What can we learn from England Lions tours?

England Lions have three First Class games in Australia, starting on 15 February. Here I look at the merit of a Lions tour, and what we can learn from them. I’ll start by busting a couple of common myths, and then consider the current squad.

Myth 1: Lions performances as auditions before Test squad selection

Only a genius or a fool would pick a player based on one performance. There are very few geniuses.

Consider the stand out performances in Lions history: 11 players have registered 150+ scores, including Michael Yardy, Chris Read and Eoin Morgan. These gentlemen didn’t have the batting to thrive in Test Cricket – so don’t assume that anyone getting a daddy hundred is the next Ollie Pope.

Nobody should be given the message that a big red ball score on tour will secure them a Test place. Even if that’s what might happen…

Myth 2: Lions performances are a predictor of Test batting success

There are very few Lions First Class matches. Just nine players have more than ten games under their belts. That means Lions averages are poor indicators of Test average. Look how scattered the plot is:

Appendix 1 – Lions vs Test batting average for players dismissed ten or more times in each

The below charts show how First Class records are more reliable for Test selection than Lions stats.

Appendix 2 – FC vs Test average, same players as Appendix 1.

Without ratings for every player the Lions come up against, any batting data is of limited use. Ideally one would combine Lions data and county stats to enrich the picture we have of a player.

A healthy average over three games doesn’t tell you a lot. A few fifties in a Lions tour doesn’t necessarily make a “horses for courses” selection next time England tour there. No matter how tempting.

Not entirely myth: Lions performances as a predictor of Test bowling success

We’ve seen that you can judge a bowler on fewer matches than a batsman.

Lions stats are almost as good at predicting Test performances as First Class Cricket (Appendix A). Consider those with 15+ Lions and Test wickets: the two highest averaging Lions found Test Cricket the hardest (Plunkett and Rashid). However, they also averaged over 30 in First Class, so they’d be expected to struggle in Tests even if we didn’t have Lions data to go on.

OK MR SMARTYPANTS … WHAT IS THE POINT OF LIONS TOURS? OTHER THAN RATING BOWLERS, A BIT.

There are five reasons I can think of to include someone in a Lions First Class squad. Three are in the following table; the other two are “getting a good look at players outside the county 1st XI” and “keeping on some white ball players that are around anyway”.

Let’s look at the red ball squad:

Lewis Gregory doesn’t tick any of the boxes, as captain I think his role is slightly different: a senior player, just falling short of the Test squad. It may be harsh to label the three “White Ball Player Kept On” players as such – Abell and Kohler-Cadmore are better than some players in the original squad. Still, that’s been their route to selection – being retained as many of the group will leave early to join the Test squad.

The Lions XI that takes the field against a Cricket Australia XI on February 15th will reveal something about the ECB’s goals.

The strongest XI? If played in English conditions it would be Sibley, Jennings, Northeast, Kohler-Cadmore, Lawrence, Bracey+, Gregory (c), Bess, OE Robinson, Overton, Gleeson.

***

A final word on a similar theme- the recent U19 World Cup has drawn a few optimistic predictions about players based on a handful of games. Note that only two players have scored three centuries in U19 World Cups. Shikhar Dhawan is a success story, yet Jack Burnham still a work in progress.

***

Appendix 1 – Correlations with Test performance

Appendix 3 – Lions vs Test bowling average for players who took 15 or more times at each level.
Appendix 4 – FC vs Test average, same players as Appendix 3.

Appendix 2 – Kookaburra red ball blues

In a recent Cricinfo article, Saqib Mahmood described the challenges of switching from the Dukes ball in England to the Kookaburra ball overseas. I was hoping for some predictive data- showing that some players struggle with the Kookaburra ball with the Lions, and those struggles would continue at Test level. The results are inconclusive.

Bowlers: Things Change

Writing last week about the careers of batsmen and the predictive power of their early performances, I glossed over something important. Batsmen don’t get magically better as they play more Tests.* Which supports my hypothesis that there is no benefit in giving a batsman experience at Test level. A batsman has a level of ability, which is revealed in Test Cricket as they play more games. It’s thus easy to model a batsman’s expected scores.**

What about bowlers?

Fig 1- Average after 25 wickets (x-axis) plotted against rest of career average for the 50 highest wicket taking Test bowlers since 2000

Noisy, as expected. This is (on average) after only seven Tests. Let’s skip forward to when they have 100 wickets:

With 100 wickets players are well into their careers – yet there’s still no consistent pattern. I’m going to split these 50 players into two groups now: the main sequence, who behave nicely and whose past performance is a good guide to future success, and the others.

