Win the toss win the match?

In watching this world cup, there have been 6 sides who have looked a step ahead of the others: Australia, England, India, New Zealand, Pakistan and South Africa. There have been 8 matches featuring two of those 6 sides.

Pakistan beat India. Pakistan beat New Zealand. New Zealand beat India. England beat Australia, Australia beat South Africa, South Africa beat England, New Zealand beat England and Australia beat Pakistan.

Interestingly, in 7 out of those 8 matches, the team who won the toss, won the match.

If the toss was independent, the chances of this happening are about 3.5% (9/256 for anyone who wants a more precise answer). That is very unlikely. But it is not so unlikely that it would be considered impossible.

If I throw 8 coins, every now and again it will come up with either 7 heads or 7 tails.

It made me wonder if the toss was a significant contributor to team's success, and if so, what can be done about it.

The first thing to do is to build a model to predict the outcome of matches. This is important, because England beating Papua New Guinea after winning the toss does not say much about the importance of the toss, because England would probably beat Papua New Guinea if they lost the toss too.

I decided to use logistic regression to build the model. I looked at every international match between 2 sets of competent (or semi-competent) side in the last 3 years. Looking back afterwards, I noticed that I had left out Nepal (who did deserve inclusion) but they were the only team that the ICC currently have ranked in the top 20 that I left out. I also included a few lower ranked sides in order to give a better picture of the difference between the teams near the bottom of the rankings for the T20 World Cup. So I also included the likes of Singapore, Kenya, USA and Malaysia.

I chose to use logistic regression because it has been helpful in the past for giving realistic probabilities of winning for limited overs matches. When I tested the model, it explained about 85% of the variation in results. When it said that a team had a 50% chance of winning, they generally won about 50% of the time. When it said that a team had a 70% chance of winning, they generally won about 70% of the time.

It is not perfect, but it is simple enough and close enough to tell us about the impact of the toss.

The factors that I used were the team, the opposition, and if the match was home, away or neutral.

The model suggested that the 6 teams that I listed above, along with Afghanistan were the 7 best teams. It probably overstated the strength of Afghanistan, due to them not playing at home at all, and so therefore missing out on the home advantage. Their players are so familiar with their adopted grounds that they have an advantage there that is not accurately reflected in the tag "neutral."

Once I had built the model, I could then make predictions about all the world cup matches.

In this graph, I have the modeled probability of winning on the x-axis and then the actual outcome on the y-axis. The green points are where a team has won the toss and the red ones are where they have lost the toss.

I have divided the data into 3 groups - expected loss, too close to say, and expected win. The numbers are the proportion of wins by the team that won the toss (green) or lost the toss (red).

We can see that the team that won the toss has won more than the team that lost the toss in each of the 3 regions.

This is fairly compelling that there is an advantage in winning the toss. But it is not nearly as dramatic as 7 out of 8 in the first sub-group that I looked at.

This made me wonder if there was some sort of accidental gerrymandering with the way that I selected the data. So I tried 5 groups instead of three.

This time I grouped them together, and looked at the expected number of wins against the actual number of wins.

This time I added in two parallel trend lines, and looked at the difference between them. The groups of teams who won the toss ended up winning about 1.5 more matches than the groups of teams who lost the toss.

This was interesting, but I was not sure what to read into this. So I decided to re-randomise the toss, to see what would have happened with an independent toss.

To do this I randomly assigned to each match one team as the designated toss winner. Then I redid the groups, and saw what the difference was. I wanted to know how rare a difference of 1.5 was.

It turned out to be more common than I would have expected. After 10000 trials, I found that roughly 10 % of the sets had a difference of more than 1.478, and roughly 10% had a difference of less than -1.478. For a re-randomisation, 10% is about the cut off where you say that it was likely or unlikely to have been caused by natural variation. 

This was a surprising result. I was expecting to find that there was clear evidence that winning the toss improved the chance of winning, but instead I found out that it might do, or it might be just natural variation.

There are two major errors in statistics: saying too much or saying too little. This situation looked like one that had the potential to put egg on my face no matter which way I went. There was not quite enough evidence to be very confident that the toss made a difference, but there was also enough evidence to be quite confident of that fact.

I wanted to try one more test before I decided that I didn't know what to say.

This time I picked 60 random innings from any match in the past 3 years. I applied the model to that innings, and then grouped the innings and found the difference between the two lines. I repeated that 1000 times.

