On the use of unfamiliar bowlers

England just whitewashed Pakistan away from home. Two innings of spin played a major part.

First Test: debutant Will Jacks took six wickets, outperforming xW by 2.5*. Third Test: debutant Rehan Ahmed took five second innings wickets for 48, outperforming xW by 3.5 (ie. on average it was really a 1-point-five for).

Jacks’ performance was on a surface where at one point 882 runs had been scored for the loss of ten wickets. Jacks has only bowled 411 first class overs, and 46 of them were at Rawalpindi.

I have a theory: unfamiliar spinners get a boost, relative to bowlers whose variations have been publicly scrutinised in HD. The boost is measurable by the outperformance vs xW (ie getting more wickets than you’d expect given the quality of balls bowled). We can’t test this directly (I don’t have the data, plus it’s subjective to know who is “unfamiliar”). But we can test a proxy: performances on debut.

The first two columns of the below chart show how it used to be. Lots of promising bowlers get a chance, very few succeed. Most wickets are take by the best bowlers (Murali & Warne) at a low average. These days that trend has reversed.

What’s driving that? I’d suggest it’s the element of mystery – spinners come in many forms, and each has a subtly different style and (possibly) variations. In the 1990s a top bowler may have been able to surprise batsmen with old tricks. Little chance of that these days. Already we know Rehan Ahmed bowls with a high arm, his leg break doesn’t turn as much as the googly, both deliveries have a scrabled seam with lots of topspin. And that’s just from one game.

There are probably some secondary factors, such as better selection, more judicious use of a second spinner, and fewer stars around to bring down the average of non-debutants.

If unfamiliar spinners have an advantage, there is an incentive to give the old ball to any vaguely capable new Test batsman. Keep an eye out for it in future, especially with England.

Take from this that one swallow does not a summer make. Give a spinner a few games to see what he’s made of**. Then we’ll decide if you’ve found the next Swann.

*I think. Didn’t write it down and Cricviz take stats off the app sharpish these days.

** Note the deliberate use of “he” – these stats aren’t based on the women’s game; so it would be inaccurate for me to assume the same principles apply.


  1. Leg spinners get better as their careers go on (courtesy of @sanderson_club). Rule of thumb: takes 700 overs to reach their peak.

2. Rehan Ahmed xW – scraped from CricViz’s app.

3. ODI Spin Debutants – Note how wide the gap used to be between debutants and all spinners in ODIs. Just like in Tests.

4. The same trend does not apply for pace bowlers

Adjusting averages (Lyon vs Ashwin)

Pick a big enough sample size and conditions should average out… At least that’s what I’d always assumed.

Let’s disprove that conceptually and then with numbers.

Is it fair to directly compare the averages of these two players? Bowler A plays his home Tests in Sri Lanka, ending his career with a bounty of Bangladesh wickets. Player B is part of a four man attack. Bowls a lot against the top order, and his home games are in Australia. He never gets a sniff of the tail – less than 10% of his wickets are batsmen averaging 10 or less.

No matter how big the sample size, A and B aren’t on a level playing field; there is a bias in favour of A.

How would we expect A or B to perform against a batsman that averaged 30, on an average pitch? Our best bet is to adjust their stats for:

  1. Ground
  2. Batsmen bowled to
  3. Innings number
  4. Specific pitch condition
  5. Ball age (maybe)
  6. Match situation (eg. Team playing for draw / declaration)

Here, I’ll do the first two, looking at the players with 50 wickets over the last four years. Will assume that factors 3-6 average out over a career.

For “Ground” take a weighted average of spinners’ averages at the stadia where each bowler has taken wickets (ie. Mehidy Hasan Miraz’s 29 wickets at 19 at Dhaka are still valuable, but worth more like 29 wickets at 21).

For “Batsman bowled to” each run conceded is worth one run – but the wickets are awarded a value based on who was dismissed – so getting Virat Kohli gives you more credit than Ishant Sharma.

Data is the four years to 17th Feb 2021. Note positive adjustments to averages are bad; negative is good.

The mean adjustment is really interesting: increasing spinners’ averages by 4%. This indicates that just looking at raw averages flatters spinners. Why is this? I think it’s a function of when spinners bowl. If they don’t get much action in the first 30 overs, three wickets will already be down. Thus they’ll disproportionately dismiss the (weaker) lower middle order.

Lyon vs Ashwin

The similarity of their adjusted records looks striking when compared to raw averages. Let’s take a closer look and see if it stacks up.

Firstly, who they dismiss:

It’s not like Ashwin is getting an easy ride, but 30% of Lyon’s wickets come against batsmen who’ve averaged over 40, while for Ashwin that figure is 20%.

Again, Ashwin plays on a mix of pitches, while Lyon has taken over half his wickets at grounds where spinners traditionally struggle.

Overall, Lyon has done amazingly well to average under 30 over the last four years given where he has bowled and to whom.

Other observations

While Ravi Jadeja’s raw average of 24.6 is flattering, he’s still right up there.

Moeen Ali can feel aggrieved not to be ahead of Dom Bess as England’s second spinner.

Roston Chase is better than his average would say – but with relatively little data the error bars get large (60 wickets means his rating is 37 +/- 5).

Nathan Lyon is the best current spinner – we adjust his average down by 11%, of which 8% comes from where he plays. He also gets a boost from who he bowls to: as part of a four man attack, Lyon does feature more against the top order.

Where do we go with this? Extending this to pace bowlers is harder, as strictly one should adjust for when in the innings they bowl (the new ball is helpful). This would need a model of wicket and run probability by ball bowled, and then to compare each player’s actual results to what the average player would achieve.

PS. This would be easy to check… if you had CricViz data. Expected averages would tell the story. Especially comparing head-to-head for the games in which both Lyon and Ashwin played. And splitting LHB and RHB so there was no bias driven by matchups.