Red and White Ducks

How does “getting your eye in” differ between Cricket’s formats? One way to measure it is through the proportion of ducks: more ducks implies it’s harder to get started.

An academic paper was brought to my attention recently. The article focused on ranking Test batsmen across eras, though what I got out of it were the ideas stimulated by reading through the full methodology. I added three entries to the “Blog Ideas” note on my phone. The first of these follows.

The academics needed to adjust for the disproportionate number of ducks in Test Cricket relative to a Geometric progression. How disproportionate? Ducks are roughly two-and-a-half times more frequent than theory would suggest. It’s hard to get one’s eye in.

What about the difference between red and white ball duck frequencies?

Fig 1 – Proportion of Ducks in Test Cricket vs Average (Top six batsmen only). Note how much more frequent Test ducks are relative to ODIs.
Fig 2 – Proportion of Ducks in the 2019 County Championship vs One Day Cup.

There’s an apparent contradiction here: in limited overs Cricket there is pressure to score from ball one, which should carry more risk – yet it’s easier to get off the mark in ODIs than in Tests. For batsmen averaging 35-40, in ODIs 6.8% of innings are ducks, while for Tests 7.5% of innings are ducks – a 10% greater frequency.

The only explanation I can offer is the defensive mindset of fielding captains in ODIs.

Conclusion: Bring the field in and have more catchers when a batsman is on nought in ODIs. I know it then looks weak to change the field after a couple of balls – but there is a clear opportunity.

Post Script: Opening the batting is easy

It should be harder to get off the mark in difficult conditions.

It should be that openers get out on nought more often than middle order batsman (if they have a similar average). Yet the opposite is true.

Here’s the chart:

Fig 3 – Proportion of Ducks in Test Cricket vs Average by batting position. To avoid cluttering the chart, I’ve not shown the lines for batsmen four or five. These are broadly in line with the ratio for openers (yes, that makes for a slightly less interesting chart).

I think this is because openers face very attacking fields, with lots of slips, so any bat on ball should find a gap (as long as you aren’t out caught!)

Comparing this to ODIs gives a sense of how openers stand out in Tests

Fig 4 – Proportion of Ducks in ODI Cricket vs Average by batting position.

Northamptonshire’s Promotion Drivers

Northamptonshire were at odds of 34-1 to win Division Two before the 2019 season began. They had a lot of work to do to get into the top three.

Things didn’t get any better when Ben Cotton was released after not managing to “reach fitness targets”.

I had them as the sixth best team in early April. Here’s what I said on the twitter:

Not a bad side, but off the pace of the top of Division 2. A batsman light, better balanced with Bavuma replacing Holder on 14 May. A shallow squad – @NorthantsCCC may have to prioritise the competitions where they have the best chance of progressing.

Now they are on the cusp of reaching Division One. Just four points from their game against Gloucestershire will secure promotion. What happened?

1.Batting outperformance

Fig 1 – Northants performance vs expectation, for an XI of the players to have featured in the most matches. Expectations based on 2016-18 red ball data.

Ricardo Vasconcelos, Adam Rossington, Rob Keogh, Nathan Buck all averaged ten or more runs above expectation.

The team as a whole averaged 53 runs per innings more than expected. I think that Rossington, Keogh and Buck had good years, and wouldn’t be expected to repeat that in 2020. Vasconcelos though. 21 years old, already has a First Class average of 37. How good could he be in a few years time?

It’s rare for a team to just have one player underperform with the bat (see the recent Ashes series). 35 batting points is the highest in the league.

2.Hutton

Ben Sanderson was always a candidate to dominate Division Two. His opening partner Brett Hutton has been the surprise package. A career average of 29 pre-summer, yet he picked up 35 wickets at 19.

Sanderson couldn’t do it on his own, Keogh, Procter & Buck bought their wickets at too high a price: without a second top bowler, it’s hard to see how Northants could have picked up five wins.

3.Sussex, Middlesex, Worcestershire

What happened guys? If time allows I’ll have a look at why these teams misfired. They are better than Northants.

What happens next?

If you believe Northamptonshire’s players have made technical changes, and they’ll play at the same level in 2020, then they could do OK in Division One. Maybe Rossington’s captaincy has made a difference.

