The first Grand Slam of 2015 is almost here, with Australian Open starting Sunday night/Monday morning UK time.
I’ve covered the contenders and conditions in my previews for Pinnacle Sports, and you can read them here:-
WTA: http://www.pinnaclesports.com/en/betting-articles/tennis/2015-wta-australian-open-betting-preview
This preview looks at the tournament from an almost entirely trading angle, and assesses some historical data to see if we can use trends to make profitable trading decisions in addition to the detailed player information including matches where projected holds are low, and break-back percentages are high which is covered by the daily trading spreadsheets.
Week Three/Four Trading Overview (only matches where point by point data is available):-
|
Only matches where point by point available |
||||||||
|
Matches |
1-0 > Set 2 train for 2-0 |
S2 Non Train |
Train % |
Set 3 2-0 Matches |
2-0 > Set 3 train for 3-0 |
S3 Non Train |
Train % |
|
|
ATP |
||||||||
|
R1 |
54 |
29 |
25 |
53.7 |
33 |
19 |
14 |
57.6 |
|
R2 |
32 |
16 |
16 |
50.0 |
22 |
9 |
13 |
40.9 |
|
R3 |
15 |
12 |
3 |
80.0 |
13 |
10 |
3 |
76.9 |
|
R4 + |
15 |
7 |
8 |
46.7 |
10 |
4 |
6 |
40.0 |
|
Austraiian Open Overall |
116 |
64 |
52 |
55.2 |
78 |
42 |
36 |
53.8 |
|
Matches |
S1 Winner Train S2 |
S2 Non Train |
Train % |
Set 3 Matches |
S3 Player Breaks 1st Trains |
S3 Non Train |
Train % |
|
|
WTA |
||||||||
|
R1 |
52 |
14 |
38 |
26.9 |
15 |
4 |
11 |
26.7 |
|
R2 |
30 |
9 |
21 |
30.0 |
13 |
6 |
7 |
46.2 |
|
R3 |
12 |
3 |
9 |
25.0 |
3 |
2 |
1 |
66.7 |
|
R4 + |
13 |
5 |
8 |
38.5 |
6 |
5 |
1 |
83.3 |
|
Australian Open Overall |
107 |
31 |
76 |
29.0 |
37 |
17 |
20 |
45.9 |
The table above illustrates the percentage of situations that ‘trained’ in these events in 2014.
As I mentioned in the week one previews, I’ve had some questions about trains on Twitter so I’ll clarify this a little better – a train would be a situation where there is no upward price swing from a given position. Therefore in these instances, a set one winner train in set two would indicate that they took a set and break lead in set two and retained this lead without being broken back. A non-train would include the player that lost the first set breaking first in set two, or recovering a set and break deficit, or even recovering one break when a set and double break down.
Likewise, a set three train in this instance would be a player that has broken first and retains this lead throughout the deciding set.
In the WTA, we can see that the 29.0% set two train percentage is very close to the week one and week two mean, and on this basis, trains should be no more or less expected than previous weeks.
This may surprise some readers, who assume that maximum effort and motivation are ensured here, and this would lead to more fightbacks. Whilst this is almost certainly true, there is a flip side in a Grand Slam – the fact that there is a higher ability differential on average in Slams than most weekly events – all the top players are playing, and are often very short priced for their opening couple of rounds, at least.
The set three percentage in the WTA, however is very high. Whilst compiling the data I noticed that these matches were typically encounters where the ‘better’ player lost set one, took the second set and then steamrollered their opponent in set 3. In these situations in the next fortnight, perhaps holding a winning position from the second set (having laid the set one winning pre-match underdog) a little longer than average (maybe even waiting to get in front in set 3) would be a good plan.
With the matches being best of 5 sets in the ATP, it’s tough to make comparisons from previous previews. However, we can see that the train percentage from 2-0 is marginally lower than that from 1-0, making a lay of a player at 2-0 (where the lay price will be extremely low) more appealing than at 1-0. Last year in Melbourne, conditions were almost unbearably hot, and this may be a contributory factor. A player 2-0 up could well take a mental breather and give up a set relatively easily.
What is also interesting in the ATP is the significantly lower train percentages in Round 4 onwards. This is, naturally, the stage of the tournament where two critical factors often prevent a one-sided match:-
1) Fatigue – players have played a number of (probably long) matches already.
2) Low ability differential – As the tournament progresses, the top players tend to be left to play each other.
On this basis, laying players in the latter stages who are 1-0 and 2-0 up appeals much more than previous rounds.
This next table illustrates the statistics from the hard court Grand Slams for previous years:-
|
Australian |
US Open |
||||||||||
|
2011 |
2012 |
2013 |
2014 |
Overall |
% |
2011 |
2012 |
2013 |
Overall |
% |
|
|
Completed Matches |
122 |
118 |
123 |
117 |
480 |
116 |
123 |
122 |
361 |
||
|
2-0 Matches |
98 |
76 |
77 |
82 |
333 |
69.38 |
87 |
80 |
79 |
246 |
68.14 |
|
2-0 -> 2-1 |
25 |
15 |
13 |
21 |
74 |
22.22 |
22 |
25 |
23 |
70 |
28.46 |
|
2-0 -> 2-2 |
10 |
10 |
12 |
6 |
38 |
11.41 |
8 |
13 |
9 |
30 |
12.20 |
As can be seen above, the number of matches where a player leads 2-0 is relatively similar across both tournaments, but the fightback percentage overall for the Australian Open is lower than the US Open, both for recovering to 2-1 and 2-2. Therefore, given the data in the first table, laying ATP players expecting fightbacks should be something that is carefully considered and performed in very select circumstances in the early rounds.
Finally, on a betting basis, backing players to cover quite ambitious long-odds handicaps, such as -2.5 sets or a high number of games, should be a good way to go, given the historically poor fightback tendency.
