Updated QF prediction chart

In my previous post I ran a simulation to find out potential quarter-final places. I received some criticism for having England so low, and Bangladesh so high, but events over the past 48 hours have shown that the respective probabilities of the two teams qualifying may not have been so far off.

The program that I wrote to do the simulation was corrupted when my computer crashed and I foolishly hadn't saved it, so I've written a different one to re-calculate. This time I made a couple of modifications. I moved from an additive model for run rates to a multiplicative one, as that seemed to be more sensible (teams are realistically a % better than other teams, rather than a fixed number of runs better. We would expect the margins to blow out more in terms of runs on better batting pitches than on difficult tracks).

I also slightly reduced the standard deviation of the simulation by moving it to one quarter of the mean rather than one third. This again made the results seem more sensible. There were too many teams scoring over 400 or under 100 previously.

Here are the new results. This table shows the probability of each team qualifying in position 1, 2, 3 or 4 in their group, and then the total probability of qualifying. Again I have not factored rain into this, and with Cyclone Pam heading towards New Zealand that may be a little optimistic.

Team	1st	2nd	3rd	4th	Quarters
New Zealand	1	0	0	0	1
Australia	0	0.976	0.024	0	1
Sri Lanka	0	0.024	0.9725	0.0035	1
Bangladesh	0	0	0.0035	0.9965	1
-	-	-	-	-	-
India	1	0	0	0	1
South Africa	0	0.976	0.024	0	1
Pakistan	0	0.017	0.664	0.1165	0.7975
Ireland	0	0.007	0.312	0.1405	0.4595
West Indies	0	0	0	0.743	0.743

The potential group results look like this:

Group A

NZ Aus SL Ban	0.9725
NZ SL Aus Ban	0.024
NZ Aus Ban SL	0.0035

Group B

Ind SA Pak WI	0.5295
Ind SA Ire WI	0.1985
Ind SA Pak Ire	0.1345
Ind SA Ire Pak	0.1135
Ind Pak SA WI	0.011
Ind Pak SA Ire	0.006
Ind Ire SA WI	0.004
Ind Ire SA Pak	0.003

The three interesting potential quarter final match-ups to watch for here are

SA vs Aus	4.7%
Ind vs SL	0.35%
Ire vs Ban	0.02%

In reality the probabilities of Ireland vs Bangladesh and Australia vs South Africa are higher, as they are both much more likely if rain starts to fall.

World-cup quarter finals simulation

After Pakistan's tremendous win over South Africa, and Ireland's remarkable victory over Zimbabwe, the make up of the quarter finals is not really much clearer.

They question as to who is likely to be going through, and who will play whom has been the subject of many, many twitter conversations.

I thought it might be helpful to run a simulation to look at some of the possibilities.

I used Microsoft Excel as it's quite convenient. I used the scores already made in this tournament to decide the probable scores. For each team I got their average rpo scored in relation to the overall group run rate, and their average conceded in relation to the overall. Hence if a team in group A averaged scoring 5.5 rpo and conceded 5.3 rpo, they got values of +0.4 for batting and +0.2 for bowling (as the average rpo in group A has been 5.1 so far). From that point I then used an inverse normal, with a random number between 0 and 1 for the area, the group run rate plus the batting run rate modifier and the other team's bowling run rate modifier as the mean. For the standard deviation, I used the smallest of one third of the mean and 1.6. This allowed me to make sure there was (almost) no chance of a team getting a negative score, but that the scores weren't going to blow out too much.  I used 1.6 as that's the standard deviation of all innings run rates this tournament..  This gave me a 50 over score for each team, and so which ever was ahead got the points for the win.

There are a few limitations with this method. I didn't take into account the quality of the teams that each side had faced. England has played Australia, New Zealand and Sri Lanka, but has yet to play Bangladesh or Afghanistan. Their numbers are not going to necessarily show how well they will do against less fancied opponents. Likewise no adjustments were made for the pitch that the match is being played on. We know that South Africa have tended to favour playing on bouncier tracks, so an innings at the 'Gaba won't necessarily tell us much about how they would go in Dunedin. I also haven't taken into account player strengths. Bangladesh's batsmen tend to struggle against tall bowlers, such as Finn and Woakes. England can expect that those two bowlers will perform better than average against Bangladesh, and hence their team is likely to do better than the numbers would suggest.

Another major limitation is that I haven't made provision for rain. That would obviously throw off all calculations. However, given the limited information I felt that a more simple model was best.