This time I found more like what I was expecting.

Less than 1% of the randomly selected innings had the impact of the winning the toss as high as it has been in the world cup. Interestingly, the teams that lost the toss actually had a slight advantage (1.7% of the time losing the toss had an advantage of 1.478 or more matches)

This tells us two interesting things:

Winning the toss seems to have given teams an advantage in this world cup, and it does not normally give teams any advantage whatsoever.

I wondered if that was due to the dew factor. It can often get harder to bowl as the match wears on due to dew in the gulf states. But that does not seem to have been the difference. Winning the toss was roughly as much of an advantage in the daytime as it was in the nighttime.

The biggest single factor seemed to be Dubai International Stadium. Matches there seemed to be much more toss dependent than almost anywhere else.

And that is where the final is being held.

Given that, the toss is likely to give an advantage to whoever wins it. Or perhaps it will revert to type, and there will be no advantage.

Assuming that the toss should be factored in, my model has the following probabilities for the final:

If Australia win the toss: Australia 67%, NZ 33%.

If New Zealand win the toss: Australia 30%, NZ 70%

Neither team is 100% or 0% in either scenario, but there's clearly an advantage.

Now it will be up to the players to see if they can overcome it.

Some Questions ahead of the 3rd test

Questions leading into the third test

1. Who will actually be fit to play for New Zealand?

There is talk that Kane Williamson, Henry Nicholls and Mitchell Santner were all too sick to get out of bed yesterday, and all are unlikely to play. Trent Boult and Lockie Ferguson have already gone home. Glenn Phillips has been called in as a late replacement, meaning that there is a chance that New Zealand will end up playing four wicket keepers, and recalling Jeet Raval to the squad simply due to lack of other options. If those 3 are all out of contention, then New Zealand’s top 7 is likely to include Raval, Tom Latham, Tom Blundell, Ross Taylor, Phillips, BJ Watling and Colin de Grandhomme.

2. Will either side opt for two spinners, and if so, who will make way?

The Sydney Cricket Ground has a reputation as a spinners track, and both teams have added an extra spinner into their squad. If Australia opt for Mitchell Swepson, then they are likely to end up either dropping a batsman, or going in with only two pace bowlers and giving the 3rd seamer role to Matthew Wade. Wade’s over against New Zealand in Melbourne was considerably less threatening than his spell in Perth, suggesting that he is less effective with the red ball than the pink one. This suggests that going with two spinners is a highly risky move for them.

Another option could be to select Michael Nesser as an all rounder to replace Wade in the side, allowing more cover for the extra spinner, but lengthening the tail considerably. This is unlikely to happen, as Australia have traditionally shied away from picking five bowlers in test sides, and Tim Paine has made it clear that he does not favour changing the formula too much.

New Zealand have taken Todd Astle on a holiday so far, not playing any tests on this tour or in the matches in New Zealand. Will Somerville has been added into the squad, and his familiarity with the conditions and point of difference with his height is likely to make him a tempting option. Somerville is a former Sydney resident, and played for New South Wales for a few years before returning to New Zealand to try to play international cricket. He is close to 2 metres tall, and so created different challenges for batsmen by being able to extract similar bounce to a bowler bowling with loop even while bowling on a flatter trajectory.

Astle provides the advantage of being a competent batsman, so bringing him into the side in place of de Grandhomme is a possibility. That would allow a 3rd genuine seam bowler into the side. Another option is for Astle and Somerville coming in with Tim Southee and Neil Wagner with de Grandhomme acting as the 3rd seamer.

3. Will the pitch actually turn, or is the spinner’s SCG a bit of a myth?

Over the past 10 years, spin bowlers have bowled over 1000 overs at the SCG, but only picked up 82 wickets at an average of over 50 at the SCG. Pace bowlers have taken 192 wickets at and average just under 35 there. Nathan Lyon has averaged 47 at the ground in that time, and collectively the leg spinners used there have averaged roughly 70. The days of Stuart McGill ripping teams apart on the SCG seem to be long gone.

However, when looking at the way that the series has progressed, Australia might consider favouring spin more. New Zealand’s two standing quick bowlers have not been much less effective than their Australian counterparts. Southee and Wagner have taken 26 wickets at less than 23 runs each, while Pat Cummins and Mitchell Starc have taken 19 wickets at just under 18 each. However, Lyon has been much more effective than Santner (10 wickets at 22.7 vs 1 wicket at 250). Giving Lyon slightly more to work with might exaggerate that difference even more.

