WASP (Winning And Score Predictor) is a calculation tool used in cricket to predict scores and

possible results of a match. It does not just take the match situation into the equation but also
factors like the ease of scoring on the day according to the pitch, weather and boundary size. It gives
the prediction of the final total in the first innings, and the probability of the chasing team winning in
the second innings. Predictions are based on the average team playing against the average team in
those conditions.[1] The models are based on a database of all non-shortened ODI and 20-20 games
played between top-eight countries since late 2006 (slightly further back for 20-20 games). The first-
innings model estimates the additional runs likely to be scored as a function of the number of balls
and wickets remaining. The second innings model estimates the probability of winning as a function
of balls and wickets remaining, runs scored to date, and the target score. Projected score or required
run rate will not qualitatively show the real picture as they fail to take into the account the quality of
the batting team and the quality of the bowling attack. As run-rate is not a parameter which we have
to not depend upon, considering the new fielding rules, WASP is a very good qualitative parameter

History[edit]
The WASP technique is a product of some extensive research from University of Canterbury (UC) Phd
Graduate Dr Scott Brooker and his supervisor Dr Seamus Hogan. They woked on this project for four
years. Actually they started to work on WASP, after they got a phone call from he university's
economics department asking him to investigate alternatives to the controversialDuckworth-Lewis
method.[3]
WASP was first introduced by Sky Sport (New Zealand) in November 2012 during Auckland's HRV
Cup game against Wellington.[4]

Theory[edit]

The WASP system is grounded in the theory of Dynamic Programming. It looks at data from past
matches and estimates the probability of runs and wickets in each game situation, and works
backwards to calculate the total runs or probability of winning in any situation.
This is how Dr Seamus Hogan - one of the creators of WASP - described the system:
Let V(b,w) be the expected additional runs for the rest of the innings when b (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.[2]

Drawbacks[edit]

If a batsman gets retired hurt, the model will not work well as it does not know the position in which
the retired hurt batsman will come to bat again. This happened in a match between England vs New
Zealand,[5] as injured Martin Guptill came to the crease again in the ninth position and gave a
staggering performance which is unusual for a ninth position batsman and helped his team to win
the match.[6][7]
References[edit]
1. Jump up^ [1][dead link]
2. ^ Jump up to:a b Hogan, Seamus (2012-11-22). "Offsetting Behaviour: Cricket and the
Wasp: Shameless self promotion (Wonkish)". Offsettingbehaviour.blogspot.in.
Retrieved 2014-02-03.
3. Jump up^ Charley Mann (2012-04-28). "Cricket formula 'fairer' - news - the-press".
Stuff.co.nz. Retrieved 2014-02-03.
4. Jump up^ "WASP: Winning and Score Predictor makes for an interesting watch on
television - Latest Cricket News, Articles & Videos at". Cricketcountry.com. Retrieved
2014-02-03.
5. Jump up^ "1st ODI: New Zealand v England at Hamilton, Feb 17, 2013 | Cricket
Scorecard". ESPN Cricinfo. Retrieved 2014-02-03.
6. Jump up^ Hogan, Seamus (2013-11-01). "Offsetting Behaviour: More Cricket: The
Return of the Wasp". Offsettingbehaviour.blogspot.in. Retrieved 2014-02-03.
7. Jump up^ Mike Selvey. "England beaten after injured Martin Guptill digs in for New
Zealand | Sport". The Guardian. Retrieved 2014-02-03.

Blog By Seamus Hogan

Cricket and the Wasp: Shameless self promotion (Wonkish).

[UPDATE: January 2014. The post below dates from November 2012 when New Zealand's Sky TV first
introduced the WASP in coverage of domestic limited overs cricket. An updated post from this
season is here; for an explanation of what cricket has to do with Economics, see here; and for all the
cricket posts on Offsetting Behaviour, see here.]

