In an excellent article in The Independent recently, it stated how in last year’s World T20 final against the West Indies, England decided to open the bowling with Joe Root because Chris Gayle had ‘a poor record against off-spin’. Although the idea worked it made me think that surely these plans are more rigorously justified beyond seeing which type of bowlers dismiss him most often. Root is a right-arm off-spin bowler just like say India’s Ravichandran Ashwin, but over a long period of time would probably come off worse against Gayle. I’m sure England and other teams develop more detailed bowling plans that include what kinds of lines and lengths to bowl to particular batsmen, when to vary their pace and where exactly to position fielders among other things. All of this can be derived from data: what kind of deliveries do batsmen generally get out to, what areas of the ground do they target etc.
Chris Gayle in T20’s
In this post, I want to use the metrics I’ve been developing and some visualisations to build up a statistical profile of a particular batsman in T20’s, in this case Chris Gayle himself. It goes without saying that Gayle has a phenomenal record in this format; closing in on 10,000 runs from over 270 innings, with an average of over 40 and a strike rate of 150. The plot below shows how Gayle’s average has fluctuated over the course of his T20 career.Early on in his career, his average somewhat dipped to about 30 before there was a resurgence in his batting from his 50th match until his 100th. He’s since been averaging in the mid 40’s.
The histogram below shows how his career scores are distributed, split up into 5 run bins.
It’s obvious that we’re not going to see a devastating Gayle innings in every match, having made 78 50+ scores in 273 innings. In fact it’s more likely that we’ll see a failure from Gayle having been dismissed for single figures 84 times in his career. This is not entirely surprising for an opener in T20’s but it does show it’s not impossible to dismiss him cheaply.
We can break this down further and see how he performs at different stages of the innings. The plot below shows the total number of runs he’s scored in each over of a T20 innings.It’s evident that the bulk of his runs come in the Powerplay overs before dropping off through the rest of the overs. This is due to the fact that it becomes more and more unlikely that he is actually still out in the middle in the later overs. To account for this we can plot his average per over.
Apart from the 11th over and the last 2 overs, Gayle’s lowest average comes in the 3rd over. In fact, this is the over where he is most frequently dismissed – a total of 28 times. It’s peculiar to see that he averages nearly 100 in the 10th over which then suddenly drops to about 30 in the next over. I’m not really sure why.
His strike rate generally increases through the course of the innings after the initial blitz in the Powerplay. The 1st over is the only time when his strike rate is below 100 suggesting he tends to be quite circumspect at the start of his innings.
The graph above shows the average number of balls it takes Gayle to reach a particular score. It illustrates his incredible ability to accelerate during an innings. It takes him on average 10 balls to score his first 10 runs. After that his 50 comes up in about 32 balls and if he gets to a century, it usually take just over 50 balls. Of course, sample sizes get quite small at that point so the graph becomes a little more erratic.
Gayle’s xR and xW
Now, we can look at what type of bowlers Gayle performs best against or otherwise. This article describes quite well exactly this. From a sample of 3 IPL seasons it shows that Gayle thrives against left-arm slow bowling striking at nearly 3 runs a ball. However, his run-scoring is somewhat restricted by right-arm fast and off-spin bowling, going for just over a run a ball.
We can go further and see if there is any variation in Gayle’s expected runs and wickets against both spinners and seamers. I use a dataset made up of the last 5 IPL seasons consisting of 70,217 balls that produce 89,329 runs and 3,612 wickets. Gayle’s IPL average and strike rate is not significantly off his career figures, so just using this data is sufficient for our analysis. As before, I train the data using a machine learning algorithm to return a xR and xW figure for every ball. The table below summarises the results.
So Gayle over-performs against both seam and spin bowlers compared to the average batsman. Although he performs better against spinner than seamers, he is dismissed more often than expected. This suggests that he takes a more hit and miss approach against spinners but balances risk and reward well against the seamers.
Where to bowl (and not bowl) to Gayle
The beehive plot above shows Gayle’s dismissals in the IPL since 2012, split by seam and spin bowlers. Spinners tend to dismiss him by bowling fairly straight while seamers tend to go wide of off stump or very short. Of course this doesn’t tell you the full story. We can also take a look at where to bowl to keep Gayle quiet.
The heat maps above show the distributions of 460 and 186 dot balls to Gayle from seamers and spinners respectively. Seam bowlers give themselves the best chance by bowling back-of-a-length outside off, although the distribution is quite broad in terms of both line and length. For spinners, keeping it very tight to the top of off stump is the way to go ensuring you’re not too full or too short. Of course, these plans are fraught with risk as the next images show.
