The great debate – how good really is Ben Stokes?

Ben Stokes had a pretty good 2019, by all accounts.

Fresh off his Sports Personality of the Year victory, he was recently named the ICC player of the year. With this achievement, as well as his heroics in the 2^nd Test in South Africa, it got us thinking about how you can compare sporting greats, and the use (and abuse) of data that might go along with it.

From a data perspective, sports played by individuals are ‘relatively’ easy to analyse, as you might look in golf at the number of majors or snooker at the number of World Championships won, but it all gets murkier with team games.

You drift into a sea of data that can be easily used and abused to make your point.

ESPN cricinfo has a wealth of cricketing statistics. Pulling up Ben Stokes’ statistics as a batsman and bowler through his test career provides the following information:

	Matches	Runs	Highest Score	Bat Avg	100s		Wickets	Bowl Avg	5 wkt Innings
Ben Stokes	61	3,906	258	36.16	8		142	32.92	4

And this is how he compares against two less recent English all-rounders…

	Matches	Runs	Highest Score	Bat Avg	100s		Wickets	Bowl Avg	5 wkt Innings
Ben Stokes	61	3,906	258	36.16	8		142	32.92	4
Ian Botham	102	5,200	208	33.54	14		383	28.40	27
Andrew Flintoff	79	3,845	167	31.77	5		226	32.78	3

So, what can we conclude from this table?  Well, based on the ‘Bat’ and ‘Bowl’ averages, Stokes is the strongest batsman and Botham the pick of the three as a bowler.  Further, the rate at which Botham accumulated 5 wickets in an innings as a bowler supports his claim to be the pick of the three as a bowler and also has the highest ratio of ‘scores over one hundred per match’, though Stokes is very close behind.

I am not sure that data can prove the point of which of Botham and Stokes is the ‘better all-rounder’.  There are too many imponderables and contextual pieces in play.  This is often true in the world of business – there is no single answer that trumps all other arguments, just analysis that can support a perspective, and allow decisions to be made.

Looking a little deeper, I found some interesting data on when within a game and within a series of games, the two great cricketers are at their best:

This chart shows their average scores by innings within a test match.Both players have the best average in the first innings – not a surprise, as the pitch will be in the best condition, and on some occasions, they will be building on the foundation of the top order, and have license to ‘go for it’. 

What is unusual in the graph is Stokes’ significant drop when he bats in the second innings of the match, and the ‘recovery’ to average almost 40 in the third innings of a match. In the 27 matches where he batted in the second innings, he has made 7 ducks, and failed to get into double figures on 13 occasions.  Is this a function of the exertions of his bowling, a ‘weaker’ top order than Ian Botham batted beneath? 

In the third innings of a match, Stokes has only 2 ducks, and has failed to get to double figures on just 10 occasions. The exertions of his bowling argument does not really work, given his efforts in the third innings of a match (again, having just been out in the field) when he is able to do so much better. Is it the case that he is playing his best cricket as he tries to drive home an advantage, or in more recent times, dig his team out of a hole?

Looking at the second chart, one can compare their relative batting performance through a test series. What is striking here is the significant increase in average in the third match of the series.  Have they both ‘warmed up’ by this point in the series?  Have they the measure of the opposition by this point?  People talk about the 3^rd day of a golf major as ‘moving day’ – is the equivalent in a test match series the 3^rd test, where the outcome is really shaped.  Do they both come to the fore when their team needs them, and they want to put their mark on the series?

Without doubt, they are both great cricketers, and the argument as to who is better will rumble on and on. 

However, by using the data available we can, at the very least, identify insights that might support an argument about what makes each player great.

By forming a hypothesis and testing their validity, we can get closer to understanding the true nature of the ‘impossible’ questions we pose, drawing conclusions to smaller, easier to answer queries, that can significantly help us make a smarter decision on the whole.