If the world economy is going through an extraordinary crisis after years of excess post the sub-prime debacle, Cricket seems to be going through a crisis of its own post the widespread acceptance of T-20 cricket. Is T-20 format the sub-prime (not literally though) of the cricket world is anybody’s guess! But cricket’s crisis seems to be rather unique. Just when the whole world was entering a recession, cricket discovered its real big money potential through – first the stupendously successful T-20world cup and then the “fill up whatever adjective you want” IPL.
Everyone (except Lalit Modi & co) is in acceptance of the fact that Cricket is in a crisis. But there’s anything but agreement when it comes to the cause of the crisis and more so with how to fix it! ICC thinks the solution lies in having 4 day test matches – I never knew David Morgan had such a great sense of humour. Allan Donald thinks it’s got to do with the balance between bat and ball – soon there’ll be a ball-tampering coach in all teams. Chris Gayle thinks that we should leave it to individuals to decide about their priorities and not get too sentimental about the good old test cricket. Oh boy…. What is the problem?
There’s a general perception that it’s all because of the new new format of the game. Infact it’s a little surprising that most people have expressed their concerns on the death of test cricket just when its going through its most healthy period in a long long time – it’s been mouth watering stuff with 3 top teams competing for the champion’s crown with no clear winner. Surely T-20 is not a threat to test cricket, in fact they are as good as 2 different sports for them to cannibalize each other!
T-20 is a welcome addition to an already rich sport and it’s probably cricket’s best bet in globalizing itself. It’s also made cricketers richer and greedier too. Chris Gayle reaching the test series in England two days before the match was ripping the soul of cricket. At the same time, it’s had a huge positive influence on the game; it brought the family audience back to the stadium along with a lot of first timers too. Not to forget the fact that it’s been a platform where spinners have reestablished themselves after their impotency started telling in ODI cricket of late – What was the last match winning spell from spinner that one can remember in ODI cricket? Ironically it has redressed the balance between bat and ball in its own way though we feared the contrary to happen. Surely it cannot be the rise of T-20 alone.
But what about the scheduling – are we killing the golden goose? How could you have a T-20 world cup barely a week after IPL – which in its current avatar is much bigger than the world cup. Oh, by the way, there’s going to be another world cup in 10 month’s time! There’s also the champions trophy and the small matter of what used to be the pinnacle event in cricket – the ODI world cup in 2011. I never ever imagined in my wildest of dreams that I would grow tired of watching cricket one day and oh boy is it becoming true! ICC needs to badly hire Operations Research experts (i.e., by their standards, which apparently could be done by a X grade student!) to fix their scheduling problem – Obviously, it requires knowledge of rocket science to figure out the distance between June 2009 and April 2010 is less than one year!
And that monster called IPL? This has been the cause of all the problems in cricket! It’s infused obscene amount of money into the game that is tempting cricketers to choose between the nation and money. What an irony – there is crisis the world over for lack of money and in cricket the crisis seems to be because of excess money! Flintoff has preferred T-20 over test cricket, Vettori has expressed the dilemmas of his team members when choosing to sign the national contract knowing a test series is clashing with the IPL, and we all know Gayle’s take on this whole issue – for him, it’s a non-issue!
But for all the large scale bad-mouthing of IPL, no one would disagree with the fact that it is simply the most spectacular cricket tournament outside of Test Cricket. For a sport in want of greater acceptance outside its traditional avenues, there cannot be a better showcase of the game than IPL to the newer audience especially in the most instantly appealing format of the game. Having said that, the monopoly that IPL is becoming to be in the cricket economy needs to be seriously controlled and regulated but does the ICC have the backbone to do it – we all know the answer!
So what is the solution? I have no idea. The problem seems to be multi-layered with a lot interactions across layers. But if you ask me, this is what I would suggest. Forget ODI cricket, dedicate a month for IPL and a week for Champions League in a yearly calendar. Schedule full test series between the top test nations and shorter series between unmatched teams, start playing bilateral T-20 series and have T-20 world cup once in two years. Strip Zimbabwe of its full member status – the most effective way to control the monopoly ways of BCCI and Mr. Lalit Modi. And pray for peace in Pakistan…
PS: For the problem of the big money of IPL luring cricketers to retire early from test cricket, let me assure that only money is capable of fixing the problem it creates – no regulation, moral science lessons or jingoistic patriotism can do it. The “how” of it, will be part of the next post.
Friday, July 31, 2009
Tuesday, July 28, 2009
Who would you want to bat for your life?
Disclaimer: I am a complete believer in the popular phrase - “Statistics are like mini-skirts, what they reveal is interesting but what they hide is vital”
It’s always a dicey proposition to make a science out of an art. But for good or bad, we as cricket fans have internalized it so much that we cannot leave statistics out of our evaluation of any player or team. Now that we have decided to live with it, let’s use numbers from as many dimensions as possible to arrive at a statistically more fair evaluation of a player.