4. Will New Zealand keep trying to out last Australia with the ball?

New Zealand have had a clear bowling plan in this series. With the new ball: pitch it up, and try to get it to swing occasionally, but mostly bowl a 4th stump line, on a good length. With the older ball, bang it in short of a length. Both tactics have been mostly designed to get the batsmen to play risky shots and get out doing so, rather than trying to actively dismiss the batsmen.

While that tactic has been reasonably successful for Southee and Wagner, it has meant that there has been a lot asked of the other bowlers, and they have not been as successful. Perhaps bowling 1m fuller, and more at the stumps. Cricviz released some interesting data recently that of all batsmen who have faced 500 balls aimed at the stumps since 2006, only Steven Smith averages over 33 against those deliveries, and of players who are still active test batsmen, Virat Kohli has the third best average against balls targeting the stumps of 24.08. That suggests that bowling straighter might be a better tactic. The odd delivery will be hit through the leg side or down the ground, but the approach may well bear more fruit.

The difference in length and line from the Australian bowlers has been clear. They have tended to bowl at the stumps more. Some of that is due to the different styles, but some of it is just that they had different plans, and those plans (especially when a batsman was new to the crease) have been much more effective.

5. Should the match even be going ahead?

Cricket is the job of the players, and of the administrators, but it is still at its heart a game. Is there a point where playing games in the midst of an ongoing natural disaster becomes a little insensitive? Should this match even be going ahead?

The smoke from the New South Wales bushfires has been so thick that the views of mountains in Southern New Zealand (over 2000 km away) has been blocked and some of the New Zealand glaciers have turned brown. In terms of distance, that would be like smoke from a fire in Dubai blocking out the view of the buildings at one end of Marine Drive in Mumbai from the other.

The Remarkables have disappeared. Pics from yesterday and today. 🙁 #AussieSmoke pic.twitter.com/KQjRgwwb5N

— Margie (@Margie186) January 1, 2020

The question has to be asked as to what point is it where player welfare comes to the fore? The atmosphere in Sydney is so polluted from the fires that one lung professor likened breathing it to smoking 40 cigarettes. The PM2.5 reading in some outer suburbs of Sydney was 734. To put that in context the match in Delhi that was called off between India and Sri Lanka had a PM2.5 reading of under 400.

Sport can be important for the morale of people who are experiencing a traumatic event, but there is such a thing as being too soon, and while the bodies of the dead from the fires are still not yet buried it may be too soon to be playing games. Even if the timing is acceptable to the public, is the safety issue to the players too extreme for such triviality.

World Cup Simulation update - 5 July

Here's the latest update for the world cup simulation. I have New Zealand at 100%, but that's simply due to the probability of Pakistan getting the required run-rate being so low that that possibility never eventuated in the 50000 trials that I used. The probability of Pakistan going through is slightly lower than the probability of someone being shot accidentally by a dog running along a beach while holding a handgun in it's mouth during the next week,

The next graph is the expected points. The simulation has had the correct top 4 from the second match on, however, the expected points and the order of the teams have changed considerably

The top 4 was looking fairly likely from about match number 6 on. There was some excitement from the two upset losses by England, but Pakistan never got beyond 40% on the simulation.

The complete make up of the semi-finalists has not yet been decided, nor has the team in 5th place. Pakistan, Bangladesh and Sri Lanka could all end up 5th. 

Next I looked at the winning probability. This is getting close to the point where it can be calculated analytically without much trouble.

 The next thing to look at is the rankings. A thing to remember here is that it is all relative to Afghanistan, so everybody going up is more an indication that Afghanistan has gone down.

The order that the teams are in here is the same as David Kendix' official rankings order, with one exception - I have India ahead of England, rather than the other way round.

Finally, a little graph to show what Pakistan needs to do to make the semi-finals. They need to keep Bangladesh below the green line.

