The 2021 ICC Men's T20 World Cup is almost upon us. The 7th edition of the tournament, and first since 2016, gets under way this weekend as Oman play against Papua New Guinea, followed by Bangladesh vs Scotland. England and India are generally considered the favourites to win the tournament, but the likes of Australia and New Zealand are also considered to be in with a shout, along with two time winners and current holders West Indies.
In this article we attempt to predict the outcome of the tournament by way of Monte Carlo simulation, which involves simulating the tournament many times (10,000 in our case) and aggregating the results. This then allows us to derive quantitative measurements for how each team can be expected to perform in the tournament.
From there we compare our results to the odds provided by the bookmakers, which then facilitates a discussion about the pros and cons of the model, and also gives some food for thought around the cricket itself.
Assessing Team Strength
The starting point for simulating a competition such as this is to derive ratings for each team, in order to get a quantitative measure of the competitors' respective strengths. For this we use the Bradley-Terry model, which has been used extensively elsewhere on the site. Using a complete set of results from some pre-determined period of time, the output of the Bradley-Terry model gives a coefficient representing the quality of each team over this time period, and these coefficients can in turn be used to simulate an outcome of a contest between any pair of teams.
The raw abilities are not sufficient to provide a reliable simulation of the tournament, however, as there will always be external factors likely to have an impact on how any given sporting competition plays out. Fortunately, the Bradley-Terry model is flexible and allows us to control for such external factors. Typically, when we do something like this we will include a factor for home advantage, which allows us to apply a 'home advantage' coefficient to a home side when we are trying to predict a match.
Unfortunately, that doesn't really apply here, since Oman are the only side in the tournament to have any games at home at all, and even then it is only in their preliminary group - in the unlikely event that they did get through then all of their remaining games would be played in U.A.E.. Since U.A.E didn't qualify, this means that every other game at the tournament will be played at a 'neutral' venue. This then means that no single side can be said to enjoy home advantage - at least in the literal sense of the term.
We do know, however, that conditions in both Oman and U.A.E are likely to favour the other Asian countries, as the pitches will be most similar to what those sides have been brought up on and are used to playing on. As such, we have applied an 'order effect' to our Bradley-Terry model, which quantifies the advantage that Asian sides gain when they play in Asian conditions against opponents from elsewhere.
The below table gives the coefficients as derived from our Bradley-Terry model, run on all T20I fixtures since the beginning of 2017. The figures on the left are the 'raw' ability ratings, and can be read as the overall strength of a team without any influencing external factors. The figures on the right are those where we have added what we'll term the Asian Coefficient to the raw ability ratings of the Asian sides to gain an understanding of how the teams are likely to stack up in a tournament in Asian conditions.
The Asian Coefficient sits at 0.21, which is small but by no means insignificant - we know that the shorter nature of T20 cricket means that home advantage in general is not as great as it in the longer formats, but in this case it is enough to see India, Afghanistan and Sri Lanka all gain a spot in the team ratings for this tournament. In practice this means, for example, that whilst our model considers South Africa as a stronger side than Afghanistan, the Asian Coefficient means that should they play each other in the upcoming tournament our model would actually favour an Afghanistan win.
Simulating the Tournament
As we noted earlier, these coefficients can now be used to simulate an outcome of a contest between any pair of teams, which is done using the inverse logit function run on the difference between the ratings of the respective sides. This single match simulator can then provide the basis of a full tournament simulator.
The tournament commences with round 1, which is something of a preliminary round as it doesn't involve many of the major sides. It comprises of two groups, each of four teams: Group A, which contains Ireland, Namibia, Netherlands and Sri Lanka and Group B, which contains Bangladesh, Oman, Papua New Guinea and Scotland.
The winner of Group A (A1) and the runner-up of Group B (B2) will join Australia, England, South Africa and West Indies in Group 1. Similarly the winner of Group B (B1) and the runner-up of Group A (A2) will join India, Pakistan, New Zealand and Afghanistan in Group 2. A slight quirk of the tournament is that should either of Sri Lanka or Bangladesh finish as runner-up in their first group phase, they will still be assigned as either A1 or B1 respectively.
Our simulation captures all of the above, but there is one slight difference around how we deal with teams who are tied on points in the group phase. The tie-breaker that will actually be used is Net Run Rate, but we do not have this information available since we are only simulating a match winner, and not the winning margin. Instead, we simply randomise the order of any set of sides who finish on equal points in the same group.
Once everyone has played each other in each of Groups 1 and 2, the winners from each group play against the runners-up from the opposite group in the semi-final, and the winners of those semi-finals will, of course, meet each other in the final.
This tournament simulator was run 10,000 times, with the results aggregated below:
It's no surprise to see India at the top here - with a 29% chance of victory - given that, with the Asian Coefficient on their side, they were ranked as the strongest team in these conditions. 2010 winners England are next favourites, with a 22% chance of claiming their second Men's T20I World Cup crown, followed closely by 2009 champions Pakistan. The top 5 are rounded out by New Zealand and Australia, which means that our top 5 favourites align exactly with the top 5 sides according to our team ratings.
