Tag Archives: Statistics

Who’s more ‘clutch’? Tendulkar, Lara or Ponting?

By Ajit Bhaskar (@ajit_bhaskar)

Who is the most clutch among these three legends from our generation?

The Stage

Given the somewhat sensitive title of the post, I tried to think of a lot of emotional, heartfelt introductory content but I failed miserably. But it suffices to say that these three players are the best from our generation, particularly in the ODI format of the game. A couple of folks (Ian Chappell and Nasser Hussain) have opined on who’s the greatest among the three ‘modern greats’. Honestly, it is a tough ask to rate the three for each is excellent in his own ways.

I’m not here to ‘rate’ which one of them is the best among the three. What I’m going to address, is each batsman’s ability to perform in the clutch, which is one of the measures of a player’s greatness. After all, such performances tend to ‘define a player’s legacy’!

I am going to compare (statistically), the performance of these three players under ‘clutch’ situations.

Also, it makes some sense to compare these three players in particular because:

  • They have played in the same era.
  • They are all top order batsmen and have spent a vast majority of their careers batting in 1-4 spots in the batting order.

Ground Rules/Assumptions

  • I’m going to restrict this conversation to ODIs alone.
  • Clutch’ is defined as chasing a target. I will try to make things more granular as I proceed further.
  • Only India, Australia, West Indies, Pakistan, New Zealand, England and South Africa have been considered for this analysis. Sorry Zimbabwe, Bangladesh et al.
  • Only run chases are considered.
  • The pronouns HE and HIS used in generic sentences encompass BOTH male and female human beings. Do not hassle me with ‘sexist’ and other epithets.

A brief note on ‘clutch’

Various images flash across our minds the instant we hear the word clutch. Like Michael Jordan’s buzzer beating “The Shot” against Cleveland (followed by Jordan jumping in the air and then throwing his elbows exactly three times after planting his feet on the ground), Javed Miandad’s last ball six off Chetan Sharma (I hate Nataraj pencils just for that) and so on. As far as ODIs are concerned, a clutch situation typically involves chasing a target. The pressure that is associated with chasing a target, particularly when two good, competitive teams are playing makes for good drama and excellent cricket. The players who shine repeatedly and consistently under such circumstances become legends of the game.

The reason for emphasis on run chase will become clearer during the course of this article.

The Statistics

These are obtained from Cricinfo directly after applying a filter for ‘fielding first’.

Key observations:

  • They’ve been involved in enough run chases to qualify for statistical analysis
  • Lara has scored nearly half his runs chasing targets!
  • The ‘chasing average’ of all three players is pretty close to their career averages. This suggests that the pressure associated with a run chase doesn’t influence their performance significantly. In fact, Lara (on an average), scores 3 more runs during chasing.
  • All players show the Jekyll and Hyde syndrome, i.e. elevated averages when their teams win during a run chase and reduced averages when their teams lose while chasing a target.
  • It’s the extent of this syndrome exhibited by the three players that is quite intriguing.
  • If we define Differential Chasing Average or D = Chasing Average during Wins – Chasing Average during Losses, it represents the degree of discrepancy in individual performance while a team goes on to win or lose. In principle, a ‘legendary’ player is expected to play the same way and produce at a high level regardless of the outcome of the game and the performance of other players on the team. So lower the D value, greater the degree of consistency of a player during run chases.
  • The D values for Tendulkar, Lara and Ponting are 19.53, 40.11 and 39 respectively.
  • Let’s pause and ponder over this for a moment. Taking Lara as example, when WI chases a total successfully, he tends to score FORTY MORE RUNS than when WI fails to chase a target. While an average of ~68 runs is fantastic during successful a run chase, that also indicates a lot of variation in performance. In other words, consistency is lacking. The same is true of Ponting (Differential = 39). However, the key difference between Lara and Ponting is that when their teams lose while chasing a target, Lara still manages to score a decent 27.5 runs, Ponting manages only 19 runs.
  • Tendulkar, on the other hand, shows the least variation (D = 19.53). In fact, the variation is half of Lara’s and Ponting’s. This indicates more consistent performance during run chases.
  • Lara has the best Chasing Average in Wins by a distance. He scores nearly 10 more runs than Ponting and 16 more runs than Tendulkar during successful run chases.
  • Tendulkar has the best Chasing Average in Losses. It’s is about 13 runs or 67% greater than Ponting’s. He also scores 4 more runs than Lara during unsuccessful rn chases.
 Figure 1. Graphical representation of performance of Sachin Tendulkar (SRT, blue), Brian Lara (BL, Red) and Ricky Ponting (RP, green) during run chases.