World Cup simulation update - 1 July

Here's the latest update to the simulation. The first two graphs disagree slightly, and that's because I have two different methods to calculate the expected net run rate. The first one seemed to be slightly more accurate than the second, but there was not a big difference when I tested them. (The margin of victory in cricket matches is actually really difficult to estimate - teams batting second tend to cruise to victory rather than try to win by as big a margin as possible) I decided to use both when doing the calculations. With the first method, New Zealand and India both have a higher than 99.98% probability of going through, while it's 99% for India and 97.7% for New Zealand with the second method. These seem more realistic.

The big thing to notice is the change to England's probability, and how England beating India damaged the chances of both Pakistan and Bangladesh. Pakistan's probability went down by slightly more than Bangladesh's probability because the ranking of India dropped slightly, and Bangladesh need to beat India to get through.

This graph shows expected value - not the most likely value. Those are actually different things. The expected value is the mean of all the expected outcomes. As a result, none of the teams will actually end up with the points that this shows, but they should mostly get close to it.

 It's now looking like there's a roughly 45% chance that net run rate will be a deciding factor in who goes through to the semi-finals.

If Bangladesh beat India (which is admittedly a fairly unlikely outcome), we could then see a situation where Pakistan and Bangladesh are playing for the opportunity to be level on points with New Zealand and India on 11 points. If that is the case, then (in all likelihood) the rained out match between New Zealand and India will have allowed both to progress at the expense of the winner of Pakistan vs Bangladesh.

The most likely semi-finals at this point are Australia vs New Zealand and England vs India, but these are by no means confirmed yet.

In individual matches, England effectively has a higher ranking than that, because teams playing at home get a ranking boost of 0.86 over their opponent. That's why I have England back on top in the next graph:
This one is quite different to what the book-makers have. I have England as favourites, while they have India and Australia both tied for favourite on roughly 30%. They also have Pakistan and Bangladesh at about double the probability that I do.

I used the first net run rate model for the winning probability, but the difference in numbers suggests that the bookies are possibly using a model that is more similar to the second one.

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 24th June

 Here's the update after the South Africa vs Pakistan match

Firstly, this pushed Pakistan's ranking back above Bangladesh's ranking, although they are both so close that the match between them is now predicted as 50.2% to 49.8%.

 Looking at the expected points, Pakistan have now jumped ahead of Sri Lanka and Bangladesh.

It's looking fairly likely that 5th place will be on 9 or 10 points, while 4th will be on 10, 11 or 12 points.

My simulation only uses net run rate as the tie breaker. Accordingly, there's actually a slightly higher probability of Sri Lanka and Pakistan getting through than this shows, and a slightly lower chance of England and Bangladesh.

It's takes a lot of processor time to improve the simulation, and it's likely to be less than 1% difference, but I might have a go at improving it once we get to the last 5 matches.

England are still the overwhelming favourite to be the 4th team to go through. There were still 41 out of the 50000 trials where New Zealand hadn't made it. So nobody is guaranteed through just yet.

If you have semi-final tickets - this is who you're likely to see.

The probabilities for Bangladesh and Pakistan being so low here are understandable. They both have about a 5% chance of making the semi-final, but, given that they both have about a 1/3 chance of winning each match against the top teams, it gives them a roughly 0.5% chance of winning the tournament from here. However, if Bangladesh, Australia and Pakistan win the next 3 matches, that number will rise.

It's starting to look like England's style that is so effective in series may not be so effective in one off matches. It will be interesting to see if that trend continues.

World Cup Simulation Update, 23 June

Here's the latest outputs from the simulation.

England's loss to Sri Lanka opened the door somewhat, but we can still be fairly confident in who the semi-finalists are.

 England's ranking has gone down, after two losses to fairly ordinary sides.

It's looking like 10 points will be the magic number. Roughly a 10% chance that we'll rely on a tie-breaker.

The average points expected certainly favour England on that count to be in fourth


Accordingly, they have a much higher chance of making it through.

What the likely match ups are. (Teams in alphabetical order, rather than placings)

England are still firm favourites by my model. Home advantage is massive.

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.

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.

Paine vs Pant

The Instagram photo. 

I wanted to quickly share my thoughts about the Paine - Pant sledge.

I've been an outspoken critic of "mental disintegration" -- the tactic of using personal abuse and insults to get under a player's skin and put them off their game, but I really liked what I heard from Paine, and think it's the sort of sledging that is totally appropriate.

Using Added Value to measure cricket performances - Part 2 ODI bowling

Reid and Matthews chat before the final over.

