How to compare Birth Quarter % data - Caps or Runs/Wickets?

Player % v Match %

Just counting the number of players in a Birth Quarter out of a cohort values them equally. Using the percentage of all matches played by the cohort gives a weighting to the very best and vice versa. I think it is useful to combine these two values in all charts. 

Caps v Runs/Wickets

Most Relative Age Effect (RAE) research uses unweighted player data. When studying the highest performing players ever, it is mostly done using the number of matches played as a qualifying factor.

When comparing the outcomes of data by caps or runs/wickets we find that there is good correlation for runs/batters but potential mis-matches for wickets/bowlers. This is because batters tend to have longer careers than bowlers and therefor are more likely to qualify above a minimum level of caps.

For example, in a recent study of male England Test Cricketers only 3 batters in the top 50 by runs hadn’t reached 50 caps, whereas half (25) bowlers in the top 50 by wickets hadn’t reached 50 caps.

Format Effect

While total runs and wickets may be good indicators in Test cricket are they the only ones? Would other measures be more appropriate for shorter formats?

In Tests perhaps also using batting and bowling averages and bowling strike rates (how may balls per wicket) may form a different picture.

In ODI and T20! cricket batting strike rates and boundary percentages are more important. For bowlers economy rate and boundaries conceded are equally important. In these formats total runs and wickets become less relevant.

Conclusions

Using a battery of indicators is desirable to cross check results to identify and analyse any mis-matches.