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:

Relative team strengths 2000-2020 based on runs per wicket batting and bowling. eg. Australia would expect to outscore England by 26% in a Test on neutral territory.

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):

Actual RPW ratios 2000-2020. For example, Australia outscored England by 45% when at home, while England were outscored by 13% when hosting Australia. Minimum 50 wickets – blanks reflect a lack of data.

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.


  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:

Home advantage (%) for specific pairs of teams – after adjustments made by me. For example, Australia get a 25% boost hosting Bangladesh

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.

Test batting: do some players gain an extra boost from playing at home?

David Warner seems to have a preference for familiar conditions. After 82 Tests he averages 66 at home, 33 away. 2019 has been a rollercoaster: averaging 9.5 touring England, then dismissed just three times amassing 551 runs in Australia.

Would we expect that trend to continue? No. I’ll exhibit two bits of evidence against some players being disproportionately dominant at home. Firstly tracking a recent crop of players, and secondly by demonstrating that the great players in home conditions are what we would expect from chance.

Recent History

We consider players that did relatively well at home up to a point in time (31/12/2016), and see if this continued, or if they regressed to the mean.

Fig 1 – Home and Away batting averages in Test Cricket. Split before and after 31st December 2016. Min 10 completed innings Home and Away pre 31/12/16, min 20 completed innings Home and Away post 31/12/16. Home advantage means the average player’s ratio is just under 1.2.

The above table indicates Home : Away Average Ratio (HAAR) history is a poor predictor of future returns. Elgar was great then OK. Amla was rubbish then brilliant. Plotting the data shows just how scattered the 12 data points are.

Fig 2 – Home to Away average ratios batting in Test Cricket. Split before and after 31st December 2016. Min 10 completed innings Home and Away pre 31/12/16, min 20 completed innings Home and Away post 31/12/16.

Putting it another way, if you had spent your Christmas 2016 holiday seeking home ground heroes, you would have been wasting your time*. Pujara, Broad and Elgar had HAAR ratios around 2 (just like Warner does now), but past performance is no guarantee of future success – all three of them subsequently performed no better than average.

And the players that favoured touring? Three of the four who were stronger away pre-31/12/16 flipped to subsequently be better at home. The one exception was Ben Stokes: in his career he averages 36 at home and 38 away. Take that nugget with a pinch of salt: if Stokes is better on tour why does he average 44% more batting at home in ODIs?

All time

Now to compare HAARs for Test cricket’s highers runscorers vs the theoretical distribution after 50 innings at home and 50 away:

Fig 3 – Actual Home:Away Average Ratios for the top 200 runscorers in Test cricket, compared with a simulation of 50 innings at home and 50 away.

Randomness plays a huge part (possibly up to 100%) in explaining the variation in Home:Away Average Ratios of Test cricketers.

There are other factors I’ve not included (for instance, a player might only struggle in swinging conditions). If there are specific cases where you think a player thrives only at home (or away), then please let me know.

Where does this leave us? Hopefully (for Warner’s sake) he has a few more years of Test cricket in him. That would also be useful for this blog – I look forward to reporting at the end of 2021 that Warner’s HAAR over the last two years has been the standard 1.2, and that past outperformance at home is no guarantee of future success.

*An aside – there’s a line from I Robot “I’m sorry: my responses are limited – you must ask the right questions”. While I wouldn’t normally take lessons from fictional holograms, I like the message in this. You can do decent-looking research, but if you start with the wrong question you’ll be wasting your time. In this example, “who are the best batsmen in home conditions” is the wrong question, one should ask “is there anything special about the ratio of a batsman’s home average to their away average?”

Debutants in Away Tests have shorter careers

Would you expect players to be disadvantaged by making their debut overseas? Surely the best players get picked and have a decent run in the side until there’s sufficient data to disprove the analysis that got them selected in the first place?

Afraid not. Away Debutants are discriminated against! Debut at home you can expect a nine Test career. If your first game is an away match, that drops to six.

Fig 1 – Average (Mean) and Median Tests played by location of Debut. Includes top nine Test teams, since 2005.

A reminder – home advantage in Test Cricket is big. Somewhere around 17%, depending on how you cut the data. If your expected batting average is 35, that’s 38 at home and 32 away. A player who starts their career overseas is likely to underperform, and is at greater risk of being dropped when the naïve assertion is made “OK, they have a decent First Class Average, but they are only averaging 29 in Tests.”

Half of Away Debutants don’t make it to seven Tests. And yet the mean number of Tests played by Home Debutants is only 1.1 matches more than Away Debutants. For some reason the early benefit to Home Debutants doesn’t persist. What happens after seven Tests to explain that?

