World Cup simulation update - 26 June

Are the wheels falling off?

England have now got a 4 win, 3 loss record, and, with 2 difficult matches coming up, have a genuine chance of not going through to the semi-finals. They are still not relying on other results, but they're getting close to the point where they are.

There's been a significant change, with Australia going up, and England going down. England are now expected to get to 10 points. That might still be enough. But it also might not be.

England's ranking has now dropped well below India's, to the point where the expected probability of England winning against India has dropped by almost 10%. They're still ahead due to home advantage, but the difference is decreasing.

There's about a 15% chance that a tie-breaker (total wins or net run rate) will be required. This may count out Sri Lanka, who have had two rain affected matches, and so will probably be on fewer wins than anyone else with the same number of points.

We see a huge drop in the semi-final probability of England, and a resultant increase in Bangladesh, Pakistan and Sri Lanka. Australia have qualified now, and there are fewer options now for New Zealand to be knocked out also (only 35 out of 50000 trials saw New Zealand miss the semi-finals.)

The decrease in England, and increase in probability of lower ranked teams making the semi-finals has meant that there are a lot more semi-final combinations with more than a 0.5% chance of happening. West Indies vs New Zealand was an epic match in the pool play, and that's now a reasonable possibility for a semi-final. The ICC and Star Sports will be licking their lips at the prospect of the 8th most likely outcome - an India Pakistan semi-final would be absolute ratings gold.

This is the first time that England has dipped below India on the winning probability graph, but it's hard to win the final if you don't get out of the group stage.

World Cup simulation update

The group stage of the World Cup is now roughly half way through, and there are 4 clear favourites to be the semi-finalists.

Afghanistan is the first team to be eliminated (they may have a mathematical possibility, but they don't have a statistical one). At this point, Sri Lanka are not far behind.

The rankings of the teams have remained fairly consistent, suggesting that the extra weighting for world cup matches is about right.

The fact that almost all the teams seem to have gone up is due to them all being relative to Afghanistan. Afghanistan do not seem to be quite as good as they were seeming to be and so they have dropped, but as they are set to 0, it's pushed everyone else up slightly.

The semi-final probability is the most interesting. 

I personally feel that this is underestimating the chances of South Africa, but we will see as the tournament progresses.

The key point on this graph is match 5, where Bangladesh overcame South Africa. If South Africa had won that match, they would be on about 40% and New Zealand and Australia would both be a lot lower.

The simulation also puts out the points for 4th, 5th and the difference between them. This suggests at the moment that there's only a fairly low chance that net run rate will come into play. However, one more rained out match, or a Bangladesh upset of Australia, and this could change dramatically. This makes the expected lines to be 9 points for 5th place, and 11 points for 4th place.

So far of the teams that I've had as favourite to win, 14 out of the 17 have won. Given the probabilities that the models assigned them, that's slightly higher than I would have expected - I would have expected there to have been 4 upsets rather than 3, but it's still telling me that my model is working quite well. That may be due to teams not always playing their best combinations in every match between the world cup, adding extra uncertainty to the results than exist inside a world cup.

It will be interesting to see if it continues to have the same success rate after the cup is finished.

Finally, applying the same system to find the probable winner gets the following results:

England are still favourites, but India are not far behind them.

Preview - World Cup group match 2 - West Indies vs Pakistan

Today's match is at Trent Bridge, Nottingham.

If any ground in the world has taken over the mantle of "most batting friendly ground in the world" from the Antigua Recreational Ground in St Johns, it's Trent Bridge. The groundsman seems to have taken WG Grace's famous statement "they came to see me bat, not you bowl" to the next level. The pitch seems to have been designed to make batting as easy as possible.

As a result the par score here is quite high.

Score 290 here, and you're on the wrong side of recent history. In order to have a 75% chance of winning after batting first, your team needs to score 349.

If anywhere is going to see 500 achieved, it is likely to be either Nottingham or Southampton (which also has a bowler-hating groundsman).

The regression model that I used in the previous article gives Pakistan a 73% chance of coming out on top in this match. However, the West Indies have been looking better recently than they were a couple of years ago, and Pakistan (conversely) have been looking like they're at a low ebb. As a result, this match feels more like it could go either way.

Pakistan have a habit of lifting their game significantly when they get momentum, and, as a result, have had a very good record recently against all the teams who are not currently ranked in the top five. The West Indies will need to start well to avoid Pakistan getting on a roll. 

This match is an important fixture for both sides, as a loss here will mean that the losing team will need to beat at least two of the top five ranked teams if they are to progress to the semi-finals.

