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.

1. Leg spinners 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.

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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.

Top five Test batsman to debut in the last eighteen months

I was on the latest Cricket only Bettor podcast talking about promising Test cricketers. Here are my thoughts in more detail.

It can be difficult to judge Test players after a few innings. Their Test average is likely to be meaningless. First Class records feature innings played a decade ago. The concept of “First Class” is lovely, but not all FC bowling attacks are created equal. South Africa has two levels of FC cricket, England has a couple of rounds of games against students each year.

I’ve used player records over the last four years in the top First Class tournament of their country to pick out the best batsmen that are just embarking on their Test career. Note that expected averages below are from here onwards (rather than career averages which should be adjusted up/down based on performances to date).

5. Oshada Fernando. Expected Test average 37

FC avg just 37 over his career, but this rises to 50 over the last four years. Likely to be under-rated.
39 sixes in nine FC matches last season (Jos Buttler gets a six roughly every other FC innings). Also averaged 74 last year.

Test average of 46 comes from four away Tests (in SA and Pakistan).
Hit 75* as Sri Lanka beat South Africa 2-0 in South Africa. (Before that only England and Australia had won a series in South Africa).

In December Pakistan soundly beat Sri Lanka in Karachi. Sri Lanka subsided for 212, with only two batsmen passing 20. One of those two was Fernando – he made 102.

4. Rassie van der Dussen. Expected Test average 40.

Took the long route to Test cricket: T20I then ODI experience before being unleashed in whites aged 30.

Tasted success in the 2019 World Cup with three fifties in six innings, even as SA’s campaign faltered (finishing seventh in the group).

Reasons to believe: last four years scored 2,302 runs at an average of 55. Some positive murmurs in the media from his first three Test innings.

3. Zubayr Hamza. Expected Test average 42.

1,563 runs at 50 L4yr. Career FC avg 49.97. Just 24 yrs old, quite a prospect.

Makes the list purely on First Class performances. Top order batsman, Poor start to Test career, but has a higher first class average than van der Dussen. Averaging 21 after eight innings, but I’m keeping the faith

2. Marnus Labuschagne. Expected Test average 45

He’s just racked up the most runs scored by an Australian in a five-match summer. So why isn’t Labuschagne #1? His FC record lately isn’t that good – four year Sheffield Shield average of 35.

His evolution is interesting. Averaged 25 in the Sheffied Shield in 2018/19, and only 26 over his first five Tests to 31st March 2019.

Began 2019 as an unknown (to me) player in an unfancied Glamorgan team but scored 1,114 at 66 and followed that up with a great run for Australia.

Will he keep it up? He’s surely not come from nowhere to be the best since Bradman. Has he? It depends on how you judge a player. One year? Two years? Four years? Their whole career?

1. Ollie Pope. Expected average 48.

Didn’t get past 30 in his first five Test innings, has back to back fifties since then.
Missed most of 2019 with a shoulder injury, though that doesn’t seem to have affected his game.
Hit an unbeaten double hundred in August against a Hampshire attack with four international bowlers (Edwards, Abbott, Holland, Dawson).
Has only played 34 first class matches – so there’s some uncertainty on exactly how good he is.

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The list started as best batsmen to debut in 2019, but I could only find three batsmen that excited me enough. Thus Pope and Labuschagne got parachuted in and the list was extended to the last 18 months. Honourable mentions go to Mayank Agarwal and Rishabh Pant who would probably have made the cut if I’d been looking at the last 18 months from the start.

Chart & Chat: A review of the 2010s

The 2010s end today. I’ve no team of the decade for you, just a chart and its implications.

Fig 1 – Batting and Bowling performances in Tests and T20Is this decade. Ratings are based on averages in Tests and Strike/Economy Rates in T20Is. Higher numbers are good – eg. India averaged 5% less with the ball in Tests than the average team. Ireland excluded as played too few games.
  1. The Big Three does not include England in Test Cricket. It’s South Africa / Australia / India based on the last ten years (and the last week).
  2. West Indies were not good at Test Cricket in the 2010s. Their record against the top three teams was W0 L21 D9. Problematic for the sport, if number eight can’t win in 30 attempts against the top three.
  3. T20Is will be closer than the average Test – As well as the obvious (one short innings rather than two long ones) the teams are far more evenly matched in 20-20 than Tests. Australia were one of the strongest teams this decade. Their W/L ratio was 1.5 in Tests but only 1.35 in T20Is.
  4. Bowling is not the differentiating factor in T20Is. This is odd, because weaker bowling should be punished by the six hitting machines out there in 20-20. Look at the distribution of the two colours of dots: the Orange ones for Tests form a line from the bottom left to top right. If you are strong in one discipline you will be strong in the other. It doesn’t work that way for 20-20: batting makes the difference.
  5. Two clusters of T20I performance: the top tier is Australia, India, England, New Zealand, South Africa. The next level down is West Indies, Sri Lanka, Afghanistan, Pakistan.
  6. What happened to New Zealand’s Test bowling this decade? Were they weak for a few years and I didn’t notice?

Please note that apart from points three and four, this chart is backwards looking: it does not have predictive power. Still, sometimes nice to take stock and see the wood not the trees. There’s a lot of noise out there, don’t miss the longer term trends.

Happy new year.

Lower order CC Division 2 runs – are they predictive of Test performance?

Jofra Archer is struggling with the bat in Test cricket, averaging eight and lengthening the tail. Yet he has a First Class average of 26. Is he getting an easy ride batting down the order for Sussex, then being found out at the highest level? Let’s find out.

