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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Sortable Advanced Stats

There are currently two pages available on this website which contain advanced statistics for batsmen and bowlers, based on the records of my ball-by-ball database, which covers over 1 million deliveries in T20 cricket. Each page houses a table powered by Tableau that allows the user to interact with the data and see which players my system ranks highly

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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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