A simulation to see who will win the World Cup

One of the main purposes of statistics is to help inform decisions. Cricket statistics are often used when deciding on selection of players, or (more often) arguments about who is the best at a particular aspect. They can help decide which strategies are best, what an equivalent score is in a reduced match (with a particular case of Duckworth Lewis Stern) or which teams should automatically qualify for the World Cup (David Kendix). They are often also used by bookmakers (both the reputable, legal variety and the more dubious underworld version) to set odds about who is going to win.

I decided to attempt to build a model to calculate the probability of each team winning, based on their previous form. This was going to allow me (hopefully) to predict the probabilities of each outcome of the world cup, by using a simulation. It didn’t prove to be as easy as I had hoped.

My first thought was to look at each team’s net run rate in each match, adjust for home advantage, and then average it out. That seemed sensible, and the first attempt at doing that looked like it would be perfect. Most teams (all except Zimbabwe) had roughly symmetrical net run rates, and they fitted a normal curve really well. The only problem was that Afghanistan was miles ahead of everyone else. The fact that they had mostly played lower quality opponents in the past 4 years meant that they had recorded a lot more convincing wins than anyone else.

This was clearly a problem. India and England both had negative net run rates, while Afghanistan, Bangladesh and West Indies were all expected to win most of their matches.

I then tried a different approach, based off David Kendix’s approach of using each result to adjust a ranking. But rather than having a ranking that was based off wins, I based it off net run rate. So if a team had an expected net run rate of 0.5, and another had an expected net run rate of 0.6, the first team would have an expected net run rate of -0.1 for their match. If they did better than that, they went up, and if they did worse than that, they went down.

However, I found that some results ended up having too much bearing. If I made it sensitive to a change in the results, it ended up changing way too much based off one big loss/win. England dropped almost a whole net run per over based on the series in the West Indies. So this was clearly not a good option.

Next, I decided to try using logistic regression, and seeing how that turned out. Logistic regression is a way of determining probabilities of events happening if there are only two outcomes. To do that, I removed every tie or match with no result, and set to work building the models.

My initial results were exciting. By just using the team, opposition and home/away status, I was able to predict the results of the previous three world cups quite accurately using the data from the preceding 4 years. (I could not go back further than that, as they included teams making their ODI debut, and there was accordingly no data to use to build the model.

The results were really pleasing. I graphed them here, grouped to the nearest 0.2 (ie the point at 0.6 represents all matches that the model gave between 0.5 and 0.7 as the chance for a team to win), compared to the actual result for that match. It seems that they slightly overstate the chance of an upset (possibly due to upsets being more common outside world cups, where players tend to be rested against smaller nations), but overall they were fairly reliable, and (most importantly) the team that the model predicted would win, generally won.

I could then use this to give a ranking of each team that directly related to their likelihood of winning against each other. The model gave everything in relation to Afghanistan, with the being 0, and any number higher than 0 being how much more likely a team was to win against the same opponent as Afghanistan. (Afghanistan was the reference simply because they were first in the alphabet).

This turns out to be fairly close to the ICC rankings. So that was encouraging.

I tried adding a number of things to the model (ground types, continents, interactions, weighting the more recent matches more highly) but the added complexity did not result in better predictions when I tested them, so I stuck to a fairly simple model, only really controlling for home advantage.

Next I applied the probabilities to every match and found the probabilities of each team making the semi-finals.

The next step was to then extend the simulation past the group stage, and find the winner.

After running through the simulation a few more times, I came out with this:

A couple of points to remember here: every simulation is an estimate. The model is almost certainly going to estimate the probabilities incorrectly, but it will get them close, and they will be close enough to give a good estimate of the actual final probabilities. It is also likely to overstate Bangladesh’s ability due to their incredible home record; overstate Pakistan’s ability as a lot of neutral matches for them they have had a degree of home advantage in UAE; and understate West Indies, due to them having not played their best players in a lot of matches in the past 4 years. But these are not likely to make a massive difference to the semi-finalist predictions.

Given this, I’d suggest that if you are wanting to bet on the winner of the world cup, these are the odds that I would consider fair for each team:

I will try to update these probabilities periodically throughout the world cup, and report on their accuracy.

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

Some stats after the first test in Jamaica

BJ Watling

BJ Watling has taken 5 dismissals again. He's now joined Parore and McCullum as the only New Zealanders to have taken 5 dismissals in an innings 4 times. Ian Smith only did it 3 times.

He also leads the way in terms of 8 dismissals in a match. He's done it 3 times now, there have only been 3 other times a kiwi has done it, Once each for Smith, Lees and McCullum. He's 5th overall for that, behind Boucher, Gilchrist, Healy and Marsh. (But they all had much longer careers)

He's taken 2.296 dismissals per innings. Nobody who has kept for more than 3 matches has managed that.

He also leads the way for NZ with the bat, averaging 47.25 when he is keeping. The next best is McCullum at 34.18, followed by Blain at 32.30, Parore at 26.94 and Smith at 25.56.