I decided to do 2000 trials, so that I could feel that the major source of uncertainly was the assumptions rather than the natural sampling variability.

First I found the probability of the different teams making the quarter finals with my simulation:

Team	Probabiity
New Zealand	100%
Australia	100%
Sri Lanka	99.95%
Bangladesh	82.51%
England	17.54%
-	-
India	100%
South Africa	100%
Pakistan	74.71%
Ireland	61.82%
West Indies	63.47%

We can see that Pool A has one crucial match (England vs Bangladesh)
Pool B, however, is still wide open. Ireland vs Pakistan is the last game of the round robin, and it's shaping up to potentially be one that has 3 team's fortunes riding on the result.

If West Indies make the final 8, they will almost definitely face New Zealand. It's very unlikely that New Zealand will not end up on top of Pool A, and impossible that West Indies will end up 3rd or higher in pool B.

Here's the full results for all possible matchups

Pool A	Pool B	Probability
New Zealand	Pakistan	14.99%
New Zealand	South Africa	0.35%
New Zealand	Ireland	21.23%
New Zealand	West Indies	63.44%
Australia	India	2.30%
Australia	Pakistan	43.11%
Australia	South Africa	27.57%
Australia	Ireland	27.02%
Sri Lanka	India	18.18%
Sri Lanka	Pakistan	15.83%
Sri Lanka	South Africa	53.75%
Sri Lanka	Ireland	12.19%
Bangladesh	India	64.74%
Bangladesh	Pakistan	0.75%
Bangladesh	South Africa	15.88%
Bangladesh	Ireland	1.15%
England	India	14.79%
England	Pakistan	0.05%
England	South Africa	2.45%
England	Ireland	0.25%

I'll redo this after tomorrow's results, and then again on Monday.

The most likely scenario at the moment is India to play Bangladesh, Australia to play Pakistan, South Africa to play Sri Lanka and New Zealand to play West Indies.

I've updated this here

Is it game over if you lose more than 2 wickets in the powerplay?

I recently observed this conversation on twitter:

It immediately made me wonder if Aakash was correct. Do you lose if you are more than 2 wickets in the power play of a T20 International.

I decided to find out. I felt that it was probably best to only look at situations where a team had batted first, as there is not any external scoreboard pressure (or lack thereof) interfering with the batsmen's mind sets.

I looked at every match where there was a result inside 20 overs (I ignored matches that had ended in a super-over or bowl-off) and looked at how many wickets down the team were after 6 overs. I didn't count "retired hurt" as a wicket, despite there being a change of batsmen and the batting team losing momentum similar to when a wicket falls.

Once I did that I came up with some quite interesting numbers.

Wickets Down	Wins	Loses	Winning %
0	41	18	69.5%
1	74	48	60.7%
2	52	51	50.5%
3	11	36	23.4%
4	5	10	33.3%
5	0	3	0%

It's fairly clear here that losing wickets early hurts the probability of winning. This is not really a surprise, often teams bat their best batsmen at the top, and the subsequent batsmen have to take fewer risks if there are not many wickets left above them. However while there are a lot of incidents of teams losing 1 or 2 wickets, our sample size is quite small for the other number of wickets. I've graphed it, adding in a 95% confidence interval. This indicates what range we can expect the actual winning probability to lie in per wicket loss: The shorter the line, the more reliable the data.

We can clearly see the trend here. But we also notice the huge gap between being 2 down and being 3 down. There does seem to be a difference between losing 2 wickets or losing more than 2 wickets.

Accordingly I broke it down into 3 groups. Less than 2 wickets, 2 wickets or More than 2 wickets. Here's how that looks:

Roughly teams win two thirds of the matches where they lose less than 2 wickets, half of the matches where they lose two wickets and about a quarter of the matches where they lose more than 2 wickets.

I also broke it down further by team, and this holds true for almost every team. The only team that has won more than half of their matches when batting first and losing more than 2 wickets in the power play is Ireland. (Interestingly Ireland has the 4th best winning record of any team batting first, and then they are not far behind Pakistan, Sri Lanka and South Africa).

Sri Lanka win just under 80% of t20's when they lose 2 or less wickets in the power play, but 20% when they lose 2 or more wickets. England win just over 60% if they keep their wickets in hand, but only 20% when they lose 3 or more in the power play.

With the World T20 getting underway, how the teams approach the first 6 overs could be a fascinating thing to keep an eye on.

