The real value of a cricket run

Last updated 12:00 08/10/2013
Sachin Tendulkar
THE MAN: Indian batting great Sachin Tendulkar.

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Numbers are ingrained in sport and they are how we define and measure them.

What is the first question you ask when someone tells you they watched the rugby/league/cricket/anything that you didn't see earlier?

"What was the score?"

While the score is essential for all sports, there is one sport where numbers mean everything.

264 for 2, 84 not out, 4 for 25, 99.94 average, 6.7 runs per over.

I grew up hearing and reading these numbers and they helped me understand things like:

"Don Bradman was the best batsman ever"

"Chris Martin was the worst batsman ever"

"Shane Watson is the biggest ........ ever"

But do the numbers we have relied on for over 130 years (runs scored, wickets taken and batting/bowling average) actually tell us how valuable a cricket player's contribution to the team really is?

Cricket is an individual sport masquerading as a team sport; a series of one-on-one matchups between batsmen and bowlers whose results are combined together to provide a team score. There is actually another sport that cricket shares this characteristic - baseball.

In the past decade there has been a revolution in the way that baseball (and baseball players) are analysed, from old school statistics and eye test scouting to new school analytics and metrics.

Analytics and metrics are all about using numbers from the game to dig deeper and break down the individual player's performances to find which players are the most and least valuable to team performance.

This helps team management construct the best team of individuals it can afford (by signing players who are undervalued by other teams), then putting the individual players in the best situations to succeed.

Many of these principles translate very well to cricket, yet so far have not been really explored or discussed by the cricket media.

When a player comes into bat or on to bowl on TV a graphic will display showing the following information:


  • Games played
  • Total runs/wickets
  • Career average
  • Career batting strike rate or bowling economy rate
  • Career best individual performance


What do these figures tell the viewer about the player and how well they are likely to perform?

Virtually nothing. It's about as useful as knowing that Sachin Tendulkar's favourite band is The Dixie Chicks.

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What those numbers lack is context, meaning the situation is which those numbers were accumulated and what value those numbers had to the team's performance. Let me give you a basic example from a recent match:

Ashton Agar 35 runs from 40 balls (SR 87.50)

With only this information, was Agar's performance good or bad? You would have no idea, right?

Was his team batting first or second? What number in the order did he bat? How did his teammates and opponents perform? 

His side batted first, with him opening the batting. His team finished its innings with six wickets lost. Both teams combined had an average strike rate of 140.

With this information in hand, it's clear that his 35 runs were scored at a strike rate so far below the average strike rate attained by all other players in the game.

He also used up 33 per cent of his entire team's allocation of balls, meaning that had he scored at an average rate for the game he should have scored 56 runs, not just 35. This was a major reason that his team were "thrashed convincingly".

So you may be asking what metrics are currently available to give this kind of 'context' to a player's performance?

To truly know who is the best, the worst, and even to compare them with players of the past.

I want to introduce you to two simple metrics that give an additional insight into individual players' performances for Twenty20 and 50 over (ODI) matches (this metric is not intended for test cricket).

They are Value Runs (VR) and Value Wicket Average (VWA):

Value Runs (VR)

Limited overs cricket (Twenty20 and ODI) is a game of two 'resources', deliveries and wickets.

The batting side has a maximum number of deliveries (120 or 300 depending on the match type), and a maximum of 10 wickets to lose, to score as many runs as possible. The second team will then bat to try and beat the first team's score under the same criteria.

So if a team bats and completes its 20 over innings at 120/2, it has used its full resource of deliveries but did not use nearly all of its wicket resources.

This indicates that the batsmen who occupied the crease should have played more aggressively in an effort to score faster.

Conversely the opposite is true that if a team scores 120/10 in 12 overs, they have achieved a much higher team run rate (having used all wicket resources) but the overall result is still not optimal as they have lost the opportunity to score from 48 deliveries (8 overs X 6 deliveries).

The goal of Value Runs (VR) is to look not only at the runs scored in a batsman's innings (which is what traditional statistics do), but see how quickly they scored those runs compared with how fast all other batsmen in the same game scored.