Globally he's 4th of all time, behind AB de Villiers (56.96) Andy Flower (53.70) and Adam Gilchrist (only 0.35 ahead on 47.60). The guy that has traditionally been considered the best ever is Ames, in 5th. He averaged 43.40.

NZ under McCullum

New Zealand have won 4 and lost 4 under McCullum. There have not been many New Zealand captains who had a winning record. Only Fleming, Coney and Howarth have winning records, and Fleming and Coney only by one match.

Under McCullum, they have averaged a collective 33.70, which is only slightly behind the 33.99 that NZ averaged under Wright, but they were ahead of it before they came out swinging to try and get quick runs in the second innings.

McCullum has led NZ to a score of 400 in 9 out of his 15 matches. Only Steve Waugh has a better % of getting 400s.

To put that in context, New Zealand averaged 26 under Taylor with roughly the same players, despite having played in South Africa and England under McCullum.

Boult - Southee combination

In matches where they have played together, Boult and Southee have a combined average of 24.08. This puts them close to the all time great mark (McGrath Gillespie averaged 23.02 and McGrath - Lee averaged 25.32). They are clear of New Zealand's other very good combinations - Bond & Martin averaged a collective 25.01 and Chatfield & Hadlee averaged 25.39.

Peter Fulton

Fulton has only scored 306 runs in his last 10 tests, at an average of 17. However he has still averaged about 30 since he came back, which is still quite high by NZ standards. Even with these games, and the ones where he came in earlier, he's still New Zealand's 11th highest averaging opener ever, one place ahead of Guptill.

His double hundreds against England were not flukes. He was in very good form at the time. In the 21 innings leading up to them he averaged 52.7 in first class cricket. But in the 31 innings since then he has averaged only 18.7. I think it is possible for the selectors to drop Fulton despite still keeping faith in him. They need to say "you are not in great form, but we know that you are a capable player. Go away and get some runs under your belt and we'll pick you straight away."

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.

Don't steal Corey Anderson's moment

Michael Jeh is a fantastic writer. When I see that he has written an article, I often read it.

Recently he wrote an article about the New Zealand - West Indies match in Queenstown, in it he suggested that Corey Anderson's innings was more a case of bad bowling than good batting. He even suggested that the game contained "possibly an unofficial record for the most full tosses bowled (including junior cricket!)"

I hadn't had a chance to see the innings before reading the article, and so I naturally assumed that Anderson's innings had involved him hitting a number of full tosses for 6. I was quite surprised, therefore, when I watched the highlights that I didn't notice a single full toss before he got to 100.

A few days later, I had a chance to sit down and watch the game closely, and actually see if Jeh's criticism was valid. Not every ball is shown on the highlights, so I wanted to be careful to not judge his article based on the work of the Sky editors.

After watching it I noticed a few things.

There are a few reasons I can think of why a bowler will deliver a full toss. Here is a list of some possible reasons:

• Perhaps they decided that a particular batsman has trouble with full tosses. 
• Perhaps they want to bowl a slower ball, and they know that slower balls are much more effective if the batsmen are attacking them. As a result a wide half-volley or a full toss often pick up wickets. 
• A good tactic for spin bowler who sees a batsman charging down the wicket is to throw in a flat full toss. The batsman often ends up just hitting the ball straight back to the bowler.
• Perhaps they just executed a yorker/full ball badly.
• A bowler who has been hit a number of times sometimes just wants to get through their over, and doesn't focus as much on where the ball lands.

Some of these are a result of poor skills, but some of them are actually caused in reaction to the batsmen. It is important that we identify which is which before we criticize too harshly.

Jesse Ryder has a history of getting out to full tosses. It is not a good idea to bowl one every ball to him, but it is a valid tactic to occasionally bowl one to him, especially outside off stump, where he has a tendency to mistime them and hit them at catching height to cover. This is a risky tactic, and not one you would try every delivery, but it is a valid option occasionally.

Corey Anderson, however, doesn't have a reputation as a bowler who is likely to get out to a full toss. He is possible only behind James Franklin and Colin de Grandhomme in his ruthlessness at dealing with full tosses.

The first 5 full tosses were all bowled to Ryder. The first one was in the 9th over. There was not a single full toss in the first 53 balls. The first one was mistimed for a single. In the next over the West Indians bowled another. It was also mistimed for a single. A couple of overs later Ryder received two in a row. He hit the first for 4, but failed to score off the next one.

About 3 overs later Ryder received another full toss, and again managed only a single off it.

The first full toss that Anderson received was the ball immediately following him bringing up his hundred. There was not a single full toss in the 36 balls that he took to get to 101.