Fig 2 – Frequencies of Number of Tests played. Includes top nine Test teams, since 2005.

The behaviour flips – from Tests 7-20 more Home Debutants are discarded than Away Debutants. I expect that this is because some players who had an easy home series to get into Test Cricket then get caught out when away from home.

After 20 Tests, a player has generally played a similar numbers of home and away Tests, so there’s no great difference between the two curves.

So What?

  1. Some Away Debutants play fewer Tests than they deserve. Conversely, some Home Debutants are kept in the team longer than they should be as a result of the stats boost they get from playing more home Tests than away.
  2. It’s time to move on from raw averages. Adjusted averages are the future. Not just adjusted for home/away, but also the ground they are playing on (think Headingley vs The Oval), the quality of opposition and the innings number. This is not a complicated task, and I’d be very surprised if it isn’t already happening behind closed doors. Admittedly I haven’t yet done this when rating Test players. But then, this is a hobby for me. Also, until a player has played 20 matches, I use their First Class average to appraise them. Which is coincidentally the point at which Debut Location ceases to matter as an input.
  3. Don’t make your Test Debut in an away game if you can help it. I appreciate this is not practical advice, so instead, if anyone reading this has made their Debut in an away game, make sure you quote your home/away adjusted average whenever possible! Ebadot Hossain, am looking at you.

It’s almost the same story for ODIs

A quick calculation says Home Advantage in ODIs is c.11%, so we would expect ODI debutants to have similar trends to Tests. Which is true for matches 0-20: Away Debutants are more readily discarded after a handful of games, then Home Debutants are in the firing line from 4-20 matches.

Fig 3 – Frequencies of Number of ODIs played. Includes top nine ODI teams, since 2005.

After 20 matches it gets more interesting. Overall, Away Debutants have greater longevity on both a mean and median basis. Of the Post-2005 players with more than 100 ODI Caps, 16 began at Home, 22 began Away.

Fig 4 – Average (Mean) and Median ODIs played by location of Debut. Includes top nine teams, since 2005.

What the dickens? I can’t confidently explain this. Could have hidden it from you, but it’s interesting and therefore worth sharing, even if I don’t understand it. I’ll offer one possibility: ODI series are often tacked onto Test series, so in an away series the star Test players stay on for the ODIs, meaning that only highly regarded red ball players make the team. At home, the top Test players can more easily be rested, so lesser known players might get a go.

The Short List: Away Test Debutants

Below is the list of players that played fewer than seven Tests, and started away from home. Have a read, see if you can pick out some players who might have had 20 Tests if given the benefit of a home debut. Luke Ronchi and Owais Shah jump out at me.

Fig 5 – Players to Debut away from home since 2005 and play fewer than seven Tests. Data implies 20 of these players would have played 20 Tests if they had debuted at home.

“Royal London One Day Cup – Group Stage Review” or “Notts and Hants FTW”

If imitation is the sincerest form of flattery, I have something of a crush on International Cricket Captain. Much of the modelling I’ve done is an attempt to recreate what that game could do in simulating whole matches in the blink of an eye. Here is a link to the International Cricket Captain website, if you think you might have 300 hours to kill this summer.

There are two parts of the International Cricket Captain engine I’ve not incorporated: Form and Fatigue. I don’t believe in form and won’t incorporate it until it shows up in the numbers (if the facts change, I’ll change my mind). Let’s look at fatigue instead…


Fixture congestion is nothing new – who can forget 1066, when Harold II’s middle order collapsed at Sussex just 19 days after an attritional fixture on a Yorkshire out-ground.

The Royal London One Day Cup (RLODC) has a punishing schedule – most matches are played less than 48 hours after the last one finished. Some teams get longer breaks- which means we have tired players against slightly less tired ones. This gives us some tasty data to measure the impact of fatigue.

Before we get into the numbers, I’d like to define the tiredness in question – it’s mid-week weariness. Not the short term fatigue that means that as a bowler goes through a spell their effectiveness drops, nor the possibility of long term decline over a season from a relentless schedule. This tiredness is like the mid-music-festival malaise one might experience on the Saturday of Glastonbury, when the preceding days take their toll.

To define a “fatigue factor” we need to see how players fare when one team has had more rest than the other.


Factors affecting RLODC Team Performance

  • Home Advantage: Home team gains 0.13 runs per over. Away team loses 0.13 runs per over. Net effect on a match 13 runs. I wasn’t specifically looking for this, but had to analyse it as a factor that needed to be controlled for before conclude on Fatigue.
  • Fatigue: Batting team better rested gains 0.23 runs per over. More rested Bowlers concede 0.23 fewer runs per over. Maximum impact on a match 23 runs.