 

Cranking up the pressure to ‘ultimate clutch’

While the analysis so far has provided an indication of the extent of consistency of these players, it hasn’t truly separated them as to who is the best among the three. So I’ll up the ante a little bit and crank up the pressure.

I’d like to evaluate these players’ performances under extreme pressure.  In many cases, teams are chasing fairly small targets of 100 or 150. While the task is still challenging, it is not as daunting as chasing a larger target. Say 250.

How do these players fare when chasing targets of 250 or above? The reason for choosing 250 becomes clearer when we take a look at how teams fare when they chase such targets.

Data Acquisition

  • Get the ODI inning by inning list for Tendulkar on cricinfo.
  • Set a filter for ‘fielding first’.
  • Open every single match/scorecard and choose only those where targets of 250 or above were chased.
  • Note the runs scored in each inning under two columns based on whether his team won or lost.
  • Calculate various parameters (Average, average during wins and losses etc.)
  • Not outs are considered as outs for calculating averages
  • Repeat the process for Lara and Ponting. Note that in Ponting’s case, a tied match is included for calculating chasing average.

Here’s how the three batsmen fare:

Key observations:

  • There is a LOT of collective failure! Just take a look at the W-L records. With these legends representing India, West Indies and Australia respectively, they have won ~30, 25 and 40% of their matches while chasing 250+ targets. The collective success rate is just 31%!
  • So, if anybody tells you chasing 250+ is an easy task, just show him this table. Even the ‘invincible Aussies’, who have boasted some of the game’s premier batsmen, bowlers and perhaps some the most balanced sides ever, have failed to win even half the games while chasing 250 or above!
  • Tendulkar’s average while chasing 250+ targets (39.9) is virtually same as his regular chasing average of 40.03. This is remarkable consistency. Lara and Ponting on the other hand, tend to score nearly 5 and 3 runs lower than their regular chasing averages respective, when chasing 250+ targets.
  • Tendulkar also averages the most during 250+ chases. While Tendulkar and Lara are separated by one run, Tendulkar scores nearly 3 more runs than Ponting.
  • The differential (D) values for Tendulkar, Lara and Ponting are 10.3, 34.2 and 46.6 respectively.
  • Let me emphasize a bit more on the D values. Regardless of W or L, you can expect consistent performance from Tendulkar. Lara and Ponting, on the other hand, tend to play extremely well when their respective teams are winning, but tend to score poorly when their sides are on the losing side. This is particularly true of Ponting, whose average of 18.5 when the Aussies lose chasing targets 250 (probability is 26 out of 44 games or 59%) or above is quite frankly, poor!
  • WI has lost 39 out of 52 games while chasing 250+. But even under these circumstances, Lara pretty much assures you 30 runs (chasing avg. during losses).
  • Tendulkar, on the other hand, gets you 7 more runs than Lara and nearly 18 more runs than Ponting on days when your team is not doing a good job at chasing. This is a very significant difference in my opinion, given the fact that India and WI do not end up on the winning side often while chasing 250+ targets.
  • But when their teams win, Lara and Ponting fire and fare much better than Tendulkar. This is clear from their chasing averages during wins.

Figure 2. Graphical representation of performance during 250+ run chases for Tendulkar (blue), Lara (red) and Ponting (green).