Ryder got 2 more full tosses. The first he managed to score 2 off and the second one dismissed him. Ryder scored 9 runs and was dismissed off the 7 full tosses that he faced. Off the other 44 balls he scored 95 runs. Overall Ryder scored at a higher rate off the balls that bounced than off the ones that didn't.

Anderson received 4 full tosses. He hit two of them for 6 and two of them for 2.

Overall the "unofficial record for the most full tosses bowled" is apparently 11. Only 2 of those 11 were in the first half of the innings. They were a result of good batting, putting the bowlers under pressure and getting them to go searching. 

They also didn't actually contribute that significantly to the overall score. Anderson and Ryder scored 25 runs off the 11 full tosses. This equates to 2.27 per ball. Off their other 87 balls they scored 210 runs, or 2.41 per ball.

The bowling performance by the West Indies may not have been the best ever, but the real story was the extraordinary batting. To focus on the bowlers bowing too many full tosses is to steal the glory that Corey Anderson and Jesse Ryder richly deserve. It is a disappointing angle for such a high quality writer to take, and makes me wonder if it would have been taken if it had been Warner or Dilshan scoring the runs.

A quick look at the Anderson innings

Corey Anderson has been talked of as the big hope for New Zealand cricket for about 7 years.

He was signed to a national contract when he was still at school to make sure he didn't chose to play rugby, a sport in which he also excelled.

He was one of only a few New Zealand players to play first class cricket while still at school since the Second World War.

And yet so far in his international career he has failed to make a splash. Until today.

Today he broke Shahid Afridi's record for the fastest ODI hundred by smiting the West Indies for 131* off 47 balls. The first 101 of which came up off only 36 balls. I honestly thought Afridi's record would never be broken, but I guess you should never say never.

Here's the full 47 balls, for those of you who are interested:

1 . 4 1 . 1 6 1 . 2 1 4 6 1 6 6 . 6 . 6 1 . . 6 6 6 6 6 1 . 1 4 4 1 1 6 6 4 1 2 4 1 6 1 2 2 1

The first question that came to my mind was how does this innings compare to other great innings of the past.

I came up with a formula that I've used a number of times before to quantify how good a limited overs innings is. Basically the score is either squared if they are out or multiplied by 5 more than itself if they are not out, then divided by the balls faced. It rewards both big scores and quick scoring. It isn't perfect, as it doesn't take into account the state of the match, the quality of the opposition the importance of the game or the conditions that the match is played in. However, it is the best simple system that I know of, so it's the one that I use.

I put Anderson's innings into the formula and then compared it to all other big innings that were scored quickly. For this comparison I looked at every innings where a batsman scored more than 75 at a strike rate of more than 110.

Here are the top 20:

Player	Score	vs	Year	Modified	Viv Points
Corey J Anderson (NZ)	131* (47)	v West Indies	2014	379.06	144.6
SR Watson (Aus)	185* (96)	v Bangladesh	2011	366.15	139.7
MV Boucher (SA)	147* (68)	v Zimbabwe	2006	328.59	125.4
V Sehwag (India)	219 (149)	v West Indies	2011	321.89	122.8
SR Tendulkar (India)	200* (147)	v South Africa	2010	278.91	106.4
RG Sharma (India)	209 (158)	v Australia	2013	276.46	105.5
ST Jayasuriya (SL)	134 (65)	v Pakistan	1996	276.25	105.4
HH Gibbs (SA)	175 (111)	v Australia	2006	275.9	105.3
IVA Richards (WI)	181 (125)	v Sri Lanka	1987	262.09	100
Shahid Afridi (Pak)	102 (40)	v Sri Lanka	1996	260.1	99.2
Saeed Anwar (Pak)	194 (146)	v India	1997	257.78	98.4
Shahid Afridi (Pak)	124 (60)	v Bangladesh	2010	256.27	97.8
RT Ponting (Aus)	164 (105)	v South Africa	2006	256.15	97.7
Yuvraj Singh (India)	138* (78)	v England	2008	253	96.5
CK Coventry (Zim)	194* (156)	v Bangladesh	2009	247.47	94.4
L Vincent (NZ)	172 (120)	v Zimbabwe	2005	246.53	94.1
BB McCullum (NZ)	80* (28)	v Bangladesh	2007	242.86	92.7
Ijaz Ahmed (Pak)	139* (84)	v India	1997	238.29	90.9
MS Dhoni (India)	183* (145)	v Sri Lanka	2005	237.27	90.5
ST Jayasuriya (SL)	157 (104)	v Netherlands	2006	237.01	90.4

The first name on the list is Corey Anderson's. This innings overcame Watson's demolition of Bangladesh from 2011.

I've also included "Viv Points." This is a comparison with what many people still consider the greatest innings of all time, Viv Richards vs Sri Lanka in 1987. 100 points means that it was equivalent to Viv's innings.

