Cricket Scoring Across Nations: A Visualization-Based Investigation
2026-04-16
Cricket is a game of complexity. As the game has spread around the globe, largely as a result of British colonialism in the late 19th and early 20th centuries, nations have established their own style of play. Key in these strategies are the ways in which the pitches are manicured within each nation. Certain parts of the globe utilize different types of dirt, varying degrees of grass on top of the pitch, as well as other variables.
Plot 1
The first plot shows how many runs each nation scores in the average One-Day International match. You will notice that some of the countries most known for their prowess in Cricket (Australia, India, South Africa, Pakistan, England) differ markedly from the rest in their average score. This provides an appropriate introduction to the general perceptions we have of who performs well in games and who doesn’t.
Plot 2
Here, we make a decisive split. A key factor in understanding Cricket metrics from an international perspective is the way in which the ICC functions. The International Cricket Council contains within it “full member” nations and “associate” nations. The full members are the teams who have generally been playing the longest and have played at the highest level. Teams vary wildly even in this group, though. As you can see from Plot 2, the violin plot shows that more recent initiates to the full-member class (Ireland, Bangladesh, Afghanistan) have distinctly different shapes from the rest. Their scores are more concentrated around the middle of their distributions with very little capability to score high numbers of runs in a match.
As the teams who have been playing for a longer period of time have acclimated to different conditions and styles of play around the world, their offensive capabilities ultimately appear quite similar.
Plot 3
However, it is important to recognize that, for all nations, full member and associate, scoring rates have increased over time. Technology in new equipment has likely contributed to this, as well as the degree of exposure that players have gotten with new opportunities to play in privately-owned leagues. These TV-tailored leagues utilize the format of T20 Cricket, where each team is given 20 overs (think of this as 20 innings in a baseball game), a limited amount of time to score as many runs as possible. More traditional forms of Cricket, like the Test format that goes for a maximum of five day, function quite differently in that they reward slow play. One-Day International matches are somewhere in between (they have 50 overs per batting team), but the influence of these shorter matches are evident through this plot of increasing ODI scores worldwide from 2002 to 2023.
## # A tibble: 0 × 2
## # ℹ 2 variables: match_id <int>, n <int>Plot 4
One particularly interesting segment of Cricket data analysis has to do with the ways in which players are able to adapt to different locations around the globe. It is intuitive that players would perform better in the country that they are from, considering that they have come of age playing in that country. We can see this in the plot below, as more batters exhibit better average scores at home than they do away from home. However, you will notice that most players cling relatively tightly to the equal-average line. These players are exceptional at making adjustments and knowing exactly how to play in varying conditions.
Plot 5
Here is a plot that displays the fifteen best batters away from home within the data provided, according to the numerical difference between their away average and their home average. What you will notice is that certain teams are prevalent. The West Indies and Australia have four players in the top 15, and New Zealand has 3. Primarily, this has to do with the fact that these nations play a lot of Cricket every year, and so they give a lot more players opportunities. However, this heightened presence is also due to the nature of pitches in these countries. Pitches in the Caribbean and Oceania strike a balance in pitch moisture, the amount of grass present, and other variables so that players from these nations generally have the ability to play spin-dominant pitches (dry and dusty) and pace-dominant pitches (healthy green grass with the right amount of moisture) equally well.
Another interesting conclusion that can be drawn is that of the associate/recent full-member players. Ireland’s Paul Stirling and Scotland’s Richie Berrington rank quite well in locational adjustment for two reasons. First, associate nations like Scotland and recent member-states like Ireland don’t have a great degree of infrastructure, so they play at home relatively infrequently. The number of professional-grade Cricket grounds in each nation is relatively slim. Second, they tend to play the same nations very often, allowing players to adapt very well to those particular locations.
Plot 6
Here, we see the other end of the spectrum. These are the fifteen worst batters away from home. You will notice that South Asian countries figure more prominently in this plot simply because their pitches are significantly more homogeneous and spin-oriented. Players often struggle when they journey to fast pitches in England, Australia, or the West Indies, for instance. You’ll also notice that South Africa and Zimbabwe have significant representation. Their pitches are quite unique due to the nature of the soil in sub-Saharan Africa, and the way in which matches unfold can vary wildly from region to region. Some players may grow up becoming fast bowlers or batsmen who are comfortable with pace on the ball, while others may mature within a spin-oriented game.
Plot 7
Within each match, there are three stages of play within each batting innings. The first ten overs are known as the powerplay because there are fielding restrictions that allow for players to swing more freely and score more runs. Overs 11-40 are the middle overs, and this is generally when scoring begins to slow down as fielders move out to the boundaries and teams bring in slower spin bowlers. In the final 10 overs, known as the death overs, scoring increases despite far-flung fielders as the batting team attempts to make one last push towards a competitive run total. However, this is all contingent upon the nature of the pitch, and this plot shows just how the distribution of scoring across phase differs from one nation to another.
Even the minor average differences shown in the plot can dramatically alter the way a team goes about setting strategy. You can see, once again, that Australia and New Zealand get very similar results, England and Ireland are nearly identical. Zimbabwe and South Africa also bear quite the resemblance to one another. What is interesting in this plot is that the South Asian nations appear to vary the most. Pakistan, in particular, has the highest proportion of runs in the death. This makes sense, considering that Pakistani pitches are known to deteriorate quite rapidly, leaving a lot of the conditions that fast bowlers can take advantage of in the powerplay nearly nonexistent by the time that the game enters the late stages.
## # A tibble: 0 × 2
## # ℹ 2 variables: match_id <int>, n <int>
Plot 8
This brings us to another vital lesson in Cricket. Bowling success is also effected by the pitch. Whether a bowler is fast (think of a pitcher who essentially only throws different variations of a fastball at 85-95 MPH) or a spinner (think of a pitcher who only throws curveballs) is a huge indicator of whether they will have success in a given location. Here is a Shiny App that allows a user to look at each host nation and the distribution between wickets taken from pace vs. wickets taken from spin.
Plot 9
With all this being said, Cricket does not simply have to do with a player’s surroundings. It also has to do with an individual’s skill! There have been great players from every nation, and some not-so-great players as well. Here is a Shiny App that allows you to investigate each player’s statistics across match locations, not only from averages but from every match they have played.