Bottom Line

The bottom line is, no matter how high the pressure is, whether the game is being played on earth or elsewhere, no matter what kind of target the team is chasing, Tendulkar provides the most steady, consistent performance. Lara is a gambling man’s pick, while Ponting is (compared to Tendulkar and Lara) more of a hit or miss case. If snoring is a problem, you may need ZQuiet.

To me, this analysis puts Tendulkar and Lara a cut above Ponting. Particularly because Ponting has enjoyed the benefit of better overall teams than Lara and Tendulkar have enjoyed over their careers. But more importantly, the averages of 18.95 during unsuccessful run chases and 18.5 during unsuccessful run chases involving 250+ targets is something I wouldn’t call ‘stuff of legends’.

In a nutshell, if I were to pick one of these three legends to help chase my team a target of 250 or above, which in my book, is a clutch situation given the rate of failure involved, I’d flip a coin. Heads – Tendulkar, Tails – Lara.

Sorry Ponting, you just don’t make the cut on my list. Certainly not in ODIs.

Advertisements

Top heavy IPL: A statistical analysis.

The IPL season is in full swing.About 3/4th of the season is over and this might be a good time to assess why certain teams are doing well and why some others aren’t. This piece aims to look at things from a purely batting perspective. The particular focus is on trying to correlate the success that certain franchises have enjoyed to the performance from their top order batsmen.

These are the current rankings:

Image

Apart from rankings, few other things are also shown. None of these statistics have been taken directly from cricinfo.

IPL is a fairly batsman friendly game. Also, a team gets to face ~120 balls per inning. So, in my opinion, the performance of the top order becomes extremely critical, regardless of whether a team is trying to set a total or chase one. While one bad over might be overcome by a good one following it immediately, a couple of quick wickets, especially among the top order batsmen puts much higher pressure in a T20 game in my opinion. This is the rationale behind assessing batting performances.

I consider the statistics listed above as a reasonable metric for analyzing the performance of top order batsmen.

All the teams have played at least 12 games. PWI is the only team that has played 13. As per current rankings, KKR, DD, MI and RCB round off the top four spots. Let’s try to look into how does their success correlates with the performance of their top order.

For the purposes of this piece, top order will refer to the first three batsmen in the lineup. Middle order will refer to batsmen no. 4 & 5 in the order.

Here’s how the table appears when the teams are sorted based on the % of total runs of the entire team scored by the top order.

Image

The results show that three out of the top four teams continue to feature in top 4. The only new entry is RR, which wasn’t too far (5th in overall rankings) to begin with. Further, there is not much change in CSK, KXIP and PWI’s positions. This suggests that there is a reasonable correlation between the performance of top order and overall success of the teams. The major outliers to this trend would be MI and DC.

A couple of interesting revelations:

  • MI slips to the last spot. This suggests they do not truly depend on their top order for their success. This in spite of one Sachin Tendulkar, but I shall not dare to ramble along those lines.
  • DD top order accounts for nearly 2/3rd of the total runs scored by the team!.

A few other details are revealed when the teams are sorted out based on the total number of runs scored per inning by the entire team.`

Image

A few interesting observations right away:

  • Top teams like KKR and DD have now slipped to the bottom of the ladder. In fact, those two teams score nearly 20 runs fewer than RCB, which leads the pack. In a nutshell, while DD and KKR don’t score much (relatively speaking), they do rely on their top orders to set or chase a target quite heavily. This accentuates the value of Gautam Gambhir, Brendon McCullum, Virender Sehwag and Kevin Pietersen.
  • MI continues to sit at the bottom of the pile. So not only does MI score the fewest runs, their top order contributes the least towards their scores. So how a does team that performs so poorly with the bat manage to do well in the overall rankings? I’ll try to address that in more detail now.

The curious case of MI:

How is MI winning games in spite of horrendous scoring compared to the rest of the league? One of the possibilities is a strong middle order. The table below sorts the IPL teams based on the average runs scored by the middle order.

