Preview - SA SL 1st ODI Paarl 11/12

This is a bit of the battle of the unknowns here.

There have only been a handful of matches at Paarl. There have only been two matches between South Africa and Sri Lanka in the last 5 years. Both teams are somewhat unpredictable, and Sri Lanka are likely to have a vastly different line up to the test matches.

So it is hard to make too many predictions really.

However there are a couple of things to look for.

1. Dilshan hit 58 and 106 in his last two innings against South Africa. He is under some scrutiny as captain and needs a score. Will this be his time?

2. De Villiers is likely to bat at 4, keep wickets and captain his team. Dhoni and Sangakkara both proved that it is possible to succeed while keeping, captaining and batting in the top 5, but Gilchrist, Germon, Taibu and Flower all struggled with the excessive workload. De Villiers is certainly a talented batsman, but so were Gilchrist and Flower. It will be interesting to see if he can hold it all together.

3. Ajantha Mendis is likely to make a return. He's only bowled 7 overs against South Africa in any form of cricket, but he took 3 for 30 in those 7 overs, picking up Smith, Kallis and Duminy. If he can repeat this then Sri Lanka are in with a big show.

4. Lasith Malinga is playing his 100th ODI match. He has played 4 ODI matches against South Africa and taken 10 wickets, roughly one wicket every 3 overs. He is always an interesting bowler to watch, and it will be good to see how he goes in this match.

5. JP Duminy looking to get on the board. Duminy's last 4 ODI innings against Sri Lanka have been 3(3), 0(2), 0(1) & 0(1). He has faced 7 deliveries scored 3 runs and been out 4 times. However his last outing at Paarl was him guiding the Cape Cobras to a win with 52* at the end of a match, so he may be in a good space to turn that round.

Some betting tips.

In the three matches that have been played at Paarl between big teams, there have been 2 century opening partnerships and one 50 opening partnership. The 2nd wicket has been even more productive. If you are looking to be over on any batsman, make sure they are in the top three. Likewise don't think the game is over just because one team has a good start. Most teams get off to a good start at this ground.

ODI team of the year

Well, it wouldn't be the end of the year, without people naming a team of the year.

Here is my attempt at ODI team of the year:

Method:
For batsmen I broke down the players into openers, top order and lower/middle order players.
I then looked at all batsmen's results batting in these positions throughout the year, giving a bonus for world cup matches and for games against harder opposition. I created a points system that took in account their runs scored, their wickets lost and their deliveries faced. The higher the score the better.

Openers:

Sehwag & Watson

Name	Matches	Runs	Average	Strike rate	Points
V Sehwag	12	645	53.75	122.58	133.432
SR Watson	22	1124	59.15	92.35	108.154
SR Tendulkar	11	513	46.63	91.98	99.282
HM Amla	15	632	45.14	87.67	73.667
MJ Guptill	16	650	54.16	68.58	73.338

Somewhat unsurprising, given that they have both dominated this year. Tendulkar was a close 3rd.

Top Order

de Villiers, Clark & Taylor

Name	Matches	Runs	Average	Strike rate	Points
AB de Villiers	10	467	51.88	108.28	113.889
LRPL Taylor	17	561	51.00	87.09	86.13
MJ Clarke	24	900	56.25	91.01	81.247
Yuvraj Singh	11	381	42.33	89.78	80.421
IJL Trott	28	1246	51.91	80.84	77.224
V Kohli	31	1349	49.96	81.49	76.953
KC Sangakkara	26	1127	51.22	83.78	75.762
G Gambhir	15	562	40.14	85.09	74.575
JP Duminy	13	523	47.54	87.61	70.696

This was a little more interesting. I was quite surprised bu Clarke's numbers, as I hadn't remembered him having such a good season. By the same token Kohli had an amazing season, but missed out.

Lower/Middle order

Dhoni & Duminy

Name	Matches	Runs	Average	Strike rate	Points
MS Dhoni	23	759	63.25	81.69	100.227
JP Duminy	11	451	50.11	100.00	79.152
KJ O'Brien	12	324	29.45	125.31	77.566
Umar Akmal	28	785	41.31	86.95	62.378
Misbah-ul-Haq	20	545	45.41	73.80	61.635
DJ Hussey	16	342	38.00	152.94	61.583
KA Pollard	20	528	31.05	151.26	57.963
YK Pathan	12	271	27.10	121.15	53.221
MEK Hussey	14	347	38.55	93.24	51.55
EJG Morgan	15	384	29.53	85.71	51.297
F du Plessis	13	288	28.80	84.12	50.829

Initally I was going to have this and wicket-keeper as separate sections, but given that Dhoni came first as a batsman and there is not a lot of point in having two wicket keepers, it's better to take an extra batsman. JP Duminy just edges out Kevin O'Brien, but O'Brien is an obvious choice for 12th man, as he can contribute with the bat or the ball. And because I like his style of play.

Bowlers.

Quantifying the records for bowlers is not as easy as batsmen. I again created a measure that valued dot balls and wickets, and put a higher value on performances in the World Cup and in matches against good teams. The lower the points the better.

