## 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)

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

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.

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.

## Kohli’s ODI run ranges are as expected for a phenomenal batsman

Clive (@vanillawallah) was looking at Kohli’s scores in ODIs since the last World Cup, suggesting that:

1. Kohli is consistent
2. He succeeds more than he failures

To check this, I compared Kohli’s performances against what my model would expect him to do – Kohli’s run ranges are broadly in line with what you would expect given his average. His consistency is a consequence of his ability, rather than a specific trait of his batting.

I modelled 1,000 innings for Kohli batting at 3 for India, with an assumed average of 95 (his average over the last 54 games / 3 ½ years).

The results show slightly more single figure scores in the real world vs model, offsetting slightly fewer scores in the teens. This is likely due to small sample sizes.

Two interesting observations:

1. In a quarter of innings he would (and did) score a hundred. Phenomenal.
2. The run distribution is skewed towards the 30-50 range by Kohli running out of time – caused by India successfully chasing down targets and the match ending while he is mid-innings.

Rest of the Top 3

Clive also pulled in data on all other top 3 ODI batsmen since the last World Cup. This is a much larger sample size- and worth checking the distribution as a way of verifying my modelling.

Simulating 1,000 innings with two openers: one of whom averages 35, one of whom averages 45 reasonably reflects the real world distribution of scores that Clive showed.

Two exceptions:

– The real world having more low scores (probably from the times when weaker openers have been selected)

– More hundreds modelled than seen.

P.S. Appreciate this is White Ball ODI Cricket rather than Red Ball Data. Don’t tell the Branding Police.