Typically the statistic that’s most abused in cricket is a batsman’s average. The first class in most statistics course start with this question on cricket - Does higher average translate into better consistency? That’s when the professor would introduce the concept of Standard Deviation, which is nothing but a measurement of risk. Very simply put, it measures the variability from the expected score (which is the average) – higher the variability higher the risk! For an intuitive explanation of this concept, click here
But the Standard Deviation is so overhyped a concept that once a lateral thinking guru told me that most cricket followers mistake average as a proxy for greatness when it should ideally be Standard Deviation! Does a batsman who scores 10 runs every time he bats more great than Sachin or Lara, because he is very very consistent – no variability at all! So standalone Standard Deviation may not be of great help, but when used along with the average, it gives a potent number (a crude version of what is famously called “Sharpe Ratio” in the world of finance). Let’s call this the consistency index – it gives the average of a batsman per unit of risk. “The higher the average per unit risk, the more reliable the batsman is” is not a bad doctrine to believe in.
In fact, if I remember right, S.Rajesh of Cricinfo had done a similar exercise a few years back for Cricinfo Magazine where he had calculated the consistency index for all great batsmen to arrive at statistically the most consistent of them all. In this exercise, I am trying to build on his work and add more dimensions to the analysis to possibly get a more complete picture. Since I don’t have the advantage of having access to a comprehensive database like S.Rajesh, I would restrict this analysis to a shortlist of batsman whom I have had the privilege of watching in my cricket following career of the past 2 decades. This list is completely my personal choice of batsmen who have staked a claim to be the best in the world at different points in time. And they are – Sachin Tendulkar, Rahul Dravid, Brian Lara, Jacques Kallis, Ricky Ponting, Kevin Peterson, Virender Sehwag, Matthew Hayden, and Adam Gilchrist. Just for novelty factor, added Sunil Gavaskar to the mix as well!
Now to begin with, let’s use the primitive method of comparing them on averages.
Average 1
Ponting 56.31
Sachin 54.73
Kallis 54.66
Lara 52.89
Dravid 52.39
Gavaskar 51.12
Sehwag 50.82
Peterson 50.82
Hayden 50.74
Gilchrist 47.61
Ponting is clearly leading the pack with a good lead over Tendulkar. Kallis is a hairline behind Tendulkar, with Lara and Dravid completing the top 5. Gilchrist might seem like a strange pick amongst the very best in the last 2 decades, but as I said before it’s a completely personal choice with no quantitative criteria to select the list.
The “Not out” factor
The problem with using average is that it is easily maneuvered by “Not Outs”. So if Lara scores a 400 and finishes not out, it can boost his average by almost 2 runs across his career. There was a phase in which Tendulkar scored nearly 700 runs without getting dismissed. Very rarely a batsman staying not out makes a critical difference to the fortunes of the team. Would it have made any difference to Team India if Tendulkar got out for 241 against Australia in Sydney? So let’s look at the averages by discounting the “Not outs”. Average will be nothing but total number of runs by the number of times the batsman has ventured out to bat. Let’s call this Average 2.
Average 2
Lara 51.52
Ponting 49.78
Sehwag 49.05
Sachin 48.98
Peterson 48.85
Gavaskar 47.30
Hayden 46.88
Kallis 46.50
Dravid 46.21
Gilchrist 40.66
Oh…what a difference this makes. It disturbs the order of the entire list with the exception of Gilchrist. Lara jumps up from fourth place to first place and that despite a 400 N.O against his name! Sehwag climbs up four positions to move from 7th to 3rd in this list. Kallis moves down five positions from 3 to 8.
Let’s quantify the “Not Out” factor for each of these batsmen and see who the biggest beneficiary of it is:
The “Not Out” factor
Kallis 8.16
Gilchrist 6.95
Ponting 6.54
Dravid 6.18
Sachin 5.75
Hayden 3.86
Gavaskar 3.82
Peterson 1.97
Sehwag 1.77
Lara 1.37
There you go, Kallis has nearly a good tail-ender’s average added to his Average 2 courtesy his unfinished innings. Gilchrist’s record as a batsman is well enhanced by the number of not outs in his career. Look at the bottom of the list there; its not at all surprising to find Sehwag there, but Lara enjoys the least benefit of not being dismissed. The amazing aspect of this list is that five of the top 6 batsmen in this list bat at either 3 or 4 (infact except Tendulkar, the rest of them bat at no.3) and have still finished as “Not out” with sizeable scores – oh boy do these guys hate getting dismissed!
