Individual Thinking v Systems Thinking

In a data rich sport like cricket we try our best to make informed decisions but do we underestimate the influence of perspective?

Take this following example when looking at Birth Quarter effects.

Currently 699 men have played Test cricket for England. Using a cohort of the 50 highest run scorers we find that Q1s (born Sept-Oct) have a higher average (+4% above the mean) than the other Birth Quarters (BQ). When we look at a subset (n=32) of those who average over 40 runs per innings this becomes more significant (9% above the mean, Q1 52, Q2 46, Q3 45, Q4 46 runs). Famous names like Boycott, Sutcliffe, Vaughan and Barrington contribute to this batting average 9% higher than the other BQs.

So intuitively it would make sense to select more Q1s? They literally score more runs in every innings, right? This is thinking from an individual perspective.

The four Q1 players mentioned above, however, are the only players that meet this criteria. 4 from 32 which is half of what you would expect. (Q1 12.5, Q2 34.38, Q3 25, Q4 28.13). 

Batters from BQs 2, 3 & 4 are far more likely to reach this elite level than Q1s who are rare.

Q1 batters only contribute 13.5% of all runs scored at this level (Q1 25194, Q2 67355, Q3 45411, Q4 48731). So intuitively it would make sense to select fewer Q1s as the other BQ batters are more (twice as) likely to reach this level. There are literally twice as many BQ 2, 3 & 4s scoring almost as many runs in every innings, right? This is thinking from a system perspective.

Selecting Q1 batters in Test cricket is like betting on red when half the red numbers are missing! But getting a 9% consolation bonus if you do win.

Rob Reed
Rob Reed

Interested in Relative Age Effects & Maturation in Player Id & Development 🏏 #OneMoreSummer