Personally, I think there will be a lot of pressure on Sanderson, Hutton won’t repeat the heroics of this summer, and Northants won’t win many games next year.

Recovery time in One Day Cricket: quick turnarounds punish bowlers

Cricket is a batsman’s game. Whether or not that’s true, we can add another pillar to that argument: 50 over matches two days apart hit the bowlers harder than the batsman.

The last iteration of the Royal London One Day Cup took place this summer. At the time, I had a look at how fatigue impacted players, and included a stat that the “Batting Team being better rested yielded an extra 0.23 runs per over”. Re-reading that piece, it became apparent that I hadn’t shown my workings. Let’s put that right: I’ll not only demonstrate the impact, but also quantify how much is that is down to bowler vs fielder fatigue.

Step One: Demonstrate that 48 hours between matches impact performance in the field.

Taking all bowling spells from the One Day Cup, I filtered on just the bowlers who bowled more than 25 overs in the tournament and looked at how they performed in each spell relative to their tournament Economy Rate.

Bowlers (and the fielding team)

Fig 1: Economy rate impact vs number of days between matches (ie. playing on the first and third of May would be two days between games).

Pretty clear trend here – if a bowler played two days beforehand, their Economy rate would be 0.16+0.06 = 0.22 runs per over higher than if they had three days between games. There’s a decent sample of 863 overs for the 3 days’ rest bucket, so the data should be reliable.

There’s also some evidence that an extra day improves Economy rate even further.

Batsmen

Fig 2 – As for Fig 1, but varying rest times for the Batting team

No clear trend here. 0.02 runs per over is one run per match, so whatever the impact here it’s not big.

Step Two: Demonstrate the impact is partly bowler and partly fielder fatigue.

We’ll start with an assumption: spinners can bowl 10 overs repeatedly without getting tired. Admittedly, the last time I bowled 18 overs in a day I tore my groin, but for professional sportsmen I think that assumption is OK.

Now we’ll break down the 2/3/4 day rest views for bowlers by the type of bowler and see how the impact varies.

Fig 3 – Relative Economy rate impacts from fatigue for Pace vs Spin bowlers.

If the spinner isn’t tired, why is he going for more runs when there’s two days between games? I think it’s because his fielders are a little ragged. The odd one becomes a two, occasionally the fielder can’t quite cut the ball off.

Interpreting the table above, playing games two days apart costs 0.13 runs per over * 50 overs = 6.5 runs due to fielder fatigue plus an extra 0.21 runs for every over a fast bowler delivers.

Discussion

  • When should a fast bowler be rested? I’d say it’s at the point when an inferior player would be expected to perform better. I came to that conclusion after reading this piece in the Daily Telegraph about Australia’s squad rotation in the Ashes. Why were Australia only rotating their weaker bowlers? Because the best bowlers, even tired, were still better than the alternative. Hazlewood is irreplaceable.
  • This analysis could be easily extended for 20-20. I’d expect a smaller effect for the shortest form of the game as each game takes half as long. Other factors to consider for 20-20 might be number of games played in the last 10 days, or the number of days travelling (a run of home games is probably less tiring than a week on the bus).

Appendix

The following chart is ugly, but I just couldn’t exclude it completely – it shows that when tired, more bowlers go for a bit more than expected, and fewer go for a bit less than expected. Can’t really construct a narrative around that, though it helped persuade me that I wasn’t just inventing patterns from the data and the effect of fatigue is real.

Fig 4 – frequencies for individual player match economy rates vs their average economy rate in the 2019 Royal London One Day Cup. Minimum 25 overs bowled.

Bowling: All County Cricketers rated

This page contains expected County Championship Division One bowling averages for all County Cricketers to have i) played during 2019; and ii) taken more than 20 wickets since 2016.

Performances in the Second Eleven Championship, County Championship and Test Cricket are included, though each performance is weighted according to the level being played at (so averaging 30 in Test Cricket is much better than averaging 40 in the Second Eleven Championship).

To give a better indication of current ability, and to partly adjust for age, ratings are weighted more heavily towards recent performances.