Recap – Linking Division 2 and Test Batting

Previous workings showed that a played would expect to average 72% as much in Tests as they do in Division 2 (D2). There isn’t that much data though: most Test players are drawn from the top division. Just four players have over 20 completed innings at both levels over the last four years:

Not a bad fit – D2 averages do have reasonable predictive power of Test performance for batsmen (please take note Mo Bobat). You just need to play a decent number of games in both formats.

But what about tail enders in Tests?

Most of the overseas players in D2 are batsmen. There aren’t many bowlers in D2 to have also played Test cricket lately. Here’s the data for the five lower order batsmen to have eight or more completed innings in Tests & D2:

Remember none of these players has 20 completed innings in both formats, so expect volatility. Archer and Mohammad Abbas are the outliers: Archer averaged nearly four times as much in D2, while Abbas has a slightly higher Test average.

Across the five players, their Test average is 63% of their D2 batting average (for all players this figure is 72%).

Tail enders in D2 vs D1

Data is lacking on tail enders in D2 and Tests. Let’s answer a different question. If we are happy with the standard of D1, then all we need to do is demonstrate similar averages for the lower order in D2 and D1, and we can conclude that Jofra Archer is good at batting.

The above chart is for all batsmen that have >15 completed innings in D1 and D2. If anything the trend is for higher averages in D1. Can’t explain that, but at least that gives some comfort that the tail isn’t getting an easier ride in the lower division.

Conclusion

Jofra Archer would be a very unusual player if he continues to average under ten in Tests. I would expect him to average 17 in Tests based on all available red-ball innings. It just happens that the County Championship has seen the best of his batting, and Test cricket the worst.

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?”

The case against Zak Crawley

Running England’s first innings (NZ vs Eng, 29th Nov 2019) through my model told me that Zak Crawley had a median first innings score of 12. Absurdly low.

Rather than just spout that opinion in a tweet, I’ll walk you through how the model got there, and we’ll see if there are any gaps in logic. Have England made a terrible selection?

Zak Crawley’s County Cricket Record – Average 31

County Championship Division Two: 2017-18 – Runs 830. Dismissed 30 times. Average 27.7

County Championship Division One: 2019 – Runs 820. Dismissed 24 times. Average 34.2.

Second XI Cricket: 2016-18 – Runs 708. Dismissed 22 times. Average 32.2.

Redballdata.com Ratings – Expected D1 Average 30 – Rating those performances, and placing more weight towards recent performances, Crawley’s expected Division One average next year is 29.6.

Adjusting for Age – Expected D1 Average 30.4 – Zak Crawley is 21. He gets a c.3% boost to his expected average because his average is based on runs scored when he was 18/19/20.

Adjust for this innings – Expected Average 19.4 – A Test Match, away, against a strong New Zealand attack is much harder than a county game. That has a severe impact on average.

Run all that expected average of 19.4 through the model, and it predicted a median score of 12.0. What you would expect from a number eight batsman, not a specialist.

Gaps and biases

Let’s look at this from England’s point of view – why is Crawley in the team? I can think of three reasons:

  • He was in the squad, they didn’t really expect him to play. (That links to home advantage getting bigger as a series goes on: in this case it’s because injury means that a squad player, there to gain experience, gets drafted into the team.
  • England selectors use a different age curve and/or bias towards recent matches – bumping up Crawley’s expected average (along with every other young player).
  • Something in performance specific data (that doesn’t show up in averages) makes the England selectors think he’ll be especially suited to batting in New Zealand.

What happened?

Crawley made one run before Wagner got him. That additional innings has moved his expected average down a little more.

Opening batsmen: the divergence of ODI and Test players

Before the Ashes Gio Colussi of The Cricket Academy analysed the two batting lineups and pointed out the White Ball bias in the England camp – they had picked batsmen who were stronger ODI players. He did not expect this to work out well for England. He was right.

Wind the clock back. The good old days. Specifically the noughties (or 2000s, or whatever). An opening batsman fulfilled the same role in Tests or ODIs. Hence their ODI and Test averages were similar, and you could use one to predict the other with a fair degree of confidence.

Fig 1 – Averages of openers to have played >20 innings in Tests and ODIs from 2000-2009

The correlation is so good that the names get all jumbled up on the straight line running from (20,20) to (50,50). Yes, there’s some Test specialists there (Cook, Strauss) but most of the 23 players that meet the criteria for inclusion behave as expected.

That correlation has broken down now.

Fig 2 – Averages of openers to have played >20 innings in Tests and ODIs from 2012-2019. Note the same axes as Fig 1.

There are three distinct types of player, reflected in the clustering in the chart:

  • Versatile elite batsmen (Warner, Iqbal) – just as good in either format, average over 40 in both.
  • Test specialists (Latham, Azhar Ali) – who are/were good enough to play in ODI Cricket, but averaged at least five lower in ODIs
  • ODI specialists (Hales, Guptill) – averaging under 30 in Tests.

I’m reminded of the film Titanic (1997) explaining the captain’s complacency: “26 years of experience working against him”. That line stuck with me – it’s easy to assume past trends will continue, and that you can use opening the batting in ODIs as a pathway into opening in Tests.

Not any more. Unless the player is good. And I mean really good, the best predictor I can see for successfully opening the batting in Tests is successfully opening the batting in red ball Cricket. Think about Jason Roy – ODI Average 43 as an opener, Test Average 19. I don’t think anyone is now expecting him to average 35 in Tests as an opener. Yet someone must have thought he could, else he wouldn’t have been picked.

redballdata.com – closing the stable door after the horse has bolted!

PS. This piece serves as another reminder to me to continually check that the trends I’ve seen still hold – else one day I could be the mug taking Fig.1 to a meeting, persuading everyone to pick the best ODI openers to open the batting in Tests.