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:
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||South Africa||0.35%|
|New Zealand||West Indies||63.44%|
|Sri Lanka||South Africa||53.75%|
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