| Futures W/L | 0% (0/0) | 0% (0/1) |
| Best of 3 Sets W% | 60% (18/30) | 33% (2/6) |
| Best of 5 Sets W% | 100% (1/1) | 0% (0/0) |
| TBs Win% (Total) | 17% (1/6) | 33% (1/3) |
| Deciding Set W% | 38% (3/8) | 0% (0/2) |
| 1st set W, W | 89% (19/17) | 50% (4/2) |
| 1st set W, L | 11% (19/2) | 50% (4/2) |
| 1st set L, W | 15% (13/2) | 0% (2/0) |
Maria Mateas Recent Matches Played
Before:
| opponent | Score | H2H | |||
|---|---|---|---|---|---|
| SF |
Stats 
Akasha Urhobo
Player
Maria Mateas
60%
(
41 of
68)
1st Serve %
68%
(
40 of
59)
4
Aces
0
1
Double Faults
0
66%
(
27 of
41)
1st Serve Won
55%
(
22 of
40)
56%
(
15 of
27)
2nd Serve Won
53%
(
10 of
19)
57%
(
4 of
7)
Break Points Won
17%
(
1 of
6)
38%
(
26 of
68)
Rtn Points Won
46%
(
27 of
59)
69
Total Points Won
58
|
6-4 6-3 | H2H | ||
| QF |
Stats
Maria Mateas
Player
Jaeda Daniel
82%
(
79 of
96)
1st Serve %
68%
(
47 of
69)
-
Aces
-
1
Double Faults
5
48%
(
38 of
79)
1st Serve Won
43%
(
20 of
47)
53%
(
9 of
17)
2nd Serve Won
32%
(
7 of
22)
53%
(
8 of
15)
Break Points Won
26%
(
5 of
19)
51%
(
49 of
96)
Rtn Points Won
61%
(
42 of
69)
89
Total Points Won
76
|
3-6 6-1 5-0 ret. | H2H | ||
| R2 |
Stats
Maria Mateas
Player
Ema Burgic Bucko
79%
(
50 of
63)
1st Serve %
71%
(
41 of
58)
-
Aces
-
3
Double Faults
8
54%
(
27 of
50)
1st Serve Won
41%
(
17 of
41)
23%
(
3 of
13)
2nd Serve Won
18%
(
3 of
17)
67%
(
8 of
12)
Break Points Won
63%
(
5 of
8)
52%
(
33 of
63)
Rtn Points Won
66%
(
38 of
58)
68
Total Points Won
53
|
6-4 6-2 | H2H | ||
| R1 |
Stats
Maria Mateas
Player
Martina Okalova
78%
(
66 of
85)
1st Serve %
85%
(
44 of
52)
1
Aces
0
3
Double Faults
3
55%
(
36 of
66)
1st Serve Won
43%
(
19 of
44)
53%
(
10 of
19)
2nd Serve Won
0%
(
0 of
8)
70%
(
7 of
10)
Break Points Won
27%
(
4 of
15)
46%
(
39 of
85)
Rtn Points Won
63%
(
33 of
52)
79
Total Points Won
58
|
6-2 7-5 | H2H | ||
| QF |
Stats
Hanna Chang
Player
Maria Mateas
54%
(
53 of
98)
1st Serve %
66%
(
69 of
104)
2
Aces
1
6
Double Faults
5
58%
(
31 of
53)
1st Serve Won
55%
(
38 of
69)
49%
(
22 of
45)
2nd Serve Won
51%
(
18 of
35)
31%
(
5 of
16)
Break Points Won
55%
(
6 of
11)
46%
(
45 of
98)
Rtn Points Won
46%
(
48 of
104)
101
Total Points Won
101
|
6-4 0-6 7-5 | H2H | ||
| R2 |
Stats
Maria Mateas
Player
Kathinka Von Deichmann
81%
(
39 of
48)
1st Serve %
80%
(
41 of
51)
1
Aces
0
2
Double Faults
1
67%
(
26 of
39)
1st Serve Won
46%
(
19 of
41)
67%
(
6 of
9)
2nd Serve Won
30%
(
3 of
10)
71%
(
5 of
7)
Break Points Won
25%
(
1 of
4)
33%
(
16 of
48)
Rtn Points Won
57%
(
29 of
51)
61
Total Points Won
38
|
6-3 6-1 | H2H | ||
| R1 |
Stats
Maria Mateas
Player
Melany Solange Krywoj
70%
(
47 of
67)
1st Serve %
59%
(
33 of
56)
-
Aces
-
2
Double Faults
4
77%
(
36 of
47)
1st Serve Won
61%
(
20 of
33)
45%
(
9 of
20)
2nd Serve Won
39%
(
9 of
23)
43%
(
3 of
7)
Break Points Won
0%
(
0 of
5)
33%
(
22 of
67)
Rtn Points Won
48%
(
27 of
56)
72
Total Points Won
51
|
6-2 6-3 | H2H | ||
| R2 |
Stats
Lulu Sun
Player
Maria Mateas
64%
(
67 of
105)
1st Serve %
72%
(
61 of
85)
6
Aces
4
3
Double Faults
4
66%
(
44 of
67)
1st Serve Won
70%
(
43 of
61)
50%
(
19 of
38)
2nd Serve Won
29%
(
7 of
24)
33%
(
3 of
9)