Here’s the 32 well behaved players:

For two thirds of the players, once they have 100 wickets their future is neatly mapped out, and you can approximate that they’ll play at that level until they get dropped. What about the others?

Let’s reveal who these miscreants are. Amazing how career averages can gloss over being rubbish to start with (Flintoff) or how the mighty fell (Harmison).

Fig 5 – Outliers – bowlers whose first 100 wickets this century were not predictive of future performance. “Wickets” and “Career” columns refer to post-2000 only.

Test career average is no good to measure these players. And they make up one third of the bowlers I’ve looked at. Crumbs, my models have been wrong all this time to use Test career average to measure current skill levels.

What causes this? Many possibilities: injury; being a late bloomer; switching from batting all rounder to bowling all rounder; getting “found out” as opponents learn your varieties and batsmen adapt.

How can we identify these players in advance? How do you know for sure who is now better or worse than their career average? With a spreadsheet, you won’t know. That’s a problem for me, because that’s all I’ve got. If you can read technique and separate the irrelevant detail from the significant change, then maybe. Perhaps there should be a “days since last ran” metric, like in horse racing, and anyone returning from a long layoff should be treated as a different player.

If we can’t identify the outliers, how can we rank every player accurately with one methodology? The good news – unlike batsmen, bowlers yield more data per match because they take lots of wickets per game. Whereas for a batsman we would use Difficulty-Adjusted-Career-Average, for a bowler we can use Difficulty-Adjusted-Last-Four-Year-Average, or similar.

Here’s the predictive power of more recent data. It may not look much better to the eye, but mathematically this is a better fit:

Fig 6 – Bowling averages for players with >100 red ball wickets in 2016-18 and >30 County Championship wickets in 2019. A strong correlation given the limited test period (2019 being only 14 matches maximum).

What have we learned? We should predict bowling performances based on what they have achieved recently – because for about a third of players their career average has limited predictive power. That means my model should pick up last four year performances, if too little data it should instead use career records.

* The line of best fit when plotting past vs future averages is a straight line that almost passes through zero.

**You also need to adjust for the age curve – batsmen get better as they get older, then drop off in their mid thirties. Also there will be the odd outlier (Ramprakash and Hick, for example, never made it at Test level), though examples of players with abnormal records after 50 Tests are likely to be rare.

How many innings before we can accurately predict T20I Strike Rate?

Last time I looked at how long it takes for averages to mean something. Thought I’d try the same analysis for 20-20 Strike Rates. How long before a player’s 20-20 SR is a fair representation of that player?

Play for long enough and a batsman’s Strike Rate reflects their ability. However, in the early stages their career Strike Rate will be volatile as the sample is small. One significant factor is the impact of average on Strike Rate: most innings accelerate as they go on, so one big score early on will give a player a temporarily favourable career SR.

The below chart shows T20I Strike Rates for all players with 60+ Innings since 2009, split by their first ten, twenty or thirty innings (x axis) and then subsequent innings (y axis). Note that the acceleration in T20 scoring in recent years means most players scored faster in their later innings.

Consider the players who had a SR of 130 in their first 30 innings: one (Dilshan) stuttered and struck at 114 afterwards. Another (Nabi) scored at 156 per hundred balls in subsequent T20Is. If you have a player that has scored at eight an over in their first 30 innings, you may only know that they’ll score at between seven and nine per over from then on. Not very insightful.

Tom Banton has a T20 SR of 160 after 25 dismissals. That’s too few innings to be confident in him maintaining that scoring rate, but enough to say he’s probably a 140+ SR batsman.

Another recent example comes from Dawid Malan:

I don’t know what else I can do to break into the team for the T20 World Cup. I don’t know how you can be under pressure with an average over 57 and a strike rate over 150

Dawid Malan, Sky Sports Cricket Blog

Malan has done very well in his nine T20Is. Yet that tells us little about how we would expect him to perform in the future. Fortunately, T20 players get a lot of stamps in their passports- Malan scored at 145 per 100 balls in the Banglasdesh Premier League and 148 in the most recent Blast. It’s just a case of doing the legwork to calculate an expected Strike Rate at international level. I’ll leave it to the T20 experts to work out whether Malan is worth a spot in the World Cup squad.

Of England’s current players, only Roy and Morgan have more than 30 completed innings in T20Is. There’s insufficient international data. Yet most batsmen have played over 100 innings in T20 leagues – plenty to have a good read on them.