Incidentally at 43rd on the list was the innings that happened at the other end, Jesse Ryder's 104 off 51 balls. It was also one of the greatest innings in ODI history, but it was completely overshadowed by the outstanding innings from Anderson.

It's still early days in Corey Anderson's career. There have been plenty of players who have had a very good day in an international match (You may notice Charles Coventry and Lou Vincent's name in the above list as evidence of this) but he has now shown that the potential that the selectors saw all those years ago when he was a teenager is closer to being realised.

There are lots of things that can be said about this innings. Poor bowling, small boundaries, big bats etc, but they can't take away from the incredible pace that Anderson managed to score at. It really was a sensational innings.

Mini-session Analysis, 3rd test, NZvWI, Seddon Park, Hamilton

Here is the final mini-session analysis for the third test between New Zealand and West Indies at Seddon Park, Hamilton

A mini-session is (normally) half a session, either between the start of the session and the drinks break or the drinks break and the end of the session. Occasionally a long session will have 3 mini-sessions where it will be broken up with 2 drinks breaks.

Tendulkulator

I'm sure that there will be tributes to Sachin Tendulkar all over the web at the moment. I wanted to do something uniquely CricketGeeky. So I've put together an interactive calculator. You enter in the 4 next scores and say if they are out or not out and then it will tell you where he will end up on the all time average leader board. The one joker in the pack is that Che Pujara is currently ahead of him, so if he has a terrible series, he might push Tendulkar up one.

I've excluded the supertest because, well, it was a bit rubbish and should never have been given test status.


I've done my best to lock it down, but it is a shared file, so if you find a way round the security, please don't vandalise it. Let everyone else play too.

The ethics of walking

What should come next?

My undergraduate degree was a Bachelor of Arts, majoring in Philosophy. While my primary interest was in logic, I also did some ethics and metaphysics papers also. I found ethics a fascinating subject, as things were very difficult to pin down. Different philosophers have argued over the existence of some sort of objective moral law. If they have agreed that there is such a thing as a moral law, they have then often disagreed as to what it is.

The idea of the moral law has led to some great works of literature. Dostoyevsky's Crime and Punishment is a fascinating look at the concept, as are a large number of Franz Kafka's short stories. But even low-brow fiction often is based on moral dilemmas or concern about the moral law. There's a theory that the Twilight series by Stephanie Meyer were so much more popular than other similar books because of some of the moral questions that they posed. Can someone be a monster by nature? Can someone overcome that nature? Is it wrong for someone to act according to a corrupted nature?

 Recently Stuart Broad's failure to walk after edging a ball from Ashton Agar set off a storm of controversy. Claim from one group of fans about cheating followed by counter claims by the other group of fans. Not long ago there was also the issue with Denesh Ramdin claiming a catch that he had actually dropped, and the ICC banning him for 2 matches as a result.

I'm going to first look at the process of the appeal, outlined in rules, then at three possible ethical frameworks and finally at these two situations, and look what the different ethical perspectives would have said about them.

More Net Run Rate issues

In my last post I looked at the problem with using net run rate in games where both teams lose a number of wickets. Only 2 days later the tournament threw up possibly the best counter-example to the net run rate system yet. New Zealand won an absolute cliff hanger over Sri Lanka. Even on the last ball there was a question of if the game was a tie or a New Zealand win. However, on the points table New Zealand were the most dominant of any team.

Because the game ended in the 37th over, New Zealand are recorded as winning with a net run rate of +1.048. The most comprehensive victory of the round, (England over Australia) only got +0.96. This is clearly not right.

Net Run Rate strikes again

Again in a big tournament, where net run rate is quite likely to be called on to separate teams, it has been exposed as an insufficient way to look at difference in performance. The West Indies vs Pakistan match was very close. When Mohammed Irfan dismissed Sunil Narine (only a couple of balls before the match was over) the game could have gone either way. West Indies were certainly favourites at that point, but they were not in a commanding position.

However they ended up winning with a commanding difference on net run rate. The match goes down as Pakistan scoring 170 in 50 overs and West Indies scoring 171 in 40.4 overs, giving West Indies a net run rate of +0.68.

+/- 0.68 is the same as a team scoring 200 and then restricting their opposition to 166. I'm not sure that these two results deserve to be weighed similarly. It is effectively a team batting first and scoring 34 runs more than their opponents. Instead I'd suggest that a better system needs to be used.

One possible suggestion is to use a modified version of Duckworth-Lewis. Duckworth-Lewis tells us how many resources a team had left. I don't have access to the professional version of Duckworth_Lewis, but using my modified version of their amateur system I found that West Indies were on track for 193. If we were to give West Indies +23 and Pakistan -23 it would make more sense to me.