Break Points Won
17%
(
2 of
12)
40%
(
42 of
105)
Rtn Points Won
41%
(
35 of
85)
98
Total Points Won
92
|
7-5 7-6(5) | H2H | ||
| R1 |
Stats
Maria Mateas
Player
Martina Okalova
66%
(
61 of
93)
1st Serve %
66%
(
55 of
83)
5
Aces
0
3
Double Faults
7
67%
(
41 of
61)
1st Serve Won
56%
(
31 of
55)
50%
(
16 of
32)
2nd Serve Won
46%
(
13 of
28)
64%
(
7 of
11)
Break Points Won
33%
(
3 of
9)
39%
(
36 of
93)
Rtn Points Won
47%
(
39 of
83)
96
Total Points Won
80
|
6-4 6-7(3) 6-0 | H2H | ||
| R1 |
Stats
Sophie Chang
Player
Maria Mateas
57%
(
39 of
68)
1st Serve %
72%
(
53 of
74)
3
Aces
0
7
Double Faults
1
74%
(
29 of
39)
1st Serve Won
58%
(
31 of
53)
45%
(
13 of
29)
2nd Serve Won
33%
(
7 of
21)
38%
(
5 of
13)
Break Points Won
43%
(
3 of
7)
38%
(
26 of
68)
Rtn Points Won
49%
(
36 of
74)
78
Total Points Won
64
|
6-4 7-5 | H2H | ||
view more
Taylor Goetz Recent Matches Played
Before:
| opponent | Score | H2H | |||
|---|---|---|---|---|---|
| R1 |
Stats
Ema Burgic Bucko
Player
Taylor Goetz
63%
(
48 of
76)
1st Serve %
68%
(
57 of
84)
-
Aces
-
10
Double Faults
3
60%
(
29 of
48)
1st Serve Won
47%
(
27 of
57)
39%
(
11 of
28)
2nd Serve Won
48%
(
13 of
27)
55%
(
6 of
11)
Break Points Won
45%
(
5 of
11)
47%
(
36 of
76)
Rtn Points Won
52%
(
44 of
84)
84
Total Points Won
76
|
7-6(6) 6-4 | H2H | ||
| Q1 | 2-6 7-6(2) 13-11 | H2H | |||
| Q1 |
Stats
Gabriella Vannessa Lindgren
Player
Taylor Goetz
75%
(
50 of
67)
1st Serve %
72%
(
38 of
53)
1
Aces
0
3
Double Faults
4
62%
(
31 of
50)
1st Serve Won
55%
(
21 of
38)
53%
(
9 of
17)
2nd Serve Won
33%
(
5 of
15)
67%
(
4 of
6)
Break Points Won
38%
(
3 of
8)
40%
(
27 of
67)
Rtn Points Won
51%
(
27 of
53)
67
Total Points Won
53
|
4-6 6-1 11-9 | H2H | ||
| R1 |
Stats
Kayla Cross
Player
Taylor Goetz
61%
(
27 of
44)
1st Serve %
72%
(
36 of
50)
-
Aces
-
3
Double Faults
1
74%
(
20 of
27)
1st Serve Won
36%
(
13 of
36)
53%
(
9 of
17)
2nd Serve Won
43%
(
6 of
14)
55%
(
6 of
11)
Break Points Won
67%
(
2 of
3)
34%
(
15 of
44)
Rtn Points Won
62%
(
31 of
50)
60
Total Points Won
34
|
6-2 6-2 | H2H | ||
| QF |
Stats
Taylor Goetz
Player
Petja Drame
74%
(
61 of
82)
1st Serve %
85%
(
82 of
97)
-
Aces
-
5
Double Faults
3
49%
(
30 of
61)
1st Serve Won
45%
(
37 of
82)
43%
(
9 of
21)
2nd Serve Won
40%
(
6 of
15)
53%
(
8 of
15)
Break Points Won
47%
(
7 of
15)
52%
(
43 of
82)
Rtn Points Won
56%
(
54 of
97)
93
Total Points Won
86
|
7-6(5) 7-5 | H2H | ||
| Q1 | 6-4 6-2 | H2H | |||
| QF | 6-3 6-2 | H2H | |||
| Q1 | 7-5 7-5 | H2H | |||
view more
Mateas Vs Goetz Prediction H2H Images To Share
Potential H2H Matches This Week For Maria Mateas
Potential H2H Matches This Week For Taylor Goetz
Today's H2H Tennis Betting Tips & Predictions
If you are looking to predict tennis or soccer matches, Matchstat.com has you covered! We have league, player and team stats, and specialise in detailed h2h stats between tennis players or soccer teams.
All our predictions are based on machine learning and AI. Matchstat is designed to help you in your analysis and predictions but the responsibility for your final predictions are your own.
It's good to talk... Get in touch with us [email protected]