Summing up, there’s too few T20Is to use them to set expected average/strike rate in later T20Is. Far better to set this expectation based on club stats, adjusted for difficulty. There’s even enough data to weight analysis towards more recent performances. Also, beware small sample sizes: even 30 completed innings are too few. Anything under 100 innings and you should apply some judgement to the data.

On the limitations of averages

Averages are the currency of red ball cricket. We know they get misused (eg. after just a handful of games Ben Foakes averages 41) and when abused they have little predictive power. What I hadn’t realised is just how limited averages are: we almost never have a satisfactory sample size for someone’s average to be the definitive measure of their ability.

Number of innings before you can rely on an average

We can all agree that averages after a couple of innings are of very little value. By “value” I mean predictive power: what does it tell you about what will happen next?

Ben Foakes averaging 42 after five Tests doesn’t mean a lot. But how about Keaton Jennings averaging 25 after 17 Tests?

The below charts show the limitations of averages by comparing them after 10/20/30 Tests (x-axis) with those players’ averages for the rest of their careers (y-axis). The sample is players since 2000 who played more than 70 Tests.

It’s quite striking how dispersed the data is. Not just the 10 Test version (Stuart Broad averaged more than Steve Smith), but even over a longer horizon: Michael Vaughan averaged 53 in his first 30 Tests of this century, then 36 in his last 50 Tests (32% less).

Modelling and True Averages

Sports models are often positively described as “simulating the game 10,000 times”. This isn’t just to make the model sound authoritative, it can take that many simulations to get an answer not influenced by the laws of chance. When I look at an innings in-running, balancing speed against accuracy, I’ll run at least a thousand simulations – any fewer and the sample size will impact results. An example from today – Asad Shafiq’s expected first innings average was 55, yet a 1,000 iteration run of the model gave his average as 54.3. Close, but not perfect.

Shouldn’t it be the same with averages? If we don’t have a thousand innings, lady luck will have played a part. We never have a thousand innings.

Looking at modelled data, I find that after 35 innings (c. 20 Tests), there is still a one-in-five chance that someone’s average differs by more than 20% from what they would average in the long term. A batsman that would average 40 in the long run could, through bad luck, average 32 after 20 Tests.

Fig 2 – Theoretical evolution of average and how it converges with true average (based on Red Ball Data model).

Sir Donald Bradman had a 99.94 average at the end of his career (70 completed innings). There’s a c.40% chance his average would have been +/- 10% if he had played enough innings for his average to have been a true reflection of his ability. We don’t know how good Bradman was*.

Implications

  • Don’t blindly slice & dice averages – they’ll tell you a story that isn’t true. Yes, if you have a mechanism to test (eg. Ross Taylor before and after eye surgery), there might be a real story. But just picking a random cutoff will mean you misread noise as signal (Virat Kohli averaged 44 up to Sept 2016, but 70 since then).
  • Use age adjusted career averages as a best view of future performance.
  • First Class data has to be a factor in judging Test batsmen, even when they have played 30 Tests. Kane Williamson averaged just 30 in his first 20 Tests. Credit to the New Zealand selectors for persevering.
  • There has to be a better metric than batting average. Times dismissed vs Expected Wickets times (Strike Rate / Mean Strike Rate) is one that I’d expect to become commonplace in future. Another might be control % in the nets. Yes, I went there: I believe there is some merit in the “he’s hitting it nicely in the nets” line of reasoning.

This analysis can be repeated for 20-20 – I’ll cover that in my next post.

Further reading

Owen Benton already covered the modelled side of averages here. His found an 80% chance that a batsman’s average is within 20% of their true average after 50 innings, which is in line with my modelling. His approach is rather practical: what’s the chance an inferior batsman has the better average after x innings?

*Factor in Bradman’s 295 completed First Class innings at an average of 95 and we can get precision on how good he was. But that sentence would lack punch, and this blog’s barely readable at the best of times.

Best bowlers of the last 50 years

During a rain delay at Johannesburg last week, the radio commentators were putting an all-time England XI together. The usual arguments ensued: how can you compare players across eras? Is bowling average the sole measure? While looking at something quite unrelated, I realised I’d stumbled upon a new way of comparing players which is perfect for this question.

The metric is “percentage impact on batsman’s average”. For instance, batsmen generally scored 31% below their average facing Malcolm Marshall, making him the best Test bowler of the last 50 years.