Keiron Pollard and the World Cup activity ratings

Last night we saw an amazing innings for Keiron Pollard. When he arrived at the wicket, West Indies were in trouble. It was 130/3 off 32 overs. Devon Smith was looking about as comfortable as a penguin in the Sahara, and Ireland had the sniff of blood.

Pollard hit 5 sixes and 8 fours, and scored 94 off 55 balls. But interestingly he scored 62 runs in boundaries, off 13 balls, meaning that off the rest of his innings, he scored 32 off 42. Even if you remove the boundaries he scored at a strike rate better than 75.

His running between wickets had an effect on Devon Smith also. When Pollard arrived at the wicket Smith had a strike rate of 72.2 During the time that they were batting together Smith scored 29 runs off 25 balls. Pollard took the pressure off Smith, and he responded by batting a lot better. (Obviously he had got his eye in also, but the change in his attitude was quite evident watching the game.)

We all know how powerful Pollard can be. We think of the big sixes that he has hit. But the subtlety that he showed last night showed us that he is capable of being much more than a big hitting circus performer.

In his whirlwind 60 against the Netherlands, there were only 18 balls that he didn't hit to the boundary, and he managed to get 16 off those. The next step for him will be to repeat this against a test playing nation.

His activity rate (runs per ball not hit for a boundary) is far above anyone in the World Cup, but there are a couple of other interesting names in the list:

Name	Runs	Balls	Runs from boundaries	Activity rate
KA Pollard	154	83	106	0.787
DPMD Jayawardene	134	123	58	0.697
V Sehwag	254	195	146	0.667
AB de Villiers	266	247	120	0.664
Younis Khan	128	159	28	0.658
HM Amla	169	196	48	0.658
IR Bell	193	227	60	0.624
KC Sangakkara	252	281	92	0.618
AJ Strauss	298	299	136	0.609
IJL Trott	289	369	76	0.609
Tamim Iqbal	152	158	66	0.606
Misbah-ul-Haq	192	259	44	0.594
V Kohli	154	161	68	0.593
G Gambhir	128	143	52	0.585
RN ten Doeschate	148	165	62	0.57
T Taibu	117	140	44	0.566
BRM Taylor	140	154	62	0.561
KJ O'Brien	164	125	110	0.535
SR Watson	141	153	68	0.533
RS Bopara	106	140	36	0.53
Shakib Al Hasan	111	144	40	0.53
TM Dilshan	283	279	158	0.521
Umar Akmal	167	193	76	0.52
CR Ervine	113	133	52	0.504

The three players who have been near the top of a lot of the activity rate charts are Sehwag, Dilshan and Tamim Iqbal. All are in this list, although Dilshan is a lot lower down than usual.

The bottom of the list features a number of players from associate nations, but there are a couple of very good batsmen who have made it into the list also.

Sorted from lowest to highest

Name	Runs	Balls	Runs from boundaries	Activity rate
AS Hansra	142	264	72	0.282
W Barresi	99	154	52	0.333
A Bagai	102	180	44	0.343
CO Obuya	144	212	72	0.367
WTS Porterfield	106	169	46	0.38
BJ Haddin	84	116	44	0.381
MJ Guptill	192	251	104	0.388
DM Bravo	112	149	58	0.397
TLW Cooper	140	213	60	0.404
Imrul Kayes	111	149	56	0.407
CH Gayle	119	149	64	0.41
Kamran Akmal	118	169	52	0.423
P Utseya	86	150	24	0.431
EC Joyce	136	207	52	0.433
KP Pietersen	131	136	80	0.436
DS Smith	202	253	102	0.439
WU Tharanga	258	292	144	0.445
SR Tendulkar	213	222	126	0.451
GC Smith	87	151	20	0.459
PW Borren	86	101	44	0.462

Haddin has managed to feature in the bottom list for other formats also, showing that he is very reliant on hitting boundaries for his usually good strike rate. Also at the wrong end of the table is Martin Guptill. Watching Guptill he doesn't seem to have a reliable single scoring shot against spin bowlers. Accordingly the more that spin bowlers bowl against him, the lower his activity rate falls.

It wil be interesting to see if Pollard manages to keep up his extrodinary rate as the competition heats up, and also if the likes of Tendulkar, Smith and Gayle can get their rating up towards 0.5.

2 potentially absorbing matches

The return to Nagpur for New Zealand, and the battle for recognition for Ireland.

New Zealand had been looking like they had left the horror Bangladesh series behind them when they drew the first two tests with India. Then came Nagpur, and a humiliating innings defeat, that paved the way for a clean sweep in the ODI series.