Just below the top 5 there is a slight difference in the win probabilities compared to how we ranked them. Our model makes South Africa and Sri Lanka as 6th and 7th favourites respectively despite only ranking the as the 7th and 8th strongest sides. This is due to them being on the 'easier' side of the draw - playing England, Australia and West Indies in their group, whereas Afghanistan (8th favourites but 6th strongest team) have to face 3 of our top 4 ranked sides in India, Pakistan and New Zealand.
We'll discuss the prospects of the potential frontrunners in more detail below, but it's worth just taking a moment to look at how the six qualifiers might get on. Both Scotland and Ireland will fancy their chances of reaching the second group phase, but will face competition from the likes of Papua New Guinea and Namibia respectively. Netherlands, who did make it past the first phase back in 2014, look to have their work cut out if they want a repeat, and Oman bring up the rear as by far the weakest team in the tournament despite playing at home.
None of these teams are given any real hope of making it into the knockout stages but a handful of our 10,000 simulations did throw up a few surprises. All of these sides managed at least 2 semi-final appearances with Scotland having the most at 17, followed by Ireland with 11. Neither managed to win any of those semi-finals but, incredibly, we did see Namibia reach two finals - albeit sadly losing out on both occasions.
Assessing our Predictions
It's impossible to know - even after the event has happened - how accurate predictions of this type will be, even if in this case they do feel roughly in line with what we would expect. What we can do, however, is compare and contrast them to another set of predictions for the same event, which can often throw up some interesting discussion points.
In this case, we have compared our win % probability as calculated by our simulation model to the implied chances of winning based on the odds (as of Monday 11th October 2021) from a specific bookmaker. The teams above the line are those that our model believes that the bookies are 'over-rating' and the sides under the line are those that our model thinks the bookies are under-rating:
It's worth noting now that the purpose of this analysis is not to look for value in the betting market, and we don't recommend anyone use the above as a guide if they do want to gamble on the tournament. Our model is relatively simple and the computation that goes into calculating the bookmakers' odds is likely far more complex and uses many more variables. Comparing the two as we have done just gives us a yardstick by which to assess our predictions, and any discrepancies can then be used as a springboard for further discussion about the respective teams.
Looking at the chart now, and first of all we can clearly see that, whoever we ask, India and England are the two favourites for this tournament. They are both in terrific form in the T20 format and also occupy the top two spots in the world rankings. Whilst England are arguably the stronger side (according to both our model and the ICC World Rankings), India's advantage in Asian conditions earn them the tag of outright favourites.
Whilst our model and the bookmakers broadly agree on most of the sides, there are a handful of outliers that merit further discussion. The most striking of these are the defending champions, West Indies - considered as joint-third favourites by the bookmakers, with an implied probability of winning sitting at 12%. This analysis, however, saw them win a measly 1% of our 10,000 simulations.
On the face of it, it's easy to see why they would be considered as one of the favourites. Firstly, they are the defending champions, and the only side to have won the tournament on more than once occasion. Secondly, their squad contains some of the most destructive players in world cricket, including the likes of Kieron Pollard, Andre Russell and, of course, the legendary Chris Gayle.
Our model, however, ranks them as the 8th best team in the competition, and they drop to 9th when we factor in the Asian Coefficient. This is supported by the ICC rankings, that also sees them in 9th spot. There isn't actually any great mystery here - this is simply due to very poor form in T20Is over the past few years. Since the start of 2019, they have lost series to England, India, Afghanistan, New Zealand, South Africa and Pakistan, with many of those defeats coming at home. They also only managed a 1-1 draw with Ireland in January 2020.
There is some mitigation here, as they would not have had a full strength side out for all of these series, but all three of Pollard, Russell and Gayle did feature in the series defeat at home to South Africa just a few months ago. There is an argument to suggest that the IPL form of each sides' respective players might be more relevant - especially given the similarity in the conditions to where the tournament will be played - and in that sense there may be some encouragement for Windies fans, particularly with the form of Shimron Hetmyer.
That being said, the fact is the West Indies have not performed well as a T20 team for some time now, and they will have to turn things around very quickly if they are to justify their position as one of the bookmakers' favourites.
It's a similar story with Australia, although not quite as extreme. They are the joint-third favourites with West Indies - with an implied win probability of 12%, but won in just 7% of our simulations.
Again, this is down to some poor recent results, particularly over the last couple of years. The same caveats around rotated teams and IPL form still hold true, but you suspect Australia would rather not be going into this competition on the back of a heavy 4-1 series defeat to Bangladesh.
On a more positive note, we see Pakistan being touted as third favourites by our simulation, winning the tournament 20% of the time. This is more than double what the bookies think, who are only giving them a roughly 8% chance of glory.
It's tough to see why Pakistan aren't more fancied by the bookmakers. Their performances have been consistently strong for a number of years now, which is reflected in their ICC T20 Ranking (3rd) and they are more familiar than any other side with conditions in the U.A.E., having played their 'home' games there for a number of years. They also have pedigree in this competition, having won it in 2009 and reached the semi-finals on three further occasions.
Our model takes both their strong recent form and the favourable conditions into account, and seems to indicate they've a good chance of winning the thing. Perhaps the bookmakers think they lack the consistency to be truly considered a contender, but time will tell!
Thanks for reading! During the tournament we will be applying our player ratings model to assess who the top performers have been, so if it sounds of interest please keep an eye out for that! Otherwise, if you liked this please check out some of the other articles on the site or follow the Twitter!
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