It was the summer of 1990/91, just before Christmas. I was on holiday at my Aunty's place in Mount Maunganui. My cousin and I were sleeping in the glass conservatory, looking out over the sand-dunes. The air smelt like salt and sand.

In our little room was a little TV, on the TV was the cricket coming out of Australia.

New Zealand were playing against Australia in Hobart.

I was 11 years old, and I was enthralled.

Danny Morrison was bowling. A year earlier, he had come to speak to my primary school assembly, then signed autographs by the school pavilion. I got him to sign my cricket bat, and I tried really hard to not get it scratched off. He was my favourite bowler. Australia needed 6 runs to win. Greg Matthews was on strike. I wasn't sure why, but as nobody seemed to like Greg Matthews, I didn't either.

Morrison bowled from around the wicket, and speared a fullish ball into leg stump. Matthews drove it, inside-out, through point for four. I really didn't like Matthews now.

New Zealand vs Australia: Head-to-Head

I've heard a number of commentators say that man-for-man, Australia have better players, but New Zealand is a better team. This strikes me as a peculiar thing to say, given that there's often no analysis included of individual head-to-head.

So I've decided to do it myself, in order to see if there actually is a clear difference, man-for-man.

I've tried to line up the players by role. Both teams have players that do similar roles generally, with only a couple of exceptions.

I'm looking at their world cup so far, as well as their numbers since 1 Jan 2013 in New Zealand and Australia.

Role 1 - Slower opener

Player	Guptill	Finch
WC Average	76.00	40.00
WC S/R	108.79	93.64
2 year Average	43.00	37.00
2 year S/R	85.08	87.13

Guptill is in better form, but the 2 year numbers are very close. These are two players of similar ability who are both playing good cricket. Both tend to be slow to start, but are capable of increasing their scoring rate once established.

Role 2 - Fast opener

Player	McCullum	Warner
WC Average	41.00	50.00
WC S/R	191.81	124.48
2 year Average	38.00	40.75
2 year S/R	135.02	104.21

Again the numbers are very close. Warner has the higher average, but McCullum scores faster. Both are remarkably good at both scoring boundaries and finding singles, but both are prone to hitting bad balls straight to fielders. McCullum has shown a weakness against left-arm spin, so there's a chance that Clarke might bring himself on to bowl early on.

Role 3 - First drop

Player	Williamson	Smith
WC Average	37.00	57.66
WC S/R	83.14	94.02
2 year Average	51.08	60.92
2 year S/R	85.64	94.77

These two players are the best batsman for their team in recent times. Smith is ahead on these numbers, but it's wrong to say that Williamson is a weakness in the New Zealand side. Both manage to score at a good rate without looking like they're trying. Both also have a big impact on their team's chances of succeeding. New Zealand win 45% of the time when Williamson scores under 40 and 62% when he scores 40+. Australia have won 53% of the time when Smith's scored under 40 and 86% when he's scored 40+.

Role 4 - Innings builder

Player	Taylor	Clarke
WC Average	30.16	29.00
WC S/R	63.06	92.94
2 year Average	46.82	23.66
2 year S/R	79.30	80.49

Clarke's had a slightly better world cup, but Taylor has produced more quality innings' over the past 2 years, averaging almost twice what Clarke has. These two are both great players, who often play roles that allow others to shine. As a result, their numbers don't truly tell the story of their contributions. Both players' numbers are also a reflection of their battle with injuries.

Role 5 - rebuild or launch

Player	Elliott	Watson
WC Average	37.83	41.20
WC S/R	107.07	107.85
2 year Average	44.76	36.82
2 year S/R	94.63	94.70

A really interesting role in modern cricket is the number 5 batsman. Their role is sometimes to steady a rocking ship, and other times it's their role to attack, and build on the foundation of the players above them. It is difficult to separate the ability of Watson and Elliott to do this role.

They also both have a role to play with the ball as the extra bowler:

Player	Elliott	Watson
WC Average	34.00	74.00
WC E/R	8.5.0	6.72
2 year Average	25.88	101.50
2 year E/R	6.56	6.37

Elliott has been more expensive, but has also broken partnerships quite regularly.

Overall, it's really difficult to separate these two with bat and ball. I'd probably back Elliott as a batsman, but Watson with the ball, despite his numbers not being as good.