Here’s the bowlers since 1970 with at least 150 wickets at under 25 apiece, ranked by their impact on a batsman’s average:

Fig 1 – Impact on Batsman’s average, leading bowlers of the last 50 years in Tests. Note that these have been adjusted to reflect players like Ravi Jadeja who has mostly played at home, mainly when conditions favour a second spinner.

There are four other players whose average flatters them, where Impact on Batsman’s Average is a better metric. Joel Garner picked up 92 of his 259 wickets against a mixed England team. Muttiah Muralitharan and Waqar Younis benefited from a disproportionate number of games against Bangladesh, Zimbabwe and (in Younis’ case) Sri Lanka. Wasim Akram is the hardest to explain: 38% of his wickets were against batsmen with career averages under 20 (a 25% figure would be more normal).

Did you spot Vernon Philander muscle in at fourth on the list? A phenomenal bowler. His average (and Impact on Batsman’s average) may be boosted by favourable conditions where he happened to play most of his away games: England, Australia and New Zealand. Still, I won’t fudge the numbers: he has a brilliant record and South Africa will miss him.

Here’s a comparison of Philander and Muralitharan

Fig 2 – Philander was better than Muralitharan.

Contextual Averages

England have been defending Joe Denly’s average (30) lately by saying that his performances are better than they appear because of the conditions he has played in.

This piece supports that approach: Marshall and Garner had the same bowling average, but Marshall was 10% better than Garner. If averages can mask that kind of difference over a whole career, imagine how skewed an average could be after ten Tests.

Further Reading

ICC’s all time rankings. The ICC have listed players according to their peak performances, while I have used their career. Consider Akram – his average puts him fourteenth on the list, but accounting for who he dismissed the ICC rankings take him all the way down to 76th. That supports my calculations that he had a -11% impact on batsmen’s averages.

Off spinners, DRS, Left Handers, and a new way of looking at averages

How can you tell if a bowler is better than average? They cause batsmen to underperform.

Muttiah Muralitharan troubled batsmen to the extent they averaged eight runs per wicket fewer than over their careers. Mark Boucher averaged a paltry 21 against Murali, whilst his career average was 30.

So what? Well, dear reader, the title of the piece gives you a clue where this is going.

***

DRS was first used in a Test match in 2008, and subsequently rolled out at the back end of 2009. This led to an increase in LBWs for spinners as the technology changed perceptions of how often spinners were delivering balls that would hit the stumps.

Fig 1 – percentage of bowled and LBW for leading off spinners this century (excludes Jeetan Patel, who evenly spanned both decades).

The chart shows a notable increase in the proportion of bowled and LBW dismissals for spinners in the 2010s.

Fig 1 indicates that off spinners only got a little benefit from DRS. This makes sense: coming over the wicket they can’t bowl too straight as that opens up run scoring opportunities. Turning the ball in to the right hander from outside off gives the batsman an escape route (by ensuring contact with the pad is outside the line of the stumps).

Now look at the left handers. A revolution. Instead of 30% of wickets coming from bowled/LBW, that rocketed to 45%. A simple post-DRS approach for an off spinner is to come round the wicket to a left hander and pitch on off stump. Any big turn catches the edge; while no turn means bowled/LBW are in play. Pre-DRS left handers had the opportunity to get a good stride in and using the pad as first line of defence. That doesn’t work with ball tracking. The camera knows.

***

UPDATE 31st JAN 2020 – In the below table, columns “RHB impact” and “LHB impact” were based on potentially inaccurate data. I need to do more work to prove this – so please don’t rely on it for now.

Fig 2

Fig 2 is a ruddy goldmine. Let’s break it down.

  • First two columns: the player and the decade they mainly played in
  • Third column: career average
  • Fourth column: impact on the averages of the batsmen they bowled to. The better the bowler, the lower this number would be. For an average Test cricketer this number would be nil. Calculated by comparing (runs conceded against each batsman) to expected runs conceded (wickets taken * batsman’s average).
  • Fifth to seventh columns split the Bowler Impact metric between right and left handed batsmen

In the 2000s, left handers were the batsmen of choice to counter a strong off spinner. Only thee batsmen averaged more than 52 against Muralitharan – all were left handers*. A left hander against Murali could expect to average three runs more than his right handed equivalent.

However, that benefit reversed last decade. The hunter has become the hunted. Swann and Ashwin didn’t adversely affect right handers’ averages, but lopped five runs per innings from left handers.

That point is really interesting, so I’ll say it again another way. It’s wrong to see Graeme Swann as a very good Test bowler against everyone – he was average against right handers yet brilliant against lefties. That averages out to “very good”.