Name	Matches	Wickets	Average	Economy	Points
DW Steyn	14	25	18.50	4.41	19.78
RJ Peterson	9	18	21.15	4.74	20.51
M Morkel	14	26	17.73	4.41	21.26
Shahid Afridi	27	45	25.65	4.35	21.76
Wahab Riaz	13	23	20.77	5.32	22.51
TG Southee	13	25	22.85	5.00	22.56
JDP Oram	12	23	23.88	4.71	23.13
SL Malinga	24	48	23.13	4.89	23.46
B Lee	19	33	23.03	4.62	23.47
Z Khan	14	30	23.75	5.08	23.64
M Muralitharan	11	17	22.35	4.22	23.81
BAW Mendis	14	17	24.13	4.46	25.15
HK Bennett	10	17	26.61	5.73	26.09
Mohammad Hafeez	32	32	24.06	3.43	26.64

There are a couple of interesting names here. Mohammad Hafeez misses out due to not taking many wickets in the World Cup. Perhaps this is a weakness in my system, as he was really one of the outstanding bowlers of the year. Peterson likewise only played 2 games outside the world cup, and went at about 6 an over in those matches. But he had a fantastic World Cup and it is fair that a player gets a bonus for performing at the highest stage.

The final team:

Sehwag
Watson
de Villiers
Taylor
Clarke
Duminy
Dhoni
Shahid Afridi
Peterson
Morkel
Steyn

12th man O'Brien

How does your team look?

Partnerships that made big contributions

In an earlier post, A different kind of hundred partnership I looked at a number of partnerships ranked by how much they increased the Duckworth-Lewis expected score.

I have come up with a new method. If you want to skip the geekery of me explaining how it works, click here to go straight to the table.

There were a number of problems with the method I used in the hundred partnership post, some of which I outlined at the time. I spent some time thinking about it, and what could be done to give a more accurate description of the contribution a partnership made to a teams cause. Part of the idea of statistics is to find a way to make the numbers tell the story, and I didn't feel that these told the story accurately enough. There were a few glaring anomalies.

The first thing that I wanted to fix up was that shorter partnerships added more than longer ones. For example Amla and Duminy's partnership of 102 off 16.2 was worth much less than Misbah-ul-Haq and Younis Khan's partnership of 89 off 13.3. These partnerships seem like they are of a similar value to their respective teams. Also there was too much of an advantage for a partnership that came in after the fall of a couple of early wickets. It didn't seem correct that Sarwan and Bravo's epic 125 runs off 22.3 overs was worth 250 more than N McCullum and Styris's 120 off 14. It was a great recovery from 0/2 to 125/3, but it still felt a little off.

I came up with a couple of options to deal with this. Firstly I divided the DL prediction at the start and end by 50, then divided by the number of overs faced. This gave me a very interesting result. The numbers suddenly looked a lot more intuitive. It also eliminated the problems with a team getting off to a bad start, or a particularly good start. Clarke and Watson's 110 run partnership that came in at a ridiculous -75 with the old method was now a more sensible 94. They were still penalised for being a lot slower than the team had been going, but not nearly as much.

I also made a slight modification to the predicted score for anything less than 10 overs, by making it a little more moderate. Again I used the Duckworth Lewis g-score of 250, and multiplied the predicted score by the number of overs used, and then 250 by 10 minus the number of overs used, and then divided by 10. Under the old method if a team was on 34/1 off 2 overs, the predicted score would be 410, now it would be 282. This seems a more realistic platform to start with. Once the over calculation in the paragraph above was added in, the start went from 16 to 11. Not a big difference, but probably more fair. This can however cause a problem in the case of a long unbeaten partnership. If a 2nd wicket partnership went from 33/1 off 2 to 333/1 off 50, they would have added 300 runs, but this method would give them 322. However given that this situation is incredibly unlikely, I am happy to live with that issue for now.

Here are the top 15 partnerships under the new method.

Batsmen Names	Score	Start	End	DL Adjusted
RR Sarwan, AB Barath	165	42/2, 12.4 Overs	207/3, 43.2 Overs	185
AB de Villiers, JP Duminy	131	82/3, 13.3 Overs	213/4, 35.2 Overs	171
MJ Guptill, JD Ryder	123	18/1, 3.4 Overs	141/2, 24.5 Overs	161
BJ Haddin, SR Watson	110	-	110/1, 19.4 Overs	156
MJ Prior, IJL Trott	113	23/1, 2.5 Overs	136/2, 22.1 Overs	156
RR Sarwan, DM Bravo	125	0/2, 1.6 Overs	125/3, 24.3 Overs	132
HM Amla, MN van Wyk	97	16/1, 2.3 Overs	113/2, 22.2 Overs	130
JP Duminy, F du Plessis	110	90/4, 23.2 Overs	200/5, 44.5 Overs	126
Misbah-ul-Haq, Mohd. Hafeez	94	56/3, 13.3 Overs	150/4, 36.1 Overs	123
SM Davies, AJ Strauss	90	-	90/1, 12.1 Overs	120
SE Marsh, CL White	100	33/4, 12.3 Overs	133/5, 32.6 Overs	117
DE Bollinger, SE Marsh	88	142/8, 36.5 Overs	230/9, 48.1 Overs	110
DJ Hussey, AC Voges	95	103/4, 25.2 Overs	198/5, 39.1 Overs	110
NL McCullum, SB Styris	120	190/5, 35.5 Overs	310/6, 49.5 Overs	110
SB Styris, KS Williamson	81	80/3, 14.5 Overs	161/4, 33.1 Overs	109

One interesting thing here is that there are three quite different partnerships all with DL adjusted results of 110. Each had a very different role in their game, but have all come out the same under this analysis. This feels good intuitively, as it is hard to separate them when the context of each game is taken into account.

Looking at this list, does this seem like a good method for ranking partnerships and their value. There aren't many that seem out of place to me here, and it seems to tell a better story than sorting them just by total runs scored.