There is still an issue with a blow-out, where (for example) a team can win by a huge margin, and therefore be uncatchable, but this encourages teams to go for it, and means that the games have something riding on them right down to the end. There are probably other problems, particularly with rain affected matches, but I think taking into account wickets and overs is better than just looking at overs used.

I'm sure that Pakistan fans will agree with me at the moment, particularly if they miss out on the semi-finals on net run rate by a very small margin.

How good was Gayle's demolition of Pune

Chris Gayle, picture from RCB's Flickr stream

Chris Gayle played one of the most incredible innings on Tuesday night. His 175* (66) broke all sorts of records, both for the IPL and for t20 and cricket in general.

He is without question an incredible player, and is an absolute phenomenon in the IPL.

But how good was that innings in the context of the rest of the IPL? I heard some people say that it was meaningless, because the pitches and grounds in India are so easy to score runs on, and the bowlers in the IPL aren't up to much. I heartily disagree with both of these statements, but the only real way to look at the innings is to look at how others have gone in similar conditions.

To do this I'm using my modified batting index for each innings. This system rewards scoring runs, and being not out, but adjusts it for the number of deliveries faced. To put these scores in context an index of 100 would be 70*(49) or 60(33). It doesn't take into account the context of the match, so there can be some high scores that cost a team, and low ones that win a match, but I think it's better than just looking at the runs scored or the strike rate by themselves.

Here's the top 20 innings in the IPL so far this year:

Name	Score	For	Against	At	Index
CH Gayle	175* (66)	RCB	Warriors	Bangalore	464.02
V Kohli	93* (47)	RCB	Sunrisers	Bangalore	184.02
SR Watson	101 (61)	Royals	Super Kings	Chennai	158.95
V Sehwag	95* (57)	Daredevils	Mum Indians	Delhi	158.33
DA Miller	80* (41)	Kings XI	Warriors	Mohali	156.1
CH Gayle	92* (58)	RCB	Mum Indians	Bangalore	145.93
KD Karthik	86 (48)	Mum Indians	Daredevils	Mumbai	145.13
CH Gayle	85* (50)	RCB	KKR	Bangalore	144.5
MEK Hussey	88 (51)	Super Kings	Royals	Chennai	143.22
MEK Hussey	86* (54)	Super Kings	Kings XI	Mohali	136.96
RG Sharma	62* (32)	Mum Indians	Warriors	Mumbai	120.13
AB de Villiers	64 (32)	RCB	Super Kings	Chennai	118
RG Sharma	73 (43)	Mum Indians	Daredevils	Delhi	115.44
RG Sharma	74* (50)	Mum Indians	Daredevils	Mumbai	109.52
Mandeep Singh	77* (58)	Kings XI	Warriors	Mohali	102.22
AB de Villiers	31 (8)	RCB	Warriors	Bangalore	100.75
DA Warner	77 (56)	Daredevils	Royals	Delhi	99
LJ Wright	34 (10)	Warriors	Kings XI	Mohali	98.6
SPD Smith	39* (16)	Warriors	Super Kings	Chennai	95.06
EJG Morgan	47 (21)	KKR	Sunrisers	Kolkata	94

There's a significant distance between Gayle and the next best innings.

Another way to look at it is to look at what percentage of Gayle's index each innings got. Here's the top 10:

Name	Score	Gayle points
CH Gayle	175* (66)	100
V Kohli	93* (47)	40
SR Watson	101 (61)	34
V Sehwag	95* (57)	34
DA Miller	80* (41)	34
CH Gayle	92* (58)	31
KD Karthik	86 (48)	31
CH Gayle	85* (50)	31
MEK Hussey	88 (51)	31
MEK Hussey	86* (54)	30

How about how it stacks up against all T20 innings anywhere:

Name	Score	For	Against	At	Index
CH Gayle	175* (66)	RCB	Warriors	Bangalore	464.02
LP van der Westhuizen	145 (50)	Namibia	Kenya	Windhoek	406
GR Napier	152* (58)	Essex	Sussex	Chelmsford	398.34
BB McCullum	158* (73)	KKR	RCB	Bangalore	341.97
CL White	141* (70)	Somerset	Worcs	Worcester	284.01
A Symonds	112 (43)	Kent	Middlesex	Maidstone	278.7
M Vijay	127 (56)	Super Kings	Royals	Chennai	276.68
ST Jayasuriya	114* (48)	Mum Indians	Super Kings	Mumbai	270.75
SB Styris	100* (37)	Sussex	Gloucs	Hove	270.27
RE Levi	117* (51)	South Africa	New Zealand	Hamilton	268.41
CG Williams	116 (48)	Namibia	Scotland	Windhoek	268.25
CH Gayle	128* (62)	RCB	Daredevils	Delhi	264.26
DA Warner	135* (69)	NSW	Super Kings	Chennai	264.13
H Davids	112* (48)	Cape Cobras	Warriors	Cape Town	261.33
KJ O'Brien	119 (52)	Gloucs	Middlesex	Uxbridge	260.88
Ahmed Shehzad	113* (49)	Barisal	Rajshahi	Dhaka	260.59
A Symonds	117* (53)	Chargers	Royals	Hyderabad (Deccan)	258.28
YK Pathan	100 (37)	Royals	Mum Indians	Mumbai (BS)	256.76
CL White	116* (53)	Somerset	Gloucs	Taunton	253.89
AC Gilchrist	109* (47)	Chargers	Mum Indians	Mumbai	252.79