Role 6 - Agressive batsman

Player	Anderson	Maxwell
WC Average	38.50	64.80
WC S/R	109.47	182.02
2 year Average	41.77	33.42
2 year S/R	125.96	125.13

One of the dangers of comparing players like Anderson and Maxwell based on statistics is that they are often asked to do different jobs. Against South Africa, Anderson's job was not to come in and score at a massive strike rate. His job was to play sensibly and carry the innings through. Over a longer term it's difficult to separate Anderson and Maxwell. Both are capable of being absolutely breathtaking with the bat.

Both play quite different roles with the ball, so I'll look at them later.

Role 7 - Wicket-keeper batsman.

Player	Ronchi	Haddin
WC Average	14.60	42.00
WC S/R	125.86	157.50
2 year Average	38.88	41.11
2 year S/R	128.84	111.11

Haddin has had a much better world cup, but it would not be difficult to argue that Ronchi has been the most effective death batsman in the world in the past couple of years.

They're also difficult to separate with the gloves. Both are solid keepers who have made a couple of key mistakes, but in general, they've done the job required of them sufficiently.

Role 8 - Bowler who bats

Player	Vettori	Faulkner
WC Average	41.00	14.66
WC S/R	164.00	176.00
2 year Average	15.66	44.25
2 year S/R	123.68	114.56

Faulkner and Vettori have very different styles, but can both be very effective. Vettori has rediscovered his batting form of 2008-2012 in this world cup, during which time he was one of New Zealand's best batsmen as well as being an outstanding bowler. Contrastingly, Faulkner hasn't found his rhythm since returning from injury.

Role 9 - Right arm opening bowler

Player	Southee	Hazlewood
WC Average	27.13	20.85
WC E/R	5.57	4.19
2 year Average	28.97	20.44
2 year E/R	5.53	4.37

Hazlewood has the advantage here numerically, but some of that is due to Southee having a role bowling at the death. I don't think many selectors would pick Hazlewood over Southee, regardless of the difference in their stats.

Role 10 - left arm opening bowlers

Player	Boult	Starc
WC Average	15.76	10.20
WC E/R	4.41	3.65
2 year Average	22.09	14.37
2 year E/R	4.58	4.34

Starc and Boult probably been the two best bowlers in the tournament. They both offer different things. Starc bowls into the pitch with a high arm action that causes the ball to bounce higher, but it also gets less movement and arrives to the batsman later, despite the quicker speed through the air. Boult bowls over his front foot and tends to bowl more deliveries along the wicket than into the wicket. As a result, the ball swings more and arrives at the batsman faster. (Ed Cowan, after facing both, commented that Starc might bowl 5-10km/h faster but you have a lot more time to face the ball. Boult certainly feels faster.)

It's difficult to separate them, but not impossible. Starc has been the premier white ball bowler in the world recently.

Role 11 - 3rd seamer

Player	Henry	Johnson
WC Average	-	24.66
WC E/R	5.00	5.43
2 year Average	19.00	25.15
2 year E/R	4.26	5.04

Henry has only bowled 8 overs this world cup, as he wasn't even in New Zealand's original squad. His first 5 overs included 2 maidens and conceded only 9 runs against South Africa. He went the distance in his next 3 overs, but even then, a large proportion of his runs came through edges and mis-hits. Johnson is a master with the red ball, but hasn't had the success recently with the white ball that he had earlier in his career. It's still difficult to complain about an average of 25 and taking a wicket every 5 overs.

Role 12 - 4th seamer

Player	Anderson	Faulkner
WC Average	16.21	23.00
WC E/R	6.45	4.90
2 year Average	22.69	26.57
2 year E/R	6.42	5.45

This is a slightly more difficult comparison, as Anderson generally bowls at the death. His economy rate here is outstanding, and he's taken a lot of wickets. However, taking wickets with bad balls isn't necessarily a trait that is repeatable. Faulkner looks like a better bowler, despite his numbers not being quite as dramatic as Anderson's.

Role 13 - Spinner

Player	Vettori	Maxwell
WC Average	18.80	36.20
WC E/R	3.98	5.83
2 year Average	35.12	32.91
2 year E/R	4.10	5.24

Vettori is in a different class here. It's probably the only place where there's a clear difference in quality between players doing similar roles in the two teams.

Overall it's really difficult to separate the two teams. Both have a team full of good players in good form. They have players doing similar jobs, often in similar ways.