Implications i. 2019 RLODC

Fatigue has an interesting effect on the semi finals: the winners of the North and South groups host the winners of quarter finals between the teams which finished second and third in the groups. The quarter finals take place on the 10th May 2019, the semi finals on the 12th May 2019.

Nottinghamshire and Hampshire have been the best teams in the group stage, and will have both home advantage and the benefit of >6 days rest, rather than the two days of rest the quarter finalists have.

I will running these extra inputs through my 50 over model this weekend to see if this insight offers any gambling opportunities. My expectation is that I’m late to the party on this, and the odds will already factor in rest periods and home advantage.

Implications ii. Selection

In a tournament like the RLODC, we should see more rotation of bowlers in and out of the team, particularly if a squad has bowling depth. Sussex only used eight bowlers in as many matches: who knows whether giving Hamza a day off might have been the difference that got them into the quarter finals, instead of mid-table disappointment. Just imagine if Sussex had had Chris Jordan available to them for the second half of the group stages, rather than on England duty.

Further Reading

Green All Over – Betting Blog, see link for a post on the impact of rest on Baseball odds (which reminded me that there was a potential input I was ignoring).

No winning on Tour

Tours are strange beasts. Anyone who has ever been on a Club Rugby tour can attest that pre-match preparation isn’t entirely conducive to peak performance.

Professional sport should be the opposite of this. Next time you are watching Cricket on TV and they cut to the pavilion balcony, count how many non-playing staff are on hand. I’m not criticising touring parties for being too large – I’ve no data to assess that on. My point is that lots of money is spent by governing bodies to ensure enough specialists are on hand to keep eleven cricketers playing at their best.

Here’s a theory – all this investment in the extra 1% is missing the wood for the trees. The tour scheduling is an unseen problem.

Recall the post-before-last regarding Home Advantage growing as a series goes on, and your correspondent having an effect with no obvious cause? Going through the archives of @Chrisps01’s blog was a possible clue to this – [link] – some analysis on rest periods between matches. A quick re-cut of the data and I could quantitatively look at this effect with two decades’ worth of data.

There’s a certain base advantage in the first Test of a series, which is kept at the same level if subsequent Tests are played back-to-back (ie with less than a seven day gap between matches). Away teams are at a much bigger disadvantage when there is a longer gap between Tests.

Think back to summer 2017 – on August 29th West Indies beat England by five wickets to square the series with just the Lord’s Test to come. On September 2nd & 3rd the full strength West Indies team toiled in a meaningless draw against Leicestershire. England rested. West Indies put up little resistance in the third Test, scoring just 300 runs over two innings.

Why might away teams struggle with longer gaps between Tests? Here’s how I rationalise it:

  1. With very short gaps between Tests, both teams are fully focused on recovery and getting the XI back ready to play the next Test. Both teams are therefore doing the same things and so no team gains an advantage over the other.
  2. Longer gaps between Tests mean tour matches for the away team, and (in the modern era) rest for the home team. Even if not all of the team are involved in a tour match, the focus of the touring party is likely to be distracted by a competitive fixture.
  3. Players for the host team may get the opportunity to go home for a few days during a break in the series – the away team will still be living in hotels.
  4. The data implies that the home team’s activities result in better performance in the next Test.

Touring teams should revisit their itinerary so they are best placed to compete throughout a series: plenty of rest, no meaningless mid-series tour matches.

Home Advantage in Test Cricket

Home advantage exists across many sports, and Cricket is no exception. Each sport has its own factors driving home advantage (1).

It’s a fascinating theme, and I plan to explore it via a series of posts, building a picture of Home advantage in Test Cricket.

In this first piece we’ll start with the magnitude of home advantage, and look at how teams fare at the start of a series in this era of condensed tours with limited match practice.

Measuring Home Advantage

So how big is home advantage? Eight of the last ten Ashes series have been won by the hosts. Casting the net a bit wider, including all Tests since 2000, we can be a bit more precise and measure home advantage a number of ways:

Figure 1: Five measures of Home Advantage. All figures presented from the home team’s perspective. Tests since 1st Jan 2000, excluding Zimbabwe, Bangladesh, Afghanistan & Ireland

The key metric is the 14% difference in runs per wicket between home and away teams. All other effects are a consequence of that. Take a player with a theoretical average of 35 – at home he’ll average 37.4; away that drops to 32.6. Over the course of an average match the 17% difference translates to a 63 run total edge to the home team, which in turn means roughly twice as many home wins as away wins in matches & series.