And then the "Gayle Points" for the top 20 innings of all time:

Name	Score	Gayle points
CH Gayle	175* (66)	100
LP van der Westhuizen	145 (50)	87
GR Napier	152* (58)	86
BB McCullum	158* (73)	74
CL White	141* (70)	61
A Symonds	112 (43)	60
M Vijay	127 (56)	60
ST Jayasuriya	114* (48)	58
SB Styris	100* (37)	58
RE Levi	117* (51)	58
CG Williams	116 (48)	58
CH Gayle	128* (62)	57
DA Warner	135* (69)	57
H Davids	112* (48)	56
KJ O'Brien	119 (52)	56
Ahmed Shehzad	113* (49)	56
A Symonds	117* (53)	56
YK Pathan	100 (37)	55
CL White	116* (53)	55
AC Gilchrist	109* (47)	54

How good was Gayle's innings? It was about 14% better than the next best innings ever. That's how good it was.

Mini-session analysis 2nd test, WI Zim, Rosseau, 2013

Here is the mini-session analysis for the first test between West Indies and Zimbabwe at Windsor Park, Roseau, Dominica

A mini-session is (normally) half a session, either between the start of the session and the drinks break or the drinks break and the end of the session. Occasionally a long session will have 3 mini-sessions where it will be broken up with 2 drinks breaks.

Mini-Session	Score	Winner
1-1a	Zimbabwe 43/2 off 10.3	West Indies
1-1b	Zimbabwe 50/1 off 16.3	draw
1-2a	Zimbabwe 27/1 off 13	West Indies
1-2b	Zimbabwe 38/3 off 16	West Indies
1-3a	Zimbabwe 17/3 off 4.5	Zimbabwe
	West Indies 57/2 off 14
1-3b	West Indies 57/0 off 13	West Indies
2-1a	West Indies 33/1 off 14	Zimbabwe
2-1b	West Indies 54/1 off 17	draw
2-2a	West Indies 52/0 off 16	West Indies
2-2b	West Indies 42/0 off 16	West Indies
2-3a	West Indies 54/0 off 13	West Indies
2-3b	West Indies 32/4 off 14	Zimbabwe
3-1a	Zimbabwe 45/1 off 12	Zimbabwe
3-1b	Zimbabwe 32/3 off 14	West Indies
3-2a	Zimbabwe 59/3 off 14.5	West Indies
3-2b	Zimbabwe 5/3 off 1.3	West Indies

Final update, click here
West Indies win the mini-session count 10 - 4


Summary West Indies win the match by an innings and 41 runs, and the mini-session count 10-4

I was mocked by a number of people for putting Shane Shillingford into my world test XI at the end of last year. He has again shown why I rate him highly. He takes wickets. Quite a lot of wickets. A few years ago West Indies vs Zimbabwe was quite a close contest. This series shows both how far West Indies have come under Sammy and how far Zimbabwe have fallen in the last 12 years.

Mini-session Analysis 1st Test, WI Zim, Bridgetown Barbados 2013

Here is the final mini-session analysis for the first test between West Indies and Zimbabwe at Kensington Oval, Bridgetown, Barbados

A mini-session is (normally) half a session, either between the start of the session and the drinks break or the drinks break and the end of the session. Occasionally a long session will have 3 mini-sessions where it will be broken up with 2 drinks breaks.

Mini-Session	Score	Winner
1-1a	Zimbabwe 30/1 off 14	West Indies
1-1b	Zimbabwe 61/1 off 13	Zimbabwe
1-2a	Zimbabwe 38/2 off 15	West Indies
1-2b	Zimbabwe 33/2 off 15	West Indies
1-3a	Zimbabwe 34/1 off 15.5	draw
1-3b	Zimbabwe 15/3 off 3.5	Zimbabwe
	West Indies 18/2 off 11
2-1a	West Indies 63/1 off 9.3	West Indies
2-1b	West Indies 63/2 off 20.1	Zimbabwe
2-2a	West Indies 73/1 off 15.2	West Indies
2-2b	West Indies 48/1 off 15	West Indies
2-3a	West Indies 42/3 off 13.2	Zimbabwe
2-3b	Zimbabwe 41/3 off 14	West Indies
3-1a	Zimbabwe 36/3 off 16	West Indies
3-1b	Zimbabwe 30/4 off 10.2	West Indies
	West Indies 9/1 off 3

Final update, click here
West Indies win the match by 9 wickets and the mini-session count 9 - 4


Lunch, Day 1: The mini-session count is tied up, 1-1

A reasonable start from Mawoyo, it's good to see him hitting some form again. - Mykuhl

Stumps, Day 1: West Indies lead the mini-session count 3-2

When Kyle Jarvis was playing for Central Districts he produced a couple of magic spells. This is certainly a good start from him.

