Matchups and Opportunity Cost

There’s a theory (which I just invented) that you could listen to old radio broadcasts of Cricket and be able to judge the date by the buzzwords of the era. For 2019, it’s “Matchups”: pitting bowlers against the optimum batsmen to stifle run scoring and take cheap wickets.

Matchups seem like a plausible proposition – get enough data, find some patterns, check you’ve got a decent sample size and out will pop some options to consider. Note the need for a plausible proposition (ie. not “Roy struggles against the flipper in the top of the hour when the bowling is from the North-West”).

There are three issues I have with the use of Matchups.

Firstly, they aren’t publicly available – if a pundit refers to X having a weakness against a particular type of bowling, the viewer/listener has no way of knowing if that’s a fact or an opinion. In times gone by, we could accept that all such utterances were opinions, and who better to go to for opinions than people who report on the game for a living? The balance has shifted – so now when hearing “Bairstow struggles against spin early in the innings”, it could be opinion, bad data*, or a solid piece of analysis. There’s something unsatisfying about that.

Secondly, we don’t know if Matchups work. If each one is a hypothesis, it should be easy to aggregate them in order to compare results and expectation. I expect much of this is – understandably – happening behind closed doors. My hunch is also that many Matchups evaporate as statistical flukes, so are of no benefit. If you’re aware of a rigorous assessment of Matchups, please do drop me a line on twitter or via the Contact page on this site.

Finally, and of relevance to the Cricket World Cup, there’s an opportunity cost associated with changing bowling plans. Especially in ODIs where bowlers need rest during an innings.

Let’s explore that Opportunity Cost – what are the downsides of opening with spin? We can expect more teams to open with spin against England after Bairstow fell first ball against Imran Tahir. Here’s how South Africa used their bowling resources that day:

Fig 1: Overs bowled by each player

Early wickets have a big impact on expected score – but one cannot fully appraise the impact of opening with Tahir without taking all factors into account.

  • Rabada didn’t get the new ball. He then had to condense 10 overs into 44, rather than across 50 – does that impact the pace he can bowl?
  • After 24 overs, with the score on 131-3, Faf du Plessis threw the ball to JP Duminy. Five of the next eight overs were bowled by Duminy and Markram. On this occasion it worked – 5-0-30-0 is not too bad. But it’s the big picture that matters, not one innings.
  • Pretorius only bowled seven overs, Phehlukwayo eight. Without a medium pacer or second spinner than can bowl 10 overs in a row, once a team opens with spin, they are probably going to underuse their fourth and fifth bowler.

What are the factors to consider when weighing up whether to open with a spinner in a four pace / one spin attack?

  1. Will it work? What is the increase in chance of a wicket versus the default option?
  2. What are the relative strengths of your sixth (and possibly seventh) best bowlers, compared to your fourth and fifth?
  3. How fit is the bowler who won’t now be opening? Are you confident they can bowl 10 out of 44 overs? How many days since your last game?

What have we learned? The value of a Matchup is the expected gain from one pairing over another, less the downsides of changing the bowling order to accommodate using a specific bowler at a particular time.

* A word on bad data: Andrew Strauss averaged 91.5 against Mitchell Johnson in Tests. It’s a nice piece of trivia, but it’s only based on Strauss scoring 183-2 against Johnson. I doubt this would have much predictive power. Using that as a basis of prediction is roughly the equivalent of writing off Graham Gooch after he bagged a pair on debut.

Further reading: Cricmetric.com claims to have Matchup data for Batsmen vs Bowlers – I’ve no reason to doubt their data.

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