Ratings are shown if each player were playing in Division One – this ensures bowlers are compared on an apples-to-apples basis.

I’ll update this page periodically, as more games are played and more information is available on each player.

This version includes matches up to 23rd August 2019.

If you’d like to discuss, please feel free to contact me on twitter @edmundbayliss or use the contact page on this site.

Best bowlers:

Full list:

Impact of “Not Outs” on Averages

A campaign will consist of many skirmishes and a handful of battles. I’m a combatant in a (meaningless) long standing disagreement with an old friend about the value of Chris Woakes. Today’s battleground is whether Woakes’ Test batting average is artificially inflated because he get a lot of “not outs” batting at eight. My opponent believes Woakes is “Bang Average” and therefore clutches any straw which supports that case.

I’d read this in Cricinfo back in 2013 and drew the conclusion that if not outs do make a difference, it’s so small I could ignore it for modelling/gambling purposes. As an aside, generally I like to look at things myself before concluding – for some reason the Cricinfo piece sufficed. Possibly because the author was trying and failing to make the case for adjusting averages to reflect not outs.

As counter-arguments go, I couldn’t rely on picking holes in someone else’s argument, I needed some data. Time for Statsguru!

Batting at seven vs eight

Players who have occupied both batting positions will give us the best data on the impact of those positions on average. I took players who had at least 15 completed innings in each role since 1990 and compared performance.

Fig 1 – Averages batting at seven or eight.
Fig 2 – Impact of additional not outs on average for players batting at eight rather than seven. Note the wide spread of results – there’s no clear trend. Excel’s trendline says the extra not outs from batting at eight do not boost average.

There’s no clear difference between batting at seven rather than eight.

Six versus seven is where it gets more interesting…

Batting at six vs seven

Fig 3 – Averages batting at six or seven. There seems to be a general benefit to batting at seven, but some outliers in Dhoni, Watling, Whittall.
Fig 4 – Impact of additional not outs on average for players batting at seven rather than six. Now we have something. The players with more not outs tend to get a higher average. Excel’s trendline agrees – cutting from bottom left to top right.

Discussion

Let’s take stock after that whirlwind of charts. Generally, players that batted at seven got a boost to their batting average, relative to batting at six. This benefit correlates with increased proportion of not outs when batting lower down.

There is no extra benefit from batting at eight rather than seven. But – that is not to say that there’s no overall benefit to batting at eight rather than six: it’s just that batting at eight has the same benefit as batting at seven rather than six.

I’m no fan of the proposals put forward so far for punishing players for being not out. Yet being not out is correlated with higher averages.

A suggested mechanism: Outrunning Bears

Remember the old joke: two guys, out in the forest, chance upon a bear. The bear starts wandering towards them, and one chap starts tying his shoelaces. Second guy asks “what are you doing – that won’t help you outrun the bear?” First guy answers “I don’t need to outrun the bear, I just need to outrun you”.

In nightmarish batting conditions, the top order have next to no chance of protecting their average. Only one batsman outruns the bear to be not out. There’s an advantage of being the last good batsman in the lineup – you just have to survive while the tail gets blown away, and it’s like the barrage never happened – your average is unscathed.

Fig 5 – Batting position of the not out batsman when a Test team has been bowled out for less than 100 since 1990 (top six Test teams only)

There we have it – number seven is having his average flattered because when the going gets tough, the number seven gets red ink. Well, 13% of the time anyway.

Extend that to all tricky batting situations, and there is likely to be a real impact to averages: the top six rarely get a not out in tricky conditions, that benefit belongs to numbers seven to eleven.

Conclusion

Let’s go back to the original question – is Woakes’ batting average benefiting from coming in at eight? I think so.

Can I quantify it? Not yet. All I’ve shown is that the lower batsman are more likely to survive in bad conditions, yet how often do they miss out on the best batting situations? If a team ends 400/4 declared, numbers seven and eight don’t see any of that action.

Does it matter? If comparing two players who bat in the same position then there’s no impact on their data. If comparing a seven and a six’s record, then yes – a rule of thumb would be:

Average adjustment = -70 * (additional not out % from batting at seven not six)

Which works out as about -1.5 runs in moving from seven to six.