Lunch, Day 2: West Indies lead the mini-session count 4-3

Zimbabwe have an opening here. If they can wrap up the tail cheaply they could be back in this match.

Final drinks, Day 2: West Indies lead the mini-session count 6-4

Darren Sammy is an interesting cricketer. Not quite a batsman, not quite a bowler, and yet he's capable of turning a match with either. That was a sensational innings in the context of this match.

Stumps, Day 2: West Indies lead the mini-session count 7-4

Things are suddenly looking very grim for Zimbabwe. They desperately need a big partnership.

End of match, Day 3: West Indies lead the mini-session count 9-4

The big partnership never came. Shane Shillingford has done this to a couple of teams now, he's starting to look a little like West Indies version of Abdul Rehman, generally doesn't look particularly special, but has days where he is almost unplayable. Despite generally being good at playing spin, the Zimbabwe batsmen were all at sea against him here.

Sorry Kieron

watch Pollard's running between wickets.He's incredible, but nobody notices, because when he hits 6's they go a long way. #AusvWI

— Michael Wagener (@Mykuhl) February 6, 2013

That was the worst jinx ever.

— Michael Wagener (@Mykuhl) February 6, 2013

End of year Mini-session Analysis review 2

One of the advantages of the mini-session analysis is that it allows me to quantify how well or badly a team went in a match in a way that is reasonably even for both teams. As a result we can get a series score, and even an annual tally.

This wouldn't be CricketGeek without some tables summarising things, so here is the complete mini-session analysis tables for the year:

Team	Won	Lost	Winning %	Match w/l
Australia	145	100	59.18%	7.00
South Africa	133	97	57.83%	no losses
West Indies	116	108	51.79%	1.00
England	173	168	50.73%	0.71
Pakistan	65	69	48.51%	3.00
New Zealand	89	108	45.18%	0.33
India	87	108	44.62%	0.60
Sri Lanka	95	124	43.38%	0.60
Bangladesh	20	32	38.46%	0.00
Zimbabwe	2	11	15.38%	0.00

Interestingly Australia came out slightly ahead of South Africa, despite South Africa not losing any matches and Australia losing one. However South Africa drew half of their matches this year, while Australia had 7 wins, 3 draws and a loss, so it makes sense that both teams would come out at a similar level in the year.

The other big surprise was how high West Indies are. It has felt like there are three tiers of cricket at the moment, with Australia, South Africa, England and Pakistan in the top group, Sri Lanka, India, New Zealand and the West Indies in the second group and then Bangladesh and Zimbabwe in the third group. Each team is competitive with teams in their own tier and at home to teams in the group above. The only exception to that has been how badly England went in Pakistan and how easily South Africa seemed to cope with English conditions.

There is also something unfair in everybody not playing everybody else. For example, New Zealand thrashed Zimbabwe 11-2, but nobody else got to play Zimbabwe. As a result I produced a weighted score. Every team got a weighting based on their performance over the year, and then I used that to calculate a ranking. I don't think this is a ranking of how good the teams are, but it is an indication of how well they have played.

Another option would be to take a football style approach, where we award 3 points for a win and 1 point for a draw. Then we use the mini-session difference as the tie breaker. The problem with this is that England have played 15 matches, while Pakistan have only played 6 matches, so it is hardly fair to compare them with an overall score. As a result I've looked at points per match, and difference per match as the way of ranking the teams.

	Team	m	w	l	d	pts	diff	ppm	dpm
	Australia	11	7	1	3	24	45	2.18	4.09
	South Africa	10	5	0	5	20	36	2	3.6
	Pakistan	6	3	1	2	11	-4	1.83	-0.67
	West Indies	10	4	4	2	14	8	1.4	0.8
	England	15	5	7	3	18	5	1.2	0.33
	India	9	3	5	1	10	-21	1.11	-2.33
	Sri Lanka	10	3	5	2	11	-29	1.1	-2.9
	New Zealand	10	2	6	2	8	-19	0.8	-1.9
	Bangladesh	2	0	2	0	0	-12	0	-6
	Zimbabwe	1	0	1	0	0	-9	0	-9

This probably feels more like a fair summary of how the teams have gone this year.