Image

No points for guessing which team has the BEST middle order in the business. It’s MI.  Here are a few other interesting observations:

  • Mumbai’s middle order contributes nearly TWICE as much to its team’s success when compared to Kolkata. This is further proven when one watches the performances of Rayudu, Pollard and the likes who have bailed Mumbai out many a times. The latest addition to this was the blitzkrieg from Dwayne Smith that flattened CSK.
  • Sorting teams based on average runs scored by the middle coincidentally sorts the teams based on the % runs contributed by no. 4 and no. 5 towards the total score. This further accentuates the contribution of the middle order of MI towards its success, particularly in close games.
  • The fact that KKR lies in the bottom of this list again accentuates how important the top order is for its success. Same rationale applies for RR that has been enjoying the success of Rahane and Dravid, and now Watson. The top order is critical for their success as well.

So I hope that I have been able to throw some light on the importance of the top order’s performance towards the success of a team. While this might be intuitive for some, analyzing statistically, nerding it up with figures and tables makes a lot more fun! Also, this could enable fantasy IPL players to choose certain players from particular teams. Too bad I won’t be getting a medal for this social service.

P.S: 3 matches have taken place since I’ve compiled this data. It would be great to put the above analysis to a litmus test.

Game 1: DD v/s DC

  • For DC, the top order scored 107/187 runs (~57.2%). While this sounds quite impressive for a team that is dead last in standings, this is still an AVERAGE if not slightly below average performance. The middle order scored 78/187 (~41.7%) runs. This is nearly twice their average production, which is probably why DC posted a significantly higher score than their season average.
  • For DD, it’s very simple. The top order won the game for them. They scored all the runs and flattened the opposition. This certainly holds true with the above analysis.

Game 2: RR v/s CSK

  • RR has relied quite a bit on it’s top order. In this game, they were fairly abysmal. They scored 26/126 (20.6%) of the team’s runs, which is barely 1/3rd of their season average! The middle order (Binny and Botha) bailed them out a little with 60 runs (47.6% of the total score). But this is a clear cut deviation from their season’s average trend.
  • CSK on the other hand, didn’t rely on their top order to win this game, which is in line with the above analysis. The top order scored only 42/127 runs (33.1%). This is below their season average of 49.8%. The middle order and the bottom order bailed them out big time and they were able to snatch a close game from RR’s hands.

Game 3: RCB v/s PWI

  • RCB batted first and their top order gave them an excellent start to set a platform for a competitive score. Chris Gayle, Tilakaratne Dilshan and Virat Kohli combined for 68.8% of the team’s runs. This is in line with their season’s trend.
  • PWI continued to showcase the fact that they have one of the worst top orders in the game This doesn’t have to do with poor quality batsmen as much as the number of times they’ve tinkered with their top order. They have changed their top order roughly 9 times in a span of 14 games. That is not the best approach towards a stable, established lineup. The game was practically over when they lost their entire top order for a mere 17 runs. The middle order did well by scoring 69/138 (50%) of the runs but it was clearly too much pressure to bail out such a poor show from the top order. The result was a crushing defeat.

So it appears that the above analysis was valid to a good extent on the games that transpired after compiling the data. As the title suggests, IPL does seem to be top heavy.

– Ajit Bhaskar.

(@ajit_bhaskar on Twitter)

Cricket Fan-tyutter-tastic!

I’ll start this post with a little trumpet-blowing and calling myself “active” on the social media “Twitter” over the past year and a half. I mostly spend my time ranting cricket or blocking ‘bots. Twitter, I found, is full of fellow cricket fans, who love the game a lot. And like every-thing that is made of people, there are categories to differentiate the people. I thought, a new twitterati must be given a guide to help understand who falls under what category, so he/she doesn’t end up following me and think Ravi Shastri is why I love cricket commentary.

Drum roll (OK, stop it, all 3 cricket teams I like get bowled out before your drum rolls can end.)…

1. Sachinists

Probably the most famous category of all. If you don’t know where you are, become this, you will have many to protect you. Recognized by periodical chants of “Sachin Is God”,even if he is not playing the game, even if India is not playing in the game. Sach is their life.
Identification marks – Sachin Tendulkar in their twitter DP, or “Sach is Life” written in their profile. Whatever the outcome of the game, they will assure you that SRT will win the world cup 2015. Along with his son (whose bio-data is also known quite well). Easier way to spot – the ones who switch off the TV or walk out of the television room when Tendulkar gets out. Since 1989..

