Factors Influencing A Batsman's Strike Rate - Extract from Strategies for Success in the Indian Premier League Book

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This chapter is taken from my book, Strategies for Success in the Indian Premier League (e-book available via the link below, with paperback release later in 2020).

For those who are unaware, a batsman’s strike rate is a measure of their scoring pace, and it is possible on various websites to see a batsman’s strike rate either on a match scorecard, as well as a summary of it for a given tournament as well with all matches in that given tournament included. The measure of it is given in runs per 100 balls, with the average Indian Premier League player from 2017-2019 striking at 134.99 runs per 100 balls (known as a strike rate of 134.99). Generally speaking, the strike rate in the Indian Premier League is one of the highest in major T20 leagues worldwide, and over the years, only the T20 Blast in England, played in very batter-friendly conditions, tends to rival the Indian Premier League for the highest average batting strike rates. 

However, this batting strike rate for batsmen is a rather simplistic measure of the way a player goes about scoring runs. For example, does a player tend to deal in boundaries, or are they consistent rotators of strike? To delve deeper into this, it is poss
ible to analyse a batsman’s boundary percentage and also their non-boundary strike rate - I supplied a chart earlier in this book which looked at these two metrics for the T20 Blast in 2017 and 2018 which highlighted Lewis Gregory’s superb numbers in that particular competition.

Of course, we can also look at the same metrics for the Indian Premier League, and the chart below assesses batsmen in the Indian Premier League between 2017 and 2019 (minimum 300 balls faced):-


Compared to a batsman’s basic strike rate, this chart gives more insight towards how a batsman actually scores their runs. We can see that potentially devastating, attacking batsmen such as Andre Russell and Sunil Narine are grouped together in the bottom right- hand corner (boundary-hitters) with elite-level boundary hitting but relatively poor non- boundary strike rates. Unsurprisingly, the West Indies veteran opener, Chris Gayle, who has a reputation for a similar playing style, isn’t far away from this bottom right-hand corner either.

It is incredibly difficult for any player to feature in the top right-hand corner - another reason why I took such a bullish approach with regards to Lewis Gregory from the T20 Blast data - and, in the Indian Premier League, Rishabh Pant is the closest player towards this ideal top-right hand corner grouping. The 22 year old Delhi Capitals batsman is a stellar talent and based on these run-scoring metrics, is the most comp
lete and well- balanced Indian Premier League batsman of this entire playing pool - domestic or overseas. It is rather perplexing as to why, at the time of writing he isn’t considered one of the first names on the team sheet for India in T20 cricket. Other players close to this ideal top-right corner include Jos Buttler, AB De Villiers, Hardik Pandya and KL Rahul - all examples of world-class players in their given roles.

Moving towards the top-left hand corner we see players who are more rotation-driven, recording below-average boundary percentages but higher than average non-boundary strike rates. These are players who either play out fewer dot balls than the average batsmen, or are excellent at converting singles into twos, or twos in to threes - or are strong in both these rotation-driven areas. A few names here, such as Ben Stokes and Virat Kohli, might surprise a few readers.

While looking at boundary percentage versus non-boundary strike-rate is useful to ascertain a profile for a batsman in how they score their runs, I want to go further now and look at the implications of the two metrics on a player’s strike rate. The chart below illustrates the boundary percentage versus strike rate for the same group of players:- 
As with the previous chart, the lines on the graph highlight the mean tournament figures from this time period, and this graph demonstrates well the impact of boundary-hitting on a player’s strike rate.  The first obvious conclusion that many readers may draw is that there is a fairly strong relationship between a player’s boundary percentage and their strike rate, with the distribution of players generally sloping from the bottom-left (low for both metrics) to the top-right (high for both metrics). It is certainly reasonable to state that a player’s boundary percentage is a considerable driver towards their overall strike rate.

This is also apparent when looking at the players with the highest strike-rates, and in particular, Sunil Narine and Andre Russell. The two West Indies all-rounders recorded, by some distance, the two highest boundary percentage figures in the Indian Premier League during this time period, and they also recorded the two highest strike rates, also by a reasonable distance. Interestingly, though, while Narine recorded a higher boundary percentage than Russell, it was Russell who recorded the higher overall strike rate. The primary reason for this was that Russell hit almost double Narine’s already stellar 10.40% six-hitting percentage, and had a six percentage of over 8% greater than any other batsman facing 300+ balls in the Indian Premier League between years 2017 to 2019 - so when Russell hits a boundary, it’s much more likely to be a six, while when Narine hits a boundary, it’s much more likely to be a four.