I don't think I can honestly say at this point which team has better players. The more you look at this match, the more mouth-watering it becomes.

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

David Warner vs Rohit Sharma

Over the past couple of days I've been called a troll, a Jonathan Agnew fan and even an Australia on twitter, because I have a position that is somewhat different from others on the David Warner vs Rohit Sharma incident. The problem is that a nuanced view doesn't fit neatly inside a 140 character window, and so my views have been missinterpreted. Part of that is because people seem to have very absolute views on the matter, when I don't think what happened is really very black and white.

First of all I'll talk about my system of ethics with sledging and other play, and what I consider acceptable, then I'll look at the Warner-Sharma confrontation specifically.

Sledging is an attempt to get a psychological advantage over another player. For me this is part of the game. However, there are limits to what is acceptable. Some examples of forms that are acceptable (in my opinion) are fielders encouraging the bowler in a way that the batsman can hear and that might get into a batsman's head. For example "That's 4 dot balls in a row now" "He's got no idea about the short one" "Look at how he's holding the bat with his bottom hand, I reckon his coach will have words with him about that afterwards. It's causing him to push the bottom of the bat in. I reckon a half volley outside off will see him nick out here." These comments make the batsman doubt either their technique or their form, and can cause them to play false shots.

Likewise batting advice to the batsman is acceptable, even if it's not always genuine. The below example (about 1:20 in) where Hadlee gives Botham some advice on how to play his bowling is a classic. Botham may well have been late on the shot because he was thinking about what Hadlee had said and had anticipated a different delivery.



I f the fielding side feel that the batsmen are doing something underhanded, such at taking a run when the ball was dead, they are entitled to express their displeasure to them.

The more interesting questions are what is unacceptable. Here is my list:

Threats of violence that don't involve the playing of the game. For example "I'm going to break your ribs with the next ball" is acceptable. Likewise "If those close fielders stay there, I'm going to still play my shots and they will get hurt." Both of these, however, need to be in context. A bowler/batsman shouldn't be randomly threatening violence willy-nilly, but in the heat of an exchange they are fine. "I'll see you in the car park afterwards and smash your face in," however is not acceptable.

Racial slurs are not acceptable. They are not acceptable directed at a player or spoken about a player. There's a story about some things that Shane Thompson said to Wasim Akram and Waqar Younis to try and goad them into bowling short at him (rather than yorkers) that are totally unacceptable things to have been said on a cricket pitch.

Abuse for the sake of it is unacceptable. This includes most (but not all) send-offs. There can be time for a witty send off, provided it is brief and concludes an ongoing conversation. Prolonged send-offs, especially abusive ones, are completely unacceptable.

Likewise abusing someone to get under their skin, without there being any relation to the game or without it being in the context of an ongoing conversation is not on. The way that Fleming subjected Smith to a torrent of nastiness when he arrived at the crease may have helped New Zealand tie the series, but it was not something that New Zealand fans should be proud of.

There are other difficult situations, but generally it is fine to sledge, provided it is done in a way that has a purpose, and doesn't cross the line into pure abuse.

Now lets look at the Warner-Sharma situation. Here's my summary of what happened, as far as I understood it.

1. Rohit Sharma was slightly outside his ground, as he's entitled to be.
2. David Warner threw the ball towards the stumps.
3. The ball was very wide of the mark, and (only just) missed Sharma, and then evaded Haddin.
4. Sharma and Raina then proceeded to run an overthrow.
5. Warner thought that the ball had deflected off Sharma and got angry that they ran an overthrow contrary to established protocol.
6. Warner told Sharma that he was unimpressed
7. Sharma said something to Warner in Hindi. Warner speaks a few words of Hindi and didn't understand the full message but was upset by what he did understand.
8. Warner shouted at Sharma to speak English.
9. Sharma repeated his message in English as the umpires separated the players.

The one key point here is number 2. David Warner is a fantastic fielder. He has produced a few blinding run outs from direct hits. One of the impressive things about his fielding is just how often he hits the stumps. Given his ability, the fact that he missed the stumps by about 3m from close range is peculiar. The fact that he almost hit Sharma was concerning. How off target it was can be seen by the fact that Haddin stepped twice, then dived full length, and still didn't get to the ball.