Further reading

The Institute & Faculty of Actuaries know a thing or two about risk. Their take is here. I didn’t find it persuasive.

Appendix: Detailed Data

Fig 6 – Full list, batsmen with more than 15 completed innings batting at seven and eight in Tests since 1990.
Fig 7 – Full list, batsmen with more than 15 completed innings batting at six and seven in Tests since 1990.

Anderson vs Woakes

When I was at university there was a rumour that one of the Geology professors was about to predict a massive earthquake in South America. This would have been a career limiting move if nothing happened.

In the end neither the bold prediction or the earthquake materialised.

I thought of that professor’s reputational gamble when I had the idea of asking whether Chris Woakes might be preferred to James Anderson for the Fourth Ashes Test. To misquote Nasser Hussain, “No Ed Bayliss, you cannot do that.”

The scenario

If you are reading this years from now, Sir James Anderson is currently England’s best bowler, though he doesn’t bat very well. Woakes is a decent batsman, and almost good enough to get into the England team as a bowler. Woakes shores up a mediocre top seven and gives the team balance, especially as Jack Leach is a non-batting spinner. Anderson pulled up during the first Test with a calf injury. He missed the next two Tests and has been added to the squad for the fourth. The series is level 1-1 with two to play. Current speculation is that Woakes might make way for Anderson.

Fig 1 – Career Test records

When weighing the merit of the two players, I’ll look at two factors: England and Australia’s expected runs. To do this, I’ll run my model using each player’s career record as the input* and see how the different teams fare.

Batting

If Woakes were dropped, England would have Broad, Leach and Anderson as a long tail. That means a higher probability that a good batsmen gets left stranded and not out. The following table shows the impact on expected runs over the course of a match of replacing Woakes with Anderson and rejigging the batting order:

Fig 2: Comparing modelled runs scored per Match by batting position in the two scenarios. Note that Bairstow would expect to score two runs fewer per game as a result of more frequently running out of partners.

England would expect to score 29 runs fewer per match with Anderson rather than Woakes.

Interesting that Broad batting at ten outscores Leach in that position by so much – I think it’s because the likely partnerships with Leach at ten (9th wicket: Broad-Leach, 10th wicket: Leach-Anderson) won’t last long.

Bowling

From a bowling perspective, Anderson has an average that’s four runs per wicket better than Woakes. Their strike rates are similar (Anderson 56, Woakes 59). It’s likely this gap is narrower in English conditions (both average 23 at home), but let’s use the raw data rather than run the risk of flattering Woakes.

Note that England have a solid fifth bowler in Ben Stokes, (unlike some teams that would need to use a part-timer if they are bowling all day).

Running this through the model, adjusting for home advantage and Austalia’s brittle batting order, the benefit of Anderson’s bowling over Woakes is 13 runs per match. Not enough to offset the weaker batting.

That seems a little low to me, four wickets per match at four extra runs per wicket would be 16 runs – I think it ends up lower because Australia are away from home and aren’t that strong at batting.

Conclusions

Bringing Anderson into the team for Woakes would be a mistake. Maybe there’s a case for such a change in a must-win match (as the odds of a draw are reduced), but the model does not support such a change for the fourth Test.

It’s important to put this analysis into context. I’m not saying that all specialist bowlers should be replaced by all-rounders. Nor am I saying that Anderson shouldn’t be in the team because he can’t bat.

The head-to-head between Woakes and Anderson is considered in this specific scenario where England have a high quality fifth bowler (Test average 32), but two weak batsmen in Broad and Leach.

James Anderson is England’s best bowler. If fit he should play. If Anderson is fit one needs to reframe the question: you can pick two of Woakes, Broad and Archer. Just make sure one of them is Woakes. Whatever you do, don’t bring in Anderson for Woakes.

*This might be slightly contentious. Any debate on this topic (though the participant may not realise it) will boil down to whether they believe that career record is the right input to use. For example, I’m not making an adjustment for Woakes’ unusually strong home record, nor am I adjusting to reflect more recent performances (which would boost Anderson’s bowling). Nor am I adjusting because Woakes hasn’t scored many runs this series.

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.