Unsold Batsmen in the Aftermath of the IPL 2020 Auction
By: Amol Desai
Tags: Data Science, Data Analysis, Analytics, Sports An, Cricket

Unsold Batsmen in the Aftermath of the IPL 2020 Auction

Shining a candle on the top unsold batsmen at IPL 2020 & their competition from the sold and retained pools

The process of forming teams for IPL 2020 which started in November 2019 with the retention and release of players culminated in late December 2019 with the auction. Some names in the list of unsold batsmen at the end of the auction caught my attention and so I decided to take a look at the margin between these batsmen and the ones that were sold.

Here, we will try to identify some of the top unsold batsmen and try to look for batsmen in the sold/retained pool, who these unsold batsmen pose a competition to. We will generally not account for player value to teams in terms of merchandise and ticket sales and instead focus on the cricket. We will briefly mention inferences related to team strategy if evident from the data, but otherwise refrain from speculations about team strategy or camaraderie on or off the field.

I used this list of retained players, and the IPL auction page to get a list of sold and unsold players listed as Batsmen & Wicket-Keepers. I ignored the behind-the-wicket contribution of the wicket keepers here. For all the players in contention, I looked at their performance in the past 5yrs in T20s.

The Raw Numbers

To start with, let’s look at averages and strike rates. Instead of using the average runs scored by a batsman per dismissal, I used average runs scored and balls faced per outing, thus treating each innings as an opportunity given to the batsman to perform on a given stage that doesn’t carry any weight beyond that game. This takes away the advantage from batsmen coming in later in the innings and not getting out, and gives that advantage to batsmen coming in earlier; but we will deal with that advantage in a bit.

Here are the average runs scored and balls faced by batsmen per inning. The slopes of the lines indicate average strike rate of all batsmen.

Fig 1.

We see that batsmen that were sold, do have a steeper slope and hence a higher strike rate than batsmen who went unsold on average. But, there are some batsmen who were sold, that seem to have similar averages and strike rates to a bunch of unsold batsmen. Here, I use ratio of averages to determine strike rate rather than average of ratios to avoid bias due to extremely short and quick innings e.g. a two ball innings of ten runs. George Munsey from Scotland stands out as the unsold batsman who has done better than most in terms of both average and strike rate.

The Need For A Model — Why Raw Numbers Don’t Cut It

All of the players in the above chart have encountered different playing conditions and match situations in the matches that they have played in. While Carey has primarily opened in the BBL & Munsey has primarily opened in International T20s, Morgan has been a middle order bat in the T20 Blast and in International T20s, and Uthappa who has primarily played in the IPL, has come in, in the first 3. Carey & Uthappa, having played mostly for Adelaide Strikers and KKR respectively, have played most of their games in Adelaide and the Eden Gardens while Munsey has played a lot of his games in the UAE.

To control for these factors, I put together a model that takes into account the following factors to determine the expected performance from a batsman.

Competition (e.g. T20I, IPL, BBL etc.)

Venue

Innings (Chasing vs Setting Target)

Team Score (Wickets, Balls & Runs when batsman faces their first ball)

I will leave the details of the model out of this writeup since I am still iterating on it with feedback from Jarrod Kimber, but it generates an expectation of runs scored and balls faced for a given inning. Essentially, we are normalizing out differences in the above factors to come up with a par performance for each inning and evaluating the effectiveness of batsmen relative to this expectation. We are not explicitly controlling for opposition strength and relying on competition, to capture some of that effect.

Looking at the Data in New Light

Let’s first take another look at performances, but this time, relative to the expectations set by the model.

Fig 2.

We still see the higher strike rate for the batsmen that were sold, and some of the same batsmen that stood out earlier, stand out here, again. But, we also see Evin Lewis & Colin Ingram and to an extent, Mohammed Shahzad stand out distinctly in addition to Munsey (They were peeking into the picture earlier as well, but the separation is more clear in this view). Another interesting outcome, that exemplifies the role of the model in normalizing expectations is the difference in relative positions of Eoin Morgan and Robin Uthappa. This is a direct result of the differences in the competitions that they have played in and their batting positions. Morgan has done better relative to his peers (in match context and situation) than has Uthappa in terms of runs scored, although Uthappa has better raw figures.