Conclusions

  • A bowler’s ability can be represented by their impact on a batsman’s average, as an alternative metric to bowling average.
  • Off spinners represent a clear and present danger to left handers.
  • The general case for matchups in Tests: RHB +8%, LHB -13% vs Off Spin. RHB -7%, LHB +14% vs Leg Spin.
  • Previously I’d adjusted a batsman’s expected average to reflect the bowling attack’s averages. Will need to add to that the type of bowler and whether the batsman is left or right handed.
  • I’m working on the basis that all off spinners are alike – explicitly assuming relative performances of right/left handers against a particular bowler are the result of low sample sizes.
  • Will look at left arm pace and slow left arm bowlers next.

*Qualification criteria: dismissed four or more times

Further reading

Ricky Ponting argued for Australia to favour right handers in the 2019 Ashes to nullify the threat of Moeen Ali.

Leg spin: What we can learn from Statsguru

My statistical goal is a theory of everything: expected averages for any situation. So far I’ve excluded the influence of match ups (specific bowler vs batsman) as being Very Difficult Indeed. That ends now: join me as I dip a toe into that field, starting with some analysis of leg spinners in Tests.

**Update 24/04/2020 – the methodology below was flawed: the Statsguru page I used reflects the score a batsman was on when dismissed, rather than the head-to-head score. Interestingly, after further work it looks like the conclusions were reasonably accurate, even if the workings weren’t.

1. Leg spinners and favour right handers

The logic for it being more expensive to bowl leg spin (LS) against left handed batsmen (LHB) in white ball cricket is that the batsman can play with the spin, and minor errors in line provide opportunities for scoring. Here’s CricViz on that topic.

In longer format cricket, I expected leg spinners to be agnostic to the batsman’s stance. Against right handers (RHB) a straight line threatens every kind of dismissal apart from timed out, while for LHB a line well outside off can still threaten the stumps and both edges, while asking the batsman to play well away from their body.

What does the data show? At the highest level of Test Cricket, nine of the ten leg spin bowlers sampled favour right handers. Expect a leggie to average 22% more against left handers in Tests.

Shane Warne took 708 Test wickets at 25, yet against LHB he was average. Still, that makes him significantly better than his competitors – none of the other recent leg spin bowlers averaged under 35 against LHB. What’s the reason? I think it’s the required line against left handers making bowled and LBW less likely. Against right handers bowled and LBW make up 37% of dismissals. For left handers that drops to 31%.

2. Elite leg spinners come into their own against the tail

There’s a neat split between Warne, MacGill, Kumble, Ahmed and the rest. The top four took 1,742 wickets at 28, while the other six took their wickets at 39. Individually, there’s not enough data on the six lesser players – so I’ve lumped them together to compare their careers to the elite four.

The ratio of Elite vs Second Rate averages reveals the trend: Elite leg spinners bamboozle lower order batsmen (anyone with a career average under 20).

What does this mean for strategy? Captains will intuitively know that a strong leg spinner is an asset against the tail. If you have an inferior leg spinner, how should you deploy them? I would argue they are best used against the top order (once the ball is no longer new), in order to keep the best bowlers fresh. It’s a question of managing resources and getting the best out of the attack over a 90 over day.

3. Elite bowlers are flattered by bowling at weaker batsmen

The weaker leg spinners claimed 58% of their wickets against batsmen who average 30+. For the elite four that figure is just 51%.

The above impact can flatter averages; for instance Stuart MacGill (42% wickets against top order, career average 29) was not so much better than Devendra Bishoo (61% wickets against top order, career average 37).

A full system would include this when rating bowlers: a rough estimate says MacGill’s true rating was 31, whilst Bishoo’s true average was 35. A quick check shows these adjusted averages are more in line with FC averages, indicating there’s a ring of truth to this.

Methodology

I’ll level with you – there are some assumptions here. Cricinfo’s excellent and free data gives a bowler’s averages split by batsmen (here’s MacGill’s). However, this doesn’t cover how many runs were conceded against batsmen who they haven’t dismissed. I’ve attributed the unallocated runs to batsmen in proportion to their average and number of matches played against that bowler.

***

That was fun! We’ve seen a hint of what matchups can do and I’m very late to the party. That said, I’ll stick to my guns: most patterns are just data mining and we need proper evidence (at the level of the above or better) before drawing conclusions. Those conclusions are best done at the “off spinner vs opening batsman” level rather than the “Moeen Ali to Dean Elgar” level.