2. The hard-core Sachin Fans

Slightly more cricket-ing nature ones involved here. Some are natural, others recruited from the Sachinist group. Crouching tiger, and hidden dragons them, will prowl at you and mince you to pieces if you say one word against His Highness. Writers, journalists, reporters, legends etc fear confrontation with this group.
Identification marks – twitter bruises on you. Sometimes filled with un-parliamentary words that are often used in parliaments. Also, they will tell me I attracted more views to this “over-rated” page, because I re-arranged the letters “N-i-c-a-h-s” in a particular manner and made it appear at multiple locations on this page to popularize it.
Affiliated group – “I Hate Steve Bucknor”.

3. The hard-core “Dada” fans

Like the title suggests, fans of the Prince of Calcutta, Saurav Ganguly make-up this space. This might sometimes need a requirement to learn Bengali, but mostly, they learn “gali” through conversations.
Identification marks – “I ❤ Dada” written across their DP or bio, constant references to off-side, and first to enter and last to leave any conversation than contain the word “captain”.
Affiliated Group – “I Hate Greg Chappell”

4. Team India Haters

Mostly English speaking, residents of England or Australia, who contribute to the world of cricket by creating a healthy battle-like atmosphere. On twitter, of course.
Identification marks – lots of Vaseline, ironic references to ICC’s world rankings, “I love DRS” written in their BIO.
Affiliated group – “Indo-Pak Unity Group”

5. Sir Donald Bradman is the Greatest

In short – we have not seen him, but we know he is the best. Because all scriptures say so, and I am under no obligation to believe Barry Richards is better. Identification marks – voracious reader of books on cricketing history, nostalgic weep at the mention of John Arlott’s name, Tendulkar hasn’t impressed enough.
Affiliated Group – “Mathematical Group for Rounding of Numbers”.

6. No Way Bradman is the Greatest. I have proof.

Internet savvy, modern day, corporate ready ‘twitteratis’, more adept with the mouse and keyboard hitting permutation than enjoying the game. They can prove that Bradman doesn’t rank among the top-5 modern day cricketers in some way or the other.
Identification mark – internet browser’s home page is CricInfo Statsguru, sometimes stutter when asked “How many tests has Bradman played in India?”. Usually at the receiving end of the other groups mentioned above.
Affiliated Group – “Gayle Is A Legend”

7. The Highway

Media people, mostly television, self-appointed chief selector of Indian cricket on screen, who pick questions making round from twitter and sounding them on air as their own and then starting a non-stop ranting that makes you feel safe twitter can’t talk.
Identification marks – utterly confusing tweets on the game, which will later be superseded by the most popular voice doing the rounds.
Affiliated Group – “I Have No Clue About DRS, But Will Take A Side. And Change Sides Often”

8. New Age Fans

Ever so lively, bubbly fans, unaffected by the turmoils suffered by their cricketers at myriad foreign lands. They are why cricket still simmers even if it is out of gas.
Identification marks – Usually have their favourite player’s photograph in their display pic. Tweet about the game very rarely. Usually tweet in the same manner as – “Ooooooooooh, Raina looks cho cute” when he grins after misfielding or “Mahiiiii, I LOVE YOU” in a yellow jersey.
Affiliated group – “I play IPL cricket”

9. Regional

Based on geographical location of self or heart, these domestic keyboard warriors show good concern to their regional/domestic cricket. In-house fights prevail, most common (in India) being the ones from The Knowledgeable Chennai Crowd, the Mumbai’s “Khadoos Army”, Delhi and considerable volume of voices from other prominent Ranji teams’ fans. This usually ends with which We-Know-There-Is-No-Way-He-Will-Be-Selected player should have been selected.
Identification marks – constant outrage at governing board and leading cricket score lending sites at the non-existence of live-updates, plan to pen the book “How To Improve Domestic Cricket Structure”.
Affiliated Group – “IPL Is Ruining Cricket”

 

Of course, I might have missed some group. I am sorry to you, fellow of “Fans of Amla’s Beard”, “Monty Is A Legend” and “KP. Keiron Pollard. That.Is.All.” etcs. Will you be kind enough and help me by describing it in a comment below? Thanks.

