Evaluating Player Impact Judiciously — T20
By: Amol Desai
Tags: Analytics, Data Science, Sports Analytics, Cricket

If actual WPA is lower than expected WPA, the normalized R-WPA is negative and the denominator uses the absolute delta between min WPA & expected WPA.

Evidence Weighted Innings/Over Win Probability Added (EW-Inn/Over-WPA): This is the evidence weighted metric that measures stint level contribution. For batsmen, this is WPA per innings, while for bowlers, this is WPA per over bowled, weighted by evidence i.e. number of innings played and overs bowled. This is one metric that is not relative to expectations.

As with the other metrics we had developed before, one can use this methodology to get splits of innings, bowler/batsman type etc. depending on what one is trying to evaluate.

Putting the metrics in perspective

We have already looked at EWR-RPO (how fast a batsman scores, relatively) and EWSF (how long a batsman bats, relatively, and hence plays more balls with their “eye in”) before, so let’s add EWR-WPA (the relative impact a batsman has per delivery) to that mix.

As one would expect, players who score faster and play longer generally are more impactful per delivery faced. However, we see that there is an interesting separation between someone like a Narine and a Perera in terms of impact even though they are close in the other two dimensions.

This has to do with the situations in which they bat. Perera, batting down the order, has more opportunities to bat in situations where different per ball outcomes can have significantly different impact on match result.

This range in possible WPA provides a larger opportunity for impact in either direction. A six in a chase of 180 is less critical in the first over than it is in the 18th over with 30 to go and 2 wickets in hand. Moreover, overall WPAs in situations where Perera bats are lower — fewer batsmen actually exceed expectations in crunch situations — giving him a higher relative WPA since he is one of those few.

The EW-NRWPA metric tries to measure how much of the available opportunity was taken up by the player, relative to the expectation, and thus it tries to “correct” for the above scenario. Note, that this just means that the two metrics complement each other, not that one is more useful or better than the other. Perera is about 9x higher than Narine on EWR-WPA, but only 1.37x on EW-NRWPA.

These metrics tell us how impactful a player is per opportunity provided (per ball) and per unit leverage that the opportunity provides (range of possible WPA). Short cameos, especially in crunch situations can be more highly rewarded by these metrics than innings that may be (but not necessarily) slightly longer, but more balanced between risk and reward. These are better captured better by the EW-Inn-WPA metric, which looks at the impact that a batsman has per stint. Perera is not higher than Narine on this metric at 0.087x.

Longer innings are more likely to be rewarded by this metric, but not always. Babar Azam, Shoaib Malik & Pollard (whose recent match winning brilliance with the bat for TKR which would have garnered an innings WPA of ~6–7%, not withstanding. In the data we were using here, when the chasing team needed between 2 and 2.5 a ball in the last over with 3 wkts in hand chasing between 140 and 160, they still lost 15 out of 19 times) who lead the pack on survival factor, produce much less impactful innings than DJM Short & Munro who are 4th and 5th on EWSF.

Note, that the stint (inn for batsmen) level metric here isn’t relative to other batsmen coming in to bat in the same situation. It is a measure of the delta between the likelihood of victory while the batsman was at the crease.

Proper use of WPA metrics

As I mentioned earlier in the piece, and as we saw in the previous section, WPA metrics are influenced by the situational opportunities that a player is subjected to, in a way that performance based measures like EWR-RPO are not.

When the possible WPA range is larger, a player has a better chance of achieving a more extreme WPA, thus favoring players who are involved in these high leverage situations. While the normalized version of this metric (EW-NRWPA) accounts for this, in isolation, it favors players who are involved with low leverage situations. The narrower the range of possible WPA, the less skill one needs to exercise to have a better score on this metric.

For example, if we look at the top bowlers who have bowled at least 300 balls in the last 3 years using just the WPA metrics in isolation, we get the following:

Apart from Rashid Khan, one would expect a lot of other names to make the top 5 cut before these names do.

Instead, if we look at top 5 bowlers by EWR-RPO and use the WPA measures to look at them, we get the following:

Note that RWPA & NRWPA can have opposite signs when aggregated per bowler (but not when computed at the delivery level).

Rashid Khan is far better than the others on both axes, pulling back win probability by ~0.1% more than average per delivery and giving away only 11–12% of the available WPA headroom above the average outcome per delivery. Narine-Mujeeb & Bumrah-Wahab are interesting here. These pairs are almost equal on one axis, but not on the other.

For the situations that they bowl in, Bumrah is just about as much more impactful that the average bowler as Wahab is. But, Wahab gives less of the available headroom away to the batsman and so there is less additional improvement opportunity available to Wahab in terms of the impact that he can have. Bumrah bowls a bit more than Wahab in the the death however, so while there is more opportunity for him to pulls things back, it may also be harder to do so.

Mujeeb & Narine are similar on wicket taking ability and economy with Narine being the slightly more economical one. They give up about the same relative leverage to the batsman, but since Mujeeb opens the bowling more often than Narine, when boundaries impact match outcomes a bit less than later in the innings, his relative impact is a bit lower on saving those boundaries.