The Real Impact of Venues — T20. Drawing out the signal from the noise…
By: Amol Desai
Tags: Data Sc, Cricket, Sports Analytics, Sports Data

Note, that all of the following statements are for measures adjusted for competition. When I say, cluster A has a higher measure than cluster B, this is relative to the measure adjusted for competitions held at the venues in each cluster.

Looking at it this way, we see that differences in the clusters anchored on Dubai & Edgbaston show up in a few different places. Margins of victory are somewhat smaller in the Edgbaston cluster. The Edgbaston cluster is also easier for openers, and more wickets fall in the middle overs. This is reflected in innings progression measures for wickets, runs as well as boundaries. The Dubai cluster has more fireworks in the death.

Between the Chittagong and Sylhet clusters, the Chittagong cluster is much smaller, with only 3 venues: Chittagong itself, Eden Gardens & Karachi. The top 3 venues in the Sylhet cluster are Sylhet, Taunton & Lahore. The Chittagong cluster has larger first innings victory margins and lower second innings victory margins although toss winners choose to bat less and teams win less when batting first for both clusters. The Chittagong cluster is also a higher scoring cluster. This indicates that while both clusters have venues where chasing is better, chases are more likely to be larger and hence go down to the wire or fizzle out more in the Chittagong cluster.

Mirpur sits in it’s own cluster because of the discrepancy between teams choosing to bat on winning the toss and teams batting first actually winning games.

Fig 7. The Mirpur anomaly

Methodology Recap & Practical Illustration

To recap, we have developed a methodology to be able to adjust venue based stats for the effect of differences in competition and sample size which allows us to compare venues in a more reliable way. As a result, we are able to identify similar venues and group them together to further boost this signal. These groups can further be used to represent individual venues either as labels, aggregations of the adjusted stats or even be readjusted to account for the new sample size of the group for more robust estimates on the measures.

Fig 8. Methodology summary

Finally, let’s look at a practical examples of how this methodology works, by looking at venues in the IPL. The venues in the IPL in the last 3 yrs are clustered as follows:

Fig 9. Groups for IPL venues

Let’s take a look at Chennai as a venue that has seen only 10 games & Mumbai that has seen a few more, just below our cutoff, at 19. While the measures for both of these grounds are heavily influenced by the IPL, there was one T20I played in Chennai and two in Mumbai. Since looking at all measures at a time can confuse the illustration, we will focus on the runs scored in the power play in the first innings measure that we looked at earlier (we will still use the grouping from the above process that used all the measures).

The average of 1st innings runs scored in the power play in the IPL is 45.09. From the raw data, in Chennai, this is 39.56 and in Mumbai this is 47.18. However, Mumbai has seen 5 innings at or under 40 and Chennai has seen 3 over 50. So, there is a lot of variance in the observations. Recall that Chennai has almost half the games as Mumbai making up this average. Our competition and sample size adjustment credits Chennai with less of the attribution. It, attributes 40.64 runs to the IPL competition and an adjustment of -0.2 runs to Chennai and +6.64 to Mumbai. Note, that this takes into account the sample sizes & hence the confidence of attribution for the venues as well as the IPL itself. It also takes into account non-IPL games and their sample size e.g the two T20I games that Mumbai hosted, saw 53 runs scored in the 1st innings power play, which is 6.24 runs above the T20I average of 46.76.

After the clustering, which takes into account other measures to assess similarity as well, the sample size that Mumbai can leverage, from other similar venues based on the dimensions we looked at, has grown from 19 to 203, while Chennai can leverage 182, instead of 10 matches. Based on this, the difference in attribution between Mumbai & Chennai goes down to 5.5 runs. In this example, we went from a raw delta of 7.62 runs to an adjusted delta of 6.66 runs and then to a readjusted delta leveraging similar venues, of 5.55 runs. Even if we just take the average of the adjusted delta per venue for all similar venues instead of readjusting that again, we get a delta of 5.69 runs.

Moreover, we are now able to leverage all of this additional information across venues and competitions to come up with a reasonable estimate for T20Is. The raw numbers from the 1–2 games played at Mumbai & Chennai don’t inspire confidence. If we use these raw numbers, we would believe that the expected 1st innings power play score in Mumbai is 53, while in Chennai, it is 46. This is 5–6 runs above the IPL average at these grounds, while the overall delta between T20Is & the IPL is only about 1 run. Using the methodology described here, we get a more realistic estimate of 48 & 43 respectively, instead.