In their coverage of the Wellington-Auckland game in the HRV cup last Friday, Sky Sport introduced
WASP—the “winning and score predictor” for use in limited-overs games, either 50-over or 20-20
format. In the first innings, the WASP gives a predicted score. In the second innings, it gives a
probability of the batting team winning the match.

I am very happy about this as it is based on research by my former doctoral student, Scott Brooker,
and me. Not surprisingly, the commentators didn’t go into any details about the way the predictions
are calculated, so I thought I would explain the inner workings in a wonkish blog post.

The first thing to note is that the predictions are not forecasts that could be used to set TAB betting
odds. Rather they are estimates about how well the average batting team would do against the
average bowling team in the conditions under which the game is being played given the current
state of the game. That is, the "predictions" are more a measure of how well the teams have done to
that point, rather than forecasts of how well they will do from that point on. As an example, imagine
that Zimbabwe were playing Australia and halfway through the second innings had done well
enough to have their noses in front. WASP might give a winning probability for Zimbabwe of 55%,
but, based on past performance, one would still favour Australia to win the game. That prediction,
however, would be using prior information about the ability of the teams, and so is not interesting as
a statement about how a specific match is unfolding. Also, the winning probabilities are rounded off
to the nearest integer, so WASP will likely show a probability of winning of either 0% or 100% before
the game actually finishes, even though the result is not literally certain at that point.

The models are based on a database of all non-shortened ODI and 20-20 games played between top-
eight countries since late 2006 (slightly further back for 20-20 games). The first-innings model
estimates the additional runs likely to be scored as a function of the number of balls and wickets
remaining. The second innings model estimates the probability of winning as a function of balls and
wickets remaining, runs scored to date, and the target score.

The estimates are constructed from a dynamic programme rather than just fitting curves through the
data. To illustrate, in the first innings model to calculate the expected additional runs when a given
number of balls and wickets remain, we could just average the additional runs scored in all matches
when that situation arose. This would work fine for situations that have arisen a lot such as 1 wicket
down after 10 overs, or 5 wickets down after 40 overs, etc.), but for rare situations like 5 wickets
down after 10 overs or 1 wicket down after 40 it would be problematic, partly because of a lack of
precision when sample sizes are small but more importantly because those rare situations will be
overpopulated with games where there was a mismatch in skills between the two teams. Instead,
what we do is estimate the expected runs and the probability of a wicket falling on the next ball
only. Let V(b,w) be the expected additional runs for the rest of the innings when b (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.

Now many authors have applied dynamic programming to analyse sporting events including limited
overs cricket (see my previous post on this here), although I don’t know of any previous uses of such
models in providing real-time information to the viewing public. Scott’s and my main contribution,
however, is in including in our models an adjustment for the ease of batting conditions. I have
previously blogged about our model for estimating ground conditions, here. Without that
adjustment, the models would overstate the advantage or disadvantage a team would have if they
made a good or bad start, respectively, since those occurrences in the data would be correlated with
ground conditions that apply to both teams. Using a novel technique we have developed, we have
been able to estimate ground conditions from historical games and so control for that confounding
effect in our estimated models.

In the games on Sky, a judgement is made on what the average first innings score would be for the
average batting team playing the average bowling team in those conditions, and the models’
predictions are normalised around this information. At this stage, I believe this judgement is just a
recent historical average for that ground, but the method of determining par may evolve.

I gather that the intention is to unveil more graphics around the use of WASP throughout the
season, with the system fully up and running by the time of the international matches against
England. It’s going to be interesting listening to what the commentators make of the WASP. Last
Friday’s game wasn’t the best showcase, since when Auckland came to bat in the second innings,
their probability of winning was already at 92% and quickly rose higher. It was fun, though, hearing
the commentators ask Wellington captain, Grant Elliot, who was wired for sound while fielding, what
he thought their chances were given that WASP had the Auckalnd Aces at 96% at that point. Grant's
reply was lovely: "Sometimes even pocket aces lose". This is worth remembering when (as will
inevitably happen), a team has a probability of winning in the 90s but still goes on to lose.