He thought that he had hit Sharma with the throw, and that, therefore, Sharma shouldn't take a run. He didn't appologise for hitting Sharma, which would normally happen. It makes me wonder if he was aiming to hit Sharma with the throw. For me that is the key thing that was wrong with that incident.

What Warner said after that was in keeping with his understanding that Sharma had taken a run he would not normally be entitled to take. Sharma speaking Hindi successfully got in the head of Warner, and I don't have a particular problem with that. Warner's reaction, likewise, was totally understandable in context. The only issue, and it's a big one, was if Warner deliberately tried to hit Sharma with the ball.

If (in the opinion of the match referee) he did, then it would be a level 2 offence and he should be banned for a couple of games. Instead Warner was charged with a level 1 offence for "using language or a gesture that is obscene, offensive or insulting." As he had been found guilty of a similar offense within the past 12 months it was automatically raised to a level 2 offense, but he received the minimum fine for that offence, of 50% of his match fee.

The thing that I don't understand is how he was found guilty of that at all. As far as I can see he didn't abuse Sharma, and he didn't use any offensive gestures that I could see. If Sharma had spoken to him in English, then asking him to "speak English" would have been offensive, but given that Sharma didn't actually speak English, it was a perfectly reasonable request (despite not being delivered in a particularly reasonable manner). In the verbal altercation, Warner and Sharma acted equally badly, but not nearly badly enough for a charge.

If the ICC Code of Conduct was applied correctly here, either Warner would have been charged with deliberately throwing the ball at Rohit Sharma or he wouldn't have been charged at all.

Maxwell joins Pringle

One of the matches that got me hooked on cricket was this one in Hobart in 1990, when Australia needed 2 runs to win going into the final over, and Chris Pringle bowled a maiden (assisted by a slightly dubious non-wide call early in the over).

Today, Glenn Maxwell has joined Pringle in the required last over maiden club by bowling Australia to victory in similar circumstances.

It was an incredible effort from Maxwell, but unfortunately, with the history of both Pakistan and of matches held in the Emirates, there is a sniff of suspicion about it.  I really hope that every player was making a genuine effort, as the result is a storybook one.

Warner in Perth

I have written before about how impressed I am with David Warner's running between wickets. I genuinely believe that he is one of the best at judging a run that I've seen.

Accordingly I was surprised to see that he had scored the same number of singles as boundaries in the second innings at Perth. I also heard the commentators describe it as a typical innings from Warner. It made me wonder if it actually was a typical innings.

First of all I looked at how Warner compared to other batsmen.  The method I chose to look at was to compare the boundary percentage (boundaries per delivery) and the activity rate (runs scored per non-boundary delivery). I filtered out any batsman who hadn't faced more than 650 deliveries since 2000, hadn't hit more than 50 fours and hadn't played in the past 2 years. I then put the rest of the batsmen on a single graph.

I divided up the batsmen into 4 categories. Aggressive, Block Bash, Pushers and Defensive. Close to the extremes of each group were players who have been reasonably successful.

In the defensive group are players like Rahul Dravid, Peter Fulton, Tino Mawoyo, Ed Cowan and JP Duminy.
Block Bash contains Angelo Mathews, Shane Watson, Chris Gayle and Yuvraj Singh.
Pushers includes Kane Williamson, Shiv Chanderpaul, Jonathan Trott and Thilan Samaraweera.
Aggressive include Ricky Ponting, Sachin Tendulkar, Darren Sammy and David Warner.

Warner has a higher activity rate than anyone in the list. But he also hits more boundaries than most batsmen.

 

Warner's activity rate for his career is 0.353 and his boundary rate is 9.3%. His innings in Perth lasted 140 deliveries. We would expect 13 boundaries, perhaps 12 fours and 1 six. Off the other 127 deliveries we would expect him to score 45 runs. Overall we would expect that he would be on about 99, rather than 112, so he scored slightly faster than we would expect, but the big difference was the make up of the innings.

Warner scored 80 runs in boundaries. That's about 40% more than we would normally expect him to get.

I used the same graph as above, to analyse Warner's other innings. I've included every innings where Warner has scored more than 30. I've drawn in lines to show which group the innings would have fit in.

We can see that Warner's innings does fit in with some of his other innings, but really is closer to the Block Bash quadrant than almost any of his other innings.

It was an interesting innings, because of the context and the opponent, but also because of the way that he scored the runs.