Ireland have won 2 of their 6 matches against Bangladesh, but have lost the three in Asia convincingly.

Here are some things to watch for in the games:

NZ vs Australia

1. Spin bowling. Nagpur has been a spinners pitch, and both team have had trouble playing spin. Australia has only managed to score 4.63 runs an over off spin over the last 2 years (compared to 5.17 overall) and New Zealand has averaged only 26.85 against spin in the same period. Both of these numbers are frighteningly bad for teams that are playing a tournament in the sub-continent.

2. Hamish Bennett in for Kyle Mills. Kyle Mills has tormented Australia recently, averaging 17.50 at 4.03 rpo. How Hamish Bennett and Tim Southee cope without him there will make a big difference to how New Zealand fare. Bennett has an awkward action and often batsmen struggle the first time they face him. (he took 3 wickets in his first match against Bangladesh, 0 in the second, 4 wickets in the first game against Pakistan, 2-54 off 7 in the second, 4 wickets in first match against Kenya). The surprise factor will work in his favour against Australia. However Bennett does seem to be much better when he doesn't open the bowling. His figures bowling at 3 or 4 are significantly better than when he's opened the bowling. No mills means that unless Vettori thrown the ball to Franklin, Bennett is likely to get given it.

3. Jason Krejza. Attacking offspin bowling is not something New Zealand has traditionally done well against. A lot of off-spinners had their career best figures against New Zealand, and so Krejza should be rubbing his hands together with glee coming into this match.

4. Mitchell Johnson vs Scott Styris. Two of the most abrasive players on the field, both love playing against each other. Styris has averaged close to 50 against Australia in the last couple of years, and Johnson has taken 27 wickets against New Zealand, making them his second favourite opponent (after India). They bumped heads in the recent Chappell Haddlee series, and it bought out the best in both of them.

5. Shane Watson. Without a doubt the stand out batsman of the Australian summer, Watson is also a player that loves the New Zealand bowlers. New Zealand is the only test-playing side that he averages over 50 against in ODI's.

6. Earthquake. There are normally 2 possible responses to a natural disaster at home for a traveling team. They either play extremely well, or extremely poorly. Accordingly it would not be a huge surprise if this game was to end in a very one-sided manner. One of New Zealand's most memorable moments in cricket was immediately following the Tangiwai Disaster.

Ireland vs Bangladesh

1. Big match players. Often Bangladesh have relied on Shakib Al Hasan and Tamim Iqbal, whil Porterfield and the O'Briens have made the big plays for Ireland. Whichever team counters these players may win the game.

2. Bounce. If there is any, then watch out Bangladesh. However the game is being played at Dhaka, and the pitch has been prepared to suit, so expect it to have about as much bounce as bowling on a sponge cake. It is likely that even Boyd Rankin will struggle to get it above waist high.

3. Where to from here. This is a must win for both teams. The winner of this match will rate their chances of knocking off the West Indies but for the loser the path to the final is up a very very steep hill.

4. A twist in the tail? Normally tail enders from lesser nations are very weak at batting. However both Bangladesh and Ireland have showed some real tenacity in recent years. Particularly Ireland with numbers 6-8 providing a lot of useful runs, quite quickly. If Ireland are to win (and I think that is unlikely given the game in in Dhaka) it may well be a late charge that gets them home. If I was going to be in-game betting, and Ireland were batting last, I would follow the Duckworth Lewis system, rather than rounding it down like I do for most minnows.

Minnow Bashing

Yesterday there were two very convincing performances from New Zealand and Sri Lanka against Kenya and Canada respectively.

However both teams have a history of convincing wins over non-test nations (New Zealand, Sri Lanka and South Africa are the only teams to have never lost an ODI against a non-test playing team). I pulled the stats of all teams against the 4 "minnow" nations at this world cup, to see which matches were most likely to provide an upset.

Here is the runs per over while batting and bowling against Kenya, Canada, Ireland and Netherlands over the last 5 years for the 8 premier teams.

Team	Batting R/R	Bowling R/R	Difference
New Zealand	6.43	3.67	2.76
India	6.29	3.63	2.66
South Africa	6.39	3.78	2.61
Sri Lanka	6.31	3.94	2.37
Australia	6.05	3.78	2.27
West Indies	5.33	3.79	1.54
England	4.99	4.31	0.68
Pakistan	4.12	3.58	0.54

This suggests that the results on day 2 are not particularly surprising. If an early game is to bring about an upset, it's most likely to be England Netherlands or Pakistan Kenya, although both are still highly unlikely.