The heat map above shows the distribution of 227 balls that have been hit to the boundary by Gayle. It’s clear that if you bowl fuller and outside off, you’re very likely to be hit to the boundary. If you’re wondering what happens if you bowl straight to Gayle as a seamer, then this is where he is mostly restricted to 1’s, 2’s and 3’s.
For spinners the margin of error between dot balls and boundaries is even smaller, comparing this to the spinner’s dot ball heat map above. The figure above illustrates the 92 boundaries Gayle has hit off spin bowling. If you bowl fractionally too full and outside off stump then you are in trouble. This confirms the hit and miss nature of bowling spinners to Gayle implied by his xR and xW figures. If you want to bowl spin to him then you have to be prepared to go for runs before his is dismissed.
Gayle vs particular bowlers
We can now look at how specific bowlers who have been successful (or otherwise) have bowled to Gayle. The table below shows the 10 bowlers he has performed worst against in terms of runs/xR and who have bowled at least 18 legal deliveries to him.
We get a mixture of both seam bowlers and spinners. Gayle scores nearly 26 runs fewer than what we would expect the average batsman to score when facing Lasith Malinga. We have to be careful with the interpretation here however. The average bowler would expect to concede 44 runs if they bowled the exact same deliveries as Malinga has. But if Malinga (and other bowlers on the list) concede less than expected against most other batsman then there is something about these bowlers our model doesn’t quite capture.
The number of dismissals per bowler isn’t really large enough to form concrete conclusions from the dismissals/xR ratios, but it should be noted that he is dismissed by the spinners Sunil Narine and Harbhajan Singh more often than expected.
Let’s look at the heat maps from some of these bowlers with their wickets shown in red.
Firstly, Malinga has two distinct areas that he bowls to Gayle mixed in with the occasional short ball. He favours a good length quite wide of off stump and also some yorker length deliveries aiming for the base of middle stump.
Steyn bowls more or less evenly between back of a length on off stump and a bouncer length towards Gayle’s helmet which has gotten his wicket once.
Narine’s heat map shows he bowls wide of off stump rather than most spinners who target top of off. His length is also incredibly consistent shown by the very narrow contours, relying on variation in spin.
Ashwin, on the other hand, varies his line and length a lot more to Gayle. He predominantly bowls quite full on off stump which, if you remember, is where Gayle hits a lot of boundaries against spinners. Perhaps this suggests Gayle is relatively more cautious when facing Ashwin.
Finally, we can take a look at a bowler who hasn’t fared quite as well.
Bhuvneshwar Kumar has gone for 78 runs from 51 balls with a runs/xR figure of 1.22 against Gayle. He mostly bowls in that area where seamers typically keep Gayle quiet. However, when he misses his length he goes for a lot of boundaries shown by the blue balls. This again stresses how little margin for error you have when bowling to Gayle.
Setting a field to Gayle
Another component to restricting and ultimately dismissing Gayle is field placement. The wagon wheel below shows 6,037 of his runs from 2,260 scoring shots.
It’s obvious from watching him that a lot of his boundaries come in the deep midwicket to long-off region. He also doesn’t run many 2’s or 3’s.
His dot balls mainly come from deliveries played back to the bowler. Having a fielder close in on the off-side is also a source of quite a few dot balls, as well as conventional point and cover fielders. I assume all those dot balls on the boundary are when he’s turned down singles although I’m not entirely sure he’s done it that many times.
Gayle is caught 64% of the time he is dismissed. The wagon wheel below shows 85 instances of him being caught in the outfield separated by seam and spin bowling.
He is caught behind and in the slips quite often to both seamers and spinners, suggesting it is worth having a slip in, especially early on in his innings, even in T20’s. Given where he scores most of his runs, it’s only a matter of time before he mishits one and gets caught at long-on or deep midwicket.
Data needs context
Overall, we’ve seen how we can use data to identify the strengths and weaknesses of a batsman and thus formulate bowling plans. However, it’s important to note that we haven’t found the silver bullet to dismissing Gayle cheaply every time we bowl to him. Just because he gets out to a particular type of delivery really often, doesn’t mean teams should focus their entire pre-match training on hitting that one spot. We know that variation is important in T20’s. Looking deeper, we might find it’s a particular string of deliveries that set him up before dismissing him, or that he is dismissed this way after getting a big score. As ever, more investigation is required.
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