We’re still friends, right?

Why Australia lost – The stats

There were a number of reasons why Australia lost the Border-Gavaskar trophy. You can always blame it on things like  toss, the pitch, team in transition, etc, etc. But some of the real reasons include things like bad captaincy, poor team composition, bad tactics and over all poor planning.

In addition, the No. 1 team in the world were outplayed in a number of areas in this series – particularly areas where India have struggled frequently in the past.

Here is my take on some of these areas:

The Opening pair

  India Australia
1st Test Innings 1 70 0
1st Test Innings 2 16 21
2nd Test Innings 1 70 0
2nd Test Innings 2 182 49
3rd Test Innings 1 5 123
3rd Test Innings 2 29 31*
4th Test Innings 1 98 32
4th Test Innings 2 116 29
Total 586 285

 

The partnership of Indian openers was 300 runs more than their counterpart and they averaged around 73 per innings. If you look at it from another angle – the total runs scored by Indian openers (Sehwag, Gambhir and Vijay) was 888, where as the Australian’s total was just 583.

The last four wickets

Apart from a rare failure in the 1st innings of the fourth test, the last four wickets have have made a significant contribution to the total. They have also pulled India out of trouble twice – in the first innings of the series, when Zaheer Khan and Harbhajan Singh top scored for India and then again in the last innings of the series – when Dhoni and Harbhajan scored fifties.  Australia on the other hand have sorely missed the services of someone like Gilchrist and failed in important situations.

  India Australia
1st Test Innings 1 165 80
1st Test Innings 2 25*
2nd Test Innings 1 143 122
2nd Test Innings 2 53
3rd Test Innings 1 132* 151
3rd Test Innings 2
4th Test Innings 1 19 89
4th Test Innings 2 129 48

 

The run rate and overs occupied

Both these are important in forcing a result. Australia in the past would score runs and do them fast – this enabled them to force results even on the 3rd or 4th day of the game. India on the other hand used to score runs slowly. In this series however, the roles were reversed.

  India Overs India RR Aus Overs Aus RR
T1 I1 119 3.02 149.5 2.86
T1 I2 73 2.42 73 3.12
T2 I1 129 3.63 101 2.63
T2 I2 65 4.83 64.4 3.01
T3 I1 161 3.80 179.3 3.21
T3 I2 77.3 2.68 8 3.87
T4 I1 124.5 3.53 134.4 2.63
T4 I2 82.4 3.56 50.2 4.15

 

Taking 20 wickets in a game

No matter how good your batting is, you need to take 20 wickets to win matches. The Indians did this twice, but the Australians without the likes of McGrath and Warne struggled with this – in fact, they were able to do this only in the last game of the tour and managed to take just 4 wickets on the last day of the 1st test – the only test where they were placed well to take the game.

India completely out bowled the Aussies. Here are some stats to go with my claim:

India played just 2 fast bowlers – Zaheer Khan and Ishant Sharma. Between them they bowled around 283 overs and took 26 wickets at an average of around 34. They also troubled the Aussies a fair bit with their reverse swing.

The Aussies, on the other hand played up to 4 fast bowlers in a game, bowled close to 545 overs and took 37 wickets at an average of around 45. Except for Shane Watson’s overs after tea in India’s last innings of the series, there was no hint of reverse swing from any one at any other time.

The difference in the stats for slow bowlers between the 2 countries is even worse – India bowled around 477 overs taking 37 wickets @ 34.8, whereas the Aussies bowled around 286 overs and took 20 wickets @ 54.

-Mahesh-