T20 Match Simulator: under the hood

When I previously wrote about my new T20 match simulator, I concentrated more on what it could do than how it was built. This time, my aim is to ‘lift the hood’ and explain exactly how the engine is constructed and how it runs. Others can then start to judge for themselves whether it can indeed answer the many, varied questions that I claimed it can

I have tried to keep things simple so that anybody interested can understand how the model works. However, there are times when I use some technical language. If you don’t understand something (and you want to understand it), you can probably find the answer on Wikipedia, a Google search, or in a library

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Trialling a T20 Captaincy Metric

The importance of captaincy in cricket is greater than in almost any other sport. In football, for just one example, the captain has almost no influence whatsoever over team selection or strategy. This is not true in cricket. Not only are cricket captains involved in most strategic and tactical decision-making, this also comes with more responsibility for the other players in the team, their mindset and their morale. Measuring the value of a good captain is incredibly difficult

One aspect of captaincy that might be measurable is the decisions that they make on the field. Here, the decision-making is observable by an outsider. Obvious, even, in the case of bowling changes. They occur 20 times in an innings and at regular intervals

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Captain Typical

My current project is a ball-by-ball simulator for T20 matches. This post isn't about that. It is about one tiny component which took way too much time to build considering how little value it contributes to the endeavour of predicting T20 outcomes. I wanted my simulator to have the ability to simulate what bowling changes the captain would make during an innings. And so I built a model to pick the next bowler

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Where should you play your best batsman?

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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T20 Player Value: Part III

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

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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T20 Player Value: Part I

In this and subsequent posts, I aim to explain my methods for T20 player evaluation. They are not set in stone. Any time I sit down to analyse a player, team, tournament, strategy, there is a decent chance that they will change. I would love to hear other people’s feedback and ideas. If nothing else, writing down my thoughts has forced me to be critical of my own work. Indeed, the methods changed several times even as I documented them

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Bowler workload

Recently, I have been wondering whether stats of less prolific bowlers (1 or 2 overs per match) are slightly over-inflated, given that they are most likely to be used when the conditions / match-ups are in their favour. Or, to put it another way, I have been wondering whether the stats of the top bowlers in a team (4 overs per match) are slightly under-inflated, given that they are required to deliver four overs whatever the circumstances

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Ageing Curves: Part II

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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Ageing curves: Part I

For teams looking to acquire new players, having a solid understanding of their value is vital. Measuring past performance in T20 can be difficult and measuring future performance is even more challenging. One reason for this is that we need to account for the unrelenting passage of time: younger players improve and older players decline

Ageing curves allow us to understand the overall shape of a typical T20 batsman's career. This post walks through the methodology I have used to calculate an approximate ageing curve for T20 batsmen

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Predicting batting performance

Even as a fairly well-informed fan, it can be hard to find stats in T20 that are as reliable and informative as batting and bowling averages in Test cricket. Mostly, I am guided by strike rates and economy but I still need to contextualise these due to variable scoring rates over the course of a T20 match, as we progress from the Powerplay, through the middle overs, and into the death overs

My aim in this article is to explore which statistics in T20 are most consistent and predictive indicators of future value. On this website, Runs Added and Win Probability Added are often used to evaluate performances but whilst they are good descriptive statistics, they may not be the best predictive statistics

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