# Country vs Country matchups in Test Cricket

It’s naive to assume that England will play as well in Mohali as they do in Melbourne.

But how to measure this? Results are misleading: a 20 run win is not as dominant as a 220 run one. Hence runs per wicket (RPW) is the best approach.

We should adjust for the relative strength of teams: Bangladesh have lost all four games in England this century – but is that purely because of the gulf in talent? If Bangladesh were as good as England, how much would they lose expect to lose by because they were playing in English conditions?

Here’s my approach: use all data since 2000 to calculate the number of runs per wicket scored by each team, and the equivalent conceded when fielding. Comparing runs per wicket when fielding to the average team gives a measure of each team’s bowling strength (eg. India’s 32 makes them 3% better than the average fielding team). New Zealand average 31 runs per wicket batting, so we would expect New Zealand to score 31 * 0.97 = 30.1 runs per wicket when playing India.

Repeating that for every pair of teams gives a set of ratios of relative strength:

See where we’re going with this? Now all we need to do is compare actual relative runs per wicket when the countries play each other to get specific country vs country matchups.

Here are the actual RPW ratios when the home team (first column) plays a specific away team (first row):

Let’s take stock. When Australia host West Indies they’ve dominated them – scoring 2.09 runs for every run scored by West Indies. Most of this can be explained by Australia being 73% better than West Indies. The remainder is from conditions and player-on-player matchups. Even if West Indies were able to field a team as strong as Australia, they would still be outscored by 2.09 / 1.73 = 1.21 times (or 21%) playing in Australia.

That 21% happens to be the average Home Advantage over the last 20 years. For the penultimate table, I’ll take the ratio of the first two tables, and adjust for the “normal” 21% home advantage to be left with specific additional adjustment factors for when two teams play each other.

This is noisy- reds and greens everywhere. Time for some judgement: I don’t think one can rely on the data for pairs of countries, because for some pairs of teams there just aren’t enough games. Instead I’ve grouped teams to pick up bigger trends.

### Findings

1. India & Australia get an average 27% home advantage (for most teams it’s 21%)
2. Asian teams in SENA (South Africa, England, New Zealand, Australia) countries do on average 10% worse than expected.
3. Sri Lanka don’t travel well

Based on that, here’s an adjusted version:

This analysis is crude. I’m not totally persuaded by it (yet). Such as why are New Zealand terrible in South Africa, when crudely similar teams like Australia and England do well there? Would we expect that trend to continue? Is it too reductive to assign characteristics to nations rather than specific players?* Perhaps, but if it helps understand why teams are winning then I’ll use it.

For instance, South Africa have a habit of beating England in England. This could be because conditions are similar in the two countries, so England lack their usual home advantage.

I’ll keep an eye on this in 2021. The four remaining series in 2020/21 are all fairly normal for home advantage. Relevant to the World Test Championship final, it’s worth noting the raw data for India and Pakistan in England hints that the location of the final suits Pakistan more than India.

Another good test for this approach will be India touring England next summer. Is this Indian team (armed with Bumrah), sufficiently talented in the pace department to avenge the 4-1 defeat from 2018? If so, that will hint that Team A being forever doomed touring Team B is twaddle.

*There’s a part of me that finds this analysis distasteful too – assigning characteristics to a whole nation.