Another Model & Perspective

So far, we looked at information on the balls and runs axes that are familiar to most of us, to understand how groups of batsmen are positioned, and how individuals stand out from the group. However, in addition to runs scored, we are also interested in how fast batsmen score normalized to the match context and situation. Let us add in one more model that includes information on the number of balls actually played to get expected runs scored.

So, we have two models for runs scored: One that takes into account match context and situation and the other that additionally takes into account balls faced. Let us use these, and table the model giving us expected balls faced, for now.

Let’s focus on the four top batsmen we have identified so far from the unsold pool, but also add in batsmen & wicket-keepers who were retained.

Fig 3.

While it is just plainly below par to be in the bottom-left quadrant, batsmen can be in the top-left or bottom-right quadrant due to team strategy. However, chronically being here could also indicate problems in team strategy or execution. Batsmen in the top-left get more runs, but bat slower and hence, longer than expected. They may be steadying the ship often after a collapse or be in a team full of hard-hitters playing anchors (or the T20 version of it). Batsmen in the bottom-right quadrant may be making way for even faster scorers better suited for the remainder of the innings or making up for partners in the top-left quadrant.

More runs in fewer balls is generally better, but there could be cases where it is in the team’s interest for a batsman to get out even if he is scoring at a decent clip, because there is a teammate in the dugout who can score faster in the deliveries that remain.

Batsmen in top-left quadrant: Retained: AM Rahane, AT Rayudu, KS Williamson, MK Pandey, MS Dhoni, S Dhawan, S Gill, SPD Smith, SS Iyer.

Batsmen in bottom-right quadrant: Retained: IP Kishan, M Vohra, MA Agarwal, PA Patel, PP Shaw, WP Saha Sold: JJ Roy, RA Tripathi, RV Uthappa, SO Hetmyer

Like-for-Like Swaps

So far, we have been looking at things from a performance standpoint only. But, since we are looking to see whether the four unsold players can replace any of the players that are set to participate in IPL 2020, we should look for players with similar roles. All four of the unsold players we are looking at are oversees players. Moreover, 3 of them have experience as openers, with Ingram being the odd one of the lot who usually comes in at the fall of the first or second wicket.

Let’s filter out the players in the sold and retained pools that don’t match these criteria. I’ve also removed some of the labels to make it less of an eye-chart.

Fig 4.

Here are some things that stand out from this:

Kane Williamson is significantly slower than the average batsman in his position, as well as other contenders. Note, that in the IPL, he has mostly come in at #3 for Sunrisers Hyderabad, while he has switched between opening and the #3 slot for New Zealand. For the Sunrisers, he has been at the crease with Shikhar Dhawan & David Warner at the top of the order and so, maybe, it is a strategic call for him to take fewer risks and stay at the wicket. Note also, that we are completely ignoring the fact that Williamson captains the Sunrisers.

Chris Gayle is vastly better than the average opener in normalized conditions than he is, coming in at #3. Gayle has mostly opened for both Kings XI as well as Royal Challengers in the IPL and he should probably continue to do so. It is noteworthy that Mohammed Shahzad actually performs very similar to Chris Gayle as opener.

Alex Carey, who went for even more than Gayle at 2.4 crore INR performs better than average but hasn’t scored much faster than par. Carey, however, also keeps wickets and can play dual roles of wicket keeper and opener for Delhi Capitals if Pant isn’t in form or otherwise unavailable. Note, that the Capitals have Shikhar Dhawan, Prithvi Shaw, Ajinkya Rahane & Jason Roy, all of whom are primarily openers. Roy, is on the other end of the spectrum to Carey. While still performing better than the average batsman, Roy is barely better than par on the total runs axis while being significantly above par in terms of runs scored per ball. This gives the Capitals an oversees opening option that they can use depending on team strategy in a given game. However, the question beckons on whether one of the unsold opening options we are considering would not have given them a good tradeoff between runs scored and the speed at which they are scored, at a good value.

