Performance Appraisals – Normal Curve on its way out….

When I first started working, performance appraisals were closely linked with the term “normalization”. What we were told was that it was not just your absolute performance that mattered, what also mattered was relative performance vis a vis peers in the organization. You not only had to be good at your job, you also had to be better than others. To take an examination analogy, normalization is like the percentile system MBA Entrance exams in India follow as opposed to the absolute grading that say a GMAT exam follows. The idea behind normalization was that by force ranking peers and rewarding the best and in some cases penalizing / letting go of the ones at the bottom we would be rewarding our best talent and also improving the “average” performance levels in the organization. While in theory it seems logical, the implementation of normal curve brings in problems at a practical level:

  1. It forces team players to compete with each other: The normal curve works well if all the team members are independently working on their tasks with no dependency on each other. However if the work is of collaborative nature, this causes a problem. To ensure that the team does its task, it is imperative for team members to teach and learn from each other – however in an environment where the sword of normalization hangs over all team members, there will be a tendency for team members to claim credit at the expense of others which in turn brings down trust in the team. Once trust falls, collaboration suffers which in turn impacts team performance which in turn leads to a blame game where every body starts blaming others for the problem which in turn leads to a further deterioration in trust and collaboration. Basically downward spiral ad infinitum.
  2. Different skill sets may be lumped together: Normalization requires a cohort of 30 individuals. Many times smaller teams are merged into larger teams to achieve this 30 number and then a normalization is applied on this agglomeration of sub groups. The outcome in this case is never satisfactory.
  3. Talent Levels Fall and Talent Risk Rises: Because its a “winner takes all” world, individuals generally ranked in the level right below the top performers will seek out other opportunities. When this happens the pressure for taking up the workload goes to the higher performers as replacements need time to be hired and come up to the level required. This in turn increases the load on the top performers which could lead to burnout. Alternatively it could make the company more dependent on the top performers and impact them negatively if they were to leave. The logic behind normalizing is to ensure that the top 20% can take care of 80% of outcomes – but this also implies putting most of your eggs in too few baskets.

Thankfully many companies are realizing the pitfalls of the model and are moving to a model where employees are measured on absolute levels and not on a relative basis. Reviews are also being done more frequently allowing employees and organisations to do appropriate course corrections. While this does certainly solve some of the problems associated with normalization, it also requires a rigorous goal setting exercise linked to the organizational goals. If the goal setting process is too dependent on the dynamics between the manager and the reportee then there is a chance that the goals may not be aligned with the organization and one may penalize / reward employees disproportionately. Another risk is that managers may be tempted to rate all employees as the same level knowing very well that this will probably minimize heartburn levels in the team. This in turn will lead to genuine high performers leaving for better opportunities. The organization also needs to have a clear view on the competencies required for each job, with clear and comprehensive learning and development paths that the employees can undertake to ensure that they can fulfill current job roles satisfactorily. In addition to this companies will also have to ensure that they have a robust career path for their employees along with an ecosystem that can help employees grow into roles of higher / different responsibilities.

If organizations get this right then we could see more happier workplaces that foster collaboration which will result in happier customers and wealthier shareholders. I am optimistic that this will happen!

T20 All Time XI – By Numbers

This is the last of my posts inspired by Cricinfo’s idea of selecting the best team across formats from the years 1995 to 2017. Earlier posts can be found here: Selection By Stats – Test XI for last 25 years , Selection By Stats – Cricket ODI XI , Cricket – All Time T20 XI , Cricket ODI XI for the last 25 Years , Cricket Test XI For The Last 25 Years.

In this post the idea is to select the Best XI in terms of statistics. I have used batting average and strike rate to select batsmen and the keeper and the bowling economy rate and bowling average to select the bowlers. I have put in the all rounders in both categories. The ideal team balance is to have 4 batsmen, 1 keeper, 2 all rounders, 4 bowlers.

Graph 1: Batting Average and Batting Strike Rate

Batting

  • Virat Kohli with a batting average of 50 at a strike rate in excess of 135 makes him probably the best T20 batsman of all time.
  • Aaron Finch selects himself based on the average of 40+ and SR of 150+.
  • Amongst the keepers ( highlighted in orange ) – it’s a choice between McCullum and Dhoni – one more comfortable opening and the other a master of finishes.
  • Amongst the all rounders, Shane Watson’s batting prowess far exceeds the others.
  • Maxwell & Munro have the best strike rates amongst all the batsmen.
  • Kevin Peterson and Chris Gayle are other giants who stand apart from the rest with healthy averages and high strike rates in excess of 140.

Graph 2: Bowling Average and Economy Rate

Bowling

  • Generally the preference is more towards containment in this form and hence the economy rate probably takes precedence over the average.
  • Rashid Khan is by far the pre-eminent T20 bowler. His average is around 13 and he has an economy rate less than 6.
  • Daniel Vettori is the only other bowler apart from Rashid Khan with an economy rate less than 6.
  • There is not much to choose between Badree and Narine. A host of other spin bowlers such as Mendis, Swann, Ajmal and Dockrell have economy rates less than 6.5 and averages less than 18. This also indicates how dominant spin bowlers have been over their fast bowling compatriots.
  • Among the all rounders Shakib Al Hasan’s average is superior to other all rounders such as Afridi, Hafeez and Mathews who all have similar economy rates.
  • 4 fast bowlers have similar economy rates – Steyn, Bukhari, Amir and Bumrah with the latter having a distinctly higher average.

The problem here is one of balance – spinners dominate the game and if you go strictly by the numbers one should just select 4 spinners. But that will make the bowling too predictable. All rounders are also a bit of a problem – none of them except ( Watson for batting ) can walk into the team on one skill only. Packing the team with all rounders adds options but comes at the cost of efficiency. The way I have chosen to resolve this is also to select atleast 2 bowlers with high batting strike rates – the rationale being they will mostly come at the end overs and try to maximize the score. Hence average does not matter as much as strike rate. Thankfully both Narine and Rashid Khan have strike rates in the range of 125 so this makes the selection problem easier! While I was tempted to choose Shakib as a bowling all rounder I decided to go with Vettori as i could not ignore the economy rate of 5.71! I am a fan of fast bowlers and find it tough to go into a team without two genuine fast bowlers – I settled on M Amir and Dale Steyn. Bukhari was another strong contender.Moving to the batting – since the team has Narine, I would use him as a pinch-hitting opener along with Aaron Finch. One down would be Virat Kohli followed by Kevin Peterson and Shane Watson.  Maxwell and Munro are alike as per numbers and I have chosen only one of them – Maxwell. At # 7 I would have MSD playing in as Keeper. 8 would be Rashid Khan followed by the tail of Vettori, Steyn and Amir. 12th man would be Shakib Al Hasan. If Shakib plays then Watson would open and Shakib would come either after Pieterson or Maxwell.

To summarize final team: S Narine, A Finch, V Kohli, K Pieterson, S Watson, G Maxwell, M Dhoni, R Khan, D Vettori, D Steyn, M Amir. 12th Man: Shakib Al Hasan.