Scoring rates vary greatly from game to game (based on factors like pitch conditions, ground size, weather, etc) which is why it is important to compare rates in each game rather than looking for a generic baseline.

The basic calculation therefore is:

Value Runs = batsman runs scored X batsman SR differential (player SR/match average SR)

Note: Strike Rate (SR) = Runs scored per 100 balls faced

Let's look at an example from the recent Champions League Twenty20 match between Otago and the Kandurata Maroons:

Neil Broom scored 25 runs from 26 balls (SR 96). The average SR from the game was 133.

25 X (96/133) = 18 VRs

In the same game Jimmy Neesham scored 32 from 19 balls (SR 168). Average SR 133.

32 X (168/133) = 40 VRs

Using simple batting averages there was only a difference of seven runs scored between them, but a difference in value runs of 22.

In games where the deliveries are a limited resource, this much more accurately represents the well above average value that Neesham's innings provided as compared with Broom.

This does not state that Broom's innings had no value, as a playing a 'steadier/slower' (Mark Richardson-ish) innings has some value.

However 'explosive/quicker' innings are a generally preferential use of a limited resource and should be acknowledged as such.

Value Wickets Average (VWA)

Bowling teams are faced with the same resource issue as batsmen: 10 wickets and 120 or 300 deliveries to concede as few runs as possible.

For bowlers however their list of desired outcomes from each delivery is:

1) Take a wicket

2) Concede no runs

3) Concede 1 run

4) Everything else is bad so I'll stare daggers at the captain for his bad field placement because it's never my fault (NB: mainly applies to Shane Watson)

This preference of taking a wicket above all else is because taking wickets will either result in the batting team losing all wickets prior to the end of their deliveries allocation (therefore losing scoring opportunities) or that the batting team will need to play more cautiously and score slower to avoid losing all wickets prior to the end of their innings.

With this in mind, looking at runs conceded per over (RPO) alone is not a good enough measure of a bowler's performance as ideally they will also be taking a higher than average rate of wickets.

To factor both of these we will again contextualise the bowling average by comparing their individual RPOs with the RPO of all bowlers in the same games they played.

Value Wickets can be used to analyse a single match bowling performance.

However its accuracy is far greater when averaging across a number of games as sometimes a bowler can have a valuable bowling spell taking no wickets but maintaining a low RPO.

This would be the exception however as the most valuable bowling performers are those who positively (for the bowling side) impact the batting side's resources (take wickets and cause below average scoring rates).

The formula used for this is:

Value Wickets = bowler average (runs conceded/wickets taken) X bowler RPO differential (bowler RPO/match average RPO)

To illustrate, here is another comparison example from the Otago Volts Twenty20 side this past season

Jacob Duffy - 13 matches played - 36 overs bowled - 13 wickets taken - average 22.5 - rpo 8.1

Neil Wagner - 7 matches played - 22 overs bowled - 7 wickets taken - average 24.0 - rpo 7.6

Who was more valuable across this stretch of games?

Conventional logic would say Duffy was marginally better with a lower average and more total wickets taken.

WVA however changes things like this

Duffy - 22.5 X (8.1 - Duffy's rpo/ 8.42 - average rpo) = 21.6 VWA

Wagner - 24.0 / (7.6 - Wagner's rpo/8.52 - average rpo) = 21.4 VWA

Both players have better than average RPOs for the games they played and have therefore seen an improvement of their averages as compared with the standard bowling average.

What has changed mainly though is that Wagner is now the player recognised as the slightly better all round bowling performer (with both runs conceded and wickets taken being accounted for in a single number).

Hopefully I haven't bored you to death with the explanations of these metrics, but they are a valuable (and relatively simple) tool to examine the true value of a player's performance versus their peers.

I hope these metrics will continue to become more popular in cricket coverage to help educate fans about which players are truly valuable or at worst you will now know one more number that proves Chris Martin was without doubt the greatest terrible batsman of all time.

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