Colin Ingram scores more than Eoin Morgan a lot faster than him coming in at 2-down. He also scores a lot more and a bit faster than Hetmyer coming in at #3. While it isn’t shown here, Morgan, coming in at #4 is a much better relative performer than Morgan coming in at #5 (in the bottom-left quadrant). Another factor that should have been in favor of Ingram is also that he has experience playing in several T20 leagues. He has played more than 20 games in T20 leagues in England, South Africa, Australia & Pakistan and around 10 games each in leagues in India (IPL) and West Indies. Interestingly, while he has mostly played in the top 3, he was used at 3-down in 8 of 12 innings at the Delhi Capitals and never in the top 3.

This is a bit of an aside and is not related to swapping in our unsold batsmen, but it is an observation that came about while exploring related data and so, I am including it here. Chris Lynn has been used by KKR to open, but he has mostly come in at #3 for Australia and the Brisbane Heat. We see that he has done significantly better than average at 1-down than he has as an opener. However, in the four occasions that he has come in at #3 for the KKR, he has done rather poorly, producing scores of -10, -2, -5 & -11 runs compared to average per outing at -9, 10, 2, 1 runs above average per outing for balls played in outing, respectively. Maybe this indicates that KKR were asking him to play a riskier game than would be optimum. Something for the Mumbai Indians & Lynn to keep in mind for 2020.

Opponent Quality

Our model considers groups of competitions but considers all International T20s in the same group. This would be OK for players that play in a number of different leagues and groups, but it could overvalue players like Munsey & Shahzad who play in fewer leagues against weaker oppositions.

Munsey has mostly played international T20s against associate teams, with only 2 games against a non-associate, Pakistan. Shahzad has played in the Bangladesh Premier League in addition to T20Is, but most of his games have been T20Is where he has played 1 game each against West Indies, South Africa, Sri Lanka & England and 3 against Bangladesh. Similarly, Gayle, Carey, Williamson, & Roy haven’t played against the associates.

While we do normalize expectations for competition quality and venue, this normalization is only relative to the conditions they have faced. Munsey innings against associates gets evaluated against the average opener against all international oppositions. Moreover, someone like Munsey & Gayle haven’t played in the same condition as the other, an argument can be made that our approach doesn’t effectively normalize for this comparison.

Let us attempt to find overlapping oppositions between the batsmen being compared and look at performances in the overlapping games. We will have to drop our threshold of 20 games for this comparison, since there aren’t enough games played against a single opposition. This will make our findings slightly less reliable due to higher variance and so the numbers here should be taken with a grain of salt.

Munsey has scored +2.5, -22.4 and -13.8 compared to expectation in his 2 innings against Pakistan & 1 against Zimbabwe. Williamson averages +10 in his 9 innings against Pakistan with a minimum of -22.8, and has a -6.8 in his lone innings against Zimbabwe. Carey averages -4.3 in 5 innings against Pakistan and has an innings of -6.7 against Zimbabwe. Jason Roy averages a -9.3 in 4 innings against Pakistan.

Munsey has scored -16.8 & +19.2 in his innings against Afghanistan. Roy in his lone innings against Afghanistan has a -21.9.

Shahzad averages +15.8 in 7 innings against Zimbabwe, has relative scores of -21.5, +20.6, -18.9, -1.1 against England, South Africa, Sri Lanka & the West Indies, respectively, and has three innings innings of +13.1, -0.9, and +2.7 against Bangladesh. Without even looking at the relative scores of other contending batsmen against the same oppositions, we can tell that these are not bad at all.

For batsmen who have played at least 10 games against both associates and non-associates, we see that the following relationship for average runs scored above model expectations.

Fig 5.

We see here, that most of these batsmen have much higher scores against associates than non-associates. The average batsman against non-associates (scores of 0 on the x-axis), scores up to +4 and an average of +2.5 runs against associates.

Overall, we can expect performances against associate teams to be less challenging and hence expect Munsey & Shahzad to be overvalued using our methods; but, we don’t have any statistically significant evidence to suggest that their performances differ from those of their competitors here, against opponents that both have faced. Especially in the case of Munsey, it would seem that even discounting his scores by ~2.5 runs based on the above keeps him in the competition.

The Universe Boss Can’t Be Discounted So Easily

So far, we have been looking at averages of relative performance across innings. While we were using separate expectations for batting first and chasing, we were aggregating the relative performance across them. Let us look at the two innings separately. This is not unlike what we did for batting position above.