Simulating T20 Matches: Pinching

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The debate on whether Sunil Narine should open is one of my favourite in T20 cricket. I stand resolutely on team ‘yes’… but standing on team ‘yes’ does not mean that I don’t still have doubt. It wouldn’t be a debate if I didn’t see merit to arguments on both sides. I have built a simulator that will play out T20 games ball-by-ball, thousands of times, so that we can see the impact of playing in a certain way, or with a certain line-up. This means that we can set up a game between the Kolkata Knight Riders and the Sunrisers Hyderabad (say) and simulate what would happen with Narine at different spots in the line-up

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Where should you play your best batsman?

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Rohit Sharma is the captain and assumed best batsman of the Mumbai Indians. He seems likely to bat at number 3 or 4 this season, unchanged from last year, when Buttler and Patel were generally preferred as the opening pair. Whilst those two are now gone, Mumbai did acquire another well-established opener at auction in the form of Evin Lewis, Ishan Kishan may also get the chance to impress

Last year, Sharma suggested that “probably three, four is the best position” for him but the stats emphatically disagree. In 52 matches as an opener, he averages 39.5 runs at a strike rate of 142. Both numbers drop noticeably when he arrives between 4-6, falling to an average of 32.4 runs at 132 (in 130 matches)

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Optimising the first over

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Six games into the 2017 T20 Blast, Kent were the slowest starters in the competition. Even enduring a maiden first over at home to Gloucestershire. In their seventh game, against Somerset, the team switched things up. Joe Denly moved into the opening slot, with his partner, Bell-Drummond replacing him at the other end

Superficially, the switch appeared to work. In the following eight games, Kent were scoring more runs after 3 balls (+0.6), more runs in the first over (+0.5), and more runs in the PowerPlay (+2.6)

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T20 Player Value: Part III

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This is the third post in a series, in which I outline my approach to assessing player value. This post walks-through an example and then adds a further three considerations on top of the ones explained previously: weighting, regression to the mean, and ageing

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T20 Player Value: Part II

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This is the second post in a series, in which I outline my approach to assessing player value. The first explains the overall objective: to measure the expected contribution of each player in runs. This post then details four main adjustments that I make to historic performances to remove any obvious biases in the data

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Ageing Curves: Part II

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My last post used historic data from over 1,500 players to construct ageing curves that show how batting performances improves and declines with age. In this post we will see how these curves change depending on the players included in the analysis. In some cases, it reveals genuine differences between player types and, in other cases, potential limitations in what was originally quite a naive approach

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Glamour from the openers, value from the middle order

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9 of the top 10 scores in T20 cricket have been posted by opening batsman. David Warner has often single handedly dragged Sunrisers Hyderabad to victories in the last two IPL seasons as an opening batsman. The average match will feature an opening batsman spending the most time at the crease and their performances stick most in the minds of fans

In contrast, the middle order batsman are often tasked with the dirty work: maximising their teams chances of winning from the situation they inherit when they come into the game. They must be versatile and their role could be wildly different from game to game

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