Having seen both the statistic, it’s fair to say that both are fair and unfair in their own ways. So let’s use both of them to compute the consistency index and see what difference it makes in the final analysis.
Risk – Variability of expected value
Let’s look at our measurement of risk for each of these batsmen – Standard Deviation.
Standard Deviation
Gilchrist 43.66
Kallis 44.19
Peterson 48.43
Dravid 48.51
Gavaskar 50.08
Hayden 50.57
Ponting 51.01
Sachin 52.41
Lara 62.37
Sehwag 62.77
If I have to go by what the renowned lateral thinking guru said, then Gilchrist is the least risky or in other words the most consistent batsmen of this lot – now then, can you believe it? Is Tendulkar a less consistent batsman than Gilchrist – Even Gilchrist would dismiss it as a joke! But watch out the last 2 names there at the bottom, can you disagree with that? So, Standard Deviation as a stand alone measure also offers some interesting insights. But let’s refine it further; let’s see what consistency index tells us…
Consistency Index (Average/Standard Deviation)
Using Average 1
Consistency Index – 1
Kallis 1.24
Ponting 1.10
Gilchrist 1.09
Dravid 1.08
Peterson 1.05
Sachin 1.04
Gavaskar 1.02
Hayden 1.00
Lara 0.85
Sehwag 0.81
Using Average 2
Consistency index – 2
Kallis 1.05
Peterson 1.01
Ponting 0.98
Dravid 0.95
Gavaskar 0.94
Sachin 0.93
Gilchrist 0.93
Hayden 0.93
Lara 0.83
Sehwag 0.78
Who would you want to bat for your life? If you believe in numbers, you will not look beyond Jacques Kallis – Take “Not outs”, discount “Not outs”, do whatever, but Kallis stands tall in the consistency index towering over the rest. And is anyone surprised by the 2 names in the bottom at all? It seems to be an apt reflection of their style of batting – high risk, high return! Except for Gilchrist, the positions of the rest of the batsmen don’t change much whether you factor in the Not out’s or not. Gilchrist seems to be enjoying the privilege of being number 7 on the batting order with too many “Not out” innings in his kitty.
This completes the first part of the two-part analysis; let’s get a little more whacky in the second part, which I promise will be a much shorter one.
Note: All the data used for the analysis are as on March 18, 2009
It’s always a dicey proposition to make a science out of an art. But for good or bad, we as cricket fans have internalized it so much that we cannot leave statistics out of our evaluation of any player or team. Now that we have decided to live with it, let’s use numbers from as many dimensions as possible to arrive at a statistically more fair evaluation of a player.
Typically the statistic that’s most abused in cricket is a batsman’s average. The first class in most statistics course start with this question on cricket - Does higher average translate into better consistency? That’s when the professor would introduce the concept of Standard Deviation, which is nothing but a measurement of risk. Very simply put, it measures the variability from the expected score (which is the average) – higher the variability higher the risk! For an intuitive explanation of this concept, click here
But the Standard Deviation is so overhyped a concept that once a lateral thinking guru told me that most cricket followers mistake average as a proxy for greatness when it should ideally be Standard Deviation! Does a batsman who scores 10 runs every time he bats more great than Sachin or Lara, because he is very very consistent – no variability at all! So standalone Standard Deviation may not be of great help, but when used along with the average, it gives a potent number (a crude version of what is famously called “Sharpe Ratio” in the world of finance). Let’s call this the consistency index – it gives the average of a batsman per unit of risk. “The higher the average per unit risk, the more reliable the batsman is” is not a bad doctrine to believe in.
In fact, if I remember right, S.Rajesh of Cricinfo had done a similar exercise a few years back for Cricinfo Magazine where he had calculated the consistency index for all great batsmen to arrive at statistically the most consistent of them all. In this exercise, I am trying to build on his work and add more dimensions to the analysis to possibly get a more complete picture. Since I don’t have the advantage of having access to a comprehensive database like S.Rajesh, I would restrict this analysis to a shortlist of batsman whom I have had the privilege of watching in my cricket following career of the past 2 decades. This list is completely my personal choice of batsmen who have staked a claim to be the best in the world at different points in time. And they are – Sachin Tendulkar, Rahul Dravid, Brian Lara, Jacques Kallis, Ricky Ponting, Kevin Peterson, Virender Sehwag, Matthew Hayden, and Adam Gilchrist. Just for novelty factor, added Sunil Gavaskar to the mix as well!
Now to begin with, let’s use the primitive method of comparing them on averages.
Average 1
Ponting 56.31
Sachin 54.73
Kallis 54.66
Lara 52.89
Dravid 52.39
Gavaskar 51.12
Sehwag 50.82
Peterson 50.82
Hayden 50.74
Gilchrist 47.61
Ponting is clearly leading the pack with a good lead over Tendulkar. Kallis is a hairline behind Tendulkar, with Lara and Dravid completing the top 5. Gilchrist might seem like a strange pick amongst the very best in the last 2 decades, but as I said before it’s a completely personal choice with no quantitative criteria to select the list.