The example of Rory Burns illustrates the effect of Away games: his county stats are excellent, but he has played six Tests, all away, and averages 25. It will take a while for his average to tick up from there, assuming he gets the opportunity. How much easier life could be if he’d started with a home series! I’ll wager that there are players whose careers stalled because they debuted away from home, and were lumbered with averages that would mark them as not-quite-good-enough. At present that’s just conjecture, it’s on the list for me to return to at a later date.

Home advantage gets bigger as a series goes on

My intention was to look at series of 3+ Tests and show that tourists were coming unstuck in the first Test (fail to prepare, prepare to fail) and then acclimatising and improving. Easy piece of analysis, right? What follows are multiple attempts to show it, and finding the opposite effect: Home advantage gets bigger as a series goes on

Here’s the Test-by-Test view:

Figure 2: 1st Jan 2000 – 9th Feb 2019, Home advantage in series of at least three Tests. Percentage advantage refers to the differential in Runs per Wicket. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland. Note the large sample sizes.

Home advantage grows though a series. The increase is insignificant from first to second Test, before jumping for later Tests of the same series. This is marked by a significant decline in away runs per wicket in later Tests in a series. Scoring 2.2 runs fewer per wicket in the later Tests is roughly the equivalent of replacing Tim Southee with a breadstick (in terms of batting contribution).

What does that mean for results? Well, if you are planning to follow your team abroad, you’d be wise to go to the early Tests in the series:

Figure 3: Home Wins increase noticeably for the third (and subsequent) Tests in a series. 1st Jan 2000 – 9th Feb 2019. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland

Worth noting that the extra home wins later in the series come from both fewer draws and fewer away wins.

Now let’s consider first Test home advantage compares to the rest of that series (by country):

Figure 4: Relative home advantage in the first Test of a 3+ Test series as compared to the rest of that series. UAE not treated as a home ground for Pakistan. Victories by wickets are translated to runs based on the average fourth innings score. Draws are recorded as nil. 1st Jan 2000 – 9th Feb 2019. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland

Generally, home advantage is actually weaker in the first Test than later matches. But note the ‘Gabba effect in Australia – this traditional series opener is especially suited to players with experience in Australian conditions. That’s the exception – in most cases, home teams have more success later in the series.

Still not convinced? One more chart, and if you’re still not convinced you can give me both barrels on twitter (@edmundbayliss) and tell me I’m wrong!

Figure 5: Home advantage by match of series. 1st Jan 2000 – 9th Feb 2019. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland

There’s a predictable trend in Figure 5: home advantage has grown over time.


Let’s recap – home advantage is worth 12% in the first two Tests of a series, and 18% in the later Tests.

Why should this be? Three hunches:

  • Away teams find themselves behind in the series; selectors panic. Perhaps a 21-year-old batsman get picked, or an unbalanced side is selected in the hope of turning the tide. Keaton Jennings being recalled to replace Foakes (a better batsman) in the recent West Indies tour is a neat example of muddled thinking
  • Modern players don’t spend much time in home conditions, but built their technique there. Playing a lengthy series allows home players to reintroduce tried and tested ways of playing. Away teams don’t have that luxury, and can’t expect to make technical changes mid-series.
  • Fatigue: a small squad gets run into the ground by back to back matches.

So, there we have it – home advantage is significant and grows as a series goes on. More analysis is needed to establish why this is the case.

Further Reading

  1. – an excellent summary by Professor David Runciman of home advantage across sports.
  2. For a thought provoking piece of analysis on modern Cricket see Tim Wigmore’s article on Cricinfo
  3. A summary of recent England tours: comparing the warm up conditions with performance in their first innings


Dan Weston (@SAAdvantage) suggested that matches after the series had already been decided could be a factor that hadn’t been taken into account:

To exclude just the “Dead Rubber” games would distort the home advantage effect, because to do so would include only the early matches in those series (probably won convincingly by the home team). The right response is to ignore all matches in a series where that series ends in a “Dead Rubber”.

Figure 7: Home advantage measured in runs, both including and excluding series that are decided before the last match of the series. Note that excluding the one sided series reduces the sample size to roughly 300 Tests- so there’s a bit more volatility between first and second Tests in the series. This is likely to be by chance, rather than a genuine effect.

Excluding one-sided series shows lower home advantage (because it excludes big home wins when a visiting team can’t compete with a superior host team). The overall effect is the same though- home advantage gets markedly bigger in the later Tests.