The first two charts in this chapter show that the ‘complete and well-balanced batsman’ identified earlier, Rishabh Pant, again recorded both the third highest figure for both strike- rate and boundary percentage, and some readers might be asking the question, ‘if Pant is the most complete and well-balanced batsman, why doesn’t he have the best strike-rate?’. On the surface, it’s a justifiable question, but it also fails to take into account the sheer weighting that a player’s boundary-hitting, and in particular, a six-hitting, has on their strike-rate.

Essentially, a player can be as good a rotator as they can possibly be, but without a strong boundary percentage and/or six-hitting percentage, they won’t be able to record high strike rates over a long period of time. This is evidenced again in the above chart, with only two players - Kane Williamson and Sanju Samson - being able to record below-average boundary-hitting percentages (and only just) but being able to record above-average strike rates. Every other player who faced 300+ balls with a boundary percentage below the tournament mean was unable to generate a strike rate in excess of the tournament mean.

As you can see from the two graphs, there are in excess of 20 batsmen in the Indian Premier League with below-average boundary-hitting percentages, and there are various reasons for their recruitment and selection. However, paying large sums for these players is not recommended, unless they offer additional skill-sets (such as captaincy or being an all-rounder), or are a high-quality and very stability-driven anchor batsman. Certainly, teams need to be very mindful of recruiting numerous below-average boundary-hitters, unless they have an elite bowling attack which is capable of regularly defending 150-160 type totals.

Several paragraphs previously, I mentioned the difference between the six-hitting exploits of Andre Russell and Sunil Narine, and the graph below illustrates major Indian Premier League batsmen facing 300+ balls between the 2017 and 2019 seasons, taking a look at the percentages each player hits fours and sixes:- 

We can see here that Russell is simply in a league of his own with regards to six-hitting, with an incredible bias towards this, as opposed to four-hitting. Conversely, Narine is out on his own as an elite four-hitter, while still being a strong six-hitter. We also see that the likes of Parthiv Patel, Prithvi Shaw and Shikhar Dhawan are superb four-hitters, but hit comparatively few sixes. Interestingly, MS Dhoni and Kieron Pollard haven’t hit a great deal of fours, but are solid six-hitters. Both have hit more sixes than fours across the last three seasons in the Indian Premier League.

If you were somehow still under any doubt that six-hitting is a huge driver towards a player’s batting strike rate, it is still possible to provide further evidence of this. Across these batsmen who faced 300+ balls in the Indian Premier League across the 2017 to 2019 seasons, the average six percentage was 5.86%. 11 players, of course, led by the aforementioned Andre Russell, hit more than 2% greater than this figure, so they had a six percentage of 7.87% or greater. Conversely, nine players recorded six-hitting percentages of over 2% worse than this average six percentage of 5.86% (therefore recording a six- hitting percentage 3.85% or below).

Now, the 11 players with this considerably higher than average six-hitting percentage played a total of 358 innings in the Indian Premier League between 2017 and 2019, and in those 358 innings recorded 52 instances of hitting at least a 200 strike rate in a 10+ ball innings (14.53%). Unsurprisingly, Andre Russell (37.04%) led the way again, with Sunil Narine (23.08%), Hardik Pandya (20.45%) and Rishabh Pant (18.18%) also recording excellent percentages of this incredibly difficult metric for a player to obtain.

On the flip side, those players with considerably worse six-hitting percentages really struggled to hit 10+ ball innings of at least a 200 strike rate, doing so just three times in 316 innings (0.95%). The above data is evidenced in the chart below:- 

To summarise, it is patently evident that it is extremely difficult for a player to generate a high strike rate, either in the short-term or long-term, without above-average boundary- hitting, and also six-hitting skills. Despite this, Indian Premier League teams, as well as teams in other T20 leagues, often prioritise batsmen - often paying large sums of money, or using early draft picks - who do not excel when looking at these metrics.