The “Not out” factor
The problem with using average is that it is easily maneuvered by “Not Outs”. So if Lara scores a 400 and finishes not out, it can boost his average by almost 2 runs across his career. There was a phase in which Tendulkar scored nearly 700 runs without getting dismissed. Very rarely a batsman staying not out makes a critical difference to the fortunes of the team. Would it have made any difference to Team India if Tendulkar got out for 241 against Australia in Sydney? So let’s look at the averages by discounting the “Not outs”. Average will be nothing but total number of runs by the number of times the batsman has ventured out to bat. Let’s call this Average 2.
Average 2
Lara 51.52
Ponting 49.78
Sehwag 49.05
Sachin 48.98
Peterson 48.85
Gavaskar 47.30
Hayden 46.88
Kallis 46.50
Dravid 46.21
Gilchrist 40.66
Oh…what a difference this makes. It disturbs the order of the entire list with the exception of Gilchrist. Lara jumps up from fourth place to first place and that despite a 400 N.O against his name! Sehwag climbs up four positions to move from 7th to 3rd in this list. Kallis moves down five positions from 3 to 8.
Let’s quantify the “Not Out” factor for each of these batsmen and see who the biggest beneficiary of it is:
The “Not Out” factor
Kallis 8.16
Gilchrist 6.95
Ponting 6.54
Dravid 6.18
Sachin 5.75
Hayden 3.86
Gavaskar 3.82
Peterson 1.97
Sehwag 1.77
Lara 1.37
There you go, Kallis has nearly a good tail-ender’s average added to his Average 2 courtesy his unfinished innings. Gilchrist’s record as a batsman is well enhanced by the number of not outs in his career. Look at the bottom of the list there; its not at all surprising to find Sehwag there, but Lara enjoys the least benefit of not being dismissed. The amazing aspect of this list is that five of the top 6 batsmen in this list bat at either 3 or 4 (infact except Tendulkar, the rest of them bat at no.3) and have still finished as “Not out” with sizeable scores – oh boy do these guys hate getting dismissed!
Having seen both the statistic, it’s fair to say that both are fair and unfair in their own ways. So let’s use both of them to compute the consistency index and see what difference it makes in the final analysis.
Risk – Variability of expected value
Let’s look at our measurement of risk for each of these batsmen – Standard Deviation.
Standard Deviation
Gilchrist 43.66
Kallis 44.19
Peterson 48.43
Dravid 48.51
Gavaskar 50.08
Hayden 50.57
Ponting 51.01
Sachin 52.41
Lara 62.37
Sehwag 62.77
If I have to go by what the renowned lateral thinking guru said, then Gilchrist is the least risky or in other words the most consistent batsmen of this lot – now then, can you believe it? Is Tendulkar a less consistent batsman than Gilchrist – Even Gilchrist would dismiss it as a joke! But watch out the last 2 names there at the bottom, can you disagree with that? So, Standard Deviation as a stand alone measure also offers some interesting insights. But let’s refine it further; let’s see what consistency index tells us…
Consistency Index (Average/Standard Deviation)
Using Average 1
Consistency Index – 1
Kallis 1.24
Ponting 1.10
Gilchrist 1.09
Dravid 1.08
Peterson 1.05
Sachin 1.04
Gavaskar 1.02
Hayden 1.00
Lara 0.85
Sehwag 0.81
Using Average 2
Consistency index – 2
Kallis 1.05
Peterson 1.01
Ponting 0.98
Dravid 0.95
Gavaskar 0.94
Sachin 0.93
Gilchrist 0.93
Hayden 0.93
Lara 0.83
Sehwag 0.78
Who would you want to bat for your life? If you believe in numbers, you will not look beyond Jacques Kallis – Take “Not outs”, discount “Not outs”, do whatever, but Kallis stands tall in the consistency index towering over the rest. And is anyone surprised by the 2 names in the bottom at all? It seems to be an apt reflection of their style of batting – high risk, high return! Except for Gilchrist, the positions of the rest of the batsmen don’t change much whether you factor in the Not out’s or not. Gilchrist seems to be enjoying the privilege of being number 7 on the batting order with too many “Not out” innings in his kitty.
This completes the first part of the two-part analysis; let’s get a little more whacky in the second part, which I promise will be a much shorter one.
Note: All the data used for the analysis are as on March 18, 2009
Friday, July 17, 2009
Finally....... I got internet connection
Oh......... Finally I got internet at home after 2 months of struggle. Hopefully should start blogging regularly from hereon.
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