The world of data (and how it is upending the game)

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The world of data (and how it is upending the game)
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In the latest episode we discuss ways in which data is upending the way teams approach the sport – and also question why there is so much resistance to the idea that smart use of data helps everyone understand the game better.

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Talking Points:

  • The importance of data in the T20 era – with teams looking for more efficiencies and for ways to exploit match-ups
  • The ‘anti-data constituency’ and why data is such a polarizing topic
  • The false-opposition to data: and why observation and experience are data too
  • The age-old cliche: ‘Numbers don’t tell the full story’
  • The terminological baggage around terms like ‘luck’, ‘fluke’ and ‘randomness’
  • The difference between descriptive data and predictive data
  • The lack of sufficient data in T20s – with so few games played every season
  • The issue of player accountability – and the fear of data revealing hard truths
  • Do TV channels have a moral responsibility to educate the public about data?
  • What does the future of cricket data look like?
  • Can one expect more systematic recording of judgement metrics?

Participants:

Siddhartha Vaidyanathan (@sidvee)

Himanish Ganjoo (@hganjoo_153)

Kartikeya Date (@cricketingview)

Ashoka (@ABVan)

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Related:

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Lead image from here.


2 thoughts on “The world of data (and how it is upending the game)”

  1. Wonderful pod with great points being made. I get it that for now this data-based analysis might be perceived as “niche” and nerdy but that is a cop-out. The goal should be to simplify things to make it accessible – better quality graphics and data animations that even kids could connect to. Boundary attempts, RAA & Misbah/jogi are excellent – just need it to be visually appealing. I think instead of waiting on broadcasters – getting on socials to post these stat-graphics would be a good start. Also to make the data/code/infra more accessible to others who want to play with it and come up with other things. I like what Jarrod Kimber does in this aspect with his videos. Not sure if he puts up his code & data for others to work with.

    But that is just the first part. Second part is this data-based analytical thinking needs to percolate to the club amateur levels in terms of understanding risk and percentage plays. Many sports have embraced it. IN my own sport tennis this is happening. Couple of examples –

    Traditionally there is tremendous focus on ground strokes consistency- players will drill for 20/30+ consecutive shots to keep the ball in play. In reality data ( based on Craig Oshannessy’s research and its ramifications) says that 70% of points end in the first 4 shots. So one needs to shift the way you train into getting first serves and returns in and follow those up with a sold shot to force an error. Rinse and repeat. This is alre

    Another example is Rafel Nadal’s domination at Roland Garros. His 112-3 record is unparalleled. But dig into it more – the actual percentage of points he won is only 56%. The world #1 at any point had an average of 55% winning record in terms of points during their reign. Domination at elite levels is really an illusion (many reasons for it- one of which is tennis’s diabolical scoring system). To quote Brad Gilbert who figured this out intuitively – ” You don’t have to play perfect tennis. You just have to be better” . This kind of analysis de-stigmatizes losing individual “crucial” points and places focus on forcing errors by constructing patterns of play. It makes the sport so much fulfilling and richer- both for the players and spectators and help improve the discourse to rise above half-baked narratives. What can be done to improve cricket in this aspect at junior/club levels? Any such stats and analysis that could help ?

    Finally we do need to be careful using data to push narratives. Tagging players as minnow-bashers IMHO is a narrative and is not helpful. You can only play the opponents you are up against. I see many examples of batters stats being sliced – minnow vs non-minnow – KLR, Babar, SCG etc. I think this form of analysis is not helpful as it lacks substance.

    1. Can you objectively prove that on that day the performance by the minnow opposition was worse than any non-minnow performance vs the same player?

    2. It can be argued that there is more pressure on an elite player to perform well against minnows as opposed to vs non-minnows- More so in shorter forms.

    @abvan you are hilarious man- nicely baited the data nerds with Raina on may 2nd haha. The nerds may strike back saying on May 2 make Raina bat for an hour at different time zones starting at Kiribati and hop TZ and bat until (super)humanly possible. Repeat yearly and you’ll have the sample size. I’ll stop here …

    1. Thanks so much for your detailed comment, Tarun. Interesting point about ‘data-based analytical thinking needs to percolate to the club amateur levels in terms of understanding risk and percentage plays’. This is a bit of a paradox for me wrt cricket at the junior levels. Do you train a player to tighten up their defense or do you train them to think about hitting every ball. As a coach – you want them to have a firm grounding in technique – which they can in turn use to build their attacking game. But are you then nurturing a muscle memory that cannot think of hitting every ball? Would be interesting to see if franchises set up their own academies in the future and find 10-year-olds to specifically teach T20 hitting. Ten-year-olds who will not be constrained by the traditional ‘back-and-across’ style but look to first hit the ball and then push it in the gap if no other option remains.

      LOL re Raina on May 2nd.

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