Having focused on batsmen so far in this chapter, we can also look at the effect boundary concession has on a bowler’s economy rate (runs per over conceded) as well. Firstly, in this area, I want to discuss a comparison between the dot ball percentage and boundary conceded percentage of bowlers bowling at least 300 balls across the last three editions of the Indian Premier League, and the chart below illustrates this, with selected spin bowlers highlighted in red, and selected non-spin bowlers highlighted in black:- 


Ideally, a bowler would be in the bottom-right hand corner (high dot ball percentage and low boundary conceded percentage) and we can see that many of the players in this area - Bhuvneshwar Kumar, Jofra Archer, Rashid Khan and Jasprit Bumrah - are renowned world-class bowlers in T20 cricket. Interestingly, the Chahar cousins - pace bowling Powerplay specialist Deepak, and the leg-spinner, Rahul, also rate extremely well via these metrics. It is completely logical that these bowlers should all boast excellent economy rates across this sample, given that they bowl a high percentage of dot balls and concede a relatively low percentage of boundaries.

Generally speaking, and this goes for other leagues as well as the Indian Premier League, spinners tend to have a lower dot ball percentage than pace bowlers, but also concede a lower percentage of boundaries. This is also logical given the phase of the matches that spinners typically bowl - the middle overs, where teams frequently don’t attack and usually rotate the strike - compared to pace bowlers, who often bowl the expensive death overs where teams attack with much more frequency. So in graphs like the one above, it is reasonably commonplace to see a grouping of spinners around the bottom-left quadrant, with pace bowlers often featuring closer to the top-right quadrant.

Rather like batsmen’s strike rates, bowler’s economy rates is hugely driven by their boundary conceded percentage, as the chart below for the same group of bowlers in the Indian Premier League over the same sample duration illustrates:- 
As with the batter’s chart, there is a general trend from bottom-left (low boundaries conceded, low economy rate - ideal) to top-right (high boundaries conceded, high economy rate - not so ideal). Only one bowler was able to record an economy rate of under eight runs per over having conceded over 18% of boundaries - the aforementioned Deepak Chahar.  So, we can see that the rate of a bowler conceding boundaries has a huge bearing on their economy rate, and teams really should be ensuring that they don’t recruit bowlers who are expected to leak boundaries in over 20% of the balls they bowl - even if they are regular death overs bowlers.

Of less relevance to a bowler’s economy rate is the frequency with which they generate dot balls, with the chart below making for interesting discussion:- 
The data behind this chart featured the same group of 300+ ball bowlers in the Indian Premier League from 2017 to 2019, but shows considerably less relationship between dot ball percentage and economy rate. Indeed, the bowler with the lowest dot ball percentage in this sample still managed to record an excellent economy rate below eight runs per over, while two bowlers bowling around 44% of dot balls still had economy rates getting on towards nine runs per over. Interestingly, both of these particular bowlers conceded boundaries in excess of 21% for the balls that they bowled in the Indian Premier League during this time period.

Essentially, to summarise, the same comparisons can be drawn between batsmen and bowlers.  Boundary-hitting is a huge driver towards a batsman’s strike-rate, while boundary-concession rates are a huge driver towards a bowler’s economy rate. However, Indian Premier League teams don’t necessarily pay attention to the above-dynamics, frequently recruiting batsmen who have unimpressive boundary-hitting data, or bowlers who have unimpressive boundary concession numbers.

Such recruitment is rather perplexing given that it is absolutely possible to model a batsman’s expected boundary hitting percentages for the Indian Premier League, no matter what league(s) around the world they have played in, and the same goes for a bowler’s expected boundary concession rates as well, and I have done so. However, given the obvious commercial application of this, I hope that you can forgive me from giving any further detail on individual players modelled boundary-hitting or boundary concession percentages - at this stage I would prefer to keep this exclusive to the teams who I am currently working with, and any teams who may be interested in acquiring this data in the future.

If this article has given you insight into the data that Sports Analytics Advantage can offer cricket teams around the world in formulating team strategies, selection, draft or auction plans, or any other work, please feel free to enquire at sportsanalyticsadvantage@gmail.com.

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