For several decades we have seen projected scores displayed by the sports channel while watching cricket but Sky Sports has offered a completely new system of projecting scores and the winner in the form of WASP. During the first ODI between India and New Zealand viewers were highly fascinated by WASP and then a highly engaging discussion caught up on twitter which made the hashtag #WASP become a trending topic for an entire day. While half of the twitter population was wondering what the term means, the other half was busy giving the answers.

## 1. What WASP really means?

**W**inning**A**nd**S**core**P**rediction abbreviated as WASP is an algorithm of projecting the total score during the first inning and the probability of the batting team being victorious during the second inning.WASP involves more of calculation and less of prediction of which team is going to win the match. It involves several factors such the past performances of the teams. The system also takes several other factors such as pitch condition, size etc. into consideration. It is an analytical way of projecting what would happen in a match. And of course, it is not essential that it will always be right but it takes all ideal conditions into account.## 2. How does it work?

This model uses dynamic programming to estimate how the match is going to unfold. The algorithm used in first innings primarily uses the number of wickets and balls remaining while in the second inning the target score, runs scored at a point and the batsmen remaining are the key factors. This is how Dr. Seamus Hogan (one of the creators of WASP) described how the algorithm worked: “*Let V(b,w) be the expected additional runs for the rest of the innings whenb (legitimate) balls have been bowled and w wickets have been lost, and let r(b,w) and p(b,w) be, respectively, the estimated expected runs and the probability of a wicket on the next ball in that situation. We can then write*V(b,w) =r(b,w) +p(b,w) V(b+1,w+1) +(1-p(b,w)))V(b+1,w)Since V(b*,w)=0*Where b* equals the maximum number of legitimate deliveries allowed in the innings (300 in a 50 over game), we can solve the model backwards. This means that the estimates for V(b,w) in rare situations depends only slightly on the estimated runs and probability of a wicket on that ball, and mostly on the values of V(b+1,w) and V(b+1,w+1), which will be mostly determined by thick data points. The second innings model is a bit more complicated, but uses essentially the same logic.”*